ARG1 single nucleotide polymorphisms rs2781666 and rs2781665 confer risk of type 2 diabetes mellitus

2023

Preprint

Coronary artery disease (CAD) is the most common cardiovascular disorder and the leading cause of heart related deaths in world. Increasing molecular targets have been discovered for CAD and CAD - related complications prognosis and therapy. However, there is still an urgent need to identify novel biomarkers. Therefore, we evaluated biomarkers that might help the diagnosis and treatment of CAD and CAD related complications. We searched next generation sequencing (NGS) dataset (GSE202625) and identified differentially expressed genes (DEGs) by comparing CAD and normal control samples using DESeq2. Gene ontology (GO) and pathway enrichment analyses of the DEGs were performed using the g:Profiler online database. The protein protein interaction (PPI) network was plotted with IMEx interactome and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC1. MiRNA hub gene regulatory network and TF hub gene regulatory network analysis was performed to identify the hub genes, miRNAs and TFs. Receiver operating characteristic (ROC) curve analysis was used to predict the diagnostic effectiveness of the hub genes. A total of 118 DEGs (479 up regulated genes and 479 down regulated genes) were detected. The GO enrichment analysis indicated that the DEGs most significantly enriched in cellular response to stimulus and biosynthetic process. The REACTOME pathway enrichment analysis revealed that the DEGs were most significantly enriched in immune system and eukaryotic translation elongation. PPI network, modules, miRNA hub gene regulatory network and TF hub gene regulatory network analysis demonstrated that EGR1, SIRT1, STAT1, LRRK2, HIF1A, CSNK2B, RPS3, RPS2, RPS4X and HDAC11 were the hub genes. On the whole, the findings of this study enhance our understanding of the potential molecular mechanisms of CAD and CAD-related complications, and provide potential targets for further research.

Section: Discussionmentioning

confidence: 99%

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

Vastrad¹,

2023

Preprint

“…S100A12 [324], CDH1 [325], S100A9 [326], ANK1 [327], KCNH2 [328], HP (haptoglobin) [329], FRMD4A [330], SNCA (synuclein alpha) [331], FBXO7 [332], PINK1 [333], B2M [334], KCNH3 [335], CA2 [336], FUZ (fuzzy planar cell polarity protein) [337], UBB (ubiquitin B) [338], C5AR1 [339], CBS (cystathionine beta-synthase) [340], F12 [341], TSPAN5 [342], NQO2 [343], NDUFA1 [344], SRXN1 [345], BASP1 [346], GPX1 [347], OLIG2 [348], EFHC2 [349], FOXA1 [350], PAX4 [351], BGN (biglycan) [352], IL33 [353], LRIG3 [354], GJA1 [355], SHANK3 [356], ARC (activity regulated cytoskeleton associated protein) [357], GFAP (glial fibrillary acidic protein) [358], UNC13A [359], MSTN (myostatin) [360], HSPG2 [361], TERT (telomerase reverse transcriptase) [362], GNB3 [363], L1CAM [364], CALB1 [365], RYR2 [366], MYO15A [367], MYH11 [368], GDF15 [369], CAV1 [370], KIRREL3 [371], COBL (cordon-bleu WH2 repeat protein) [372], LOX (lysyl oxidase) [373], SPARCL1 [374], FLNC (filamin C) [375], TMPRSS4 [376], VWA2 [377], OGDHL (oxoglutarate dehydrogenase L) [378], CYP3A5 [379] and FBXO40 [380] were revealed to be associated with cognitive dysfunction. S100A12 [381], SLC6A19 [382], TXN (thioredoxin) [383], TLR9 [384], S100P [385], S100A9 [386], ANK1 [387], CA1 [388], HP (haptoglobin) [389], ARG1 [390], SNCA (synuclein alpha) [391], STARD10 [392], PINK1 [393], TFR2 [394], B2M [395...…”

Section: Discussionmentioning

confidence: 99%

Identification of Key Genes and Biological Pathways in Bipolar Disorder by Bioinformatics and Next Generation Sequencing Data Analysis

Vastrad¹,

2022

Preprint

Bipolar disorder (BD), also known as psychiatric disorder, affects millions of people all over the world. The aim of this investigation was to screen and verify hub genes involved in BD as well as to explore potential molecular mechanisms. The next generation sequencing (NGS) dataset GSE124326 was downloaded from the Gene Expression Omnibus (GEO) database, which contained 480 samples, including 240 BD and 240 normal controls. Differentially expressed genes (DEGs) were filtered and subjected to gene ontology (GO) and pathway enrichment analyses. A protein–protein interaction (PPI) network and module analysis were used to identify hub genes. The miRNA-hub gene regulatory network and TF-hub gene regulatory network were also constructed. Receiver operating characteristic curve (ROC) analysis was used to validate hub genes. In this work, 957 DEGs, including 477 up regulated and 480 down regulated, were obtained from NGS data. The GO and pathway enrichment analyses of the DEGs showed that the up regulated genes were enriched in the neutrophil degranulation, immune system, transport, cytoplasm and enzyme regulator activity, and the down regulated genes were enriched in extracellular matrix organization, diseases of metabolism, multicellular organismal process, cell periphery and metal ion binding. Finally, through analyzing the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, we screened hub genes UBB, UBE2D1, TUBA1A, RPL11, RPS24, NOTCH3, CAV1, CNBD2, CCNA1 and MYH11 by the Cytoscape software. The current investigation illustrates a characteristic NGS data in BD, which might contribute to the interpretation of the progression of BD and provide novel biomarkers and therapeutic targets for BD.

“…MMP9 [262], IGF2 [273], SOCS3 [274], MMP8 [265], OSM (oncostatin M) [266], PLAU (plasminogen activator, urokinase) [275], ADAMDEC1 [269], IL1RN [270], TLR4 [271], CD80 [276] and TXNIP (thioredoxin interacting protein) [272] make great contributions to the progression of nasal polyps. MMP9 [277], S100A12 [278], IGF2 [279], GPR84 [280], SOCS3 [281], ANXA3 [282], IGFBP2 [283], NOS1AP [284], G0S2 [285], HP (haptoglobin) [286], MMP8 [287], SLC6A19 [288], OSM (oncostatin M) [289], S100A8 [290], FFAR3 [291], ARG1 [292], ADM (adrenomedullin) [293], S100A9 [294], NRG1 [295], IL1R1 [296], IL4R [297], TSPO (translocator protein) [298], TXN (thioredoxin) [299], PLAU (plasminogen activator, urokinase) [300], SLC11A1 [301], TRPM2 [302], RETN (resistin) [303], NLRC4 [304], CD55 [305], ALOX5AP [306], GPR160 [307], TRPM6 [308], ALOX5 [309], IL1RN [310], ADM2 [311], DGAT2 [312], TLR4 [313], ENTPD1 [314], GRB10 [315], IL17RA [316], IRS2 [317], MGAM (maltase-glucoamylase) [318], FADS2 [319], SLC22A4 [320], KCNJ15 [321], PPP1R3B [322], CTSD (cathepsin D) [323], SERPINB11 [324], COL4A3 [325], ADAM12 [326], SOX5 [327], CD80 [328], ERBB2 [329], POU2AF1 [330], FASLG (Fas ligand) [331], SOX13 [332], BANK1 [333], IL2RA [334], AQP3 [335], CCR4 [336], CD74 [337], FCRL3 [338], ITGB7 [339],...…”

Section: Discussionmentioning

confidence: 99%

Identification of Key Genes and Underlying Mechanisms in Cystic Fibrosis and its Associated Complications Based on Bioinformatics Analysis of Next Generation Sequencing Data Analysis

Vastrad¹,

2022

Preprint

Cystic fibrosis (CF) is one of the inherited autosomal recessive disorders with very complicated pathogenesis. Identifying the molecular signatures and specific biomarkers of CF might provide novel clues for CF and and CF associated complications prognosis and targeted therapy. Based on the nexgeneration sequencing (NGS) dataset GSE136371 downloaded from the Gene Expression Omnibus (GEO) database, the differentially expressed genes (DEGs) between CF samples and normal controls were identified by using DESeq2 bioconductor package of R software. Gene ontology (GO) and REACTOME pathway enrichment analyses were applied for the DEGs. Then protein-protein interaction (PPI) network of these DEGs was visualized by Cytoscape with IMEx interactome database. The most significant module from the PPI network was selected for GO and pathway enrichment analysis. Subsequently, a miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed to identify hub genes, miRNAs and TFs. Finally, receiver operating characteristic curve (ROC) analysis was established to validate these hub genes. Total of 917 DEGs were identified between CF and normal control samples in GSE136371 dataset, including 479 up regulated and 438 down regulated genes. The most enriched DEGs in GO and pathway enrichment analysis were mainly associated with response to stimulus, regulation of cellular process, Neutrophil degranulation and rRNA processing. PPI network, module analysis, miRNA-hub gene regulatory network and TF-hub gene regulatory network predicted ten hub genes (FN1, UBE2D1, SRPK1, MAPK14, CEBPB, HSP90AB1, HSPA8, XRCC6, NCL and PARP1). In conclusion, the DEGs, relative pathways and hub genes identified in the present study might aid in understanding of the molecular mechanisms underlying CF and CF associated complications progression and provide potential molecular targets and biomarkers for CF and CF associated complications.