ArticleJournal of ovarian research2022
A risk model of gene signatures for predicting platinum response and survival in ovarian cancer.
Article in Journal of ovarian research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Efficacy and safety of hyperthermic intraperitoneal intraoperative chemotherapy plus surgery in advanced ovarian cancer patients.The Journal of international medical research · 2025Trial
- ER-Localized DeadenylaseExploration (Beijing, China) · 2026Article
- SULT1A1 at a differentiation branch point regulates osteosarcoma cell proliferation and melatonin-mediated anti-tumor activity.Frontiers in oncology · 2026Article
- Identification of a Novel Pyroptosis-Related lncRNAs Prognosis Model and Subtypes in Ovarian Cancer.Phenomics (Cham, Switzerland) · 2025Article
- Integrating bulk RNA-seq and scRNA-seq analyses with machine learning to predict platinum response and prognosis in ovarian cancer.Scientific reports · 2025Article
- Prognosis and immune infiltration prediction in neuroblastoma based on neutrophil extracellular traps-related gene signature.Scientific reports · 2025Article
- Hesperidin inhibits colon cancer progression by downregulating SLC5A1 to suppress EGFR phosphorylation.Journal of Cancer · 2025Article
- Senescence-Related LncRNAs: Pioneering Indicators for Ovarian Cancer Outcomes.Phenomics (Cham, Switzerland) · 2024Review
- Article
- Establishment of an ovarian cancer exhausted CD8+T cells-related genes model by integrated analysis of scRNA-seq and bulk RNA-seq.European journal of medical research · 2024Article
- Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer.Translational oncology · 2024Article
- Prediction of Response to Chemoradiotherapy by Dynamic Changes of Circulating Exosome Levels in Patients with Esophageal Squamous Cell Carcinoma.International journal of nanomedicine · 2024Article
- Personalization of Therapy in High-Grade Serous Tubo-Ovarian Cancer-The Possibility or the Necessity?Journal of personalized medicine · 2023Review
- Endoplasmic Reticulum Stress-Related Ten-Biomarker Risk Classifier for Survival Evaluation in Epithelial Ovarian Cancer andInternational journal of molecular sciences · 2023Article
- Development of Nomogram Models Based on Peripheral Blood Score and Clinicopathological Parameters to Predict Preoperative Advanced Stage and Prognosis for Epithelial Ovarian Cancer Patients.Journal of inflammation research · 2023Article
- Increased response to TPF chemotherapy promotes immune escape in hypopharyngeal squamous cell carcinoma.Frontiers in pharmacology · 2022Article
- Assessing the Impact of Patient Selection and Analytical Methods on mRNA Biomarker Identification in Ovarian Cancer Using TCGA Data.Cancer control : journal of the Moffitt Cancer CenterArticle
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundOvarian cancer (OC) is the deadliest tumor in the female reproductive tract. And increased resistance to platinum-based chemotherapy represents the major obstacle in the treatment of OC currently. Robust and accurate gene expression models are crucial tools in distinguishing platinum therapy response and evaluating the prognosis of OC patients.
methodsIn this study, 230 samples from The Cancer Genome Atlas (TCGA) OV dataset were subjected to mRNA expression profiling, single nucleotide polymorphism (SNP), and copy number variation (CNV) analysis comprehensively to screen out the differentially expressed genes (DEGs). An SVM classifier and a prognostic model were constructed using the Random Forest algorithm and LASSO Cox regression model respectively via R. The Gene Expression Omnibus (GEO) database was applied as the validation set.
resultsForty-eight differentially expressed genes (DEGs) were figured out through integrated analysis of gene expression, single nucleotide polymorphism (SNP), and copy number variation (CNV) data. A 10-gene classifier was constructed which could discriminate platinum-sensitive samples precisely with an AUC of 0.971 in the training set and of 0.926 in the GEO dataset (GSE638855). In addition, 8 optimal genes were further selected to construct the prognostic risk model whose predictions were consistent with the actual survival outcomes in the training cohort (p = 9.613e-05) and validated in GSE638855 (p = 0.04862). PNLDC1, SLC5A1, and SYNM were then identified as hub genes that were associated with both platinum response status and prognosis, which was further validated by the Fudan University Shanghai cancer center (FUSCC) cohort.
conclusionThese findings reveal a specific risk model that could serve as effective biomarkers to identify patients' platinum response status and predict survival outcomes for OC patients. PNLDC1, SLC5A1, and SYNM are the hub genes that may serve as potential biomarkers in OC treatment.
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