Meta‐analysis of microarray data to determine gene indicators involved in cisplatin resistance in non‐small cell lung cancer

Abstract Background Lung cancer is a major cause of cancer‐related mortality worldwide, with a 5‐year survival rate of approximately 22%. Cisplatin is one of the standard first‐line chemotherapeutic agents for non‐small cell lung cancer (NSCLC), but its efficacy is often limited by the development of resistance. Despite extensive research on the molecular mechanisms of chemoresistance, the underlying causes remain elusive and complex. Aims We analyzed three microarray datasets to find the gene signature and key pathways related to cisplatin resistance in NSCLC. Methods and Results We compared the gene expression of sensitive and resistant NSCLC cell lines treated with cisplatin. We found 274 DEGs, including 111 upregulated and 163 downregulated genes, in the resistant group. Gene set enrichment analysis showed the potential roles of several DEGs, such as TUBB2B, MAPK7, TUBAL3, MAP2K5, SMUG1, NTHL1, PARP3, NTRK1, G6PD, PDK1, HEY1, YTHDF2, CD274, and MAGEA1, in cisplatin resistance. Functional analysis revealed the involvement of pathways, such as gap junction, base excision repair, central carbon metabolism, and Notch signaling in the resistant cell lines. Conclusion We identified several molecular factors that contribute to cisplatin resistance in NSCLC cell lines, involving genes and pathways that regulate gap junction communication, DNA damage repair, ROS balance, EMT induction, and stemness maintenance. These genes and pathways could be targets for future studies to overcome cisplatin resistance in NSCLC.

One of the standard clinical trials applied for the management of NSCLC is a regimen based on platinum (cisplatin or carboplatin), a cytotoxic chemotherapeutic agent. 3,4However, the survival rate is poor in NSCLC due to the occurrence of drug resistance. 5,6splatin exerts cytotoxic effects by crosslinking with the purine bases on the DNA and interfering with DNA replication, DNA repair mechanisms, and cell division in cancer cells.Various signal transduction pathways are implicated in the cellular fate upon absorption of cisplatin into the cancer cell, including cisplatin-induced oxidative stress, DNA damage, calcium signaling, cell apoptosis, activation/ downregulation of mitogen-activated protein kinase (MAPK), Jun amino-terminal kinase (JNK), and phosphoinositide 3-kinase (PI3K)-Akt signaling pathways. 7,8spite the increasing prevalence of advanced therapeutics such as molecular targeted therapies to improve overall survival, drug resistance remains a frustrating, unsolved challenge in the management of cancer.Although a large number of studies have been dedicated to discovering the molecular events underlying the process of cancer drug resistance, the biological story behind drug resistance remains ambiguous, and the high mortality of lung cancer resulting from chemo-resistance poses a significant ongoing clinical problem.In addition to the accumulation of genomic mutations, alterations at the transcriptomic levels during cancer progression can also be responsible for limiting the efficacy of anti-tumor agents and the development of chemotherapy resistance.As a result, drug insensitivity of cancer cells may emerge at many levels, including the dysregulation of drug transporters, drug-targeted agents, and pro-survival and anti-apoptotic molecules. 9croarray technology is a high-throughput approach used to detect the expression values of thousands of genes simultaneously and uncover differences in the transcriptome of cells and tissues. 10cordingly, bioinformatics analysis of microarray data and system biology, including identification of gene signature, gene ontology, and pathway enrichment analysis, could be noteworthy to highlight potential key genes and pathways within a biological process. 11Therefore, microarray data analysis has been widely used in cancer pathogenesis and pharmacology research in recent years.
In the current report, we aimed to reveal the differentially expressed genes and underlying mechanisms associated with cisplatin resistance in NSCLC cell lines.This can help delineate biological subsets of resistant cancer cells and identify novel plausible therapeutic targets to overcome drug resistance.For this purpose, an integrative microarray analysis of non-small cell lung cancer was conducted to compare the expression profiles of NSCLC cell lines under treatment with cisplatin, focusing on different responses to this anti-tumor agent, whether sensitive (responder) or resistant (non-responder).To reveal key genes and pathways involved in the development of cisplatin resistance, differentially expressed genes (DEGs) with statistical significance were subjected to gene and pathway enrichment analysis.
Our results indicated that Epithelial-mesenchymal transition (EMT) acts as a central regulator of cisplatin resistance, which is reinforced by several cross-talking pathways.

| METHOD 2.1 | Integrative analysis of GEO datasets
Three microarray datasets were obtained from the National Center for Biotechnology Information-Gene Expression Omnibus (NCBI-GEO).These datasets contain the mRNA expression profiles of NSCLC samples that had received cisplatin antitumor agent treatment.
The following key terms were searched in GEO to retrieve the datasets by filtering for "Expression profiling by array" and "Homo sapiens": "Neoplasms/Carcinoma/Lung cancer/NSCLC" AND "Drug resistance/Recurrence/Relapse" AND "Cisplatin/CDDP/platinumbased."Samples were divided into two categories: Cisplatin-sensitive and -resistant.All expression data were preprocessed, log2 transformed, and normalized using affy, gene filter, and limma Bioconductor packages provided in the R platform version 4.2.1 (https:// www.r-project.org/). 12,13r the comparative study and to remove unwanted variations across studies included, samples of datasets were integrated and batch corrections were conducted using an empirical Bayes (Combat) method from the sva package. 14,15The logarithmic fold change between the two chemo-sensitive and -resistant groups were calculated using a linear model procedure as implemented in the limma package. 12Principal component analysis (PCA), an unsupervised exploratory data analysis approach, was applied to control quality and visualize the variation between the arrays. 16After the elimination of sample arrays with poor quality and removal of batch effect, the tstatistics test was applied to adjusted samples to evaluate differential expression gene values between cisplatin-resistant and -sensitive NSCLC cells.The threshold of jlogFCj ≥ 2 and p.value <.01 was considered to screen the significantly differentially expressed genes, and the Benjamini-Hochberg (BH) method was also used to reduce false positive values. 17

| Gene set enrichment analysis
The Enrichr online tool (https://maayanlab.cloud› Enrichr) was utilized to perform functional enrichment analysis of statistically significant DEGs.This analysis categorized and annotated the genes in terms of molecular function (MF), biological process (BP), and cellular component (CC). 18It also enriched the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to NSCLC resistance to cisplatin, an anti-tumor agent.Gene Ontology (GO) and significant pathways enriched by differentially expressed genes (DEGs) using the Enrichr tool were considered significant with a p.value of <.05.The STRING database (https:// www.string-db.org/) was utilized to assess protein-protein interaction (PPI) and identify hub genes involved in cisplatin resistance in NSCLC.The full STRING network option with a required high confidence of 0.700 was applied to construct the PPI network of differentially expressed genes.A functional enrichment analysis was also conducted to identify the over-represented biological processes, pathways, and domains among the proteins in the network.The default FDR stringency of 0.05 was used for the enrichment analysis.

| Receiver operating characteristic curve analysis
To assess the predictive values of identified DEGs in cisplatin resistance of NSCLC cells, the ROC curve was plotted for several key genes based on the gene expression data using GraphPad Prism 9.4.0 software.In the context of receiver operating characteristic (ROC) curve analysis, genes that demonstrate an Area Under the Curve (AUC) value exceeding 0.7, along with a statistically significant p.value of less than .01,are considered to exhibit a robust predictive potential for cisplatin resistance in NSCLC cells.

| Microarray data analysis
Three datasets, GSE108214, GSE84146, and GSE21656, which contain expression profiles of cisplatin-resistant and -sensitive lung cancer cell lines, were included in this study.The datasets were as follows: 1-GSE108214 consists of expression profiling of 22 sensitive and resistant non-small cell lung cancer cells (A549) that were treated with 11 or 34 μM cisplatin or with the drug-free medium, 2-GSE84146 involved gene expression profiles of two paired cisplatin-sensitive and -resistant lung cancer cell lines (H460, H23), 3-GSE21656 encompassed expression profiling of cisplatin-resistant lung cancer cells derived from the H460 lung cell line and the parental cells.The detailed information of selected datasets is presented in Table 1.
A total of 29 samples, consisting of 7 responder cells to cisplatin, referred to as sensitive cells in this study, and 22 cisplatin-resistant lung cancer cell lines, were selected for further analysis.The expression values of GSE108214, GSE84146, and GSE21656 were integrated based on common Entrez gene IDs. Figure 1A shows the PCA plot visualizing samples as two clusters, cisplatin-resistant or -sensitive, based on overall patterns of the expression signatures and supports the removal of batch effect.Among 10 667 common genes in all three datasets, 274 DEGs were significantly identified by considering p.value <.01 and jlogFCj ≥ 2 as a threshold, consisting of 111 upregulated genes and 163 downregulated genes associated with cisplatin resistance (Tables 2,3, respectively).Figure 1B shows the volcano plot for differentially expressed genes between cisplatin-resistant and -sensitive NSCLC cell lines.The expression value of each gene in both cisplatin-resistant and -sensitive samples are provided in Table S2.
These levels are also visually represented through a heatmap, which was created using unsupervised hierarchical cluster analysis (Figure 2A).

| Gene set enrichment analysis
The enrichment analysis of KEGG pathways suggests a potential correlation between the differentially expressed genes (DEGs) and drug resistance via multiple pathways.Figure 2B  MAP2K5) are potentially implicated in the gap junction pathway, which is posited to be one of the most critical pathways in this context.
The Gene Ontology term enrichment analysis has provided a comprehensive understanding of the probable functions of differentially expressed genes (DEGs) in the development of chemotherapy resistance in NSCLC cells.Through the application of Gene Ontology (GO) term enrichment analysis, genes differentially expressed in relation to cisplatin resistance were categorized into three functional groups: biological process (BP), cellular component (CC), and molecular function (MF).These categories are depicted in Figure 3 and further detailed in Tables S1-S3.In order to evaluate the potential involvement of seven differentially expressed genes (DEGs)-TUBB2B, TUBA3D, MAPK7, TUBAL3, ADCY1, TUBB4A, and MAP2K5-within the gap junction pathway, a module was assembled.This module incorporated an additional gene, connexin43 (GJA1), known for its pivotal role within the gap junction pathway.The module associated with the gap junction pathway has been denoted by the red nodes.

| DISCUSSION
Cisplatin, a widely used first-line chemotherapeutic agent for advanced NSCLC, has been the focus of extensive clinical trials over the past few decades.Despite these efforts, the high mortality rate of lung cancer due to chemoresistance remains a significant clinical challenge.
In this study, we aimed to investigate the prognostic implications of cisplatin responsiveness in NSCLC cells.We conducted an integrative microarray analysis to identify dysregulated genes and key pathways involved in the development of resistance to the cisplatin antitumor agent.[21][22][23][24] F I G U R E 3 Gene Ontology (GO) of DEGs.The gene ontology of differentially expressed genes (DEGs) reveals the top 20 terms in three categories: biological process (BP), molecular function (MF), and cellular component (CC).The color of the bubbles ranges from red to blue, indicating a progression from smaller to larger p.value.The size of the bubbles corresponds to the number of genes, with larger bubbles representing a greater number of genes.
T A B L E 2 Upregulated genes in Cisplatin-resistant NSCLC.Connexins are a group of transmembrane proteins that form gap junctions, which are intercellular channels that facilitate the exchange of small molecules and potentially control cellular growth and differentiation processes. 25,26 has been reported that the expression and phosphorylation of Connexin43 (Cx43) can affect the epithelial-mesenchymal transition (EMT) and the cisplatin sensitivity of A549 cell lines. 23This is because Cx43 is modified by mitogen-activated protein kinases (MAPK), a group of proteins that can control gap junction intercellular communication (GJIC) by phosphorylating Cx43. 20,27,28 shown in Figure 4, a network of interactions between proteins TUBB2B, MAPK7, TUBAL3, MAP2K5, and GJA1 (Cx43) suggests that reduced expression of MAPK and tubulin genes could be responsible for the attenuation of the gap junction pathway and insensitivity of lung cancer cells to cisplatin.
Consistent with our hypothesis, a recent study has interestingly highlighted the importance of gap junction activation and MAPK pathways for overcoming cisplatin resistance in NSCLC. 29Huang et al.
have reported a novel adjuvant compound, arteannuin B (Art B), that enhances the cytotoxicity of cisplatin by facilitating its uptake into the cancer cells.This is mediated by the upregulation of connexin43, the activation of gap junction and MAPK pathways, and the increase of intracellular Fe2+ and calcium influx. 29A damage, a common feature of many anticancer drugs, leads to genome instability and cell death in cancer cells treated with cisplatin.The base excision repair (BER) pathway modulates the Consistent with these data, the downregulation of NTHL1 and SMUG1 might solve the problem of chemoresistance to cisplatin in lung cancer cells, but this finding required further experimental validation.
Our study reveals that PARP3 expression is elevated in NSCLC cells that exhibit cisplatin resistance.According to previous studies, PARP3 participates in the ROS signaling pathway triggered by TGFβ, which promotes EMT and stemness in cancer cells. 30PARP3 also regulates the expression of TG2, an enzyme that influences EMT and stemness, by altering the chromatin state of TGFβ-responsive genes. 30Interestingly, TGFβ has been shown to confer EMT-mediated chemoresistance in NSCLC cell lines treated with cisplatin. 31In addition, PARP3 enhances the DNA repair proficiency and genomic stability of cancer cells by supporting the NHEJ process. 30Therefore, our study corroborates the importance of PARP3 upregulation in the development of cisplatin resistance in NSCLC.We suggest that PARP3 could be a potential therapeutic target for reversing EMTmediated chemoresistance in NSCLC.
Our finding also shows that PARP3 is highly expressed in NSCLC cells that are resistant to cisplatin.PARP3 is involved in the ROS signaling pathway activated by TGFβ, which leads to EMT and stemness in cancer cells. 30PARP3 also controls the TG2 expression, an enzyme that plays a role in EMT and stemness, by changing the chromatin state of TGFβ-responsive genes.Interestingly, TGFβ is involved in EMT-mediated chemoresistance in NSCLC cell lines exposed to cisplatin. 31Furthermore, PARP3 improves the DNA repair ability and genomic stability of cancer cells by assisting the NHEJ process. 30erefore, our finding supports the relevance of upregulated PARP3 and the acquisition of cisplatin drug resistance.PARP3 could be potential target for anticancer therapies and its inhibiting may increase the effectiveness of cancer treatment by reversing the EMTmediated chemoresistance in NSCLC.
Metabolic reprogramming is a key feature of tumors that enables them to resist first-line chemotherapy drugs. 32,33One of the main pathways involved in this process is the central carbon metabolism, which includes the pentose phosphate pathway (PPP) and the tricarboxylic acid (TCA) cycle. 34These pathways support cancer growth and survival by providing energy and biosynthetic precursors. 32,35,36 the present study, we identified two genes related to central carbon metabolism, G6PD and PDK1, that were overexpressed in cisplatinresistant NSCLC cell lines.G6PD and PDK1 are known to regulate the Warburg Effect, a phenomenon in which cancer cells preferentially use glycolysis for energy production. 36,372][43][44] Pyruvate dehydrogenase kinase 1 (PDK1) is an enzyme that inhibits the entry of pyruvate into the TCA cycle, thereby promoting glycolysis and epithelialmesenchymal transition (EMT). 45,467][48] Our findings are consistent with these reports and suggest that G6PD and PDK1 (logFC: 2.114258, p.value: 1.19E-06) are potential targets for overcoming cisplatin resistance in NSCLC.
Our results also showed an increased expression of NTRK1 in cisplatin-resistant NSCLC cells compared to sensitive cells (logFC: 2.00152, p.value: 1.15E-09).Neurotrophic tropomyosin receptor kinase 1 (NTRK1) encodes TRKA, a tyrosine kinase receptor that mediates neurotrophin signaling and neural development. 49,50TRKA is overexpressed in various cancers including pancreas, breast, lung, glioblastoma, lymphoid, oral squamous cell carcinoma, and adenoid cystic carcinoma and confers drug resistance and poor prognosis. 51,522][53] Moreover, NTRK1 gene fusions are oncogenic and prevalent in some tumors, such as NSCLC, melanoma, glioma, and thyroid cancers. 49,54The frequency of NTRK1 fusions in NSCLC is about 0.07%-3.3%. 557][58][59][60] These evidences are in agreement with our findings and suggest NTRK1 as a potential target of cisplatin resistance in NSCLC, although further studies are needed on this gene in lung cancer.
The GO enrichment analysis revealed that the genes YTHDF2, HEY1, PDCD10, MAGEA1 might be involved in the chemoresistance of NSCLC cells through the Notch signaling pathway (GO:0008593).[63][64][65] In lung cancer, the Notch signaling pathway maintains the properties of cancer stem cells (CSCs) and confers drug resistance. 66CSCs are responsible for therapy resistance and tumor recurrence, as they can evade the effects of chemotherapy, radiotherapy, and immunotherapy.They can also modulate the tumor immune microenvironment (TIME) to create an immunosuppressive niche that protects them from immune attack. 67r result also showed a significant upregulation of YTHDF2 (logFC: 2.022421721, p.value: 1.43E-12) in cisplatin-resistant NSCLC cells.YTHDF2 is a major m6A reader that recognizes and binds to m6A-modified RNAs, a type of epigenetic modification that affects RNA stability and translation. 68A modulators can affect the CSC phenotype and function by altering the expression and activity of key genes and pathways involved in stemness, epithelial-mesenchymal transition (EMT), DNA damage response (DDR), autophagy, and immune evasion. 69YTHDF2 can have different effects on cancer therapy resistance depending on the type and context of the cancer. 68For example, in lung cancer, YTHDF2 promotes cancer progression and resistance to Erlotinib therapy through activating the Notch signaling pathway. 70 leukemia, YTHDF2 supports the survival and growth of leukemia stem cells (LSCs) by enhancing the translation of genes that are important for stemness and proliferation, such as MYC and BCL2. 71In hepatocellular carcinoma (HCC), YTHDF2 has a dual role: it increases the stemness and tumor growth of liver cancer stem cells (LCSCs) by increasing the translation of OCT4, but it also inhibits the proliferation and growth of HCC cells by decreasing the stability of EGFR. 72nversely, the expression of YTHDF2 affects the sensitivity of melanoma cells to immunotherapy by regulating the level of PD-1, an immune checkpoint protein. 73YTHDF2 promotes the degradation of PD-1 mRNA, leading to lower PD-1 protein expression on the surface of melanoma cells. 73As a result, YTHDF2 knockdown could increase the resistance of melanoma cells to immunotherapy.This is in line with the observation that high expression of PD1 and PD-L1 is associated with cisplatin resistance in small cell lung cancer (SCLC) cell lines (H69R, H82R), relative to their parental counterparts. 74It has been proposed that intracellular PD1/PD-L1 signaling may be a determinant of poor response to cisplatin treatment, and that blocking this pathway may enhance the chemosensitivity of aggressive SCLC.
6][77] However, we also found that the expression of CD274, also known as PD-L1, was decreased in resistant cells compared with sensitive cells (logFC: À5.89, p.value: 1.78E-15).This contradicts the previous studies that showed that PD-L1 expression is associated with poor response to immune checkpoint inhibitors in NSCLC patients.
Our results are consistent with the study by Tsuchiya et al., who reported that YTHDF1 and YTHDF2 were associated with a better prognosis and an inflamed tumor-immune microenvironment in NSCLC by regulating the expression of PD-1 and PD-L1. 78These findings challenge the notion that CD274 is a marker for poor response to cisplatin treatment in lung cancer and suggest that cisplatin-resistant NSCLC cells may have developed other mechanisms to evade the immune system.Further studies are needed to elucidate the function of YTHDF and CD274 in the tumor microenvironment and the m6Amediated control of transcripts and proteins.
0][81][82] It has been found to be linked with cancer stem cells (CSCs) in a variety of cancers, including glioblastoma, breast cancer, and lung cancer. 83HEY1 is known to influence the self-renewal, survival, and resistance of CSCs by suppressing the expression of genes involved in apoptosis, cell cycle arrest, differentiation, and senescence. 82For instance, in glioblastoma stem cells (GSCs), HEY1 is upregulated by the STAT3/NF-κB signaling pathway, which is constitutively activated in these cells.HEY1, in turn, helps to maintain the stemness and self-renewal of GSCs by repressing the expression of CTBP1 and RBPJ, which are negative regulators of the Notch pathway. 84Recent studies have reported high levels of HEY1 expression in cisplatin-resistant lung adenocarcinoma tissues and A549/DDP cell lines. 80K1 appears to act as a dual-directional effector in initiating the EMT and inducing cisplatin resistance.The positive role of the PDK1/Notch axis in promoting metastasis through EMT activation in Hypopharyngeal squamous cell carcinoma (HSCC) has been recently emphasized. 85Therefore, alongside its role in energy metabolism and cell survival, PDK1 could be a key regulator of the Notch1 signaling pathway in NSCLC.However, the protein expression level of PDK1 in two NSCLC cell lines (H358 and H520) remains unobserved and warrants further investigation. 79As a result, based on the existing evidence, it seems that PDK1's impact on drug resistance in NSCLC is part of a multifaceted network of factors, requiring in-depth laboratory analysis for a complete understanding.
In contrast to PDK1, MAGEA1 seems to exert a negative influ- PDCD10, a protein that interacts with various molecules, plays a crucial role in regulating numerous biological processes.The role of PDCD10 in cell survival and proliferation has been confirmed in various types of cancer, including NSCLC, bladder cancer, ovarian cancer, cervical cancer, and prostate cancer. 88However, it is demonstrated that the expression of PDCD10 vary based on the specific type of cancer cell and the anti-cancer drug utilized.For instance, in colon cancer, PDCD10 is upregulated, protecting cancer cells from cisplatin-induced apoptosis. 88Conversely, in breast cancer, PDCD10 is downregulated, making cancer cells more resistant to other anti-cancer drugs, such as doxorubicin, docetaxel, and etoposide. 89 addition to its role in cancer, PDCD10 also influences the Notch signaling pathway a critical regulator of cellular destiny, differentiation, and intercellular communication.This regulation may impact the proliferation, migration, and angiogenesis of endothelial cells, potentially leading to conditions like Cerebral cavernous malformation (CCM) when PDCD10 is lost in these cells. 88is highlights the complex and significant relationship between First, our study focused on specific aspects of the cisplatin resistance in NSCLC, and there may be other relevant factors that were not considered in our analysis.Given the multifaceted characteristics of NSCLC, as well as the multifactorial nature of cisplatin resistance in cancer cells, such as the initiation of anti-apoptotic signals, active drug expulsion from the cell cytoplasm, miRNA-mediated epigenetic regulation, growth regulatory pathway deregulation leading to growth factor independence, immune system suppression, and low antigen expression that activates T lymphocyte cells (mimicry)-this study has concentrated on several of these elements. 90These include the activity of gap junctions, DNA repair mechanisms, and the deregulation of growth regulatory pathways.
Second, our research did not directly investigate the interaction between the NOTCH pathway and cancer stem cells (CSCs), which have been identified as key players in chemo-and radiotherapy resistance in NSCLC.Previous studies have shown that CSCs, particularly the side population (SP) cells in the A549 cell line, possess increased proliferation, higher clonogenicity, stronger tumorigenicity, and resistance to chemotherapy. 91,92However, our current analysis did not specifically evaluate the expression of CSC-associated genes or their potential impact on treatment resistance.This represents an area for future research, which could provide a more comprehensive understanding of the complex interplay between the NOTCH pathway, CSCs, and treatment resistance in NSCLC.
Third, our study was purely computational and did not include any in vitro or in vivo experiments to validate the findings.Therefore, the results should be interpreted with caution until validated experimentally.

| CONCLUSION
We have discovered several molecular determinants of cisplatin resistance in NSCLC cell lines, involving genes and pathways related to

F I G U R E 1
The principal component analysis (PCA) and volcano plot.(A) The PCA plot was conducted based on the normalized integrated expression data.Clustering the samples into two distinct groups using this unsupervised approach demonstrates the removal of unwanted variations among datasets.(B) The volcano plot for differentially expressed genes (DEGs) in cisplatin-resistant NSCLC cell lines versus sensitive ones shows the fold-change (x-axis) versus the significance (Àlog10 (p.value) on the y-axis) of the identified DEGs.The significant DEGs were identified based on p.value <.01 and jlogFCj > 2. Two vertical lines show the 2-fold change boundaries and the horizontal line shows the cutoff of statistical significance ( p.value ≤ .01).Pink and blue dots display upregulated and downregulated genes, respectively.F I G U R E 2 Heatmap and KEGG pathway analysis of differentially expressed genes.(A) The heatmap provides a visual representation of the varying levels of gene expression in cisplatin-sensitive and -resistant NSCLC cell lines.High expression is denoted by the color red, while low expression is indicated by blue (S represents sensitive; R represents resistance).(B) The bar plot presents the 20 most representative altered canonical KEGG pathways that are affected by the differentially expressed genes (DEGs) associated with cisplatin resistance in NSCLC.Each term's statistical significance was evaluated using a p.value <.05, with the most significantly enriched pathways depicted in a progressively redder color.The biological process group includes several significant processes such as the biosynthesis of phospholipids (GO:0008654), cellular macromolecules (GO:0034645), and the cellular response to glucose starvation (GO:0042149).It also encompasses the regulation of multiple signaling pathways involved in primary metabolic processes (GO:0080090), including the Notch (GO:0008593) and p38MAPK (GO:1900744) cascades, as well as protein deubiquitination (GO:0090085).The cellular component group primarily consists of the P-body, endoplasmic reticulum membrane, recycling endosome membrane, microtubule cytoskeleton, and cytosolic small ribosomal subunit.In the molecular function group, the most significant enrichments were found in purine ribonucleotide binding and protein binding.

Figure 4
Figure4illustrates the protein-protein interaction network and module selection, which were constructed utilizing the STRING database with a false discovery rate (FDR) stringency of 0.05.The functional enrichment analysis pinpointed a singular pathway, namely the gap junction (hsa04540), as being significantly over-represented among the proteins within the network, with an FDR of 0.0165.

F I G U R E 5
Receiver operating characteristic (ROC) analysis for DEGs.ROC plots illustrate the predictive accuracy of 14 DEGs in relation to the occurrence of cisplatin resistance in NSCLC cell lines.AUC refers to the area under the ROC curve.(A-N) are ROC plots of G6PD, MAGEA1, MAPK7, PDK1, MAP2K5, NTRK1, PARP3, NTHL1, HEY1, SMUG1, TUBB2B, TUBB4A, TUBAL3 and ADCY1 genes, respectively.
ence on the Notch signaling pathway within cells.MAGEA1 is part of the MAGEA gene family, and the exact functional role of MAGEA proteins is yet to be determined.It is reported that MAGE-A proteins exhibit varying activity depending on their subcellular localization.MAGE-A1 has been shown to interfere with Notch as a potent transcriptional repressor by inhibiting the intracellular transactivation domain of Notch1.Moreover, forced expression of MAGEA1 is known to increase the drug sensitivity of cisplatin-resistant ovarian cancer cells due to induced epigenetic changes. 86,87Our findings suggest that the downregulated expression of MAGEA1 (logFC: À3.8555, p.value: 8.44E-16) in cisplatin-resistant cells could reduce the inhibitory effects of MAGEA1 on Notch1, leading to cell survival and endurance of resistant cells.
PDCD10 and Notch signaling in various diseases.Our research reveals a decrease in the levels of PDCD10 (logFC: À3.717, p.value: 6.65E-16) in cisplatin-resistant NSCLC cells compared to those that are sensitive, highlighting the need for additional experimental investigations for a more thorough comprehension.The findings of this study were derived through a comprehensive examination of high-throughput studies, utilizing in silico tools to predict the molecular mechanisms underlying cisplatin resistance.The results obtained provide a foundation upon which researchers can formulate hypotheses for future experimental studies in the field of drug resistance and targeted therapy.Nonetheless, this study does present certain limitations that must be acknowledged.
gap junction communication, DNA damage response, ROS modulation, EMT induction, and stemness maintenance.We have also revealed the potential therapeutic value of targeting SMUG1, NTHL1, PARP3, NTRK1, YTHDF2, PDK1, CD274, HEY1, PDCD10 and MAGEA1 which are differentially expressed in cisplatin-resistant NSCLC cells.Our study sheds light on the complex mechanisms underlying cisplatin resistance and offers novel opportunities for improving the treatment outcomes of NSCLC.Nevertheless, further investigations are required to confirm the clinical utility and biological importance of these genes and pathways in NSCLC patients.Elucidating the precise role of candidate DEGs and pathways associated with drug resistance in the cancer cell metabolism and survival could facilitate the clinical management and pave the way for the development of personalized medicine.
Details of the microarray data used.