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Minerva Medica 2022 June;113(3):532-41

DOI: 10.23736/S0026-4806.21.07478-4


language: English

Integrative global co-expression analysis identifies key microRNA-target gene networks as key blood biomarkers for obesity

Abdulhadi I. BIMA 1, Ayman Z. ELSAMANOUDY 1, 2, Abdulhakeem S. ALAMRI 3, 4, Raed FELIMBAN 5, 6, Majed FELEMBAN 5, 7, Kawthar S. ALGHAMDI 8, 9, Prabhakar R. KAIPA 10, Ramu ELANGO 9, 11, Noor A. SHAIK 9, 11, Babajan BANAGANAPALLI 9, 11

1 Department of Clinical Biochemistry, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia; 2 Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Mansoura University, Mansoura, Egypt; 3 Department of Clinical Laboratories Sciences, College of Applied Medical Sciences, University of Taif, Taif, Saudi Arabia; 4 Center of Biomedical Sciences Research (CBSR), Deanship of Scientific Research, University of Taif, Taif, Saudi Arabia; 5 Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia; 6 3D Bioprinting Unit, Center of Innovation in Personalised Medicine (CIPM), King Abdulaziz University, Jeddah, Saudi Arabia; 7 Center of Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah, Saudi Arabia; 8 Department of Biology, Faculty of Science, Hafar Al-Batin University, Hafar Al-Batin, Saudi Arabia; 9 Princess Al-Jawhara Al-Brahim Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia; 10 Department of Genetics, College of Science, Osmania University, Hyderabad, India; 11 Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia

BACKGROUND: Obesity is associated with the quantitative changes in miRNAs and their target genes. However, the molecular basis of their dysregulation and expression status correlations is incompletely understood. Therefore, this study aims to examine the shared differentially expressed miRNAs and their target genes between blood and adipose tissues of obese individuals to identify potential blood-based biomarkers.
METHODS: In this study, 3 gene expression datasets (two mRNA and one miRNA), generated from blood and adipose tissues of 68 obese and 39 lean individuals, were analyzed by a series of robust computational concepts, like protein interactome mapping, functional enrichment of biological pathways and construction of miRNA-mRNA and transcription factor gene networks.
RESULTS: The comparison of blood versus tissue datasets has revealed the shared differential expression of 210 genes (59.5% upregulated) involved in lipid metabolism and inflammatory reactions. The blood miRNA (GSE25470) analysis has identified 79 differentially expressed miRNAs (71% downregulated). The miRNA-target gene scan identified regulation of 30 shared genes by 22miRNAs. The gene network analysis has identified the inverse expression correlation between 8 target genes (TP53, DYSF, GAB2, GFRA2, NACC2, FAM53C, JNK and GAB2) and 3 key miRNAs (hsa-mir-940, hsa-mir-765, hsa-mir-612), which are further regulated by 24 key transcription factors.
CONCLUSIONS: This study identifies potential obesity related blood biomarkers from large-scale gene expression data by computational miRNA-target gene interactome and transcription factor network construction methods.

KEY WORDS: Obesity; Gene expression; MicroRNAs; Transcription factors

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