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Identifying module biomarkers from gastric cancer by differential correlation network

View Article: PubMed Central - PubMed

ABSTRACT

Gastric cancer (stomach cancer) is a severe disease caused by dysregulation of many functionally correlated genes or pathways instead of the mutation of individual genes. Systematic identification of gastric cancer biomarkers can provide insights into the mechanisms underlying this deadly disease and help in the development of new drugs. In this paper, we present a novel network-based approach to predict module biomarkers of gastric cancer that can effectively distinguish the disease from normal samples. Specifically, by assuming that gastric cancer has mainly resulted from dysfunction of biomolecular networks rather than individual genes in an organism, the genes in the module biomarkers are potentially related to gastric cancer. Finally, we identified a module biomarker with 27 genes, and by comparing the module biomarker with known gastric cancer biomarkers, we found that our module biomarker exhibited a greater ability to diagnose the samples with gastric cancer.

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Related in: MedlinePlus

Schematic flowchart of identifying potential genes based on differential correlation network and gene expression for each phase.Notes: The threshold for high correlation was set to FDR corrected P-value of 0.05. “Remove common edges” means removing the interactions that appear in both normal and cancer network, and “Differential expression between normal and cancer” means identifying the differentially expressed genes between normal and cancer with P-value 0.05 by Student’s t-test.Abbreviation: FDR, false discovery rate.
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f1-ott-9-5701: Schematic flowchart of identifying potential genes based on differential correlation network and gene expression for each phase.Notes: The threshold for high correlation was set to FDR corrected P-value of 0.05. “Remove common edges” means removing the interactions that appear in both normal and cancer network, and “Differential expression between normal and cancer” means identifying the differentially expressed genes between normal and cancer with P-value 0.05 by Student’s t-test.Abbreviation: FDR, false discovery rate.

Mentions: The gene expression profiles were divided into three phases based on the stages of gastric cancer, as shown in Table 1; in every phase, the gene expression profiles were divided into two groups of normal and cancer, and each group only contained normal or cancer samples in this phase. The correlation coefficient of each interaction within the correlation network was calculated based on the samples of every group. The highly correlated edges were reserved, and lowly correlated edges were deleted. The highly correlated edges were able to identify the interactions among nodes at the corresponding phenotypes. The correlation network was rewired based on these highly correlated edges from the normal and cancer samples individually. The new correlation networks were called network in normal and network in cancer, and are shown in Figure 1. The correlation coefficient threshold value was set to FDR corrected P-value ≤0.05, and was used to divide the highly correlated edges and lowly correlated edges. The context-specific network will only keep the highly correlated edges in the network. The differential network between normal and cancer samples could show the dynamic changes of interactions between the two samples. The normal-specific and cancer-specific networks were individually built by removing the common edges of correlation network in normal and cancer status. The differential networks were formed to combine the normal-specific and cancer-specific networks (Figure 1).


Identifying module biomarkers from gastric cancer by differential correlation network
Schematic flowchart of identifying potential genes based on differential correlation network and gene expression for each phase.Notes: The threshold for high correlation was set to FDR corrected P-value of 0.05. “Remove common edges” means removing the interactions that appear in both normal and cancer network, and “Differential expression between normal and cancer” means identifying the differentially expressed genes between normal and cancer with P-value 0.05 by Student’s t-test.Abbreviation: FDR, false discovery rate.
© Copyright Policy
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5036598&req=5

f1-ott-9-5701: Schematic flowchart of identifying potential genes based on differential correlation network and gene expression for each phase.Notes: The threshold for high correlation was set to FDR corrected P-value of 0.05. “Remove common edges” means removing the interactions that appear in both normal and cancer network, and “Differential expression between normal and cancer” means identifying the differentially expressed genes between normal and cancer with P-value 0.05 by Student’s t-test.Abbreviation: FDR, false discovery rate.
Mentions: The gene expression profiles were divided into three phases based on the stages of gastric cancer, as shown in Table 1; in every phase, the gene expression profiles were divided into two groups of normal and cancer, and each group only contained normal or cancer samples in this phase. The correlation coefficient of each interaction within the correlation network was calculated based on the samples of every group. The highly correlated edges were reserved, and lowly correlated edges were deleted. The highly correlated edges were able to identify the interactions among nodes at the corresponding phenotypes. The correlation network was rewired based on these highly correlated edges from the normal and cancer samples individually. The new correlation networks were called network in normal and network in cancer, and are shown in Figure 1. The correlation coefficient threshold value was set to FDR corrected P-value ≤0.05, and was used to divide the highly correlated edges and lowly correlated edges. The context-specific network will only keep the highly correlated edges in the network. The differential network between normal and cancer samples could show the dynamic changes of interactions between the two samples. The normal-specific and cancer-specific networks were individually built by removing the common edges of correlation network in normal and cancer status. The differential networks were formed to combine the normal-specific and cancer-specific networks (Figure 1).

View Article: PubMed Central - PubMed

ABSTRACT

Gastric cancer (stomach cancer) is a severe disease caused by dysregulation of many functionally correlated genes or pathways instead of the mutation of individual genes. Systematic identification of gastric cancer biomarkers can provide insights into the mechanisms underlying this deadly disease and help in the development of new drugs. In this paper, we present a novel network-based approach to predict module biomarkers of gastric cancer that can effectively distinguish the disease from normal samples. Specifically, by assuming that gastric cancer has mainly resulted from dysfunction of biomolecular networks rather than individual genes in an organism, the genes in the module biomarkers are potentially related to gastric cancer. Finally, we identified a module biomarker with 27 genes, and by comparing the module biomarker with known gastric cancer biomarkers, we found that our module biomarker exhibited a greater ability to diagnose the samples with gastric cancer.

No MeSH data available.


Related in: MedlinePlus