AUTHOR=Guo Haonan , Tang Hui , Zhao Yang , Zhao Qianwen , Hou Xianliang , Ren Lei 

TITLE=Molecular Typing of Gastric Cancer Based on Invasion-Related Genes and Prognosis-Related Features

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.848163

DOI=10.3389/fonc.2022.848163

ISSN=2234-943X

ABSTRACT=Background: In the present study, we aimed to construct a prognostic stratification system for gastric cancer (GC) by using tumor invasion-related genes to more accurately predict clinical prognosis of GC.
Methodology: Tumor invasion-related genes were downloaded from CancerSEA, and their expression data in the TCGA-STAD dataset were used to cluster STAD samples via non-negative matrix factorization (NMF). Differentially expressed genes (DEGs) between subtypes were identified using the limma package. KEGG pathway analysis and GO functional enrichment analysis were conducted using the WebGestaltR package (v0.4.2). The ImmuneScore between molecular subtypes were evaluated using the R package ESTIMATE, MCPcounter, and the ssGSEA methods of the GSCA package, respectively. Univariate, multivariate, and lasso regression analyses of DEGs were performed using the R package survival coxph function and the glmnet package, which was used to construct the RiskScores. Model robustness was validated using internal and external datasets. The nomogram was constructed based on the RiskScore.
Results: Based on 97 tumor invasion-related genes, 353 GC samples from TCGA were categorized into two subtypes, thereby indicating the presence of inter-subtype differences in prognosis. A total of 569 DEGs existed between subtypes, and four genes were selected to construct the risk model. This four-gene signature had the robustness and stable predictive performance in different platform datasets (GSE26942 and GSE66229); thus, showing that the study’s model performed better than other existing models.
Conclusion: A four-gene signature prognostic stratification system was developed with a desirable area under the curve in the training and independent validation sets. Therefore, the use of this classifier as a molecular diagnostic test to assess the prognostic risk of GC patients is recommended.