Screening and validation of differentially expressed genes in adipose tissue of patients with obesity and type 2 diabetes mellitus

White adipose tissue (WAT) plays a pivotal role in the onset of type 2 diabetes mellitus (T2DM) and obesity. Despite its significance the underlying pathogenesis and key genes associated with it remain elusive. In our study, we screened the differentially expressed genes (DEGs) in intra-abdominal WAT of T2DM patients with obesity, as well as those with simple obesity, aiming to lay a foundational theory for an in-depth investigation of T2DM pathogenesis and the identification of novel therapeutic targets. Gene expression datasets (GSE16415 and GSE71416) were retrieved from the Gene Expression Omnibus (GEO) database. We employed R for screening DEGs and conducted a functional enrichment analysis using the Metascape database. Combined Lasso regression and Boruta feature selection algorithms were used to identify key DEGs. Subsequently, these were cross-verified using the GSE29231 dataset. Samples and medical records were collected from clinical study participants. The mRNA and protein expressions of the key DEGs were verified using quantitative reverse transcription polymerase chain reaction and western blotting, respectively. We discerned a total of 130 DEGs, with 40 being upregulated and 90 downregulated. Functional and pathway enrichment analyses illuminated that these genes are instrumental in mediating metabolite and energy production, neutrophil-mediated immunity, and other associated biological processes. This includes their involvement in the tricarboxylic acid cycle, glycolysis/gluconeogenesis, peroxisome proliferator-activated receptors, and other signaling pathways. Two genes, CIDEA and FSCN1 emerged as key DEGs. The low expression of CIDEA and high expression of FSCN1 in the T2DM and obesity groups were verified in clinical samples (P < 0.05). We established that CIDEA and FSCN1 manifest significant differential expression in T2DM patients who are obese. This suggests their potential as risk assessment markers and therapeutic targets for T2DM.

[1]  Chao Tan,et al.  Integrated profiling identifies ITGB3BP as prognostic biomarker for hepatocellular carcinoma , 2021, Bosnian journal of basic medical sciences.

[2]  Yongyan Wu,et al.  Fascin actin-bundling protein 1 in human cancer: promising biomarker or therapeutic target? , 2021, Molecular therapy oncolytics.

[3]  S. Meri,et al.  The Effects of Type 2 Diabetes Mellitus on Organ Metabolism and the Immune System , 2020, Frontiers in Immunology.

[4]  Yi-xuan Yang,et al.  Identification of the fatty acid synthase interaction network via iTRAQ-based proteomics indicates the potential molecular mechanisms of liver cancer metastasis , 2020, Cancer Cell International.

[5]  J. Florez,et al.  Genetics of diabetes mellitus and diabetes complications , 2020, Nature Reviews Nephrology.

[6]  Á. Gil,et al.  Are we close to defining a metabolomic signature of human obesity? A systematic review of metabolomics studies , 2019, Metabolomics.

[7]  C. Liang,et al.  Neuroendocrine Regulation of Energy Metabolism Involving Different Types of Adipose Tissues , 2019, International journal of molecular sciences.

[8]  V. Puri,et al.  CIDE Proteins in Human Health and Disease , 2019, Cells.

[9]  M. Kratz,et al.  Contribution of Adipose Tissue Inflammation to the Development of Type 2 Diabetes Mellitus. , 2018, Comprehensive Physiology.

[10]  Keping Chen,et al.  Proteomics, metabolomics and metagenomics for type 2 diabetes and its complications , 2018, Life sciences.

[11]  J. Shaw,et al.  IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. , 2018, Diabetes research and clinical practice.

[12]  B. M. Abdullah,et al.  Genetic polymorphisms associated with overweight and obesity in uncontrolled Type 2 diabetes mellitus. , 2016, Biomarkers in medicine.

[13]  Robert R. Henry,et al.  Type 2 diabetes mellitus , 2015, Nature Reviews Disease Primers.

[14]  P. Arner,et al.  Cidea improves the metabolic profile through expansion of adipose tissue , 2015, Nature Communications.

[15]  S. Klein,et al.  Metabolically normal obese people are protected from adverse effects following weight gain. , 2015, The Journal of clinical investigation.

[16]  Josephine C. Adams,et al.  The roles of fascins in health and disease , 2011, The Journal of pathology.

[17]  A. Chawla,et al.  Cidea is associated with lipid droplets and insulin sensitivity in humans , 2008, Proceedings of the National Academy of Sciences.

[18]  M. Czech,et al.  Adipocyte dysfunctions linking obesity to insulin resistance and type 2 diabetes , 2008, Nature Reviews Molecular Cell Biology.

[19]  B. Fagerberg,et al.  Relations of adipose tissue CIDEA gene expression to basal metabolic rate, energy restriction, and obesity: population-based and dietary intervention studies. , 2007, The Journal of clinical endocrinology and metabolism.

[20]  P. Arner,et al.  A human-specific role of cell death-inducing DFFA (DNA fragmentation factor-alpha)-like effector A (CIDEA) in adipocyte lipolysis and obesity. , 2005, Diabetes.

[21]  Bei-fan Zhou.  [Predictive values of body mass index and waist circumference to risk factors of related diseases in Chinese adult population]. , 2002, Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi.

[22]  J. Shaw,et al.  Global and societal implications of the diabetes epidemic , 2001, Nature.

[23]  P. Zimmet,et al.  Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. Provisional report of a WHO Consultation , 1998, Diabetic medicine : a journal of the British Diabetic Association.

[24]  Tanya Barrett,et al.  The Gene Expression Omnibus Database , 2016, Statistical Genomics.

[25]  Karin Kraft,et al.  [Type-2 diabetes]. , 2010, MMW Fortschritte der Medizin.

[26]  Radha D. Pyarasani,et al.  Review Article Peroxisome Proliferator-activated Receptor Targets for the Treatment of Metabolic Diseases , 2022 .