A novel prognostic model based on epithelial-mesenchymal transition-related genes predicts patient survival in gastric cancer

Background Gastric cancer (GC) represents a major malignancy and is the third deathliest cancer globally. Several lines of evidence indicate that the epithelial-mesenchymal transition (EMT) has a critical function in the development of gastric cancer. Although plentiful molecular biomarkers have bee...

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Published inWorld journal of surgical oncology Vol. 19; no. 1; pp. 1 - 216
Main Authors Song, Wanting, Bai, Yi, Zhu, Jialin, Zeng, Fanxin, Yang, Chunmeng, Hu, Beibei, Sun, Mingjun, Li, Chenyan, Peng, Shiqiao, Chen, Moye, Sun, Xuren
Format Journal Article
LanguageEnglish
Published London BioMed Central Ltd 19.07.2021
BioMed Central
BMC
Subjects
Age
Analysis
Biomarkers
Cancer
Cancer patients
Cancer therapies
Care and treatment
Diagnosis
Epithelial-mesenchymal transition
Gastric cancer
Gene
Gene expression
Genes
Genetic aspects
Genomes
Malignancy
Medical prognosis
Mesenchyme
Mortality
Patients
Performance prediction
Prognosis
Regression analysis
Risk
Risk groups
Software
Stomach cancer
Survival
Survival analysis
Taiwan
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Summary:Background Gastric cancer (GC) represents a major malignancy and is the third deathliest cancer globally. Several lines of evidence indicate that the epithelial-mesenchymal transition (EMT) has a critical function in the development of gastric cancer. Although plentiful molecular biomarkers have been identified, a precise risk model is still necessary to help doctors determine patient prognosis in GC. Methods Gene expression data and clinical information for GC were acquired from The Cancer Genome Atlas (TCGA) database and 200 EMT-related genes (ERGs) from the Molecular Signatures Database (MSigDB). Then, ERGs correlated with patient prognosis in GC were assessed by univariable and multivariable Cox regression analyses. Next, a risk score formula was established for evaluating patient outcome in GC and validated by survival and ROC curves. In addition, Kaplan-Meier curves were generated to assess the associations of the clinicopathological data with prognosis. And a cohort from the Gene Expression Omnibus (GEO) database was used for validation. Results Six EMT-related genes, including CDH6, COL5A2, ITGAV, MATN3, PLOD2, and POSTN, were identified. Based on the risk model, GC patients were assigned to the high- and low-risk groups. The results revealed that the model had good performance in predicting patient prognosis in GC. Conclusions We constructed a prognosis risk model for GC. Then, we verified the performance of the model, which may help doctors predict patient prognosis. Keywords: Gastric cancer, Epithelial-mesenchymal transition, Gene, Survival
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ISSN:1477-7819
1477-7819
DOI:10.1186/s12957-021-02329-9