ArticleTranslational cancer research2021
A comprehensive bioinformatics analysis to identify a candidate prognostic biomarker for ovarian cancer.
Article in Translational cancer research, 2021. 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.
- Identification of a novel chemotherapy benefit index for patients with advanced ovarian cancer based on Bayesian network analysis.PloS one · 2025Article
- FoxO1 promotes ovarian cancer by increasing transcription and METTL14-mediated mCancer science · 2024Article
- Identification ofTranslational cancer research · 2023Article
- Association ofAging · 2023Article
- Elevated BTG2 improves the radiosensitivity of non-small cell lung cancer (NSCLC) through apoptosis.Thoracic cancer · 2022Article
- SMC4 serves as a potential marker for the diagnosis and prognosis of colon adenocarcinoma.International journal of immunopathology and pharmacologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to investigate prognostic genes in ovarian cancer (OC) and to explore their potential underlying biological mechanisms through a comprehensive bioinformatics analysis.
methodsCommon differentially expressed genes (DEGs) in 3 OC datasets from the Gene Expression Omnibus (GEO) (GSE26712, GSE18520, and GSE14407) were screened out. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed by Metascape. The protein-protein interaction (PPI) network of the DEGs was constructed using the STRING database. The prognostic value of DEGs were determined using the Kaplan-Meier plotter. The ONCOMINE and Human Protein Atlas databases were used to verify the expression levels of prognostic genes in OC. Genomic analysis of prognostic genes were also investigated by cBio Cancer Genomics Portal (cBioPortal) database, UCSC Xena browser and UALCAN. Gene set enrichment analysis (GSEA) was used to predict the possible pathways and biological processes of the prognostic genes.
resultsIntegration of the 3 datasets have found 879 common DEGs. A high expression of structural maintenance of chromosomes protein 4 (
conclusionsHigh
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.