ArticleJournal of ovarian research2024
Development and clinical validation of a seven-gene signature based on tumor stem cell-related genes to predict ovarian cancer prognosis.
Article in Journal of ovarian research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 4 citations in OpenAlex.
- Repurposing the liquid-based Pap test for the detection of ovarian cancer protein biomarkers.Clinical proteomics · 2026Article
- Article
- Targeted immunotherapies and nanomedicines for ovarian cancer: the way forward.NPJ precision oncology · 2026Review
- The uterine secretome initiates growth of gynecologic tissues in ectopic locations: re-evaluating the evidence.Pathology oncology research : POR · 2026Review
- A Prognostic Nomogram Based on an Immunogenic Cell Death and Endoplasmic Reticulum Stress-Related Gene Signature for Ovarian Cancer.International journal of women's health · 2025Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveTumors are highly heterogeneous, and within their parenchyma, a small population of tumor-stem cells possessing differentiation potential, high oncogenicity, and self-renewal capabilities exists. These cells are pivotal in mediating tumor development, chemotherapy resistance, and recurrence. Ovarian cancer shares characteristics with tumor stem cells, making it imperative to investigate molecular markers associated with these cells.
methodsStem cell-related genes were collected, and molecular subtypes were established based on gene expression profiles from The Cancer Genome Atlas using the R package tool "ConsensusClusterPlus." Multi-gene prognostic markers were identified using LASSO regression analysis. Gene set enrichment analysis was employed to gain insights into the potential molecular mechanisms of these identified markers. The robustness of these prognostic markers was analyzed across different cohorts, and their clinical independence was determined through multivariate Cox analysis. A nomogram was constructed to assess the model's clinical applicability. Immunohistochemistry was performed to validate the expression of hub genes.
resultsUtilizing 49 tumor stem cell-related genes associated with prognosis, 362 ovarian cancer samples were divided into two distinct clusters, revealing significant prognostic disparities. A seven-gene signature (GALP, CACNA1C, COL16A1, PENK, C4BPA, PSMA2, and CXCL9), identified through LASSO regression, exhibited stability and robustness across various platforms. Multivariate Cox regression analysis confirmed the signature's independence in predicting survival in patients with ovarian cancer. Furthermore, a nomogram combining the gene signature demonstrated strong predictive abilities. Immunohistochemistry results indicated significantly elevated GALP, CACNA1C, COL16A1, PENK, C4BPA, PSMA2, and CXCL9 expression in cancer tissues.
conclusionThe seven-gene signature holds promise as a valuable tool for decision-making and prognosis prediction in patients with ovarian cancer.
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