ArticleJournal of gastrointestinal oncology2022
Identification of a chemotherapy-associated gene signature for a risk model of prognosis in gastric adenocarcinoma through bioinformatics analysis.
Article in Journal of gastrointestinal oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 10 citations in OpenAlex.
- A DIA-MS-based proteomics approach to find potential serum prognostic biomarkers in glioblastoma patients.Molecular oncology · 2025Article
- The prognostic impact of body mass index on patients with gastric adenocarcinoma and mucinous adenocarcinoma: a retrospective cohort study.Journal of gastrointestinal oncology · 2025Article
- [High expression of CRTAC1 promotes proliferation, migration and immune cell infiltration of gastric cancer by regulating the PI3K/AKT signaling pathway].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- CRTAC1 identified as a promising diagnosis and prognostic biomarker in lung adenocarcinoma.Scientific reports · 2024Article
- Article
- A prognostic signature for lung adenocarcinoma by five genes associated with chemotherapy in lung adenocarcinoma.The clinical respiratory journal · 2023Article
- Evaluation of vital genes correlated with CD8 + T cell infiltration as prognostic biomarkers in stomach adenocarcinoma.BMC gastroenterology · 2023Article
- Prostate transmembrane androgen inducible protein 1Frontiers in oncology · 2023Review
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Over the past few years, the overall survival rate of patients with gastric adenocarcinoma who have received different chemotherapy regimens has increased. However, not all gastric cancer patients who receive chemotherapy have a longer survival. We need better predictive biomarkers. This study is to construct a new risk model of chemotherapy-associated genes in gastric adenocarcinoma (GA) for prognostication. Methods: RNA-seq data and clinical information of GSE26901 (containing 44 chemotherapy samples and 65 patients without chemotherapy) in Gene Expression Omnibus (GEO) and stomach adenocarcinoma (STAD, containing 360 cancer tissue samples and 50 paired normal tissue samples) in The Cancer Genome Atlas (TCGA) were selected for screening differentially expressed genes (DEGs). Multivariate Cox regression was conducted to screen prognosis-associated genes and its link to patients' prognosis were screened by least absolute shrinkage and selection operator (LASSO) regression analysis. Based on the key genes, a risk scoring equation for the prognosis model was established, and constructed survival prognosis model. The model was tested for predictive ability through training set (TCGA datasets) and validation set (GSE84437). The correlations of the risk score with clinical pathological features, immune score and drug sensitivity score were evaluated. Results: In total, 179 overlapping genes were obtained by screening DEGs. Univariate Cox analysis revealed 36 prognosis-related genes, and LASSO regression analysis revealed 8 key genes ( Conclusions: According to the results of our training set and validation set, the risk model based on the eight chemotherapy-related gene signatures predicting prognosis has certain predictive accuracy in predicting the survival of GA patients which can be a promising prognostic parameter for GA. However, its efficacy remains to be proved in clinical practice.
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