Abstract
Background
Acetyl-CoA carboxylase alpha (ACACA) is a key enzyme in fatty acid biosynthesis and a proposed therapeutic target in prostate cancer. However, its role in androgen receptor-independent prostate cancer (ARIPC), an aggressive and treatment-resistant subtype, remains unclear. This study aimed to investigate the effects of ACACA depletion on ARIPC, with a focus on inflammation and metastasis.
Methods
ACACA expression patterns were analyzed across multiple metastatic castration-resistant prostate cancer (mCRPC) datasets. In ARIPC cell lines, ACACA was inhibited via both shRNA and the pharmacological inhibitor TOFA. Transcriptomic, metabolomic, and single-cell RNA sequencing data were used to identify downstream changes. Inflammatory signaling was assessed by qPCR, western blotting, and immunofluorescence. Cell migration was evaluated via wound healing and transwell assays, and the metastatic potential was examined in a mouse tail vein injection model. The roles of arachidonic acid (AA), cytosolic phospholipase A2 (cPLA2), and NF-κB signaling were further tested through targeted inhibition.
Results
ACACA expression was reduced in ARIPC and was negatively correlated with inflammatory pathways. Its inhibition upregulated proinflammatory cytokines and chemokines, elevated AA and eicosanoid levels, and increased cPLA2 expression. Single-cell RNA sequencing confirmed NF-κB signaling enrichment in ACACA-low tumor cells. Mechanistically, elevated AA activated NF-κB signaling. ACACA depletion enhanced cell migration and metastasis, along with macrophage infiltration. Inhibiting cPLA2 or NF-κB signaling reversed these effects.
Conclusions
This study reveals a previously unrecognized tumor-promoting effect of ACACA depletion in ARIPC. Targeting ACACA in this context enhances inflammation and metastasis via arachidonic acid-mediated activation of NF-κB signaling. These findings highlight a context dependent, tumor-promoting role of ACACA inhibition and underscore the need for combinational strategies to avoid potential adverse outcomes in metabolic therapies.
Trial registration
Not applicable.
Graphical Abstract
Supplementary Information
The online version contains supplementary material available at 10.1186/s12964-025-02363-0.
Keywords: ACACA, ARIPC, Arachidonic acid metabolism, Metastasis, Inflammation, NF-κB, cPLA2
Background
Metabolic reprogramming is a hallmark of cancer, enabling tumor cells to meet increased bioenergetic and biosynthetic demands [1]. One of the most prominent metabolic features in prostate cancer is the activation of de novo fatty acid synthesis [2–4], in which acetyl-CoA carboxylase alpha (ACACA) serves as a key rate-limiting enzyme [5, 6]. ACACA catalyzes the conversion of acetyl-CoA to malonyl-CoA, initiating fatty acid biosynthesis that supports membrane synthesis, energy storage, and signaling [7]. As such, ACACA has been widely studied and proposed as a potential therapeutic target in prostate cancer [8, 9]. However, prostate cancer is a biologically heterogeneous disease composed of multiple subtypes with distinct molecular features and metabolic dependencies [10].
Among these subtypes, androgen receptor-independent prostate cancer (ARIPC) represents a particularly aggressive and therapy-resistant subtype, encompassing neuroendocrine prostate cancer (NEPC) and double-negative prostate cancer (DNPC), which lack expression of both androgen receptor (AR) and neuroendocrine markers [11, 12]. ARIPC often arises as a result of lineage plasticity and therapeutic pressure following androgen deprivation therapy (ADT) [13], and is resistance to AR-targeted agents [14]. Despite its clinical importance, the metabolic vulnerabilities of ARIPC remain poorly understood.
One emerging but underappreciated feature of aggressive prostate cancer is tumor-promoting inflammation, characterized by the aberrant expression of inflammatory cytokines and chemokines such as IL-1β, IL-6, TNFα, and CXCL family members [1, 15, 16]. This inflammatory environment not only alters tumor cell behavior but also shapes the tumor microenvironment to favor immune evasion, epithelial‒mesenchymal transition (EMT), and metastasis [17–19]. Arachidonic acid (AA), a polyunsaturated fatty acid derived from membrane phospholipids via cytosolic phospholipase A2 (cPLA2), serves as a critical substrate for the production of proinflammatory lipid mediators including prostaglandins and thromboxanes [20–22]. These metabolites are well-known activators of the nuclear factor kappa B (NF-κB) signaling pathway, which is a central regulator of inflammatory gene expression that contributes to tumor cell survival, EMT, and dissemination [23–25].
In this study, we investigated the role of ACACA in ARIPC and revealed an unexpected link between lipid metabolism and inflammation. Using integrated transcriptomic, metabolomic, and in vivo approaches, we demonstrated that ACACA depletion in ARIPC cells induces cPLA2-mediated arachidonic acid release and downstream activation of NF-κB signaling, leading to enhanced inflammatory responses, migration, and metastasis. These findings highlight a paradoxical tumor-promoting effect of targeting ACACA in AR-independent prostate cancer and provide new insight into potential combination strategies for metabolic therapy.
Methods
Cell culture
The human androgen receptor-independent prostate cancer (ARIPC) cell lines DU145 and PC3, the androgen receptor positive prostate cancer cell line LNCaP, the benign prostatic hyperplasia cell line BPH1, and HEK293T cells were obtained from the American Type Culture Collection (ATCC, Manassas, USA). The cells were cultured in DMEM (Gibco, #C11995500CP) supplemented with 10% fetal bovine serum (FBS, Gibco, #10270-106) or 10% delipidated FBS (VivaCell, #C3840-0100), and 1% penicillin‒streptomycin (Gibco, #15140122) at 37 °C in a humidified incubator with 5% CO₂. The ACACA inhibitor TOFA (Abcam, #ab141578), the NF-κB inhibitor QNZ/EVP4593 (Selleckchem, #S4902), arachidonic acid (Selleckchem, #S6185), AR agonist R1881 ( GLPBIO, #GC19800), and AR antagonist Enzalutamide (Selleckchem, #S1250) were used at specified concentrations and time points as indicated.
Cell line construction
We obtained ACACA-specific and control shRNA plasmids from Ke Lei Biological Technology Co., Ltd. These plasmids contained sequences encoding puromycin resistance proteins, luciferase or GFP, and shRNAs. For the transfection, a mixture consisting of shRNA, psPAX2, pMD2.G, and TurboFect Transfection Reagent (Thermo Fisher Scientific, #01300669) was prepared to target 293T cells at 30-40% confluency. After a 48-hour incubation for viral assembly, the medium supernatant was collected to infect the target cells. Following 72 h of infection, the recombinant cells were selected by growing them in medium supplemented with 2.0 µg/ml puromycin (Solarbio, #P8230). Single clones were isolated from the transfected cells for further verification and analysis.
siRNAs were purchased from Guangzhou IGE Biotechnology Co. Ltd. Cells were plated to reach 40‒50% confluency by the next day. For transfection, siRNA, siRNA-mate (GenePharma, #G04002), and DMEM were mixed. After vortexing, the mixture was allowed to sit for 15 min, and then added to the flasks with complete medium. The medium was replaced after 24 h, and proteins and RNA were harvested at the appropriate times for further validation. The sequences of the shRNAs and siRNAs are shown in Supplementary Material Table S1.
Western blot analysis
Cell lysates were prepared via RIPA buffer supplemented with PMSF protease inhibitor (Beyotime, #ST506) and phosphatase inhibitor (BoCai Biology, #R0127). the samples were mixed with SDS loading buffer (Biosharp, #BL502A) and boiled at 98 °C for 10 min. Proteins were separated on SDS‒PAGE gels (Beyotime, #P0012A) and transferred to PVDF membranes. After blocking, the membranes were incubated with primary antibodies (1:1000) overnight at 4 °C. The membranes were subsequently incubated with secondary antibodies in PBST at room temperature for 1 h. ECL detection reagent was used to visualize the signals, which were then captured via a ChemiDoc MP Imaging System (Bio-Rad, #12003154). Details of the antibodies used are provided in Supplementary Material Table S2.
Real-time quantitative PCR (qPCR) analysis
Total RNA was extracted via NucleoZol (MN, #740404.200) per the manufacturer’s instructions. RNA concentration and quality were assessed via spectrophotometry. The residual genomic DNA was removed, and the RNA was reverse transcribed into cDNA via the HiScript III RT SuperMix with a qPCR (+ gDNA wiper) kit (Vazyme, #R323). The qPCR mixture, consisting of cDNA, target-specific primers, and 2×ChamQ Universal SYBR qPCR Master Mix (Vazyme, #Q711), was prepared and dispensed into a 96-well qPCR plate. Amplification and real-time fluorescence monitoring were performed via the QuantStudio 1 system (Thermo Fisher, #A40427). Relative mRNA expression was calculated via the 2 − ΔΔCt method and normalized to that of beta-actin (ACTB). For details, refer to our previous study [26].The primer sequences are shown in Supplementary Material Table S3.
Histology
For immunohistochemistry, mouse lung metastases from patiences were dissected, fixed in 4% paraformaldehyde (Biosharp, #BL539A-1), and paraffin-embedded. Section (4 μm) were deparaffinized in xylen and dehydrated in graded alcohol, and antigen retrieval was performed in citrate buffer at 100 °C for 15 min. The staining was performed using the same protocol as previously described [27]. For Immunocytochemistry, please refer to our previous study [28, 29]. Details of the antibodies used are provided in Supplementary Material Table S2.
Bioinformatics analysis
ACACA expression profiles were retrieved from the Prostate Cancer Atlas (https://prostatecanceratlas.com/). The expression data for the cohorts (Cambridge, CIT, GSE29079, GSE62872, and GSE79021) were obtained from the Prostate Cancer Database (PCaDB, http://bioinfo.jialab-ucr.org/PCaDB/). For the mCRPC cohorts (GSE126078, GSE32269, SU2C Capture, and SU2C PolyA), normalized mRNA expression and ACACA expression data were also sourced from the PCaDB, with samples without mCRPC removed. The mRNA expression data for the GSE229394, GSE243329, GSE273738, GSE220097 and GSE225481 cohorts were downloaded from the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/). Processed data for ACACA expression profiles in prostate cancer (PCa) cell lines were obtained from the Cancer Cell Line Encyclopedia (CCLE; https://sites.broadinstitute.org/ccle/datasets). Additionally, 50 hallmark gene sets were obtained from the GSEA Molecular Signature Database (MSigDB) (https://www.gsea-msigdb.org/gsea/msigdb/collections.jsp#H).
Gene set enrichment analysis (GSEA) was performed between the high- and low-expression subgroups on the basis of the median expression levels of the specified genes in each dataset. Genes were ranked by decreasing Log2FoldChange values. The ordered gene list was subjected to GSEA via the R package “clusterProfiler”. Pathways with an adjusted p-value < 0.05 and a normalized enrichment score (NES) > 1 were considered activated, whereas pathways with an NES < − 1 were considered suppressed.
Gene Ontology (GO) enrichment analysis of differentially expressed genes (DEGs) between the high- and low-ACACA expression subgroups (based on the median expression levels) was performed in R, with|Log2FoldChange| >1 and adjusted p-value < 0.05 used to define DEGs.
Correlation analysis was performed in R via the Pearson and Spearman methods. Significance was tested with cor.test(), and visualizations were created via ggplot2 for scatter plots.
Single-cell transcriptomic data from GSE137829 were processed via R with standard preprocessing steps. Luminal epithelial tumor cells were identified via the use of known marker genes. AR activity scores were derived through gene set variation analysis (GSVA), which groups cells into AR-high and AR-low categories. The AR score genes were derived from references [12, 30]. Complete gene lists are provided in Supplementary Material Table S4.
RNA-seq analysis
For RNA isolation, we utilized TRIzol reagent (Invitrogen, CA, USA, #50175111) to lyse the cells according to the manufacturer’s protocol. All procedures of RNA extraction, purification, reverse transcription, sequencing, and subsequent bioinformatics analysis were performed by LC-Bio Technology Co. Ltd. Gene expression heatmaps, and Gene Ontology (GO) enrichment analysis bubble plots and so on were generated via the OmicStudio cloud platform (https://www.omicstudio.cn/tool).
Gene set enrichment analysis (GSEA) was performed via GSEA software (v4.1.0) and the MSigDB database to identify significant differences in gene sets across specific GO terms and KEGG pathways between the two groups. Briefly, a gene expression matrix was input, and genes were ranked according to the signal-to-noise ratio. Enrichment scores and p-values were calculated under default settings, considering GO terms and KEGG pathways with a normalized enrichment score (NES) > 1 and NOM p value < 0.05 as significantly different between groups.
The enrichment analysis highlighted GO terms that were significantly enriched in the differentially expressed genes (DEGs) compared with the genomic background. DEGs were mapped to the Gene Ontology database (http://www.geneontology.org/), and the number of genes per term was calculated. Significantly enriched GO terms in DEGs relative to the genomic background were identified through hypergeometric testing.
Mouse metastasis model
DU145 cells expressing luciferase (DU145-Luc) were generated through a lentiviral system as described in the cell line construction protocol. Five-week-old BALB/c nude mice, obtained from GemPharmatech (Guangzhou, China), were randomly divided into experimental and control groups. A total of 1.5 × 106 cells were diluted in 100 µL of sterile PBS and administered to the mice via tail vein injection via an insulin syringe. Bioluminescence was measured via an in vivo imaging system (IVIS). All animal experiments were approved by the Institutional Animal Care and Use Committee (IACUC) of South China University of Technology and conducted in accordance with institutional guidelines and ethical regulations (Approval no. S-2023‐078‐01).
Transwell assay
After specific treatments, the cells were prepared for a transwell migration assay in a 24-well plate. The lower chamber of each well received 600 µL of DMEM containing 10% FBS. The cells were digested, resuspended, counted, and adjusted to 50,000 per well. After centrifugation, the cells were resuspended in 200 µL of serum-free DMEM and added to the upper chamber. The plate was then incubated for 24 h. After incubation, the cells in the lower chamber were fixed with 4% paraformaldehyde, washed with PBS, and stained with 0.1% crystal violet. The membranes were removed, placed on slides, covered with neutral balsam, and scanned after drying. Cell migration was quantified via ImageJ to assess their migration ability.
Wound healing assay
The cells were cultured in specific media until the appropriate points reached. Following digestion and resuspension, the cells were seeded at a density of 150,000 per well in a 6-well plate and allowed to adhere overnight to form a confluent monolayer. A uniform scratch was then made across the cell monolayer via a 200 µL pipette tip. After the samples were washed twice with PBS, serum-free DMEM was added, and initial photographs were taken. Additional photographs were captured after 24 h. The migration area rate was quantified via ImageJ software, which calculats the percentage of area covered by migrating cells relative to the initial wound area.
ELISA assay
The samples were pretreated with PBS at 4 °C, and protease inhibitor (100×) was added (Beyotime, #ST506). After appropriate treatments, 1 × 106 cells per group were collected and resuspended in 200 µL of PBS. The cells were lysed via ultrasonication, and the lysate was stored at 4 °C until use. The Arachidonic Acid Detection Kit (MEIMIAN, #MM-926637O1) was used to measure the absorbance of each well at 450 nm following the manufacturer’s protocol. A standard curve was created on the basis of the absorbance values of the standards, and the arachidonic acid content in the samples was calculated via this curve.
Arachidonic acid metabolomics
Arachidonic acid metabolism in adherent tumor cell samples was analyzed via gas chromatography‒mass spectrometry (GC‒MS) by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China). Briefly, cells were extracted using dichloromethane: methanol (1:1, v/v) under cryogenic grinding, followed by low-temperature ultrasonication, phase separation, and derivatization with sodium hydroxide in methanol. The resulting fatty acid methyl esters, including arachidonic acid derivatives, were extracted with n-hexane and subjected to GC‒MS analysis on an Agilent 8890B GC coupled with a 5977B/7000D mass spectrometer. Chromatographic separation was performed on a DB-FastFAME or CP-Sil88 capillary column using helium as the carrier gas, and analytes were detected in selected ion monitoring (SIM) mode. Quantification was based on external calibration curves via MassHunter software. The quality control (QC) samples were analyzed periodically to ensure data reliability and system stability.
Statistical analysis
All experimental data in this study were replicated at least three times to ensure validity. Statistical analyses and data visualizations were conducted via GraphPad Prism version 9.0 (GraphPad, San Diego, CA, USA). An independent two-tailed t test was employed to evaluate differences between experimental groups, with the means ± standard deviations (SDs) depicted as error bars, and p < 0.05 was considered statistically significant.
Results
ACACA expression is negatively corelated with inflammation in ARIPC
To explore the role of ACACA in prostate cancer, we performed a comprehensive analysis of its expression across various tumor subtypes via publicly available datasets. ACACA expression was significantly upregulated in early-stage primary tumors and androgen receptor-positive castration-resistant prostate cancer (ARPC) tissues compared with normal prostate tissues. In contrast, its expression was markedly decreased in androgen receptor-independent prostate cancer (ARIPC) (Fig. 1A). Similarly, in the GSE48403 dataset, ACACA expression was lower in postandrogen deprivation (ADT) samples than in pre-ADT samples, suggesting that AR suppression is associated with the downregulation of ACACA (Fig. 1B). To validate these findings, we stratified AR expression into high and low groups across multiple metastatic CRPC (mCRPC) datasets to model the ARPC and ARIPC subtypes, respectively. Consistently, ACACA expression was significantly lower in the ARIPC groups (Fig. 1C–F), supporting a negative correlation between ACACA and AR signaling in this context. This finding was validated by using both public datasets and independent experiments (Fig. S1). In the public datasets, AR expression was downregulated upon androgen (R1881) stimulation and upregulated under ENZA treatment, suggesting a compensatory response. Consistently, classical AR downstream targets (KLK2, TMPRSS2, and NKX3-1) were upregulated by R1881 and downregulated by ENZA, as expected (Fig. S1A-D). Notably, ACACA expression also increased with R1881 and decreased with ENZA treatment in both public datasets and our independent experiments (Fig. S1A-G), indicating that ACACA is positively regulated by AR signaling. Notably, while ACACA expression was elevated in primary prostate cancer tissues compared with normal tissues (Fig. 1G–L), it was not consistently associated with tumor grade or AR status across all subtypes, suggesting that the role of ACACA is highly context dependent and may differ between disease stages and molecular phenotypes. Furthermore, the ACACA mRNA expression levels (Fig. 1M) and protein expression levels (Fig. 1N) in normal prostate tissue-derived BPH1 cells, the androgen receptor-positive PCa cell line LNCaP, and the androgen receptor-independent cell lines DU145 and PC3 displayed a similar expression pattern.
Fig. 1.
ACACA expression is negatively correlated with inflammation in patients with ARIPC. (A) Violin plots illustrating ACACA expression levels across distinct molecular subtypes of PCa in ProstateCancerAlta. (B) Violin plots illustrating ACACA expression levels in the preADT and postADT groups of advanced/metastatic PCa samples from the GSE48403 cohort. (C-F) Violin plots showing ACACA expression levels in mCRPC samples divided into high and low AR expression groups across the indicated cohorts. (G-L) Violin plots illustrating ACACA expression levels in normal and primary tumor samples from the indicated cohorts. (M) Transcriptome data of ACACA and AR expression, and (N) Western blot analysis of ACACA expression in BPH1 and indicated PCa cell lines. (O) GSEA of hallmark pathways in the mCRPC cohorts (GSE126078, GSE229394, GSE32269, SU2C Capture, and SU2C PloyA), comparing the high and low ACACA expression groups. (P-Q) GO enrichment analysis of DEGs between the high- and low-ACACA expression groups in the indicated mCRPC cohorts revealed enrichment of inflammatory pathways. PCa, prostate cancer; ARPC, androgen receptor-positive castration-resistant prostate cancer; NEPC, neuroendocrine prostate cancer; ARIPC, androgen receptor-independent prostate cancer; DEG, differentially expressed gene. Data are presented as the means ± SD in (A-L). *P < 0.05
To further investigate the functional significance of ACACA expression in ARIPC, we conducted gene set enrichment analysis (GSEA) via hallmark gene sets across five mCRPC cohorts (GSE126078, GSE229394, GSE32269, SU2C Capture, and SU2C PolyA). A total of 45 pathways were significantly associated with ACACA expression (Fig. 1O). Importantly, all inflammation-related pathways—including TNFα signaling via NF-κB, the interferon-α/γ response, the inflammatory response, the IL-6–JAK–STAT3 signaling pathway, and the IL-2–STAT5 signaling pathway—were consistently and negatively correlated with ACACA levels. Furthermore, differential gene enrichment analysis in the SU2C Capture and PolyA cohorts confirmed significant upregulation of inflammatory pathways in the ACACA low-expression groups (Fig. 1P–Q). Together, these results indicate that ACACA expression is negatively correlated with inflammatory signaling in ARIPC.
Depletion of ACACA promotes inflammation in ARIPC cells
To determine whether reduced ACACA expression drives inflammatory signaling, stable ACACA-knockdown DU145 and PC3 cell lines were constructed via lentiviral transduction and transcriptomic sequencing was performed. GO enrichment analysis of the DEGs in the ACACA-depleted cells revealed significant enrichment in inflammation response-related signaling pathways (Fig. 2A-B). Additionally, GSEA revealed a negative correlation between ACACA expression and the inflammatory response (Fig. 2C-D). Further analysis of the transcriptomics data revealed that representative biomarkers associated with inflammatory cytokines and chemokines, were upregulated in ACACA-depleted cells compared with control cells (Fig. 2E-F). This upregulation was further verified by qPCR and Western blot analysis, which revealed increased mRNA (Fig. 2G) and protein levels (Fig. 7A) of the inflammatory biomarkers in ACACA-depleted DU145 cells. Consistently, treating DU145 cells with TOFA (10 µg/ml), an allosteric inhibitor of the ACACA protein, led to elevated mRNA levels of inflammatory biomarkers (Fig. 2H). Collectively, these data indicate that depletion of ACACA promotes inflammation in ARIPC cells.
Fig. 2.
Depletion of ACACA promotes inflammation in ARIPC cells. (A-B) Inflammatory pathways significantly enriched in ACACA-depleted ARIPC cells. (C-D) GSEA showed that the ‘Regulation of inflammatory response’ was activated in ACACA-depleted ARIPC cells on the basis of the transcriptome data. (E-F) Heatmap of inflammatory biomarker-related gene expression in ACACA-depleted ARIPC cells on the basis of transcriptome data. (G) Quantification of the qPCR analysis results of inflammation biomarker-related gene expression in ACACA depleted DU145 cells. (H) Quantification of qPCR analysis results of inflammation biomarker-related gene expression in DU145 cells after TOFA treatment (10 µg/mL, 72 h). Data are represented as mean ± SD in (G and H). *P < 0.05
Fig. 7.
ACACA depletion activates NF-κB signaling in ARIPC cells. (A) Western blot analysis of key proteins of the NF-κB signaling pathway and inflammatory signaling in ACACA-depleted DU145 cells. (B-C) Western blot analysis of P65 and pP65 after treatment with TOFA for 72 h at the indicated concentrations (µg/mL) in the indicated ARIPC cells. (D) and (E) Western blot analysis of P65 and pP65 expression following ACACA silencing in the indicated ARIPC cells. (F-I) Immunofluorescence analysis of P65 in ACACA-depleted DU145 cells generated with different shRNA sequences (shACACA#1 and shACACA#2), with the quantification presented in (G) and (I), respectively. (J-K) Immunofluorescence analysis of P65 in DU145 cells after TOFA treatment (72 h), with quantification presented in (K). (L) Western blot analysis of P65 and pP65 and (M) qPCR analysis of P65 after arachidonic acid (AA) treatment (50 µM, 72 h). (N) Western blot analysis of P65 and pP65 and (M) qPCR analysis of P65 after cPLA2 silencing in ACACA-depleted DU145 cells. (O) qPCR analysis of P65. Data are presented as the means ± SD in (G, I, K, M, O). *P < 0.05
ACACA depletion promotes inflammation and macrophage recruitment during metastatic prostate cancer establishment in vivo
To further verify the relationship between ACACA expression and inflammation in ARIPC, an animal model is needed for in vivo validation. On the basis of the results shown in Fig. 1M, which revealed that ACACA expression is negatively correlated with the hallmark of epithelial‒mesenchymal transition (EMT), we established a nude mouse model of tail vein metastasis by injecting ACACA-depleted DU145 cells and control cells. Sixty days injection, the mice were euthanized, and metastases were harvested (Fig. 3A). Bioluminescence imaging revealed that ACACA-depleted DU145 cells developed significantly more metastatic lesions than control cells did (Fig. 3B-C). Further immunohistochemical analysis of the metastatic tissues revealed that the ACACA-depleted tumors presented increased expression of classic inflammatory biomarkers, including TNFα, IL-1β, and IL-6, along with macrophage markers F4/80 and CD68 (Fig. 3D). These findings demonstrate that ACACA depletion not only facilitates metastatic colonization but also promotes a proinflammatory tumor microenvironment characterized by elevated cytokine expression and macrophage recruitment.
Fig. 3.
ACACA depletion promotes inflammation and macrophage recruitment during metastatic prostate cancer establishment in vivo. (A) Schematic diagram of the nude mouse tail vein metastasis model. (B) Representative images and (C) bioluminescence quantitative results from living imaging of the tail vein metastasis mode. (D) Representative images of the immunohistochemistry of mouse lung tissues, respectively. Data are represented as mean ± SD in (C). *P < 0.05
Depletion of ACACA causes upregulation of arachidonic acids activity in ARIPC cells
Given that ACACA is an important metabolism-related enzyme, we first investigated the changes in metabolic products to determine which specific metabolites or categories of metabolites were altered, leading to a significant increase in inflammation in ACACA-depleted ARIPC cells. Interestingly, untargeted metabolome analysis revealed elevated levels of arachidonic acid and its precursor linoleic acid in ACACA-depleted cells compared with controls (Fig. 4A). Arachidonic acid is a well-known precursor of proinflammatory eicosanoids including prostaglandins and thromboxanes [20, 22]. We further performed targeted metabolomic profiling to specifically quantify AA-related metabolites. Notably, all detectable arachidonic acid-related metabolites including prostaglandins (PGD2, PGE2, PGF2α, and 8-iso-PGF2α) and thromboxane, were increased in ACACA-depleted ARIPC cells (Fig. 4B-M). Transcriptome sequencing data of ACACA-depleted ARIPC cells revealed elevated expression of key enzymes related to AA signaling activity (Fig. 4N-O). Additionally, the arachidonic acid and prostaglandin-related signaling pathways were significantly enriched in the GO enrichment analysis of DEGs in ACACA-depleted ARIPC cells (Fig. 4P-Q). Collectively, these data suggest that ACACA depletion leads to increased arachidonic acid accumulation and metabolism in ARIPC cells.
Fig. 4.
Depletion of ACACA causes increased arachidonic acid activity in prostate cancer cells. (A) Levels of two metabolites associated with arachidonic acid from metabolomic analysis data after ACACA knockdown in DU145 cells. (B-M) Heatmap and individual panels of arachidonic acid and its metabolite levels in ACACA-depleted PC3 cells. (N-O) Heatmaps showing the transcriptional expression levels of arachidonic acid pathway metabolic enzymes in the indicated ARIPC cells. (P-Q) Indicated pathways significantly enriched in ACACA-depleted DU145 cells. Data are represented as mean ± SD in (A, C-M). *P < 0.05
cPLA2-driven arachidonic acid promotes inflammation and migration in ACACA-depleted ARIPC cells
Arachidonic acid (AA) is primarily stored in the phospholipids of the cell membrane. When cells are subjected to specific stimuli, such as mechanical or chemical signals, AA is released from phospholipids by phospholipase A2 (cPLA2, encoded by PLA2G4A) [20]. Additionally, AA can also be acquired exogenously and incorporated into intracellular signaling pathways [31]. To assess the functional role of AA in ACACA-depleted ARIPC cells, exogenous AA (50 µM) was added to the cells. This treatment significantly increased the mRNA expression of inflammatory biomarkers, including chemokines, interleukins, and tumor necrosis factor (Fig. 5A). In parallel, AA supplementation markedly enhanced the migratory ability of ACACA-deficient cells, as evidenced by wound healing and transwell assays (Fig. 5B–E). In contrast, culturing cells in lipid-deprived media failed to suppress the elevated inflammatory biomarker expression in ACACA-depleted cells, suggesting that the proinflammatory phenotype is mediated primarily by endogenously released AA rather than by extracellular uptake (Fig. S2). Consistent with these findings, compared with control cells, ACACA-depleted ARIPC cells presented significant upregulation of cPLA2 at both the mRNA and protein levels (Fig. 5F–G). Analysis of mCRPC patient datasets revealed a significant negative correlation between ACACA and cPLA2 expression (Fig. 5H), and tumors with low ACACA expression presented higher cPLA2 levels (Fig. 5I).
Fig. 5.
cPLA2-driven arachidonic acid (AA) promotes inflammation and migration in ACACA-depleted ARIPC cells. (A) qPCR analysis of inflammation biomarker-related gene expression in ACACA-depleted DU145 cells after 72 h of treatment with 50 µM AA. (B-E) Evaluation of the migration potential of ACACA-depleted DU145 cells following AA treatment (72 h) at the indicated concentrations, via wound healing (B) and Transwell assays (D), with the quantification presented in (C) and (E), respectively. (F) Transcriptome data and (G) Western blot analysis of cPLA2 expression in ACACA-depleted DU145 cells. (H) Correlation analysis between cPLA2 and ACACA expression levels in the SU2C PolyA cohort. (I) Violin plots illustrating cPLA2 expression levels in mCRPC samples divided into high and low ACACA expression groups in the SU2C PolyA cohort. (J) qPCR analysis of cPLA2 expression after 72 h of treatment with silencing RNA in ACACA-depleted DU145 cells. (K) AA concentration measured by ELISA following cPLA2 silencing for 72 h in ACACA-depleted DU145 cells. (L) qPCR analysis of inflammation biomarker-related gene expression after cPLA2 silencing in ACACA-depleted DU145 cells. (M-P) Evaluation of the migration potential of ACACA-depleted ARIPC cells after cPLA2 silencing via wound healing (M) and Transwell (O) assays, with the quantification presented in (N) and (P), respectively. The data are presented as the means ± SDs in A, C, E, F, I, J-L, N, and P. *P < 0.05
The NF-κB signaling pathway is enriched in ACACA-depleted ARIPC cells
To investigate which inflammatory signaling transduction pathways dominate in ACACA-depleted ARIPC cells, transcriptome data were analyzed via GSEA. GSEA revealed significant enrichment of 6 pathways in DU145 cells (Fig. 6A) and 8 pathways in PC3 cells (Fig. 6B), with 3 pathways shared between the two cell lines (Fig. 6C). Further analysis of key genes within these 3 pathways revealed significant genes (P < 0.05) in DU145 (Fig. 6D) and PC3 (Fig. 6E) cells, with an overlap of 11 genes, 9 of which were enriched in the TNF and NF-κB signaling pathways (Fig. 6F). Additionally, GO enrichment analysis of DEGs in ACACA-depleted ARIPC cells revealed significant enrichment of signaling pathways related to the NF-κB signaling pathway (Fig. 6G-H).
Fig. 6.
The NF-κB signaling pathway was enriched in ACACA-depleted ARIPC cells. (A-B) GSEA-derived significant pathways in KEGG signaling transduction identified from transcriptome data of ACACA-depleted ARIPC cells. (C) Venn diagram of KEGG signaling transduction pathways in ACACA-depleted DU145 and PC3 cells. The intersection indicates the number of shared pathways. (D-E) Heatmap of the expression of key genes associated with the intersecting pathways in (C). (F) Venn diagram illustrating the overlapping genes between the DU145 (D) and PC3 (E) cell lines. (G-H) NF-κB-related pathways significantly enriched in ACACA-depleted ARIPC cells. (I) UMAP plots of single-cell RNA sequencing (scRNA-seq) data from 6 metastatic prostate cancer patients after ARSI treatment (GSE137829), with luminal epithelial cells highlighted by a black circle for subsequent analysis. (J) UMAP plots of luminal epithelial cells in (I) clustered into AR-positive and AR-negative subpopulations on the basis of the GSVA score. (K) UMAP plots of AR-negative cells in (J), which were further clustered into ACACA-high (n = 91) and ACACA-low (n = 285) subpopulations on the basis of mRNA expression. (L) GO enrichment analysis of DEGs between the ACACA-high and ACACA-low subpopulations in (K) revealed significant enrichment of the NF-κB and TNF signaling pathways
To corroborate these findings in patient-derived samples, we analyzed six single-cell RNA-sequencing (scRNA-seq) datasets from metastatic prostate cancer patients after AR pathway inhibitor (ARSI) treatment (GSE137829). Luminal epithelial tumor cells were first identified on the basis of marker expression (Fig. 6I) and further stratified into AR-positive and AR-negative subpopulations via gene set variation analysis (GSVA) (Fig. 6J). AR-negative cells (which model ARIPC) were further divided into ACACA-high and ACACA-low subgroups on the basis of mRNA expression (Fig. 6K). KEGG pathway enrichment analysis between the ACACA-high and ACACA-low subgroups revealed that the TNF and NF-κB signaling pathways were significantly enriched in the FAS-low subgroup, whereas no enrichment of the JAK-STAT pathway was detected (Fig. 6L). Given that the TNF pathway is typically transduced by the NF-κB signaling pathway, and in light of previous findings, we preliminarily hypothesize that the NF-κB signaling pathway may serve as the dominant mediator of arachidonic acid-induced inflammation in ACACA-depleted ARIPC cells.
ACACA depletion activates NF-κB signaling in ARIPC cells
To validate the activation of NF-κB signaling, we examined key signaling molecules in this pathway via Western blotting. ACACA-depleted DU145 cells presented increased phosphorylation of IKKα/β, IκBα, and P65, along with decreased total IκBα, indicating activation of the canonical NF-κB signaling cascade (Fig. 7A). Pharmacological inhibition of ACACA by TOFA led to a dose-dependent increase in P65 phosphorylation in the DU145 and PC3 cell lines (Fig. 7B–C), supporting the direct regulatory role of ACACA in NF-κB activity. Consistently, the expression of P65 and pP65 was upregulated in ACACA-silenced ARIPC cells (Fig. 7D-E). Furthermore, immunofluorescence staining confirmed the nuclear translocation and increased fluorescence intensity of P65 in both ACACA-depleted DU145 cells and TOFA-treated DU145 cells (Fig. 7F–K). To determine whether arachidonic acid–driven inflammation contributes to NF-κB activation, we first treated ACACA-depleted DU145 cells with exogenous AA. This stimulation significantly increased P65 mRNA levels, as well as P65 and phospho-P65 protein levels (Fig. 7L–M). Consistently, silencing cPLA2 led to a notable decrease in P65 and phospho-P65 protein levels, along with a reduction in P65 mRNA expression (Fig. 7N–O). These data confirm that ACACA depletion activates NF-κB signaling through a cPLA2-arachidonic acid-dependent mechanism.
Inhibition of NF-κB suppresses inflammation and migration in ACACA-depleted ARIPC cells
To further test the functional role of NF-κB in mediating inflammation and metastasis induced by ACACA depletion, we treated ACACA-knockdown DU145 cells with QNZ (EVP4593), a potent NF-κB pathway inhibitor. Western blot analysis revealed that QNZ treatment markedly reduced the protein levels of P65, phospho-P65, IL-1β, and IL-6 (Fig. 8A). qPCR analysis confirmed the downregulation of multiple inflammatory cytokines and chemokines, including TNFα, IL-1β, IL-6, CXCL1, CXCL5, and CXCL8 (Fig. 8B). Functionally, NF-κB inhibition suppressed cell migration in both the wound healing (Fig. 8C–D) and Transwell assays (Fig. 8E–F). Moreover, the expression levels of key epithelial‒mesenchymal transition (EMT) transcription factors, Slug and Snail, were elevated in ACACA-depleted cells (Fig. 8G) but significantly reduced after QNZ treatment (Fig. 8H), further indicating that NF-κB activity drives EMT and metastatic potential downstream of ACACA depletion. Together, these findings establish that the NF-κB signaling pathway is essential for mediating the proinflammatory and promigratory phenotypes induced by ACACA depletion in ARIPC. Blocking this pathway effectively reversed the tumor-promoting effects associated with ACACA depletion.
Fig. 8.
Inhibition of NF-κB suppresses inflammation and migration in ACACA-depleted ARIPC cells. (A) Western blot analysis of key proteins in the NF-κB signaling pathway and inflammatory signaling pathway after treatment with QNZ (10 nM, 72 h) in ACACA-depleted DU145 cells. (B) qPCR analysis of inflammation-related genes after treatment with QNZ (10 nM, 72 h) in ACACA-depleted DU145 cells. (C-F) Evaluation of the migration potential of ACACA-depleted ARIPC cells following treatment with QNZ (10 nM, 72 h) via wound healing (C) and Transwell (E) assays, with the quantification presented in (D) and (F), respectively. (G) qPCR analysis of Slug and Sanil in ACACA-depleted DU145 cells. (H) qPCR analysis of Slug and Sanil in the ACACA-depleted DU145 cells after treatment with QNZ (10 nM, 72 h). The data are presented as the means ± SD in (B, D, F, G, H). *P < 0.05
Discussion
Aberrant lipid metabolism has emerged as a key feature of prostate cancer progression, with de novo fatty acid synthesis being one of its most prominent metabolic hallmarks [3, 7, 32]. As a central enzyme in this pathway, acetyl-CoA carboxylase alpha (ACACA) has been extensively studied for its role in regulating tumor growth, mitochondrial function, and redox homeostasis [28, 33–36]. Consistent with prior studies, targeting ACACA has been shown to suppress tumor cell proliferation and induce apoptosis, suggesting its therapeutic potential [28, 33, 34]. However, our study reveals a distinct and unexpected role of ACACA in androgen receptor-independent prostate cancer (ARIPC), where its depletion paradoxically promotes tumor progression [29].
We demonstrate for the first time that ACACA depletion in ARIPCs activates a cascade of proinflammatory events driven by increased arachidonic acid (AA) metabolism and NF-κB signaling. This is in stark contrast to the traditional view of ACACA as solely a tumor-supportive enzyme, highlighting the context-specific consequences of targeting metabolic pathways. Mechanistically, depletion of ACACA upregulated cytosolic phospholipase A2 (cPLA2), facilitating the release of AA from membrane phospholipids. AA is then converted into prostaglandins and thromboxanes, which are potent inflammatory mediators that activate NF-κB, leading to increased production of cytokines (IL-1β, IL-6, and TNFα) and chemokines (CXCL1, CXCL5, and CXCL8). These changes collectively drive epithelial–mesenchymal transition (EMT), migration, and metastatic potential.
Our in vivo metastasis models corroborate the in vitro findings, showing that ACACA-depleted ARIPC cells induce stronger metastatic colonization and enhanced macrophage infiltration. These data support a model in which ACACA acts as a suppressor of tumor-promoting inflammation in the specific context of ARIPC. This unexpected protumorigenic consequence of ACACA depletion highlights a critical caveat in the design of metabolic therapies for prostate cancer: targeting a metabolic node without considering tumor subtype and compensatory pathways may lead to adverse effects.
Moreover, our results suggest that cotargeting cPLA2 or NF-κB may mitigate the inflammatory and prometastatic effects induced by ACACA inhibition. This combination approach could improve the efficacy of lipid metabolism-targeted therapies in AR-independent tumors. While previous studies, including our own prior work in prostate cancer, have shown that ACACA depletion may promote EMT via MAPK/ERK activation [29], the current study uncovers a distinct compensatory program in ARIPC centered on inflammatory remodeling via the cPLA2–arachidonic acid–NF-κB axis. This highlights the plasticity of stress response programs depending on AR status and provides a rationale for developing subtype-specific metabolic interventions in prostate cancer.
Collectively, these findings build upon our prior research and reveal an unanticipated inflammatory consequence of disrupting fatty acid synthesis in AR-independent contexts. They establish a previously unrecognized link between metabolic vulnerability and immune signaling plasticity in ARIPC, providing a novel therapeutic rationale for targeting the cPLA2–arachidonic acid–NF-κB axis in this aggressive prostate cancer subtype.
Conclusions
we revealed that ACACA depletion in ARIPC enhances arachidonic acid-driven inflammation and metastasis via NF-κB signaling. These findings redefine the role of ACACA in prostate cancer and provide new insight into the metabolic and inflammatory vulnerabilities of AR-independent tumors. Future therapeutic strategies should consider tumor subtype-specific metabolic contexts and may benefit from combinatorial approaches targeting both lipid metabolism and inflammation.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
Not applicable.
Abbreviations
- AA
Arachidonic acid
- ACACA
Acetyl-CoA carboxylase alpha
- ADT
Androgen deprivation therapy
- AR
Androgen receptor
- ARIPC
Androgen receptor-independent prostate cancer
- ARPC
Androgen receptor-positive castration-resistant prostate cancer
- ARSI
Aandrogen receptor signaling inhibitor
- cPLA2
Cytosolic phospholipase A2
- mCRPC
Metastatic castration-resistant prostate cancer
- DEG
Differentially expressed genes
- EMT
Epithelial-Mesenchymal Transition
- GSEA
Gene set enrichment analysis
- GSVA
Gene set variation analysis
- HEK293T
Human embryonic kidney 293T cells
- IHC
Immunohistochemistry
- IL
Interleukin
- KEGG
Kyoto Encyclopedia of Genes and Genomes
- mCRPC
Metastatic castration-resistant prostate cancer
- NEPC
Neuroendocrine prostate cancer
- NF-κB
Nuclear factor kappa B
- NES
Normalized enrichment score
- PCR
Polymerase chain reaction
- qPCR
Real-time quantitative PCR
- RNA-seq
RNA sequencing
- ROS
Reactive oxygen species
- scRNA-seq
Single-cell RNA sequencing
- shRNA
Short hairpin RNA
- siRNA
Small interfering RNA
- TNFα
Tumor necrosis factor alpha
- TOFA
5-(tetradecyloxy)-2-furoic acid
- WB
Western blot
Author contributions
S.L., J.L., Y.C., and W.Z. contributed to investigation and concept establishment. S.L., Y.C., and H.L. were responsible for study design and coordination. Y.C., J.Li., J.C., J.L., Z.L., X.M., and H.T. performed experiments. Y.F. and Y.C. conducted bioinformatic analyses. F.J., Z.H., and Y.W. curated the data. S.L., Y.C., J.L., and J.C. carried out data analyses and visualization. Y.C. and J.L. wrote the original draft. S.L. and W.Z. reviewed and edited the manuscript. W.Z. and H.H. supervised the project and provided funding. All authors reviewed the manuscript.
Funding
This work was supported by grants from the National Natural Science Foundation of China (82072813, 82373166), the Science and Technology Development Fund (FDCT) of Macao SAR (0090/2022/A, 0116/2023/RIA2), the Emergency Key Program of Guangzhou Laboratory (EKPG21-04), the Guangzhou Municipal Science and Technology Project (202201020346, 202201020386), and the Guangdong S&T Program (2023B1111030006).
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval and consent to participate
All animals were maintained according to the guidelines outlined in the Guide for the Care and Use of Laboratory Animals. All experimental protocols involving animals received approval from South China University of Technology.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Shaoyou Liu, Yupeng Chen, Jian Chen and Jiarun Lai contributed equally to this work.
Contributor Information
Huichan He, Email: xiaohejian@21cn.com.
Jiarun Lai, Email: jrlql@hotmail.com.
Weide Zhong, Email: zhongwd2009@live.cn.
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Data Availability Statement
No datasets were generated or analysed during the current study.