cSurvival: a web resource for biomarker interactions in cancer outcomes and in cell lines

Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning

Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of renal cell carcinoma. However, our understanding of ccRCC risk genes remains limited. This gap in knowledge poses challenges to the effective diagnosis and treatment of ccRCC. To address this problem, we propose a deep reinforcement learning-based computational approach named RL-GenRisk to identify ccRCC risk genes. Distinct from traditional supervised models, RL-GenRisk frames the identification of ccRCC risk genes as a Markov Decision Process, combining the graph convolutional network and Deep Q-Network for risk gene identification. Moreover, a well-designed data-driven reward is proposed for mitigating the limitation of scant known risk genes. The evaluation demonstrates that RL-GenRisk outperforms existing methods in ccRCC risk gene identification. Additionally, RL-GenRisk identifies eight potential ccRCC risk genes. We successfully validated epidermal growth factor receptor (EGFR) and piccolo presynaptic cytomatrix protein (PCLO), corroborated through independent datasets and biological experimentation. This approach may also be used for other diseases in the future.

Integrative bioinformatics analysis of high-throughput sequencing and in vitro functional analysis leads to uncovering key hub genes in esophageal squamous cell carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCA) is a type of cancer that starts in the cells lining the esophagus, the tube connecting the throat to the stomach. It is known for its aggressive nature and poor prognosis. Understanding the key factors that drive this cancer is crucial for developing better diagnostic tools and treatments.

METHODS

Gene expression profiles of ESCA were analyzed using Gene Expression Omnibus (GEO) datasets (GSE23400, GSE29001, GSE92396, and GSE1420) from the GEO database. Differentially expressed genes (DEGs) were identified using the limma package, and a protein-protein interaction (PPI) network was constructed using the STRING database. Hub genes were identified based on the degree method. Further validation was performed through reverse transcription quantitative PCR (RT-qPCR), mutational and copy number variation (CNV) analysis via the cBioPortal database, promoter methylation analysis using the OncoDB and GSCA databases, survival analysis, immune infiltration analysis through the GSCA database, and functional assays, including knockdown of key genes.

RESULTS

We identified four key hub genes, COL3A1, COL4A1, COL5A2, and CXCL8 that play significant roles in ESCA. These genes were highly expressed in ESCA tissues and cell lines, with expression levels significantly (p-value < 0.001) elevated compared to normal controls. Receiver operating characteristic (ROC) curve analysis revealed exceptional diagnostic performance for all four genes, with area under the curve (AUC) values of 1.0, indicating perfect sensitivity and specificity in distinguishing ESCA from normal controls. Mutational analysis revealed that COL3A1 was altered in 67% of ESCA samples, primarily through missense mutations, while COL5A2 exhibited alterations in 50% of the samples, including splice site and missense mutations. Additionally, gene amplification patterns were observed in all four hub genes, further validating their oncogenic potential in ESCA progression. A significant (p-value < 0.05) promoter hypomethylation was detected in these genes, suggesting a potential regulatory role in their expression. Functional assays demonstrated that knocking down COL3A1 and COL4A1 led to decreased cell proliferation, colony formation, and migration, indicating their critical roles in tumor progression. Additionally, these genes were involved in pathways related to the extracellular matrix and immune system modulation.

CONCLUSION

COL3A1, COL4A1, COL5A2, and CXCL8 are crucial in ESCA development and progression, particularly in remodeling the extracellular matrix, modulating the immune system, and promoting metastasis. These findings suggest that these genes could serve as potential biomarkers for diagnosing ESCA and targets for future therapies. Future research should focus on in vivo validation of these findings and clinical testing to assess the therapeutic potential of targeting these genes in ESCA treatment.

Regulation of KIF23 by miR-107 controls replicative tumor cell fitness in mouse and human hepatocellular carcinoma

BACKGROUND & AIMS

In hepatocellular carcinoma (HCC), successful translation of experimental targets identified in mouse models to human patients has proven challenging. In this study, we used a comprehensive transcriptomic approach in mice to identify novel potential targets for therapeutic intervention in humans.

METHODS

We analyzed combined genome-wide miRNA and mRNA expression data in three pathogenically distinct mouse models of liver cancer. Effects of target genes on hepatoma cell fitness were evaluated by proliferation, survival and motility assays. TCGA and GEO databases, in combination with tissue microarrays, were used to validate the mouse targets and their impact on human HCC prognosis. Finally, the functional effects of the identified targets on tumorigenesis and tumor therapy were tested in hydrodynamic tail vein injection-based preclinical HCC models in vivo.

RESULTS

The expression of miR-107 was found to be significantly reduced in mouse models of liver tumors of various etiologies and in cohorts of humans with HCC. Overexpression of miR-107 or inhibition of its novel target kinesin family member 23 (Kif23) significantly reduced proliferation by interfering with cytokinesis, thereby controlling survival and motility of mouse and human hepatoma cells. In humans, KIF23 expression was found to be a prognostic marker in liver cancer, with high expression associated with poor prognosis. Hydrodynamic tail vein injection of vectors carrying either pre-miR-107 or anti-Kif23 shRNA inhibited the development of highly aggressive c-Myc-NRAS-induced liver cancers in mice.

CONCLUSIONS

Disruption of the miR-107/Kif23 axis inhibited hepatoma cell proliferation in vitro and prevented oncogene-induced liver cancer development in vivo, offering a novel potential avenue for the treatment of HCC in humans.

IMPACT AND IMPLICATIONS

Our study revealed the central role of the miR-107/KIF23 axis in controlling tumor cell fitness and hepatocellular carcinoma progression. The results demonstrate that the overexpression of miR-107 or silencing of its target, KIF23, markedly suppresses the proliferation, survival, and motility of human and mouse hepatoma cells. In this work, we demonstrate that the disruption of miR-107/Kif23 signaling effectively protects mice from an aggressive form of oncogene-induced liver cancer in vivo, implying that targeting miR-107/KIF23 might be a novel therapeutic approach for hepatocellular carcinoma in humans.

The translational impact of bioinformatics on traditional wet lab techniques

Bioinformatics has taken a pivotal place in the life sciences field. Not only does it improve, but it also fine-tunes and complements the wet lab experiments. It has been a driving force in the so-called biological sciences, converting them into hypothesis and data-driven fields. This study highlights the translational impact of bioinformatics on experimental biology and discusses its evolution and the advantages it has brought to advancing biological research. Computational analyses make labor-intensive wet lab work cost-effective by reducing the use of expensive reagents. Genome/proteome-wide studies have become feasible due to the efficiency and speed of bioinformatics tools, which can hardly be compared with wet lab experiments. Computational methods provide the scalability essential for manipulating large and complex data of biological origin. AI-integrated bioinformatics studies can unveil important biological patterns that traditional approaches may otherwise overlook. Bioinformatics contributes to hypothesis formation and experiment design, which is pivotal for modern-day multi-omics and systems biology studies. Integrating bioinformatics in the experimental procedures increases reproducibility and helps reduce human errors. Although today's AI-integrated bioinformatics predictions have significantly improved in accuracy over the years, wet lab validation is still unavoidable for confirming these predictions. Challenges persist in multi-omics data integration and analysis, AI model interpretability, and multiscale modeling. Addressing these shortcomings through the latest developments is essential for advancing our knowledge of disease mechanisms, therapeutic strategies, and precision medicine.

CDCA genes as prognostic and therapeutic targets in Colon adenocarcinoma

OBJECTIVES

The study investigates the role of Cell Division Cycle Associated (CDCA) genes in colorectal cancer (COAD) by analyzing their differential expression, epigenetic alterations, prognostic significance, and functional associations.

METHODOLOGY

This study employed a detailed in silico and in vitro experiments-based methodology.

RESULTS

RT-qPCR assays reveal significantly elevated mRNA levels of CDCA2, CDCA3, CDCA4, CDCA5, CDCA7, and CDCA8 genes in COAD cell lines compared to controls. Bisulfite sequencing indicates reduced promoter methylation of CDCA gene promoters in COAD cell lines, suggesting an epigenetic regulatory mechanism. Analysis of large TCGA datasets confirms increased CDCA gene expression in COAD tissues. Survival analysis using cSurvival database demonstrates negative correlations between CDCA gene expression and patient overall survival. Additionally, Lasso regression-based models of CDCA genes predict survival outcomes in COAD patients. Investigating immune modulation, CDCA gene expression inversely correlates with immune cell infiltration and immune modulators. miRNA-mRNA network analysis identifies regulatory miRNAs targeting CDCA genes, validated by RT-qPCR showing up-regulation of has-mir-10a-5p and has-mir-20a-5p in COAD cell lines and tissues. Drug sensitivity analysis suggests resistance to specific drugs in COAD patients with elevated CDCA gene expression. Furthermore, CDCA gene expression correlates with crucial functional states in COAD, including "angiogenesis, apoptosis, differentiation, hypoxia, inflammation, and metastasis." Additional in vitro experiments revealed that CDCA2 and CDCA3 knockdown in SW480 and SW629 cells significantly reduced cell proliferation and colony formation while enhancing cell migration.

CONCLUSION

Overall, the study elucidates the multifaceted role of CDCA genes in COAD progression, providing insights into potential diagnostic, prognostic, and therapeutic implications.

Oncolytic reprogramming of tumor microenvironment shapes CD4 T-cell memory via the IL6ra-Bcl6 axis for targeted control of glioblastoma

Oncolytic viruses (OVs) emerge as a promising cancer immunotherapy. However, the temporal impact on tumor cells and the tumor microenvironment, and the nature of anti-tumor immunity post-therapy remain largely unclear. Here we report that CD4+ T cells are required for durable tumor control in syngeneic murine models of glioblastoma multiforme after treatment with an oncolytic herpes simplex virus (oHSV) engineered to express IL-12. The upregulated MHCII on residual tumor cells facilitates programmed polyfunctional CD4+ T cells for tumor control and for recall responses. Mechanistically, the proper ratio of Bcl-6 to T-bet in CD4+ T cells navigates their enhanced anti-tumor capacity, and a reciprocal IL6ra-Bcl-6 regulatory axis in a memory CD4+ T-cell subset, which requires MHCII signals from reprogrammed tumor cells, tumor-infiltrating and resident myeloid cells, is necessary for the prolonged response. These findings uncover an OV-induced tumor/myeloid-CD4+ T-cell partnership, leading to long-term anti-tumor immune memory, and improved OV therapeutic efficacy.

Pan-cancer secreted proteome and skeletal muscle regulation: insight from a proteogenomic data-driven knowledge base

Large-scale, pan-cancer analysis is enabled by data driven knowledge bases that link tumor molecular profiles with phenotypes. A debilitating cancer-related phenotype is skeletal muscle loss, or cachexia, which occurs partly from tumor products secreted into circulation. Using the LinkedOmicsKB knowledge base assembled from the Clinical Proteomics Tumor Analysis Consortium proteogenomic analysis, along with catalogs of human secretome proteins, ligand-receptor pairs and molecular signatures, we sought to identify candidate pan-cancer proteins secreted to blood that could regulate skeletal muscle phenotypes in multiple solid cancers. Tumor proteins having significant pan-cancer associations with muscle were referenced against secretome proteins secreted to blood from the Human Protein Atlas, then verified as increased in paired tumor vs. normal tissues in pan-cancer manner. This workflow revealed seven secreted proteins from cancers afflicting kidneys, head and neck, lungs and pancreas that classified as protein-binding activity modulator, extracellular matrix protein or intercellular signaling molecule. Concordance of these biomarkers with validated molecular signatures of cachexia and senescence supported relevance to muscle and cachexia disease biology, and high tumor expression of the biomarker set associated with lower overall survival. In this article, we discuss avenues by which skeletal muscle and cachexia may be regulated by these candidate pan-cancer proteins secreted to blood, and conceptualize a strategy that considers them collectively as a biomarker signature with potential for refinement by data analytics and radiogenomics for predictive testing of future risk in a non-invasive, blood-based panel amenable to broad uptake and early management.

A bioinformatics analysis of gene expression in endometrial cancer, endometriosis and obesity

Endometrial cancer (EC), endometriosis (ENDO), and obesity (OBY) are interconnected conditions in women that may share underlying genetic pathways. This study aimed to identify shared genetic pathways and differential gene expressions across these conditions to uncover potential therapeutic targets. A bioinformatics pipeline was applied using gene expression datasets from the GEO database, incorporating differential expression analysis, functional and pathway enrichment, PPI network construction, survival analysis, and mutational profiling across 198 samples. The analysis revealed 26 shared differentially expressed genes (DEGs), with IGF-1, CREBBP, EP300, and PIAS1 identified as key hub genes. Elevated IGF-1 expression was significantly linked to poorer survival outcomes in EC patients (p < .05). Frequent mutations were observed in these hub genes, suggesting their critical role in disease mechanisms. This study highlights genetic links among EC, ENDO, and OBY, emphasizing high IGF-1 expression as a potential prognostic marker in EC and recurrent alterations in hub genes as promising therapeutic targets. These findings provide insights into the shared genetic underpinnings of these conditions and present new avenues for targeted therapies.

Leveraging Bioinformatics and Machine Learning for Identifying Prognostic Biomarkers and Predicting Clinical Outcomes in Lung Adenocarcinoma

Background/Objectives: There exist significant challenges for lung adenocarcinoma (LUAD) due to its poor prognosis and limited treatment options, particularly in the advanced stages. It is crucial to identify genetic biomarkers for improving outcome predictions and guiding personalized therapies. Methods: In this study, we utilize a multi-step approach that combines principled sure independence screening, penalized regression methods and information gain to identify the key genetic features of the ultra-high dimensional RNA-sequencing data from LUAD patients. We then evaluate three methods of survival analysis: the Cox model, survival tree, and random survival forests (RSFs), to compare their predictive performance. Additionally, a protein-protein interaction network is used to explore the biological significance of identified genes. Results:DKK1 and TNS4 are consistently selected as significant predictors across all feature selection methods. The Kaplan-Meier method shows that high expression levels of these genes are strongly correlated with poorer survival outcomes, suggesting their potential as prognostic biomarkers. RSF outperforms Cox and survival tree methods, showing higher AUC and C-index values. The protein-protein interaction network highlights key nodes such as VEGFC and LAMA3, which play central roles in LUAD progression. Conclusions: Our findings provide valuable insights into the genetic mechanisms of LUAD. These results contribute to the development of more accurate prognostic tools and personalized treatment strategies for LUAD.

Segmentation of patients with small cell lung cancer into responders and non-responders using the optimal cross-validation technique

BACKGROUND

The timing of treating cancer patients is an essential factor in the efficacy of treatment. So, patients who will not respond to current therapy should receive a different treatment as early as possible. Machine learning models can be built to classify responders and nonresponders. Such classification models predict the probability of a patient being a responder. Most methods use a probability threshold of 0.5 to convert the probabilities into binary group membership. However, the cutoff of 0.5 is not always the optimal choice.

METHODS

In this study, we propose a novel data-driven approach to select a better cutoff value based on the optimal cross-validation technique. To illustrate our novel method, we applied it to three clinical trial datasets of small-cell lung cancer patients. We used two different datasets to build a scoring system to segment patients. Then the models were applied to segment patients into the test data.

RESULTS

We found that, in test data, the predicted responders and non-responders had significantly different long-term survival outcomes. Our proposed novel method segments patients better than the standard approach using a cutoff of 0.5. Comparing clinical outcomes of responders versus non-responders, our novel method had a p-value of 0.009 with a hazard ratio of 0.668 for grouping patients using the Cox proportion hazard model and a p-value of 0.011 using the accelerated failure time model which approved a significant difference between responders and non-responders. In contrast, the standard approach had a p-value of 0.194 with a hazard ratio of 0.823 using the Cox proportion hazard model and a p-value of 0.240 using the accelerated failure time model indicating the responders and non-responders do not differ significantly in survival.

CONCLUSION

In summary, our novel prediction method can successfully segment new patients into responders and non-responders. Clinicians can use our prediction to decide if a patient should receive a different treatment or stay with the current treatment.

Explorative Discovery of Gene Signatures and Clinotypes in Glioblastoma Cancer Through GeneTerrain Knowledge Map Representation

This study introduces the GeneTerrain Knowledge Map Representation (GTKM), a novel method for visualizing gene expression data in cancer research. GTKM leverages protein-protein interactions to graphically display differentially expressed genes (DEGs) on a 2-dimensional contour plot, offering a more nuanced understanding of gene interactions and expression patterns compared to traditional heatmap methods. The research demonstrates GTKM's utility through four case studies on glioblastoma (GBM) datasets, focusing on survival analysis, subtype identification, IDH1 mutation analysis, and drug sensitivities of different tumor cell lines. Additionally, a prototype website has been developed to showcase these findings, indicating the method's adaptability for various cancer types. The study reveals that GTKM effectively identifies gene patterns associated with different clinical outcomes in GBM, and its profiles enable the identification of sub-gene signature patterns crucial for predicting survival. The methodology promises significant advancements in precision medicine, providing a powerful tool for understanding complex gene interactions and identifying potential therapeutic targets in cancer treatment.

Nicotinamide phosphoribosyltransferase modulates PD-L1 in bladder cancer and enhances immunotherapeutic sensitivity

Bladder cancer (BLCA) is one of the most prevalent malignancies worldwide with a high mortality rate and poor response to immunotherapy in patients expressing lower programmed death ligand 1 (PD-L1) levels. Nicotinamide phosphoribosyltransferase (NAMPT), a rate-limiting enzyme responsible for the biosynthesis of nicotinamide adenine dinucleotide (NAD+) from nicotinamide was reported to be overexpressed in various cancers; however, the role of NAMPT in BLCA is obscure. Immunohistochemistry of tissue microarrays, a real-time polymerase chain reaction, Western blotting, proliferation assay, NAD+ quantification, transwell-migration assay, and colony-formation assay were performed to measure NAMPT and PD-L1 expression levels in patients and the effect of NAMPT inhibition on T24 cells. Our study revealed that NAMPT expression was upregulated in BLCA patients with different grades and associated with poor T-cell infiltration. Notably, FK866-mediated NAMPT inhibition decreased cell viability by depleting NAD+, and reducing the migration ability and colony-formation ability of T24 cells. Interestingly, NAMPT negatively regulated PD-L1 under an interferon (IFN)-γ-mediated microenvironment. However, exogenous NAMPT activator has no effect on PD-L1. NAD+ supplementation also only increased PD-L1 in the absence of IFN-γ. Conclusively, NAMPT is crucial for BLCA tumorigenic properties, and it regulates expression of the PD-L1 immune checkpoint protein. NAMPT could be considered a target for modulating sensitivity to immunotherapy.

Estimating cutoff values for diagnostic tests to achieve target specificity using extreme value theory

BACKGROUND

Rapidly developing tests for emerging diseases is critical for early disease monitoring. In the early stages of an epidemic, when low prevalences are expected, high specificity tests are desired to avoid numerous false positives. Selecting a cutoff to classify positive and negative test results that has the desired operating characteristics, such as specificity, is challenging for new tests because of limited validation data with known disease status. While there is ample statistical literature on estimating quantiles of a distribution, there is limited evidence on estimating extreme quantiles from limited validation data and the resulting test characteristics in the disease testing context.

METHODS

We propose using extreme value theory to select a cutoff with predetermined specificity by fitting a Pareto distribution to the upper tail of the negative controls. We compared this method to five previously proposed cutoff selection methods in a data analysis and simulation study. We analyzed COVID-19 enzyme linked immunosorbent assay antibody test results from long-term care facilities and skilled nursing staff in Colorado between May and December of 2020.

RESULTS

We found the extreme value approach had minimal bias when targeting a specificity of 0.995. Using the empirical quantile of the negative controls performed well when targeting a specificity of 0.95. The higher target specificity is preferred for overall test accuracy when prevalence is low, whereas the lower target specificity is preferred when prevalence is higher and resulted in less variable prevalence estimation.

DISCUSSION

While commonly used, the normal based methods showed considerable bias compared to the empirical and extreme value theory-based methods.

CONCLUSIONS

When determining disease testing cutoffs from small training data samples, we recommend using the extreme value based-methods when targeting a high specificity and the empirical quantile when targeting a lower specificity.

TheMarker: a comprehensive database of therapeutic biomarkers

Distinct from the traditional diagnostic/prognostic biomarker (adopted as the indicator of disease state/process), the therapeutic biomarker (ThMAR) has emerged to be very crucial in the clinical development and clinical practice of all therapies. There are five types of ThMAR that have been found to play indispensable roles in various stages of drug discovery, such as: Pharmacodynamic Biomarker essential for guaranteeing the pharmacological effects of a therapy, Safety Biomarker critical for assessing the extent or likelihood of therapy-induced toxicity, Monitoring Biomarker indispensable for guiding clinical management by serially measuring patients' status, Predictive Biomarker crucial for maximizing the clinical outcome of a therapy for specific individuals, and Surrogate Endpoint fundamental for accelerating the approval of a therapy. However, these data of ThMARs has not been comprehensively described by any of the existing databases. Herein, a database, named 'TheMarker', was therefore constructed to (a) systematically offer all five types of ThMAR used at different stages of drug development, (b) comprehensively describe ThMAR information for the largest number of drugs among available databases, (c) extensively cover the widest disease classes by not just focusing on anticancer therapies. These data in TheMarker are expected to have great implication and significant impact on drug discovery and clinical practice, and it is freely accessible without any login requirement at: https://idrblab.org/themarker.

An extended transcription factor regulatory network controls hepatocyte identity

Cell identity is specified by a core transcriptional regulatory circuitry (CoRC), typically limited to a small set of interconnected cell-specific transcription factors (TFs). By mining global hepatic TF regulons, we reveal a more complex organization of the transcriptional regulatory network controlling hepatocyte identity. We show that tight functional interconnections controlling hepatocyte identity extend to non-cell-specific TFs beyond the CoRC, which we call hepatocyte identity (Hep-ID)CONNECT TFs. Besides controlling identity effector genes, Hep-IDCONNECT TFs also engage in reciprocal transcriptional regulation with TFs of the CoRC. In homeostatic basal conditions, this translates into Hep-IDCONNECT TFs being involved in fine tuning CoRC TF expression including their rhythmic expression patterns. Moreover, a role for Hep-IDCONNECT TFs in the control of hepatocyte identity is revealed in dedifferentiated hepatocytes where Hep-IDCONNECT TFs are able to reset CoRC TF expression. This is observed upon activation of NR1H3 or THRB in hepatocarcinoma or in hepatocytes subjected to inflammation-induced loss of identity. Our study establishes that hepatocyte identity is controlled by an extended array of TFs beyond the CoRC.

Spatially resolved transcriptomics revealed local invasion-related genes in colorectal cancer

Objective

Local invasion is the first step of metastasis, the main cause of colorectal cancer (CRC)-related death. Recent studies have revealed extensive intertumoral and intratumoral heterogeneity. Here, we focused on revealing local invasion-related genes in CRC.

Methods

We used spatial transcriptomic techniques to study the process of local invasion in four CRC tissues. First, we compared the pre-cancerous, cancer center, and invasive margin in one section (S115) and used pseudo-time analysis to reveal the differentiation trajectories from cancer center to invasive margin. Next, we performed immunohistochemical staining for RPL5, STC1, AKR1B1, CD47, and HLA-A on CRC samples. Moreover, we knocked down AKR1B1 in CRC cell lines and performed CCK-8, wound healing, and transwell assays to assess cell proliferation, migration, and invasion.

Results

We demonstrated that 13 genes were overexpressed in invasive clusters, among which the expression of CSTB and TM4SF1 was correlated with poor PFS in CRC patients. The ribosome pathway was increased, while the antigen processing and presentation pathway was decreased along CRC progression. RPL5 was upregulated, while HLA-A was downregulated along cancer invasion in CRC samples. Pseudo-time analysis revealed that STC1, AKR1B1, SIRPA, C4orf3, EDNRA, CES1, PRRX1, EMP1, PPIB, PLTP, SULF2, and EGFL6 were unpregulated along the trajectories. Immunohistochemic3al staining showed the expression of STC1, AKR1B1, and CD47 was increased along cancer invasion in CRC samples. Knockdown of AKR1B1 inhibited CRC cells' proliferation, migration, and invasion.

Conclusions

We revealed the spatial heterogeneity within CRC tissues and uncovered some novel genes that were associated with CRC invasion.

Systematic Analysis of the Therapy Resistance Genes and their Prognostic Relevance in Cervical Cancer

INTRODUCTION

Critical issues in the therapeutic management of cervical cancer (CC) include therapy resistance and treatment failure. The development of therapy resistance is a multifaceted, progressive process, including genetic and epigenetic abnormalities. The present study aimed to identify genes that may contribute to therapy resistance in CC.

MATERIALS AND METHODS

We have created an extensive list of the genes in cancer that are therapy-resistant using a text-mining approach. The list was compared with the TCGA-CESC dataset to identify the differentially expressed therapy resistance genes (DETRGs) in CC. We used online resources (UALCAN, DNMIVD, cBio- Portal, HCMDB, OncoDB, ShinyGO, HPA, KM Plotter, TIMER, and DGIdb) to determine the potential association between methylation and expression of therapy resistance genes with the prognosis and clinical outcomes in CC.

RESULTS

The systematic analysis identified 71 out of 91 DETRGs showed aberrant DNA methylation. The overlapping analysis identified 25 genes to show an inverse correlation between methylation and expression. Further, differential expression or methylation could be helpful in CC staging, HPV association, prediction of metastasis and prognosis. The study identified seven driver genes in CC. The PPIN identifies ten hub genes (HGs) associated with CC staging, cancer hallmarks, and prognosis to affect long-term survival.

CONCLUSION

Our thorough investigation uncovered several novel genes and pathways that might contribute to therapy resistance in CC. The genes identified in our study may serve as a biomarker, prognostic indicator, and therapeutic target in CC.