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biomoleculesArticleMulti-Omics Information Analysis Uncovers Molecular Networks and Gene Regulators for Metabolic BiomarkersSu Yon JungTranslational Sciences Section, Jonsson Comprehensive Cancer Center, College of Nursing, University of California, Los Angeles, Los Angeles, CA 90095, USA; [email protected]: Jung, S.Y. Multi-Omics Data Evaluation Uncovers Molecular Networks and Gene Regulators for Metabolic Biomarkers. Biomolecules 2021, 11, 406. https://doi.org/ ten.3390/biom11030406 Academic Editor: Jorge Joven Received: 12 February 2021 Accepted: 7 March 2021 Published: ten MarchAbstract: The insulin-like development things (IGFs)/insulin resistance (IR) axis will be the key metabolic hormonal pathway mediating the biologic mechanism of many complicated human ailments, like sort two diabetes (T2DM) and cancers. The genomewide association study (GWAS)-based approach has neither totally characterized the phenotype variation nor offered a extensive understanding on the regulatory biologic mechanisms. We applied systematic genomics to integrate our previous GWAS data for IGF-I and IR with multi-omics datasets, e.g., whole-blood expression quantitative loci, molecular pathways, and gene network, to capture the full range of genetic functionalities related with IGF-I/IR and key drivers (KDs) in gene-regulatory networks. We identified both shared (e.g., T2DM, lipid metabolism, and estimated glomerular filtration signaling) and IR-specific (e.g., mechanistic target of rapamycin, phosphoinositide 3-kinases, and erb-b2 receptor tyrosine kinase 4 signaling) molecular biologic processes of IGF-I/IR axis regulation. Subsequent, by utilizing tissuespecific gene ene interaction networks, we identified each well-established (e.g., IRS1 and IGF1R) and novel (e.g., AKT1, HRAS, and JAK1) KDs in the IGF-I/IR-associated subnetworks. Our outcomes, if validated in extra genomic stud.