OBJECTIVE: Liver cancer (LC) is a frequently occurring lethal malignancy worldwide, yet the molecular mechanisms of carcinogenesis and their development remain uncharacterized. In this study, bioinformatics methods were used to find candidate hub genes for prognosis assessment and clinical treatment of LC. METHODS: Differential analysis was carried out based on the evidence of gene expression profiling in LC on The Cancer Genome Atlas (TCGA). The differentially expressed genes (DEGs) were constructed into co-expression networks and divided into modules by virtue of weighted gene co-expression network analysis (WGCNA). Based on the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), the module genes were subjected to functional enrichment analysis. The LC microarray (GSE105130) in the Gene Expression Omnibus was selected to verify the hub genes' expression profiles. The validity of the hub genes was verified via survival analysis, as well as expression correlation with the clinicopathological features. Thereafter, gene set variation analysis (GSVA) and single-sample gene set enrichment analysis (GSEA) were applied to investigate the possible biological functions of the hub genes. RESULTS: In total, 3780 DEGs and 17 co-expression modules were obtained. The blue module had the strongest correlation with the tumour stage and the module genes were principally enriched in tumour-associated GO terms, as well as pathways such as Ras protein signal transduction, ERK1/2 cascade, Ras signal pathway, and ECM-receptor interaction. RASAL1, which is highly expressed in LC, was identified as a hub gene for LC progression. Its high expression suggested unfavorable patient prognosis and was correlated with T stage, gender and tumour stage. Further analysis identified that the overexpression of RASAL1 was substantially enriched in cancer-associated gene sets. CONCLUSION: RASAL1 is a hub gene that influences LC progression, constituting a novel biomarker and molecular target in the future diagnosis and therapy of LC. AJTR
OBJECTIVE: Liver cancer (LC) is a frequently occurring lethal malignancy worldwide, yet the molecular mechanisms of carcinogenesis and their development remain uncharacterized. In this study, bioinformatics methods were used to find candidate hub genes for prognosis assessment and clinical treatment of LC. METHODS: Differential analysis was carried out based on the evidence of gene expression profiling in LC on The Cancer Genome Atlas (TCGA). The differentially expressed genes (DEGs) were constructed into co-expression networks and divided into modules by virtue of weighted gene co-expression network analysis (WGCNA). Based on the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), the module genes were subjected to functional enrichment analysis. The LC microarray (GSE105130) in the Gene Expression Omnibus was selected to verify the hub genes' expression profiles. The validity of the hub genes was verified via survival analysis, as well as expression correlation with the clinicopathological features. Thereafter, gene set variation analysis (GSVA) and single-sample gene set enrichment analysis (GSEA) were applied to investigate the possible biological functions of the hub genes. RESULTS: In total, 3780 DEGs and 17 co-expression modules were obtained. The blue module had the strongest correlation with the tumour stage and the module genes were principally enriched in tumour-associated GO terms, as well as pathways such as Ras protein signal transduction, ERK1/2 cascade, Ras signal pathway, and ECM-receptor interaction. RASAL1, which is highly expressed in LC, was identified as a hub gene for LC progression. Its high expression suggested unfavorable patient prognosis and was correlated with T stage, gender and tumour stage. Further analysis identified that the overexpression of RASAL1 was substantially enriched in cancer-associated gene sets. CONCLUSION: RASAL1 is a hub gene that influences LC progression, constituting a novel biomarker and molecular target in the future diagnosis and therapy of LC. AJTR
Authors: Francisco Sanchez-Vega; Marco Mina; Joshua Armenia; Walid K Chatila; Augustin Luna; Konnor C La; Sofia Dimitriadoy; David L Liu; Havish S Kantheti; Sadegh Saghafinia; Debyani Chakravarty; Foysal Daian; Qingsong Gao; Matthew H Bailey; Wen-Wei Liang; Steven M Foltz; Ilya Shmulevich; Li Ding; Zachary Heins; Angelica Ochoa; Benjamin Gross; Jianjiong Gao; Hongxin Zhang; Ritika Kundra; Cyriac Kandoth; Istemi Bahceci; Leonard Dervishi; Ugur Dogrusoz; Wanding Zhou; Hui Shen; Peter W Laird; Gregory P Way; Casey S Greene; Han Liang; Yonghong Xiao; Chen Wang; Antonio Iavarone; Alice H Berger; Trever G Bivona; Alexander J Lazar; Gary D Hammer; Thomas Giordano; Lawrence N Kwong; Grant McArthur; Chenfei Huang; Aaron D Tward; Mitchell J Frederick; Frank McCormick; Matthew Meyerson; Eliezer M Van Allen; Andrew D Cherniack; Giovanni Ciriello; Chris Sander; Nikolaus Schultz Journal: Cell Date: 2018-04-05 Impact factor: 41.582