ArticleFrontiers in immunology2024
Explore the expression of mitochondria-related genes to construct prognostic risk model for ovarian cancer and validate it, so as to provide optimized treatment for ovarian cancer.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Mitochondrial gene expression-based model for the prediction of thyroid cancer prognosis.Endocrine connections · 2026Article
- Adenosine Pathway-Based Prognostic Signature for Predicting Clinical Outcomes and Immune Microenvironment Characteristics in Epithelial Ovarian Cancer.Journal of clinical practice and research · 2026Article
- Identification and validation of a sphingolipid metabolism-related prognostic signature for predicting prognosis and immune microenvironment-related characteristics in ovarian cancer.Translational cancer research · 2026Article
- Mitochondrial apoptosis gene-based pathomics for ovarian cancer prognosis.Scientific reports · 2026Article
- Targeting Osteoclastogenesis: Sabutoclax reduces tumor-associated osteolysis and tumor burden within the bone microenvironment.American journal of cancer research · 2026Article
- Expression profiling of KRAS and NOXA genes as prospective biomarkers in ovarian carcinoma.Scientific reports · 2025Article
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Abstract
Background: The use of gene development data from public database has become a new starting point to explore mitochondrial related gene expression and construct a prognostic prediction model of ovarian cancer. Methods: Data were obtained from the TCGA and ICGC databases, and the intersection with mitochondrial genes was used to obtain the differentially expressed genes. q-PCR, Cox proportional risk regression, minimal absolute contraction and selection operator regression analysis were performed to construct the prognostic risk model, and ROC curve was used to evaluate the model for centralized verification. The association between risk scores and clinical features, tumor mutation load, immune cell infiltration, macrophage activation analysis, immunotherapy, and chemosensitivity was further evaluated. Results: A prognostic risk score model for ovarian cancer patients was constructed based on 12 differentially expressed genes. The score was highly correlated with ovarian cancer macrophage infiltration and was a good predictor of the response to immunotherapy. M1 and M2 macrophages in the ovarian tissue in the OV group were significantly activated, providing a reference for the study of the polarity change of tumor-related macrophages for the prognosis and treatment of ovarian cancer. In terms of drug sensitivity, the high-risk group was more sensitive to vinblastine, Acetalax, VX-11e, and PD-0325901, while the low-risk group was more sensitive to Sabutoclax, SB-505124, cisplatin, and erlotinib. Conclusion: The prognostic risk model of ovarian cancer associated to mitochondrial genes built on the basis of public database better evaluated the prognosis of ovarian cancer patients and guided individual treatment.
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