Identification of a Novel Prognostic Signature for Gastric Cancer Based on Multiple Level Integration and Global Network Optimization - Cui, Lin; Wang, Ping; Ning, Dandan; Shao, Jing; Tan, Guiyuan; Li, Dajian; Zhong, Xiaoling; Mi, Wanqi; Zhang, Chunlong; Jin, Shizhu (Frontiers in Cell and Developmental Biology, 2021)
Gastric Cancer (GC) is a common cancer worldwide with a high morbidity and mortality rate in Asia. Many prognostic signatures from genes and ncRNA levels have been identified by high-throughput expression profiling for GC. However, the integrated optimization analysis based on GC global lncRNA-miRNA-mRNA network to study the prognostic mechanism has not been reported. In the present work, a Gastric Cancer specific lncRNA-miRNA-mRNA regulatory network (GCsLMM) was first constructed based on the ceRNA hypothesis, by combining miRNA-target interactions and expression data set of GC. To mine novel prognostic signatures associated with GC, we performed topological analysis, a random walk with restart algorithm, in the GCsLMM from three levels, miRNA-, mRNA- and lncRNA-levels. And we further obtained candidate prognostic signatures by calculating integrated score, and analyzed the robust of these signatures by combination strategy. In the meanwhile, the biological roles of key candidate signatures were explored. Finally, we targeted a gene, PHF10 and analyzed the expression patterns of PHF10 in independent datasets. The findings of this study will improve our understanding of the competing endogenous RNA (ceRNA) regulatory mechanisms and further facilitate the discovery of novel prognostic biomarkers for GC clinical guideline.