Dataset Information

Discovery and Validation of an Epithelial-Mesenchymal Transition-Based Signature in Gastric Cancer by Genomics and Prognosis Analysis

ABSTRACT:

Objective

Epithelial-mesenchymal transition (EMT) exerts a key function in cancer initiation and progression. Herein, we aimed to develop an EMT-based prognostic signature in gastric cancer.

Methods

The gene expression profiles of gastric cancer were obtained from TCGA dataset as a training set and GSE66229 and GSE84437 datasets as validation sets. By LASSO regression and Cox regression analyses, key prognostic EMT-related genes were screened for developing a risk score (RS) model. Potential small molecular compounds were predicted by the CMap database based on the RS model. GSEA was employed to explore signaling pathways associated with the RS. ESTIMATE and seven algorithms (TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCPCOUNTER, XCELL, and EPIC) were applied to assess the RS and immune microenvironment.

Results

This study developed an EMT-related gene signature comprised of SERPINE1, PCOLCE2, MATN3, and DKK1. High-RS patients displayed poorer survival outcomes than those with low RS. ROC curves demonstrated the robustness of the model in predicting the prognosis. After external validation, the RS model was an independent risk factor for gastric cancer. Several compounds were predicted for gastric cancer treatment based on the RS model. ECM receptor interaction, focal adhesion, pathway in cancer, TGF-beta, and WNT pathways were distinctly activated in high-RS samples. Also, high RS was significantly associated with increased stromal and immune scores and increased infiltration of CD4+ T cell, CD8+ T cell, cancer-associated fibroblast, and macrophage in gastric cancer tissues.

Conclusion

Our findings suggested that the EMT-related gene model may robustly predict gastric cancer prognosis, which could improve the efficacy of personalized therapy.

SUBMITTER: Xu H

PROVIDER: S-EPMC8570100 | biostudies-literature |

REPOSITORIES: biostudies-literature

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