scholarly journals Identification of Lumican and Fibromodulin as Hub Genes Associated with Accumulation of Extracellular Matrix in Diabetic Nephropathy

2021 ◽  
pp. 1-11
Author(s):  
Songtao Feng ◽  
Yueming Gao ◽  
Di Yin ◽  
Linli Lv ◽  
Yi Wen ◽  
...  
Keyword(s):  
Extracellular Matrix ◽  
Diabetic Nephropathy ◽  
Clinical Importance ◽  
Gene Expression Omnibus ◽  
Potential Candidate ◽  
Hub Genes ◽  
Glomerular Diseases ◽  
Go Analysis ◽  
Diagnostic Potential ◽  
Renal Biopsies

<b><i>Introduction:</i></b> Diabetic nephropathy (DN) remains a major cause of end-stage renal disease. 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). <b><i>Methods:</i></b> Gene Expression Omnibus database was searched for microarray data including distinct types of 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. In addition, cross-validation was performed in an independent dataset and in samples of renal biopsies with DN and other types of glomerular diseases. <b><i>Results:</i></b> 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. Twenty-three modules of the total 10,947 genes were grouped by WGCNA, and a module was specifically correlated with DN (<i>r =</i> 0.54, <i>p =</i> 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. Cross verification showed LUM and FMOD were higher in the DN group and were negatively correlated with estimated glomerular filtration rate (eGFR). In renal biopsies, expression levels of LUM and FMOD were higher in DN than IgA nephropathy, membranous nephropathy, and normal controls. <b><i>Conclusion:</i></b> By using WGCNA approach, we identified LUM and FMOD related to ECM accumulation and were specific for DN. These 2 genes may represent potential candidate diagnostic biomarkers of DN.

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

2020 ◽  
Author(s):  
Song-Tao Feng ◽  
Yue-Ming Gao ◽  
Di Yin ◽  
Lin-Li Lv ◽  
Yi Wen ◽  
...  
Keyword(s):  
Extracellular Matrix ◽  
Diabetic Nephropathy ◽  
Kidney Diseases ◽  
External Validation ◽  
Clinical Importance ◽  
Potential Candidate ◽  
Hub Genes ◽  
Glomerular Diseases ◽  
Go Analysis ◽  
Diagnostic Potential

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.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Bojun Xu ◽  
Lei Wang ◽  
Huakui Zhan ◽  
Liangbin Zhao ◽  
Yuehan Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Diabetic Nephropathy ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Topological Analysis ◽  
Gene Expression Omnibus ◽  
Complement Cascade ◽  
Hub Genes ◽  
Novel Biomarkers

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.

scholarly journals An in-silico method leads to recognition of hub genes and crucial pathways in survival of patients with breast cancer

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sepideh Dashti ◽  
Mohammad Taheri ◽  
Soudeh Ghafouri-Fard
Keyword(s):  
Breast Cancer ◽  
Clinical Importance ◽  
Interaction Network ◽  
R Package ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ncbi Gene Expression Omnibus ◽  
Kaplan Meier ◽  
Microarray Datasets

Abstract Breast cancer is a highly heterogeneous disorder characterized by dysregulation of expression of numerous genes and cascades. In the current study, we aim to use a system biology strategy to identify key genes and signaling pathways in breast cancer. We have retrieved data of two microarray datasets (GSE65194 and GSE45827) from the NCBI Gene Expression Omnibus database. R package was used for identification of differentially expressed genes (DEGs), assessment of gene ontology and pathway enrichment evaluation. The DEGs were integrated to construct a protein–protein interaction network. Next, hub genes were recognized using the Cytoscape software and lncRNA–mRNA co-expression analysis was performed to evaluate the potential roles of lncRNAs. Finally, the clinical importance of the obtained genes was assessed using Kaplan–Meier survival analysis. In the present study, 887 DEGs including 730 upregulated and 157 downregulated DEGs were detected between breast cancer and normal samples. By combining the results of functional analysis, MCODE, CytoNCA and CytoHubba 2 hub genes including MAD2L1 and CCNB1 were selected. We also identified 12 lncRNAs with significant correlation with MAD2L1 and CCNB1 genes. According to The Kaplan–Meier plotter database MAD2L1, CCNA2, RAD51-AS1 and LINC01089 have the most prediction potential among all candidate hub genes. Our study offers a framework for recognition of mRNA–lncRNA network in breast cancer and detection of important pathways that could be used as therapeutic targets in this kind of cancer.

P0971IDENTIFICATION OF HUB GENES ASSOCIATED WITH THE DEPOSITION OF EXTRACELLULAR MATRIX AND SPECIFIC FOR DIABETIC NEPHROPATHY BY WEIGHTED GENE CO-EXPRESSION NETWORK ANALYSIS

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Songtao Feng ◽  
Linli Lv ◽  
Gao Yueming ◽  
Cao Jingyuan ◽  
Di Yin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Extracellular Matrix ◽  
Diabetic Nephropathy ◽  
Network Analysis ◽  
Expression Level ◽  
Hub Genes ◽  
Hub Gene ◽  
Gene Screening ◽  
Gene Modules ◽  
Geo Database

Abstract Background and Aims Diabetic nephropathy (DN) and its most severe manifestation, end-stage renal disease (ESRD), remains one of the leading causes of reduced lifespan in people with diabetes. Identifying novel molecules that are involved in the pathogenesis of DN has both diagnostic and therapeutic implications. The gene co-expression network analysis (WGCNA) algorithm represents a novel systems biology approach that provide the approach of association between gene modules and clinical traits to find the module involvement into the certain phenotypic trait. The goal of this study was to identify hub genes and their roles in DN from the aspect of whole gene transcripts analysis. Method Various types of chronic kidney diseases (CKD), including DN, microarray-based mRNA gene expression data, listed in the Gene Expression Omnibus (GEO) database, were analyzed. Next, we constructed a weighted gene co-expression network and identified modules distinguishing DN from normal or other types of CKD by WGCNA. Functional annotations of the genes in modules specialized for DN were analyzed by Gene Ontology (GO) enrichment analysis. Through protein-protein interaction (PPI) analysis and hub gene screening, the hub genes specific for DN were obtained. Furthermore, we drew ROC curves to determine the diagnosis and differential diagnosis value to DN of hub genes. Finally, another study of microarray in the GEO database was selected to verify the expression level of hub genes and in the “Nephroseq” database, the correlation between the gene expression level and eGFR was analyzed. Results “GSE99339”, glomerular tissue microarray in 187 patients with a total of 10947 genes, was selected for analysis. After excluding the inappropriate cases, a total of 179 specimens were analyzed, including 14 cases of DN, 22 cases of focal segmental glomerulosclerosis (FSGS), 15 cases of hypertensive nephropathy (HT), 26 cases of IgA nephropathy (IgAN), 13 cases of minimal change disease (MCD), 21 cases of membranous nephropathy (MGN), 23 cases of rapidly progressive glomerulonephritis (RPGN), 30 cases of lupus nephritis (LN) and 14 cases of kidney tissue adjacent to tumor. Co-expression network analysis by WGCNA identified 23 distinct gene modules of the total 10947 genes and revealed “MEsaddlegreen” module was strongly correlated with DN (r=0,54), but not with other groups. GO functional annotation showed that these 64 genes in the “MEsaddlegreen” module mainly enriched in the deposition of extracellular matrix, which represents the specific and diagnostic pathophysiological process of DN. Further PPI and hub gene screening analysis revealed that LUM, ELN, FBLN1, MMP2, FBLN5 and FMOD can be served as hub genes, which had been proved to play an important role in the deposition of extracellular matrix. Furthermore, we found that the expression of hub genes was the highest in DN group and for the diagnosis value of DN by each gene, the area under the ROC curve is about 0.75∼0.95. The external verification of another study showed that compared with the normal control group, the expression of these hub genes was the highest in the DN group, and their expression level was negatively correlated with eGFR. Conclusion Using WGCNA and further bioinformatics approach, we identified six hub genes that appear to be identical to DN development. As such, they may represent potential diagnostic biomarkers as well as therapeutic targets with clinical utility.

scholarly journals Identification of Hub Genes Associated With Clinical Characteristics and Potential Therapy of Diabetic Nephropathy by Bioinformatics Analysis.

2020 ◽  
Author(s):  
Si Xu ◽  
Sha Wu ◽  
Min Yang ◽  
Xiaoning Li
Keyword(s):  
Extracellular Matrix ◽  
Diabetic Nephropathy ◽  
Signaling Pathway ◽  
Bioinformatic Analysis ◽  
Molecular Therapy ◽  
Akt Signaling Pathway ◽  
Targeted Molecular Therapy ◽  
Hub Genes ◽  
Diagnostic Biomarkers ◽  
Gene Modules

Abstract Background: To provide molecular markers and potential targeted molecular therapy for diabetic nephropathy by screening hubgenes based on bioinformatic analysis. Results: We found 91 differentially expressed genes (DEGs) between diabetic nephropathy tissues and normal kidney tissues. Majority DEGs were significantly enriched in the extracellular matrix structural constituent, collagen-containing extracellular matrix. KEGG pathway analysis showed that most of DEGs participated in PI3K-Akt signaling pathway, AGE-RAGE signaling pathway in diabetic complications. Five high relevant sub-networks and the top 16 genes according to 12 topological algorithms were screened out and also five co-expressed gene modules were identified by WGCNA. Eventually, 5 hub genes were identified by taking the intersection which might be involved in the progression of DN. And 11 microRNAs were associated with related genes in WebGestalt. Conclusions: We identified five hub genes, namely COL1A2, COL6A3, COL15A1, CLU and LUM, and their related microRNAs(especially miR29 and miR196), which might be used as diagnostic biomarkers and therapeutic targets for diabetic nephropathy.

scholarly journals Identification of Vital Hub Genes and Potential Molecular Pathways of Dermatomyositis by Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xueren Ouyang ◽  
Yuning Zeng ◽  
Xiaotao Jiang ◽  
Hua Xu ◽  
Yile Ning
Keyword(s):  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Pathway Enrichment Analysis ◽  
Molecular Pathways ◽  
Potential Candidate ◽  
Molecular Signaling ◽  
Hub Genes ◽  
High Association

Dermatomyositis is an autoimmune disease characterized by severe symmetrical muscle dysfunction and pain. This study was aimed at discovering vital hub genes and potential molecular pathways of DM through bioinformatics analysis, which contributes to identifying potential diagnostic or therapeutic biomarkers and targets. In this study, a total of 915 DEGs in DM samples including 167 upregulated genes and 748 downregulated genes were screened out by the limma package based on the GSE142807 dataset from the Gene Expression Omnibus (GEO) database. Furthermore, the results of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis indicated that these downregulated genes were highly associated with the immune-related biological processes and pathways. Therefore, 41 genes closely related to DM were extracted for further study based on the subcluster analysis through the Molecular Complex Detection (MCODE) software plugin in Cytoscape. Ultimately, 10 hub genes (including ISG15, DDX58, IFIT3, CXCL10, and STAT1) were identified as the potential candidate biomarkers and targets. Besides, we found that the identified hub genes directly or indirectly communicated with each other via molecular signaling pathways on the protein and transcription level. In general, under the guidance of bioinformatics analysis, 10 vital hub genes and molecular mechanisms in DM were identified and the expression of proinflammatory factors and interferon family proteins and genes showed high association with DM, which might help provide a theoretical foundation for the development of point-to-point targeted therapy in the future treatment of DM.

scholarly journals Identification of VWF as a Novel Biomarker in Lung Adenocarcinoma by Comprehensive Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Yi He ◽  
Ruijie Liu ◽  
Mei Yang ◽  
Wu Bi ◽  
Liuyin Zhou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Extracellular Matrix ◽  
Lung Adenocarcinoma ◽  
Malignant Tumors ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
High Morbidity ◽  
Hub Genes ◽  
Normal Tissues ◽  
New Biomarkers

Lung adenocarcinoma (LUAD) is one of the most malignant tumors with high morbidity and mortality worldwide due to the lack of reliable methods for early diagnosis and effective treatment. It’s imperative to study the mechanism of its development and explore new biomarkers for early detection of LUAD. In this study, the Gene Expression Omnibus (GEO) dataset GSE43458 and The Cancer Genome Atlas (TCGA) were used to explore the differential co-expressed genes between LUAD and normal samples. Three hundred sixity-six co-expressed genes were identified by differential gene expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA) method. Those genes were mainly enriched in ameboidal-type cell migration (biological process), collagen-containing extracellular matrix (cell component), and extracellular matrix structure constituent (molecular function). The protein-protein network (PPI) was constructed and 10 hub genes were identified, including IL6, VWF, CDH5, PECAM1, EDN1, BDNF, CAV1, SPP1, TEK, and SELE. The expression level of hub genes was validated in the GEPIA database, compared with normal tissues, VWF is lowly expressed and SPP1 is upregulated in LUAD tissues. The survival analysis showed increased expression of SPP1 indicated unfavorable prognosis whereas high expression of VWF suggested favorable prognosis in LUAD (p &lt; 0.05). Based on the immune infiltration analysis, the relationship between SPP1 and VWF expression and macrophage, neutrophil, and dendritic cell infiltration was weak in LUAD. Quantitative real-time PCR (qRT-PCR) and western blotting were used to validate the expression of VWF and SPP1 in normal human bronchial epithelial (HBE) cell and three LUAD cell lines, H1299, H1975, and A549. Immunohistochemistry (IHC) was further performed to detect the expression of VWF in 10 cases LUAD samples and matched normal tissues. In summary, the data suggest that VWF is a potential novel biomarker for prognosis of LUAD.

scholarly journals Bioinformatics analysis of common differential genes of coronary artery disease and carotid atherosclerosis

2021 ◽  
Author(s):  
Luxia Song ◽  
Jie Zhang ◽  
Yixuan Fan ◽  
Qiyu Liu ◽  
Baoyi Guan ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Carotid Atherosclerosis ◽  
Gene Expression Omnibus ◽  
Potential Candidate ◽  
Hub Genes ◽  
Blood Samples ◽  
The Common ◽  
Artery Disease ◽  
Differential Genes

Abstract Coronary artery disease(CAD) is one of the most fatal diseases in the world, which seriously threatens human health. Studies have demonstrated that the appearance of carotid plaque is related to the risk of CAD, but the common differential genes and mechanism between these two conditions are still unclear. Our study identified the common differential genes between carotid atherosclerosis tissues and blood samples of CAD patients, aiming to search promising biomarkers in CAD predicting and diagnosing. We obtain datasets of GSE100927 and GSE56885 from GENE EXPRESSION OMNIBUS (GEO) database. Through scanning their mutual differentially expressed genes(DEGs), we performed Gene Ontology (GO), Kyoto Encyclopedia of Genes, Genomes (KEGG) analysis, and PPI analysis to get hub genes between these two conditions. We found that both CAD blood samples and carotid atherosclerotic plaque tissues were related to immune response, inflammatory response and cell chemotaxis. Followed by PPI network construction, MCODE analysis found that 1 subnetwork, including CCR5, CCR2, CXCR4 and C5AR1, was extracted, which concerned as hub genes of the two datasets. Indicating that CCR5, CCR2, CXCR4 and C5AR1maybe potential candidate biomarkers for CAD prediction in patients with carotid plaques.

scholarly journals Identification of hub genes and important pathways in determination of breast cancer survival using bioinformatics approaches

2019 ◽  
Author(s):  
Sepideh Dashti ◽  
Soudeh Ghafouri-Fard
Keyword(s):  
Breast Cancer ◽  
Signaling Pathway ◽  
Cancer Survival ◽  
Clinical Importance ◽  
Gene Expression Omnibus ◽  
Receptor Interaction ◽  
Peroxisome Proliferator ◽  
Ppi Network ◽  
Hub Genes ◽  
Pathologic Classification

Abstract Backgrounds Breast cancer is a highly heterogeneous disorder characterized by dysregulation of expression of numerous genes and cascades. The conventional pathologic classification of breast cancer is not sufficient for the prediction of breast cancer behavior and response to therapy.Methods We have retrieved data of two microarray datasets (GSE65194 and GSE45827) from the NCBI Gene Expression Omnibus database (GEO). R package was used for identification of differentially expressed genes (DEGs), assessment of gene ontology (GO) and pathway enrichment evaluation. The DEGs were integrated to construct a protein-protein interaction (PPI) network. Next, hub genes were recognized using the Cytoscape software and lncRNA-mRNA co-expression analysis was performed to evaluate the potential roles of lncRNAs. The interactive information among DEGs and the PPI network was obtained using the STRING online database. Finally, the clinical importance of the obtained genes was assessed using Kaplan-Meier survival analysis.Results After excluding the outliers from the GSE65194 and GSE45827 datasets and data normalization, 866 DEGs including 712 upregulated and 154 downregulated DEGs were detected between breast cancer and normal samples. Up-regulated DEGs were enriched in six pathways including ‘Cell cycle’, ‘Oocyte meiosis’ and ‘Focal adhesion’. Down-regulated DEGs were enriched in five pathways including ‘Peroxisome-proliferator-activated receptors (PPAR) signaling pathway’, ‘Metabolism of xenobiotics by cytochrome P450’, ‘Adipocytokine signaling pathway’ and ‘Cytokine-cytokine receptor interaction’ pathways. CCNA2, CDK1, MAD2L1, and CCNB2 were significantly enriched in several biological pathways. These four genes showed strong expression in breast cancer samples as compared to normal breast tissue. We also identified 12 lncRNAs with a significant correlation with MAD2L1 and CCNB2 genes. MAD2L1, CCNA2, RAD51-AS1, and LINC01089 have the most prediction potential among all candidate hub genes.Conclusion Our study offers a framework for recognition of the mRNA-lncRNA network in breast cancer and the detection of important pathways that could be used as therapeutic targets in this kind of cancer.

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