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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.

No MeSH data available.


Schematic flowchart of identifying candidate biomarker genes or module biomarkers for gastric cancer.Note: The overlapped genes of the potential biomarker genes or module biomarkers in the three phases were the candidate biomarker genes or module biomarkers for the method.
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f2-ott-9-5701: Schematic flowchart of identifying candidate biomarker genes or module biomarkers for gastric cancer.Note: The overlapped genes of the potential biomarker genes or module biomarkers in the three phases were the candidate biomarker genes or module biomarkers for the method.

Mentions: Every edge in the differential network represents the dynamic interaction between the normal and cancer samples, and the edges in the differential network only appear at one phenotype of normal or cancer samples. The disease-related genes were the common genes in both the normal-specific and cancer-specific networks. These genes appear in both normal-specific and cancer-specific network. It is highly possible that these genes play crucial roles in the transformation from normal to cancer status. Then, the differentially expressed genes were detected by P-value of 0.05 obtained by Student’s t-test, and the genes in the differential network were filtered by the differential expressed genes to obtain the potential biomarker genes or module biomarkers for each phase (Figure 1). The common members of the intersection genes at the three phases of gastric cancer were obtained and regarded as candidate biomarker genes or module biomarkers (Figure 2). The candidate biomarker genes or module biomarkers stably appear in the differential network of every phase and always connect with both normal-specific and cancer-specific networks, and these indicate their important dysfunctions from normal to cancer status.


Identifying module biomarkers from gastric cancer by differential correlation network
Schematic flowchart of identifying candidate biomarker genes or module biomarkers for gastric cancer.Note: The overlapped genes of the potential biomarker genes or module biomarkers in the three phases were the candidate biomarker genes or module biomarkers for the method.
© Copyright Policy
Related In: Results  -  Collection

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

f2-ott-9-5701: Schematic flowchart of identifying candidate biomarker genes or module biomarkers for gastric cancer.Note: The overlapped genes of the potential biomarker genes or module biomarkers in the three phases were the candidate biomarker genes or module biomarkers for the method.
Mentions: Every edge in the differential network represents the dynamic interaction between the normal and cancer samples, and the edges in the differential network only appear at one phenotype of normal or cancer samples. The disease-related genes were the common genes in both the normal-specific and cancer-specific networks. These genes appear in both normal-specific and cancer-specific network. It is highly possible that these genes play crucial roles in the transformation from normal to cancer status. Then, the differentially expressed genes were detected by P-value of 0.05 obtained by Student’s t-test, and the genes in the differential network were filtered by the differential expressed genes to obtain the potential biomarker genes or module biomarkers for each phase (Figure 1). The common members of the intersection genes at the three phases of gastric cancer were obtained and regarded as candidate biomarker genes or module biomarkers (Figure 2). The candidate biomarker genes or module biomarkers stably appear in the differential network of every phase and always connect with both normal-specific and cancer-specific networks, and these indicate their important dysfunctions from normal to cancer status.

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.