Shuan Jin1, Wenhua Zhu2, Jun Li2. 1. Department of Anesthesia, Jinan Central Hospital Affiliated to Shandong University, Jinan, 250013, Shandong Province, China. 2. Department of Gastroenterology, Chinese PLA General Hospital, Beijing, 100853, China.
Abstract
AIM: The purpose of this study was to identify predictive biomarkers used for clinical therapy and prognostic evaluation of high-risk gastrointestinal stromal tumors (GISTs). MATERIALS AND METHODS: In this study, microarray data GSE31802 were used to identify differentially expressed genes (DEGs) between high-risk GISTs and low-risk GISTs. Then, enrichment analysis of DEGs was conducted based on the gene ontology and kyoto encyclopedia of genes and genomes pathway database. In addition, the transcription factors and cancer-related genes in DEGs were screened according to the TRANSFAC, TSGene, and TAG database. Finally, protein-protein interaction (PPI) network was constructed and analyzed to look for critical genes involved in high-risk GISTs. RESULTS: A total of forty DEGs were obtained and these genes were mainly involved in four pathways, including melanogenesis, neuroactive ligand-receptor interaction, malaria, and hematopoietic cell lineage. The enriched biological processes were related to the regulation of insulin secretion, integrin activation, and neuropeptide signaling pathway. Transcription factor analysis of DEGs indicated that POU domain, class 2, associating factor 1 (POU2AF1) was significantly downregulated in high-risk GISTs. By constructing the PPI network of DEGs, ten genes with high degrees formed local networks, such as PNOC, P2RY14, and SELP. CONCLUSIONS: Four genes as POU2AF1, PNOC, P2RY14, and SELP might be used as biomarkers for prognosis of high-risk GISTs.
AIM: The purpose of this study was to identify predictive biomarkers used for clinical therapy and prognostic evaluation of high-risk gastrointestinal stromal tumors (GISTs). MATERIALS AND METHODS: In this study, microarray data GSE31802 were used to identify differentially expressed genes (DEGs) between high-risk GISTs and low-risk GISTs. Then, enrichment analysis of DEGs was conducted based on the gene ontology and kyoto encyclopedia of genes and genomes pathway database. In addition, the transcription factors and cancer-related genes in DEGs were screened according to the TRANSFAC, TSGene, and TAG database. Finally, protein-protein interaction (PPI) network was constructed and analyzed to look for critical genes involved in high-risk GISTs. RESULTS: A total of forty DEGs were obtained and these genes were mainly involved in four pathways, including melanogenesis, neuroactive ligand-receptor interaction, malaria, and hematopoietic cell lineage. The enriched biological processes were related to the regulation of insulin secretion, integrin activation, and neuropeptide signaling pathway. Transcription factor analysis of DEGs indicated that POU domain, class 2, associating factor 1 (POU2AF1) was significantly downregulated in high-risk GISTs. By constructing the PPI network of DEGs, ten genes with high degrees formed local networks, such as PNOC, P2RY14, and SELP. CONCLUSIONS: Four genes as POU2AF1, PNOC, P2RY14, and SELP might be used as biomarkers for prognosis of high-risk GISTs.