Identification of 5-methylcytosine-related signature for predicting prognosis in ovarian cancer.
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ABSTRACT: Background
Ovarian cancer is one of the most common malignancies often resulting in a poor prognosis. 5-methylcytosine (m5C) is a common epigenetic modification with roles in eukaryotes. However, the expression and function of m5C regulatory factors in ovarian cancer remained unclear.Results
Two molecular subtypes with different prognostic and clinicopathological features were identified based on m5C regulatory factors. Meanwhile, functional annotation showed that in the two subtypes, 452 differentially expressed genes were significantly related to the malignant progression of ovarian cancer. Subsequently, four m5C genes were screened to construct a risk marker predictive of overall survival and indicative of clinicopathological features of ovarian cancer, also the robustness of the risk marker was verified in external dataset and internal validation set. multifactorial cox regression analysis and nomogram demonstrated that risk score was an independent prognostic factor for ovarian cancer prognosis.Conclusion
In conclusion, our results revealed that m5C-related genes play a critical role in tumor progression in ovarian cancer. Further detection of m5C methylation could provide a novel targeted therapy for treating ovarian cancer.
SUBMITTER: Wang L
PROVIDER: S-EPMC8240302 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionOvarian cancer (OV) is a highly lethal gynecological malignancy with a poor prognosis. Lactate metabolism is crucial for tumor cell survival, proliferation, and immune evasion. Our study aims to investigate the role of lactate metabolism-related genes (LMRGs) in OV and their potential as biomarkers for prognosis, immune microenvironment, and immunotherapy response.MethodsOvarian samples were collected from the TCGA cohort. And 12 lactate-related pathways were identified from the MsigDB database. Differentially expressed genes within these pathways were designated as LMRGs, which undergo unsupervised clustering to identify distinct clusters based on LMRGs. Subsequently, we assessed survival outcomes, immune cell infiltration levels, Hallmaker pathway activation patterns, and chemotaxis among different subtypes. After conducting additional unsupervised clustering based on differentially expressed genes (DEGs), significant differences in the expression of LMRGs between the two clusters were observed. The differentially expressed genes were subjected to subsequent functional enrichment analysis. Furthermore, we construct a model incorporating LMRGs. Subsequently, the lactate score for each tumor sample was calculated based on this model, facilitating the classification of samples into high and low groups according to their respective lactate scores. Distinct groups examined disparities in survival prognosis, copy number variation (CNV), single nucleotide variation (SNV), and immune infiltration. The lactate score served as a quantitative measure of OV's lactate metabolism pattern and an independent prognostic factor.ResultsThis study investigated the potential role of LMRGs in tumor microenvironment diversity and prognosis in OV, suggesting that LMRGs play a crucial role in OV progression and the tumor microenvironment, thus serving as novel indicators for prognosis, immune microenvironment status, and response to immunotherapy.
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Project description:Ovarian cancer (OC) is a highly malignant gynaecological tumour that has a very poor prognosis. Pyroptosis has been demonstrated in recent years to be an inflammatory form of programmed cell death. However, the expression of pyroptosis-related genes in OC and their correlations with prognosis remain unclear. In this study, we identified 31 pyroptosis regulators that were differentially expressed between OC and normal ovarian tissues. Based on these differentially expressed genes (DEGs), all OC cases could be divided into two subtypes. The prognostic value of each pyroptosis-related gene for survival was evaluated to construct a multigene signature using The Cancer Genome Atlas (TCGA) cohort. By applying the least absolute shrinkage and selection operator (LASSO) Cox regression method, a 7-gene signature was built and classified all OC patients in the TCGA cohort into a low- or high-risk group. OC patients in the low-risk group showed significantly higher survival possibilities than those in the high-risk group (P < 0.001). Utilizing the median risk score from the TCGA cohort, OC patients from a Gene Expression Omnibus (GEO) cohort were divided into two risk subgroups, and the low-risk group had increased overall survival (OS) time (P = 0.014). Combined with the clinical characteristics, the risk score was found to be an independent factor for predicting the OS of OC patients. Gene ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) analyses indicated that immune-related genes were enriched and that the immune status was decreased in the high-risk group. In conclusion, pyroptosis-related genes play important roles in tumour immunity and can be used to predict the prognosis of OCs.
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Project description:Background Colorectal cancer (CRC) is the third most common cancer. Nearly a decade of studies had shown that cancer regimens tailored to molecular and pathological features lead to improved overall survival. Oxidative stress (OS) refers to a state in which oxidation and antioxidant effects are unbalanced in the body. However, the molecular mechanism of OS-related CRC remains unclear. Methods Univariate Cox regression analysis gained OS signature genes related to CRC prognosis, and then, different CRC molecular subtypes were obtained by consensus clustering analysis. Differential expression analysis and least absolute shrinkage and selection operator (LASSO) algorithm were used to obtain prognostic-related signature genes. Significantly, risk score was calculated by RiskScore = Σβi × Expi. Moreover, the Kaplan-Meier survival analysis, immune cell infiltration, and sensitivity to treatment regimens were performed to assess the model's validity and adaptability. Finally, RiskScore incorporated clinicopathological features to further improve prognostic models and survival prediction. Results 63 OS-related prognostic genes were obtained, and four distinct molecular subtypes of CRC were identified based on the expression characteristics. 230 differentially expressed genes (DEGs) between different molecular subtypes were compressed by LASSO algorithm, and finally, 6 OS-related genes were obtained. The Kaplan-Meier survival analysis indicated that the high RiskScore groups had poorer prognosis and the RiskScore model showed better predictive performance in all three other independent datasets. Moreover, immunotherapy/chemosensitivity analysis found that the low-risk group was more sensitive to different treatment options and could achieve better treatment outcomes. Conclusion Oxidative stress-related RiskScore model built in this work has good predictive performance for CRC.
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