ArticleFrontiers in molecular biosciences2024
Single-sample gene set enrichment analysis reveals the clinical implications of immune-related genes in ovarian cancer.
Article in Frontiers in molecular biosciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
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- An integrated analysis of MEN1 reveals its associations with clinical outcomes, cellular pathways, and the immune microenvironment.World journal of surgical oncology · 2026Article
- Integrated Bulk and Single-Cell Transcriptomic Analysis Followed by Clinical Validation Reveal Programmed Cell Death-Related Shared Molecular Signatures in OA and MDD.International journal of molecular sciences · 2026Article
- The Tip of the Iceberg: Pathway Biology Must Anchor the Next Generation of Critical Care Trials.Critical care explorations · 2026Article
- PINK1‑mediated mitophagy enhances breast cancer proliferation through metabolic reprogramming.Oncology reports · 2026Article
- A transcriptomic-based model associated with PARP inhibitor sensitivity and immunogenic signatures in ovarian cancer.Discover oncology · 2026Article
- ZNF473 as a biomarker and potential therapeutic target in cancer: integrated bioinformatics and experimental evidence with a focus on hepatocellular carcinoma.Frontiers in oncology · 2026Article
- Convergent Immune-Coagulation Programs Underlie Gastrointestinal Bleeding Risk in Portal Vein Tumor Thrombosis-Associated Hepatocellular Carcinoma and Portal Hypertension.Human mutation · 2026Article
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- Bioinformatics analysis reveals C5AR1's impact on thyroid cancer development via immune infiltration.Scientific reports · 2025Article
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- Robust machine-learning based prognostic index using fatty acid metabolism genes predicts prognosis and therapy responses in glioblastoma.Journal of Cancer · 2025Article
- Identification of immune and major depressive disorder-related diagnostic markers for early nonalcoholic fatty liver disease by WGCNA and machine learning.Frontiers in bioinformatics · 2025Article
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7 authors.
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Abstract
Purpose: Ovarian cancer (OC) is a common gynecological malignancy with poor prognosis and substantial tumor heterogeneity. Due to the complex tumor immune microenvironment (TIME) among ovarian cancer, only a few patients have an immune response to immunotherapy. To investigate the differences in immune function and identify potential biomarkers in OC, we established a prognostic risk scoring model (PRSM) with differential expression of immune-related genes (IRGs) to identify critical prognostic IRG signatures. Methods: Single-sample gene set enrichment analysis (ssGSEA) was used to investigate the infiltration of various immune cells in 372 OC patients. Then, COX regression analysis and Lasso regression analysis were used to screen IRGs and construct PRSM. Next, the immunotherapy sensitivity of different risk groups regarding the immune checkpoint expression and tumor mutation burden was evaluated. Finally, a nomogram was created to guide the clinical evaluation of the patient prognosis. Results: In this study, 320 immune-related genes (IRGs) were identified, 13 of which were selectively incorporated into a Prognostic Risk Scoring Model (PRSM). This model revealed that the patients in the high-risk group were characterized as having poorer prognosis, lower expression of immune checkpoints, and decreased tumor mutation load levels compared with those in the low-risk group. The nomogram based on the risk score can distinguish the risk subtypes and individual prognosis of patients with OC. Additionally, M1 macrophages may be the critical target for immunotherapy in OC patients. Conclusion: With the in-depth analysis of the immune microenvironment of OC, the PRSM was constructed to predict the OC patient prognosis and identify the subgroup of the patients benefiting from immunotherapy.
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