Integrated profiling identifies SLC5A6 and MFAP2 as novel diagnostic and prognostic biomarkers in gastric cancer patients

作者：Tao Sun, Danhua Wang, Ping Ying, Yiwen Sang, Yibei Dai, Yiyun Wang, Zhenping Liu, Xiuzhi Duan, Zhihua Tao, Weiwei Liu · 发表于：International Journal of Oncology · 年份：2019 · DOI：10.3892/ijo.2019.4944 · 被引用次数：23 · 研究领域：RNA modifications and cancer、Ferroptosis and cancer prognosis、Cancer-related molecular mechanisms research

Gastric cancer (GC) is one of the leading causes of malignancy‑associated mortality worldwide. However, the underlying molecular mechanisms of GC are unclear and the prognosis of GC is poor. Therefore, it is important and urgent to explore the underlying mechanisms and screen for novel diagnostic and prognostic biomarkers, as well as therapeutic targets. In the current study, scale‑free gene co‑expression networks were constructed using weighted gene co‑expression network analysis, the potential associations between gene sets and clinical features were investigated, and the hub genes were identified. The gene expression profiles of GSE38749 were downloaded from the Gene Expression Omnibus database. RNA‑seq and clinical data for GC from The Cancer Genome Atlas were utilized for verification. Furthermore, the expression of candidate biomarkers in gastric tissues was investigated. Survival analysis was performed using Kaplan‑Meier and log‑rank test. The predictive role of candidate biomarkers in GC was evaluated using a receiver operator characteristic (ROC) curve. Gene Ontology, gene set enrichment analysis and gene set variation analysis methods were used to interpret the function of candidate biomarkers in GC. A total of 29 modules were identified via the average linkage hierarchical clustering. A significant module consisting of 48 genes associated with clinical traits was found; three genes with high connectivity in the clinical significant module were identified as hub gen...