Exploration of crucial oxidative stress-related genes as prognostic markers and therapeutic targets in cervical squamous cell carcinoma

Exploration of crucial oxidative stress-related genes as prognostic markers and therapeutic targets in cervical squamous cell carcinoma | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 28 March 2025 V1 Latest version Share on Exploration of crucial oxidative stress-related genes as prognostic markers and therapeutic targets in cervical squamous cell carcinoma Authors : Mlambo Andrea 0009-0001-6531-3855 , Yuyang Zhang 0000-0002-6524-8133 [email protected] , Kowthar Mohamed Shaie , Shuyue Su 0009-0000-8593-9292 , Tianle Weng , Jingying Bai , Chunchun Fang , and Xiaodie Bai Authors Info & Affiliations https://doi.org/10.22541/au.174317604.46164400/v1 395 views 136 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Cervical squamous cell carcinoma (CESC) is a leading cause of cancer-related deaths in women, with oxidative stress playing a pivotal role in its progression. However, the role of oxidative stress-related genes (OSRGs) in modulating the tumor microenvironment, immune response, and prognosis remains poorly understood. This study aims to identify key OSRGs influencing CESC progression and survival outcomes, with a focus on their potential as prognostic biomarkers and therapeutic targets. We analyzed TCGA CESC data from the GDC Portal, clinical data from cBioPortal, and normal tissue expression data from GTEx. Differentially expressed genes (DEGs) between CESC and normal tissues were identified using GEPIA2. Functional annotation was performed with DAVID v6.8, and Protein-Protein Interaction (PPI) networks were constructed using STRING v11.0. A prognostic model was developed using Kaplan-Meier curves, univariate, LASSO, and multivariate regression analysis. Validation was performed using the GSE63514 dataset. We identified 3,528 upregulated and 6,865 downregulated genes in CESC, including 308 upregulated and 345 downregulated OSRGs. Key upregulated genes included CDKN2A, SLPI, and LCN2, while downregulated genes included DES, GPX3, and GSTM5. Functional enrichment analysis revealed significant associations with immune, cancer-related, and metabolic pathways. A prognostic model based on OSRG expression stratified patients into high-risk and low-risk groups with distinct survival outcomes. Validation in the GSE63514 dataset confirmed the role of CXCL8, SPP1, and PDIA3 as prognostic biomarkers. This study highlights the critical role of OSRGs in shaping the tumor microenvironment and immune response in CESC. Key prognostic markers, including CXCL8, SPP1, PDIA3, DES, and ATP13A2, were associated with poor prognosis and drug resistance. The developed risk model provides a valuable tool for survival prediction and may guide personalized treatment strategies. These findings underscore the importance of oxidative stress in CESC progression and suggest potential therapeutic targets for further investigation. Exploration of crucial oxidative stress-related genes as prognostic markers and therapeutic targets in cervical squamous cell carcinoma Andrea Mlambo 1 , Yuyang Zhang 2* , Kowthar Mohamed Shaie 1 , Shuyue Su 1 , Tianle Weng 1 , Jingying Bai 1 , Chunchun Fang 1 , Xiaodie Bai 1 , 1 The First School of Medicine, School of Information and Engineering, Wenzhou Medical University, Wenzhou, 325000, China. 2 Department of Gynecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China. *Correspondence to: Yuyang Zhang，Email: [email protected] Address: The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, People’s Republic of China. Keywords: Cervical squamous cell carcinoma, oxidative stress-related genes, Differentially expressed genes, risk model, Immune infiltration, drug resistance Number of words (6,214) Figures :6 main figures 8 supplementary figures Tables :5 main tables 13 supplementary tables Cervical squamous cell carcinoma (CESC) is a leading cause of cancer-related deaths in women, with oxidative stress playing a pivotal role in its progression. However, the role of oxidative stress-related genes (OSRGs) in modulating the tumor microenvironment, immune response, and prognosis remains poorly understood. This study aims to identify key OSRGs influencing CESC progression and survival outcomes, with a focus on their potential as prognostic biomarkers and therapeutic targets. We analyzed TCGA CESC data from the GDC Portal, clinical data from cBioPortal, and normal tissue expression data from GTEx. Differentially expressed genes (DEGs) between CESC and normal tissues were identified using GEPIA2. Functional annotation was performed with DAVID v6.8, and Protein-Protein Interaction (PPI) networks were constructed using STRING v11.0. A prognostic model was developed using Kaplan-Meier curves, univariate, LASSO, and multivariate regression analysis. Validation was performed using the GSE63514 dataset. We identified 3,528 upregulated and 6,865 downregulated genes in CESC, including 308 upregulated and 345 downregulated OSRGs. Key upregulated genes included CDKN2A, SLPI, and LCN2, while downregulated genes included DES, GPX3, and GSTM5. Functional enrichment analysis revealed significant associations with immune, cancer-related, and metabolic pathways. A prognostic model based on OSRG expression stratified patients into high-risk and low-risk groups with distinct survival outcomes. Validation in the GSE63514 dataset confirmed the role of CXCL8, SPP1, and PDIA3 as prognostic biomarkers. This study highlights the critical role of OSRGs in shaping the tumor microenvironment and immune response in CESC. Key prognostic markers, including CXCL8, SPP1, PDIA3, DES, and ATP13A2, were associated with poor prognosis and drug resistance. The developed risk model provides a valuable tool for survival prediction and may guide personalized treatment strategies. These findings underscore the importance of oxidative stress in CESC progression and suggest potential therapeutic targets for further investigation. Introduction Cervical cancer, originating in the cervix, is the fourth most common cancer in women globally, following breast, lung, and colorectal cancer (1). It is largely preventable through widespread Papanicolaou (Pap) smear screening, which enables early detection and removal of pre-cancerous lesions. However, cervical cancer remains a significant public health challenge in low- and middle-income countries (LMICs) due to limited access to screening (1, 2). Most cases are squamous cell carcinomas caused by persistent infection with high-risk strains of human papillomavirus (HPV), particularly HPV types 16 and 18. HPV integration into the host genome disrupts key cellular pathways, including those regulating cell cycle control and apoptosis, driving carcinogenesis (1, 3). In 2018, cervical cancer accounted for approximately 570,000 cases and 311,000 deaths worldwide, with the majority occurring in LMICs where screening and vaccination programs are underutilized (2). In contrast, high-resource countries have significantly reduced cervical cancer incidence and mortality through widespread Pap smear screening and HPV vaccination (4). Clinically, cervical cancer often presents asymptomatically in early stages, with symptoms such as abnormal bleeding, pelvic pain, and weight loss appearing in advanced disease. Early detection is associated with favorable outcomes, as treatments like surgery, radiation, and chemotherapy are highly effective (5). Nonetheless, advanced or treatment-resistant cases exhibit unfavorable prognoses, highlighting the necessity for a more profound comprehension of the molecular and genetic determinants of cervical squamous cell carcinoma (CESC) (6). Genetic studies have identified numerous genes associated with CESC development and progression. For instance, Yi Liu et al. highlighted the overexpression of PRAME, HMGA2, ETV4, MEX3A, and TTYH3 in HPV-negative cervical cancer, with MEX3A and TTYH3 linked to shorter overall survival (7). Key genes like TOP2A, AURKA, CCNA2, and CDKN2A have been found in several research to be important contributors to the pathophysiology of cervical cancer (8-10). These results emphasize the intricate genetic makeup of CESC and the necessity of investigating other molecular pathways, such as those linked to oxidative stress. In the biology of cancer, reactive oxygen species (ROS) have two functions. Although they control regular physiological processes, dysregulated ROS levels encourage DNA damage, cell survival, and proliferation, which leads to carcinogenesis (11, 12). In order to maintain a pro-tumorigenic redox equilibrium that promotes their survival and resistance to apoptosis, cancer cells respond to oxidative stress by upregulating antioxidant systems (11, 13). Oxidative stress-related genes (OSRGs) are becoming more widely acknowledged as important contributors to the development of cancer, impacting therapy responses, tumor microenvironment dynamics, and immunological regulation (14, 15). For instance, research on ovarian, renal cell, and colorectal cancers has shown the prognostic and therapeutic potential of OSRGs, connecting them to patient outcomes and immune infiltration (13, 16, 17). The function of OSRGs in CESC is still poorly understood in spite of these developments.In order to shed light on their physiological functions and potential as biomarkers or therapeutic targets, this work intends to discover and describe important OSRGs in CESC. This study may help develop better predictive instruments and individualized treatment plans for patients with cervical cancer by clarifying the molecular pathways that connect oxidative stress to the development of CESC. Methodology 2.1 Collection of Datasets We gathered information on 306 tumor samples and Cervical Squamous Cell Carcinoma (CESC) from the GDC Data Portal (https://portal.gdc.cancer.gov/). The cBioPortal provided the clinical data for CESC (https://www.cbioportal.org). Normal tissue gene expression profiles were obtained from the Genotype-Tissue Expression (GTEx) database (https://www.gtexportal.org/home/downloads/adult-gtex/overview). For validation, we used the GSE63514 dataset (18), which includes 24 normal and 28 cancer specimens hybridized to Affymetrix U133-Plus2.0 arrays. This multi-source approach ensures robust analysis of gene expression differences in CESC. 2.2 Collection of Oxidative Stress-Related Genes (OSRGs) Oxidative stress-related genes (OSRGs) were identified using the GeneCards database (https://www.genecards.org/) (19). Genes with a relevance score ≥ 7 were selected, resulting in 1,399 OSRGs (13). These genes, listed in Supplementary Table 1, were used for subsequent analyses. 2.3 Differential Expression Analysis of OSRGs in CESC Differentially expressed genes (DEGs) between CESC and normal tissues were identified using GEPIA2 (http://gepia2.cancer-pku.cn/\#index) (20). The analysis included 306 CESC samples and 13 normal samples, with a cutoff of |log2FC| > 0.585 and adjusted p-value < 0.05. OSRGs were further filtered using a Venn diagram approach. 2.4 Functional Enrichment Analysis Functional annotation of DEGs was performed using DAVID v6.8 (https://david.ncifcrf.gov/) (21). Enriched KEGG pathways, Reactome pathways, biological processes, cellular components, and molecular functions were identified with a false discovery rate (FDR) < 0.05 (22). 2.5 PPI Network Analysis and Identification of Hub Genes Protein-Protein Interaction (PPI) networks were constructed using STRING v11.0 (https://string-db.org/) (23), with an interaction score threshold of ≥0.40. Hub genes were identified using the cytoHubba plugin in Cytoscape (https://cytoscape.org/) (24), selecting genes with an interaction degree of ≥5. 2.6 Construction of a Survival and Prognosis Prediction Model TCGA CESC clinical data were used for survival analysis. Patients were stratified into high- and low-risk groups based on gene expression levels. The log-rank test and Kaplan-Meier curves were used to assess survival differences. LASSO-Cox regression with 10-fold cross-validation was performed using the ”glmnet” package (25). The prognostic risk score was calculated as: Risk score= \(\sum_{\mathbf{1}}^{\mathbf{n}}{\mathbf{\text{Coefficient\ i\ }}\text{*}\mathbf{X}\mathbf{i}}\) Patients were classified into high- and low-risk groups based on the median risk score. 2.7 Evaluation of Diagnostic Efficacy The ”pROC” R package was used to generate ROC curves and calculate AUC values for prognostic genes (26). AUC > 0.5 indicated diagnostic capability, with higher values reflecting better discriminatory power (27). 2.8 Validation of Key Hub Genes The GSE63514 dataset was analyzed using GEO2R to validate key hub genes (18). Differential expression between normal and cancerous tissues was confirmed with a p-value < 0.05. 2.9 Immune Infiltration Analysis Single-sample gene-set enrichment analysis (ssGSEA) was used to calculate immune cell enrichment scores based on established immune signature markers (Supplementary Table 2) (28-32). Spearman’s correlation analysis was performed to assess relationships between hub genes and immune signatures, with a significance threshold of |R| > 0.20 and P-value < 0.05. 2.10 Drug Sensitivity and Resistance Analysis The GSCA Lite platform (http://bioinfo.life.hust.edu.cn/web/GSCALite/) (33) was used to analyze gene-drug interactions. Pearson correlation analysis evaluated relationships between gene expression and drug sensitivity (IC50 values) from the GDSC database (34). Positive correlations indicated drug resistance, while negative correlations suggested sensitivity (35). 2.11 Statistical Analysis All analyses were conducted using R programming (version 4.2.3). Results 3.1 Exploration of Differentially Expressed Oxidative Stress-Related Genes in CESC We identified differentially expressed genes (DEGs) in cervical squamous cell carcinoma (CESC) by comparing tumor and normal samples from the TCGA and GTEx datasets. A total of 3,528 upregulated and 6,865 downregulated genes were identified, as visualized in the volcano plot (Figure 1A). These DEGs were intersected with a curated list of oxidative stress-related genes (OSRGs), revealing 308 upregulated and 345 downregulated OSRGs in CESC (Table 1 and Figure 1B). The upregulated OSRGs suggest an enhanced oxidative stress response in CESC tumors, potentially contributing to tumor progression and treatment resistance. Conversely, the downregulated OSRGs may indicate impaired antioxidant defense mechanisms, exacerbating cellular damage and cancer progression. These results suggest possible biomarkers and therapeutic targets for more research, offering crucial insights into the oxidative stress landscape of CESC. In cervical squamous cell carcinoma (CESC), we found 25 increased oxidative stress-related genes (OSRGs) (Table 2) , which are linked to important functions like immunological modulation, cell cycle regulation, apoptosis suppression, and extracellular matrix remodeling. Notably, CDKN2A , a cyclin-dependent kinase inhibitor, exhibited the highest upregulation (LOG2FC of 6.728), highlighting its significant role in tumor progression. Other key upregulated genes include SLPI , which regulates immune responses, and LCN2 , involved in tissue injury response, both showing high fold changes (LOG2FC of 6.023 and 5.939, respectively). Genes such as S100A9 and S100A8 , known for their roles in inflammation and oxidative stress, were also notably upregulated, indicating their involvement in CESC progression. Additionally, genes like FOXM1 and CCNB1 , crucial for cell cycle progression, suggest active tumor cell division, while BIRC5, an inhibitor of apoptosis, supports cancer cell survival. The upregulation of CXCL10 and CXCL1 emphasizes the importance of immune cell recruitment and inflammation in the tumor microenvironment. MMP9, KRT8 , and KRT18 are associated with tumor invasion and epithelial integrity. Moreover, AURKA , a regulator of mitotic progression, and GPX2 , involved in antioxidant defense, underscore the complex interplay between cell proliferation and oxidative stress in CESC. These findings collectively point to a disrupted oxidative stress response in CESC, with these upregulated genes potentially serving as biomarkers or therapeutic targets for further investigation. (Table 3) presents 25 downregulated oxidative stress-related genes identified in cervical squamous cell carcinoma (CESC) by comparing tumor samples to normal tissue samples. These genes exhibit significantly lower expression in tumor tissues, suggesting a disruption in the oxidative stress response and cellular protection mechanisms in CESC. For example, DES (Desmin), with a log2 fold change (LOG2FC) of -7.83, shows a drastic reduction in tumor samples, indicating potential involvement in tumor cell structural integrity. Similarly, GPX3 (Glutathione Peroxidase 3), a key antioxidant enzyme, is also downregulated (LOG2FC -5.248), suggesting a diminished antioxidant defense in CESC. Other notable downregulated genes include GSTM5 and GSTM2 (Glutathione S-transferases), which are involved in detoxification and oxidative stress response, both exhibiting significant reductions in tumor tissues. CXCL12, a chemokine involved in immune cell signaling, and SOD3 (Superoxide Dismutase 3), which protects against oxidative damage, are also downregulated, indicating potential immune evasion, and increased oxidative stress in CESC. Additionally, genes like FGF7 (Fibroblast Growth Factor 7) and PDGFRB (Platelet Derived Growth Factor Receptor Beta) are involved in cell signaling and tumor microenvironment interactions, and their downregulation may contribute to impaired tumor progression and tissue remodeling. The significant reductions in these genes underscore the altered oxidative stress landscape in CESC, which may promote tumorigenesis and progression through compromised cellular defense mechanisms. These findings highlight potential biomarkers for diagnostic and therapeutic strategies targeting oxidative stress pathways in CESC. 3.2 Differentially expressed oxidative-stress related genes are associated with functional enrichment in CESC The functional enrichment analysis of the top 25 KEGG pathways associated with upregulated oxidative stress-related genes in cervical squamous cell carcinoma (CESC) revealed significant findings (Supplementary Table 3). Among these pathways, the ”Lipid and atherosclerosis” pathway (hsa05417) showed the highest fold enrichment value of 6.99, with a very low false discovery rate (FDR) of 1.64E-23, indicating a strong and significant association with the upregulated genes. Similarly, pathways such as ”Prion disease” (hsa05020) and ”Diabetic cardiomyopathy” (hsa05415) also exhibited substantial fold enrichment values of 4.96 and 5.77, respectively, with corresponding FDR values of 9.49E-16 and 1.75E-15, further emphasizing the relevance of these pathways in CESC. Other pathways, like ”Apoptosis” (hsa04210), and ”Human cytomegalovirus infection” (hsa05163), showed significant fold enrichment values ranging from 4.63 to 4.64, with FDR values in the range of 2.62E-11 to 2.62E-11. The pathways related to ”Chemical carcinogenesis - reactive oxygen species” (hsa05208), ”Pathways in cancer” (hsa05200), and ”Cellular senescence” (hsa04218) also demonstrated significant findings in the functional enrichment analysis. The hsa05208 pathway, which focuses on the role of reactive oxygen species (ROS) in chemical carcinogenesis, exhibited a fold enrichment value of 4.63 with an FDR of 2.63E-11, indicating a notable association with the upregulated oxidative stress-related genes. This highlights the critical role that ROS play in cancer development, especially in CESC, as they are involved in the initiation and progression of tumorigenesis. The hsa05200 pathway, ”Pathways in cancer,” showed a fold enrichment of 2.95 with a highly significant FDR value of 3.29E-10, reinforcing its involvement in the oxidative stress response. This pathway encompasses various cancer-related signaling cascades, many of which are critical in the regulation of gene expression, cell cycle, and apoptosis. The high fold enrichment and low FDR emphasize the potential therapeutic value of targeting these cancer pathways in CESC. We identified that several upregulated genes that are significantly implicated in key cancer-related pathways, including apoptosis, cell cycle regulation, and immune response. GSK3B, CXCL8, PIK3CB, BRCA2, NRAS, and MMP1 were strongly involved in these pathways (Figure 2) . These genes interact with prominent signaling cascades such as PI3K-AKT, p53, and MAPK, which regulate cell survival, migration, and metastasis. Their upregulation underscores their potential as therapeutic targets for managing oxidative stress-induced cancer progression. Overall, these findings reveal robust associations between upregulated OSRGs and critical pathways in CESC, highlighting their potential as therapeutic targets and biomarkers for oxidative stress-related tumorigenesis. The top 25 Reactome pathways for upregulated genes revealed significant involvement in immune system-related processes and cell death mechanisms (Supplementary Table 4). The most enriched pathway, Cytokine Signaling in the Immune System (R-HSA-1280215), included 73 genes such as CD86, TNF, IL1RN, and CXCL8, with a fold enrichment of 3.55 and a p-value of 5.68E-22, highlighting the critical role of cytokine-mediated immune responses in CESC. Other notable pathways, such as Immune System (R-HSA-168256) and Signaling by Interleukins (R-HSA-449147), featured key immune modulators like TNF, IL1A, and IL18, with fold enrichments of 2.25 and 4.25, respectively. Pathways related to cell death, including Programmed Cell Death (R-HSA-5357801), Apoptosis (R-HSA-109581), Regulated Necrosis (R-HSA-5218859), and Pyroptosis (R-HSA-5620971), were also significantly enriched, with genes such as CASP3, BCL2L11, BAX, and CASP1 playing pivotal roles in cell survival and death regulation. Stress-response pathways, such as Cellular Response to Chemical Stress (R-HSA-9711123), and inflammation-related pathways, including Interleukin-4 and Interleukin-13 Signaling (R-HSA-6785807) and VEGFA-VEGFR2 Pathway (R-HSA-4420097), further underscored the importance of immune modulation, angiogenesis, and cellular adaptation to oxidative stress. Overall, the upregulated genes across these pathways illustrate the intricate network of immune response, apoptosis, and cellular stress adaptation in the studied conditions. The Gene Ontology (GO) analysis of upregulated genes revealed several biological processes (BP), cellular components (CC), and molecular functions (MF) associated with these genes (Supplementary Table 5) . The Gene Ontology (GO) analysis of upregulated genes revealed a range of enriched biological processes (BPs) that reflect significant cellular and systemic responses. The apoptotic process (GO:0006915) and positive regulation of the neuron apoptotic process (GO:0043525) indicate a strong involvement of apoptosis, particularly in neurons. The inflammatory response (GO:0006954) and innate immune response (GO:0045087) highlight the genes’ roles in mediating immune responses and inflammation, while the positive regulation of apoptotic processes (GO:0043065) suggests a regulated control of cell death. Additionally, processes like the cellular response to lipopolysaccharide (GO:0071222) and the response to xenobiotic stimulus (GO:0009410) point to the genes’ involvement in immune and stress responses triggered by external factors. Mitochondrial-related activities, such as aerobic respiration (GO:0009060), were also upregulated, indicating metabolic activity. For CC, the mitochondrion (GO:0005739) showed the highest fold enrichment (3.6, FDR = 1.47E-25), emphasizing its central role, while the extracellular exosome (GO:0070062) and mitochondrial matrix (GO:0005759) also demonstrated significant enrichment, indicating their involvement in extracellular communication and energy production. For MF, protein binding (GO:0005515) and electron transfer activity (GO:0009055) were highly enriched, with fold enrichments of 1.282 and 16.630, respectively, highlighting their roles in protein interactions and redox reactions. Other key terms, such as cytokine activity (GO:0005125) and p53 binding (GO:0002039), underscored the importance of immune signaling and tumor suppression. Collectively, these findings illustrate the multifaceted roles of upregulated genes in apoptosis, immune response, metabolism, and cellular stress management, providing insights into their functional contributions to CESC progression. We identified significant KEGG pathways associated with downregulated genes (Supplementary Table 6), highlighting their potential roles in cervical squamous cell carcinoma (CESC). Key pathways included the FoxO signaling pathway (hsa04068), MAPK signaling pathway (hsa04010), and AGE-RAGE signaling pathway in diabetic complications (hsa04933), all exhibiting high fold enrichment values and low false discovery rates (FDRs), indicating their strong association with the downregulated gene set. Pathways related to cancer, such as Pathways in cancer (hsa05200), Chemical carcinogenesis - reactive oxygen species (hsa05208), and Proteoglycans in cancer (hsa05205), were also significantly enriched, underscoring their relevance in CESC progression. Additionally, pathways involved in neurotrophin signaling, HIF-1 signaling, and those related to atherosclerosis, diabetes, and neurodegeneration were notably enriched, suggesting their potential roles in the observed gene expression changes. These findings highlight the involvement of downregulated genes in critical biological processes, including cancer progression, stress responses, and metabolic regulation, providing insights into their functional contributions to CESC pathogenesis. We identified several Reactome pathways significantly associated with downregulated oxidative stress-related genes (Supplementary Table 7), demonstrating varying levels of fold enrichment and false discovery rates (FDRs). The FOXO-mediated transcription pathway (R-HSA-9614085) exhibited the highest fold enrichment (9.56, FDR = 5.44E-09), highlighting its critical role in oxidative stress response. Similarly, the PI3K/AKT Signaling in Cancer pathway (R-HSA-2219528) showed a fold enrichment of 6.71, while the MAPK family signaling cascades (R-HSA-5683057) pathway had a fold enrichment of 3.87, emphasizing their relevance in cancer-related processes. Other notable pathways included Cellular responses to stimuli (R-HSA-8953897) (fold enrichment = 2.48) and Signaling by Interleukins (R-HSA-449147) (fold enrichment = 2.78), which demonstrated moderate yet significant involvement. The Regulation of FOXO transcriptional activity by acetylation pathway (R-HSA-9617629) stood out with an exceptionally high fold enrichment of 25.60, underscoring its importance in oxidative stress regulation. The FDR values for these pathways, ranging from 2.18E-10 (e.g., Signaling by Receptor Tyrosine Kinases) to 3.75E-05 (e.g., Signaling by NTRKs), confirmed their statistical robustness. These findings suggest that the identified pathways are reliably associated with downregulated oxidative stress-related genes and warrant further investigation into their roles in CESC progression. The GO analysis of downregulated oxidative stress-related genes revealed their significant involvement in critical cellular functions (Supplementary Table 8). The process response to hypoxia (GO:0001666) has a fold enrichment of 12.72, emphasizing its importance in cellular adaptation to low oxygen circumstances. The insulin-like growth factor receptor signaling pathway (GO:0048009, fold enrichment = 13.98) indicates a connection between oxidative stress and growth factor signaling, pointing to their role in cell growth and survival. Additionally, response to xenobiotic stimulus (GO:0009410, fold enrichment = 6.83) and positive regulation of gene expression (GO:0010628, fold enrichment = 4.71) highlight how these genes help modulate cellular responses to environmental toxins and enhance gene transcription. The process of response to oxidative stress (GO:0006979, fold enrichment = 9.36) shows that these genes play an essential role in defending cells against oxidative damage. Similarly, positive regulation of phosphatidylinositol 3-kinase/protein kinase B signal transduction (GO:0051897, fold enrichment = 7.22) reflects how oxidative stress-related genes regulate vital signaling pathways involved in cell survival. Furthermore, positive regulation of cell population proliferation (GO:0008284, fold enrichment = 4.10) and insulin receptor signaling pathway (GO:0008286, fold enrichment = 9.27) suggest the role of these genes in promoting cellular growth and metabolic processes. The positive regulation of endothelial cell proliferation (GO:0001938, fold enrichment = 12.41) emphasizes their impact on vascular health and tissue repair. Finally, cellular response to oxidative stress (GO:0034599, fold enrichment = 9.80) reinforces their role in managing oxidative damage within cells, crucial for maintaining cellular integrity and function. Together, these enriched biological processes reflect the importance of downregulated oxidative stress-related genes in various cellular responses, survival mechanisms, and metabolic regulation under stress conditions. The cellular components (CC) associated with downregulated oxidative stress-related genes provide insights into the specific locations within the cell where these genes exert their influence. The extracellular region (GO:0005576) and extracellular space (GO:0005615), with fold enrichments of 2.54 and 2.63 respectively, indicate that these genes are implicated in processes occurring outside the cell, such as signaling or interactions with the extracellular matrix. The cytosol (GO:0005829, fold enrichment = 1.76) and cytoplasm (GO:0005737, fold enrichment = 1.71) suggest that these genes play a role in maintaining cellular functions within the liquid matrix of the cell. The sarcolemma (GO:0042383, fold enrichment = 10.57) indicates a strong association with the muscle cell membrane, suggesting involvement in muscle function or cell signaling related to oxidative stress. The platelet alpha granule lumen (GO:0031093, fold enrichment = 13.25) and collagen-containing extracellular matrix (GO:0062023, fold enrichment = 4.46) highlight the involvement of these genes in platelet function and extracellular matrix structure, important for tissue integrity and repair. The mitochondrion (GO:0005739, fold enrichment = 2.25) points to the role of oxidative stress-related genes in mitochondrial function, where oxidative damage often occurs. The axon (GO:0030424, fold enrichment = 4.27) suggests their role in the nervous system, particularly in nerve cell function and signaling. Finally, the caveola (GO:0005901, fold enrichment = 10.41) points to the involvement of these genes in specialized membrane structures that are key to cellular signaling and endocytosis. Together, these enriched cellular components provide a detailed picture of where the downregulated oxidative stress-related genes are acting, from extracellular spaces to critical intracellular structures, underscoring their diverse roles in cellular function and stress responses. Downregulated oxidative stress-related genes were shown to have molecular functions that included protein interactions, enzyme activity, and metabolic control. Identical protein binding (GO:0042802, fold enrichment = 2.99) and protein binding (GO:0005515, fold enrichment = 1.27) suggest that these genes are involved in forming protein complexes or interacting with other proteins to mediate cellular processes. The enzyme binding (GO:0019899, fold enrichment = 5.84) and protein homodimerization activity (GO:0042803, fold enrichment = 3.35) indicate that these genes interact with enzymes or facilitate the formation of homodimers, which are often important in cellular signaling or metabolic regulation. The involvement of genes in amyloid-beta binding (GO:0001540, fold enrichment = 9.22) suggests a potential role in Alzheimer’s disease or neurodegenerative diseases, where amyloid-beta aggregation is a key factor. Transmembrane transporter binding (GO:0044325, fold enrichment = 6.48) points to these genes’ participation in transporting molecules across cell membranes, which is essential for maintaining cellular homeostasis. The high fold enrichment of protein tyrosine kinase activity (GO:0004713, fold enrichment = 7.43) suggests that these genes may influence signaling pathways that regulate cell growth, differentiation, and metabolism. Additionally, the histone H2AXY142 kinase activity (GO:0140801, fold enrichment = 7.30) and histone H3Y41 kinase activity (GO:0035401, fold enrichment = 7.30) indicate a role in DNA repair mechanisms, particularly in response to oxidative stress and DNA damage. Finally, flavin adenine dinucleotide binding (GO:0050660, fold enrichment = 10.34) points to involvement in redox reactions, where FAD plays a crucial role in cellular energy metabolism. Overall, these molecular functions underscore the complex interactions of downregulated oxidative stress-related genes, particularly in protein interactions, cellular signaling, and metabolic processes. 3.3 Protein-Protein Interaction (PPI) network analysis of upregulated and downregulated genes for exploring hub OSRG and gene clusters To gain deeper insights into the functional relationships between the upregulated and downregulated oxidative stress-related genes, a Protein-Protein Interaction (PPI) network analysis was performed. This analysis aims to map how these genes interact with one another at the protein level, revealing critical cellular pathways and biological processes that may be impacted by the differential regulation of oxidative stress-related genes.There were 5,401 edges (interactions) and 305 nodes (proteins) in the PPI network for elevated oxidative stress-related genes (Supplementary Figure 1) . Each protein interacts with roughly 35 additional proteins, according to the average node degree of 35.4, which shows a densely integrated network. Each protein interacts with roughly 35 additional proteins, as indicated by the average node degree of 35.4, which shows a highly interconnected network. The average local clustering coefficient of 0.52 suggests moderate interconnectedness among proteins. The observed number of edges (5,401) significantly exceeded the expected number (2,747), indicating enriched interactions. The PPI enrichment p-value (< 1.0e-16) further confirmed the biological relevance of these interactions, demonstrating that the network is not random but reflects significant molecular processes. To further explore the Protein-Protein Interaction (PPI) network, we identified the top 50 hub genes using the CytoHubba plugin in Cytoscape, based on their degree of interaction (Supplementary Table 9) (Figure 3). These hub genes play central roles in the network, with the top 20 upregulated hub genes being particularly influential in various cellular processes. GAPDH emerged as the most connected gene, with a degree of 210, followed by TP53 (degree = 173) and TNF (degree = 166), which are pivotal in cellular stress response, inflammation, and apoptosis. Other key hub genes included IL1B (degree = 140), CASP3 (degree = 136), and HSP90AA1 (degree = 121), which are involved in inflammation, programmed cell death, and protein folding, respectively. Genes such as CYCS (degree = 120), MMP9 (degree = 118), and IFNG (degree = 115) play critical roles in mitochondrial function, tissue remodeling, and immune response regulation. Additionally, CD4 (degree = 113) and HSPA4 (degree = 106) are implicated in immune system modulation and stress response . Other notable hub genes such as ICAM1 (95), CXCL8 (94), and CDH1 (93) are integral to cell adhesion, immune signaling, and epithelial integrity. Overall, these hub genes, with varying degrees of interaction, highlight crucial pathways in cellular stress responses, inflammation, and immune regulation. After identifying the hub genes in the upregulated oxidative stress-related gene set, we performed cluster analysis using the MCODE (Molecular Complex Detection) algorithm in Cytoscape to identify densely connected sub-networks within the PPI network (Supplementary Figure 4) . The analysis revealed five distinct clusters, with a density score threshold set to >4.5, which represents the most significant and biologically relevant protein interactions (Supplementary Table 10). The MCODE cluster results highlight key gene networks involved in oxidative stress response. Cluster 1, with the highest MCODE score of 36.821, indicates a highly interconnected network of immune and inflammatory genes, such as CXCL8, CCL4 , and TNF , suggesting a dominant role in immune activation during oxidative stress. This cluster’s high score signifies a strong, tightly-knit gene network, implying a central role in the oxidative stress response. Cluster 2, with an MCODE score of 11.308, focuses on genes like PARP1, BCL2A1 , and COX5A , involved in DNA repair, apoptosis, and mitochondrial functions, showing a moderately dense network with a clear emphasis on cellular protection and mitochondrial integrity under stress. Cluster 3, with a score of 10.308, includes genes like TP53 and HSP90AA1 , pointing to stress response pathways and cell cycle regulation, reflecting a moderately strong network with a focus on genomic stability. Cluster 4, with a lower score of 7.765, suggests a less dense network, focusing on cell proliferation and tissue repair, involving genes like MMP7 and MKI67 . Cluster 5 (MCODE score = 7.714) highlighted immune activation and tissue remodeling through genes like SYK, IL2RB, and MMP1. Collectively, these clusters reveal a hierarchy of gene network connectivity, with Cluster 1 being the most integral to the oxidative stress response, while the others represent specialized yet crucial roles in cellular stress adaptation. The protein-protein interaction (PPI) network for downregulated oxidative stress-related genes comprises 344 nodes and 4,660 edges (Supplementary Figure 3), indicating a highly interconnected network involved in cellular responses to oxidative stress. The average node degree of 27.1 demonstrates that each gene interacts with over 27 others on average, reflecting a densely connected system. The average local clustering coefficient of 0.439 suggests moderate local connectivity, where genes in proximity are more likely to interact, indicating functional organization. The observed number of edges (4,660) significantly exceeds the expected number (2,021), confirming that the network is highly enriched with interactions. The PPI enrichment p-value (< 1.0e-16) further validates the biological relevance of the network, indicating that the interactions are statistically significant and not random. These findings strongly suggest that the downregulated oxidative stress-related genes form a coordinated network, likely influencing key biological pathways and processes related to cellular stress responses and the suppression of oxidative damage. Using the CytoHubba plugin in Cytoscape, we identified the top 50 hub genes from the PPI network of downregulated oxidative stress-related genes based on their degree of interaction (Supplementary Figure 4, Supplementary Table 10). Among these, the top 20 hub genes with the highest interaction scores emerged as crucial players in the network. ALB exhibited the highest interaction score (163), followed by MAPK3 and BCL2 (scores: 124 each). Other prominent genes included JUN (score: 123), PPARG (score: 106), and ESR1 (score: 105), which are involved in apoptosis, signal transduction, and metabolic regulation. Additionally, genes such as IGF1 (score: 104), PTEN (score: 102), and FGF2 (score: 96) play critical roles in cell survival, proliferation, and vascular signaling. These findings highlight the central involvement of these hub genes in oxidative stress response pathways, offering valuable insights into potential therapeutic targets for managing oxidative stress-related diseases. We applied MCODE clustering to the PPI network of downregulated oxidative stress-related genes, identifying six distinct clusters based on network density and node interactions (Supplementary Figure 5, Supplementary Table 11). Cluster 1, with the highest score of 23, included 51 nodes and 575 edges, featuring key genes such as TJP1, SIRT3, FOXO3, PPARG, MAPK3, BCL2, IGF2, and AKT2, which are involved in cell survival, apoptosis, signal transduction, and metabolism. This cluster’s high density suggests a central role in regulating cellular survival and repair mechanisms under oxidative stress. Cluster 2, with a score of 19.769, includes 27 nodes and 257 edges, and features genes like ANGPT1, MMP2, PPARGC1A, VEGFC, PECAM1, ESR1, and CXCL12 . These genes are involved in angiogenesis, vascular regulation, and cell migration, indicating that Cluster 2 is crucial for processes like tissue remodeling and immune response under oxidative stress conditions. Cluster 3, with a score of 7.714, consists of 8 nodes and 27 edges, including genes such as ANK2, CASQ2 , and CACNA1C, which are associated with ion channel regulation and muscle function. This suggests that Cluster 3 may have a more specialized role in cellular communication and ion transport under oxidative stress. Cluster 4, with a score of 6.75, contains 25 nodes and 81 edges, including genes like ABCB1, PIK3R1, MAPK14, TSC2, and BCL6 , which are involved in signal transduction, cellular transport, and apoptosis. This cluster may contribute to cellular stress responses by regulating signal pathways related to cell survival and metabolism. Cluster 5, with a score of 5.684, consists of 20 nodes and 54 edges, featuring genes such as DUSP1, PINK1, GSTM4, MAPT, and CYP1B1 . These genes are involved in oxidative stress response, detoxification, and protein degradation, indicating Cluster 5’s role in mitigating cellular damage caused by oxidative stress and maintaining cellular homeostasis. Cluster 6, with the lowest score of 4.667, contains 10 nodes and 21 edges, including genes like TARDBP, SIRT2, and MT-ND4 . This cluster is associated with mitochondrial function, neurodegenerative diseases, and protein aggregation, suggesting a role in cellular damage and repair mechanisms related to mitochondrial stress under oxidative conditions. Together, these clusters represent distinct yet interconnected networks of genes involved in regulating cellular responses to oxidative stress, including cell survival, apoptosis, angiogenesis, mitochondrial function, and metabolic regulation. Each cluster may reflect different facets of the cellular adaptation to oxidative damage, providing insights into potential therapeutic targets for diseases associated with oxidative stress. 3.4 Exploring the Prognostic Significance of Oxidative Stress-Related Genes in CESC Survival In this study, we investigated the relationship between the expression of oxidative stress-related genes and survival outcomes in cervical squamous cell carcinoma (CESC) patients. We identified a set of upregulated genes that were significantly associated with poor prognosis, as determined by Kaplan-Meier survival analysis. CESC samples were stratified into high and low expression groups based on the median expression of each gene. Notably, the high-expression group of upregulated genes, including ACACA, ADAM17, ANXA2, ATP13A2, CXCL8, ENO1, FASN, VDAC1, G3BP1, HK2, HSPA8, IL1B, IL11, MMP1, MMP3, PGK1, RHOD, SPP1, SLC7A1, TFRC, TNF, and PDIA3, showed a significant association with decreased survival probability (Figure 4). Many of these genes are involved in critical cellular processes such as inflammation, immune response, metabolic reprogramming, and cell survival, all of which contribute to cancer progression and resistance to apoptosis. The upregulation of these genes likely reflects adaptive mechanisms that enable tumor cells to survive under oxidative stress conditions, evade immune surveillance, and promote metastatic potential. Our findings suggest that the overexpression of these oxidative stress-related genes serves as a prognostic indicator of worse survival in CESC patients, highlighting their potential as biomarkers for poor prognosis and therapeutic targets. Further studies are needed to validate these associations and explore the underlying mechanisms contributing to cancer progression in CESC. In addition to the upregulated oxidative stress-related genes, we identified a set of downregulated genes that were significantly associated with poor prognosis in cervical squamous cell carcinoma (CESC) patients (Supplementary Figure 6) , as determined by Kaplan-Meier survival analysis (p<0.05). These genes include BCL2, CPT1A, DES, EPHX2, F8, FRZB, ISCU, SLC40A1, and SNTA1. Interestingly, the low-expression group for each of these genes showed a significantly worse survival outcome, indicating that reduced expression of these genes is linked to a lower survival probability. The downregulation of these genes suggests impaired cellular functions critical for oxidative stress management, cell survival, and tumor suppression. For example, BCL2 , a regulator of apoptosis, when downregulated, could decrease anti-apoptotic activity, enhancing tumor cell survival under stress. Similarly, genes like CPT1A and ISCU , involved in mitochondrial metabolism and oxidative stress management, may impair cancer cell metabolic reprogramming, further promoting tumor progression.When downregulated, genes like FRZB, which is involved in Wnt signaling, and EPHX2, which detoxifies reactive oxygen species, may impair tissue homeostasis and cellular defenses, respectively. Furthermore, abnormalities in cellular communication and iron homeostasis are reflected in the downregulation of SLC40A1 and SNTA1, which exacerbates tumor aggression. These results highlight how crucial genes linked to oxidative stress are in controlling tumor activity, and they imply that downregulating these genes may accelerate the spread of cancer and worsen patient outcomes. These genes could therefore be used as therapeutic targets for upcoming therapies as well as possible biomarkers for poor survival in CESC. 3.5 Univariate Cox Regression Analysis Identifies Prognostic Factors and Oxidative Stress-Related Genes Associated with Survival in CESC Using a univariate Cox regression analysis, we assessed clinical parameters (such as age, cancer stage, fraction of the genome altered, and mutation count) and genes previously linked to the prognosis of cervical squamous cell carcinoma (CESC) using Kaplan-Meier survival analysis to find potential prognostic factors for survival in CESC. The analysis revealed several significant factors associated with poor survival (Table 4). Stage was the most prominent risk factor (HR = 2.3, 95% CI: 1.4–3.8, p = 6e-04), indicating that advanced-stage patients face more than double the risk of poor survival. Among genes, ACACA (HR = 1.8, 95% CI: 1.2–2.6, p = 0.0033), PGK1 (HR = 1.8, 95% CI: 1.4–2.5, p = 1e-04), and FASN (HR = 1.5, 95% CI: 1.2–1.8, p = 0.00068) were strongly associated with poor prognosis, reflecting their roles in lipid metabolism and metabolic reprogramming. Other upregulated genes, such as ENO1, HSPA8, IL11, G3BP1, HK2, PDIA3, IL1B, CXCL8, MMP1, MMP3, SLC7A1, SPP1, ADAM17, TFRC, and TNF, also significantly contributed to tumor progression and poorer outcomes, highlighting their involvement in inflammation, immune response, and metabolic adaptation. Conversely, several factors were associated with a reduced risk of poor survival, acting as protective factors. BCL2 (HR = 0.69, 95% CI: 0.55–0.86, p = 0.00088), a key regulator of apoptosis, was the most notable protective gene, with lower expression linked to a 31% reduction in poor survival risk. ISCU (HR = 0.32, 95% CI: 0.18–0.56, p = 6.8e-05) emerged as the strongest protective gene, showing a 68% reduction in risk. CPT1A, DES, and F8 were other protective genes that may have functions in cellular integrity, mitochondrial metabolism, and oxidative stress response. These results highlight the intricate interactions between genes linked to oxidative stress in CESC, with both upregulated and downregulated genes having a major impact on patient survival. The discovered genes could be useful prognostic biomarkers and possible treatment targets in CESC, especially those with substantial hazard ratios (HRs) and p-values. 3.6 LASSO and multivariate analysis to create a risk model based on prognostic factors and oxidative stress-related genes in CESC We used LASSO analysis with 10-fold cross-validation to find important risk genes linked to survival in cervical squamous cell carcinoma (CESC) (Figure 5A-B). Different influences on patient prognosis were revealed by the identification of genes with nonzero coefficients. Positive coefficients for ACACA (0.0499), FASN (0.0019), PDIA3 (0.0073), CXCL8 (0.0009), IL11 (0.0069), MMP3 (0.0130), PGK1 (0.0276), SLC7A1 (0.0151), SPP1 (0.0179), ADAM17 (0.0056), TFRC (0.0581), and ATP13A2 (0.0559) suggest that higher expression of these genes is associated with poorer survival outcomes, indicating their role as risk factors. On the other hand, genes with negative coefficients—like ISCU (-0.0250), F8 (-0.0256), HSPA8 (-0.0094), DES (-0.0107), ENO1 (-0.0465), BCL2 (-0.0174), CPT1A (-0.0357), and ISCU (-0.0250)—seem to be protective, with increased expression associated with improved survival results. Genes like HK2, IL1B, MMP1, TNF, and G3BP1 had zero coefficients, suggesting no significant contribution to survival prognosis. Subsequent multivariate regression analysis on genes with non-zero coefficients identified six statistically significant genes for predicting survival outcomes in CESC (Table 5, Figure 5C). DES (coefficient: -0.1159, p = 0.0447) and ENO1 (coefficient: -0.6474, p = 0.0088) exhibited negative coefficients, indicating that higher expression is associated with better survival outcomes. In contrast, PDIA3 (coefficient: 0.4887, p = 0.0280), CXCL8 (coefficient: 0.1881, p = 0.0211), SPP1 (coefficient: 0.1782, p = 0.0214), and ATP13A2 (coefficient: 0.5692, p = 0.0191) showed positive coefficients, suggesting that higher expression levels are linked to poorer survival outcomes. These findings highlight the potential of these six genes as key prognostic biomarkers for CESC, with their expression profiles offering valuable insights for risk stratification and survival prediction in clinical practice. Using the coefficients and expression levels of the six significant genes identified through multivariate analysis, we constructed a risk model as follows: Risk Score = CXCL8 × 0.1881 - DES × 0.1159 - ENO1 × 0.6474 + PDIA3 × 0.4887 + SPP1 × 0.1782 + ATP13A2 × 0.55692 Based on the calculated risk scores, patients were stratified into high-risk and low-risk groups. With Kaplan-Meier analysis showing noticeably lower survival rates in the high-risk group relative to the low-risk group, the risk model successfully differentiated between the two groups (Figure 5D) . This illustrates how useful the model is for forecasting patient outcomes and directing medical judgment. We used time-dependent ROC analysis to further assess the prediction accuracy of the model (Figure 5E) . With AUC values of 0.657 at one year (20 cases, 57 controls), 0.648 at three years (56 cases, 158 controls), and 0.652 at five years (64 cases, 198 controls), the model showed strong predictive performance over time. The model’s capacity to reliably differentiate between high-risk and low-risk patients for up to five years is demonstrated by these persistent AUC values over 0.6, highlighting its promise as a predictive tool in clinical practice. The ROC curves (E) evaluate the predictive accuracy of the constructed risk score model at 1, 3, and 5 years, with AUC values demonstrating the model’s ability to distinguish between high-risk and low-risk patients over time. 3.7 Differential expression and diagnostic value of six key genes in risk stratification of patients The expression levels of the six genes included in the risk model (CXCL8, PDIA3, DES, ATP13A2, SPP1, and ENO1) were significantly different between the high-risk and low-risk groups (Supplementary Figure 7A). CXCL8 (logFC = 1.8444) and PDIA3 (logFC = 0.38055) were upregulated in the high-risk group, while DES (logFC = -1.5669) and ENO1 (logFC = -0.2484) were downregulated. All genes exhibited statistically significant differential expression, with exceptionally low p-values and adjusted p-values (ranging from 1.13E-14 to 0.0019768), further validating their relevance in stratifying patients based on prognosis. ROC curve analysis was performed to evaluate the predictive ability of these genes (Supplementary Figure 7B). All six genes demonstrated AUC values greater than 0.5, indicating their potential to discriminate between high-risk and low-risk groups. CXCL8 showed the highest AUC value (0.74), reflecting its strong predictive ability. Other genes, such as ATP13A2 (AUC = 0.67), PDIA3 (AUC = 0.69), and DES (AUC = 0.68), also exhibited moderate accuracy, while ENO1 (AUC = 0.58) and SPP1 (AUC = 0.64) provided additional predictive value. These results underscore the clinical relevance of these genes as potential biomarkers for risk stratification and prognosis prediction in CESC. 3.8 Validation of key prognostic OSRGs in an independent cohort Validation analysis using the GSE63514 dataset, comprising 24 normal and 28 cancer specimens, confirmed the differential expression of five key prognostic genes (CXCL8, DES, ENO1, PDIA3, and SPP1) between normal and cancer tissues (Supplementary Table 12). CXCL8 (p = 5.52E-06, logFC = 3.927) and SPP1 (p = 2.61E-06, logFC = 3.857) were significantly upregulated in cancer samples, while DES (p = 7.35E-06, logFC = -0.899) was significantly downregulated. PDIA3 (p = 5.00E-02, logFC = 0.534) showed upregulation with statistical significance, and ENO1 (p = 5.00E-02, logFC = -0.327) exhibited a trend toward downregulation. Although ATP13A2 (p = 1.31E-01, logFC = 0.708) was upregulated, it did not reach statistical significance. These findings validate the differential expression of the key prognostic genes identified in the risk model and further support their potential as biomarkers for cancer prognosis. 3.9 Association of Immune infiltration with key prognostic OSRGs We analyzed immune infiltration signatures in cervical squamous cell carcinoma (CESC) related to oxidative stress genes, covering various immune cell types and functions, including B cells, CAFs (cancer-associated fibroblasts), CD4+ and CD8+ regulatory T cells, cytolytic activity, immune checkpoint genes, M2 macrophages, MDSCs (myeloid-derived suppressor cells), neutrophils, NK cells, pDCs (plasmacytoid dendritic cells), T cell activation, T cell exhaustion, TAMs (tumor-associated macrophages), Tfh cells, Th17 cells, and TILs (tumor-infiltrating lymphocytes).Correlation analysis between immune signatures and the expression of prognostic genes revealed significant relationships (cutoff: ±0.20, Figure 6). SPP1 exhibited strong positive correlations with CAFs (r = 0.563) and M2 macrophages (r = 0.486), as well as moderate correlations with macrophages (r = 0.421) and Tfh cells (r = 0.34), suggesting a role for fibroblasts and macrophages in promoting SPP1 expression within the tumor microenvironment. CXCL8 showed a moderate negative correlation with CD8+ regulatory T cells (r = -0.209) and a significant positive correlation with Th17 cells (r = 0.209), indicating its potential role in modulating immune cell populations and promoting pro-inflammatory responses. ATP13A2 demonstrated a moderate negative correlation with CD4+ regulatory T cells (r = -0.217), suggesting its influence on immune suppression. DES exhibited a strong positive correlation with CAFs (r = 0.563), highlighting its potential role in promoting fibroblast activity and tumor microenvironment remodeling. A weak negative correlation was observed between ENO1 and neutrophils (r = -0.195), suggesting a slight reduction in neutrophil infiltration with higher ENO1 expression. These findings provide insights into the immune-tumor interactions in CESC, emphasizing the roles of specific immune cells, such as macrophages and fibroblasts, in shaping the tumor microenvironment and influencing prognosis. 3.10 Drug sensitivity and resistance analysis of key prognostic OSRGs To explore the correlation between the expression of six hub genes (CXCL8, DES, ENO1, PDIA3, SPP1, and ATP13A2) and cancer cell sensitivity to small-molecule drugs, we analyzed GDSC IC50 drug data from the GSCA Lite database. Drug-gene pairs with an absolute correlation coefficient > 0.1 and FDR < 0.05 were considered significant. Our analysis revealed that CXCL8, SPP1, PDIA3, and ATP13A2 expressions are associated with resistance to several drugs, including PIK-93, AICAR, AZD8055, BX-912, CAL-101, FK866, Methotrexate, and Y-39983 (Supplementary Figure 8). Conversely, these genes were found to be sensitive to drugs such as SB590885, Dabrafenib, PLX4720, Selumetinib, Trametinib, and RDEA119 (Supplementary Figure 8). Additional drugs exhibiting resistance and sensitivity relationships with the hub genes are also depicted in the figure. These findings highlight the potential of these hub genes as biomarkers for predicting drug response and guiding personalized therapeutic strategies in CESC. We further explored the resistant and sensitive drugs for all prognostic oxidative stress-related genes (OSRGs) with an absolute correlation coefficient > 0.2 and FDR < 0.05 (Supplementary Table 13). CXCL8, DES, PDIA3, and SPP1 exhibited resistance to most of the small-molecule drugs tested. However, several sensitive drugs were identified for these genes. For CXCL8, sensitive drugs included Dasatinib, AZ628, CI-1040, Bleomycin (50 µM), Docetaxel, Selumetinib, PD-0325901, RDEA119, 17-AAG, and Trametinib, suggesting that high CXCL8 expression may enhance sensitivity to these agents. DES showed sensitivity primarily to GNF-2, while PDIA3 was sensitive to AZ628, Trametinib, RDEA119, PD-0325901, TGX221, and 17-AAG. SPP1 exhibited sensitivity to Dasatinib, Dabrafenib, and TGX221.The comprehensive list of resistant and sensitive drugs for CXCL8, DES, PDIA3, and SPP1 is provided in Supplementary Table 13 . These findings highlight the potential utility of specific small-molecule drugs in overcoming resistance mechanisms in Cervical cancer, emphasizing the importance of further investigating the activity and clinical application of these sensitive drugs in this cancer type. Discussion The molecular mechanisms of oxidative stress-related genes (OSRGs) in cervical squamous cell carcinoma (CESC) can be effectively deciphered using gene expression data. In this investigation, we discovered a group of OSRGs that were both elevated and downregulated in CESC tissues in contrast to normal tissues, suggesting their possible roles in the development of cancer and therapeutic targeting. Gene expression data can serve as a powerful tool for unraveling the biological mechanisms of various gene classes, including oxidative stress-related genes, in cervical squamous cell carcinoma (CESC). We identified a set of upregulated and downregulated oxidative stress-related genes in CESC tissues compared to normal tissues. The oxidative stress response is known to play a crucial role in cancer progression, and the dysregulation of oxidative stress pathways can contribute to carcinogenesis (36). Our findings revealed that several genes were significantly differentially expressed and their role in CESC could provide insights into potential therapeutic targets or biomarkers for this disease. Several upregulated OSRGs identified in this study play critical roles in the progression and survival of cervical squamous cell carcinoma (CESC) and other cancers. CDKN2A , which encodes important cell cycle regulators, is frequently disrupted in cancers, and its upregulation in cancers suggests a potential involvement in cell cycle checkpoint responses under oxidative stress (37). FOXM1 , a transcription factor involved in cell cycle regulation and DNA repair, is often overexpressed in cancers, such as breast and liver cancers, where it promotes tumor cell proliferation and resistance to apoptosis (38). Similarly, MKI67 , a well-known proliferation marker, is associated with aggressive tumor behavior and poor prognosis across various cancers (39). BIRC5 (Survivin), an inhibitor of apoptosis, is upregulated in CESC and is also commonly seen in breast and lung cancers, where it helps tumor cells evade cell death and contributes to therapy resistance (40). Furthermore, MMP9 , an enzyme involved in extracellular matrix remodeling, is elevated in cancers, promoting tumor invasion and metastasis, a hallmark of many cancers (41). These genes highlight the complex molecular mechanisms driving CESC progression, with implications for targeted therapeutic strategies in CESC and other cancers. Several downregulated OSRGs identified in this study play crucial roles in cellular defense mechanisms, tissue remodeling, and tumor progression in cervical squamous cell carcinoma (CESC). DES (Desmin), a structural protein involved in maintaining cellular integrity, is downregulated in cancer, which may contribute to altered cell structure and motility (42). Similarly, GPX3 , an antioxidant enzyme, is reduced in CESC, suggesting impaired oxidative stress response and enhanced tumor cell vulnerability to oxidative damage, a pattern also seen in other cancers (43). Additionally, CXCL12 , a chemokine involved in immune cell recruitment, is reduced in CESC, possibly impairing the immune response and promoting immune evasion in cancers (44). The downregulation of SOD3 , an antioxidant enzyme, in CESC further suggests that oxidative stress may be exacerbated, allowing for the promotion of cancerous transformations (45). Other gene such as KLF2 , which are involved in cell signaling and inhibits cancer progression and metastasis, are also downregulated in CESC, potentially contributing to impaired tumor vascularization and metastasis (46). These genes collectively highlight a compromised cellular defense system, contributing to the pathophysiology of CESC and similar alterations observed in other cancers, providing potential targets for therapeutic intervention. Following the identification of differentially expressed OSRGs in CESC, we performed functional enrichment analysis to better understand the biological processes, pathways, and molecular functions associated with these genes. The functional enrichment results revealed several key pathways and biological processes that are closely linked to the altered gene expression patterns in CESC, highlighting the significant role of oxidative stress in the disease. These functional enrichment results emphasize the central role of oxidative stress in shaping the molecular landscape of CESC, where an imbalance between oxidative damage and the body’s antioxidant defense system contributes to tumor progression, immune evasion, and metastasis (47). The findings also suggest potential therapeutic targets, such as antioxidant genes and cell cycle regulators, which could be explored to mitigate oxidative stress and its harmful effects in CESC. To further understand the functional relationships and key regulatory roles of the identified oxidative stress-related genes in CESC, we constructed Protein-Protein Interaction (PPI) networks. These networks help visualize the complex interactions between proteins encoded by the differentially expressed genes and reveal how they work together to influence cellular processes. By analyzing the PPI networks, we identified hub genes, which are central nodes in the network, suggesting that they play crucial roles in the regulation of oxidative stress and tumor progression in CESC. Among the upregulated hub genes identified, several are critical in CESC and other cancers. TP53, a tumor suppressor, regulates the cell cycle and apoptosis in response to DNA damage, and its upregulation suggests a response to oxidative stress and genomic instability (48). MMP9, involved in extracellular matrix remodeling, plays a key role in tumor invasion and metastasis, commonly observed in aggressive cancers (49). HSP90AA1, a heat shock protein, helps tumor cells survive under stress by stabilizing proteins, contributing to cancer cell resilience (50). Lastly, CD274 (PD-L1) enables immune evasion, allowing the tumor to escape immune surveillance (51). These upregulated hub OSRGs are central to key cancer processes, highlighting their potential as therapeutic targets in CESC and beyond. Among the downregulated oxidative stress-related hub genes, several play crucial roles in regulating tumor growth and resistance to stress-induced damage. ALB is important for maintaining oxidative balance and immune function, and its downregulation could impair detoxification and immune surveillance, promoting tumor progression and metastasis (52). PTEN , a tumor suppressor that regulates the PI3K/AKT pathway, is crucial for managing oxidative stress; its downregulation compromises stress response mechanisms, enhancing tumor cell survival and resistance to therapies (53). Similarly, SIRT1 , which regulates oxidative stress and DNA repair, when downregulated, reduces the tumor’s ability to repair oxidative damage, leading to increased genomic instability and accelerated tumor progression (54). Lastly, FOXO1 , a key transcription factor protecting against oxidative damage, if downregulated, may impair the tumor’s ability to undergo apoptosis in response to oxidative stress, promoting cancer cell survival and facilitating disease progression (54). These downregulated genes collectively highlight the tumor’s adaptation to oxidative stress, fostering growth, metastasis, and resistance to treatment. In addition to hub genes, we conducted gene module analysis to identify groups of genes that are co-expressed and functionally related. By analyzing these gene modules and their relationships within the PPI network, we gained a more comprehensive understanding of the molecular mechanisms driving CESC. This approach not only highlighted the importance of oxidative stress-related genes in CESC progression but also identified potential biomarkers and therapeutic targets for further exploration. To identify key prognostic markers in CESC, we performed a series of analyses, starting with Kaplan-Meier survival curves to screen for survival-associated genes. Through univariate analysis, we identified significant genes associated with patient survival. We then applied LASSO regression to further narrow down the list of potential risk genes, focusing on those with the strongest predictive value. Subsequently, multivariate regression analysis was performed to identify independent prognostic markers, ensuring that the selected genes were not influenced by confounding factors. From these analyses, we constructed a risk model using six genes: ATP13A2, CXCL8, DES, ENO1, PDIA3, and SPP1 . The model revealed that the high-risk group exhibited a significantly lower survival probability compared to the low-risk group, highlighting the clinical relevance of these genes in predicting poor outcomes for CESC patients. To validate the robustness of the model, we used time-dependent receiver operating characteristic (ROC) curves, which confirmed that the risk model was reliable and had strong discriminatory power in predicting survival outcomes. Following the construction of the risk model, we further explored the expression levels of the six identified genes ( ATP13A2, CXCL8, DES, ENO1, PDIA3 , and SPP1 ) between the high-risk and low-risk groups. We found significant differential expression of these genes, with distinct patterns emerging between the two groups, which further supported their relevance in CESC prognosis. To assess the diagnostic utility of these genes, we performed ROC curve analysis between the high-risk and low-risk groups, and the results confirmed that these genes had strong diagnostic efficacy in distinguishing between the two groups. The ROC curves demonstrated high accuracy and predictive power, reinforcing the potential of these genes as biomarkers for CESC. Finally, to validate the robustness and external applicability of our findings, we tested the model in an independent GEO cohort. The validation results were consistent with our initial analysis, further solidifying the importance of these six genes in CESC prognosis. Taken together, this validation strongly indicates that CXCL8, DES, ENO1, PDIA3, and SPP1 are crucial in CESC and could serve as valuable biomarkers for clinical stratification and therapeutic decision-making. The validated genes— CXCL8, DES, ENO1, PDIA3, and SPP1 —play crucial roles in cancer progression by regulating oxidative stress, inflammation, and tumor survival. CXCL8 promotes angiogenesis, immune evasion, and metastasis under oxidative stress, contributing to poor prognosis in cancers (55). DES , involved in cell integrity, has been linked to oxidative stress adaptation and tumor metastasis (42). ENO1 enhances glycolytic activity and resistance to oxidative stress, supporting tumor growth and therapy resistance (56). PDIA3 aids in redox homeostasis and cancer cell survival under oxidative stress, promoting resistance to chemotherapy (57). Finally, SPP1 regulates cell adhesion and migration, facilitating metastasis and immune evasion in cancer (58). These genes are key players in maintaining tumor resilience and progression under oxidative stress, making them potential therapeutic targets in CESC. The immune tumor microenvironment plays a crucial role in the progression and prognosis of cancers, including CESC. Tumor-associated immune cells such as macrophages, fibroblasts, and T cells influence tumor growth, immune evasion, and metastasis. Specifically, Cancer-associated fibroblasts (CAFs) are known to promote tumor progression by secreting extracellular matrix proteins and growth factors, and they are often associated with immune suppression. CAFs contribute to remodeling the tumor microenvironment, facilitating immune cell infiltration, and supporting tumor metastasis (59). In particular, SPP1, a secreted glycoprotein, has been shown to enhance macrophage recruitment and activity in various cancers, including cervical cancer, promoting an immunosuppressive environment and facilitating tumor progression (60). The role of macrophages, especially the M2 macrophages subset, is well-established in cancer as they not only participate in immune suppression but also help remodel the tumor stroma and support angiogenesis (61). Tumor-associated macrophages (TAMs), in particular, are involved in immune escape mechanisms, such as inhibiting cytotoxic T cell activity and enhancing tumor cell survival (62). CXCL8 , a key chemokine, is involved in the recruitment of neutrophils and other immune cells to the tumor site, where it can have both pro- and anti-tumor effects depending on the context. Elevated CXCL8 levels have been associated with poor prognosis in several cancers, where it may promote tumor progression by enhancing the infiltration of pro-inflammatory immune cells such as Th17 cells (63, 64). Th17 cells, known for their pro-inflammatory activity, can have dual roles in cancer, sometimes promoting anti-tumor immunity but in other cases contributing to tumor progression through inflammation (65). Moreover, ATP13A2 , a gene involved in regulating cellular stress and maintaining lysosomal function, has shown potential in modulating the immune response. In various cancers, ATP13A2 expression can influence CD4+ T regulatory cells, which are key suppressors of the immune response (66). Understanding how genes like ATP13A2 affect CD4+ T regulatory cells and other immune cell populations could provide insights into potential therapeutic targets to enhance anti-tumor immunity. Finally, the role of metabolic enzymes such as ENO1 has gained attention in cancer immunology due to their involvement in cellular metabolism and immune modulation. ENO1 is associated with various metabolic processes that support tumor growth and immune evasion (67). Its relationship with immune cells such as neutrophils could provide valuable insights into the metabolic shifts occurring within the tumor microenvironment and their impact on immune cell function. To the end, immune cell infiltration and their interactions with key prognostic OSRGs play a pivotal role in shaping the tumor microenvironment in CESC. Targeting specific immune cell subsets or their interactions with tumor-promoting factors like SPP1, CXCL8 , and ENO1 may offer new therapeutic opportunities for improving immune responses and clinical outcomes in cervical cancer patients. The correlation analysis between the expression of key oxidative stress-related genes (OSRGs)— CXCL8, DES, ENO1, PDIA3, SPP1, and ATP13A2 —and drug sensitivity in cancer cells provides valuable insights into the potential mechanisms of drug resistance and sensitivity in CESC. Our findings, derived from the GDSC IC 50 data in the GSCA Lite platform, demonstrate that CXCL8, SPP1, PDIA3, and ATP13A2 are associated with resistance to several small-molecule drugs, including PIK-93, AICAR, AZD8055, BX-912, and Methotrexate. These genes are implicated in promoting an adverse tumor environment, suggesting their potential role in mediating chemoresistance. Interestingly, these same genes were also found to be sensitive to a distinct set of drugs, such as SB590885, Dabrafenib, PLX4720, and Trametinib, highlighting the dual potential of these markers to influence both resistance and sensitivity depending on the context of the tumor microenvironment and the specific drug used. In addition to the significant correlations observed for CXCL8, SPP1, PDIA3, and ATP13A2 , further analysis of the prognostic OSRGs revealed a broader landscape of drug sensitivity and resistance. CXCL8 exhibited sensitivity to multiple agents, including Dasatinib, AZ628, Docetaxel, and Trametinib, which suggests that targeting CXCL8 might be a viable strategy to overcome resistance to certain treatments. DES , although generally associated with resistance, showed sensitivity primarily to GNF-2, providing a potential therapeutic pathway for targeting DES in specific contexts. Likewise, PDIA3 was sensitive to several drugs, such as AZ628, Trametinib, and RDEA119, indicating that drugs targeting PDIA3 may help circumvent resistance mechanisms associated with its high expression. Similarly, SPP1 was sensitive to Dasatinib, Dabrafenib, and TGX221, suggesting that these drugs might be effective in tumors with elevated SPP1 expression. These findings emphasize the complexity of gene-drug interactions in cancer and the need to consider individual gene expressions when selecting targeted therapies. The observed resistance mechanisms mediated by CXCL8, SPP1, PDIA3, and ATP13A2 suggest that these genes could play a crucial role in immune evasion and tumor progression, while their sensitivity to certain drugs underscores the potential for personalized treatment strategies. As we move forward, it is essential to further investigate the clinical activity of these sensitive drugs, particularly in CESC, to better understand their efficacy and potential role in overcoming therapeutic resistance. Our study has both notable strengths and weaknesses. A key strength of our research lies in the identification of differentially expressed OSRGs using a variety of bioinformatics methods. To address CESC, we successfully identified resistant and sensitive drug profiles, pinpointing specific drugs that could target OSRG biomarkers in CESC. To the best of our knowledge, this is the first study to undertake such an investigation. However, the primary limitation of our study is the lack of experimental validation in CESC patients for the altered essential genes and drugs identified through bioinformatics analysis. As such, further experimental and clinical validation will be essential to translate these findings into practical applications for cervical cancer treatment. Nonetheless, our results offer promising therapeutic targets and biomarkers that could benefit CESC patients in the future. Conclusions In this study, we explored the role of OSRGs in the prognosis and immune landscape of CESC. Our comprehensive analysis of TCGA and GTEx datasets identified a large number of upregulated and downregulated genes in CESC, with significant overlap with OSRGs. These genes, such as CDKN2A, SLPI, and LCN2 (upregulated) and DES, GPX3 , and GSTM5 (downregulated), play critical roles in tumor progression, immune modulation, and cellular protection. Functional enrichment analysis highlighted the involvement of OSRGs in various immune, cancer-related, and metabolic pathways, indicating their crucial influence on the tumor microenvironment and cellular metabolism. We constructed a prognostic model based on the expression of key OSRGs, which successfully stratified patients into high- and low-risk groups, with distinct survival outcomes. Genes such as PDIA3, CXCL8, SPP1 , and MMP1 were found to be significantly associated with poor prognosis, while BCL2 and ISCU emerged as protective markers. The robustness of the model was validated by ROC analysis, confirming its predictive accuracy and potential clinical applicability for survival prediction in CESC. Additionally, immune infiltration analysis revealed key correlations between OSRGs and various immune cells, including macrophages, regulatory T cells, and cancer-associated fibroblasts, highlighting the role of immune modulation in CESC prognosis. Our findings also suggest that several OSRGs, including CXCL8, SPP1, PDIA3, and ATP13A2 , could serve as therapeutic targets for precision medicine, as they were found to influence drug sensitivity and resistance. In conclusion, this study underscores the pivotal role of OSRGs in shaping the immune landscape and prognosis of CESC, offering potential biomarkers for risk stratification and therapeutic targets for future cancer treatments. Legends Supplementary Table 1. List of oxidative stress-related genes (OSRGs) identified from the GeneCards database. Supplementary Table 2. Immune indicators that are used to determine scores for single-sample gene set enrichment analysis (ssGSEA). Figure 1. investigation of oxidative stress-related genes (OSRGs) that are differently expressed in CESC. (A) Differentially expressed genes (DEGs) in a volcano graphic. (B) Venn diagram illustrating how DEGs and OSRGs overlap, revealing 345 downregulated and 308 upregulated OSRGs. Table 1. Oxidative stress-related genes (OSRGs) that are expressed differently in CESC. By comparing tumor and normal samples from the TCGA and GTEx datasets, 308 upregulated and 345 downregulated OSRGs were found, as summarized in the table. Table 2. The top 25 genes linked to oxidative stress (OSRGs) that are elevated in CESC. Gene symbols, Entrez IDs, complete gene names, median expression values (tumor vs. normal), log2 fold change (LOG2FC), and adjusted p-values are all included in the table. These genes play a role in immunological modulation, cell cycle regulation, extracellular matrix remodeling, and apoptosis inhibition. Table 3. The top 25 oxidative stress-related genes (OSRGs) that are downregulated in CESC. Genes related to immunological modulation, cell signaling, antioxidant defense, and extracellular matrix remodeling are listed in the table along with their expression levels, log2 fold changes, and adjusted p-values. Supplementary Table 3. KEGG pathways linked to increased oxidative stress-related genes (OSRGs) in CESC were functionally enriched. The top 25 pathways are listed in the table along with fold enrichment, p-values, percentages, gene counts, and false discovery rates (FDR). Figure 2. increased expression of genes linked to oxidative stress (OSRGs) in cancerous processes. Key genes and pathways (such as GSK3B, CXCL8, and TP53) linked to the advancement of cancer are highlighted in the figure. Supplementary Table 4. The top 25 enriched biological pathways for CESC genes that are elevated. The table contains gene counts, percentages, p-values, fold enrichment, and FDR for pathways pertaining to cytokine signaling, immunological modulation, apoptosis, and cellular stress responses. Supplementary Table 5. Gene Ontology (GO) term enrichment analysis for increased OSRGs (oxidative stress-related genes) in CESC. The table uses fold enrichment and FDR values to classify molecular functions (MF), cellular components (CC), and biological processes (BP). Supplementary Table 6. In CESC, reactome pathways are substantially linked to downregulated oxidative stress-related genes (OSRGs). Pathway names, gene counts, percentages, p-values, fold enrichment, and FDR are all included in the table. Supplementary Table 7. Enrichment analysis of Gene Ontology (GO) terms for downregulated oxidative stress-related genes (OSRGs) in CESC. The table categorizes biological processes (BP), cellular components (CC), and molecular functions (MF), with fold enrichment and FDR values. Supplementary Table 8. Enrichment Analysis of Biological Processes (BP), Cellular Components (CC), and Molecular Functions (MF) with Downregulated OSRG. Fold enrichment indicates the degree of association between the gene set and the GO term, while FDR provides statistical significance after multiple testing correction. This analysis identifies key downregulated biological functions, processes, and components in the dataset. Supplementary Figure 1. Protein-protein interaction (PPI) network of upregulated oxidative stress-related genes (OSRGs) in CESC. The network includes 305 nodes and 5,401 edges, with an average node degree of 35.4. Supplementary Table 9: Top 50 upregulated and downregulated hub genes in the PPI network of oxidative stress-related genes (OSRGs) in CESC. Figure 3. PPI network clusters of upregulated oxidative stress-related genes (OSRGs) identified using the MCODE algorithm (density score >4.5). The figure shows clusters with significant biological interactions. Supplementary Figure 2. PPI network of downregulated oxidative stress-related genes (OSRGs) in CESC. The network includes 344 nodes and 4,660 edges, with an average node degree of 27.1. Supplementary Table 10. Protein-protein interaction (PPI) network clusters of upregulated oxidative stress-related genes (OSRGs) identified using the MCODE algorithm (density score >4.5). The table lists clusters, scores, node counts, edge counts, and gene IDs. Supplementary Figure 3. PPI network of downregulated oxidative stress-related genes (OSRGs). The network consists of 344 nodes (genes) and 4,660 edges (interactions), with an average node degree of 27.1 and a local clustering coefficient of 0.439. The observed number of edges significantly exceeds the expected value (2,021), indicating strong enrichment (PPI enrichment p-value < 1.0e-16). Node color intensity reflects interaction degree (red: high; yellow: low). Supplementary Figure 4. Top 50 hub genes in the PPI network of downregulated oxidative stress-related genes (OSRGs) in CESC. The figure highlights key genes (e.g., ALB, MAPK3, BCL2) based on their interaction degrees. Supplementary Figure 5. PPI network clusters of downregulated oxidative stress-related genes (OSRGs) identified using the MCODE algorithm (density score >4.5). The figure shows six clusters with distinct biological roles. Supplementary Table 11. Clusters of downregulated oxidative stress-related genes (OSRGs) identified from the PPI network using MCODE analysis. The table includes cluster scores, node counts, edge counts, and gene IDs. Figure 4. Kaplan-Meier survival analysis of CESC patients stratified by the expression levels of upregulated oxidative stress-related genes (OSRGs). Higher expression of genes (e.g., CXCL8, SPP1, PDIA3) is associated with worse survival. Supplementary Figure 6. Kaplan-Meier survival analysis of CESC patients stratified by the expression levels of downregulated oxidative stress-related genes (OSRGs). Lower expression of genes (e.g., BCL2, DES, ISCU) is associated with worse survival. Table 4. Univariate Cox regression analysis of clinical parameters and oxidative stress-related genes (OSRGs) associated with overall survival in CESC. The table provides hazard ratios, 95% confidence intervals, and p-values. Figure 5. Development and evaluation of a prognostic risk model for CESC based on LASSO-Cox and multivariate regression analysis. (A-B) LASSO-Cox regression identifies key prognostic genes. (C) Multivariate regression highlights significant genes. (D) Survival curve comparing high-risk and low-risk groups. (E) ROC curves evaluating predictive accuracy at 1, 3, and 5 years. Table 5. Multivariate analysis of prognostic genes in CESC. The table includes regression coefficients, hazard ratios, standard errors, z-scores, and p-values. Supplementary Figure 7. Differential expression and predictive value of six key prognostic genes (ATP13A2, CXCL8, DES, ENO1, PDIA3, SPP1) in risk stratification of CESC patients. (A) Expression levels in high-risk vs. low-risk groups. (B) ROC curves showing predictive ability Supplementary Table 12. Differential expression of prognostic oxidative stress-related genes (OSRGs) in the GSE63514 dataset. The table lists p-values, log2 fold changes, and gene symbols for key OSRGs. Figure 6. Correlation of immune infiltration signatures with the expression of prognostic oxidative stress-related genes (OSRGs) in CESC. The figure illustrates relationships between immune cell types (e.g., CAFs, M2 macrophages, T cells) and key OSRGs. Supplementary Figure 8. Bubble plot summarizing correlations between key oxidative stress-related genes (OSRGs) and drug sensitivity/resistance. Blue bubbles indicate sensitivity, and red bubbles indicate resistance. 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Journal of Gastrointestinal Surgery. 2023;27(1):56-6667. Li Y, Liu L, Li B. Role of ENO1 and its targeted therapy in tumors. Journal of Translational Medicine. 2024;22(1):1025 Supplementary Material File (figure 1.tif) Download 13.60 MB File (figure 3.tif) Download 21.81 MB File (figure 4.tif) Download 30.41 MB File (figure 5.tif) Download 22.24 MB File (figure 6.tif) Download 27.02 MB File (tables.xlsx) Download 20.14 KB Information & Authors Information Version history V1 Version 1 28 March 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords cervical squamous cell carcinoma differentially expressed genes drug resistance immune infiltration oxidative stress-related genes risk model Authors Affiliations Mlambo Andrea 0009-0001-6531-3855 Wenzhou Medical University View all articles by this author Yuyang Zhang 0000-0002-6524-8133 [email protected] The First Affiliated Hospital of Wenzhou Medical University Department of Gynecology View all articles by this author Kowthar Mohamed Shaie Wenzhou Medical University View all articles by this author Shuyue Su 0009-0000-8593-9292 Wenzhou Medical University View all articles by this author Tianle Weng Wenzhou Medical University View all articles by this author Jingying Bai Wenzhou Medical University View all articles by this author Chunchun Fang Wenzhou Medical University View all articles by this author Xiaodie Bai Wenzhou Medical University View all articles by this author Metrics & Citations Metrics Article Usage 395 views 136 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Mlambo Andrea, Yuyang Zhang, Kowthar Mohamed Shaie, et al. 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