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Bioinformatics analysis of metastasis-related proteins in hepatocellular carcinoma.

ABSTRACT: AIM:To analyze the metastasis-related proteins in hepatocellular carcinoma (HCC) and discover the biomarker candidates for diagnosis and therapeutic intervention of HCC metastasis with bioinformatics tools. METHODS:Metastasis-related proteins were determined by stable isotope labeling and MS analysis and analyzed with bioinformatics resources, including Phobius, Kyoto encyclopedia of genes and genomes (KEGG), online mendelian inheritance in man (OMIM) and human protein reference database (HPRD). RESULTS:All the metastasis-related proteins were linked to 83 pathways in KEGG, including MAPK and p53 signal pathways. Protein-protein interaction network showed that all the metastasis-related proteins were categorized into 19 function groups, including cell cycle, apoptosis and signal transduction. OMIM analysis linked these proteins to 186 OMIM entries. CONCLUSION:Metastasis-related proteins provide HCC cells with biological advantages in cell proliferation, migration and angiogenesis, and facilitate metastasis of HCC cells. The bird's eye view can reveal a global characteristic of metastasis-related proteins and many differentially expressed proteins can be identified as candidates for diagnosis and treatment of HCC.

SUBMITTER: Song PM

PROVIDER: S-EPMC2751890 | biostudies-literature | 2008 Oct

REPOSITORIES: biostudies-literature

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Bioinformatics analysis of metastasis-related proteins in hepatocellular carcinoma.

Song Pei-Ming PM Zhang Yang Y He Yu-Fei YF Bao Hui-Min HM Luo Jian-Hua JH Liu Yin-Kun YK Yang Peng-Yuan PY Chen Xian X

World journal of gastroenterology 20081001 38

<h4>Aim</h4>To analyze the metastasis-related proteins in hepatocellular carcinoma (HCC) and discover the biomarker candidates for diagnosis and therapeutic intervention of HCC metastasis with bioinformatics tools.<h4>Methods</h4>Metastasis-related proteins were determined by stable isotope labeling and MS analysis and analyzed with bioinformatics resources, including Phobius, Kyoto encyclopedia of genes and genomes (KEGG), online mendelian inheritance in man (OMIM) and human protein reference d ...[more]

PMID: 18855979

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Project description:BackgroundHepatocellular carcinoma (HCC) is a common malignant tumor affecting the digestive system and causes serious financial burden worldwide. Hepatitis B virus (HBV) is the main causative agent of HCC in China. The present study aimed to investigate the potential mechanisms underlying HBV-related HCC and to identify core biomarkers by integrated bioinformatics analyses.MethodsIn the present study, HBV-related HCC GSE19665, GSE55092, GSE94660 and GSE121248 expression profiles were downloaded from the Gene Expression Omnibus database. These databases contain data for 299 samples, including 145 HBV-related HCC tissues and 154 non-cancerous tissues (from patients with chronic hepatitis B). The differentially expressed genes (DEGs) from each dataset were integrated and analyzed using the RobustRankAggreg (RRA) method and R software, and the integrated DEGs were identified. Subsequently, the gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the DAVID online tool, and the protein-protein interaction (PPI) network was constructed using STRING and visualized using Cytoscape software. Finally, hub genes were identified, and the cBioPortal online platform was used to analyze the association between the expression of hub genes and prognosis in HCC.ResultsFirst, 341 DEGs (117 upregulated and 224 downregulated) were identified from the four datasets. Next, GO analysis showed that the upregulated genes were mainly involved in cell cycle, mitotic spindle, and adenosine triphosphate binding. The majority of the downregulated genes were involved in oxidation reduction, extracellular region, and electron carrier activity. Signaling pathway analysis showed that the integrated DEGs shared common pathways in retinol metabolism, drug metabolism, tryptophan metabolism, caffeine metabolism, and metabolism of xenobiotics by cytochrome P450. The integrated DEG PPI network complex comprised 288 nodes, and two important modules with high degree were detected using the MCODE plug-in. The top ten hub genes identified from the PPI network were SHCBP1, FOXM1, KIF4A, ANLN, KIF15, KIF18A, FANCI, NEK2, ECT2, and RAD51AP1. Finally, survival analysis revealed that patients with HCC showing altered ANLN and KIF18A expression profiles showed worse disease-free survival. Nonetheless, patients with FOXM1, NEK2, RAD51AP1, ANLN, and KIF18A alterations showed worse overall survival.ConclusionsThe present study identified key genes and pathways involved in HBV-related HCC, which improved our understanding of the mechanisms underlying the development and recurrence of HCC and identified candidate targets for the diagnosis and treatment of HBV-related HCC.

Project description:BackgroundThe number of patients with hepatocellular carcinoma (HCC) is showing a growing trend all over the world. The metabolic microenvironment has been shown to play a key role in the pathogenesis of HCC in recent studies. The expression of metabolites and metabolic processes in tumor cells can be regulated by gene regulation mediated by long non-coding RNAs (lncRNAs), the abnormal expression of which is closely related to tumor occurrence and metastasis. However, the fundamental mechanism of applying metabolism-related lncRNAs to predicting HCC is still unclear.MethodsWith the complete RNA sequence data and clinical data obtained from The Cancer Genome Atlas database and metabolism-related genes downloaded from the Kyoto Encyclopedia of Genes and Genomes database, with false discovery rate < 0.001, log fold change > 1.5 selected as the screening criteria for lncRNA, the relationship between the expression level of metabolism-related LncRNAs (MRLs) and the overall survival rate was determined by the Univariate Cox regression analyses with the establishment of the metabolic prognosis model by the application of Multivariate Cox regression analyses, revealing the different biological processes and signaling pathways in both high-risk groups and low-risk groups by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and gene set enrichment analysis, leading the expression of lncRNA to be assessed by the reverse transcription-polymerase chain reaction results.ResultsThe overall survival rate of HCC patients is significantly correlated with signature of 5-MRLs. The prognosis characteristics of lncRNA reveal the relatively high death rate of patients in the high-risk groups, with the predicted signals by functional and pathway enrichment analysis related to biosynthesis, metabolic process, and metabolic pathway, with the prognostic characteristics of 5-MRLs by the combined analysis showing that it is an independent factor of HCC superior to the traditional clinical indicators in predicting the prognosis. A trend of high-expression was shown in MRLs in tumors by reverse transcription-polymerase chain reaction.ConclusionThe new 5-MRLs as potential biomarkers provide more powerful prognostic information for HCC patients. In the future clinical treatment of HCC, it will provide doctors with more methods.

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Bioinformatics analysis of metastasis-related proteins in hepatocellular carcinoma.

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Bioinformatics analysis of metastasis-related proteins in hepatocellular carcinoma.

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