Integrated Datasets of Proteomic and Metabolomic Biomarkers to Predict Its Impacts on Comorbidities of Type 2 Diabetes Mellitus

Amrita K Cheema,1 Prabhjit Kaur,1 Amina Fadel,2 Noura Younes,3 Mahmoud Zirie,4 Nasser M Rizk2,5 1Department of Oncology, Lombardi Comprehensive Cancer Center at Georgetown University Medical Center, Washington, DC, USA; 2Biomedical Sciences Department, College of Health Sciences and Biomedical Research Center, QU Health, Qatar University, Doha, Qatar; 3Clinical Chemistry Lab, Hamad Medical Corporation, Doha, Qatar; 4Endocrine Department, Hammad Medical Corporation, Doha, Qatar; 5Physiology Department, Mansoura Faculty of Medicine, Mansoura, EgyptCorrespondence: Nasser M RizkBiomedical Sciences Department, College of Health Sciences, Biomedical Research Center, QU Health, Qatar University, Doha, QatarEmail nassrizk@qu.edu.qaObjective: The objective of the current study is to accomplish a relative exploration of the biological roles of differentially dysregulated genes (DRGs) in type 2 diabetes mellitus (T2DM). The study aimed to determine the impact of these DRGs on the biological pathways and networks that are related to the associated disorders and complications in T2DM and to predict its role as prospective biomarkers.Methods: Datasets obtained from metabolomic and proteomic profiling were used for investigation of the differential expression of the genes. A subset of DRGs was integrated into IPA software to explore its biological pathways, related diseases, and their regulation in T2DM. Upon entry into the IPA, only 94 of the DRGs were recognizable, mapped, and matched within the database.Results: The study identified networks that explore the dysregulation of several functions; cell components such as degranulation of cells; molecular transport process and metabolism of cellular proteins; and inflammatory responses. Top disorders associated with DRGs in T2DM are related to organ injuries such as renal damage, connective tissue disorders, and acute inflammatory disorders. Upstream regulator analysis predicted the role of several transcription factors of interest