Development and validation of a prognostic and drug sensitivity model for gastric cancer utilizing telomere-related genes.

Abstract

Background: Gastric cancer (GC) poses a major global health challenge because of its unfavorable prognosis. Elevated telomerase activity has been linked to the rapid growth and invasiveness of GC tumors. Investigating the expression profiles of telomerase could improve our understanding of the mechanisms underlying telomere-related GC advancement and its applicability as potential targets for diverse therapeutic strategies for GC.

Methods: The TCGA and GEO databases were utilized to access transcriptome and clinical data related to GC. After assessing differentially expressed genes (DEGs), a prognostic risk model was developed through Cox univariate regression, LASSO-Cox regression. The prognostic risk model was validated using data from the GSE62254 cohort. The significant influence of the risk model on the tumor immune microenvironment (TIME) and its sensitivity to various drugs was assessed.

Results: Differential expression analysis identified 328 significantly telomere-related DEGs in GC, with 35 of them showing a significant association with GC prognosis. A predictive risk model composed of four telomere-related genes (TRGs) was established, enabling the accurate stratification of GC patients into two distinct prognostic groups. The LASSO risk model demonstrated notable variations in immune-cell infiltration and drug sensitivity patterns between high- and low-risk groups.

Conclusions: The study establishes suggestive relationships between four TRGs (LRRN1, SNCG, GAMT, and PDE1B) and the prognosis of GC. The comprehensive characterization of the TRG model reveals their possible roles in the prognosis, TIME, and drug sensitivity in GC.