PIK3R1, SPNB2, and CRYAB as Potential Biomarkers for Patients with Diabetes and Developing Acute Myocardial Infarction

Zheng, Yue; Lang, Yuheng; Qi, Zhenchang; Gao, Wenqing; Hu, Xiaomin; Li, Tong

doi:10.1155/2021/2267736

Cited by 7 publications

(5 citation statements)

References 32 publications

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“…In previous bioinformatic analysis, PIK3R1, RAC1, GNG3, GNAI1, CDC42, and ITGB1 have been identified as candidate genes of the pathogenesis of type 2 diabetes [ 33 ]. For instance, epidemiological studies have demonstrated that PIK3R1 exerts a critical role in insulin signal transduction during type 2 diabetes development [ 34 , 35 ]. In vitro and in vivo evidence supports that RAC results in the onset of mitochondrial dysregulation via mediating phagocyte-like NADPH oxidase- (Nox-) reactive oxygen species- (ROS-) JNK1/2 signaling pathway in the islet β cells [ 36 ].…”

Section: Discussionmentioning

confidence: 99%

[Retracted] Establishment and Verification of a Gene Signature for Diagnosing Type 2 Diabetics by WGCNA, LASSO Analysis, and In Vitro Experiments

Shao

Zhang

Liu

et al. 2022

BioMed Research International

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Objective. The incidence and prevalence of type 2 diabetes are increasing with age. Nevertheless, there is lack of sensitive diagnostic tools and effective therapeutic regimens. We aimed to establish and verify a practical and valid diagnostic tool for this disease. Methods. WGCNA was presented on the expression profiling of type 2 diabetic and normal islets in combined GSE25724 and GSE38642 datasets. By LASSO Cox regression analyses, a gene signature was constructed based on the genes in diabetes-related modules. ROC curves were plotted for assessing the diagnostic efficacy. Correlations between the genes and immune cell infiltration and pathways were analyzed. BST2 and BTBD1 expression was verified in glucotoxicity-induced and normal islet β cells. The influence of BST2 on β cell dysfunction was investigated under si-BST2 transfection. Results. Totally, 14 coexpression modules were constructed, and red and cyan modules displayed the correlations to diabetes. The LASSO gene signature (BST2, BTBD1, IFIT1, IFIT3, and RTP4) was developed. The AUCs in the combined datasets and GSE20966 dataset were separately 0.914 and 0.910, confirming the excellent performance in diagnosing type 2 diabetes. Each gene in the model was distinctly correlated to immune cell infiltration and key signaling pathways (TGF-β and P53, etc.). The abnormal expression of BST2 and BTBD1 was confirmed in glucotoxicity-induced β cells. BST2 knockdown ameliorated β cell dysfunction and altered the activation of TGF-β and P53 pathways. Conclusion. Our findings propose a gene signature with high efficacy to diagnose type 2 diabetes, which could assist and improve early diagnosis and therapy.

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Section: Discussionmentioning

confidence: 99%

[Retracted] Establishment and Verification of a Gene Signature for Diagnosing Type 2 Diabetics by WGCNA, LASSO Analysis, and In Vitro Experiments

Shao

Zhang

Liu

et al. 2022

BioMed Research International

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“…A previous study reported that LXN (latexin) [84], LMNA (lamin A/C) [85], PFKFB3 [86], NEU1 [87], TBK1 [88], GRN (granulin precursor) [89], CTSD (cathepsin D) [90], ACADS (acyl-CoA dehydrogenase short chain) [91], IRF7 [92], S1PR1 [93], ZAP70 [94], IDH1 [95], IL15 [96], PIK3R1 [97], OSM (oncostatin M) [98], SOCS3 [99], USP21 [100], CEP19 [101], KDM2A [102], TP53 [103], BRD2 [104], ATP6 [105], BRD4 [106], COX2 [107], RPS6 [108], ND2 [109], CYTB (cytochrome b) [110] and COX1 [111] are altered expressed in obesity. Altered expression of BCL3 [112], TRAF2 [113], NEU1 [114], SNAP29 [115], AGPAT2 [116], LPCAT3 [117], ADORA2B [118], CTSD (cathepsin D) [119], ACADS (acyl-CoA dehydrogenase short chain) [120], ACAD9 [121], E4F1 [122], IRF7 [123], TAF1 [124], S1PR1 [125], RASSF1 [126], ELAC2 [127], RNF146 [128], COX15 [129], SMYD2 [130], IDH1 [131], MTO1 [132], IL15 [133], PIK3R1 [134], ASB1 [135], OSM (oncostatin M) [136], ZNF791 [137], GBA (glucosylceramidase beta) [138], SOCS3 [139], SLC39A7 [140], AKIP1 [141], AMIGO2 [142], GLUL (glutamate-ammonia ligase) [143], SEMA4D [144], KDM2A [145], TP53 [146], JARID2 [147], CTBP1 [148], ATP6 [149], RPL7 [150], HSP90AA1 [151], BRD4 [152], PSMB4 [153], COX2 [154], JUND (JunD proto-oncogene, AP-1 transcription factor subunit) [155], RPS5 [156], RACK1 […”

Section: Discussionmentioning

confidence: 99%

“…Altered expression of BCL3 [112], TRAF2 [113], NEU1 [114], SNAP29 [115], AGPAT2 [116], LPCAT3 [117], ADORA2B [118], CTSD (cathepsin D) [119], ACADS (acyl-CoA dehydrogenase short chain) [120], ACAD9 [121], E4F1 [122], IRF7 [123], TAF1 [124], S1PR1 [125], RASSF1 [126], ELAC2 [127], RNF146 [128], COX15 [129], SMYD2 [130], IDH1 [131], MTO1 [132], IL15 [133], PIK3R1 [134], ASB1 [135], OSM (oncostatin M) [136], ZNF791 [137], GBA (glucosylceramidase beta) [138], SOCS3 [139], SLC39A7 [140], AKIP1 [141], AMIGO2 [142], GLUL (glutamate-ammonia ligase) [143], SEMA4D [144], KDM2A [145], TP53 [146], JARID2 [147], CTBP1 [148], ATP6 [149], RPL7 [150], HSP90AA1 [151], BRD4 [152], PSMB4 [153], COX2 [154], JUND (JunD proto-oncogene, AP-1 transcription factor subunit) [155], RPS5 [156], RACK1 [157], ND1 [158], CCND2 [159], COX1 [160], TLK1 [161] and TMPO (thymopoietin) [162] are associated with cardiovascular complications. A previous study found that LMNA (lamin A/C) [85], SLC11A2 [163], CRTC2 [164], TBK1 [165], GRN (granulin precursor) [166], CTSD (cathepsin D) [167], STARD10 [168], PGRMC1 [169], TFE3 [170], POR (cytochrome p450 oxidoreductase) [171], SESN1 [172], IL15 [173], PIK3R1 [134], OSM (oncostatin M) [98], SOCS3 [174], USP21 [100], GLUL (glutamate-ammonia ligase) [175], IL1R1 [176], TP53 [177], PPM1A [178], CTBP1 [179], DNAJC3 […”

Section: Discussionmentioning

confidence: 99%

“…Pathways include metabolism [ [89], CTSD (cathepsin D) [90], ACADS (acyl-CoA dehydrogenase short chain) [91], IRF7 [92], S1PR1 [93], ZAP70 [94], IDH1 [95], IL15 [96], PIK3R1 [97], OSM (oncostatin M) [98], SOCS3 [99], USP21 [100], CEP19 [101], KDM2A [102], TP53 [103], BRD2 [104], ATP6 [105], BRD4 [106], COX2 [107], RPS6 [108], ND2 [109], CYTB (cytochrome b) [110] and COX1 [111] are altered expressed in obesity. Altered expression of BCL3 [112], TRAF2 [113], NEU1 [114], SNAP29 [115], AGPAT2 [116], LPCAT3 [117], ADORA2B [118], CTSD (cathepsin D) [119], ACADS (acyl-CoA dehydrogenase short chain) [120], ACAD9 [121], E4F1 [122], IRF7 [123], TAF1 [124], S1PR1 [125], RASSF1 [126], ELAC2 [127], RNF146 [128], COX15 [129], SMYD2 [130], IDH1 [131], MTO1 [132], IL15 [133], PIK3R1 [134], ASB1 [135], OSM (oncostatin M)…”

Section: Discussionmentioning

confidence: 99%

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Bioinformatics Analysis of next generation sequencing data for Risk Prediction in Patients with Type 1 diabetes mellitus

Vastrad¹,

Vastrad²

2022

Preprint

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Type 1 diabetes mellitus (T1DM) comprise the most common forms of autoimmune disease. The aim of this investigation was to apply a bioinformatics approach to reveal related pathways or genes involved in the development of T1DM. The next generation sequencing (NGS) dataset GSE182870 was downloaded from the gene expression omnibus (GEO) database. Differentially expressed gene (DEG) analysis was performed using DESeq2. The g:Profiler was utilized to analyze the functional enrichment, gene ontology (GO) and REACTOME pathway of the differentially expressed genes. Protein-protein interaction (PPI) network, modules, miRNA-hub gene regulatory network, and TF-hub gene regulatory network were conducted via comprehensive target prediction and network analyses. Finally, hub genes were validated by using receiver operating characteristic curve analysis. A total of 860 DEGs were screened out from NGS dataset, among which 477 genes were up regulated and 383 genes were down regulated. GO enrichment analysis indicated that up regulated genes were mainly involved in cellular metabolic process, intracellular anatomical structure and catalytic activity, and the down regulated genes were significantly enriched in cellular nitrogen compound biosynthetic process, protein containing complex and heterocyclic compound binding. REACTOME pathway enrichment analysis showed that the up regulated genes were mainly enriched in metabolism of carbohydrates and the down regulated genes were significantly enriched in metabolism of RNA. A PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed, and hub genes (MAPK14, RHOC, MAD2L1, TAF1, TRAF2, HSP90AA1, TP53, HSP90AB1, UBA52 and RACK1) were identified. This study provides further insights into the underlying pathogenesis of T1DM.

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“…Among the extensive T2DM-related complications, acute myocardial infarction is a life-threatening and severe complication ( Rosenblit, 2019 ). More than one-third of T2DM patients with myocardial infarction (MI) die within 10 years, and long-term all-cause mortality and cardiovascular mortality are even higher in younger patients than in elderly patients ( Singh et al, 2020 ; Zheng et al, 2021 ). Numerous studies have shown that strict glycemic control promotes a decrease in non-fatal MI ( Rodriguez-Gutierrez et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Systematic investigation of the underlying mechanisms of GLP-1 receptor agonists to prevent myocardial infarction in patients with type 2 diabetes mellitus using network pharmacology

Deng

Ren²,

Li³

et al. 2023

Front. Pharmacol.

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Background: Several clinical trials have demonstrated that glucagon-like peptide-1 (GLP-1) receptor agonists (GLP-1RAs) reduce the incidence of non-fatal myocardial infarction (MI) in patients with type 2 diabetes mellitus (T2DM). However, the underlying mechanism remains unclear. In this study, we applied a network pharmacology method to investigate the mechanisms by which GLP-1RAs reduce MI occurrence in patients with T2DM.Methods: Targets of three GLP-1RAs (liraglutide, semaglutide, and albiglutide), T2DM, and MI were retrieved from online databases. The intersection process and associated targets retrieval were employed to obtain the related targets of GLP-1RAs against T2DM and MI. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genes (KEGG) enrichment analyses were performed. The STRING database was used to obtain the protein-protein interaction (PPI) network, and Cytoscape was used to identify core targets, transcription factors, and modules.Results: A total of 198 targets were retrieved for the three drugs and 511 targets for T2DM with MI. Finally, 51 related targets, including 31 intersection targets and 20 associated targets, were predicted to interfere with the progression of T2DM and MI on using GLP-1RAs. The STRING database was used to establish a PPI network comprising 46 nodes and 175 edges. The PPI network was analyzed using Cytoscape, and seven core targets were screened: AGT, TGFB1, STAT3, TIMP1, MMP9, MMP1, and MMP2. The transcription factor MAFB regulates all seven core targets. The cluster analysis generated three modules. The GO analysis for 51 targets indicated that the terms were mainly enriched in the extracellular matrix, angiotensin, platelets, and endopeptidase. The results of KEGG analysis revealed that the 51 targets primarily participated in the renin-angiotensin system, complement and coagulation cascades, hypertrophic cardiomyopathy, and AGE-RAGE signaling pathway in diabetic complications.Conclusion: GLP-1RAs exert multi-dimensional effects on reducing the occurrence of MI in T2DM patients by interfering with targets, biological processes, and cellular signaling pathways related to atheromatous plaque, myocardial remodeling, and thrombosis.

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PIK3R1, SPNB2, and CRYAB as Potential Biomarkers for Patients with Diabetes and Developing Acute Myocardial Infarction

Cited by 7 publications

References 32 publications

[Retracted] Establishment and Verification of a Gene Signature for Diagnosing Type 2 Diabetics by WGCNA, LASSO Analysis, and In Vitro Experiments

[Retracted] Establishment and Verification of a Gene Signature for Diagnosing Type 2 Diabetics by WGCNA, LASSO Analysis, and In Vitro Experiments

Bioinformatics Analysis of next generation sequencing data for Risk Prediction in Patients with Type 1 diabetes mellitus

Systematic investigation of the underlying mechanisms of GLP-1 receptor agonists to prevent myocardial infarction in patients with type 2 diabetes mellitus using network pharmacology

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