1. A prognostic model based on regulatory T‐cell‐related genes in gastric cancer: Systematic construction and validation.
- Author
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Tong, Qin and Ling, Yingjie
- Subjects
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REGULATOR genes , *CANCER genes , *PROGNOSTIC models , *STOMACH cancer , *REGULATORY T cells , *T cells , *GENE regulatory networks - Abstract
Human gastrointestinal tumours have been shown to contain massive numbers of tumour infiltrating regulatory T cells (Tregs), the presence of which are closely related to tumour immunity. This study was designed to develop new Treg‐related prognostic biomarkers to monitor the prognosis of patients with gastric cancer (GC). Treg‐related prognostic genes were screened from Treg‐related differentially expressed genes in GC patients by using Cox regression analysis, based on which a prognostic model was constructed. Then, combined with RiskScore, survival curve, survival status assessment and ROC analysis, these genes were used to verify the accuracy of the model, whose independent prognostic ability was also evaluated. Six Treg‐related prognostic genes (CHRDL1, APOC3, NPTX1, TREML4, MCEMP1, GH2) in GC were identified, and a 6‐gene Treg‐related prognostic model was constructed. Survival analysis revealed that patients had a higher survival rate in the low‐risk group. Combining clinicopathological features, we performed univariate and multivariate regression analyses, with results establishing that the RiskScore was an independent prognostic factor. Predicted 1‐, 3‐ and 5‐year survival rates of GC patients had a good fit with the actual survival rates according to nomogram results. In addition patients in the low‐risk group had higher tumour mutational burden (TMB) values. Gene Set Enrichment Analysis (GSEA) demonstrated that genes in the high‐risk group were significantly enriched in pathways related to immune inflammation, tumour proliferation and migration. In general, we constructed a 6‐gene Treg‐associated GC prognostic model with good prediction accuracy, where RiskScore could act as an independent prognostic factor. This model is expected to provide a reference for clinicians to estimate the prognosis of GC patients. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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