ArticleAging2020
A nine-gene signature related to tumor microenvironment predicts overall survival with ovarian cancer.
Article in Aging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 55 citations in OpenAlex.
- Pan cohort immune biomarker of CD8 lymphocyte activation enabling HGSOC outcome prediction and treatment response.Discover oncology · 2025Article
- Acetylation model predicts prognosis of patients and affects immune microenvironment infiltration in epithelial ovarian carcinoma.Journal of ovarian research · 2024Article
- Development and validation of an individualized gene expression-based signature to predict overall survival of patients with high-grade serous ovarian carcinoma.European journal of medical research · 2023Article
- Suppressive stroma-immune prognostic signature impedes immunotherapy in ovarian cancer and can be reversed by PDGFRB inhibitors.Journal of translational medicine · 2023Article
- Leveraging various extracellular matrix levels to assess prognosis and sensitivity to immunotherapy in patients with ovarian cancer.Frontiers in oncology · 2023Article
- A risk score system based on a six-microRNA signature predicts the overall survival of patients with ovarian cancer.Journal of ovarian research · 2022Article
- Review
- Construction and validation of a transcription factors-based prognostic signature for ovarian cancer.Journal of ovarian research · 2022Article
- CXCL12, a potential modulator of tumor immune microenvironment (TIME) of bladder cancer: From a comprehensive analysis of TCGA database.Frontiers in oncology · 2022Article
- Establishment, immunological analysis, and drug prediction of a prognostic signature of ovarian cancer related to histone acetylation.Frontiers in pharmacology · 2022Article
- A Six-Gene Risk Model Based on the Immune Score Reveals Prognosis in Intermediate-Risk Acute Myeloid Leukemia.BioMed research international · 2022Article
- Identification of a glycolysis-related gene signature for survival prediction of ovarian cancer patients.Cancer medicine · 2021Article
- CircRNAs as potent biomarkers in ovarian cancer: a systematic scoping review.Cellular & molecular biology letters · 2021Article
- Identification of tumor microenvironment-related prognostic genes in colorectal cancer based on bioinformatic methods.Scientific reports · 2021Article
- Prognostic value of tumour microenvironment-related genes by TCGA database in rectal cancer.Journal of cellular and molecular medicine · 2021Article
- Article
- Establishment of a Prognostic Signature of Stromal/Immune-Related Genes for Gastric Adenocarcinoma Based on ESTIMATE Algorithm.Frontiers in cell and developmental biology · 2021Article
- Construction of an Immune Cell Infiltration Score to Evaluate the Prognosis and Therapeutic Efficacy of Ovarian Cancer Patients.Frontiers in immunology · 2021Article
- Identification of a tumor microenvironment-associated prognostic gene signature in bladder cancer by integrated bioinformatic analysis.International journal of clinical and experimental pathology · 2021Article
- Construction of Prognostic Risk Prediction Model of Oral Squamous Cell Carcinoma Based on Nine Survival-Associated Metabolic Genes.Frontiers in physiology · 2021Article
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mounting evidence suggests that immune cell infiltration within the tumor microenvironment (TME) is a crucial regulator of carcinogenesis and therapeutic efficacy in ovarian cancer (OC). In this study, 593 OC patients from TCGA were divided into high and low score groups based on their immune/stromal scores resulting from analysis utilizing the ESTIMATE algorithm. Differential expression analysis revealed 294 intersecting genes that influencing both the immune and stromal scores. Further Cox regression analysis identified 34 differentially expressed genes (DEGs) as prognostic-related genes. Finally, the nine-gene signature was derived from the prognostic-related genes using a Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression. This nine-gene signature could effectively distinguish the high-risk patients in the training (TCGA database) and validation (GSE17260) cohorts (all p < 0.01). A time-dependent receiver operating characteristic (ROC) analysis showed that the nine-gene signature had a reasonable predictive accuracy (AUC = 0.707, AUC =0.696) in both cohorts. In addition, this nine-gene signature is associated with immune infiltration in TME by Gene Set Variation Analysis (GSVA), and can be used to predict the survival of patients with OC.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.