Research Papers:
Systematic analysis of molecular mechanisms for HCC metastasis via text mining approach
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Cheng Zhen1, Caizhong Zhu1, Haoyang Chen1, Yiru Xiong1, Junyuan Tan1, Dong Chen1, Jin Li1
1Beijing 302 Hospital, Beijing, 100039, China
Correspondence to:
Jin Li, email: [email protected]
Keywords: hepatocellular carcinoma, metastasis, text mining
Received: April 05, 2016 Accepted: January 03, 2017 Published: January 17, 2017
ABSTRACT
Objective: To systematically explore the molecular mechanism for hepatocellular carcinoma (HCC) metastasis and identify regulatory genes with text mining methods.
Results: Genes with highest frequencies and significant pathways related to HCC metastasis were listed. A handful of proteins such as EGFR, MDM2, TP53 and APP, were identified as hub nodes in PPI (protein-protein interaction) network. Compared with unique genes for HBV-HCCs, genes particular to HCV-HCCs were less, but may participate in more extensive signaling processes. VEGFA, PI3KCA, MAPK1, MMP9 and other genes may play important roles in multiple phenotypes of metastasis.
Materials and methods: Genes in abstracts of HCC-metastasis literatures were identified. Word frequency analysis, KEGG pathway and PPI network analysis were performed. Then co-occurrence analysis between genes and metastasis-related phenotypes were carried out.
Conclusions: Text mining is effective for revealing potential regulators or pathways, but the purpose of it should be specific, and the combination of various methods will be more useful.