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.

Identifying Differential Expression Genes and Prognostic Signature Based on Subventricular Zone Involved Glioblastoma

“…Among them, PCOLCE2, ERP27, PSMD13, C14orf39, C16orf86, UGCG, and HPX were identified as prognostic factors for glioma for the first time. PCOLCE2 has been identified as a biomarker in multiple cancers, including colorectal and gastric cancers (L. Xu et al, 2021). ERP27 is highly expressed in colorectal cancers, and its expression is correlated with patient survival (Yu et al, 2021).…”

Cancer-Associated Fibroblasts: Mechanisms of Tumor Progression and Novel Therapeutic Targets

“…SERPINE1 is a member of the validated five-member EMT- or plasticity-related prognostic gene set in gastric cancer and the six-gene signature that accurately predicts reduced overall survival in patients with HNSCC, where it partitions to the aggressive tumor signature subset, indicative of poor prognosis and higher risk score. SERPINE1 is generally classified as a hub or core gene in a wide spectrum of cancer types [ 50 , 118 , 141 , 146 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , 177 , 178 , 179 , 180 , 181 , 182 , 183 ]. For many of the poor outcome cancers, identification of SERPINE1 in the complement of hub or signature genes is a strong indicator of reduced patient survival [ 141 , 184 , 185 , 186 , 187 , 188 , 189 ].…”

Identification of candidate biomarkers associated with gastric cancer prognosis based on an integrated bioinformatics analysis

“…Functional analyses have revealed that most DEGs are predominantly involved in the ECM-receptor interaction pathway (20,21). Notably, Liu et al previously performed a graph-based clustering analysis and found that the ECMreceptor interaction pathway was correlated with the underlying molecular mechanisms of GC development (22).…”