ArticleWorld journal of gastrointestinal oncology2020
Identification of a nine-gene prognostic signature for gastric carcinoma using integrated bioinformatics analyses.
Article in World journal of gastrointestinal oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 17 citations in OpenAlex.
- Identification of candidate biomarkers correlated with the pathogenesis of breast cancer patients.Scientific reports · 2025Article
- Progress of arylacetamide deacetylase research in metabolic diseases.Frontiers in oncology · 2025Review
- Glycolysis-related five-gene signature correlates with prognosis and immune infiltration in gastric cancer.World journal of gastrointestinal oncology · 2024Article
- Article
- Prediction of recurrence of early gastric carcinoma after endoscopic submucosal dissection.African health sciences · 2023Article
- Impact of AADAC gene expression on prognosis in patients with Borrmann type III advanced gastric cancer.BMC cancer · 2022Article
- Construction and validation of a novel coagulation-related 7-gene prognostic signature for gastric cancer.Frontiers in genetics · 2022Article
- Evaluation of gene expression of PLEKHS1, AADAC, and CDKN3 as novel genomic markers in gastric carcinoma.PloS one · 2022Article
- Identification and Verification of Potential Biomarkers in Gastric Cancer By Integrated Bioinformatic Analysis.Frontiers in genetics · 2022Article
- A Prognostic Signature Constructed byFrontiers in genetics · 2021Article
- Identification of Core Prognosis-Related Candidate Genes in Chinese Gastric Cancer Population Based on Integrated Bioinformatics.BioMed research international · 2020Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGastric carcinoma (GC) is one of the most aggressive primary digestive cancers. It has unsatisfactory therapeutic outcomes and is difficult to diagnose early.
aimTo identify prognostic biomarkers for GC patients using comprehensive bioinformatics analyses.
methodsDifferentially expressed genes (DEGs) were screened using gene expression data from The Cancer Genome Atlas and Gene Expression Omnibus databases for GC. Overlapping DEGs were analyzed using univariate and multivariate Cox regression analyses. A risk score model was then constructed and its prognostic value was validated utilizing an independent Gene Expression Omnibus dataset (GSE15459). Multiple databases were used to analyze each gene in the risk score model. High-risk score-associated pathways and therapeutic small molecule drugs were analyzed and predicted, respectively.
resultsA total of 95 overlapping DEGs were found and a nine-gene signature (
conclusionThe nine-gene signature-derived risk score allows to predict GC prognosis and might prove useful for guiding therapeutic strategies for GC patients.
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