Genomic discovery of EF-24 targets unveils antitumorigenic mechanisms in leukemia cells

Ajeet P Singh; Noah Wax; James Duncan; Ana S Fernandes; Jonathan L Jacobs

doi:10.1371/journal.pone.0330906

. 2025 Sep 23;20(9):e0330906. doi: 10.1371/journal.pone.0330906

Genomic discovery of EF-24 targets unveils antitumorigenic mechanisms in leukemia cells

Ajeet P Singh ^1,^*, Noah Wax ¹, James Duncan ¹, Ana S Fernandes ¹, Jonathan L Jacobs ^1,^*

Editor: Kota V Ramana²

PMCID: PMC12456773 PMID: 40986633

Abstract

Curcumin, a polyphenolic compound derived from the plant Curcuma longa L., has demonstrated a wide range of therapeutic properties, including potential anticancer effects. However, its clinical efficacy is limited due to poor bioavailability and stability. To overcome these challenges, curcumin analogs like EF-24 have been developed with improved pharmacological properties. In this study, in order to improve our understanding of EF-24’s potential mechanisms of action, we used whole-transcriptome sequencing to identify genome-wide functional impacts of EF-24 treatment in leukemia cells. These results enabled the development of a testable model system for associating druggable genes with clinical disease targets related to EF-24 treatment. To develop our model of the transcriptional response to EF-24 treatment, we used four well studied model cell lines for leukemia research, specifically the chronic myeloid leukemia (CML) cell line K-562 and acute myeloid leukemia (AML) cell lines HL-60, Kasumi-1, and THP-1. Cell viability was significantly decreased in all four of these leukemia models following EF-24 treatment as compared to untreated controls. We discovered that the genes ATF3, CLU, HSPA6, OSGIN1, ZFAND2A, and CXCL8, which are associated with reduced cell viability and proliferation, were consistently upregulated in all EF-24–treated cell lines. Further analysis of the tested cell lines revealed the activation of various signaling pathways, but notably the S100 family signaling pathway was consistently activated in all four cell lines. Our results provide critical insights into the molecular underpinnings of EF-24’s antitumor efficacy against leukemia subtypes, highlighting its multifaceted impact on signaling pathways and gene networks that regulate cell survival, proliferation, and immune responses in cell line models of myeloid leukemia subtypes.

Introduction

Chronic (CML) and acute myeloid leukemia (AML) are heterogeneous hematological malignancies that arise from the rapid, uncontrolled proliferation of immature or abnormal myeloid blood cells [1]. The complexity of these diseases is further exacerbated by diverse subtypes, each characterized by distinct genetic alterations and clinical behaviors. While traditional chemotherapy has improved survival rates for many leukemia patients, its efficacy is often limited by drug resistance, off-target toxicity, and disease relapse [2]. This underscores the urgent need for more selective and less toxic therapeutic alternatives.

Curcumin, a bioactive polyphenolic compound extracted from the rhizomes of Curcuma longa L., has garnered significant attention for its potential anticancer properties [3]. Despite its promising effects in preclinical studies, curcumin’s clinical translation has been hindered by its poor bioavailability and rapid metabolism. To overcome these limitations, synthetic curcumin analogs with enhanced pharmacokinetic properties and greater potency have been developed [4]. Among these, EF-24 (diphenyl difluoroketone) has emerged as a promising anticancer agent due to its potent antiproliferative and pro-apoptotic effects across multiple cancer models [3]. EF-24 exerts its anticancer activity by modulating key signaling pathways implicated in cell cycle regulation, apoptosis, and inflammation.⁵ Notably, it has been shown to induce oxidative stress, disrupt NF-κB signaling, and trigger mitochondrial-mediated apoptosis in various cancer types.⁶ Additionally, EF-24 has been reported to inhibit PI3K/Akt and STAT3 pathways, which are frequently dysregulated in leukemia and contribute to cell survival and chemoresistance.⁷ Despite these advances, the precise molecular targets and genome-wide transcriptional effects of EF-24 in leukemia subtypes remain poorly understood [5,6].

To elucidate EF-24’s mechanisms of action in leukemia, we investigated its effects on a panel of well-characterized CML (K-562 (ATCC^® CCL-243™) and AML HL-60 (ATCC^® CCL-240™), Kasumi-1 (ATCC^® CRL-2724™), and THP-1 (ATCC^® TIB-202™) cell lines. The CML cell line K-562 (ATCC® CCL-243™) was selected due to its BCR-ABL fusion gene, which drives leukemogenesis and is a key therapeutic target in CML [7]. The AML cell lines HL-60 (ATCC® CCL-240™) [8], Kasumi-1 (ATCC® CRL-2724™), and THP-1 (ATCC® TIB-202™) were chosen to represent distinct AML subtypes, each with unique genetic and phenotypic features. HL-60 cells model early-stage AML and are widely used for studying myeloid differentiation and chemotherapeutic responses [9]. Kasumi-1 cells harbor the t(8;21) translocation, a hallmark of core-binding factor AML, which is associated with specific transcriptional dysregulation [9]. THP-1 cells, derived from an M5 monocytic AML subtype, provide a model for investigating leukemic monocyte-macrophage differentiation and inflammatory signaling [9].

These cell lines represent specific myeloid leukemia subtypes and have been extensively characterized and widely used in research, offering unique insights into the pathogenesis and therapeutic vulnerabilities of the disease. Here, our objective was to first illuminate the baseline transcriptional profiles of these leukemia cell lines, emphasizing the existing molecular discrepancies among them, then conduct RNA-sequencing to explore global gene expression landscape in responses to EF-24 exposure. Leveraging comparative differential gene expression analysis of untreated and EF-24–treated cells, we uncovered the molecular pathways and gene regulatory networks underlying EF-24’s anticancer effects. Overall, this approach provided an unbiased assessment of global gene expression and target gene relative abundance, enabling the identification of differences and similarities among each cell line in response to EF-24 treatment.

Results

EF-24 exhibits potent cell killing activity in leukemia cell lines

We evaluated the antitumor efficacy of EF-24 across four genetically distinct leukemia cell lines—K-562, Kasumi-1, THP-1, and HL-60—selected to represent key subtypes of myeloid leukemia [10–14]. Building on the foundational work by Hsiao et al., which demonstrated EF-24-induced cytotoxicity in AML cell lines, we sought to validate and extend those findings [5]. Their study reported elevated levels of cleaved caspase-3 and PARP following treatment with 2 µM EF-24, indicative of pronounced apoptotic cell death [5]. To assess EF-24’s cytotoxic potential, we treated each cell line with 2 µM EF-24 and measured viability using the CellTiter-Glo® assay (Promega®) 24 hours post-treatment. The results revealed substantial reductions in cell viability: approximately 60% in K-562, 75% in THP-1 and HL-60, and 90% in Kasumi-1 (Fig 1A). These findings were corroborated by direct cell counts, which consistently showed decreased numbers of viable cells in treated samples compared to untreated controls (Fig 1B,1C). Collectively, these data reinforce previous findings and highlight EF-24’s potent cytotoxic activity across diverse myeloid leukemia subtypes, supporting its continued evaluation as a promising therapeutic candidate with broad applicability across lineage-specific, hematologic and solid tumor models, including translational relevance in primary patient samples [15].

Fig 1 — (A) Relative cell viability measured using the Promega® CellTiter-Glo® luminescent assay shows a marked decrease in EF-24–treated cells compared to controls. (B) Percent viability and (C) total cell counts assessed using the Beckman Coulter® Vi-CELL® XR analyzer further confirm the cytotoxic effect of EF-24, demonstrating consistent reductions in both viability and cell number across treatment groups. Statistical p- value ** < 0.01.

Chronic (CML) and Acute Myeloid Leukemia (AML) cell lines exhibit transcriptional heterogeneity

To understand the inherent molecular characteristics of the myeloid leukemia cell lines, we carried out whole-transcriptome profiling to establish a reference baseline for gene expression in naive cells. Principal component analysis (PCA) revealed close clustering of biological replicates (n = 5), while leukemia cell lines were stratified into four distinct clusters based on their transcript and gene expression levels, indicating that each of these cell lines represents a distinct model system and transcriptional “starting point” for studying the impact of drug (EF-24) treatment response. Comparative gene expression analysis between the cell lines revealed differential patterns in the top 100 genes, providing insights into their roles in specific cell types (Fig 2A). This facilitated the identification of marker genes specific to each leukemia cell line. For example, HBG1, RHAG, HBG2, HBA1, and NMU are enriched in K-562 cells as compared to THP-1, Kasumi-1, and HL-60. In contrast, SMAP2, ZMPSTE24, CDC20, NFYC, and RPS4Y1 are enriched in THP-1. Similarly, CD34, KIT, EEF1A1, PRSS57, RPLP0, and RPS24 are enriched in Kasumi-1, while MT-ND4L, MT-ND4, MT-CO1, MT-ND3, and CST7 are enriched in HL-60 cells (Fig 2A). The expression patterns of these genes in particular cell lines could serve as biomarkers for characterizing specific leukemia subtypes and as potential pharmacological targets.

Fig 2 — (A) Heatmap demonstrates the clustering pattern of genes differentially expressed among leukemia cell lines (red = induced; black = reduced). Each cell line has 5 biological replicates. (B) Venn diagram showing the shared and unique genes differentially expressed in the AML subtypes HL-60, THP-1, and Kasumi-1 as compared to the CML subtype K-562. (C) Venn diagram showing the shared and uniquely expressed genes in K-562, THP-1, and Kasumi-1 as compared to HL-60. (D) Venn diagram showing the shared and uniquely expressed genes in THP-1, K-562, and HL-60 that differ from those in Kasumi-1. (E) Venn diagram showing the shared and uniquely expressed genes in Kasumi-1, HL-60, and K-562 that differ from those in THP-1. For each Venn diagram, the differences in circle size reflect the number of genes. (F) The heatmap displays the DEGs genes exhibiting inconsistent expression patterns in THP-1, Kasumi-1, and HL-60 as compared to K-562. (G) The heatmap displays the genes exhibiting a consistent expression pattern in the THP-1, Kasumi-1, and HL-60 as compared to K-562. Absolute fold change ≥5, false discovery rate (FDR) p-value <0.01.

When further analyzing the number of differentially expressed genes (DEGs) either shared or unique in the AML cell lines as compared to the CML cell line, we identified 2011 genes that were found to be differentially regulated and shared across the THP-1, Kasumi-1, and HL-60 cell lines and distinct from the K-562 cell line (Fig 2B). We also discovered numerous genes unique to HL-60 (853), THP-1 (978), and Kasumi-1 (632) as compared to K-562 (Fig 2B,2F). Further investigation of HL-60, Kasumi-1, and THP-1 revealed genes that shared similar expression patterns among multiple cell lines as well as those unique to specific cell lines (Fig 2C,2D,2E,2F). We then looked for the genes shared by THP-1, Kasumi-1, and HL-60 that could be a common factor driving the AML subtypes; here, we identified genes with consistent expression patterns among the cell lines. For instance, PDCL2, CT45A2, SSX4, SSX2B, TUBAL3, HOXB5, AHSP, and SEC14L6 are consistently downregulated whereas GSDMD, ARHGAP15, PDK3, PLPPR3, MDFIC, LGALS9, ZNF141, and GFI1 are upregulated in HL-60, Kasumi-1, and THP-1 as compared to K-562 (Fig 2G). Next, we identified genes inconsistently regulated in THP-1, Kasumi-1, and HL-60 cells and differentially expressed as compared to K-562 cells. For example, TUBB4A4, TSPAN9, CLEC2B, COL4A5, SPARC, and SNCA are highly augmented in Kasumi-1 cells but attenuated in HL-60 and THP-1 cells as compared to K-562 cells (Fig 2F). Similarly, we also identified a subset of genes differentially enriched or decreased in HL-60 (e.g., TUBB4A, TSPAN9, CLEC2B, COL4A5, SPARC, SNCA decreased; ZNF229, ZNF502, ZNF67, CHST15, EPX increased) and THP-1 (e.g., IL2RG, DYSF, SV2B, ZSCAN18 decreased; TKTL1, TWIST1, CAV1, MTX3, PAQR9, KLHL29, FILIP1, SLC4A4, ACVR1C increased) as compared to K-562 (Fig 2F). This comprehensive analysis revealed the global molecular heterogeneity between cell lines and marker genes, underscoring the importance of evaluating different subtypes when reviewing a new treatment as it may affect each cell line differently.

EF-24 treatment impacts the expression of genes controlling cell survival and proliferation

To identify the gene expression changes responsible for EF-24’s anticancer effects in leukemia cell lines, we performed RNAseq on EF-24-treated cells and compared them against untreated baseline controls. Principal Component Analysis (PCA) carried out RNAseq profiles for EF-24 treated and untreated samples, with all biological replicates of each condition clustering together (Fig 3A). Furthermore, differential gene expression analysis highlighted a shift in the pattern of gene expression in EF-24–treated cells as compared to control cells (Fig 3B).

Fig 3 — (A) PCA plot illustrates sample clustering in leukemia cell lines and experimental groups. The variations within the samples resulted in distinct clustering (red = control samples; black = EF-24 samples). (B) Heatmap of the top 100 DEGs in leukemia cell lines that shows the highest fold change in EF-24–treated cells as compared to untreated controls. There are 5 biological replicates of each condition/cell line (red = control; blue = treated). (C) Venn diagram depicts shared and unique genes that are differentially expressed in EF-24–treated cells as compared to untreated controls. Threshold absolute fold change ≥5, FDR p-value 0.01. (D) Pan differentially expressed genes in EF-24–treated cells as compared to untreated controls. (E) Violin plots display the relevant genes’ quantitative enrichment in EF-24–treated cells as compared to untreated controls. (F) IPA shows shared top canonical pathways activated in myeloid leukemia cell lines were treated with EF-24. (G) IL-6 is an upstream regulator of genes induced in the EF-24 cell lines. (H) Regulatory network of genes induced in the EF-24 treated cell lines. False Discovery Rate (FDR) p-value ** < 0.01.

Regardless of the distinct myeloid leukemia subtypes, subsequent analysis uncovered 63 differentially expressed genes shared among all four leukemia cell lines treated with EF-24 as compared to the untreated controls of each cell line (Fig 3C). The number of genes altered in individual cell lines following EF-24 treatment was identified (Fig 3C). Several genes known to inhibit cell viability (e.g., HSPA6, CLU, OSGIN1, ATF3, CXCL8, ZFAND2A, and S100A10) exhibited a multifold increase in expression level in all four EF-24–treated cell lines as compared to the respective untreated control for each cell line (Fig 3D,3E). Further, Ingenuity Pathway Analysis (IPA) of genes that were both differentially expressed and shared in all four EF-24–treated leukemia cell lines revealed the top canonical pathways, including the S100 family signaling pathway (Fig 3F). The activation of S100 signaling causes the induction of downstream effector molecules such as JAK1, TYK2, and P38 MAPK, which induce NFkB-regulated proinflammatory cytokines linked to cell proliferation, survival, and viability [16].

We then investigated upstream regulators of genes differentially expressed in EF-24-treated cells. IL-6 emerged as a key regulator, activating JUN, NFkB, and STAT1 that causing induction of downstream genes S100A10, ABCB1, ATF3 and CRYAB adversely impacting cell viability and survival (Fig 3G). Moreover, NUPR1, appeared to be a novel transcription regulator, upregulated in response to EF-24 treatment and directly control expression of induced genes such as ATF3, CXCL8, DUSP8, MMP10, UPP1, ZFAND2A, AND ADM. Additionally, networks of upregulated genes in EF-24–treated cells suggest associations with cell death and survival (Fig 3H). Overall, EF-24 treatment causes adverse effects on myeloid leukemia cell lines through alterations in key pathways and a highly interconnected network of genes that negatively impact cell survival, proliferation, and viability. To further understand the transcriptional response to EF-24 therapy in specific leukemia subtypes, we explored differentially expressed genes and pathways at the individual cell line level.

EF-24 treatment activated the wound healing signaling pathway in K-562 cells

To identify the specific genes and pathways in K-562 cells affected by EF-24, we evaluated the differentially expressed genes. The expression patterns in heatmap were color-coded: red indicated increased expression, while black indicated decreased expression in treated cells compared to untreated control cells (Fig 4A). Among the differentially regulated genes, 488 genes exhibited a ≥ 5-fold change (p-value 0.01) in EF-24–treated cells as compared to untreated K-562 controls; only a small subset of DEGs (9 out of 488 genes) were downregulated. We then focused on 25 upregulated genes that showed the highest fold change in the list of DEGs (Fig 4B). Interestingly, CLU, PTPRN, NDRG1, GBP2, OSGIN1, ATF3, IFI16, HLA-C, BEX2, and VWA5A—known for their tumor suppressive function—were induced in many folds (Fig 4C) [17]. Count-per-million (CPM) values are used to filter genes in RNA-seq analysis with a minimum cut-off of 0.5 CPM. Gene falling below this threshold across all samples were excluded from further analysis.

Next, analysis in IPA unveiled canonical pathways and gene regulatory networks of the genes activated in response to EF-24 treatment, shedding light on EF-24 mechanisms influencing cell viability. In this pursuit, IPA illuminated the wound healing signaling pathways and their downstream effector molecules that potentially orchestrate gene expression changes in EF-24–treated cells (Fig 4D). This activation triggered MAP3K7-mediated NFkB and its target genes, which are crucial players in controlling cell survival and death [18]. Additionally, within the wound healing signaling pathway, the EGFR signaling cascade was activated, leading to the activation of downstream genes like RAS, MEK, and ERK1/2. These, in turn, transactivated AP1 transcription factor–regulated suppression of clusterin (CLU) gene that contributes to tissue repair [19].

Advancing in our transcriptional analysis, we uncovered upstream molecules governing gene expression in EF-24–treated cells (Fig 4E,4F). The activation of proinflammatory cytokines IL-1B and TGFB1 modulates downstream genes such as NFKB1, RELA, TNF, JUN, TP53, CEBPB, and FOS; each of these functions as tumor suppressors (Fig 4E). Meanwhile, the induced expression of various genes, including STAT3, SP1, and TNF is indirectly promoted and impeded by anti-inflammatory dexamethasone respectively (Fig 4F), which directly activates the repressed NR3C1 glucocorticoid receptor (GR) [20]. This comprehensive analysis not only enhances our understanding of the molecular responses triggered by EF-24 but also underscores the intricate interplay of pathways and regulatory networks contributing to the observed cellular effects in K-562 cells.

EF-24–activated pathways cause neutrophil degranulation in HL-60 cells

We performed full transcriptome analysis on HL-60 cells treated or untreated with EF-24 to understand transcriptional alterations and uncover signaling pathways and gene regulatory networks. Differential expression analysis revealed that EF-24 treatment had dramatically changed genes as compared to that of the untreated controls (Fig 5A). Quantitative analysis of DEGs showed that EF-24 treatment significantly increased gene expression levels (red color in heatmap) as compared to untreated controls.

Fig 5 — (A) Heatmap of the top 100 genes altered in HL-60 treated cells as compared to untreated control (red = induced; black = reduced). (B) Box plots show relative mRNA level of the indicated genes induced in EF-24–treated as compared to untreated control HL-60 cells. (C) Box plots show relative mRNA level of the indicated genes reduced in EF-24–treated as compared to untreated control HL-60 cells. Threshold absolute fold change ≥5, FDR p-value 0.01. (D) IPA shows top canonical pathways activated in HL-60 cells were treated with EF-24. (E) Gene network of genes regulated by TNF in HL-60 cells treated with EF-24. (F, G) Causal gene networks controlled by SMARCD1 and EGR1 in EF-24–treated HL-60 cells. (H) Bar plot shows relative mRNA level of the indicated downstream target genes of the IL-10 signaling in EF-24 treated as compared to untreated control HL-60 cells. False Discovery Rate (FDR) p-value ** < 0.01.

To understand the functional implications of top fold changed DEGs, we employed gene ontology analysis with GeneCards. The analysis indicated that the genes MAFA, CLU, RHOB, RASD1, HSPA1B, HSPA1A, HMOX1, CXCL8, DNAJB1, NCF2, SAT1, IER5, BAG3, B4GALNT1, CCL3, ANXA2, PPP1R15A, CES1, and JUN, which were induced in EF-24–treated cells (Fig 5B), may participate in cancer progression [17,21–23]. However, it is noteworthy that their excessive expression level causes adverse effects such as the disruption of homeostasis and cell death [22,24,25]. This indicates that the overexpression of genes that promote tumor growth affected cellular homeostasis, resulting in a decrease in cell survival and an increase in cell death. Conversely, the genes FTH1, SQSTM1, HSPA6, TSPYL2, PHLDA2, ITGAX, and DNAJA4 are expected to act as versatile protein regulators, potentially suppressing tumor cell genetic instability and promoting autophagy, apoptosis, and other forms of cell death [26–31].

We then focused on down-regulated genes associated with cancer, revealing several genes that play pivotal roles in various aspects of cancer development, including cell proliferation, adhesion, migration, phagocytosis, integrin signal transduction, and immunogenic cell death (ICD) (Fig 5C). Interestingly, certain repressed genes like B4GALT2, ELANE, CTSG, MLC1, NMP3, and SLC43A3 act as cancer suppressors, inhibiting malignant features and cancer development [32–34]. This suggests that EF-24 triggers a defense process under stressed physiological conditions that influences both protumorigenic and antitumorigenic gene expression.

Using IPA, we explored the pathways and upstream regulators of DEGs in EF-24–treated HL-60 cells and identified a downregulation of the neutrophil degranulation pathway. This downregulation contributes to an unfavorable environment for leukemia cell survival, leading to cell death [35]. On the other hand, several signaling pathways, with IL-10 ranking as the top upregulated pathway, were identified (Fig 5D). These findings demonstrate that EF-24 activates IL-10 signaling–associated transcriptional programs in leukemia cell lines, with both the direction and magnitude of gene expression changes supporting its engagement in tumor-suppressive pathways.

Notably, EF-24 treatment activated signaling cascades, including STAT3, that traditionally impede apoptosis, support cell proliferation, facilitate angiogenesis, and suppress antitumor immune responses [36]. This paradoxical activation of proinflammatory and cell survival pathways suggests a unique mechanism of EF-24-induced cell death in leukemia subtypes. TNF and dexamethasone modulated the expression of altered genes in EF-24 treated HL-60 cells and function as upstream regulators (Fig 5E). Causal network analysis in IPA highlighted SMARCD1 as an upstream regulator, playing a vital role in gene expression regulation through chromatin remodeling [37]. SMARCD1 activity directly transactivates P53, CDKN1a, and NR3C1 in EF-24–treated HL-60 cells (Fig 5F), inhibiting cell viability. Additionally, EGR1 activation and its target genes were observed in EF-24–treated cells (Fig 5G). Given IL-10’s context-dependent role in cancer—exhibiting both tumor-promoting and tumor-suppressive effects [38], we extend our analysis to explore IL-10 signaling associated genes in HL-60 cells. We performed a focused analysis of genes identified through IPA. EF-24 treatment was associated with upregulation of IL-10–responsive genes including HMOX1, DUSP1, CDKN1A, JUN, NFKB2, BLVRB, IL1R1, IL1RN, BCL3, and RELB (Fig 5H), all previously linked to cell death [39]. These findings suggest that EF-24–induced IL-10 activation contributes to cell death in HL-60 cells. Additional IL-10–associated genes such as CSF1, CXCL8, and CCL4 also showed significant expression changes (S1A Fig). Extending this analysis to K-562, Kasumi-1, and THP-1 cells revealed consistent modulation of IL-10 signaling–regulated genes across all four cell lines following EF-24 treatment (S1B–D Fig). Functional annotation revealed that the genes were activated associated with activation of leukocytes, differentiation of hematopoietic progenitor cells, and cell death of tumor. In summary, our comprehensive analysis of EF-24–treated HL-60 cells revealed a complex interplay of gene expression changes, signaling pathways, and regulatory networks contributing to cell death. The findings underscore the potential of EF-24 as a promising agent for leukemia treatment, warranting further investigation in preclinical and clinical settings.

EF-24 activates tumor suppressor genes in Kasumi-1 cells

To investigate the gene expression patterns in Kasumi-1 cells in response to EF-24, we conducted a differential gene expression analysis in treated versus untreated cells. Our findings revealed DEGs were either upregulated or downregulated (red-increase, dark-decrease) in EF-24–treated cells when compared to control cells (Fig 6A). The treatment with EF-24 induced a significant multifold change in the relative expression levels of various genes; notably, many of these induced genes, including FTL, UBC, FTH1, UBB, SQSTM1, JUN, MLLT1, GADD45A, KLF6, PPP1R15A, TSPYL2, SRGN, SRXN1, NDRG1, and ZFP36, function as tumor suppressors. In contrast, tumor promoters such as MYBL2, TYMS, MYC, MCM7, and EGFL7 were downregulated in EF-24–treated cells (Fig 6B).

Fig 6 — (A) Heatmap of the top 100 genes differentially expressed in the EF-24–treated Kasumi-1 cells (red = induced; black = reduced). Absolute fold change >5, FDR p-value 0.01. (B) Box plots display the relative levels of candidate genes differentially expressed in the RNA-Seq datasets of EF-24–treated Kasumi-1 cells. (C) Top canonical pathways activated in EF-24–treated Kasumi-1 cells. (D) TP53 and TP63 are the upstream regulators of genes induced in the EF-24–treated Kasumi-1 cells. (E) Top canonical pathway down regulated in the EF-24–treated Kasumi-1 cells. (F) Cell cycle and chromosomal replication pathway associated with down regulated genes in the EF-24–treated Kasumi-1 cells. (G, H) Network of down regulated genes in the EF-24–treated Kasumi-1 cells regulated by TBX3 and E2F1. () Table of top-down regulated genes show highest fold change in EF-24–treated Kasumi-1 cells. False Discovery Rate (FDR) p-value ** < 0.01.

Pathway analysis further unveiled top dysregulated canonical pathways, including the Pathogen Induced Cytokine Storm Signaling (PICSS) Pathway and IL-10 signaling, which were not only activated in EF-24–treated Kasumi-1 cells (Fig 6C) but also in HL-60 cells. Excessive production of inflammatory signals by the immune system can result in a cytokine storm, potentially leading to cell death.³⁴ Moreover, TP53 and TP63, which function as tumor suppressors, appeared as upstream regulators of genes induced in EF-24–treated Kasumi-1 cells (Fig 6D).

Considering the downregulation of a substantial number of genes by EF-24, we investigated the pathways controlling these downregulated genes. Pathways crucial for cell cycle control, chromosomal replication, and DNA synthesis were downregulated, leading to a multifold decrease in the expression levels of core molecules such as TOP2A, MCM7, MCM2, CDK1, MCM4, and MCM3 (Fig 6E, 6F). Additionally, TBX3 and E2F, identified as upstream regulators, were inhibited in EF-24–treated cells, resulting in the downregulation of their downstream target genes (Fig 6G,6H,6I).

The antitumorigenic effects of EF-24 in THP-1 cells are mediated by interferon alpha/beta signaling

Our analysis of differential gene expression in the comprehensive transcriptomic data from EF-24–treated and untreated THP-1 cells has unveiled notable changes in specific genes (Fig 7A). Seeking a deeper understanding of the biological implications stemming from EF-24–induced gene dysregulation, we delved into IPA. Within the EF-24–treated THP-1 cells, we observed the activation of interferon alpha/beta signaling—a pathway acknowledged for its role in inhibiting malignant cell growth through programmed cell death (Fig 7B) [40]. This underscores that the antitumorigenic effects of EF-24 in THP-1 cells are mediated by immune signaling. The genes upregulated and regulated by interferon signaling play pivotal roles in various biological processes and molecular functions.

Fig 7 — (A) Heatmap of the top 100 genes differentially expressed in EF-24–treated THP-1 cells (red = induced; black = reduced). (B, C) IPA revealed top canonical signaling pathways activated in EF-24–treated THP-1 cells. (D) TNF and (E) PYCARD activation observed in THP-1 cells treated with EF-24, functioning upstream of induced genes. (F) Box plots display quantitative levels of indicated genes in treated and untreated THP-1 cells. (G, H,) In EF-24–treated THP-1 cells, cell viability and NFκB activity show an inverse correlation. (G) Cell viability decreased in EF-24–treated THP-1 cells as compared to untreated control cells, whereas (H) NFκB-luciferase activity increased in EF-24–treated THP-1 cells as compared to untreated control cells. False Discovery Rate (FDR) p-value ** < 0.01.

Immunogenic cell death emerges as a significant mechanism in cancer therapy, harnessed by chemotherapy, radiation therapy, and targeted anticancer agents. This results in clinically relevant tumor-targeting immune responses [41] and culminates in immunogenic cell death (ICD) [42]. In our exploration of upstream regulators influencing genes altered in EF-24-treated THP-1 cells, TNF and P53 emerged as key orchestrators of these changes (Fig 7C,D). TNF, acting as a death ligand, binds to its cognate receptors, instigating complex I and complex II, which regulate caspase-8 activation. While complex I is associated with cell survival and proliferation, complex II executes cell death by activating caspase-3 and caspase-7 [42]. P53, a master regulator, suppresses tumorigenesis and surfaced as a prominent upstream regulator in EF-24–treated THP-1 cells [43]. P53 partially mediates the effects of EF-24 in inducing cell death in THP-1 cells.

Similarly, TNF regulatory networks displayed the activation of genes such as IFNG, STAT1, JUN, CDKN2A, RALA, and NFKB1 in EF-24–treated THP-1 cells (Fig 7D). Activation of PYCARD resulted in downstream activation of genes such as P53, MAPT, CASP1, SMAD2, and SMAD3 (Fig 7E). Subsequently, we identified candidate genes with differential expression and explored their biological associations; these genes exhibited a substantial increase in relative expression levels in EF-24–treated cells as compared to untreated THP-1 cells (Fig 7F). All these genes were found to be associated with cell death and survival, and their heightened activity contributed to induced cell death following EF-24 treatment.

To confirm TNF signaling activation in EF-24–treated cells, we performed a luciferase reporter assay using the THP-1-NFkB-LUC2 (ATCC^® TIB-202-NFkB-LUC2™) cell line, which was derived from parental THP-1 cells by introducing NFkB-LUC2 genes to measure NFkB activity. The cells were subjected to EF-24 treatment or left untreated for 24 hours, after which we measured cell viability and NFkB luciferase activity. As depicted in earlier assays, cell viability significantly decreased in the EF-24–treated cells as compared to untreated cells (Fig 7G). In contrast, NFkB activity saw a significant multifold increase (Fig 7H). This further substantiates the transcriptomics results and establishes the activation of TNF signaling in EF-24–treated THP-1 cells.

EF-24 differentially modulates p38/MAPK/ERK pathway targets in leukemia cell lines

In a previous study, Hsiao et al. demonstrated that EF-24 treatment activates the p38 mitogen-activated protein kinase (MAPK) pathway while attenuating ERK signaling in HL-60 acute myeloid leukemia (AML) cells after 24 hours [5]. To further investigate the transcriptional consequences of EF-24 exposure, we examined the expression of key downstream targets of the KEGG p38/MAPK pathway across several myeloid leukemia cell lines. In K-562 cells, EF-24T treatment led to significant upregulation of STAT1, PAX6, MEF2D, ATF3, HSPB1, DDIT3 and MEF2A, while MAX, RARA, HMGN1, CREB1, and RPS6KA5, were downregulated (S2A Fig). In contrast, CCL-240 cells exhibited a general upward trend in gene expression, except for a moderate decrease in MAPKAPK2, NFKB1, MAX, HMGN1, and ELK1 (S2B Fig), highlighting a divergent response compared to K-562 cells. In Kasumi-1 cells, EF-24T induced MEF2D, MAPKAPK2, ATF3, ATF2, CREB1, HSPBAP1, DDIT3, MAX, RPS6KA5, MEF2A, MEF2B, and HSPB1, while suppressing STAT1, NFKB1, and ELK1 (S2C Fig). Finally, in THP-1 cells, EF-24T treatment resulted in increased expression of STAT1, ETS1, RARA, ATF3, DDIT3, and MEF2A, accompanied by reduced levels of MYC, HMGN1, MEF2D, CREB1 and RPS6KA5 (S2D Fig).We observed consistent upregulation of ATF3 and DDIT3 across all four cell lines, suggesting that EF-24 may exert part of its effect through these stress-responsive transcription factors downstream of p38/MAPK signaling. While several genes showed statistically significant changes following treatment, the overall magnitude of expression shifts was generally subtle to moderate. Moreover, only a limited subset of genes demonstrated consistent regulation across all cell lines. In addition to canonical KEGG p38/MAPK pathway target genes, Ingenuity Pathway Analysis identified a set of shared genes modulated by p38/MAPK activity across all four cell lines (S2E Fig). These findings underscore the context-dependent nature of EF-24’s transcriptional impact, highlighting its selective modulation of p38/MAPK pathway targets involved in cellular stress responses and differentiation.

Discussion

In this study, we examined the impact of EF-24 on the transcriptome of leukemia cell lines [44]. Despite numerous treatments available, myeloid leukemia – a disease with several genetically distinct subtypes – has seen few significant therapeutic advances [45]. EF-24 is known to inhibit growth and induce apoptosis in leukemia and other cancers via established molecular pathways; however, its effects on global transcription remain unknown [5,6,46–48]. To understand EF-24’s molecular mechanisms, we used RNA sequencing to perform a whole transcriptome analysis on four well-established leukemia cell lines representing leukemia subtypes. Our goal was to identify gene expression signatures predictive of EF-24’s effects on cancer cells for future studies. To ensure the reliability of our transcriptomic analysis, we employed a rigorous experimental design with a minimum of five biological replicates per condition, enhancing statistical power. Differentially expressed genes (DEGs) were identified using stringent criteria: a minimum transcript read count of 10–15 (approximately CPM ≥ 0.5) and an FDR-adjusted p-value < 0.05. Genes were retained only if they met these thresholds in at least one sample, reducing the likelihood of including transcripts with negligible or absent expression. Rather than preselecting genes for validation, we adopted a hypothesis-free, data-driven approach, prioritizing top-ranked DEGs with strong endogenous expressions in control samples. This strategy minimized the inclusion of low-abundance transcripts, which are more susceptible to technical noise and less likely to reflect biologically meaningful changes. The consistency and magnitude of expression changes across replicates further support the robustness of our findings. We acknowledge, however, that this approach may have excluded low-expressing genes with potential biological relevance, particularly in mediating EF-24’s effects. While such transcripts are more prone to background noise and off-target responses, and less likely to be directly linked to primary phenotypic outcomes (e.g., reduced cell viability), their exclusion represents a limitation of our study. Future investigations incorporating orthogonal validation methods may help elucidate the roles of these low-abundance genes.

We verified that EF-24 treatment exhibited potent antitumor activity, significantly reducing cell viability across all four treated myeloid leukemia cell lines that coincided with previous reports [5]. It was also noted that EF-24 treatment sensitivity is higher at lower concentrations in leukemia cell lines compared to solid tumor cell lines [49]. Transcriptomic analysis showed that EF-24’s effects are mediated by a combination of genes and signaling molecules rather than a single pathway, highlighting its complex mechanism of action. The activation of pathways such as the S100 family signaling pathway [16], along with the identification of key molecules like IL6 and TNF, provided nuanced insights into the intricate molecular dynamics influenced by EF-24 [50,51]. Besides identifying the multifactorial genetic effects of EF-24 in selected myeloid leukemia cell lines, our analysis also confirmed the previously reported modulation of molecules and pathways in various cell lines from multiple tissue types treated with EF-24 [5,49]. In vitro, EF-24 significantly reduced the proliferation of various solid cancer cells while demonstrating non-cytotoxicity to normal cells. For instance, Zou et al. found that EF-24 inhibited the survival of gastric cancer cell lines SGC-7901 and BGC-823 but did not affect the survival of the normal human gastric epithelial cell line GES-1 or the rat kidney proximal tubular epithelial cell line NRK-52E [52]. In vivo, EF-24 demonstrated high oral bioavailability and low toxicity in mice, while effectively inhibiting the growth of human breast cancer in a mouse xenograft model [53]. Our findings explicitly underscore the EF-24’s multifaceted nature in modulating genes associated with myeloid leukemia cell survival and proliferation.

In our analysis of K-562 cells, we uncovered a myriad of differentially regulated genes post-EF-24 treatment, shedding light on its mechanisms. Surprisingly, EF-24 treatment induced NFkB activity in K-562 cells, contrasting with previous reports of NFkB suppression in EF-24 treated lung, breast, ovarian, and cervical cancer cells [54]. Additionally, unlike the inhibition of HIF1α observed in certain cell lines, HIF1α was activated in myeloid leukemia cell lines upon EF-24 treatment [55,56]. Accordingly, EF-24-mediated inhibition of NFkB and HIF1α in cancer cells appears to be contextual, and its antitumorigenic effects are not simply exerted by regulating specific genes or pathways. However, we observed that the increased expression of transporter genes ABCB1, ABCB11, ABCA4, ABCA9, and many others did not contribute to the antiproliferative effects of EF-24, which is consistent with previous reports by Skoupa et al. [6]. Noteworthy inductions of apoptosis associated genes CLU and CRYAB, coupled with the activation of the wound healing signaling pathway unveiled EF-24’s potential in orchestrating cellular responses [57,58]. This revelation emphasizes its prospective role in the context of chronic myeloid leukemia. Previous reports indicate that Clusterin (CLU) mediates apoptosis by interacting with Bcl-XL [59], aligning with the observed reduced cell viability in EF-24–treated cells. Attention was then directed to genes that exhibit higher levels of basal expression in untreated K-562 cells and overexpressed by EF-24, including CLU, PTPRN, NDRG1, GBP2, OSGIN1, AFT3, IFI16, HLA-C, BEX2, and VWA5A. Gene ontology analysis of the induced genes shed light on their potential antitumorigenic roles, suggesting tumor suppressor functions in EF-24–treated cells [60–68]. Notably, activation of wound healing pathway suggests a counter response to the acute cell-killing activity of EF-24 treatment in K-562 cells. Induction of cell death is inexorably linked with cancer therapy, but this can also initiate wound-healing processes that have been linked to cancer progression and therapeutic resistance [69,70]. Because the wound healing pathway was activated in EF-24–treated K-562 cells, understanding the therapeutic benefit of the curcumin analog EF-24 in chronic myeloid leukemia required further investigations. K-562 cells are known for their multipotentiality, exhibiting spontaneous differentiation into erythroid, granulocytic, and monocytic lineages. This capacity to activate alternative differentiation programs reflects characteristics of normal multipotent hematopoietic stem cells [71]. However, given their malignant origin, K-562 cells may not fully recapitulate the behavior of non-leukemic hematopoietic progenitors. To more comprehensively understand the effects of EF-24 on hematopoietic signaling pathways, future studies should incorporate CD34 ⁺ normal hematopoietic stem and progenitor cells as a comparative reference. Such analyses would help delineate EF-24’s impact on leukemic versus non-leukemic transcriptional programs and clarify whether its modulatory effects are lineage-specific or broadly applicable across hematopoietic contexts.

Shifting our focus to HL-60 cells, EF-24 treatment induced significant alterations in the expression of genes associated with cancer progression. The downregulation of the neutrophil degranulation pathway suggested an unfavorable environment for leukemia cell survival, while the concomitant upregulation of IL-10 signaling hinted at a complex interplay between proinflammatory, and cell survival pathways triggered by EF-24 [72]. Examining gene regulatory networks associated with molecular and cellular function, we observed the activation of leukocytes and differentiation of hematopoietic progenitor cells in EF-24–treated cells. While the activation of leukocytes is known to be part of the immune response, the impact on tumor burden requires further exploration in an in vivo setting. Additionally, the activation of genes associated with the differentiation of hematopoietic progenitor cells suggests that EF-24 exerts multifaceted antitumor effects in HL-60 cells [70].

Investigating upstream regulators of genes aberrantly activated in EF-24–treated cells, TNF emerged as a central regulator in HL-60 and THP-1 cells. TNF directly regulates upregulated genes, including EGR1, NFkB complex, RELA, and IKBKB, leading to downstream upregulation of TP53. EGR1 and TP53, known tumor suppressors, upregulate p21, inducing tumor cell apoptosis [73,74]. Glucocorticoid receptor (NR3C1) signaling, activated by dexamethasone, a known treatment for leukemia, was found to be negligible change, aligning with the observed negative control of genes regulated by dexamethasone in EF-24–treated HL-60 cells [75].

In Kasumi-1 cells, EF-24 induced a distinct shift toward tumor-suppressive mechanisms. The activation of TP53 and TP63, coupled with the downregulation of tumor promoters, pointed towards EF-24’s potential to create a tumor-suppressive microenvironment. The modulation of key pathways, including the Pathogen Induced Cytokine Storm Signaling Pathway and IL-10 Signaling [40,72], added depth to our understanding of EF-24’s impact on gene regulatory networks.

Finally, our investigation extended to THP-1 cells, revealing EF-24’s prominent activation of the interferon alpha/beta signaling pathway [40]. This immune-mediated pathway, coupled with the involvement of TNF and P53 as upstream regulators, substantiated EF-24’s potential to induce programmed cell death in malignant cells. Luciferase reporter assays further validated the activation of the TNF signaling cascade, reinforcing our transcriptomic findings.

It is also important to consider the dual roles that many signaling pathways play in cancer biology. Several of the pathways altered by EF-24 treatment—such as [insert specific pathways, e.g., NF-κB, PI3K/AKT, MAPK, IL-10, TNF]—are known to have context-dependent functions [76–80]. While these pathways often promote survival and proliferation in malignant cells, they can also trigger apoptotic or anti-proliferative responses under certain conditions. This complexity underscores the need for careful interpretation of pathway modulation and highlights the importance of validating these findings in diverse cellular and genetic contexts. Future studies will aim to dissect these dual roles more thoroughly, particularly in primary leukemia samples and across different lineage backgrounds.

While our study provides important insights into the effects of EF-24 on leukemia cell lines, we acknowledge several limitations that warrant further investigation. Notably, our current analysis was conducted using a limited panel of established leukemia cell lines—three AML and one CML—which, while informative, may not fully capture the heterogeneity and complexity of primary patient-derived leukemia samples. We have now explicitly acknowledged this limitation and emphasized the need for future studies involving primary leukemia cells to validate the translational relevance of our findings. Additionally, we recognize an imbalance in the number of AML versus CML cell lines used in this study. The inclusion of only a single CML cell line limits our ability to draw robust conclusions about lineage-specific responses to EF-24. While the study did not include FLT3-ITD mutated AML—a well-characterized and clinically significant subtype—the signaling principles elucidated here may still be relevant across broader AML contexts. Future work will expand the panel of CML cell lines, FLT-3-ITD mutated AML and other common subtypes and incorporate lymphoid leukemia models to enable a more comprehensive comparison across hematologic lineages to assess the generalizability potential of the observed signaling dynamics. This will also allow for a more detailed investigation into whether EF-24 elicits differential responses based on leukemia lineage and maturation stage. Importantly, several keys signaling pathways were consistently altered by EF-24 treatment across the cell lines studied. These pathways, identified through transcriptomic analyses, will serve as focal points in subsequent studies aimed at delineating lineage-specific mechanisms of action. Such efforts will be critical for refining the therapeutic potential of EF-24 and identifying biomarkers predictive of response in diverse leukemia subtypes.

Conclusion

Our comprehensive RNAseq analyses not only unraveled EF-24’s potent antitumorigenic activity and its influence on transcriptional landscapes but also delved into its multifaceted impact on specific signaling pathways and gene networks controlling cell survival, proliferation, and immune responses in myeloid leukemia cells. These insights lay a solid foundation for the exploration of EF-24 as a promising therapeutic agent in myeloid leukemia treatment. Moving forward, further investigations in preclinical and clinical settings are imperative to harness the full potential of EF-24 for effective leukemia interventions.

Materials and methods

Cell lines

Preserved cell vials of the cell lines K-562 (ATCC^® CCL-243™), HL-60 (ATCC^® CCL-240™), Kasumi-1 (ATCC^® CRL-2724™), and THP-1 (ATCC^® TIB-202™) were acquired from the ATCC^® repository (Manassas, VA, USA) and cultured according to specified parameters. These parameters were tailored for each cell line and adhered to the ATCC^® cell culture laboratory, which operates under industry safety standards and quality guidelines and complies with ISO 9001 regulations. In brief, HL-60 cells were cultured in Iscove’s Modified Dulbecco’s Medium (IMDM; ATCC^® 30–2005™) supplemented with fetal bovine serum (FBS; ATCC^® 30–2020™) to a final concentration of 20%. Complete medium for K-562 consisted of IMDM with 10% FBS. For Kasumi-1, complete medium was prepared using RPMI-1640 (ATCC^® 30–2001™) supplemented with 20% FBS. THP-1 cells were cultured in RPMI-1640 supplemented with 10% FBS. All cell lines were maintained at 37°C in an atmosphere of 95% air and 5% CO₂.

Chemical and reagents

EF-24 (with a purity of ≥98%; determined by HPLC (High Performance Liquid Chromatography) was procured from Sigma-Aldrich^® (St. Louis, MO, USA; catalog number E8409-25MG). It was dissolved in dimethyl sulfoxide (DMSO; ATCC^® 4-X™) at a concentration of 25 mg (milligram) EF-24 per 2.5 mL (milliliter) DMSO, yielding a stock concentration of 32.1203 mM (millimolar) according to the manufacturer’s instructions. Subsequently, a 10 mM solution was prepared from the stock by combining 312.5 µL (microliter) of the 32 mM EF-24 solution with 687.5 µL of DMSO to achieve a final volume of 1 mL. The final treatment concentration was 2 µM; for instance, 10 µL of the 10 mM EF-24 solution was added to 50 mL of media to attain a final concentration of 2 µM in cell culture.

Cytotoxicity assay

The cytotoxic effect of EF-24 on AML cell lines was assessed using the CellTiter-Glo^® Luminescent Cell Viability Assay kit (Promega^® catalog number G7570). K-562, HL-60, Kasumi-1, and THP-1 were seeded into T25 flasks (Corning, part no. 430639) at a density of 3 x 10⁶ cells per flask. Subsequently, the cells were treated with a concentration of 2 µM EF-24 for 24 hours. Following treatment, 100 µL of cell suspension was combined with 100 µL of CellTiter-Glo^® solution in a 96-well plate with each well containing 3 x 10⁴ cells. The plate was then incubated at room temperature for 10 minutes, after which the absorbance was measured at 450 nm using a microplate reader (Molecular Devices SpectraMax^®). Each experiment was performed twice with a minimum of five biological replicates.

RNA extraction and quality control (QC)

RNA isolation was performed using the QIAGEN^® QIAcube^® automated system with the RNeasy Mini QIAcube^® Kit. Frozen samples were thawed and prepared for RNA extraction according to ATCC’s work instructions. Extracted samples were tested for RNA integrity and quality using the Agilent^® TapeStation™ (RNA Integrity Number (RIN) ≥ 6.5), RNA purity using the Thermofisher^® Nanodrop™ (A260/A280 1.8 ≥ x ≤ 2.2), and concentration using the Qubit^®™.

RNA-Seq library preparation and sequencing

Automated RNA-seq NGS library preparation was performed on the Eppendorf epMotion^® 5075 Liquid Handler using the Illumina^® Stranded mRNA Prep, Ligation kit. Prepared NGS libraries were assessed by quantitative analysis using the Invitrogen^®™ Qubit^®™ dsDNA High Sensitivity Assay Kit and qualitative analysis using the Agilent^® 4200 TapeStation^®™ and D5000 ScreenTape System. Libraries were loaded on an Illumina^® P3 200-cycle Reagent kit and sequenced on the NextSeq^® 2000 platform.

RNA-Seq and bioinformatics analysis

Our data analysis pipeline included quality control, read trimming, alignment to the reference transcriptome, and quantification of gene expression. Utilizing the CLC Genomics Workbench v23 (QIAGEN Digital Insights), an end-to-end pipeline was created that, briefly, entailed the following steps. First, raw paired end Illumina reads were trimmed and filtered to a minimum quality of Q30 and a maximum of 2 ambiguous bases. Furthermore, potential “read-through” adapter sequences and 3’ polyG sequences (due to using the 2-color NextSeq platform) were also automatically identified and trimmed. Reads below 50 bp were then discarded. Next, reads were mapped to the human genome hg38 reference genome (obtained from Ensemble) using the default settings for bulk-RNAseq experiments: mismatch cost 2; InDel cost 3; length fraction 0.8; similarity fraction 0.8; maximum hits per reads 10; reversed strand-specific mapping 1; ignore broken read pairs 1. A minimum of 18M mapped reads per library were required for each biological replicate, and TMM normalization was carried out for each library (Robinson and Oshlack, 2010). Statistical comparisons between groups were conducted using a Wald test, and fold-change values calculated from the GLM model (Robinson et al., 2010). Outliers were down weighted and iteratively re-fit to the GLM model (Zhou et al., 2014). Low expression genes were filtered prior to FDR correction and calculation (Love et al., 2014). Count-per-million (CPM) values are used to filter genes in RNA-seq analysis. A CPM value of 0.5 is usually equivalent to 10–15 reads. A gene is typically retained if it has a CPM above 0.5 in at least one sample, as a smaller count indicates that the gene is not expressed in that sample. The gene list obtained from the differential gene expression analysis was further analyzed using the Ingenuity Pathway Analysis (IPA) platform (Krämer et al., 2014). IPA automatically carried out statistical comparisons for gene-set enrichment analysis, statistically significant pathway activation, identification of upstream regulators and downstream targets, gene and pathway associated diseases and biofunctions. IPA was also used to produce network diagrams of pathways and associated differentially expressed genes.

Supporting information

S1 Fig. Expression analysis of downstream target genes in the p38/MAPK signaling pathway across myeloid leukemia cell lines.

(A–D) Bar plots show the relative mRNA expression levels of the indicated genes in EF-24–treated versus untreated control samples for each cell line representing distinct myeloid leukemia subtypes. False Discovery Rate (FDR) p-value ** < 0.01.

(TIF)

pone.0330906.s001.tif^{(5.3MB, tif)}

S2 Fig. Expression analysis of downstream target genes in the IL-10 signaling pathway across myeloid leukemia cell lines.

(A–C) Bar plots show the relative mRNA expression levels of the indicated genes in EF-24–treated versus untreated control samples for each cell line representing distinct myeloid leukemia subtypes. False Discovery Rate (FDR) p-value ** < 0.01.

(TIF)

pone.0330906.s002.tif^{(10.2MB, tif)}

Abbreviations

AML: Acute myeloid leukemia
ATCC: American Type Culture Collection
CML: Chronic myeloid leukemia
DEG: Differentially expressed genes
FBS: fetal bovine serum
IMDM: Iscove’s Modified Dulbecco’s Medium
PCA: Principal component analysis
RNAseq: RNA sequencing
RPMI: Roswell Park Memorial Institute Medium

Data Availability

All RNAseq data produced for this study is available under NCBI Bioproject PRJNA1165739.

Funding Statement

The author(s) received no specific funding for this work.

References

1.Chennamadhavuni A, Lyengar V, Mukkamalla SKR, Shimanovsky A. Leukemia. Treasure Island (FL): StatPearls; 2024. [Google Scholar]
2.Advancing cancer therapy. Nat Cancer. 2021;2(3):245–6. doi: 10.1038/s43018-021-00192-x [DOI] [PubMed] [Google Scholar]
3.Sohn S-I, Priya A, Balasubramaniam B, Muthuramalingam P, Sivasankar C, Selvaraj A, et al. Biomedical applications and bioavailability of curcumin-an updated overview. Pharmaceutics. 2021;13(12):2102. doi: 10.3390/pharmaceutics13122102 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.He Y, Li W, Hu G, Sun H, Kong Q. Bioactivities of EF24, a novel curcumin analog: a review. Front Oncol. 2018;8:614. doi: 10.3389/fonc.2018.00614 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Hsiao P-C, Chang J-H, Lee W-J, Ku C-C, Tsai M-Y, Yang S-F, et al. The Curcumin Analogue, EF-24, Triggers p38 MAPK-Mediated Apoptotic Cell Death via Inducing PP2A-Modulated ERK Deactivation in Human Acute Myeloid Leukemia Cells. Cancers (Basel). 2020;12(8):2163. doi: 10.3390/cancers12082163 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Skoupa N, Dolezel P, Ruzickova E, Mlejnek P. Apoptosis Induced by the Curcumin Analogue EF-24 Is Neither Mediated by Oxidative Stress-Related Mechanisms nor Affected by Expression of Main Drug Transporters ABCB1 and ABCG2 in Human Leukemia Cells. Int J Mol Sci. 2017;18(11):2289. doi: 10.3390/ijms18112289 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Baykal-Köse S, Acikgoz E, Yavuz AS, Gönül Geyik Ö, Ateş H, Sezerman OU, et al. Adaptive phenotypic modulations lead to therapy resistance in chronic myeloid leukemia cells. PLoS One. 2020;15(2):e0229104. doi: 10.1371/journal.pone.0229104 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Koeffler HP, Golde DW. Human myeloid leukemia cell lines: a review. Blood. 1980;56(3):344–50. doi: 10.1182/blood.v56.3.344.bloodjournal563344 [DOI] [PubMed] [Google Scholar]
9.Skopek R, Palusińska M, Kaczor-Keller K, Pingwara R, Papierniak-Wyglądała A, Schenk T, et al. Choosing the right cell line for acute myeloid leukemia (AML) research. Int J Mol Sci. 2023;24(6):5377. doi: 10.3390/ijms24065377 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.El-Tanani M, Nsairat H, Matalka II, Lee YF, Rizzo M, Aljabali AA, et al. The impact of the BCR-ABL oncogene in the pathology and treatment of chronic myeloid leukemia. Pathol Res Pract. 2024;254:155161. doi: 10.1016/j.prp.2024.155161 [DOI] [PubMed] [Google Scholar]
11.Reitsma PH, Rothberg PG, Astrin SM, Trial J, Bar-Shavit Z, Hall A, et al. Regulation of myc gene expression in HL-60 leukaemia cells by a vitamin D metabolite. Nature. 1983;306(5942):492–4. doi: 10.1038/306492a0 [DOI] [PubMed] [Google Scholar]
12.Basu J, Madhulika S, Murmu KC, Mohanty S, Samal P, Das A, et al. Molecular and epigenetic alterations in normal and malignant myelopoiesis in human leukemia 60 (HL60) promyelocytic cell line model. Front Cell Dev Biol. 2023;11:1060537. doi: 10.3389/fcell.2023.1060537 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Larizza L, Magnani I, Beghini A. The Kasumi-1 cell line: a t(8;21)-kit mutant model for acute myeloid leukemia. Leuk Lymphoma. 2005;46(2):247–55. doi: 10.1080/10428190400007565 [DOI] [PubMed] [Google Scholar]
14.Chanput W, Mes JJ, Wichers HJ. THP-1 cell line: an in vitro cell model for immune modulation approach. Int Immunopharmacol. 2014;23(1):37–45. doi: 10.1016/j.intimp.2014.08.002 [DOI] [PubMed] [Google Scholar]
15.Hsiao PC, Chang JH, Lee WJ, Ku CC, Tsai MY, Yang SF, et al. The Curcumin Analogue, EF-24, Triggers p38 MAPK-Mediated Apoptotic Cell Death via Inducing PP2A-Modulated ERK Deactivation in Human Acute Myeloid Leukemia Cells. Cancers (Basel). 2020;12(8). doi: 10.3390/cancers12082163 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Allgöwer C, Kretz A-L, von Karstedt S, Wittau M, Henne-Bruns D, Lemke J. Friend or Foe: S100 proteins in cancer. Cancers (Basel). 2020;12(8):2037. doi: 10.3390/cancers12082037 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Shannan B, Seifert M, Boothman DA, Tilgen W, Reichrath J. Clusterin and DNA repair: a new function in cancer for a key player in apoptosis and cell cycle control. J Mol Histol. 2006;37(5–7):183–8. doi: 10.1007/s10735-006-9052-7 [DOI] [PubMed] [Google Scholar]
18.Peng F, Liao M, Qin R, Zhu S, Peng C, Fu L, et al. Regulated cell death (RCD) in cancer: key pathways and targeted therapies. Signal Transduct Target Ther. 2022;7(1):286. doi: 10.1038/s41392-022-01110-y [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Gross C, Le-Bel G, Desjardins P, Benhassine M, Germain L, Guérin SL. Contribution of the Transcription Factors Sp1/Sp3 and AP-1 to Clusterin Gene Expression during Corneal Wound Healing of Tissue-Engineered Human Corneas. Int J Mol Sci. 2021;22(22):12426. doi: 10.3390/ijms222212426 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Jia W-Y, Zhang J-J. Effects of glucocorticoids on leukocytes: genomic and non-genomic mechanisms. World J Clin Cases. 2022;10(21):7187–94. doi: 10.12998/wjcc.v10.i21.7187 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Wang Y, Gu W, Wen W, Zhang X. SERPINH1 is a potential prognostic biomarker and correlated with immune infiltration: a pan-cancer analysis. Front Genet. 2022;12:756094. doi: 10.3389/fgene.2021.756094 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Ning Y, Li Y, Wang H. ANXA2 is a potential biomarker for cancer prognosis and immune infiltration: a systematic pan-cancer analysis. Front Genet. 2023;14:1108167. doi: 10.3389/fgene.2023.1108167 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Eychène A, Rocques N, Pouponnot C. A new MAFia in cancer. Nat Rev Cancer. 2008;8(9):683–93. doi: 10.1038/nrc2460 [DOI] [PubMed] [Google Scholar]
24.Ecker V, Stumpf M, Brandmeier L, Neumayer T, Pfeuffer L, Engleitner T, et al. Targeted PI3K/AKT-hyperactivation induces cell death in chronic lymphocytic leukemia. Nat Commun. 2021;12(1):3526. doi: 10.1038/s41467-021-23752-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Luu Hoang KN, Anstee JE, Arnold JN. The Diverse Roles of Heme Oxygenase-1 in Tumor Progression. Front Immunol. 2021;12:658315. doi: 10.3389/fimmu.2021.658315 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Tang J, Li Y, Xia S, Li J, Yang Q, Ding K, et al. Sequestosome 1/p62: a multitasker in the regulation of malignant tumor aggression (Review). Int J Oncol. 2021;59(4):77. doi: 10.3892/ijo.2021.5257 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Shen S, Wei C, Fu J. RNA-Sequencing Reveals Heat Shock 70-kDa Protein 6 (HSPA6) as a Novel Thymoquinone-Upregulated Gene That Inhibits Growth, Migration, and Invasion of Triple-Negative Breast Cancer Cells. Front Oncol. 2021;11:667995. doi: 10.3389/fonc.2021.667995 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Liu J, Ren Z, Yang L, Zhu L, Li Y, Bie C, et al. The NSUN5-FTH1/FTL pathway mediates ferroptosis in bone marrow-derived mesenchymal stem cells. Cell Death Discov. 2022;8(1):99. doi: 10.1038/s41420-022-00902-z [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Tao KP, Fong SW, Lu Z, Ching YP, Chan KW, Chan SY. TSPYL2 is important for G1 checkpoint maintenance upon DNA damage. PLoS One. 2011;6(6):e21602. doi: 10.1371/journal.pone.0021602 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Yang X, Wang Z, Samovich SN, Kapralov AA, Amoscato AA, Tyurin VA, et al. PHLDA2-mediated phosphatidic acid peroxidation triggers a distinct ferroptotic response during tumor suppression. Cell Metab. 2024;36(4):762–777.e9. doi: 10.1016/j.cmet.2024.01.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Galluzzi L, Kepp O, Hett E, Kroemer G, Marincola FM. Immunogenic cell death in cancer: concept and therapeutic implications. J Transl Med. 2023;21(1):162. doi: 10.1186/s12967-023-04017-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Peng B, Hu J, Fu X. ELANE: an emerging lane to selective anticancer therapy. Signal Transduct Target Ther. 2021;6(1):358. doi: 10.1038/s41392-021-00766-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Chan S, Wang X, Wang Z, Du Y, Zuo X, Chen J, et al. CTSG suppresses colorectal cancer progression through negative regulation of Akt/mTOR/Bcl2 signaling pathway. Int J Biol Sci. 2023;19(7):2220–33. doi: 10.7150/ijbs.82000 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Wu Z, Ni J, Zhang H, Zhang Y, Lv C, Wang Y, et al. MLC1 overexpression inhibits tumor progression through PI3K/AKT signal pathway in prostate cancer. Adv Biol (Weinh). 2024;8(1):e2300060. doi: 10.1002/adbi.202300060 [DOI] [PubMed] [Google Scholar]
35.Wu L, Saxena S, Singh RK. Neutrophils in the tumor microenvironment. Adv Exp Med Biol. 2020;1224:1–20. doi: 10.1007/978-3-030-35723-8_1 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Tolomeo M, Cascio A. The multifaced role of STAT3 in cancer and its implication for anticancer therapy. Int J Mol Sci. 2021;22(2):603. doi: 10.3390/ijms22020603 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Singh AP, Archer TK. Analysis of the SWI/SNF chromatin-remodeling complex during early heart development and BAF250a repression cardiac gene transcription during P19 cell differentiation. Nucleic Acids Res. 2014;42(5):2958–75. doi: 10.1093/nar/gkt1232 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Oft M. IL-10: master switch from tumor-promoting inflammation to antitumor immunity. Cancer Immunol Res. 2014;2(3):194–9. doi: 10.1158/2326-6066.CIR-13-0214 [DOI] [PubMed] [Google Scholar]
39.Verma R, Balakrishnan L, Sharma K, Khan AA, Advani J, Gowda H, et al. A network map of Interleukin-10 signaling pathway. J Cell Commun Signal. 2016;10(1):61–7. doi: 10.1007/s12079-015-0302-x [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Kotredes KP, Gamero AM. Interferons as inducers of apoptosis in malignant cells. J Interferon Cytokine Res. 2013;33(4):162–70. doi: 10.1089/jir.2012.0110 [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Fucikova J, Kepp O, Kasikova L, Petroni G, Yamazaki T, Liu P, et al. Detection of immunogenic cell death and its relevance for cancer therapy. Cell Death Dis. 2020;11(11):1013. doi: 10.1038/s41419-020-03221-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Ahmed A, Tait SWG. Targeting immunogenic cell death in cancer. Mol Oncol. 2020;14(12):2994–3006. doi: 10.1002/1878-0261.12851 [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Thomas AF, Kelly GL, Strasser A. Of the many cellular responses activated by TP53, which ones are critical for tumour suppression? Cell Death Differ. 2022;29(5):961–71. doi: 10.1038/s41418-022-00996-z [DOI] [PMC free article] [PubMed] [Google Scholar]
44.The Lancet Haematology. The global burden of haematological diseases. Lancet Haematol. 2018;5(1):e1. doi: 10.1016/S2352-3026(17)30233-8 [DOI] [PubMed] [Google Scholar]
45.Short NJ, Rytting ME, Cortes JE. Acute myeloid leukaemia. Lancet. 2018;392(10147):593–606. doi: 10.1016/S0140-6736(18)31041-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Tang C, Liu J, Yang C, Ma J, Chen X, Liu D, et al. Curcumin and its analogs in non-small cell lung cancer treatment: challenges and expectations. Biomolecules. 2022;12(11):1636. doi: 10.3390/biom12111636 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Ibáñez Gaspar V, McCaul J, Cassidy H, Slattery C, McMorrow T. Effects of Curcumin Analogues DMC and EF24 in Combination with the Cytokine TRAIL against Kidney Cancer. Molecules. 2021;26(20):6302. doi: 10.3390/molecules26206302 [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Mbese Z, Khwaza V, Aderibigbe BA. Curcumin and its derivatives as potential therapeutic agents in prostate, colon and breast cancers. Molecules. 2019;24(23):4386. doi: 10.3390/molecules24234386 [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Bertazza L, Barollo S, Mari ME, Faccio I, Zorzan M, Redaelli M, et al. Biological effects of EF24, a curcumin derivative, alone or combined with mitotane in adrenocortical tumor cell lines. Molecules. 2019;24(12):2202. doi: 10.3390/molecules24122202 [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Choy EH, De Benedetti F, Takeuchi T, Hashizume M, John MR, Kishimoto T. Translating IL-6 biology into effective treatments. Nat Rev Rheumatol. 2020;16(6):335–45. doi: 10.1038/s41584-020-0419-z [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Wang X, Yang C, Körner H, Ge C. Tumor necrosis factor: what is in a name? Cancers (Basel). 2022;14(21):5270. doi: 10.3390/cancers14215270 [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Zou P, Xia Y, Chen W, Chen X, Ying S, Feng Z, et al. EF24 induces ROS-mediated apoptosis via targeting thioredoxin reductase 1 in gastric cancer cells. Oncotarget. 2016;7(14):18050–64. doi: 10.18632/oncotarget.7633 [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Adams BK, Ferstl EM, Davis MC, Herold M, Kurtkaya S, Camalier RF, et al. Synthesis and biological evaluation of novel curcumin analogs as anti-cancer and anti-angiogenesis agents. Bioorg Med Chem. 2004;12(14):3871–83. doi: 10.1016/j.bmc.2004.05.006 [DOI] [PubMed] [Google Scholar]
54.Kasinski AL, Du Y, Thomas SL, Zhao J, Sun S-Y, Khuri FR, et al. Inhibition of IkappaB kinase-nuclear factor-kappaB signaling pathway by 3,5-bis(2-flurobenzylidene)piperidin-4-one (EF24), a novel monoketone analog of curcumin. Mol Pharmacol. 2008;74(3):654–61. doi: 10.1124/mol.108.046201 [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Thomas SL, Zhong D, Zhou W, Malik S, Liotta D, Snyder JP, et al. EF24, a novel curcumin analog, disrupts the microtubule cytoskeleton and inhibits HIF-1. Cell Cycle. 2008;7(15):2409–17. doi: 10.4161/cc.6410 [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Liang Y, Zheng T, Song R, Wang J, Yin D, Wang L, et al. Hypoxia-mediated sorafenib resistance can be overcome by EF24 through Von Hippel-Lindau tumor suppressor-dependent HIF-1α inhibition in hepatocellular carcinoma. Hepatology. 2013;57(5):1847–57. doi: 10.1002/hep.26224 [DOI] [PubMed] [Google Scholar]
57.Shannan B, Seifert M, Leskov K, Willis J, Boothman D, Tilgen W, et al. Challenge and promise: roles for clusterin in pathogenesis, progression and therapy of cancer. Cell Death Differ. 2006;13(1):12–9. doi: 10.1038/sj.cdd.4401779 [DOI] [PubMed] [Google Scholar]
58.Zhang J, Liu J, Wu J, Li W, Chen Z, Yang L. Progression of the role of CRYAB in signaling pathways and cancers. Onco Targets Ther. 2019;12:4129–39. doi: 10.2147/OTT.S201799 [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Kim N, Yoo JC, Han JY, Hwang EM, Kim YS, Jeong EY, et al. Human nuclear clusterin mediates apoptosis by interacting with Bcl-XL through C-terminal coiled coil domain. J Cell Physiol. 2012;227(3):1157–67. doi: 10.1002/jcp.22836 [DOI] [PubMed] [Google Scholar]
60.Khoi C-S, Xiao C-Q, Hung K-Y, Lin T-Y, Chiang C-K. Oxidative Stress-Induced Growth Inhibitor (OSGIN1), a Target of X-Box-Binding Protein 1, Protects Palmitic Acid-Induced Vascular Lipotoxicity through Maintaining Autophagy. Biomedicines. 2022;10(5):992. doi: 10.3390/biomedicines10050992 [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Yan C, Jamaluddin MS, Aggarwal B, Myers J, Boyd DD. Gene expression profiling identifies activating transcription factor 3 as a novel contributor to the proapoptotic effect of curcumin. Mol Cancer Ther. 2005;4(2):233–41. doi: 10.1158/1535-7163.233.4.2 [DOI] [PubMed] [Google Scholar]
62.Joshi V, Lakhani SR, McCart Reed AE. NDRG1 in Cancer: A Suppressor, Promoter, or Both? Cancers (Basel). 2022;14(23):5739. doi: 10.3390/cancers14235739 [DOI] [PMC free article] [PubMed] [Google Scholar]
63.Luo Y, Jin H, Kim JH, Bae J. Guanylate-binding proteins induce apoptosis of leukemia cells by regulating MCL-1 and BAK. Oncogenesis. 2021;10(7):54. doi: 10.1038/s41389-021-00341-y [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Li D, Xie L, Qiao Z, Mai S, Zhu J, Zhang F, et al. STING-mediated degradation of IFI16 negatively regulates apoptosis by inhibiting p53 phosphorylation at serine 392. J Biol Chem. 2021;297(2):100930. doi: 10.1016/j.jbc.2021.100930 [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Schaafsma E, Fugle CM, Wang X, Cheng C. Pan-cancer association of HLA gene expression with cancer prognosis and immunotherapy efficacy. Br J Cancer. 2021;125(3):422–32. doi: 10.1038/s41416-021-01400-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
66.Mu N, Wang Y, Li X, Du Z, Wu Y, Su M, et al. Crotonylated BEX2 interacts with NDP52 and enhances mitophagy to modulate chemotherapeutic agent-induced apoptosis in non-small-cell lung cancer cells. Cell Death Dis. 2023;14(9):645. doi: 10.1038/s41419-023-06164-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
67.Koh J, Jeong D, Park SY, Han D, Kim DS, Kim HY, et al. Identification of VWA5A as a novel biomarker for inhibiting metastasis in breast cancer by machine-learning based protein prioritization. Sci Rep. 2024;14(1):2459. doi: 10.1038/s41598-024-53015-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
68.Li P, Chen F, Yao C, Zhu K, Zhang B, Zheng Z. PTPRN serves as a prognostic biomarker and correlated with immune infiltrates in low grade glioma. Brain Sci. 2022;12(6):763. doi: 10.3390/brainsci12060763 [DOI] [PMC free article] [PubMed] [Google Scholar]
69.Hänggi K, Ruffell B. Cell death, therapeutics, and the immune response in cancer. Trends Cancer. 2023;9(5):381–96. doi: 10.1016/j.trecan.2023.02.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
70.Enane FO, Saunthararajah Y, Korc M. Differentiation therapy and the mechanisms that terminate cancer cell proliferation without harming normal cells. Cell Death Dis. 2018;9(9):912. doi: 10.1038/s41419-018-0919-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
71.Lozzio BB, Lozzio CB, Bamberger EG, Feliu AS. A multipotential leukemia cell line (K-562) of human origin. Proc Soc Exp Biol Med. 1981;166(4):546–50. doi: 10.3181/00379727-166-41106 [DOI] [PubMed] [Google Scholar]
72.Ni G, Zhang L, Yang X, Li H, Ma B, Walton S, et al. Targeting interleukin-10 signalling for cancer immunotherapy, a promising and complicated task. Hum Vaccin Immunother. 2020;16(10):2328–32. doi: 10.1080/21645515.2020.1717185 [DOI] [PMC free article] [PubMed] [Google Scholar]
73.Inoue K, Fry EA. Tumor suppression by the EGR1, DMP1, ARF, p53, and PTEN Network. Cancer Invest. 2018;36(9–10):520–36. doi: 10.1080/07357907.2018.1533965 [DOI] [PMC free article] [PubMed] [Google Scholar]
74.Singh AP, Foley JF, Rubino M, Boyle MC, Tandon A, Shah R, et al. Brg1 enables rapid growth of the early embryo by suppressing genes that regulate apoptosis and cell growth arrest. Mol Cell Biol. 2016;36(15):1990–2010. doi: 10.1128/MCB.01101-15 [DOI] [PMC free article] [PubMed] [Google Scholar]
75.Aoyagi S, Archer TK. Differential glucocorticoid receptor-mediated transcription mechanisms. J Biol Chem. 2011;286(6):4610–9. doi: 10.1074/jbc.M110.195040 [DOI] [PMC free article] [PubMed] [Google Scholar]
76.Fu D, Hu Z, Xu X, Dai X, Liu Z. Key signal transduction pathways and crosstalk in cancer: Biological and therapeutic opportunities. Transl Oncol. 2022;26:101510. doi: 10.1016/j.tranon.2022.101510 [DOI] [PMC free article] [PubMed] [Google Scholar]
77.Fu D, Jiang H, Long A, Harris E, Guo H, Capitano ML, et al. Dual targeting of tumoral cells and immune microenvironment by blocking the IL-33/IL1RL1 pathway. Nat Commun. 2025;16(1):6369. doi: 10.1038/s41467-025-61567-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
78.Saraiva M, Vieira P, O’Garra A. Biology and therapeutic potential of interleukin-10. J Exp Med. 2020;217(1):e20190418. doi: 10.1084/jem.20190418 [DOI] [PMC free article] [PubMed] [Google Scholar]
79.Yi M, Li T, Niu M, Zhang H, Wu Y, Wu K, et al. Targeting cytokine and chemokine signaling pathways for cancer therapy. Signal Transduct Target Ther. 2024;9(1):176. doi: 10.1038/s41392-024-01868-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
80.Wang X, Lin Y. Tumor necrosis factor and cancer, buddies or foes? Acta Pharmacol Sin. 2008;29(11):1275–88. doi: 10.1111/j.1745-7254.2008.00889.x [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0330906.r001

Decision Letter 0

Kota Ramana

11 Jul 2025

PONE-D-25-29261 Genomic Discovery of EF-24 Targets Unveils Antitumorigenic Mechanisms in Leukemia Cells PLOS ONE

Dear Dr. Jacobs,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Aug 25 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Kota V Ramana, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf.

2. Thank you for stating the following in the Acknowledgments Section of your manuscript:

“This work was funded entirely by internal support from the American Type Culture Collection.”

We note that you have provided additional information within the Acknowledgements Section that is not currently declared in your Funding Statement. Please note that funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form.

Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows:

“The author(s) received no specific funding for this work.”

Please include your amended statements within your cover letter; we will change the online submission form on your behalf.

3. We note that you have included the phrase “data not shown” in your manuscript. Unfortunately, this does not meet our data sharing requirements. PLOS does not permit references to inaccessible data. We require that authors provide all relevant data within the paper, Supporting Information files, or in an acceptable, public repository. Please add a citation to support this phrase or upload the data that corresponds with these findings to a stable repository (such as Figshare or Dryad) and provide and URLs, DOIs, or accession numbers that may be used to access these data. Or, if the data are not a core part of the research being presented in your study, we ask that you remove the phrase that refers to these data.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: I Don't Know

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: 1.The discussion could appropriately mention the dual roles of signaling pathways in cancer.

2.Please standardize terminology; for instance, is the phrase "complete cell death" (line 77) scientifically justified?

3.While the study emphasizes coverage of diverse leukemia subtypes, it fails to justify the exclusion of common subtypes (e.g., FLT3-ITD mutated AML). Please clarify this rationale and discuss the generalizability of conclusions to other subtypes.

Reviewer #2: In the manuscript entitled “Genomic Discovery of EF-24 Targets Unveils Antitumorigenic Mechanisms in Leukemia Cells”, Singh, A.P. et al. perform whole transcriptome sequencing on 4 human leukemia cell lines after treatment with the curcumin analog, EF-24. The authors show that there are both conserved and unique gene expression changes upon a 24 hour treatment with the drug. Some of the affected pathways are important in cell viability and inflammatory response, therefore suggesting an antitumorigenic response. This is a useful dataset to understand the acute effects to the EF-24 compound which is still being characterized for its efficacy. However, there is very limited orthogonal validation of many of the DEGs discussed in the manuscript. Moreover, given that most of the manuscript is focused on the results in individual cell lines, it is difficult to discern how applicable these results are for the use of EF-24 in primary leukemia samples.

Major critiques/questions:

1) The authors highlight that EF-24 has shown efficacy in leukemia cells in a prior publication. However, between the prior publication and the manuscript herein, there has been no assays with non-transformed hematopoietic cells. If the authors want to be able to state that the gene expression differences they are seeing are specific to the leukemic cells, they should perform the same whole transcriptome sequencing in CD34+ bone marrow cells to determine how many of the pathways changing are leukemia specific.

2) There has been a previous publication detailing a mechanism for EF-24 in killing leukemia cells. However, that publication focused only on the protein and signaling pathway activation aspect of the response. The data described herein, if analyzed through the lens of the p38/MAPK/ERK pathway target genes, may be a way to strengthen the manuscript and integrate the data from each cell line if they are responding through the upregulation or downregulation of certain pathway members.

3) While many of the genes differentially expressed are notable and described for their potential importance in aiding leukemic cell death, there is very little orthogonal validation outside of replotting their own transcriptome sequencing data. The authors should validate some of the key genes through qPCR or evaluate the overall change to certain pathways via Western. For instance, in Figure 5, the data suggests a central IL-10 signaling signature and mention that this would lead to the upregulation of certain target genes. However, the expression of those genes is not shown even from the transcriptome sequencing data. Further validation of the genes/pathway highlighted for each cell line would add impact to the findings.

4) The authors utilize 3 AML cell lines but only 1 CML cell line. It would be worthwhile to add additional CML cell lines and then perform an analysis examining the differences between lymphoid/myeloid leukemia cell lines to determine if there is a significant divergence in cellular response based on the lineage of the leukemia. May also prioritize some of the pathways already identified as central pathways altered by EF-24 treatment.

5) It is unclear if statistical analysis was performed on any of the data in Figure 1 or statistical values provided for any of the gene expression data highlighted throughout.

Minor critiques/questions:

1) More detailed Figure titles denoting the main finding in the figure would be helpful to orient the reader.

2) Many panels in the Figures are quite hard to read at their current size. It would be helpful to make many of the network maps larger and more legible.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Attachment

Submitted filename: comments.docx

pone.0330906.s003.docx^{(12.5KB, docx)}

Attachment

Submitted filename: EF24_Singh_review PLOSone.docx

pone.0330906.s004.docx^{(16.2KB, docx)}

PLoS One. 2025 Sep 23;20(9):e0330906. doi: 10.1371/journal.pone.0330906.r002

Author response to Decision Letter 1

28 Jul 2025

Reviewer-1

In the manuscript entitled “Genomic Discovery of EF-24 Targets Unveils Antitumorigenic Mechanisms in Leukemia Cells”, Singh, A.P. et al. perform whole transcriptome sequencing on 4 human leukemia cell lines after treatment with the curcumin analog, EF-24. The authors show that there are both conserved and unique gene expression changes upon a 24 hour treatment with the drug. Some of the affected pathways are important in cell viability and inflammatory response, therefore suggesting an antitumorigenic response. This is a useful dataset to understand the acute effects to the EF-24 compound which is still being characterized for its efficacy. However, there is very limited orthogonal validation of many of the DEGs discussed in the manuscript. Moreover, given that most of the manuscript is focused on the results in individual cell lines, it is difficult to discern how applicable these results are for the use of EF-24 in primary leukemia samples.

Response: We sincerely thank the reviewers for their thoughtful and constructive feedback. We are pleased that you recognize the value of our dataset in elucidating the acute transcriptomic effects of EF-24 treatment in leukemia cells, and we appreciate your acknowledgment of both the conserved and cell line–specific gene expression changes, as well as the relevance of the affected pathways to EF-24’s antitumor activity.

Regarding the concern about limited orthogonal validation of differentially expressed genes (DEGs): The reviewer made thoughtful comments regarding the importance of orthogonal validation. While traditional methods such as qPCR have long been used to confirm transcriptomic findings, the advancement of next-generation sequencing (NGS) technologies has made RNA-seq a highly sensitive and accurate tool for quantifying gene expression, especially when independent biological replicates with sufficiently deep coverage are available (as in our case). To ensure scientific rigor, we included a minimum of five independent biological replicates per condition for each cell line and used standardized cell culture and sample processing methodologies across all samples - providing robust comparisons across cell lines and high statistical power. Additionally, we applied stringent filtering criteria, including a minimum transcript read count of 10 (approximately equivalent to a CPM value of 0.5) and a false-discovery rate corrected (FDR) p-value < 0.05, to enhance the reliability of identified differentially expressed genes (DEGs). Genes were retained only if they exceeded these criteria threshold in at least one cell line, as lower counts typically indicate negligible or absent expression.

Rather than selecting specific genes for analysis, we adopted a hypothesis-free, unbiased approach. We focused on top-ranked DEGs that showed strong endogenous expression in at least the control samples. This strategy helped minimize the inclusion of low-abundance transcripts that may be prone to technical variability and less likely to reflect meaningful biological changes. The consistency and magnitude of gene expression changes across replicates further support the robustness of our findings.

That said, we fully acknowledge the value of orthogonal validation, particularly for genes with low basal expression or subtle changes that may still have biological significance. While our filtering approach helped reduce false positives, it may have excluded potentially relevant low-abundance transcripts. We have now addressed this point in the revised Discussion section as a limitation of the current study and a direction for future investigation—specifically, the potential role of low-expressing genes in mediating EF-24’s effects.

We also note however that changes in low-expressing genes are more likely to reflect background noise or off-target effects and are less likely to be directly associated with the primary phenotypic outcomes observed—such as reduced cell viability. For this reason, we made a deliberate decision to exclude them from our final analysis.

Regarding the applicability of our findings to primary leukemia samples: We appreciate this important point. As this study was designed as an initial discovery effort using established leukemia cell lines, we recognize that the findings may not fully capture the complexity of primary patient-derived samples. To address this, we have revised the Discussion to explicitly acknowledge this limitation. We also outline potential follow-up studies involving primary leukemia cells to assess the translational relevance of our results.

We hope these revisions clarify the scope and impact of our work and provide a solid foundation for future investigations into EF-24’s therapeutic potential. We have addressed each point in detail below and made substantial revisions to the manuscript to enhance its scientific rigor and clarity. All changes are reflected in the revised version, with newly added figures and sections referenced accordingly.

Major critiques/questions:

1) The authors highlight that EF-24 has shown efficacy in leukemia cells in a prior publication. However, between the prior publication and the manuscript herein, there have been no assays with non-transformed hematopoietic cells. If the authors want to be able to state that the gene expression differences, they see are specific to the leukemic cells, they should perform the same whole transcriptome sequencing in CD34+ bone marrow cells to determine how many of the pathways changing are leukemia specific.

Response: We thank the reviewer for the suggestion regarding the inclusion of non-transformed hematopoietic cells. Our current study focuses on leukemia-derived cell lines to capture both the heterogeneity and conserved transcriptional responses to EF-24. However, we agree that incorporating normal CD34⁺ hematopoietic stem/progenitor cells would provide valuable insight into the selectivity and potential therapeutic window of EF-24. We note that CD34⁺ cells have a limited lifespan in culture, and manufacturers advise against maintaining them without application-specific growth factors. Optimizing culture conditions to support their viability and function would require substantial time and resources. As such, transcriptomic profiling of CD34⁺ cells were beyond the scope of the present study. Nonetheless, we have now explicitly acknowledged this limitation in the revised Discussion section. We also emphasize that future studies will incorporate both primary CD34⁺ cells and patient-derived leukemia samples to more comprehensively define EF-24’s selectivity and its impact on leukemia-specific gene networks.

Response: We thank the reviewer for pointing out this important opportunity to integrate our findings with prior mechanistic studies. In response, we re-analyzed our transcriptomic data with a focus on genes downstream of the p38/MAPK/ERK signaling axis. To further investigate the transcriptional consequences of EF-24 exposure, we examined the expression of key downstream targets of the p38/MAPK pathway across several myeloid leukemia cell lines. In K-562 cells, EF-24T treatment led to significant upregulation of STAT1, PAX6, MEF2D, ATF3, HSPB1, DDIT3 and MEF2A, while MAX, RARA, HMGN1, CREB1, and RPS6KA5, were downregulated (Supplementary Fig. 2A). In contrast, CCL-240 cells exhibited a general upward trend in gene expression, except for a moderate decrease in MAPKAPK2, NFKB1, MAX, HMGN1, and ELK1 (Supplementary Fig. 2B), highlighting a divergent response compared to K-562 cells. In Kasumi-1 cells, EF-24T induced MEF2D, MAPKAPK2, ATF3, ATF2, CREB1, HSPBAP1, DDIT3, MAX, RPS6KA5, MEF2A, MEF2B, and HSPB1, while suppressing STAT1, NFKB1, and ELK1 (Supplementary Fig. 2C). Finally, in THP-1 cells, EF-24T treatment resulted in increased expression of STAT1, ETS1, RARA, ATF3, DDIT3, and MEF2A, accompanied by reduced levels of MYC, HMGN1, MEF2D, CREB1 and RPS6KA5 (Supplementary Fig. 2D).We observed consistent upregulation of ATF3 and DDIT3 across all four cell lines, suggesting that EF-24 may exert part of its effect through these stress-responsive transcription factors downstream of p38/MAPK signaling. While several genes showed statistically significant changes following treatment, the overall magnitude of expression shifts was generally subtle to moderate. Moreover, only a limited subset of genes demonstrated consistent regulation across all cell lines. These findings underscore the context-dependent nature of EF-24’s transcriptional impact, highlighting its selective modulation of p38/MAPK pathway targets involved in cellular stress responses and differentiation. These findings are now included as a new Supplementary Figure 2 and described in the Results section. We also expanded our discussion to highlight the convergence between transcriptomic signatures and previously described post-translational mechanisms, providing a more cohesive understanding of EF-24’s multi-layered activity.

Response: We have explicitly outlined the RNA-seq analysis criteria and subsequent differential gene expression (DEG) analysis in the manuscript and above. To further explore the impact of IL-10 signaling activation on the transcriptomic landscape, we performed a focused analysis of key DEGs associated with IL-10 signaling. In EF-24–treated HL-60 cells, we observed significant expression changes in genes such as HMOX1, DUSP1, CDKN1A, JUN, NFKB2, BLVRB, IL1R1, IL1RN, BCL3, and RELB (Figure 5H), all previously implicated in cell death pathways. These findings suggest that EF-24–mediated IL-10 activation contributes to cell death in HL-60 cells. Additional IL-10–associated genes, including CSF1, CXCL8, and CCL4, also exhibited significant expression changes (Supplementary Figure 1A). Extending this analysis to K-562, Kasumi-1, and THP-1 cells revealed consistent modulation of IL-10 signaling–regulated genes across all four cell lines following EF-24 treatment (Supplementary Figures 1B–D). Collectively, these results confirm that EF-24 activates IL-10–associated transcriptional programs, with both the direction and magnitude of gene expression changes supporting pathway engagement. These data are now presented in a new panel (Figure 5H), with additional supporting information provided in Supplementary Figure 1, and are referenced in the revised Results section. We believe these additions substantially strengthen the manuscript’s conclusions.

Response: We agree that differential responses to EF-24 between myeloid and lymphoid leukemias could have implications for its therapeutic application. However, in the present study, our design was optimized to assess transcriptomic responses in a representative panel of genetically and phenotypically diverse myeloid lineage of leukemia models, with an emphasis on myeloid malignancies where EF-24 has previously demonstrated strong efficacy. It is important to note that K562 (the CML line used) exhibits characteristics of myeloid lineage and poses erythroid differentiation capability, and thus, while informative, may not fully represent lymphoid lineage responses. Likewise, HL-60, Kasumi-1 and THP1 also are myeloid leukemia cell lineage. Moreover, many lymphoid leukemia cell lines (e.g., Jurkat, NALM-6) are known to have different redox states and apoptotic thresholds than myeloid cells, potentially impacting EF-24's response kinetics. While we acknowledge that a full lineage comparison (e.g., including B-ALL or T-ALL cell lines) would potentially be of interest, we believe such an analysis requires a larger panel of representative lymphoid cell lines and potentially primary patient samples, which is beyond the scope of this initial transcriptomic study. We have now added a statement in the discussion acknowledging this limitation and outlining future directions that will include broader lineage stratification to better inform the clinical utility of EF-24.

5) It is unclear if statistical analysis was performed on any of the data in Figure 1 or statistical values provided for any of the gene expression data highlighted throughout.

Response: We apologize for this oversight. We have now clarified all statistical analyses in the revised Methods section, including the use of t-tests for Figure 1 and DESeq2 adjusted p-values for DEG analyses. Figures now display statistical annotations, including significance thresholds and error bars where appropriate.

Minor critiques/questions:

1) More detailed Figure titles denoting the main finding in the figure would be helpful to orient the reader.

Response: Revised figure titles now succinctly describe the main experimental outcome or biological insight for each figure.

2) Many panels in the Figures are quite hard to read at their current size. It would be helpful to make many of the network maps larger and more legible.

Response: We have reformatted all network maps and increased the resolution and font size for enhanced readability.

Reviewer-2

1) The discussion could appropriately mention the dual roles of signaling pathways in cancer.

Response: In the revised discussion section, we have elaborated on the dual roles of signaling pathways in cancer, emphasizing how certain pathways can exhibit both oncogenic and tumor-suppressive functions depending on cellular context, mutation status, and microenvironmental cues. This addition provides a more nuanced understanding of pathway dynamics in leukemogenesis.

2) Please standardize terminology; for instance, is the phrase "complete cell death" (line 77) scientifically justified?

Response: Thank you for pointing this out. We have revised the phrase to better reflect the biological outcome observed, and we have ensured consistent and scientifically accurate terminology throughout the manuscript.

3) While the study emphasizes coverage of diverse leukemia subtypes, it fails to justify the exclusion of common subtypes (e.g., FLT3-ITD mutated AML). Please clarify this rationale and discuss the generalizability of conclusions to other subtypes.

Response: We acknowledge the reviewer’s concern regarding the exclusion of FLT3-ITD mutated AML. Our study focused on subtypes with distinct signaling profiles that are less represented in current literature, aiming to highlight underexplored therapeutic vulnerabilities. However, we now include a justification and discuss the potential implications for generalizability in the revised Discussion. We also note that while FLT3-ITD AML is a well-characterized subtype, the signaling mechanisms explored in our study may still offer insights applicable across subtypes, including those with FLT3 mutations.

Attachment

Submitted filename: Singh et al Plos One review rebuttal_clean.docx

pone.0330906.s006.docx^{(43.3KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0330906.r003

Decision Letter 1

Kota Ramana

8 Aug 2025

<p>Genomic Discovery of EF-24 Targets Unveils Antitumorigenic Mechanisms in Leukemia Cells

PONE-D-25-29261R1

Dear Dr. Jacobs,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact billing support.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Kota V Ramana, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

No additional comments.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #2: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

**********

PLoS One. doi: 10.1371/journal.pone.0330906.r004

Acceptance letter

Kota Ramana

PONE-D-25-29261R1

PLOS ONE

Dear Dr. Jacobs,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

* All references, tables, and figures are properly cited

* All relevant supporting information is included in the manuscript submission,

* There are no issues that prevent the paper from being properly typeset

You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days to review your paper and let you know the next and final steps.

Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

You will receive an invoice from PLOS for your publication fee after your manuscript has reached the completed accept phase. If you receive an email requesting payment before acceptance or for any other service, this may be a phishing scheme. Learn how to identify phishing emails and protect your accounts at https://explore.plos.org/phishing.

If we can help with anything else, please email us at customercare@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Kota V Ramana

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Expression analysis of downstream target genes in the p38/MAPK signaling pathway across myeloid leukemia cell lines.

(TIF)

pone.0330906.s001.tif^{(5.3MB, tif)}

S2 Fig. Expression analysis of downstream target genes in the IL-10 signaling pathway across myeloid leukemia cell lines.

(TIF)

pone.0330906.s002.tif^{(10.2MB, tif)}

Attachment

Submitted filename: comments.docx

pone.0330906.s003.docx^{(12.5KB, docx)}

Attachment

Submitted filename: EF24_Singh_review PLOSone.docx

pone.0330906.s004.docx^{(16.2KB, docx)}

Attachment

Submitted filename: Singh et al Plos One review rebuttal_clean.docx

pone.0330906.s006.docx^{(43.3KB, docx)}

Data Availability Statement

All RNAseq data produced for this study is available under NCBI Bioproject PRJNA1165739.

[pone.0330906.ref001] 1.Chennamadhavuni A, Lyengar V, Mukkamalla SKR, Shimanovsky A. Leukemia. Treasure Island (FL): StatPearls; 2024. [Google Scholar]

[pone.0330906.ref002] 2.Advancing cancer therapy. Nat Cancer. 2021;2(3):245–6. doi: 10.1038/s43018-021-00192-x [DOI] [PubMed] [Google Scholar]

[pone.0330906.ref003] 3.Sohn S-I, Priya A, Balasubramaniam B, Muthuramalingam P, Sivasankar C, Selvaraj A, et al. Biomedical applications and bioavailability of curcumin-an updated overview. Pharmaceutics. 2021;13(12):2102. doi: 10.3390/pharmaceutics13122102 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref004] 4.He Y, Li W, Hu G, Sun H, Kong Q. Bioactivities of EF24, a novel curcumin analog: a review. Front Oncol. 2018;8:614. doi: 10.3389/fonc.2018.00614 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref005] 5.Hsiao P-C, Chang J-H, Lee W-J, Ku C-C, Tsai M-Y, Yang S-F, et al. The Curcumin Analogue, EF-24, Triggers p38 MAPK-Mediated Apoptotic Cell Death via Inducing PP2A-Modulated ERK Deactivation in Human Acute Myeloid Leukemia Cells. Cancers (Basel). 2020;12(8):2163. doi: 10.3390/cancers12082163 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref006] 6.Skoupa N, Dolezel P, Ruzickova E, Mlejnek P. Apoptosis Induced by the Curcumin Analogue EF-24 Is Neither Mediated by Oxidative Stress-Related Mechanisms nor Affected by Expression of Main Drug Transporters ABCB1 and ABCG2 in Human Leukemia Cells. Int J Mol Sci. 2017;18(11):2289. doi: 10.3390/ijms18112289 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref007] 7.Baykal-Köse S, Acikgoz E, Yavuz AS, Gönül Geyik Ö, Ateş H, Sezerman OU, et al. Adaptive phenotypic modulations lead to therapy resistance in chronic myeloid leukemia cells. PLoS One. 2020;15(2):e0229104. doi: 10.1371/journal.pone.0229104 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref008] 8.Koeffler HP, Golde DW. Human myeloid leukemia cell lines: a review. Blood. 1980;56(3):344–50. doi: 10.1182/blood.v56.3.344.bloodjournal563344 [DOI] [PubMed] [Google Scholar]

[pone.0330906.ref009] 9.Skopek R, Palusińska M, Kaczor-Keller K, Pingwara R, Papierniak-Wyglądała A, Schenk T, et al. Choosing the right cell line for acute myeloid leukemia (AML) research. Int J Mol Sci. 2023;24(6):5377. doi: 10.3390/ijms24065377 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref010] 10.El-Tanani M, Nsairat H, Matalka II, Lee YF, Rizzo M, Aljabali AA, et al. The impact of the BCR-ABL oncogene in the pathology and treatment of chronic myeloid leukemia. Pathol Res Pract. 2024;254:155161. doi: 10.1016/j.prp.2024.155161 [DOI] [PubMed] [Google Scholar]

[pone.0330906.ref011] 11.Reitsma PH, Rothberg PG, Astrin SM, Trial J, Bar-Shavit Z, Hall A, et al. Regulation of myc gene expression in HL-60 leukaemia cells by a vitamin D metabolite. Nature. 1983;306(5942):492–4. doi: 10.1038/306492a0 [DOI] [PubMed] [Google Scholar]

[pone.0330906.ref012] 12.Basu J, Madhulika S, Murmu KC, Mohanty S, Samal P, Das A, et al. Molecular and epigenetic alterations in normal and malignant myelopoiesis in human leukemia 60 (HL60) promyelocytic cell line model. Front Cell Dev Biol. 2023;11:1060537. doi: 10.3389/fcell.2023.1060537 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref013] 13.Larizza L, Magnani I, Beghini A. The Kasumi-1 cell line: a t(8;21)-kit mutant model for acute myeloid leukemia. Leuk Lymphoma. 2005;46(2):247–55. doi: 10.1080/10428190400007565 [DOI] [PubMed] [Google Scholar]

[pone.0330906.ref014] 14.Chanput W, Mes JJ, Wichers HJ. THP-1 cell line: an in vitro cell model for immune modulation approach. Int Immunopharmacol. 2014;23(1):37–45. doi: 10.1016/j.intimp.2014.08.002 [DOI] [PubMed] [Google Scholar]

[pone.0330906.ref015] 15.Hsiao PC, Chang JH, Lee WJ, Ku CC, Tsai MY, Yang SF, et al. The Curcumin Analogue, EF-24, Triggers p38 MAPK-Mediated Apoptotic Cell Death via Inducing PP2A-Modulated ERK Deactivation in Human Acute Myeloid Leukemia Cells. Cancers (Basel). 2020;12(8). doi: 10.3390/cancers12082163 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref016] 16.Allgöwer C, Kretz A-L, von Karstedt S, Wittau M, Henne-Bruns D, Lemke J. Friend or Foe: S100 proteins in cancer. Cancers (Basel). 2020;12(8):2037. doi: 10.3390/cancers12082037 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref017] 17.Shannan B, Seifert M, Boothman DA, Tilgen W, Reichrath J. Clusterin and DNA repair: a new function in cancer for a key player in apoptosis and cell cycle control. J Mol Histol. 2006;37(5–7):183–8. doi: 10.1007/s10735-006-9052-7 [DOI] [PubMed] [Google Scholar]

[pone.0330906.ref018] 18.Peng F, Liao M, Qin R, Zhu S, Peng C, Fu L, et al. Regulated cell death (RCD) in cancer: key pathways and targeted therapies. Signal Transduct Target Ther. 2022;7(1):286. doi: 10.1038/s41392-022-01110-y [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref019] 19.Gross C, Le-Bel G, Desjardins P, Benhassine M, Germain L, Guérin SL. Contribution of the Transcription Factors Sp1/Sp3 and AP-1 to Clusterin Gene Expression during Corneal Wound Healing of Tissue-Engineered Human Corneas. Int J Mol Sci. 2021;22(22):12426. doi: 10.3390/ijms222212426 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref020] 20.Jia W-Y, Zhang J-J. Effects of glucocorticoids on leukocytes: genomic and non-genomic mechanisms. World J Clin Cases. 2022;10(21):7187–94. doi: 10.12998/wjcc.v10.i21.7187 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref021] 21.Wang Y, Gu W, Wen W, Zhang X. SERPINH1 is a potential prognostic biomarker and correlated with immune infiltration: a pan-cancer analysis. Front Genet. 2022;12:756094. doi: 10.3389/fgene.2021.756094 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref022] 22.Ning Y, Li Y, Wang H. ANXA2 is a potential biomarker for cancer prognosis and immune infiltration: a systematic pan-cancer analysis. Front Genet. 2023;14:1108167. doi: 10.3389/fgene.2023.1108167 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref023] 23.Eychène A, Rocques N, Pouponnot C. A new MAFia in cancer. Nat Rev Cancer. 2008;8(9):683–93. doi: 10.1038/nrc2460 [DOI] [PubMed] [Google Scholar]

[pone.0330906.ref024] 24.Ecker V, Stumpf M, Brandmeier L, Neumayer T, Pfeuffer L, Engleitner T, et al. Targeted PI3K/AKT-hyperactivation induces cell death in chronic lymphocytic leukemia. Nat Commun. 2021;12(1):3526. doi: 10.1038/s41467-021-23752-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref025] 25.Luu Hoang KN, Anstee JE, Arnold JN. The Diverse Roles of Heme Oxygenase-1 in Tumor Progression. Front Immunol. 2021;12:658315. doi: 10.3389/fimmu.2021.658315 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref026] 26.Tang J, Li Y, Xia S, Li J, Yang Q, Ding K, et al. Sequestosome 1/p62: a multitasker in the regulation of malignant tumor aggression (Review). Int J Oncol. 2021;59(4):77. doi: 10.3892/ijo.2021.5257 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref027] 27.Shen S, Wei C, Fu J. RNA-Sequencing Reveals Heat Shock 70-kDa Protein 6 (HSPA6) as a Novel Thymoquinone-Upregulated Gene That Inhibits Growth, Migration, and Invasion of Triple-Negative Breast Cancer Cells. Front Oncol. 2021;11:667995. doi: 10.3389/fonc.2021.667995 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref028] 28.Liu J, Ren Z, Yang L, Zhu L, Li Y, Bie C, et al. The NSUN5-FTH1/FTL pathway mediates ferroptosis in bone marrow-derived mesenchymal stem cells. Cell Death Discov. 2022;8(1):99. doi: 10.1038/s41420-022-00902-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref029] 29.Tao KP, Fong SW, Lu Z, Ching YP, Chan KW, Chan SY. TSPYL2 is important for G1 checkpoint maintenance upon DNA damage. PLoS One. 2011;6(6):e21602. doi: 10.1371/journal.pone.0021602 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref030] 30.Yang X, Wang Z, Samovich SN, Kapralov AA, Amoscato AA, Tyurin VA, et al. PHLDA2-mediated phosphatidic acid peroxidation triggers a distinct ferroptotic response during tumor suppression. Cell Metab. 2024;36(4):762–777.e9. doi: 10.1016/j.cmet.2024.01.006 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref031] 31.Galluzzi L, Kepp O, Hett E, Kroemer G, Marincola FM. Immunogenic cell death in cancer: concept and therapeutic implications. J Transl Med. 2023;21(1):162. doi: 10.1186/s12967-023-04017-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref032] 32.Peng B, Hu J, Fu X. ELANE: an emerging lane to selective anticancer therapy. Signal Transduct Target Ther. 2021;6(1):358. doi: 10.1038/s41392-021-00766-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref033] 33.Chan S, Wang X, Wang Z, Du Y, Zuo X, Chen J, et al. CTSG suppresses colorectal cancer progression through negative regulation of Akt/mTOR/Bcl2 signaling pathway. Int J Biol Sci. 2023;19(7):2220–33. doi: 10.7150/ijbs.82000 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref034] 34.Wu Z, Ni J, Zhang H, Zhang Y, Lv C, Wang Y, et al. MLC1 overexpression inhibits tumor progression through PI3K/AKT signal pathway in prostate cancer. Adv Biol (Weinh). 2024;8(1):e2300060. doi: 10.1002/adbi.202300060 [DOI] [PubMed] [Google Scholar]

[pone.0330906.ref035] 35.Wu L, Saxena S, Singh RK. Neutrophils in the tumor microenvironment. Adv Exp Med Biol. 2020;1224:1–20. doi: 10.1007/978-3-030-35723-8_1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref036] 36.Tolomeo M, Cascio A. The multifaced role of STAT3 in cancer and its implication for anticancer therapy. Int J Mol Sci. 2021;22(2):603. doi: 10.3390/ijms22020603 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref037] 37.Singh AP, Archer TK. Analysis of the SWI/SNF chromatin-remodeling complex during early heart development and BAF250a repression cardiac gene transcription during P19 cell differentiation. Nucleic Acids Res. 2014;42(5):2958–75. doi: 10.1093/nar/gkt1232 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref038] 38.Oft M. IL-10: master switch from tumor-promoting inflammation to antitumor immunity. Cancer Immunol Res. 2014;2(3):194–9. doi: 10.1158/2326-6066.CIR-13-0214 [DOI] [PubMed] [Google Scholar]

[pone.0330906.ref039] 39.Verma R, Balakrishnan L, Sharma K, Khan AA, Advani J, Gowda H, et al. A network map of Interleukin-10 signaling pathway. J Cell Commun Signal. 2016;10(1):61–7. doi: 10.1007/s12079-015-0302-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref040] 40.Kotredes KP, Gamero AM. Interferons as inducers of apoptosis in malignant cells. J Interferon Cytokine Res. 2013;33(4):162–70. doi: 10.1089/jir.2012.0110 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref041] 41.Fucikova J, Kepp O, Kasikova L, Petroni G, Yamazaki T, Liu P, et al. Detection of immunogenic cell death and its relevance for cancer therapy. Cell Death Dis. 2020;11(11):1013. doi: 10.1038/s41419-020-03221-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref042] 42.Ahmed A, Tait SWG. Targeting immunogenic cell death in cancer. Mol Oncol. 2020;14(12):2994–3006. doi: 10.1002/1878-0261.12851 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref043] 43.Thomas AF, Kelly GL, Strasser A. Of the many cellular responses activated by TP53, which ones are critical for tumour suppression? Cell Death Differ. 2022;29(5):961–71. doi: 10.1038/s41418-022-00996-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref044] 44.The Lancet Haematology. The global burden of haematological diseases. Lancet Haematol. 2018;5(1):e1. doi: 10.1016/S2352-3026(17)30233-8 [DOI] [PubMed] [Google Scholar]

[pone.0330906.ref045] 45.Short NJ, Rytting ME, Cortes JE. Acute myeloid leukaemia. Lancet. 2018;392(10147):593–606. doi: 10.1016/S0140-6736(18)31041-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref046] 46.Tang C, Liu J, Yang C, Ma J, Chen X, Liu D, et al. Curcumin and its analogs in non-small cell lung cancer treatment: challenges and expectations. Biomolecules. 2022;12(11):1636. doi: 10.3390/biom12111636 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref047] 47.Ibáñez Gaspar V, McCaul J, Cassidy H, Slattery C, McMorrow T. Effects of Curcumin Analogues DMC and EF24 in Combination with the Cytokine TRAIL against Kidney Cancer. Molecules. 2021;26(20):6302. doi: 10.3390/molecules26206302 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref048] 48.Mbese Z, Khwaza V, Aderibigbe BA. Curcumin and its derivatives as potential therapeutic agents in prostate, colon and breast cancers. Molecules. 2019;24(23):4386. doi: 10.3390/molecules24234386 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref049] 49.Bertazza L, Barollo S, Mari ME, Faccio I, Zorzan M, Redaelli M, et al. Biological effects of EF24, a curcumin derivative, alone or combined with mitotane in adrenocortical tumor cell lines. Molecules. 2019;24(12):2202. doi: 10.3390/molecules24122202 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref050] 50.Choy EH, De Benedetti F, Takeuchi T, Hashizume M, John MR, Kishimoto T. Translating IL-6 biology into effective treatments. Nat Rev Rheumatol. 2020;16(6):335–45. doi: 10.1038/s41584-020-0419-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref051] 51.Wang X, Yang C, Körner H, Ge C. Tumor necrosis factor: what is in a name? Cancers (Basel). 2022;14(21):5270. doi: 10.3390/cancers14215270 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref052] 52.Zou P, Xia Y, Chen W, Chen X, Ying S, Feng Z, et al. EF24 induces ROS-mediated apoptosis via targeting thioredoxin reductase 1 in gastric cancer cells. Oncotarget. 2016;7(14):18050–64. doi: 10.18632/oncotarget.7633 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref053] 53.Adams BK, Ferstl EM, Davis MC, Herold M, Kurtkaya S, Camalier RF, et al. Synthesis and biological evaluation of novel curcumin analogs as anti-cancer and anti-angiogenesis agents. Bioorg Med Chem. 2004;12(14):3871–83. doi: 10.1016/j.bmc.2004.05.006 [DOI] [PubMed] [Google Scholar]

[pone.0330906.ref054] 54.Kasinski AL, Du Y, Thomas SL, Zhao J, Sun S-Y, Khuri FR, et al. Inhibition of IkappaB kinase-nuclear factor-kappaB signaling pathway by 3,5-bis(2-flurobenzylidene)piperidin-4-one (EF24), a novel monoketone analog of curcumin. Mol Pharmacol. 2008;74(3):654–61. doi: 10.1124/mol.108.046201 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref055] 55.Thomas SL, Zhong D, Zhou W, Malik S, Liotta D, Snyder JP, et al. EF24, a novel curcumin analog, disrupts the microtubule cytoskeleton and inhibits HIF-1. Cell Cycle. 2008;7(15):2409–17. doi: 10.4161/cc.6410 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref056] 56.Liang Y, Zheng T, Song R, Wang J, Yin D, Wang L, et al. Hypoxia-mediated sorafenib resistance can be overcome by EF24 through Von Hippel-Lindau tumor suppressor-dependent HIF-1α inhibition in hepatocellular carcinoma. Hepatology. 2013;57(5):1847–57. doi: 10.1002/hep.26224 [DOI] [PubMed] [Google Scholar]

[pone.0330906.ref057] 57.Shannan B, Seifert M, Leskov K, Willis J, Boothman D, Tilgen W, et al. Challenge and promise: roles for clusterin in pathogenesis, progression and therapy of cancer. Cell Death Differ. 2006;13(1):12–9. doi: 10.1038/sj.cdd.4401779 [DOI] [PubMed] [Google Scholar]

[pone.0330906.ref058] 58.Zhang J, Liu J, Wu J, Li W, Chen Z, Yang L. Progression of the role of CRYAB in signaling pathways and cancers. Onco Targets Ther. 2019;12:4129–39. doi: 10.2147/OTT.S201799 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref059] 59.Kim N, Yoo JC, Han JY, Hwang EM, Kim YS, Jeong EY, et al. Human nuclear clusterin mediates apoptosis by interacting with Bcl-XL through C-terminal coiled coil domain. J Cell Physiol. 2012;227(3):1157–67. doi: 10.1002/jcp.22836 [DOI] [PubMed] [Google Scholar]

[pone.0330906.ref060] 60.Khoi C-S, Xiao C-Q, Hung K-Y, Lin T-Y, Chiang C-K. Oxidative Stress-Induced Growth Inhibitor (OSGIN1), a Target of X-Box-Binding Protein 1, Protects Palmitic Acid-Induced Vascular Lipotoxicity through Maintaining Autophagy. Biomedicines. 2022;10(5):992. doi: 10.3390/biomedicines10050992 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref061] 61.Yan C, Jamaluddin MS, Aggarwal B, Myers J, Boyd DD. Gene expression profiling identifies activating transcription factor 3 as a novel contributor to the proapoptotic effect of curcumin. Mol Cancer Ther. 2005;4(2):233–41. doi: 10.1158/1535-7163.233.4.2 [DOI] [PubMed] [Google Scholar]

[pone.0330906.ref062] 62.Joshi V, Lakhani SR, McCart Reed AE. NDRG1 in Cancer: A Suppressor, Promoter, or Both? Cancers (Basel). 2022;14(23):5739. doi: 10.3390/cancers14235739 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref063] 63.Luo Y, Jin H, Kim JH, Bae J. Guanylate-binding proteins induce apoptosis of leukemia cells by regulating MCL-1 and BAK. Oncogenesis. 2021;10(7):54. doi: 10.1038/s41389-021-00341-y [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref064] 64.Li D, Xie L, Qiao Z, Mai S, Zhu J, Zhang F, et al. STING-mediated degradation of IFI16 negatively regulates apoptosis by inhibiting p53 phosphorylation at serine 392. J Biol Chem. 2021;297(2):100930. doi: 10.1016/j.jbc.2021.100930 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref065] 65.Schaafsma E, Fugle CM, Wang X, Cheng C. Pan-cancer association of HLA gene expression with cancer prognosis and immunotherapy efficacy. Br J Cancer. 2021;125(3):422–32. doi: 10.1038/s41416-021-01400-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref066] 66.Mu N, Wang Y, Li X, Du Z, Wu Y, Su M, et al. Crotonylated BEX2 interacts with NDP52 and enhances mitophagy to modulate chemotherapeutic agent-induced apoptosis in non-small-cell lung cancer cells. Cell Death Dis. 2023;14(9):645. doi: 10.1038/s41419-023-06164-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref067] 67.Koh J, Jeong D, Park SY, Han D, Kim DS, Kim HY, et al. Identification of VWA5A as a novel biomarker for inhibiting metastasis in breast cancer by machine-learning based protein prioritization. Sci Rep. 2024;14(1):2459. doi: 10.1038/s41598-024-53015-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref068] 68.Li P, Chen F, Yao C, Zhu K, Zhang B, Zheng Z. PTPRN serves as a prognostic biomarker and correlated with immune infiltrates in low grade glioma. Brain Sci. 2022;12(6):763. doi: 10.3390/brainsci12060763 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref069] 69.Hänggi K, Ruffell B. Cell death, therapeutics, and the immune response in cancer. Trends Cancer. 2023;9(5):381–96. doi: 10.1016/j.trecan.2023.02.001 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref070] 70.Enane FO, Saunthararajah Y, Korc M. Differentiation therapy and the mechanisms that terminate cancer cell proliferation without harming normal cells. Cell Death Dis. 2018;9(9):912. doi: 10.1038/s41419-018-0919-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref071] 71.Lozzio BB, Lozzio CB, Bamberger EG, Feliu AS. A multipotential leukemia cell line (K-562) of human origin. Proc Soc Exp Biol Med. 1981;166(4):546–50. doi: 10.3181/00379727-166-41106 [DOI] [PubMed] [Google Scholar]

[pone.0330906.ref072] 72.Ni G, Zhang L, Yang X, Li H, Ma B, Walton S, et al. Targeting interleukin-10 signalling for cancer immunotherapy, a promising and complicated task. Hum Vaccin Immunother. 2020;16(10):2328–32. doi: 10.1080/21645515.2020.1717185 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref073] 73.Inoue K, Fry EA. Tumor suppression by the EGR1, DMP1, ARF, p53, and PTEN Network. Cancer Invest. 2018;36(9–10):520–36. doi: 10.1080/07357907.2018.1533965 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref074] 74.Singh AP, Foley JF, Rubino M, Boyle MC, Tandon A, Shah R, et al. Brg1 enables rapid growth of the early embryo by suppressing genes that regulate apoptosis and cell growth arrest. Mol Cell Biol. 2016;36(15):1990–2010. doi: 10.1128/MCB.01101-15 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref075] 75.Aoyagi S, Archer TK. Differential glucocorticoid receptor-mediated transcription mechanisms. J Biol Chem. 2011;286(6):4610–9. doi: 10.1074/jbc.M110.195040 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref076] 76.Fu D, Hu Z, Xu X, Dai X, Liu Z. Key signal transduction pathways and crosstalk in cancer: Biological and therapeutic opportunities. Transl Oncol. 2022;26:101510. doi: 10.1016/j.tranon.2022.101510 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref077] 77.Fu D, Jiang H, Long A, Harris E, Guo H, Capitano ML, et al. Dual targeting of tumoral cells and immune microenvironment by blocking the IL-33/IL1RL1 pathway. Nat Commun. 2025;16(1):6369. doi: 10.1038/s41467-025-61567-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref078] 78.Saraiva M, Vieira P, O’Garra A. Biology and therapeutic potential of interleukin-10. J Exp Med. 2020;217(1):e20190418. doi: 10.1084/jem.20190418 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref079] 79.Yi M, Li T, Niu M, Zhang H, Wu Y, Wu K, et al. Targeting cytokine and chemokine signaling pathways for cancer therapy. Signal Transduct Target Ther. 2024;9(1):176. doi: 10.1038/s41392-024-01868-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330906.ref080] 80.Wang X, Lin Y. Tumor necrosis factor and cancer, buddies or foes? Acta Pharmacol Sin. 2008;29(11):1275–88. doi: 10.1111/j.1745-7254.2008.00889.x [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Genomic discovery of EF-24 targets unveils antitumorigenic mechanisms in leukemia cells

Ajeet P Singh

Noah Wax

James Duncan

Ana S Fernandes

Jonathan L Jacobs

Roles

Abstract

Introduction

Results

EF-24 exhibits potent cell killing activity in leukemia cell lines

Fig 1. EF-24 treatment significantly reduces cell viability in leukemia cell lines.

Chronic (CML) and Acute Myeloid Leukemia (AML) cell lines exhibit transcriptional heterogeneity

Fig 2. Distinct and shared gene expression patterns among leukemia cell lines following EF-24 treatment.

EF-24 treatment impacts the expression of genes controlling cell survival and proliferation

Fig 3. EF-24 treatment disrupts key molecular pathways associated with cell survival, proliferation, and viability.

EF-24 treatment activated the wound healing signaling pathway in K-562 cells

Fig 4. EF-24 induces significant transcriptional changes in K-562 cells.

EF-24–activated pathways cause neutrophil degranulation in HL-60 cells

Fig 5. EF-24 treatment alters gene expression profiles in HL-60 cells.

EF-24 activates tumor suppressor genes in Kasumi-1 cells

Fig 6. EF-24 treatment induces distinct gene expression changes in Kasumi-1 cells.

The antitumorigenic effects of EF-24 in THP-1 cells are mediated by interferon alpha/beta signaling

Fig 7. EF-24 treatment modulates gene expression in THP-1 cells.

EF-24 differentially modulates p38/MAPK/ERK pathway targets in leukemia cell lines

Discussion

Conclusion

Materials and methods

Cell lines

Chemical and reagents

Cytotoxicity assay

RNA extraction and quality control (QC)

RNA-Seq library preparation and sequencing

RNA-Seq and bioinformatics analysis

Supporting information

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Kota Ramana

Roles

Author response to Decision Letter 1

Decision Letter 1

Kota Ramana

Roles

Acceptance letter

Kota Ramana

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases