Systematic analysis of molecular mechanisms for HCC metastasis via text mining approach

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.

DOI: 10.18632/oncotarget.14692

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Cite this paper

@inproceedings{Zhen2017SystematicAO, title={Systematic analysis of molecular mechanisms for HCC metastasis via text mining approach}, author={Cheng Zhen and Caizhong Zhu and Haoyang Chen and Yiru Xiong and Junyuan Tan and Dong Chen and Jin Li}, booktitle={Oncotarget}, year={2017} }