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Identifying module biomarker in type 2 diabetes mellitus by discriminative area of functional activity.

Zhang X, Gao L, Liu ZP, Chen L - BMC Bioinformatics (2015)

Bottom Line: This module biomarker is enriched with known causal genes and related functions of T2DM.Further analysis shows that the module biomarker is of superior performance in classification, and has consistently high accuracies across tissues and experiments.The proposed approach can efficiently identify robust and functionally meaningful module biomarkers in T2DM, and could be employed in biomarker discovery of other complex diseases characterized by expression profiles.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Technology, Xidian University, Xi'an, 710000, China. zxd841@163.com.

ABSTRACT

Background: Identifying diagnosis and prognosis biomarkers from expression profiling data is of great significance for achieving personalized medicine and designing therapeutic strategy in complex diseases. However, the reproducibility of identified biomarkers across tissues and experiments is still a challenge for this issue.

Results: We propose a strategy based on discriminative area of module activities to identify gene biomarkers which interconnect as a subnetwork or module by integrating gene expression data and protein-protein interactions. Then, we implement the procedure in T2DM as a case study and identify a module biomarker with 32 genes from mRNA expression data in skeletal muscle for T2DM. This module biomarker is enriched with known causal genes and related functions of T2DM. Further analysis shows that the module biomarker is of superior performance in classification, and has consistently high accuracies across tissues and experiments.

Conclusion: The proposed approach can efficiently identify robust and functionally meaningful module biomarkers in T2DM, and could be employed in biomarker discovery of other complex diseases characterized by expression profiles.

Show MeSH
Network structure of identified module. Network structure of identified module which contains 32 genes, where diamond denotes that the gene is a causal gene of T2DM by quering T2D-Db or GAD, hexagon denotes that the gene is a T2DM related gene by functional correlation.
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Fig3: Network structure of identified module. Network structure of identified module which contains 32 genes, where diamond denotes that the gene is a causal gene of T2DM by quering T2D-Db or GAD, hexagon denotes that the gene is a T2DM related gene by functional correlation.

Mentions: To capture significant changes of genes in transcriptional expression level, we first identified 203 differentially expressed genes as seeds with adjusted p-value <0.01 by t-test, and then generated a discriminative module for each seed by a greedy strategy. Figure 2 shows the main idea of the seed-growth strategy (see Methods for details). Hence, by removing modules of discriminative area disa (M) >0.2, 40 modules remained after selection. The activities of these 40 modules are highly correlated PCC >0.6, which indicates that these modules have a poor effect on improving discriminative ability, and each of them could be regarded as a potential module biomarker for the original data (GSE18732). Then, we used a function-similarity based method to detect a module which would be more reproducible across data sets. Finally, a module of 32 genes with the highest score was identified. Figure 3 shows interactions of these 32 genes in module biomarker, and Additional file 1: Table S1 for the details of these 32 genes.Figure 2


Identifying module biomarker in type 2 diabetes mellitus by discriminative area of functional activity.

Zhang X, Gao L, Liu ZP, Chen L - BMC Bioinformatics (2015)

Network structure of identified module. Network structure of identified module which contains 32 genes, where diamond denotes that the gene is a causal gene of T2DM by quering T2D-Db or GAD, hexagon denotes that the gene is a T2DM related gene by functional correlation.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4374500&req=5

Fig3: Network structure of identified module. Network structure of identified module which contains 32 genes, where diamond denotes that the gene is a causal gene of T2DM by quering T2D-Db or GAD, hexagon denotes that the gene is a T2DM related gene by functional correlation.
Mentions: To capture significant changes of genes in transcriptional expression level, we first identified 203 differentially expressed genes as seeds with adjusted p-value <0.01 by t-test, and then generated a discriminative module for each seed by a greedy strategy. Figure 2 shows the main idea of the seed-growth strategy (see Methods for details). Hence, by removing modules of discriminative area disa (M) >0.2, 40 modules remained after selection. The activities of these 40 modules are highly correlated PCC >0.6, which indicates that these modules have a poor effect on improving discriminative ability, and each of them could be regarded as a potential module biomarker for the original data (GSE18732). Then, we used a function-similarity based method to detect a module which would be more reproducible across data sets. Finally, a module of 32 genes with the highest score was identified. Figure 3 shows interactions of these 32 genes in module biomarker, and Additional file 1: Table S1 for the details of these 32 genes.Figure 2

Bottom Line: This module biomarker is enriched with known causal genes and related functions of T2DM.Further analysis shows that the module biomarker is of superior performance in classification, and has consistently high accuracies across tissues and experiments.The proposed approach can efficiently identify robust and functionally meaningful module biomarkers in T2DM, and could be employed in biomarker discovery of other complex diseases characterized by expression profiles.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Technology, Xidian University, Xi'an, 710000, China. zxd841@163.com.

ABSTRACT

Background: Identifying diagnosis and prognosis biomarkers from expression profiling data is of great significance for achieving personalized medicine and designing therapeutic strategy in complex diseases. However, the reproducibility of identified biomarkers across tissues and experiments is still a challenge for this issue.

Results: We propose a strategy based on discriminative area of module activities to identify gene biomarkers which interconnect as a subnetwork or module by integrating gene expression data and protein-protein interactions. Then, we implement the procedure in T2DM as a case study and identify a module biomarker with 32 genes from mRNA expression data in skeletal muscle for T2DM. This module biomarker is enriched with known causal genes and related functions of T2DM. Further analysis shows that the module biomarker is of superior performance in classification, and has consistently high accuracies across tissues and experiments.

Conclusion: The proposed approach can efficiently identify robust and functionally meaningful module biomarkers in T2DM, and could be employed in biomarker discovery of other complex diseases characterized by expression profiles.

Show MeSH