Comprehensive Computational Assessment of SNAI1 and SNAI2 in Gastric Cancer: Linking EMT, Tumor Microenvironment, and Survival Outcomes.

ABSTRACT:

Background

Gastric cancer is aggressive with poor prognosis due to high invasion and metastasis rates, a hallmark of cancer. The Snail family (SNAI1 and SNAI2) drives EMT, enabling epithelial cells to gain migratory and invasive traits.

Methods

We used "limma" package to identify genes with differential expression between high and low levels of SNAI1/SNAI2 in TCGA stomach adenocarcinoma dataset, intersecting these with cancer invasion and metastasis genes obtained from 5 databases. Using Cox regression analysis, we developed a risk score model and created a nomogram incorporating clinical data. The model's prognostic accuracy was validated with survival and ROC analyses in both TCGA and GEO datasets. Additionally, we performed WGCNA and constructed a ceRNA network to investi

SUBMITTER: Kalantari-Dehaghi M 

PROVIDER: S-EPMC12214337 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature