Microarray meta-analysis reveals IL6 and p38β/MAPK11 as potential targets of hsa-miR-124 in endothelial progenitor cells: Implications for stent re-endothelization in diabetic patients

Abstract

Circulating endothelial progenitor cells (EPCs) play an important role in the repair processes of damaged vessels, favoring re-endothelization of stented vessels to minimize restenosis. EPCs number and function is diminished in patients with type 2 diabetes, a known risk factor for restenosis. Considering the impact of EPCs in vascular injury repair, we conducted a meta-analysis of microarray to assess the transcriptomic profile and determine target genes during the differentiation process of EPCs into mature ECs. Five microarray datasets, including 13 EPC and 12 EC samples were analyzed, using the online tool ExpressAnalyst. Differentially expressed genes (DEGs) analysis was done by Limma method, with an | log2FC| > 1 and FDR < 0.05. Combined p-value by Fisher exact method was computed for the intersection of datasets. There were 3,267 DEGs, 1,539 up-regulated and 1,728 down-regulated in EPCs, with 407 common DEGs in at least four datasets. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed enrichment for terms related to “AGE-RAGE signaling pathway in diabetic complications.” Intersection of common DEGs, KEGG pathways genes and genes in protein-protein interaction network (PPI) identified four key genes, two up-regulated (IL1B and STAT5A) and two down-regulated (IL6 and MAPK11). MicroRNA enrichment analysis of common DEGs depicted five hub microRNA targeting 175 DEGs, including STAT5A, IL6 and MAPK11, with hsa-miR-124 as common regulator. This group of genes and microRNAs could serve as biomarkers of EPCs differentiation during coronary stenting as well as potential therapeutic targets to improve stent re-endothelization, especially in diabetic patients.

The Identification of Key Genes and Biological Pathways in Polycystic Ovary Syndrome and its Complications by Next Generation Squancing (NGS) and Bioinformatics Analysis

“…This study indicated that enriched genes might play important role in PCOS. As previously reported, FOXP3 [446], TIMP4 [447], OAS3 [448], SOCS1 [128], CD74 [129], PLK1 [449], TGFB1 [450], RAPGEF1 [451], SERPINH1 [452], ATF5 [66], GATA6 [133], IGF2BP1 [453], IRX3 [454], FOXC2 [455], SNHG17 [456], TAF1C [457], HHIP (hedgehog interacting protein) [458], POMC (proopiomelanocortin) [459], DOK5 [76], COL1A1 [460], POSTN (periostin) [461], SOD3 [462], ADAMTS13 [463], FABP5 [464], MAPK11 [465], KCTD15 [83], HSPG2 [466], MST1 [467], MGAT2 [468], LGR5 [469], SPINK1 [470], CYP19A1 [471], PLAC8 [90], CD36 [472], AQP5 [473], GIPR (gastric inhibitory polypeptide receptor) [474], ARG1 [475], SORL1 [476], CD4 [477], F11R [478], LEPR (leptin receptor) [479], CDH13 [480], AR (androgen receptor) [481], FOXO1 [102], MALT1 [482], PAM (peptidylglycine alpha-amidating monooxygenase) [483], B2M [484], TCF4 [485], CAV2 [486], HSBP1 [487], COLEC12 [488], ADRA2A [489], SLC38A4 [490], TM6SF2 [491], GLP2R [111], KLB (klotho beta) [492], NFAT5 [493], PON2 [494], ZMAT3 [495], DST (dystonin) [426], MGST3 [496], COQ2 [118], EPHX2 [497], C2 [181] and FAM3C [120] are altered expression in type 2 diabetes mellitus. Previous studies have shown that DRD4 [498], TIMP4 [499], SOCS1 [500], CD74 [501], TGFB1 [502], ACTG2 [503], ISG15 [504], HOXC8 [...…”