SynthesisJournal of cellular and molecular medicine2018
Prognostic value of gastric cancer-associated gene signatures: Evidence based on a meta-analysis using integrated bioinformatics methods.
Synthesis in Journal of cellular and molecular medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic Review of Prognostic Gene Signature in Gastric Cancer Patients.Frontiers in bioengineering and biotechnology · 2020Pooled it
- Prognostic value of gastric cancer-associated gene signatures: Evidence based on a meta-analysis using integrated bioinformatics methods.Journal of cellular and molecular medicine · 2018Pooled it
- Article
- Identification of differentially expressed mRNAs as novel predictive biomarkers for gastric cancer diagnosis and prognosis.World journal of gastrointestinal oncology · 2024Article
- A prognostic gene signature for gastric cancer and the immune infiltration-associated mechanism underlying the signature gene, PLG.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2023Article
- Article
- Article
- Meta-Analysis of Microdissected Breast Tumors Reveals Genes Regulated in the Stroma but Hidden in Bulk Analysis.Cancers · 2021Article
- Gastric cancer-associated microRNA expression signatures: integrated bioinformatics analysis, validation, and clinical significance.Annals of translational medicine · 2021Article
- A practical method to screen and identify functioning biomarkers in nasopharyngeal carcinoma.Scientific reports · 2021Article
- Identification of Potential Biomarkers Associated with Prognosis in Gastric Cancer via Bioinformatics Analysis.Medical science monitor : international medical journal of experimental and clinical research · 2021Article
- The prediction of survival in Gastric Cancer based on a Robust 13-Gene Signature.Journal of Cancer · 2021Article
- Identifying Diagnostic and Prognostic Biomarkers and Candidate Therapeutic Drugs of Gastric Cancer Based on Transcriptomics and Single-Cell Sequencing.Pathology oncology research : POR · 2021Article
- Identification of three predictors of gastric cancer progression and prognosis.FEBS open bio · 2020Article
- Identification of New Therapeutic Targets for Gastric Cancer With Bioinformatics.Frontiers in genetics · 2020Article
- Reciprocal Interplay Between Fibrillar Collagens and Collagen-Binding Integrins: Implications in Cancer Progression and Metastasis.Frontiers in oncology · 2020Review
- Application value of nomogram and prognostic factors of gastric cancer patients who underwent D2 radical lymphadenectomy.BMC gastroenterology · 2019Article
- Identification and validation of four hub genes involved in the plaque deterioration of atherosclerosis.Aging · 2019Article
- Identification of Key Genes and Circular RNAs in Human Gastric Cancer.Medical science monitor : international medical journal of experimental and clinical research · 2019Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Selecting differentially expressed genes (DEGs) based on integrated bioinformatics analyses has been used in previous studies to explore potential biomarkers in gastric cancer (GC) with microarray and RNA sequencing data. However, the genes obtained may be inaccurate because of noisy data and errors, as well as insufficient clinical sample sizes. Thus, we aimed to find robust and strong DEGs with prognostic value for GC, where the robust rank aggregation method was employed to select significant DEGs from eight Gene Expression Omnibus data sets with a total of 140 up-regulated and 206 down-regulated genes. Network data mining was then used to screen hub genes, and 11 genes were filtered using Fisher's exact test. Based on these results, we built a prognostic signature with seven genes (FBN1, MMP1, PLAU, SPARC, COL1A2, COL2A1 and ATP4A) using stepwise multivariate Cox proportional hazard regression. According to the risk score for each patient, we found that high-risk group patients had significantly worse survival results compared with those in the low-risk group (log-rank test P-value < 0.001). This seven-gene signature was then validated with an external data set. Thus, we established a signature based on seven DEGs with prognostic value for GC patients using multi-steps bioinformatics methods, which may provide novel insights and potential biomarkers for prognosis, as well as possibly serving as new therapeutic targets in clinical applications.
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