Identification of biomarkers, pathways and potential therapeutic agents for white adipocyte insulin resistance using bioinformatics analysis

Abstract

For the better understanding of insulin resistance (IR), the molecular biomarkers in IR white adipocytes and its potential mechanism, we downloaded two mRNA expression profiles from Gene Expression Omnibus (GEO). The white adipocyte samples in two databases were collected from the human omental adipose tissue of IR obese (IRO) subjects and insulin-sensitive obese (ISO) subjects, respectively. We identified 86 differentially expressed genes (DEGs) between the IRO and ISO subjects using limma package in R software. Gene Set Enrichment Analysis (GSEA) provided evidence that the most gene sets enriched in kidney mesenchyme development in the ISO subjects, as compared with the IRO subjects. The Gene Ontology (GO) analysis indicated that the most significantly enriched in cellular response to interferon-gamma. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the DEGs were most significantly enriched in cytokine-cytokine receptor interaction. Protein-Protein Interaction (PPI) network was performed with the STRING, and the top 10 hub genes were identified with the Cytohubba. CMap analysis found several small molecular compounds to reverse the altered DEGs, including dropropizine, aceclofenac, melatonin, and so on. Our outputs could empower the novel potential targets to treat omental white adipocyte insulin resistance, diabetes, and diabetes-related diseases.

Identification of potential hub genes via bioinformatics analysis combined with experimental verification in colorectal cancer

“…At present, microarrays technology combined with bioinformatics analysis is a very efficient method to identify potential hub genes in CRC. It is possible to detect differentially expressed messenger RNAs (mRNAs), microRNAs, and long noncoding RNAs 8‐10 . By using this technique, we can find many differentially expressed genes, which play key roles in the occurrence and development of CRC.…”

“…It is possible to detect differentially expressed messenger RNAs (mRNAs), microRNAs, and long noncoding RNAs. [8][9][10] By using this technique, we can find many differentially expressed genes, which play key roles in the occurrence and development of CRC.…”

Underlying Mechanism of Insulin Resistance: A Bioinformatics Analysis Based on Validated Related-Genes from Public Disease Databases

“…In this study, we explored the key molecules, functions, and pathways to display a comprehensive molecular mechanism of insulin resistance based on IRRGs collected from public disease databases. Although basic researches of insulin resistance mechanism were prevalent, the responding bioinformatics analysis reported by Yang et al in 2014 [ 22 ] and Zhang et al in 2019 [ 23 ]. In the previous studies, bioinformatics analysis was performed to construct regulatory networks and identify molecular biomarkers for insulin resistance based on DEGs shared in the GEO databases.…”

“…Recently, bioinformatics analysis as an interdisciplinary field has been viewed as an efficient tool in the study of complex mechanisms [ 21 ]. Two studies of insulin resistance were reported based on differential expressed genes (DEGs) between insulin sensitive and insulin resistant tissues extracted from the Gene Expression Omnibus (GEO) [ 22 , 23 ]. One study disclosed insulin resistance related transcription factors, including ETS1, AR, ESR1, and Myc [ 22 ], and the other revealed functions, signal pathway, and hub genes significantly related to insulin resistance [ 23 ].…”

Identification of Key Genes and Molecular Mechanisms Associated With Docetaxel Resistance in Castration Resistant Prostate Cancer Based on Bioinformatics Analysis

“…As an innovative and high-throughput research method, bioinformatics analysis of microarray data has been widely used in new drug target discovery, molecular diagnosis, and the molecular mechanisms of drug resistance [11,12]. Based on the bioinformatics analyses, You and Gao [13] found neuromedin U is the key gene conferring the alectinib resistance in Non-small cell lung cancer and Li, Wang [14] identi ed 8 hub genes and 4 molecular complex detections for progesterone resistance in endometrial cancer.…”