Sleeve gastrectomy improves lipid dysmetabolism by downregulating the USP20-HSPA2 axis in diet-induced obese mice

Zhang, Wenjie; Shi, Bowen; Li, Shirui; Liu, Zenglin; Li, Songhan; Dong, Shuohui; Zhao, Yuan; Zhu, Jiankang; Zhong, Maiying

doi:10.3389/fendo.2022.1041027

Cited by 3 publications

(2 citation statements)

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“…RUNX1 [395], CDKN2A [403], HSPA2 [423], hsa-mir-132-3p [615], hsa-mir-155-5p [610] and USF2 [616] have been reported to be expressed in Alzheimer’s disease. RUNX1 [449], CAV1 [472], STAT6 [468], CDKN2A [226], HSPA2 [485], hsa-mir-10b-5p [617] and HOXA5 [618] were related to the obesity. RUNX1 [511], CAV1 [534], CACNA1A [543], STAT6 [528], CDKN2A [105], hsa-mir-4516 [619], SRY (sex determining region Y) [620], FOXC1 [621] and PRDM1 [622] might offer useful information for treating hypertension.…”

Section: Discussionmentioning

confidence: 99%

Screening of the Key Genes and Signaling Pathways for Schizophrenia Using Bioinformatics and Next Generation Sequencing Data Analysis

Vastrad,

Vastrad

2023

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Schizophrenia is thought to be the most prevalent chronic psychiatric disorder. Numerous proteins have been identified that are associated with the occurrence and development of schizophrenia. This study aimed to identify potential core genes and pathways involved in schizophrenia, through exhaustive bioinformatic and next generation sequencing (NGS) data analyses using GSE20966 NGS data of neural progenitor cells and neurons obtained from healthy controls and patients with schizophrenia. The NGS data were downloaded from the Gene Expression Omnibus database. NGS data was processed by the DESeq2 package in R software and the differentially expressed genes (DEGs) were identified. Gene Ontology (GO) enrichment analysis and REACTOME pathway enrichment analysis were carried out to identify potential biological functions and pathways of the DEGs. Protein-protein interaction (PPI) network, module, miRNA-hub gene regulatory network and TF-hub gene regulatory network analysis were performed to identify the hub genes, miRNA and TFs. Potential hub genes were analyzed using receiver operating characteristic (ROC) curves in the R package (pROC). In this investigation, an overall 955 DEGs were identified: 478 genes were remarkably up regulated and 477 genes were distinctly down regulated. These genes were enriched for GO terms and pathways mainly involved in the multicellular organismal process, GPCR ligand binding, regulation of cellular process and amine ligand-binding receptors. MYC, FN1, CDKN2A, EEF1G, CAV1, ONECUT1, SYK, MAPK13, TFAP2A and BTK were considered the potential hub genes. miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed successfully. On the whole, the findings of this investigation enhance our understanding of the potential molecular mechanisms of schizophrenia and provide potential targets for further investigation.

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Section: Discussionmentioning

confidence: 99%

Screening of the Key Genes and Signaling Pathways for Schizophrenia Using Bioinformatics and Next Generation Sequencing Data Analysis

Vastrad,

Vastrad

2023

Preprint

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“…CCR2 [461], FASLG (Fas ligand) [462], CD28 [463], LYZ (lysozyme) [464], CCL5 [465], CCL3 [466], C6 [467], MSR1 [468], HGF (hepatocyte growth factor) [469], VEGFA (vascular endothelial growth factor A) [470], ADM (adrenomedullin) [471], CR2 [472], MAG (myelin associated glycoprotein) [473], CDH1 [474], OLIG2 [475], SEMA4D [476], ADAMTS4 [477], BMP2 [478], ERBB3 [479] and NKX2-2 [480] have been identified as a hub gene in spinal cord injury in several studies. CCR2 [481], FASLG (Fas ligand) [286], CD24 [482], CD28 [483], LYZ (lysozyme) [484], IL7R [485], CCL5 [486], LTF (lactotransferrin) [487], GPNMB (glycoprotein nmb) [488], CTLA4 [489], IRF4 [490], CHIT1 [491], NPY2R [492], TAB2 [131], CD52 [302], CD163 [493], MSR1 [306], HGF (hepatocyte growth factor) [494], TRPM8 [495], TTR (transthyretin) [496], F13A1 [497], ADM (adrenomedullin) [498], COL9A3 [499], ANGPTL4 [500], CHI3L1 [501], BMP8A [502], SREBF1 [503], CDKN1C [504], INSIG1 [505], ATF3 [506], HK2 [507], TF (transferrin) [508], MOG (myelin oligodendrocyte glycoprotein) [509], ADAMTS4 [510], BMP2 [511], HSPA2 [512], LMNA (lamin A/C) [330], HSD11B1 [513], NKX2-2 [514], NHLH2 [433], FGF11 [515], ASPA (aspartoacylase) [336], FASN (fatty acid synthase) [516], PNPLA3 [517], MMP15 [518], CBR1 [519], LRP2 [520], WNT7A [340], GPR142 [521], GPD1 [343], LIPE (lipase E, hormone sensitive type) [522], CNDP1 […”

Section: Discussionmentioning

confidence: 99%

Identification of candidate biomarkers and pathways associated with multiple sclerosis using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2023

Preprint

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Multiple sclerosis (MS) is the main reason for disability caused by autoimmunity. However, effective biomarkers for MS have not been identified. This study explored the molecular mechanisms and potential therapeutic targets for MS occurrence and progression using bioinformatics and next generation sequencing (NGS) data analysis. NGS data GSE138614, including patients with MS and 25 normal control samples, were obtained from Gene Expression Omnibus (GEO) database Differentially expressed genes (DEGs) were filtered and subjected to gene ontology (GO) and pathway enrichment analyses. Protein–protein interaction (PPI) network and module analyses were performed based on the DEGs. MiRNA-hub gene regulatory network and TF-hub gene regulatory network analysis were built by Cytoscape to predict the underlying microRNAs (miRNAs) and transcription factors (TFs) associated with hub genes. We also validated the identified hub genes via receiver operating characteristic (ROC) curve analysis. A total of 959 DEGs (479 up regulated genes and 480 down regulated genes) were detected. The GO terms and pathways of DEGs involve immune system process, developmental process, immune system and regulation of cholesterol biosynthesis by SREBP (SREBF). The network analysis revealed that hub genes include LCK, PYHIN1, SLAMF1, DOK2, TAB2, CFTR, RHOB, LMNA, EGLN3 and ERBB3 might be involved in the development of MS. The present investigation illustrates a characteristic gene profile in MS, which might contribute to the interpretation of the progression of MS and provide novel biomarkers and therapeutic targets for MS.

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Screening and identification of key biomarkers in diabetic kidney disease and its complications: Evidence from bioinformatics and next generation sequencing data analysis

Vastrad¹,

Vastrad²

2023

Preprint

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Diabetic patients are prone to diabetic kidney disease (DKD), which may cause cardiovascular damage, hypertension and obesity, and reduce quality of life. As a result, the life quality of patients was seriously reduced. However, the pathogenesis of diabetic kidney disease (DKD) has not been fully elucidated, and current treatments remain inadequate. Therefore, it is essential to explore the molecular mechanism of DKD and its complications. Next Generation Sequancing (GSE217709) dataset was obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were picked out by R software. Then Gene ontology (GO) and REACTOME pathway enrichment analysis were performed by g:Profiler database, protein-protein interaction (PPI) of DEGs was constructed by Human Integrated Protein-Protein Interaction rEference (HIPPIE) database . Module analysis was carried out by Cytoscape plug-in PEWCC. Subsequently, miRNA-hub gene regulatory network and TF- hub gene regulatory network were performed by miRNet database and NetworkAnalyst database. Finally, validation of hub genes was performed by receiver operating characteristic (ROC) curve analysis to predict the diagnostic effectiveness of the hub genes. In total, 958 DEGs, including 479 up regulated and 479 down regulated genes, were identified. The GO and pathway enrichment changes of DEGs were mainly enriched in biological regulation, multicellular organismal process, signaling by GPCR and extracellular matrix organization. Ten hub genes (HSPA8, HSP90AA1, HSPA5, SDCBP, HSP90B1, VCAM1, MYH9, FLNA, MDFI and PML) associated with DKD and its complications were identified. Bioinformatics analysis is a useful tool to explore the molecular mechanism and pathogenesis of DKD and its complications. The identified hub genes may participate in the onset and development of DKD and its complications and serve as therapeutic targets.

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Sleeve gastrectomy improves lipid dysmetabolism by downregulating the USP20-HSPA2 axis in diet-induced obese mice

Cited by 3 publications

References 50 publications

Screening of the Key Genes and Signaling Pathways for Schizophrenia Using Bioinformatics and Next Generation Sequencing Data Analysis

Screening of the Key Genes and Signaling Pathways for Schizophrenia Using Bioinformatics and Next Generation Sequencing Data Analysis

Identification of candidate biomarkers and pathways associated with multiple sclerosis using bioinformatics and next generation sequencing data analysis

Screening and identification of key biomarkers in diabetic kidney disease and its complications: Evidence from bioinformatics and next generation sequencing data analysis

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