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. 2015 Mar 18:16:92.
doi: 10.1186/s12859-015-0519-y.

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

Xindong Zhang  1   2 , Lin Gao  3   4 , Zhi-Ping Liu  5 , Luonan Chen  6   7   8
Affiliations

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

Xindong Zhang et al. BMC Bioinformatics. .

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.

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Figures

Figure 1
Figure 1
Overview of the proposed framework for identifying module biomarker.
Figure 2
Figure 2
Computational strategy for generating discriminative modules. Computational strategy for generating discriminative modules by maximizing discriminative area of module activity. The discriminative area is defined as the area under two probability density functions of module activities corresponding to normal samples and case (disease) samples.
Figure 3
Figure 3
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.
Figure 4
Figure 4
Performance analysis of the identified module biomarker. (A) The robustness of classification accuracy in perturbation data with different ratio of artificial noises. The mean accuracy of the proposed classifier decreases progressively from 84.02% to 73.26% when ratio of noise increases from 1% to 10%. (B) Comparison of biomarkers identified by different methods in GSE18732. ROC curves shows a superior performance in classification of module biomarker identified in this work (AUC = 0.96). (C) Histogram of mean accuracy with variance for biomarkers identified by our method, SVM-RFE and PAC. We also randomized the interactions of background network (PPIs) 50 times and identified a module biomarker using the proposed method, then mean accuracy and variance are calculated for 10-fold cross-validation across 5 datasets used in this work. Results show a stable performance across tissues for identified biomarkers.
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