The identification of gene signature and critical pathway associated with childhood-onset type 2 diabetes

Jia, Keren; Wu, Yingcheng; Ju, Jingyi; Wang, Liyang; Shi, Lili; Wu, Huiqun; Jiang, Kui; Dong, Jiancheng

doi:10.7717/peerj.6343

Cited by 6 publications

(8 citation statements)

References 46 publications

(44 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, a study revealed that the expression of NAMPT was of no difference between simple steatosis and NASH [ 25 ]. NAMPT also contributed to the regulation of insulin secretion in the pancreatic β-cells [ 22 ] and diabetes mellitus [ 27 , 28 ].…”

Section: Discussionmentioning

confidence: 99%

A nomogram for predicting metabolic steatohepatitis: The combination of NAMPT, RALGDS, GADD45B, FOSL2, RTP3, and RASD1

Liao

Zeng

et al. 2021

Open Medicine

View full text Add to dashboard Cite

Objective To identify differentially expressed and clinically significant mRNAs and construct a potential prediction model for metabolic steatohepatitis (MASH). Method We downloaded four microarray datasets, GSE89632, GSE24807, GSE63067, and GSE48452, from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis were performed to screen significant genes. Finally, we constructed a nomogram of six hub genes in predicting MASH and assessed it through receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA). In addition, qRT-PCR was used for relative quantitative detection of RNA in QSG-7011 cells to further verify the expression of the selected mRNA in fatty liver cells. Results Based on common DEGs and brown and yellow modules, seven hub genes were identified, which were NAMPT, PHLDA1, RALGDS, GADD45B, FOSL2, RTP3, and RASD1. After logistic regression analysis, six hub genes were used to establish the nomogram, which were NAMPT, RALGDS, GADD45B, FOSL2, RTP3, and RASD1. The area under the ROC of the nomogram was 0.897. The DCA showed that when the threshold probability of MASH was 0–0.8, the prediction model was valuable to GSE48452. In QSG-7011 fatty liver model cells, the relative expression levels of NAMPT, GADD45B, FOSL2, RTP3, RASD1 and RALGDS were lower than the control group. Conclusion We identified seven hub genes NAMPT, PHLDA1, RALGDS, GADD45B, FOSL2, RTP3, and RASD1. The nomogram showed good performance in the prediction of MASH and it had clinical utility in distinguishing MASH from simple steatosis.

show abstract

Section: Discussionmentioning

confidence: 99%

A nomogram for predicting metabolic steatohepatitis: The combination of NAMPT, RALGDS, GADD45B, FOSL2, RTP3, and RASD1

Liao

Zeng

et al. 2021

Open Medicine

View full text Add to dashboard Cite

show abstract

“…Exploring the molecular level dysfunction of islet cells of type 2 diabetes can provide effective treatment and more predictive and diagnostic biomarkers. For example, Jia et al [12] identified marker genes for childhood-onset type 2 diabetes from one GEO dataset by performing DEGs, weighted co-expression network analysis (WGCNA), GO and KEGG enrichment, PPI network, diseases and transcription factors (TFs) related to critical genes analysis. Shao et al [13] identified that the glucocorticoid was central in the pathogenesis of type 2 diabetes from three GEO datasets by integrated method including DEGs screen, gene functional enrichment, PPI network analyses, drug–gene interactions and genetic association of DEGs.…”

Section: Discussionmentioning

confidence: 99%

Identification of key genes involved in type 2 diabetic islet dysfunction: a bioinformatics study

Ming

et al. 2019

Bioscience Reports

View full text Add to dashboard Cite

Aims: To identify the key differentially expressed genes (DEGs) in islet and investigate their potential pathway in the molecular process of type 2 diabetes. Methods: Gene Expression Omnibus (GEO) datasets (GSE20966, GSE25724, GSE38642) of type 2 diabetes patients and normal controls were downloaded from GEO database. DEGs were further assessed by enrichment analysis based on the Database for Annotation, Visualization and Integrated Discovery (DAVID) 6.8. Then, by using Search Tool for the Retrieval Interacting Genes (STRING) 10.0 and gene set enrichment analysis (GSEA), we identified hub gene and associated pathway. At last, we performed quantitative real-time PCR (qPCR) to validate the expression of hub gene. Results: Forty-five DEGs were co-expressed in the three datasets, most of which were down-regulated. DEGs are mostly involved in cell pathway, response to hormone and binding. In protein–protein interaction (PPI) network, we identified ATP-citrate lyase (ACLY) as hub gene. GSEA analysis suggests low expression of ACLY is enriched in glycine serine and threonine metabolism, drug metabolism cytochrome P450 (CYP) and NOD-like receptor (NLR) signaling pathway. qPCR showed the same expression trend of hub gene ACLY as in our bioinformatics analysis. Conclusion: Bioinformatics analysis revealed that ACLY and the pathways involved are possible target in the molecular mechanism of type 2 diabetes.

show abstract

“…We analyzed the DEG to evaluate the different 5-hmC levels of cfDNA in plasma between healthy individuals and CRC patients by the limma package [ 14 ]. The parameter settings were set according to a previous study [ 15 ].…”

Section: Methodsmentioning

confidence: 99%

“…We used the WGCNA package [ 16 ] to determine the coexpression modules and the interconnectedness between each module, and the coexpressed genes in the control and CRC patients according to a previously published method [ 15 ].…”

Section: Methodsmentioning

confidence: 99%

Identification of a 5-Hydroxymethylation Signature in Circulating Cell-Free DNA for the Noninvasive Detection of Colorectal Cancer

Liu

Tang

Zhang

et al. 2022

Journal of Oncology

View full text Add to dashboard Cite

Background. As a crucial epigenetic modification, DNA 5-hydroxymethylcytosine (5-hmC) plays a key role during colorectal cancer (CRC) carcinogenesis. Nevertheless, the levels of 5-hmC-related genes in the circulating DNA of CRC remain largely unknown. Methods and Results. The GSE81314 dataset from the Gene Expression Omnibus (GEO), which was generated by chemical marking-based low-input shotgun sequencing to detect 5-hmC in circulating cell-free DNA (cfDNA) was used in the present study. The GSE81314 dataset includes data for 8 plasma samples from healthy individuals and 4 plasma samples from CRC patients. The difference in the 5-hmC levels in cfDNA between the CRC group and healthy individuals was analyzed by the differentially expressed genes (DEG) package. Weighted gene coexpression network analysis (WGCNA) was conducted to analyze gene coexpression modules associated with sample characteristics. DEG analysis identified 19 upregulated and 9 downregulated 5-hmC-related genes. WGCNA showed that the pink, purple, and brown modules, which contain 531 genes in total, were significantly correlated with CRC (0.66, 0.61, and -0.59, respectively). We used gene set enrichment analysis (GSEA) software to compare 5-hmC-related genes and pathways between CRC patients and healthy controls. We further performed a protein–protein interaction (PPI) analysis and identified 4 nodes (LCN2, LRG1, S100P, and TACSTD2) that played key roles in the network, and we analyzed the expression of these nodes S100P in the GEPIA database. Consistent with the 5-hmC levels in CRC patient plasma, our external validation results from the GEPIA and UALCAN databases showed that LCN2, LRG1, S100P, and TACSTD2 were highly expressed in CRC tissue compared with controls. The DNA promoter methylation levels of LCN2, LRG1, and S100P were lower in CRC tissue than in normal control tissue. Conclusion. The present findings suggest that abnormality in cell-free DNA hydroxylation in plasma may be associated with CRC. In addition, the 5-hmC levels of LCN2, LRG1, S100P, and TACSTD2 in circulating cfDNA may be used as potential noninvasive markers for CRC.

show abstract

The identification of gene signature and critical pathway associated with childhood-onset type 2 diabetes

Cited by 6 publications

References 46 publications

A nomogram for predicting metabolic steatohepatitis: The combination of NAMPT, RALGDS, GADD45B, FOSL2, RTP3, and RASD1

A nomogram for predicting metabolic steatohepatitis: The combination of NAMPT, RALGDS, GADD45B, FOSL2, RTP3, and RASD1

Identification of key genes involved in type 2 diabetic islet dysfunction: a bioinformatics study

Identification of a 5-Hydroxymethylation Signature in Circulating Cell-Free DNA for the Noninvasive Detection of Colorectal Cancer

Contact Info

Product

Resources

About