Nan Jiang1,2,3, Xinzhuo Zhang3, Dalian Qin4, Jing Yang4, Anguo Wu4, Long Wang4, Yueshan Sun4, Hong Li4, Xin Shen4, Jing Lin4, Fahsai Kantawong1, Jianming Wu2. 1. Department of Medical Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand. 2. School of Pharmacy, Southwest Medical University, Luzhou, China. 3. International Education School, Southwest Medical University, Luzhou, China. 4. Education Ministry Key Laboratory of Medical Electrophysiology, Sichuan Key Medical Laboratory of New Drug Discovery and Drugability Evaluation, Luzhou Key Laboratory of Activity Screening and Drugability Evaluation for Chinese Materia Medica, Southwest Medical University, Luzhou, China.
Abstract
BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most leading causes of cancer death with a poor prognosis. However, the underlying molecular mechanisms are largely unclear, and effective treatment for it is limited. Using an integrated bioinformatics method, the present study aimed to identify the key candidate prognostic genes that are involved in HCC development and identify small-molecule drugs with treatment potential. METHODS AND RESULTS: In this study, by using three expression profile datasets from Gene Expression Omnibus database, 1,704 differentially expressed genes were identified, including 671 upregulated and 1,033 downregulated genes. Then, weighted co-expression network analysis revealed nine modules are related with pathological stage; turquoise module was the most associated module. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway analyses (KEGG) indicated that these genes were enriched in cell division, cell cycle, and metabolic related pathways. Furthermore, by analyzing the turquoise module, 22 genes were identified as hub genes. Based on HCC data from gene expression profiling interactive analysis (GEPIA) database, nine genes associated with progression and prognosis of HCC were screened, including ANLN, BIRC5, BUB1B, CDC20, CDCA5, CDK1, NCAPG, NEK2, and TOP2A. According to the Human Protein Atlas and the Oncomine database, these genes were highly upregulated in HCC tumor samples. Moreover, multivariate Cox regression analysis showed that the risk score based on the gene expression signature of these nine genes was an independent prognostic factor for overall survival and disease-free survival in HCC patients. In addition, the candidate small-molecule drugs for HCC were identified by the CMap database. CONCLUSION: In conclusion, the nine key gene signatures related to HCC progression and prognosis were identified and validated. The cell cycle pathway was the core pathway enriched with these key genes. Moreover, several candidate molecule drugs were identified, providing insights into novel therapeutic approaches for HCC.
BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most leading causes of cancer death with a poor prognosis. However, the underlying molecular mechanisms are largely unclear, and effective treatment for it is limited. Using an integrated bioinformatics method, the present study aimed to identify the key candidate prognostic genes that are involved in HCC development and identify small-molecule drugs with treatment potential. METHODS AND RESULTS: In this study, by using three expression profile datasets from Gene Expression Omnibus database, 1,704 differentially expressed genes were identified, including 671 upregulated and 1,033 downregulated genes. Then, weighted co-expression network analysis revealed nine modules are related with pathological stage; turquoise module was the most associated module. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway analyses (KEGG) indicated that these genes were enriched in cell division, cell cycle, and metabolic related pathways. Furthermore, by analyzing the turquoise module, 22 genes were identified as hub genes. Based on HCC data from gene expression profiling interactive analysis (GEPIA) database, nine genes associated with progression and prognosis of HCC were screened, including ANLN, BIRC5, BUB1B, CDC20, CDCA5, CDK1, NCAPG, NEK2, and TOP2A. According to the Human Protein Atlas and the Oncomine database, these genes were highly upregulated in HCC tumor samples. Moreover, multivariate Cox regression analysis showed that the risk score based on the gene expression signature of these nine genes was an independent prognostic factor for overall survival and disease-free survival in HCC patients. In addition, the candidate small-molecule drugs for HCC were identified by the CMap database. CONCLUSION: In conclusion, the nine key gene signatures related to HCC progression and prognosis were identified and validated. The cell cycle pathway was the core pathway enriched with these key genes. Moreover, several candidate molecule drugs were identified, providing insights into novel therapeutic approaches for HCC.
Authors: Paul G Richardson; Ashraf Z Badros; Sundar Jagannath; Stefano Tarantolo; Jeffrey L Wolf; Maher Albitar; David Berman; Marianne Messina; Kenneth C Anderson Journal: Br J Haematol Date: 2010-07-07 Impact factor: 6.998
Authors: Ethan Cerami; Jianjiong Gao; Ugur Dogrusoz; Benjamin E Gross; Selcuk Onur Sumer; Bülent Arman Aksoy; Anders Jacobsen; Caitlin J Byrne; Michael L Heuer; Erik Larsson; Yevgeniy Antipin; Boris Reva; Arthur P Goldberg; Chris Sander; Nikolaus Schultz Journal: Cancer Discov Date: 2012-05 Impact factor: 39.397