Identification of biomarkers in breast cancer metastasis by integrating protein-protein interaction network and gene expression data

2011 ◽  
Author(s):  
Md Jamiul Jahid ◽  
Jianhua Ruan
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Data ◽  
Protein Interaction Network ◽  
Cancer Metastasis ◽  
Interaction Network ◽  
Breast Cancer Metastasis ◽  
Expression Data ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

scholarly journals Integrating Protein-Protein Interaction Networks with Gene- Gene Co-Expression Networks improves Gene Signatures for Classifying Breast Cancer Metastasis

2011 ◽  
Vol 8 (2) ◽  
pp. 222-238 ◽  
Author(s):  
Erik van den Akker ◽  
Bas Verbruggen ◽  
Bas Heijmans ◽  
Marian Beekman ◽  
Joost Kok ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Metastasis ◽  
Gene Selection ◽  
De Novo ◽  
Tumor Development ◽  
Interaction Network ◽  
Breast Cancer Metastasis ◽  
Breast Cancers ◽  
Proteinprotein Interaction

Summary Multiple studies have illustrated that gene expression profiling of primary breast cancers throughout the final stages of tumor development can provide valuable markers for risk prediction of metastasis and disease sub typing. However, the identification of a biologically interpretable and universally shared set of markers proved to be difficult. Here, we propose a method for de novo grouping of genes by dissecting the proteinprotein interaction network into disjoint sub networks using pair wise gene expression correlation measures. We show that the obtained sub networks are functionally coherent and are consistently identified when applied on a compendium composed of six different breast cancer studies. Application of the proposed method using different integration approaches underlines the robustness of the identified sub network related to cell cycle and identifies putative new sub network markers for metastasis related to cell-cell adhesion, the proteasome complex and JUN-FOS signalling. Although gene selection with the proposed method does not directly improve upon previously reported cross study classification performances, it shows great promises for applications in data integration and result interpretation.

scholarly journals Identification of differentially expressed genes in hepatocellular carcinoma by integrated bioinformatic analysis

2019 ◽  
Author(s):  
Guangxin Yan ◽  
Zhaoyu Liu
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Dna Replication ◽  
Liver Cancer ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

AbstractHepatocellular carcinoma is one of the most common tumors in the world and has a high mortality rate. This study elucidates the mechanism of hepatocellular carcinoma- (HCC) related development. The HCC gene expression profile (GSE54238, GSE84004) was downloaded from Gene Expression Omnibus for comprehensive analysis. A total of 359 genes were identified, of which 195 were upregulated and 164 were downregulated. Analysis of the condensed results showed that “extracellular allotrope” is a substantially enriched term. “Cell cycle”, “metabolic pathway” and “DNA replication” are three significantly enriched Kyoto Encyclopedia of Genes and Genomespathways. Subsequently, a protein-protein interaction network was constructed. The most important module in the protein-protein interaction network was selected for path enrichment analysis. The results showed thatCCNA2, PLK1, CDC20, UBE2CandAURKAwere identified as central genes, and the expression of these five hub genes in liver cancer was significantly increased in The Cancer Genome Atlas. Univariate regression analysis was also performed to show that the overall survival and disease-free survival of patients in the high expression group were longer than in the expression group. In addition, genes in important modules are mainly involved in “cell cycle”, “DNA replication” and “oocyte meiosis” signaling pathways. Finally, through upstream miRNA analysis, mir-300 and mir-381-3p were found to coregulateCCNA2,AURKAandUBE2C. These results provide a set of targets that can help researchers to further elucidate the underlying mechanism of liver cancer.

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