Identification of key biomarkers in diabetic nephropathy via <b>bioinformatic analysis</b>

Mi, Zeng; Liu, Jialu; Yang, Wenxia; Zhang, Shumin; Liu, Fuyou; Dong, Zheng; Peng, Yong; Sun, Lin; Xiao, Li

doi:10.1002/jcb.28155

Cited by 25 publications

(20 citation statements)

References 65 publications

(110 reference statements)

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“…Researchers have used microarray data from DN models of different species to determine molecular mechanisms and genetic factors involved in DN [15,16]. Previous bioinformatics analyses using human DN gene chip data (GSE47183) from the GEO database found that the VSIG4, CD163, C1QA, C1QB, MS4A6A, COL6A3, COL1A2, CD44, FN1, NPHS1, WT1, PLCE1, TNNT2, TNNI1, and TNNC1 genes played important roles in DN progression through ECM-receptor interaction, PI3K-Akt signaling pathway, focal adhesion, proteoglycans in cancer, and complement and coagulation cascades [17]. Yang et al [18] identified hub genes associated with DN using GSE30528 chip data, including VEGFA, ITGA3, ITGB5, COL4A3, COL4A5, CBLB, and CCL19.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis

Wang

Zhan

et al. 2021

Journal of Diabetes Research

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Objectives. Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD) throughout the world, and the identification of novel biomarkers via bioinformatics analysis could provide research foundation for future experimental verification and large-group cohort in DN models and patients. Methods. GSE30528, GSE47183, and GSE104948 were downloaded from Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs). The difference of gene expression between normal renal tissues and DN renal tissues was firstly screened by GEO2R. Then, the protein-protein interactions (PPIs) of DEGs were performed by STRING database, the result was integrated and visualized via applying Cytoscape software, and the hub genes in this PPI network were selected by MCODE and topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to determine the molecular mechanisms of DEGs involved in the progression of DN. Finally, the Nephroseq v5 online platform was used to explore the correlation between hub genes and clinical features of DN. Results. There were 64 DEGs, and 32 hub genes were identified, enriched pathways of hub genes involved in several functions and expression pathways, such as complement binding, extracellular matrix structural constituent, complement cascade related pathways, and ECM proteoglycans. The correlation analysis and subgroup analysis of 7 complement cascade-related hub genes and the clinical characteristics of DN showed that C1QA, C1QB, C3, CFB, ITGB2, VSIG4, and CLU may participate in the development of DN. Conclusions. We confirmed that the complement cascade-related hub genes may be the novel biomarkers for DN early diagnosis and targeted treatment.

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Section: Introductionmentioning

confidence: 99%

Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis

Wang

Zhan

et al. 2021

Journal of Diabetes Research

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“…WGCNA was performed in an independent study of large number of samples of multiple kidney diseases, avoiding the heterogeneity among different studies caused by a comprehensive analysis of multiple GEO datasets. Different from the analysis of DEGs [14][15][16], the genome-wide transcriptional co-expression network analysis has provided an unbiased description of gene expression network in DN. A gene module speci c for DN was identi ed re ecting the unique pathogenesis of DN.…”

Section: Discussionmentioning

confidence: 99%

Identification of hub genes associated with accumulation of extracellular matrix as biomarkers of diabetic nephropathy

Feng

Gao

Yin

et al. 2020

Preprint

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Abstract Background Diabetic nephropathy (DN) remains a major cause of end stage renal disease (ESRD). The development of novel biomarkers and early diagnosis of DN are of great clinical importance. The goal of this study was to identify hub genes with diagnostic potential for DN by weighted gene co-expression network analysis (WGCNA). Methods Gene Expression Omnibus (GEO) database was searched for microarray data including distinct types of chronic kidney diseases (CKD). Gene co-expression network was constructed and modules specific for DN were identified by WGCNA. Gene Ontology (GO) analysis was performed and the hub genes were screened out within the selected gene modules. Furthermore, receiver operating characteristic (ROC) curves were generated to evaluate the diagnostic values of hub genes. In addition, an external validation was performed in an independent dataset. Results Dataset GSE99339 was selected and a total of 179 microdissected glomeruli samples were analyzed, including DN, normal control and 7 groups of other glomerular diseases. 23 modules of the total 10947 genes were grouped by WGCNA and a module was specifically correlated with DN (r = 0.54, 9e-15). GO analysis showed that module genes were mainly enriched in the accumulation of extracellular matrix (ECM). LUM, ELN, FBLN1, MMP2, FBLN5 and FMOD were identified as hub genes. Furthermore, levels of hub genes were the highest in DN compared to other groups, which could differentially diagnose DN (AUC, 0.67 ~ 0.95). External verification showed hub genes were higher in DN group and were negatively correlated with eGFR. Conclusions By using WGCNA approach, we identified 6 hub genes, LUM, ELN, FBLN1, MMP2, FBLN5 and FMOD, related to ECM accumulation and were specific for DN. These genes may represent potential candidate diagnostic biomarkers of DN.

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“…Search Tool for the Retrieval of Interacting Genes (STRING) (version10.5) database was used to construct PPI network. Figuring out the protein-protein functional interactions would help us to nd possible mechanisms underlying the development of diseases, as described in previous study (29). In the current study, STRING database was used to construct PPI network of DEGs, and the combined score was set as more than 0.9.…”

Section: Methodsmentioning

confidence: 99%

Identification of Genes in Patients for Predicting Ulcerative Colitis-Associated Colorectal Cancer

Wei

Zhang

Chi

et al. 2020

Preprint

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BackgroundUlcerative colitis (UC) has been considered as a risk factor for colorectal cancer (CRC). However, effective biomarkers for predicting UC-associated CRC are lacking. Therefore, it is necessary to screen biomarkers associated with UC-related CRC, which could be used to evaluate UC-associated CRC early, and provide possible mechanisms involved in UC-associated CRC. Efficient bioinformatics analysis could help us to explore potential biomarkers.MethodsTwo public datasets, including 44 UC without CRC samples and 17 UC-associated CRC samples were chosen from Gene Expression Omnibus (GEO) database. Sva package was used to remove batch effects, and then we screened out differentially expressed genes (DEGs) with limma package. STRING and Cytoscape were used to achieve protein-protein interaction (PPI) network analysis. The survival curves between high and low gene expression were performed by log rank test based on the cancer genome atlas (TCGA) program. The expression of three identified hub genes was validated based on Oncomine. To validate the expression of three hub genes, we compared the expression of three hub genes between normal and colorectal cancer based on Oncomine.Results405 DEGs were identified, including 256 down-regulated genes and 149 up-regulated genes in UC-associated CRC tissues. 16 hub genes were identified. And among them, RPL6, RPL7, and RPL35 were related to poor prognosis of patients in survival analysis. Higher expression of RPL6, RPL7, and RPL35 was validated in CRC tissues based on Oncomine.ConclusionsOur study showed that overexpressed RPL6, RPL7, and RPL 35 may be potential tumor oncogenes and could act as a prognostic factor in clinical diagnosis and treatment.

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Identification of key biomarkers in diabetic nephropathy via bioinformatic analysis

Cited by 25 publications

References 65 publications

Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis

Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis

Identification of hub genes associated with accumulation of extracellular matrix as biomarkers of diabetic nephropathy

Identification of Genes in Patients for Predicting Ulcerative Colitis-Associated Colorectal Cancer

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