Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0345904 (liver cancer)
 15,188 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Liver cancer is one of the leading causes of cancer mortality worldwide. Hepatocellular carcinoma (HCC) is the main type of liver cancer. We applied a machine learning approach with maximum-relevance-minimum-redundancy (mRMR) algorithm followed by incremental feature selection (IFS) to a set of microarray data generated from 43 tumor and 52 nontumor samples. With the machine learning approach, we identified 117 gene probes that could optimally separate tumor and nontumor samples. These genes not only include known HCC-relevant genes such as MT1X, BMI1, and CAP2, but also include cancer genes that were not found previously to be closely related to HCC, such as TACSTD2. Then, we constructed a molecular interaction network based on the protein-protein interaction (PPI) data from the STRING database and identified 187 genes on the shortest paths among the genes identified with the machine learning approach. Network analysis reveals new potential roles of ubiquitin C in the pathogenesis of HCC. Based on gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, we showed that the identified subnetwork is significantly enriched in biological processes related to cell death. These results bring new insights of understanding the process of HCC.
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PMID:Identification of hepatocellular carcinoma-related genes with a machine learning and network analysis. 2524 52

Increasing evidence has revealed that cancer cells undergoing an intermediate state, partial epithelial mesenchymal transition (p-EMT), tend to metastasize rather than complete EMT. We performed a comprehensive analysis of E-cadherin and 25 p-EMT-related genes in HCC to explore the roles and regulatory mechanisms of them in HCC. We analysed E-cadherin and 25 p-EMT-related genes in HCC and constructed an mRNA-miRNA-lncRNA ceRNA subnetwork containing p-EMT-related genes by bioinformatic approaches. IHC was used to identify the protein expression of key p-EMT-related genes, P4HA2, ITGA5, MMP9, MT1X and SPP1. Complete EMT is not necessary for HCC progression. Overexpression of P4HA2, ITGA5, MMP9, SPP1 and down-regulation of MT1X were found in HCC tissues, which were significantly associated with poor prognosis of HCC patients. By means of stepwise reverse prediction and validation from mRNA to lncRNA, an mRNA-miRNA-lncRNA ceRNA subnetwork correlated with HCC prognosis was identified by expression and survival analysis. This study implied that key p-EMT-related genes P4HA2, ITGA5, MMP9, MT1X, SPP1 could be prognostic biomarkers and potential targets of therapy for HCC patients. We constructed an mRNA-miRNA-lncRNA subnetwork containing p-EMT-related genes successfully, among which each component might be utilized as a prognostic biomarker of HCC.
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PMID:Comprehensive analysis of partial epithelial mesenchymal transition-related genes in hepatocellular carcinoma. 3321 60