ArticleBioscience reports2020
Construction of a novel prognostic-predicting model correlated to ovarian cancer.
Article in Bioscience reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Single-Cell-Derived Malignant Epithelial Programs Define Prognostic Risk and Therapeutic Vulnerability in Ovarian Cancer.Journal of Cancer · 2026Article
- Article
- Development and clinical validation of a seven-gene signature based on tumor stem cell-related genes to predict ovarian cancer prognosis.Journal of ovarian research · 2024Article
- Combination of Immune-Related Genomic Alterations Reveals Immune Characterization and Prediction of Different Prognostic Risks in Ovarian Cancer.Frontiers in cell and developmental biology · 2021Article
- Potential Prognostic Immune Biomarkers of Overall Survival in Ovarian Cancer Through Comprehensive Bioinformatics Analysis: A Novel Artificial Intelligence Survival Prediction System.Frontiers in medicine · 2021Article
- Clinical Significance and Potential Role ofFrontiers in genetics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOvarian cancer (OC) is one of the most lethal gynecological cancers worldwide. The pathogenesis of the disease and outcomes prediction of OC patients remain largely unclear. The present study aimed to explore the key genes and biological pathways in ovarian carcinoma development, as well as construct a prognostic model to predict patients' overall survival (OS).
resultsWe identified 164 up-regulated and 80 down-regulated differentially expressed genes (DEGs) associated with OC. Gene Ontology (GO) term enrichment showed DEGs mainly correlated with spindle microtubes. For Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, cell cycle was mostly enriched for the DEGs. The protein-protein interaction (PPI) network yielded 238 nodes and 1284 edges. Top three modules and ten hub genes were further filtered and analyzed. Three candidiate drugs targeting for therapy were also selected. Thirteen OS-related genes were selected and an eight-mRNA model was present to stratify patients into high- and low-risk groups with significantly different survival.
conclusionsThe identified DEGs and biological pathways may provide new perspective on the pathogenesis and treatments of OC. The identified eight-mRNA signature has significant clinical implication for outcome prediction and tailored therapy guidance for OC patients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.