ArticleScientific reports2024
Construction of an immune-related risk score signature for gastric cancer based on multi-omics data.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Pesticidal efficacy of some innovative phenyl benzoate-based heterocycles againstRSC advances · 2025Article
- Placental DNA methylation key topics: sex- and cell-type specificity, mediation, multi-omics, and biomarker discovery.Epigenomics · 2025Review
- Correlation between dyslipidaemia and gastric cancer: pathogenesis to prevention and treatment strategies.Lipids in health and disease · 2025Review
- Estimating progression of Alzheimer's disease with extracellular vesicle-related multi-omics risk models.Frontiers in aging neuroscience · 2025Article
- Natural Compounds for Preventing Age-Related Diseases and Cancers.International journal of molecular sciences · 2024Review
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6 authors.
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Abstract
Early identification of gastric cancer (GC) is associated with a superior survival rate compared to advanced GC. However, the poor specificity and sensitivity of traditional biomarkers suggest the importance of identifying more effective biomarkers. This study aimed to identify novel biomarkers for the prognosis of GC and construct a risk score (RS) signature based on these biomarkers, with to validation of its predictive performance. We used multi-omics data from The Cancer Genome Atlas to analyze the significance of differences in each omics data and combined the data using Fisher's method. Hub genes were subsequently subjected to univariate Cox and LASSO regression analyses and used to construct the RS signature. The RS of each patient was calculated, and the patients were divided into two subgroups according to the RS. The RS signature was validated in two independent datasets from the Gene Expression Omnibus and subsequent analyses were subsequently conducted. Five immune-related genes strongly linked to the prognosis of GC patients were obtained, namely CGB5, SLC10A2, THPO, PDGFRB, and APOD. The results revealed significant differences in overall survival between the two subgroups (p < 0.001) and indicated the high accuracy of the RS signature. When validated in two independent datasets, the results were consistent with those in the training dataset (p = 0.003 and p = 0.001). Subsequent analyses revealed that the RS signature is independent and has broad applicability among various GC subtypes. In conclusion, we used multi-omics data to obtain five immune-related genes comprising the RS signature, which can independently and effectively predict the prognosis of GC patients with high accuracy.
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