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

被引:0
|
作者
Tian, Ruyue [1 ,2 ]
Hu, Jiexuan [1 ]
Ma, Xiao [1 ]
Liang, Lei [2 ]
Guo, Shuilong [3 ]
机构
[1] Capital Med Univ, Beijing Friendship Hosp, Dept Oncol, Beijing 100050, Peoples R China
[2] Aero Space Cent Hosp, Dept Ultrasound, Beijing 100050, Peoples R China
[3] Capital Med Univ, Beijing Friendship Hosp, Natl Clin Res Ctr Digest Dis, Beijing Digest Dis Ctr,Dept Gastroenterol,Beijing, Beijing 100050, Peoples R China
来源
AGING-US | 2021年 / 13卷 / 02期
基金
中国国家自然科学基金;
关键词
gastric cancer; microsatellite instability; immune-related genes; survival analysis; The Cancer Genome Atlas; MISMATCH REPAIR DEFICIENCY; HORMONE-RELATED PEPTIDE; PROGNOSTIC-SIGNIFICANCE; PARATHYROID-HORMONE; BACH2; EXPRESSION; STAGE-II; CHEMOTHERAPY; STATISTICS; SUBTYPES; TUMORS;
D O I
暂无
中图分类号
Q2 [细胞生物学];
学科分类号
071009 ; 090102 ;
摘要
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.
引用
收藏
页码:2418 / 2435
页数:18
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