Identification of Gene Signature Associated with Type 2 Diabetes Mellitus by Integrating Mutation and Expression Data.

Background: Type 2 Diabetes Mellitus (T2DM) is a chronic disease. The molecular diagnosis should be helpful for the treatment of T2DM patients. With the development of sequencing technology, a large number of differentially expressed genes were identified from expression data. However, the method of machine learning can only identify the local optimal solution as the signature.

Objective: The mutation information obtained by inheritance can better reflect the relationship between genes and diseases. Therefore, we need to integrate mutation information to more accurately identify the signature.

Methods: To this end, we integrated Genome-Wide Association Study (GWAS) data and expression data, combined with expression Quantitative Trait Loci (eQTL) technology to get T2DM predictive signature (T2DMSig-10). Firstly, we used GWAS data to obtain a list of T2DM susceptible loci. Then, we used eQTL technology to obtain risk Single Nucleotide Polymorphisms (SNPs), and combined with the pancreatic β-cells gene expression data to obtain 10 protein-coding genes. Next, we combined these genes with equal weights.

Results: After Receiver Operating Characteristic (ROC), single-gene removal and increase method, gene ontology function enrichment and protein-protein interaction network were used to verify the results showed that T2DMSig-10 had an excellent predictive effect on T2DM (AUC=0.99), and was highly robust.

Conclusion: In short, we obtained the predictive signature of T2DM, and further verified it.