Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2026 Aug 3;127(8):e70115. doi: 10.1002/jcb.70115

Integrated Bioinformatic and Experimental Analysis Reveals the Molecular Mechanisms Underlying KDM1B/LSD2 Inhibition as a Therapeutic Strategy in Human Lung Adenocarcinoma

Kayalvizhi Samuvel Muthiah 1, Sathan Raj Natarajan 2, Udesh Dhawan 3, Yu‐Chien Lin 4,5, Ren‐Jei Chung 1,6,✉
PMCID: PMC13434360  PMID: 42548171

ABSTRACT

Lung cancer remains a major global health challenge, and the oncogenic function of KDM1B (Lysine‐specific Demethylase 1B) is still poorly characterized. This study employed integrated bioinformatics and experimental approaches to investigate KDM1B's function in lung cancer. Pan‐cancer analysis using databases such as TIMER revealed notably elevated KDM1B mRNA expression in LUAD datasets, suggesting its potential as a diagnostic biomarker. A strong association was also found between increased KDM1B levels and immune cell infiltration in LUAD datasets. Protein interaction networks constructed using STRING and Cytoscape revealed close associations between KDM1B and key regulatory genes in NSCLC. KEGG enrichment analysis linked KDM1B to the mTOR signaling, which is critical for cell proliferation and survival. RT‐PCR and western blotting for experimental validation showed KDM1B expression was significantly increased in A549 and NCI‐H460 lung cancer cells. The deletion of KDM1B inhibits cell growth, induces G0/G1 phase cell cycle arrest, and promotes apoptosis in A54 cells. Moreover, cell proliferation was significantly inhibited by the KDM1B inhibitor, tranylcypromine, and induced G0/G1 phase cell cycle arrest, increased apoptosis, ROS, and glycolytic activity in A549 cells. Collectively, these findings highlight KDM1B as a valuable therapeutic target in lung adenocarcinoma and emphasize its key role in lung cancer development.

Keywords: apoptosis, KDM1B, lung cancer, mTOR signaling, NSCLC, proteins, warburg effect

1. Introduction

Non‐small cell lung carcinoma (NSCLC) accounts for about 85% of lung cancer cases and is the most common type [1]. Unlike small cell lung carcinoma (SCLC), the other major lung cancer type, NSCLC generally progresses and spreads more slowly. Key risk factors for NSCLC include smoking, secondhand smoke exposure, radon, asbestos, other environmental pollutants, and genetic predispositions [2, 3]. Lung cancer remains a leading cause of cancer death worldwide, with non‐small cell lung cancer (NSCLC) being the most common subtype [4]. The WHO estimates 1.8 million deaths from lung cancer each year, most from NSCLC [5]. Both incidence and mortality rates of NSCLC vary greatly between regions of the world and are influenced by factors such as smoking patterns, air quality, and access to health care services. High‐income countries tend to benefit from improved screening programs and advanced treatments, leading to earlier detection and improved survival rates [6, 7]. Advances in molecular biology have paved the way for targeted therapies and immunotherapies, providing new hope for NSCLC patients by enhancing treatment effectiveness and survival. Despite these innovations, the prognosis for NSCLC remains poor, particularly at advanced stages, underscoring the ongoing need for research and improved global health care strategies [8, 9, 10].

Advancements in targeted therapies and immunotherapy have introduced more personalized and effective treatment options for lung cancer [11]. Current treatment strategies are multifaceted, with molecular targeted gene therapy emerging as a key component within this broader approach. While traditional treatments such as surgery, chemotherapy, and radiotherapy remain essential, integrating targeted therapies enables more precise interventions by targeting specific molecular abnormalities in cancer cells [12, 13]. Gene therapy through molecular targeted approaches in lung cancer typically aims to disrupt the signaling pathways involved in tumorigenesis. The molecular profiling of an individual patient's cancer informs a tailored treatment plan by taking into consideration the genetics of the individual tumor [14]. Ongoing research is dedicated to unraveling the complexities of lung cancer's molecular landscape to develop more effective, targeted therapies [12]. Bioinformatics tools are critical for identifying potential cancer and normal tissue data [15, 16]. Additionally, bioinformatics contributes to drug discovery by identifying potential therapeutic targets. Through genomic and proteomic data analysis, most uncover specific molecular pathways and gene alterations that can be targeted in developing novel cancer therapies. Moreover, bioinformatic analyses help identify genetic alterations and molecular pathways linked to drug resistance, offering valuable insights into strategies to overcome treatment challenges [17].

KDM1B (Lysine‐specific demethylase 1B), a histone demethylase, plays a crucial role in epigenetic regulation by removing methyl groups from lysine residues on histones, thereby modulating gene expression [18, 19, 20, 21]. By demethylating histone H3K4me1/me2 and H3K9me1/me2, KDM1B alters the chromatin structure, resulting in either activation or repression of genes involved in cell cycle regulation [22]. This study explored KDM1B's role in NSCLC progression using bioinformatics and experimental validation. Elevated KDM1B expression across cancer stages and its interaction with immune cells and macrophages suggest diagnostic potential (Scheme 1). It influences PI3K‐AKT signaling and complex cellular networks. Experimental findings confirm upregulation of KDM1B in lung cancer cells, supporting its role as a promising diagnostic and therapeutic target.

Scheme 1.

Scheme 1

Open in a new tab

The study workflow of KDM1B in lung adenocarcinoma.

2. Materials and Methods

2.1. Transcription Level of KDM1B From TCGA Datasets

An extensive examination of KDM1B across diverse cancers was undertaken using the TIMER dataset, in which KDM1B expression was measured in TPM units [23]. In total, 515 lung cancer tumor samples and 59 adjacent normal tissues were examined to delineate KDM1B expression differences at the transcriptional level. All procedures adhered to the ethical principles of the Declaration of Helsinki, each process was undertaken with strict ethical consideration.

2.2. Immune Infiltration of KDM1B in LUAD Datasets

The TIMER database (https://timer.cistrome.org/) was utilized to evaluate the immune infiltration level of KDM1B in LUAD [24]. Moreover, the investigated patterns of overall survival in lung cancer by comparing high versus low KDM1B expression levels for each of the six immune cell types.

2.3. Survival and Protein Expression of KDM1B in LUAD Datasets

A clinicopathological analysis of KDM1B in lung adenocarcinoma (LUAD) was performed using TCGA datasets. Kaplan−Meier plot analysis of overall survival was conducted using the KM plotter web server (https://kmplot.com/) [25], assessing survival probability (%) and time (months) for high and low KDM1B expression. Additionally, we utilized the CPTAC datasets to evaluate KDM1B protein expression in lung cancer by comparing z‐scores between normal lung tissues (n = 111) and lung cancer samples (n = 111). Protein expression levels were obtained from the HPA database [26].

2.4. PPI by STRING and Cytoscape

The protein network was analyzed using targets from the CTD database (https://ctdbase.org/) [5], GSE33532 [6], and GeneCards (https://www.genecards.org/) [27], identified using a Venn diagram generated with the FunRich tool. Additionally, the GSE33532 dataset was used for independent validation of KDM1B expression, with differentially expressed genes (DEGs) identified using (log2 fold change) ≥ 1 and p < 0.05. Genes overlapping between the STRING‐ and GeneCards‐derived datasets were considered KDM1B‐related candidate targets. Additional analysis of protein‐protein interactions was conducted using the STRING database (https://string.embl.de/) [28]. For STRING analysis, protein–protein interactions with a confidence score ≥ 0.4 (medium confidence) were retained. GeneCards relevance scores were used to prioritize functionally associated genes, and only genes with a relevance score above the median were included for further analysis. These DEGs were then utilized to construct and visualize the PPI network using Cytoscape software (version 3.5.1; http://www.cytoscape.org) [29]. Differential expression analyses were performed using the default statistical settings provided by each platform, with a significance threshold of p < 0.05. In this network, the edges connecting proteins were assigned widths based on their combined scores, which reflect the strength of the interactions. Visualization was further enhanced using ClustVis plugins.

2.5. Acquisition of KDM1B for GO/KEGG Analysis

To investigate the molecular mechanism associated with the upregulated biomolecules. To do this, we used the g:Profiler database (https://biit.cs.ut.ee/gprofiler/) [30], which provides a systematic and standardized process for gene annotation based on three main aspects: molecular functions (MF), cellular components (CC), and biological processes (BP).

2.6. Cell Culture and Transfection

The human NSCLC cell lines A549 and NCI‐H460, and the normal lung epithelial cell line L‐132 from ATCC. These cell lines were authenticated by short tandem repeat (STR) profiling prior to use. Routine mycoplasma contamination testing was performed using a PCR‐based detection kit, and only mycoplasma‐negative cultures were used for all experiments. Cells were passaged as needed and maintained in a CO2 incubator. The culture medium used was DMEM from Himedia, supplemented with 10% FBS and 1% penicillin‐streptomycin. Moreover, the transfection was carried out using the transfection reagent Lipofectamine 3000, and the KDM1B plasmid (sc‐405078) was purchased from SCBT. The transfection was performed in NSCLC cells, which were incubated for 96 h. The transfection was confirmed by western blotting.

2.7. Cell Viability by MTT Assay

The MTT assay was carried out following standard protocols [31] to evaluate the cytotoxic effects of the KDM1B‐specific inhibitor, tranylcypromine (TCP), and then mTOR signaling‐specific inhibitor, Rapamycin (553210, Sigma‐Aldrich), on transfected A549 and NCI‐H460 cells (WT and KDM1B KO) and on L‐132 cells, representing normal mucosa. The experiment began by seeding 1 × 104 cells, which were then incubated overnight. Tranylcypromine (TCP, Cat no. 616431, Merck) was used at concentrations of 0−20 µM for 24 h. TCP was dissolved in dimethyl sulfoxide (DMSO, Cat no. TC185, Himedia) to prepare stock solutions (50 mg/mL). The final concentration of DMSO in all treatment groups did not exceed 0.1% (v/v). Cells treated with an equivalent concentration of DMSO alone were used as solvent controls in all experiments. Following exposure to MTT reagent (Cat no. 475989, Merck), we solubilized the generated formazan crystals in DMSO. Cell viability was determined by measuring absorbance at a wavelength of 570 nm using a microplate reader (Bio‐Rad).

2.8. Metabolic Assays

Briefly, 1 × 104 A549 and NCI‐H460 cells were plated and incubated for 24 h. After 24 h, cells were treated with TCP for 24 h. Glucose uptake was measured following the manufacturer's protocols [32]. Lactate was measured with colorimetric kits following the manufacturer's instructions. Cell lysates were harvested, incubated with the lactate enzymatic mix for 1 h, and then measured at 470 nm using a microplate reader (Bio‐Rad).

2.9. Measurement of Cellular SOD Activity

Cellular SOD activity was measured using a SOD Assay Kit‐WST from Fluka Co., Milwaukee, WI, USA. A549 and NCI‐H460 cells were incubated for 24 h with TCP at a concentration of 1 × 104 cells. After determining the concentration with a Bradford assay, SOD activity was measured from 30 μg of total protein in the supernatant [33]. Absorbance was measured at 450 nm using a microplate reader (Bio‐Rad).

2.10. Measurement of Intracellular ROS Activity by DCFH‐DA

The fluorescent dye probe DCFH‐DA (D6883, Merck) was used to assess intracellular ROS production [34]. In short, 1 × 104 A549 and NCI‐H460 cells were plated 1 day in advance and treated with TCP for a sufficient duration preceding the incubation. After washing the cells in PBS, the cells were treated with 10 μM DCFH‐DA and incubated for 15 min at 37°C (in the dark). Finally, the samples were analyzed for absorbance at 525 nm with a microplate reader (Bio‐Rad).

2.11. Cell Cycle Analysis and Apoptosis Using Flow Cytometry

Briefly, A549 and NCI‐H460 cells were seeded into wells at approximately 2 × 106 cells per well and treated with KDM1B and TCP deletion, or a KDM1B inhibitor, at their respective IC50 concentrations after 24 h incubation. After fixation, the cells were stained with propidium iodide (PI) [35]. Apoptotic cells were evaluated by an Annexin V‐FITC/PI apoptosis kit according to the manufacturer's instructions [36]. Subsequent analyses were performed on fluorescence intensity histograms and dot plots acquired in the PE‐A channel. For the apoptosis assay, Annexin V–FITC–positive and PI–positive populations were quantified to distinguish early‐ and late‐apoptotic cells from necrotic cells. The fluorescence of the PI‐labeled nuclei was measured using a BD FACS Calibur flow cytometer (Becton Dickinson, USA).

2.12. Immunofluorescence Staining by PI, DAPI, and DCFH‐DA Staining

Investigation of the morphological alteration of TCP‐treated A549 and NCI‐H460 cells was performed with immunofluorescent staining. Evaluation of ROS formation in A549 and NCI‐H460 cells was performed using a non‐fluorescent intracellular probe, DCFH‐DA, at 5 µM [37]. The study's experimental conditions entailed treating 2 × 105 A549 and NCI‐H460 cells with TCP for 40 min, followed by the addition of DCFH‐DA. Also, in the investigation, nuclear stains, PI [38] and DAPI [39], were employed in conjunction with the previously outlined treatment for 24 h, and afterwards all stained cells were captured with the Olympus fluorescence microscopy (Tokyo, Japan).

2.13. Nuclear Staining by Acridine Orange (AO)/Ethidium Bromide (EtBr)

Nuclear staining using AO/EtBr was employed to identify apoptotic cells [40]. This staining technique utilizes AO and EtBr to differentiate between apoptotic and normal cells. In the experimental procedure, 10 μL of the AO/EtBr solution was applied to the A549 and NCI‐H460 cells treated with TCP and evenly distributed by sample. In contrast, normal cells exhibited green fluorescence, which was also recorded at 20x magnification using an Olympus fluorescence microscope (Tokyo, Japan).

2.14. Gene Expression Studies

L‐132, A549, and NCI‐H460 cells were seeded at a density of 2 × 106 cells per well in a six‐well plate. The RNA concentration was quantified in micrograms (µg) following the method outlined in [41]. The forward and reverse primers specific for the KDM1B gene: Forward: (5′‐AACCGAACCTAGTCCCAAA G−3′) and Reverse: (5′‐GGCTATCTGTGGAGTAAGCT‐3′) [42], β‐actin, forward: 5′‐ AGAGCTACGAGCTGCCTGAC‐3”; Reverse: 5′‐ AGCACTGTGTTGGCGTACAG‐3′.

2.15. Protein Expression by Western Blot

The lysates of L‐132, A549, and NCI‐H460 cells were prepared using RIPA buffer to achieve suboptimal protein extraction, as previously described [42, 43]. Primary antibodies used in this study included anti‐KDM1B (sc‐515565, SCBT, dilution 1:1000), anti‐BAX (sc‐7480, SCBT, 1:1000), anti‐CDKN1A/p21 (sc‐166630, SCBT, 1:1000), anti‐LDHA (sc‐137244, SCBT, 1:1000), and anti‐β‐actin (sc‐517582, SCBT, 1:5000) as a loading control. Corresponding HRP‐conjugated secondary antibodies (sc‐2005, SCBT) were used at a dilution of 1:5000 was used to probe the PVDF membrane, which was subsequently incubated with secondary antibodies to detect the chemiluminescent signal. Protein bands were analyzed and quantified using the One image analysis system software, with β‐actin for normalization and loading control.

2.16. Molecular Docking

The study investigated the binding interactions between the prodrug TCP (CID: 19493) and the KDM1B protein. The protein structure was retrieved from the Protein Data Bank (https://www.pdb.org/pdb), and molecular docking was performed using PyRx. Following docking, both 3D and 2D structural interaction analyses were conducted using Discovery Studio 2021 [44].

2.17. Ethical Statement

In this study, we used online bioinformatic databases and in vitro cell line models with ethical approval. The bioinformatic techniques we used were conducted in accordance with the principles of the Declaration of Helsinki, so ethical review and approval from human or animal review boards were not required for the methods.

2.18. Statistical Analysis

To determine the significance of mRNA and protein expression levels, the log‐rank test was performed in GraphPad Prism 8, yielding the corresponding p‐values. Kaplan−Meier survival curves were used to illustrate overall survival trends. Statistical differences using the following significance levels: *p < 0.05, **p < 0.01, ***p < 0.0001, and ****p < 0.0001.

3. Results

3.1. Pan‐Cancer View of KDM1B Expression Status

To explore the specific role of KDM1B in normal versus tumor conditions, a comprehensive analysis was performed using TCGA datasets. KDM1B expression across different cancer types was examined through TIMER 2.0 and GEPIA, revealing notable upregulation in several cancers, including breast, liver, kidney, lung, and prostate (Figures 1a,b). Among these, LUAD showed particularly high KDM1B expression. Further analysis of immunohistochemical data from the Human Protein Atlas (HPA) confirmed a marked elevation in KDM1B protein levels in lung cancer tissues compared with normal tissues (Figure 1c,d).

Figure 1.

Figure 1

Open in a new tab

Pan‐cancer view of KDM1B expression from (a) TIMER 2.0 and (b) GEPIA database retrieved from TCGA datasets. Expression levels were represented in TPM (Transcript Per Million). Blue color indicates normal and red color denotes tumor samples. The red box highlights LUAD. (c, d) KDM1B expression from the HPA (Human Protein Atlas) database.

3.2. Clinicopathological Features of KDM1B in Lung Cancer Datasets

KDM1B mRNA expression levels were further analyzed respectively using the UALCAN database of 59 normal tissue samples and 515 lung tumor samples. KDM1B was found to be significantly elevated in primary lung tumors when compared with normal tissues, as demonstrated in Figure 2a. To validate our bioinformatic results, performed gene expression experiments for KDM1B in NCI‐H460 and A549 lung cancer cells, as well as L‐132 normal lung epithelial cells. The findings, presented in Figure 2b, demonstrate a notable upregulation of KDM1B expression in NSCLC cells compared to control cells. Moreover, KDM1B expression levels were elevated in clinical tissue samples from smokers and non‐smokers compared with controls, as shown in Figure 2c. Additionally, Figure 2d shows that KDM1B expression gradually increased with age in these clinical samples. Figures 2e,f further highlight the diagnostic significance of KDM1B upregulation across different LUAD stages (I‐IV) and nodal metastasis (N0‐N3). This finding was supported by data from CPTAC samples, which demonstrated a significant increase in KDM1B protein expression in lung cancer tissues (111 tumor samples and 111 normal samples), as illustrated in Figure 2g.

Figure 2.

Figure 2

Open in a new tab

Expressional status of KDM1B in LUAD. (a) mRNA expression status of KDM1B for LUAD. (b) KDM1B gene expression by RT‐PCR in L‐132, NCI‐H460, and A549 cells, (c) KDM1B expression status compared with smoker, non‐smoker, and control, (d) KDM1B expression levels were compared in different age groups 0−100 years), (e) nodal metastasis property (for KDM1B was visualized, (f) prognostic value of KDM1B in LUAD was examined by analyzing early and advance Stages comparing with normal lung samples (n = 59), KDM1B expression for lung cancer different stages were visualized by GEPIA database, (g). protein expression level for KDM1B for LUAD was retrieved from CPTAC samples comparing tumor (n = 111) and normal adjacent tissues (n = 111), (h) Protein expression by western blot for KDM1B in normal lung, L‐132, and lung cancer cells, NCI‐H460 and A549 cells. The values presented indicate the mean ± standard error of the mean (SEM) derived from three separate experiments. Levels in the remaining groups were determined relative to those of the control group. (i) Overall survival rate of KDM1B (HR‐1.1, p−0.76) in LUAD. CPTAC, Clinical Proteomic Tumor Analysis Consortium; HR, Hazard ratio. Statistical significance is represented as follows: (*p < 0.05, **p < 0.01, ***p < 0.0001, and ****p < 0.0001) compared to the control group.

We also confirmed KDM1B protein expression by western blot analysis, which showed elevated levels in NCI‐H460 and A549 cells compared with L‐132 normal lung epithelial cells (Figure 2h). The alignment of gene and protein expression results further supports the significance of KDM1B in lung cancer. Additionally, the overall survival analysis for KDM1B in LUAD, depicted in Figure 2i, revealed between high KDM1B expression (HR = 1.1, p = 0.76) and reduced overall survival in lung cancer patients.

3.3. KDM1B Related to PPI Interaction With Potent Targets

CTD database. the GSE33532, and Genecards to identify the significant targets and pathway enriched for KDM1B in lung cancer. The Venn diagram in Figure 3a shows that 19 targets overlapped and suggests their important nature associated with KDM1B in lung cancer. To further explore these interactions, the PPI network from the STRING database was utilized, as depicted in Figure 3b. The network visualization was enhanced using Cytoscape software, illustrating the interactions of KDM1B with its associated targets in Figure 3c. Additional analysis with the ClustVis plugin of Cytoscape identified 29 nodes and 414 edges, with a score of 14.786, as shown in Figure 3d. Furthermore, the top 10 hub genes such as BECN1, RPS6KB1, TSC2, EIF4EBP1, BCL2, BCL2L11, MCL1, BAK1, BCL2L1, and BAX were identified in Figures 3e,f. These protein interaction networks suggest that KDM1B is closely associated with apoptosis and cell proliferation‐related targets in lung cancer.

Figure 3.

Figure 3

Open in a new tab

KDM1B interconnects with hub genes in LUAD. (a) CTD database, GSE33532, and gene cards databases. (b, c) Using STRING, a web server, and Cytoscape, a networking tool, visualized PPI with high nodal strength. (d). Using the MCODE plugin, 29 nodes and 414 edges were scrutinized. (e, f) The CytoHubba plugin evaluated the targets in accordance with their MCC, degree, stress, closeness, betweenness, and radiality.

3.4. Functional Annotations and Pathway Enriched in KDM1B in LUAD

In expanding our investigation into KDM1B's functional roles, gene ontology analysis revealed its involvement in critical Biological Processes. It is also associated with Cellular Components such as extracellular vesicles, the nucleus, and the nuclear membrane. Molecular Functions attributed to KDM1B include histone demethylase activity, channel activity, histone H3K9 demethylase activity, and binding to methylated histones, as illustrated in Figure 4a. Additionally, the butterfly plots in Figure 4b highlight the significant involvement of the top 10 genes in enriched pathways, including apoptosis, the mTOR signaling pathway, and protein metabolism.

Figure 4.

Figure 4

Open in a new tab

Functional annotation and pathway regulation of KDM1B in lung cancer. (a) Gene ontologies (b) A two‐way plot represents the pathway‐enriched analysis.

3.5. Genetic Alterations and Immune Infiltration of KDM1B in LUAD Datasets

Further, explored KDM1B mutations, genetic alterations, and immune cell responses in LUAD patient samples using data from the cBioPortal and TIMER databases to gain deeper insights into its complex interactions. As shown in Figure 5a, copy number alterations and mutation analysis revealed a 1.2% alteration rate in KDM1B within LUAD samples. The box plot in Figure 5b,c illustrates KDM1B's involvement in immune cell infiltration. Analysis of mutation counts from the cBioPortal database also indicated that diploid mutations are more frequently upregulated than deletions and amplifications in KDM1B‐LUAD clinical samples. We also assessed overall survival rates for KDM1B in early and advanced lung cancer in KMplot, which showed a distinct pattern: KDM1B expression was lower in early stages and higher in advanced stages (Figure 5d). Also, Kaplan‐Meier analysis of overall survival in lung cancer with immune cells and KDM1B expression showed a worse overall survival rate with high KDM1B expression (Top 50%) (Figure 5e). Overall, these bioinformatic analyses of KDM1B mutations and immune infiltration emphasize the interaction of immune cells and lung cancer.

Figure 5.

Figure 5

Open in a new tab

Genetic alterations, mutations, and Immune profiling of KDM1B with immune cells in LUAD. (a) Genetic alterations and copy number variants of KDM1B in LUAD samples; (b) Immune infiltration levels for KDM1B in LUAD. (c) Mutation count was also evaluated for KDM1B; (d) Different stages (I to IV) of overall survival rate for KDM1B in lung adenocarcinoma were obtained from KM plotter; (e) KDM1B in LUAD datasets retrieved from TIMER 2.0 database.

3.6. KDM1B Promotes Cell Growth and Inhibits Cell Death in Lung Cancer Cells

To validate the bioinformatic findings indicating the oncogenic role of KDM1B in lung cancer, in vitro experiments were performed. Deletion of KDM1B significantly suppressed cell proliferation in transfected A549 and NCI‐H460 cells in a time‐dependent manner, as shown in Figure 6a. Flow cytometric analysis further demonstrated that KDM1B deletion led to a reduction in the S phase population with a concomitant accumulation of cells in the G0/G1 phase, indicating cell cycle arrest in both lung cancer cells (Figure 6b). Successful deletion of KDM1B was confirmed by Western blot analysis (Figure 6c). Additionally, transfected A549 and NCI‐H460 cells lacking KDM1B exhibited a marked increase in cell death compared with the wild‐type group (Figure 6d). Collectively, these experimental results corroborate the bioinformatic analyses and confirm the oncogenic potential of KDM1B in lung cancer.

Figure 6.

Figure 6

Open in a new tab

Deletion of KDM1B reduced cell growth and promoted cell death in A549 and NCI‐H460 cells. (a) The cell viability by MTT assay for 24h‐72h in the presence and absence of KDM1B KO in time‐dependent in A549 and NCI‐H460 cells. (b) With and without treatment of KDM1B shows reduced S‐phase cell cycle arrest in A549 and NCI‐H460 cells. (c) The deletion of KDM1B was confirmed by Western blot in both lung cancer cells. (d) The flow cytometry by annexin V‐FITC/PI dual staining also reveals the induction of cell death by treatment of KDM1B deletion in A549 and NCI‐H460 cells. Statistical significance (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001).

3.7. TCP Reduces Cell Growth and Promotes Apoptosis in Lung Cancer Cells

To validate prior results, molecular docking analysis revealed a strong binding interaction between TCP and KDM1B (−7.1 kcal/mol), as shown in Figure 7a. Figure 7b illustrates the hydrogen bond binding interaction that stabilizes the KDM1B‐TCP complex. To confirm the molecular docking results, we assessed TCP's cytotoxicity using the MTT assay by treating cells with TCP (0−20 µM) for 24 h. Compared to normal lung cells (L‐132), A549 and NCI‐H460 tumor cells exhibited significant growth inhibition following TCP treatment, as shown in Figure 7c. The IC50 of TCP was calculated to be 14.69 µM for A549 and 16.12 µM for NCI‐H460 were used in subsequent experiments. To assess TCP's impact on cellular morphology, A549 and NCI‐H460 cells were treated with the IC50 concentration for 24 h. Untreated cells showed no significant changes, whereas TCP‐treated cells exhibited pronounced cytomorphological alterations and aggregation, as observed by PI, DAPI, and DCFH‐DA staining. Complementing these findings, we investigated the effect of TCP on ROS activity in A549 and NCI‐H460 cells using DCF‐DA staining. Results showed reduced ROS activity in untreated A549 and NCI‐H460 cells, while TCP treatment induced a notable increase in ROS activity, highlighting TCP's influence on oxidative stress. Nuclear staining with AO/EB further confirmed the apoptotic response, with A549 and NCI‐H460 cells showing increased early and late apoptosis following TCP treatment, as illustrated in Figure 7d. Flow cytometry analysis further corroborated TCP's effectiveness against lung cancer. Treating A549 and NCI‐H460 cells with TCP for 24 h resulted in a reduction in the synthesis phase compared to the untreated group as depicted in Figure 7e, suggesting that TCP induces G1 phase cell cycle arrest. Moreover, annexin V‐FITC/PI double labeling indicated that TCP treatment induced early and late apoptosis in A549 and NCI‐H460 cells compared to the control group, as shown in Figure 7f. Figure 7g,h, demonstrate that TCP treatment over time led to a decrease in glucose accumulation and LDH activity in A549 and NCI‐H460 cells. Furthermore, cellular superoxide dismutase (SOD) and ROS activity were evaluated, revealing that TCP decreased SOD levels and elevated ROS activity, as depicted in Figures 7i,j. Furthermore, protein expression levels were measured by Western blotting in A549 cells after treatment with TCP. The results demonstrated that KDM1B and LDHA protein levels significantly decreased, and that A549 cells showed increased expression of BAX and CDKN1A, as shown in Figure 7k. These findings suggest TCP's influence on oxidative stress and its therapeutic potential in lung cancer. This experimental approach deepens our understanding of TCP's impact on ROS activity and apoptosis mechanisms, underscoring its potential as a treatment for lung cancer. Overall, the correlation between our bioinformatic analyses and experimental validations reinforces the identification of KDM1B as an oncogene and indicates that it may be an important therapeutic biomarker in NSCLC and LUAD tumorigenesis. The findings in this study highlight KDM1B as a therapeutic target in lung cancer.

Figure 7.

Figure 7

Open in a new tab

Tranylcypromine inhibits cell growth, Warburg effect, increases G1 phase cell cycle arrest, and induces apoptosis in A549 cells. (a) Interaction between TCP and KDM1B targets. (b) Hydrogen bond interaction of the TCP‐KDM1B complex. (c) Cytotoxicity assay for cell viability shows the increasing concentration of TCP (0−20 μM) for 24h intervals in A549, NCI‐H460, and L‐132 cells, (d) TCP were treated in A549 and NCI‐H460 cells for 24 h, and visualized the morphological changes. PI and DAPI for nuclei staining, DCFH‐DA for intracellular ROS activity, and AO/EtBr staining for early and late apoptosis in A549 cells at 20x magnification. (e) Cell cycle analysis and (f) Cell death was also measured by flow cytometry in A549 and NCI‐H460 cells. Metabolic assays (g) Glucose uptake, (h) LDH activity, (i) Cellular SOD activity, and (j) ROS production assays were measured. (k) Protein expression levels of KDM1B, BAX, LDHA, and CDKN1A by western blot. Statistical significance (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001).

3.8. KDM1B Regulates Cell Proliferation and Induces Apoptosis Through Mtor Signaling Pathway in Lung Cancer Cells

KDM1B knockout (KO), rapamycin (RAPA), and their combination significantly inhibited the growth of A549 and NCI‐H460 lung cancer cells. Cell viability assays showed that KDM1B depletion and rapamycin treatment significantly reduced cell proliferation, and that the combined treatment resulted in a significant decrease, suggesting enhanced growth inhibition as shown in Figure 8a. The cell cycle analysis by flow cytometry revealed a gradual increase in A549 and NCI‐H460 cells in the G0/G1 phase in KDM1B knock‐out and rapamycin‐treated cells, with a reduction in the S and G2/M phases. The combination group exhibited the most significant G0/G1 arrest, indicating effective inhibition of cell‐cycle progression and DNA synthesis in A549 and NCI‐H460 cells, as depicted in Figure 8b. Annexin V‐FITC/PI staining demonstrated that KDM1B KO significantly increased the population of apoptotic cells, a change further augmented by rapamycin. Increased early and late apoptosis was observed in combined KDM1B KO and rapamycin treatment, with concomitant decrease in viable A549 and NCI‐H460 cells, as shown in Figure 8c. Collectively, these results suggest that KDM1B promotes proliferation and survival of NSCLC cells and that its inhibition synergizes with mTOR blockade to suppress tumor progression via cell cycle arrest and apoptosis in lung cancer cells.

Figure 8.

Figure 8

Open in a new tab

KDM1B knockout enhances the antiproliferative and pro‐apoptotic effects of rapamycin treatment in A549 and NCI‐H460 lung cancer cells. (a) Cell viability assays demonstrating the impact of wild‐type (WT), KDM1B knockout (KDM1B KO), rapamycin (RAPA) and KDM1B KO + RAPA on A549 and NCI‐H460 cells. Depletion of KDM1B and rapamycin significantly reduced cell viability, and the combination treatment showed the greatest inhibition of cell proliferation. Values are presented as mean ± SD. (b) Representative flow cytometric histograms of cell cycle distribution in A549 and NCI‐H460 cells. KDM1B knockout increased G0/G1 phase arrest and decreased S and G2/M phase populations, with the most profound G0/G1 accumulation occurring with combined KDM1B KO and rapamycin treatment. (c) Representative Annexin V‐FITC/PI flow cytometry plots of apoptosis in both cell lines. KDM1B knockout and rapamycin alone increased the population of apoptotic cells, whereas the combination treatment significantly increased apoptosis at both early and late stages and decreased the percentage of viable cells, indicating synergistic induction of programmed cell death. Statistical significance (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001).

4. Discussion

Cancer is a complex and heterogeneous collection of diseases characterized by uncontrolled proliferation and dissemination of abnormal cells; it is also a major public health crisis worldwide [45, 46]. Lung cancer usually begins with precancerous polyps that eventually become malignancies [47]. While lung cancer can progress slowly, allowing for early intervention and treatment, advanced stages present greater challenges, particularly when metastasis to other organs occurs, complicating treatment success [48]. The identification of biomarkers has become a key strategy in modern lung cancer therapy, enabling more personalized and effective treatment approaches [49]. Biomarkers, which may be genetic, molecular, or biochemical, are essential for predicting prognosis, guiding treatment decisions, and monitoring treatment responses. In lung cancer, the discovery and use of biomarkers have significantly advanced our understanding of the disease and reshaped therapeutic strategies [50, 51, 52].

Our results, illustrated in Figures 1a,b, showed significant upregulation of KDM1B in several cancers. Specifically, we focused on LUAD, in which KDM1B expression was notably higher than in other cancer types. Using immunohistochemical data from the HPA database (Figure 1c) I did further analysis, which revealed a significant increase in KDM1B protein expression between cancerous and normal lung tissue, indicating it may have an important role in lung cancer progression. As shown in Figure 2a, we saw a marked induction of KDM1B expression in primary lung tumors. These bioinformatic results were confirmed by RT‐PCR, in which we compared the levels of KDM1B expression between normal L‐132 lung epithelial cells and lung cancer cells, NCI‐H460 or A549. The results, presented in Figure 2b, showed a marked upregulation of KDM1B in LUAD cells compared to controls. Additionally, KDM1B expression levels were analyzed in LUAD clinical tissue samples from smokers and reformed smokers, where increased expression was observed (Figure 2c). Furthermore, KDM1B expression levels gradually rose with age in these clinical samples (Figure 2d). In Figures 2e,f, we highlighted the diagnostic importance of KDM1B upregulation across various stages (I‐IV) of LUAD and nodal metastasis (N0‐N3). Data from CPTAC samples further confirmed increased KDM1B protein expression in lung cancer tissues compared with normal tissues (Figure 2g), and this was validated by western blot analysis, which showed higher levels in lung cancer cells (Figure 2h). Consistency in gene and protein expression patterns underscores the relevance of KDM1B in lung cancer. Overall survival analysis (Figure 2i) also underscores the relationship between high KDM1B expression and diminished survival in LUAD patients, supporting the possibility as a prognostic marker [53].

Our expanded investigation into KDM1B's role in lung cancer revealed its involvement in critical BP and MF. KDM1B was also linked to CCs, including extracellular vesicles, the nucleus, and the nuclear membrane. To further understand the role of KDM1B in LUAD, we explored genetic alterations, mutations, and immune responses using data from the cBioPortal and TIMER databases. Figure 5a shows that KDM1B had a 1.2% rate of copy number alterations and mutations in LUAD samples. Mutation count analysis from the cBioPortal database further revealed that diploid mutations in KDM1B are more frequently upregulated than deletions and amplifications in LUAD samples. We also investigated the impact of KDM1B on lung cancer progression by examining overall survival across disease. This discovery implies that KDM1B may play a major role in disease progression, particularly in advanced lung cancer [54, 55]. In addition, we investigated the association between KDM1B expression and immune cell responses in lung cancer.

The combined use of computational and experimental approaches revealed the potential of TCP as a potent inhibitor of KDM1B in lung cancer treatment. Molecular docking demonstrated a strong binding interaction between TCP and KDM1B (−7.1 kcal/mol), with hydrogen bonding stabilizing the complex. To confirm this, an MTT assay showed that TCP inhibited A549 tumor cell growth, with an IC50 of 14.69 µM, while sparing normal lung cells (L‐132). Flow cytometry showed that TCP treatment induced G1‐phase cell‐cycle arrest and apoptosis in A549 cells. This was supported by annexin V‐FITC/PI labeling, which detected the following percentages of early (19.69%) and late (3.45%) apoptosis. In addition, we investigated cellular morphology and observed significant changes, including cell shrinkage and cell death, following TCP treatment. Additionally, TCP increased ROS activity in A549 cells, reducing glucose uptake, lactate production, and SOD levels, indicating its influence on oxidative stress [56]. These findings highlight TCP's role in inducing apoptosis and oxidative stress in lung cancer cells. The overall molecular mechanistic role of KDM1B was illustrated in Figure 8. Overall, the results align with bioinformatic analyses, reinforcing KDM1B's role as an oncogene in LUAD and its potential as a therapeutic target. TCP shows promise as an effective treatment, targeting KDM1B to inhibit lung cancer progression.

Despite providing important insights into the oncogenic role of KDM1B in lung cancer, this study has limitations that warrant attention. The in vitro functional validation was performed using a limited number of lung cancer cell lines, which may not fully capture the molecular heterogeneity of lung cancer (Figure 9). Although large‐scale public datasets were used for expression and clinical correlation analyses, validation of KDM1B expression in self‐collected clinical samples was not performed, limiting the translational relevance of the findings. Several molecular interactions and signaling pathways proposed in the mechanistic model were derived primarily from bioinformatic predictions and literature evidence, without direct experimental verification. Although L‐132 cells were used as a representative normal lung epithelial control, it should be noted that L‐132 is a transformed cell line with embryonic origin and may not fully recapitulate the molecular and physiological characteristics of primary normal lung epithelial cells. Although the present study demonstrates that KDM1B promotes lung cancer cell proliferation and apoptosis via mTOR signaling, the effects of KDM1B on cell migration and invasion were not investigated. Further studies, including wound‐healing and Transwell migration/invasion assays, are needed to better understand the role of KDM1B in lung cancer metastasis. Additionally, although genetic KO models were employed, complementary knockdown approaches and global transcriptomic profiling, such as RNA‐seq, were not performed, limiting insights into downstream regulatory networks.

Figure 9.

Figure 9

Open in a new tab

The molecular mechanisms through which KDM1B operates in lung adenocarcinoma have garnered significant attention due to its oncogenic properties.

5. Conclusion

Overall, the study demonstrated a notable association between elevated KDM1B expression and increased immune cell infiltration in LUAD datasets. Protein–protein interaction network analysis using STRING and cytoscape identified a KDM1B‐centered gene network, providing insight into its key molecular interactions in LUAD datasets. KEGG pathway enrichment analysis indicated a potential involvement of KDM1B in mTOR‐related signaling pathways that regulate cell growth and survival. Experimental validation by RT‐PCR and Western blotting confirmed higher KDM1B expression in lung cancer cells. Functional studies showed that KDM1B deletion suppresses cell proliferation, induces G0/G1‐phase cell‐cycle arrest, and promotes apoptosis in A549 and NCI‐H460 cells. In addition, treatment with TCP, a specific inhibitor of KDM1B, significantly reduced cell growth and modulated cancer cell metabolism, consistent with altered Warburg effects in A549 and NCI‐H460 lung cancer cells. Collectively, these findings suggest that KDM1B plays an important role in lung carcinogenesis and may represent a potential therapeutic target in NSCLC, although further mechanistic and clinical validation is warranted.

Author Contributions

Kayalvizhi Samuvel Muthiah: conceptualization, investigation, formal analysis, writing – original draft, writing – review and editing. Sathan Raj Natarajan: investigation, formal analysis, writing – review and editing. Udesh Dhawan: resources, writing – review and editing. Yu‐Chien Lin: resources, writing – review and editing. Ren‐Jei Chung: supervision, project administration, methodology, funding acquisition, conceptualization, writing – review and editing.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supporting File

JCB-127-e70115-s001.docx (925.2KB, docx)

Acknowledgments

The authors are grateful for the financial support provided by the National Science and Technology Council of Taiwan (NSTC 112‐2221‐E‐027‐038‐MY3; NSTC 112‐2321‐B‐A49‐008; NSTC 113‐2314‐B‐182A‐128). We also would like to acknowledge the technical assistance from the Precision Analysis and Material Research Center of the National Taipei University of Technology (Taipei Tech).

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

The raw data supporting the conclusions of this article will be made available by the authors without undue reservation.

References

  • 1. Duma N., Santana‐Davila R., and Molina J. R., “Non–Small Cell Lung Cancer: Epidemiology, Screening, Diagnosis, and Treatment,” Mayo Clinic Proceedings, Elsevier 94 (2019): 1623–1640. [DOI] [PubMed] [Google Scholar]
  • 2. Mustafa M., Azizi A. J., IIIzam E., Nazirah A., Sharifa S., and Abbas S., “Lung Cancer: Risk Factors, Management, and Prognosis,” IOSR Journal of Dental and Medical Sciences 15, no. 10 (2016): 94–101. [Google Scholar]
  • 3. S Cheng E., Weber M., Steinberg J., and Qin Yu X., “Lung Cancer Risk in Never‐Smokers: An Overview of Environmental and Genetic Factors,” Chinese Journal of Cancer Research 33, no. 5 (2021): 548–562. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Chaitanya Thandra K., Barsouk A., Saginala K., Sukumar Aluru J., and Barsouk A., “Epidemiology of Lung Cancer,” Współczesna Onkologia 25, no. 1 (2021): 45–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Balata H., Fong K. M., Hendriks L. E., et al., “Prevention and Early Detection for NSCLC: Advances in Thoracic Oncology 2018,” Journal of Thoracic Oncology 14, no. 9 (2019): 1513–1527. [DOI] [PubMed] [Google Scholar]
  • 6. Meghji J., Mortimer K., Agusti A., et al., “Improving Lung Health in Low‐Income and Middle‐Income Countries: From Challenges to Solutions,” Lancet 397, no. 10277 (2021): 928–940. [DOI] [PubMed] [Google Scholar]
  • 7. Zvolska K., Pankova A., Nohavova I., et al., “A Narrative Review of Facilitators and Barriers to Smoking Cessation and Tobacco‐Dependence Treatment in Patients with Tuberculosis in Low‐ and Middle‐Income Countries,” Tobacco Induced Diseases 18 (2020): 67. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Bertolaccini L., Casiraghi M., Uslenghi C., Maiorca S., and Spaggiari L., “Recent Advances in Lung Cancer Research: Unravelling the Future of Treatment,” Updates in Surgery 76 (2024): 2129–2140. [DOI] [PubMed] [Google Scholar]
  • 9. Miller M. and Hanna N., “Advances in Systemic Therapy for Non‐Small Cell Lung Cancer,” BMJ 375 (2021): 375. [DOI] [PubMed] [Google Scholar]
  • 10. Megyesfalvi Z., Gay C. M., Popper H., et al., “Clinical Insights into Small Cell Lung Cancer: Tumor Heterogeneity, Diagnosis, Therapy, and Future Directions,” CA: A Cancer Journal for Clinicians 73, no. 6 (2023): 620–652. [DOI] [PubMed] [Google Scholar]
  • 11. Pakkala S. and Ramalingam S. S., “Personalized Therapy for Lung Cancer: Striking a Moving Target,” JCI Insight 3, no. 15 (2018): e120858. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Huang M., Shen A., Ding J., and Geng M., “Molecularly Targeted Cancer Therapy: Some Lessons From the Past Decade,” Trends in Pharmacological Sciences 35, no. 1 (2014): 41–50. [DOI] [PubMed] [Google Scholar]
  • 13. Hensing T., Chawla A., Batra R., and Salgia R., “A Personalized Treatment for Lung Cancer: Molecular Pathways, Targeted Therapies, and Genomic Characterization,” Systems Analysis of Human Multigene Disorders 799 (2013): 85–117. [DOI] [PubMed] [Google Scholar]
  • 14. Malone E. R., Oliva M., Sabatini P. J. B., Stockley T. L., and Siu L. L., “Molecular Profiling for Precision Cancer Therapies,” Genome Medicine 12 (2020): 8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Chen L., Lu D., Sun K., et al., “Identification of Biomarkers Associated with Diagnosis and Prognosis of Colorectal Cancer Patients Based on Integrated Bioinformatics Analysis,” Gene 692 (2019): 119–125. [DOI] [PubMed] [Google Scholar]
  • 16. Jafari S., Ravan M., Karimi‐Sani I., et al., “Screening and Identification of Potential Biomarkers for Pancreatic Cancer: An Integrated Bioinformatics Analysis,” Pathology‐Research and Practice 249 (2023): 154726. [DOI] [PubMed] [Google Scholar]
  • 17. Li K., Du Y., Li L., and Wei D.‐Q., “Bioinformatics Approaches for Anti‐Cancer Drug Discovery,” Current Drug Targets 21, no. 1 (2019): 3–17. [DOI] [PubMed] [Google Scholar]
  • 18. Kang M., Mehrazarin S., Park N. H., and Wang C. Y., “Epigenetic Gene Regulation by Histone Demethylases: Emerging Role in Oncogenesis and Inflammation,” Oral Diseases 23, no. 6 (2017): 709–720. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Kim H.‐M. and Liu Z., “LSD2 is an Epigenetic Player in Multiple Types of Cancer and Beyond,” Biomolecules 14, no. 5 (2024): 553. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. D'Oto A., Tian Q.‐w, Davidoff A. M., and Yang J., “Histone Demethylases and Their Roles in Cancer Epigenetics,” Journal of Medical Oncology and therapeutics 1, no. 2 (2016): 34. [PMC free article] [PubMed] [Google Scholar]
  • 21. Wang Y., Sun L., Luo Y., and He S., “Knockdown of KDM1B Inhibits Cell Proliferation and Induces Apoptosis of Pancreatic Cancer Cells,” Pathology‐Research and Practice 215, no. 5 (2019): 1054–1060. [DOI] [PubMed] [Google Scholar]
  • 22. Katz T. A., Vasilatos S. N., Harrington E., Oesterreich S., Davidson N. E., and Huang Y., “Inhibition of Histone Demethylase, LSD2 (KDM1B), Attenuates DNA Methylation and Increases Sensitivity to DNMT Inhibitor‐Induced Apoptosis in Breast Cancer Cells,” Breast Cancer Research and Treatment 146 (2014): 99–108. [DOI] [PubMed] [Google Scholar]
  • 23. Duarte Y., Márquez‐Miranda V., Miossec M. J., and González‐Nilo F., “Integration of Target Discovery, Drug Discovery and Drug Delivery: A Review on Computational Strategies,” WIREs Nanomedicine and Nanobiotechnology 11, no. 4 (2019): e1554. [DOI] [PubMed] [Google Scholar]
  • 24. Fioravanti R., Romanelli A., Mautone N., et al., “Tranylcypromine‐Based LSD1 Inhibitors: Structure‐Activity Relationships, Antiproliferative Effects in Leukemia, and Gene Target Modulation,” ChemMedChem 15, no. 7 (2020): 643–658. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Tomczak K., Czerwińska P., and Wiznerowicz M., “Review The Cancer Genome Atlas (TCGA): An Immeasurable Source of Knowledge, Contemporary Oncology/Współczesna,” Onkologia 2015, no. 1 (2015): 68–77. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26. Qi C., Lei L., Hu J., Wang G., Liu J., and Ou S., “T Cell Immune Regulator 1 is a Prognostic Marker Associated with Immune Infiltration in Glioblastoma Multiforme,” Oncology Letters 21, no. 4 (2021): 252. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Jia B., Zhao X., Wang Y., Wang J., Wang Y., and Yang Y., “Prognostic Roles of MAGE Family Members in Breast Cancer Based on KM‐Plotter Data,” Oncology Letters 18 (2019): 3501–3516. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Pontén F., Jirström K., and Uhlen M., “The Human Protein Atlas—A Tool for Pathology,” Journal of Pathology 216, no. 4 (2008): 387–393. [DOI] [PubMed] [Google Scholar]
  • 29. Davis A. P., Grondin C. J., Johnson R. J., et al., “Comparative Toxicogenomics Database (CTD): Update 2021,” Nucleic Acids Research 49, no. D1 (2021): D1138–D1143. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Xie H., Zhang J. F., and Li Q., “Identification and Analysis of Genes Associated with Lung Adenocarcinoma by Integrated Bioinformatics Methods,” Annals of Human Genetics 85, no. 3–4 (2021): 125–137. [DOI] [PubMed] [Google Scholar]
  • 31. Safran M., Dalah I., Alexander J., et al., “Genecards Version 3: the Human Gene Integrator,” Database 2010 (2010): baq020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Von Mering C., Jensen L. J., Snel B., et al., “P. Bork, STRING: Known and Predicted Protein–Protein Associations, Integrated and Transferred Across Organisms,” supplement, Nucleic Acids Research 33, no. S1 (2005): D433–D437. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Shannon P., Markiel A., Ozier O., et al., “Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks,” Genome Research 13, no. 11 (2003): 2498–2504. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. Reimand J., Arak T., Adler P., et al., “G: Profiler—A Web Server for Functional Interpretation of Gene Lists (2016 Update),” Nucleic Acids Research 44, no. W1 (2016): W83–W89. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. Jayaraman S., Raj Natarajan S., Ponnusamy B., Veeraraghavan V. P., and Jasmine S., “Unlocking the Potential of Beta Sitosterol: Augmenting the Suppression of Oral Cancer Cells Through Extrinsic and Intrinsic Signalling Mechanisms,” Saudi Dental Journal 35, no. 8 (2023): 1007–1013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. Jayaraman S., Natarajan S. R., Veeraraghavan V. P., and Jasmine S., “Unveiling the Anti‐Cancer Mechanisms of Calotropin: Insights into Cell Growth Inhibition, Cell Cycle Arrest, and Metabolic Regulation in Human Oral Squamous Carcinoma Cells (HSC‐3),” Journal of Oral Biology and Craniofacial Research 13, no. 6 (2023): 704–713. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37. Batinic‐Haberle I., Tovmasyan A., Roberts E. R., Vujaskovic Z., Leong K. W., and Spasojevic I., “Sod Therapeutics: Latest Insights into Their Structure‐Activity Relationships and Impact on the Cellular Redox‐Based Signaling Pathways,” Antioxidants & Redox Signaling 20, no. 15 (2014): 2372–2415. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38. Pelicano H., Carney D., and Huang P., “ROS Stress in Cancer Cells and Therapeutic Implications,” Drug Resistance Updates 7, no. 2 (2004): 97–110. [DOI] [PubMed] [Google Scholar]
  • 39. Jayaraman S., Veeraraghavan V. P., Natarajan S. R., and Jasmine S., “Exploring the Therapeutic Potential of Curcumin in Oral Squamous Cell Carcinoma (HSC‐3 Cells): Molecular Insights into Hypoxia‐Mediated Angiogenesis,” Pathology‐Research and Practice 254 (2024): 155130. [DOI] [PubMed] [Google Scholar]
  • 40. Chen S., Cheng A.‐C., Wang M.‐S., and Peng X., “Detection of Apoptosis Induced by New Type Gosling Viral Enteritis Virus in Vitro Through Fluorescein Annexin V‐FITC/PI Double Labeling,” World Journal of Gastroenterology 14, no. 14 (2008): 2174. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41. Rutley N. and Miller G., “Large‐Scale Analysis of Pollen Viability and Oxidative Level Using H 2 DCFDA‐Staining Coupled with Flow Cytometry,” Pollen and Pollen Tube Biology: Methods and Protocols 2160 (2020): 167–179. [DOI] [PubMed] [Google Scholar]
  • 42. Riccardi C. and Nicoletti I., “Analysis of Apoptosis by Propidium Iodide Staining and Flow Cytometry,” Nature Protocols 1, no. 3 (2006): 1458–1461. [DOI] [PubMed] [Google Scholar]
  • 43. Tarnowski B. I., Spinale F. G., and Nicholson J. H., “DAPI as a Useful Stain for Nuclear Quantitation,” Biotechnic & Histochemistry 66, no. 6 (1991): 296–302. [PubMed] [Google Scholar]
  • 44. Vivek R., Thangam R., Muthuchelian K., Gunasekaran P., Kaveri K., and Kannan S., “Green Biosynthesis of Silver Nanoparticles From Annona Squamosa Leaf Extract and its In Vitro Cytotoxic Effect on MCF‐7 Cells,” Process Biochemistry 47, no. 12 (2012): 2405–2410. [Google Scholar]
  • 45. Green M. R. and Sambrook J., “Quantifying and Storing RNA,” Cold Spring Harbor Protocols 2020, no. 3 (2020) pdb. top101709.): pdb.top101709. [DOI] [PubMed] [Google Scholar]
  • 46. Cai S., Wang J., Zeng W., Cheng X., Liu L., and Li W., “Lysine‐Specific Histone Demethylase 1B (LSD2/KDM1B) Represses p53 Expression to Promote Proliferation and Inhibit Apoptosis in Colorectal Cancer Through LSD2‐mediated H3K4me2 Demethylation,” Aging 12, no. 14 (2020): 14990–15001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47. Peach M., Marsh N., Miskiewicz E. I., and MacPhee D. J., “Solubilization of Proteins: The Importance of Lysis Buffer Choice,” Western Blotting: Methods in Molecular Biology (Clifton, N.J.) 1312 (2015): 49–60. [DOI] [PubMed] [Google Scholar]
  • 48. Jayaraman S., Natararaj S., and Veeraraghavan V. P., “Hesperidin Inhibits Oral Cancer Cell Growth via Apoptosis and Inflammatory Signaling‐Mediated Mechanisms: Evidence from In Vitro and In Silico Analyses,” Cureus 16(2) (2024): e53458. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49. Ali E. S., Sharker S. M., Islam M. T., et al., “Targeting Cancer Cells with Nanotherapeutics and Nanodiagnostics: Current Status and Future Perspectives,” Seminars in Cancer Biology 69 (2021): 52–68. [DOI] [PubMed] [Google Scholar]
  • 50. Piña‐Sánchez P., Chávez‐González A., Ruiz‐Tachiquín M., et al., “Cancer Biology, Epidemiology, and Treatment in the 21st Century: Current Status and Future Challenges from a Biomedical Perspective,” Cancer Control 28 (2021): 10732748211038735. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51. Keith R. L., “Chemoprevention of Lung Cancer,” Proceedings of the American Thoracic Society 6, no. 2 (2009): 187–193. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52. Spiro S. G. and Porter J. C., “Lung Cancer—Where are We Today? Current Advances in Staging and Nonsurgical Treatment,” American Journal of Respiratory and Critical Care Medicine 166, no. 9 (2002): 1166–1196. [DOI] [PubMed] [Google Scholar]
  • 53. Wang M., Herbst R. S., and Boshoff C., “Toward Personalized Treatment Approaches for Non‐Small‐Cell Lung Cancer,” Nature Medicine 27, no. 8 (2021): 1345–1356. [DOI] [PubMed] [Google Scholar]
  • 54. Vargas A. J. and Harris C. C., “Biomarker Development in the Precision Medicine Era: Lung Cancer as a Case Study,” Nature Reviews Cancer 16, no. 8 (2016): 525–537. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55. Bharti A., Ma P. C., and Salgia R., “Biomarker Discovery in Lung Cancer—Promises and Challenges of Clinical Proteomics,” Mass Spectrometry Reviews 26, no. 3 (2007): 451–466. [DOI] [PubMed] [Google Scholar]
  • 56. Li T., Kung H.‐J., Mack P. C., and Gandara D. R., “Genotyping and Genomic Profiling of Non–Small‐Cell Lung Cancer: Implications for Current and Future Therapies,” Journal of Clinical Oncology 31, no. 8 (2013): 1039–1049. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Supporting File

JCB-127-e70115-s001.docx (925.2KB, docx)

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

The raw data supporting the conclusions of this article will be made available by the authors without undue reservation.

Articles from Journal of Cellular Biochemistry are provided here courtesy of Wiley

RESOURCES