Identification of Inflammation-Related Biomarkers in Diabetes of the Exocrine Pancreas With the Use of Weighted Gene Co-Expression Network Analysis

Li, Guoqing; Sun, Jinfang; Zhang, Jun; Lv, Yingqi; Liu, Dechen; Zhu, Xiangyun; Qi, Liang; Chen, Zhiwei; Zheng, Yi; Su, Xianghui; Li, Ling

doi:10.3389/fendo.2022.839865

Cited by 6 publications

(3 citation statements)

References 41 publications

(43 reference statements)

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“…The enrichment analysis of differentially expressed genes was conducted using bioinformatics analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG). The liver fibrosis-related miRNAs were analyzed using the following databases: GSE33857 ( 19 ), GSE49012 ( 20 ), GSE59492 ( 21 ), GSE74872 ( 22 ) from Gene Expression Omnibus (GEO) ( http://www.ncbi.nih.gov/geo ) ( 23 ), miRWalk ( http://mirwalk.umm.uni-heidelberg.de/ ) ( 24 ), miR2Disease ( http://www.mir2disease.org/ ) ( 25 ) and HMDD ( http://www.cuilab.cn/hmdd ) ( 26 ). The potential interactions between VEGFA and HSP60 protein were predicted using the following database: GeneMania ( http://www.genemania.org/ ) ( 27 ).…”

Section: Methodsmentioning

confidence: 99%

Dihydroartemisinin inhibits the activation and proliferation of hepatic stellate cells by regulating miR‑29b‑3p

et al. 2023

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Liver fibrosis is an early pathological feature of hepatic diseases. Hepatic stellate cell (HSC) activation and disordered proliferation are associated with liver fibrosis. The present study identified significant differences in the expression levels of microRNA (miRNA/miR)-29b-3p in clinical samples and multiple miRNA databases. Subsequently, the specific antifibrotic mechanism of miR-29b-3p was further elucidated. Reverse transcription-quantitative PCR, western blot, ELISA and immunofluorescence were used to detect the expression levels of target genes and proteins. Oil red O, Nile red and trypan blue staining were used to evaluate HSC activation and cell viability. A luciferase assay was used to detect the relationship between miR-29b-3p and VEGFA. Adhesion, wound healing, apoptosis double staining and JC-1 assays were used to detect the effects of VEGFR1 and VEGFR2 knockdown on HSCs. Immunoprecipitation and fluorescence colocalization were used to identify interactions between the proteins. Furthermore, a rat fibrosis model was constructed to investigate the effects of dihydroartemisinin (DHA) and miR-29b-3p in vivo and in vitro . The results indicated that miR-29b-3p both inhibited the activation of HSCs and limited the proliferation of activated HSCs via lipid droplet recovery and VEGF pathway regulation. VEGFA was identified as a direct target of miR-29b-3p, and knockdown of VEGFA induced cell apoptosis and autophagy. Notably, VEGFR1 and VEGFR2 knockdown both promoted apoptosis; however, VEGFR1 knockdown inhibited autophagy, whereas VEGFR2 knockdown induced autophagy. Furthermore, it was revealed that VEGFR2 regulated autophagy by mediating the PI3K/AKT/mTOR/ULK1 pathway. VEGFR2 knockdown also led to ubiquitination of heat shock protein 60, ultimately inducing mitochondrial apoptosis. Finally, DHA was identified as a natural agonist of miR-29-3p that effectively prevented liver fibrosis in vivo and in vitro . Overall, the present study determined the molecular mechanism by which DHA inhibited HSC activation and prevented liver fibrosis.

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Section: Methodsmentioning

confidence: 99%

Dihydroartemisinin inhibits the activation and proliferation of hepatic stellate cells by regulating miR‑29b‑3p

et al. 2023

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“…We used the processed data to filter DEGs by using the “limma” packages of R software (version 4.1.2) with the screening criteria of adjusted P -value <0.05 and |log2 fold change (FC)| >0.5 ( 28 ). The Venn online mapping tool was used to screen for intersections of DEG and cellular senescence-associated genes, namely, cellular senescence-associated DEGs, which were visualized with heatmaps and volcano plots by the “ggplot2” package in R software ( 29 ).…”

Section: Methodsmentioning

confidence: 99%

Deciphering the endometrial immune landscape of RIF during the window of implantation from cellular senescence by integrated bioinformatics analysis and machine learning

Zhao

Zhao²,

Jiang³

et al. 2022

Front. Immunol.

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Recurrent implantation failure (RIF) is an extremely thorny issue in in-vitro fertilization (IVF)-embryo transfer (ET). However, its intricate etiology and pathological mechanisms are still unclear. Nowadays, there has been extensive interest in cellular senescence in RIF, and its involvement in endometrial immune characteristics during the window of implantation (WOI) has captured scholars’ growing concerns. Therefore, this study aims to probe into the pathological mechanism of RIF from cellular senescence and investigate the correlation between cellular senescence and endometrial immune characteristics during WOI based on bioinformatics combined with machine learning strategy, so as to elucidate the underlying pathological mechanisms of RIF and to explore novel treatment strategies for RIF. Firstly, the gene sets of GSE26787 and GSE111974 from the Gene Expression Omnibus (GEO) database were included for the weighted gene correlation network analysis (WGCNA), from which we concluded that the genes of the core module were closely related to cell fate decision and immune regulation. Subsequently, we identified 25 cellular senescence-associated differentially expressed genes (DEGs) in RIF by intersecting DEGs with cellular senescence-associated genes from the Cell Senescence (CellAge) database. Moreover, functional enrichment analysis was conducted to further reveal the specific molecular mechanisms by which these molecules regulate cellular senescence and immune pathways. Then, eight signature genes were determined by the machine learning method of support vector machine-recursive feature elimination (SVM-RFE), random forest (RF), and artificial neural network (ANN), comprising LATS1, EHF, DUSP16, ADCK5, PATZ1, DEK, MAP2K1, and ETS2, which were also validated in the testing gene set (GSE106602). Furthermore, distinct immune microenvironment abnormalities in the RIF endometrium during WOI were comprehensively explored and validated in GSE106602, including infiltrating immunocytes, immune function, and the expression profiling of human leukocyte antigen (HLA) genes and immune checkpoint genes. Moreover, the correlation between the eight signature genes with the endometrial immune landscape of RIF was also evaluated. After that, two distinct subtypes with significantly distinct immune infiltration characteristics were identified by consensus clustering analysis based on the eight signature genes. Finally, a “KEGG pathway–RIF signature genes–immune landscape” association network was constructed to intuitively uncover their connection. In conclusion, this study demonstrated that cellular senescence might play a pushing role in the pathological mechanism of RIF, which might be closely related to its impact on the immune microenvironment during the WOI phase. The exploration of the molecular mechanism of cellular senescence in RIF is expected to bring new breakthroughs for disease diagnosis and treatment strategies.

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“…But in the vast majority of cases, it focuses more on the effect of individual genes during analysis of gene differential expression, while ignoring the interaction of genes in complex biological gene networks, and fails to establish the relationship between illnesses and genes. Currently, the researches about WGCNA and diabetes were primarily focused on the function of pancreas [ 9 ], diabetic kidney [ 10 ], diabetic nephropathy [ 11 ], diabetic cardiomyopathy [ 12 ], and etc. Genetic factors occupy an irreplaceable role both in the pathogenesis of stroke and diabetes mellitus.…”

Section: Introductionmentioning

confidence: 99%

Identification of potential biological processes and key genes in diabetes-related stroke through weighted gene co-expression network analysis

He,

Bai,

Huang

et al. 2024

BMC Med Genomics

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Background Type 2 diabetes mellitus (T2DM) is an established risk factor for acute ischemic stroke (AIS). Although there are reports on the correlation of diabetes and stroke, data on its pathogenesis is limited. This study aimed to explore the underlying biological mechanisms and promising intervention targets of diabetes-related stroke. Methods Diabetes-related datasets (GSE38642 and GSE44035) and stroke-related datasets (GSE16561 and GSE22255) were obtained from the Gene Expression omnibus (GEO) database. The key modules for stroke and diabetes were identified by weight gene co-expression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes Genomes (KEGG) analyses were employed in the key module. Genes in stroke- and diabetes-related key modules were intersected to obtain common genes for T2DM-related stroke. In order to discover the key genes in T2DM-related stroke, the Cytoscape and protein–protein interaction (PPI) network were constructed. The key genes were functionally annotated in the Reactome database. Results By intersecting the diabetes- and stroke-related crucial modules, 24 common genes for T2DM-related stroke were identified. Metascape showed that neutrophil extracellular trap formation was primarily enriched. The hub gene was granulin precursor (GRN), which had the highest connectivity among the common genes. In addition, functional enrichment analysis indicated that GRN was involved in neutrophil degranulation, thus regulating neutrophil extracellular trap formation. Conclusions This study firstly revealed that neutrophil extracellular trap formation may represent the common biological processes of diabetes and stroke, and GRN may be potential intervention targets for T2DM-related stroke.

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Identification of Inflammation-Related Biomarkers in Diabetes of the Exocrine Pancreas With the Use of Weighted Gene Co-Expression Network Analysis

Cited by 6 publications

References 41 publications

Dihydroartemisinin inhibits the activation and proliferation of hepatic stellate cells by regulating miR‑29b‑3p

Dihydroartemisinin inhibits the activation and proliferation of hepatic stellate cells by regulating miR‑29b‑3p

Deciphering the endometrial immune landscape of RIF during the window of implantation from cellular senescence by integrated bioinformatics analysis and machine learning

Identification of potential biological processes and key genes in diabetes-related stroke through weighted gene co-expression network analysis

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