4.6 Article

Immune-related gene signature predicts overall survival of gastric cancer patients with varying microsatellite instability status

Journal

AGING-US
Volume 13, Issue 2, Pages 2418-2435

Publisher

IMPACT JOURNALS LLC

Keywords

gastric cancer; microsatellite instability; immune-related genes; survival analysis; The Cancer Genome Atlas

Funding

  1. Aero Space Central Hospital foundation
  2. National Natural Science Foundation of China [81670474]
  3. Natural Science Foundation of Beijing [7152043]

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The study aimed to construct a risk-stratification model based on immune-related genes for gastric cancer patients with varying microsatellite instability (MSI) status, identifying prognostically significant gene signatures. These findings provide new targets for personalized treatment and immunotherapy in gastric cancer patients, potentially advancing the development of tailored therapies and immunotherapies.
Purpose: Gastric cancer (GC) is one of the most common and fatal malignancies globally. While microsatellite instability (MSI) index has earlier been correlated with survival outcome in gastric cancer patients, the present study aims to construct a risk-stratification model based on immune-related genes in GC patients with varying MSI status. Results: The univariate and multivariate Cox regression analyses identified SEMA7A, NUDT6, SCGB3A1, NPR3, PTH1R, and SHC4 as signature genes, which were used to build the prognostic model for GC patients with microsatellite instability-low (MSI-L) and microsatellite stable (MSS). Whereas, for GC patients with microsatellite instability-high (MSI-H), prognostic model was established with three genes (SEMA6A, LTBP1, and BACH2), based on the univariate and multivariate Cox regression, and Kaplan-Meier survival analyses. Conclusion: The prognostic immune-related gene signature identified in this study may offer new targets for personalized treatment and immunotherapy for GC patients with MSI-H or MSI-L/MSS status. Methods: The Cancer Genome Atlas (TCGA) and ImmPort databases were used to extract expression data and to explore prognostic genes from the immune-related genes (IRGs), respectively. Univariate and multivariate Cox regression analysis were applied to identify IRGs correlated with patient prognosis. The regulatory network between prognostic IRGs and TFs were performed using R software.

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