ArticleCancer cell international2020
Establishment of a 5-gene risk model related to regulatory T cells for predicting gastric cancer prognosis.
Article in Cancer cell international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Use of machine learning-based integration to develop an immune-related signature for improving prognosis in patients with gastric cancer.Scientific reports · 2023Trial
- STLBRF: an improved random forest algorithm based on standardized-threshold for feature screening of gene expression data.Briefings in functional genomics · 2025Article
- MUC15 acts as a tumor suppressor gene which correlates with prognosis and immune infiltration in esophageal squamous cell carcinoma.International journal of medical sciences · 2025Article
- Low Expression MCEMP1 Promotes Lung Adenocarcinoma Progression and its Clinical Value.Current cancer drug targets · 2025Article
- Analysis of Zinc and Stromal Immunity in Disuse Osteoporosis: Mendelian Randomization and Transcriptomic Analysis.Orthopaedic surgery · 2023Article
- Six potential biomarkers in septic shock: a deep bioinformatics and prospective observational study.Frontiers in immunology · 2023Observational
- The Expression, Prognostic Value, and Immunological Correlation of MCEMP1 and its Potential Role in Gastric Cancer.Journal of oncology · 2022Article
- Article
- Early B-cell factors involve in the tumorigenesis and predict the overall survival of gastric cancer.Bioscience reports · 2021Article
- ADAMTS-12: Functions and Challenges for a Complex Metalloprotease.Frontiers in molecular biosciences · 2021Review
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Abstract
backgroundGastric cancer (GC) is one of the high-risk cancers that lacks effective methods for prognosis prediction. Therefore, we searched for immune cells related to the prognosis of GC and studied the role of related genes in GC prognosis.
methodsIn this study, we collected the mRNA data of GC from The Cancer Genome Atlas (TCGA) database and studied the immune cells that were closely related to the prognosis of GC. Spearman correlation analysis was performed to show the association between immune cell-related genes and the differentially expressed genes (DEGs) of GC. Univariate and multivariate Cox regression analyses were conducted on the immune cell-related genes with a high correlation with GC. A prognostic risk score model was constructed and the most significant feature genes were identified. Kaplan-Meier method was then used to compare the overall survival (OS) of patients with high-risk and low-risk, and receiver operating characteristic (ROC) analysis was used to assess the accuracy of the risk model. In addition, GC patients were grouped according to the median expression of the features genes, and survival analysis was further carried out.
resultsIt was noted that regulatory T cells (Tregs) were significantly correlated with the prognosis of GC, and 172 genes related to Tregs were found to be closely associated with GC. An optimal prognostic risk model was constructed, and a 5-gene (including LRFN4, ADAMTS12, MCEMP1, HP and MUC15) signature-based risk score was established. Survival analysis showed significant difference in OS between low-risk and high-risk samples. ROC analysis results indicated that the risk model had a high accuracy for the prognosis prediction of samples (AUC = 0.717). The results of survival analysis on each feature gene based on expression levels were consistent with the results of multivariate Cox analysis for predicting the risk rate of the 5 genes.
conclusionThese results proved that the 5-gene signature-based risk score could be used to predict the survival of GC patients, and these 5 genes were closely related to Tregs. These findings are of great significance for studying the role of immune cells and related immune factors in regulating the prognosis of GC.
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