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Identification of biomarkers for hepatocellular carcinoma using network-based bioinformatics methods.

Zhang L, Guo Y, Li B, Qu J, Zang C, Li F, Wang Y, Pang H, Li S, Liu Q - Eur. J. Med. Res. (2013)

Bottom Line: In total, 29 phenotype-related differentially expressed genes were included in the PPI network.Hierarchical clustering showed that the gene expression profile of these 29 genes was able to differentiate HCC samples from non-cancerous liver samples.This study provides a portfolio of targets useful for future investigation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Radiology, College of Basic Medicine, Chongqing Medical University, No,1 Yixueyuan Road, Yuzhong District, Chongqing 400016, P,R, China. lishaolin@cqmu.edu.cn.

ABSTRACT

Background: Hepatocellular carcinoma (HCC) is one of the most common types of cancer worldwide. Despite several efforts to elucidate molecular mechanisms involved in this cancer, they are still not fully understood.

Methods: To acquire further insights into the molecular mechanisms of HCC, and to identify biomarkers for early diagnosis of HCC, we downloaded the gene expression profile on HCC with non-cancerous liver controls from the Gene Expression Omnibus (GEO) and analyzed these data using a combined bioinformatics approach.

Results: The dysregulated pathways and protein-protein interaction (PPI) network, including hub nodes that distinguished HCCs from non-cancerous liver controls, were identified. In total, 29 phenotype-related differentially expressed genes were included in the PPI network. Hierarchical clustering showed that the gene expression profile of these 29 genes was able to differentiate HCC samples from non-cancerous liver samples. Among these genes, CDC2 (Cell division control protein 2 homolog), MMP2 (matrix metalloproteinase-2) and DCN (Decorin were the hub nodes in the PPI network.

Conclusions: This study provides a portfolio of targets useful for future investigation. However, experimental studies should be conducted to verify our findings.

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Related in: MedlinePlus

Hierarchical clustering of genes in the protein-protein interaction (PPI) network. Rows represent genes and columns represents sample. The samples under the green bar were noncancerous liver samples and the samples under the purple bar were hepatocellular (HCC) samples.
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Figure 2: Hierarchical clustering of genes in the protein-protein interaction (PPI) network. Rows represent genes and columns represents sample. The samples under the green bar were noncancerous liver samples and the samples under the purple bar were hepatocellular (HCC) samples.

Mentions: To verify whether the 29 genes in the PPI network could be used to differentiate between HCC and non-cancerous liver, we performed hierarchical clustering using R based on gene expression level (FigureĀ 2). We found that although the 29 gene profiles could notdifferentiate HCV-related HCCs from HBV-related HCCs, they could differenttiate HCC samples from non-cancerous livers. In addition, hierarchical clustering portioned the genes into two groups. In total, 15 genes were upregulated in HCC, including THBS1 (Thrombospondin 1), IGFBP3 (insulin-like growth factor binding protein 3), GPRASP1 (G protein-coupled receptor associated sorting protein 1), DPT (dermatopontin), and MMP2. The other 14 genes were downregulated in HCC, and included TUBG1 (tubulin, gamma 1), CDKN2C (Cyclin-dependent kinase 4 inhibitor C), CDKN2A and RRM2 (ribonucleotide reductase M2).


Identification of biomarkers for hepatocellular carcinoma using network-based bioinformatics methods.

Zhang L, Guo Y, Li B, Qu J, Zang C, Li F, Wang Y, Pang H, Li S, Liu Q - Eur. J. Med. Res. (2013)

Hierarchical clustering of genes in the protein-protein interaction (PPI) network. Rows represent genes and columns represents sample. The samples under the green bar were noncancerous liver samples and the samples under the purple bar were hepatocellular (HCC) samples.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4016278&req=5

Figure 2: Hierarchical clustering of genes in the protein-protein interaction (PPI) network. Rows represent genes and columns represents sample. The samples under the green bar were noncancerous liver samples and the samples under the purple bar were hepatocellular (HCC) samples.
Mentions: To verify whether the 29 genes in the PPI network could be used to differentiate between HCC and non-cancerous liver, we performed hierarchical clustering using R based on gene expression level (FigureĀ 2). We found that although the 29 gene profiles could notdifferentiate HCV-related HCCs from HBV-related HCCs, they could differenttiate HCC samples from non-cancerous livers. In addition, hierarchical clustering portioned the genes into two groups. In total, 15 genes were upregulated in HCC, including THBS1 (Thrombospondin 1), IGFBP3 (insulin-like growth factor binding protein 3), GPRASP1 (G protein-coupled receptor associated sorting protein 1), DPT (dermatopontin), and MMP2. The other 14 genes were downregulated in HCC, and included TUBG1 (tubulin, gamma 1), CDKN2C (Cyclin-dependent kinase 4 inhibitor C), CDKN2A and RRM2 (ribonucleotide reductase M2).

Bottom Line: In total, 29 phenotype-related differentially expressed genes were included in the PPI network.Hierarchical clustering showed that the gene expression profile of these 29 genes was able to differentiate HCC samples from non-cancerous liver samples.This study provides a portfolio of targets useful for future investigation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Radiology, College of Basic Medicine, Chongqing Medical University, No,1 Yixueyuan Road, Yuzhong District, Chongqing 400016, P,R, China. lishaolin@cqmu.edu.cn.

ABSTRACT

Background: Hepatocellular carcinoma (HCC) is one of the most common types of cancer worldwide. Despite several efforts to elucidate molecular mechanisms involved in this cancer, they are still not fully understood.

Methods: To acquire further insights into the molecular mechanisms of HCC, and to identify biomarkers for early diagnosis of HCC, we downloaded the gene expression profile on HCC with non-cancerous liver controls from the Gene Expression Omnibus (GEO) and analyzed these data using a combined bioinformatics approach.

Results: The dysregulated pathways and protein-protein interaction (PPI) network, including hub nodes that distinguished HCCs from non-cancerous liver controls, were identified. In total, 29 phenotype-related differentially expressed genes were included in the PPI network. Hierarchical clustering showed that the gene expression profile of these 29 genes was able to differentiate HCC samples from non-cancerous liver samples. Among these genes, CDC2 (Cell division control protein 2 homolog), MMP2 (matrix metalloproteinase-2) and DCN (Decorin were the hub nodes in the PPI network.

Conclusions: This study provides a portfolio of targets useful for future investigation. However, experimental studies should be conducted to verify our findings.

Show MeSH
Related in: MedlinePlus