Weighted Gene Co-Expression Network Analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathy

Abstract Objective To reveal the molecular mechanism underlying the pathogenesis of HCM and find new effective therapeutic strategies using a systematic biological approach. Methods The WGCNA algorithm was applied to building the co-expression network of HCM samples. A sample cluster analysis was performed using the hclust tool and a co-expression module was constructed. The WGCNA algorithm was used to study the interactive connection between co-expression modules and draw a heat map to show the strength of interactions between modules. The genetic information of the respective modules was mapped to the associated GO terms and KEGG pathways, and the Hub Genes with the highest connectivity in each module were identified. The Wilcoxon test was used to verify the expression level of hub genes between HCM and normal samples, and the “pROC” R package was used to verify the possibility of hub genes as biomarkers. Finally, the potential functions of hub genes were analyzed by GSEA software. Results Seven co-expression modules were constructed using sample clustering analysis. GO and KEGG enrichment analysis judged that the turquoise module is an important module. The hub genes of each module are RPL35A for module Black, FH for module Blue, PREI3 for module Brown, CREB1 for module Green, LOC641848 for module Pink, MYH7 for module Turquoise and MYL6 for module Yellow. The results of the differential expression analysis indicate that MYH7 and FH are considered true hub genes. In addition, the ROC curves revealed their high diagnostic value as biomarkers for HCM. Finally, in the results of the GSEA analysis, MYH7 and FH highly expressed genes were enriched with the “proteasome” and a “PPAR signaling pathway,” respectively. Conclusions The MYH7 and FH genes may be the true hub genes of HCM. Their respective enriched pathways, namely the “proteasome” and the “PPAR signaling pathway,” may play an important role in the development of HCM.

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Weighted Gene Co-Expression Network Analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathy

10.21203/rs.3.rs-34084/v4 ◽

2020 ◽

Author(s):

Xin Li ◽

Chenxin Wang ◽

Xiaoqing Zhang ◽

Jiali Liu ◽

Yu Wang ◽

...

Keyword(s):

Signaling Pathway ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Roc Curves ◽

R Package ◽

Diagnostic Value ◽

Wilcoxon Test ◽

Hub Genes ◽

Kegg Pathways ◽

Ppar Signaling

Abstract Objective: To reveal the molecular mechanism underlying the pathogenesis of HCM and find new effective therapeutic strategies using a systematic biological approach.Methods: The WGCNA algorithm was applied to building the co-expression network of HCM samples. A sample cluster analysis was performed using the hclust tool and a co-expression module was constructed. The WGCNA algorithm was used to study the interactive connection between co-expression modules and draw a heat map to show the strength of interactions between modules. The genetic information of the respective modules was mapped to the associated GO terms and KEGG pathways, and the Hub Genes with the highest connectivity in each module were identified. The Wilcoxon test was used to verify the expression level of hub genes between HCM and normal samples, and the "pROC" R package was used to verify the possibility of hub genes as biomarkers. Finally, the potential functions of hub genes were analyzed by GSEA software. Results: Seven co-expression modules were constructed using sample clustering analysis. GO and KEGG enrichment analysis judged that the turquoise module is an important module. The hub genes of each module are RPL35A for module Black, FH for module Blue, PREI3 for module Brown, CREB1 for module Green, LOC641848 for module Pink, MYH7 for module Turquoise and MYL6 for module Yellow. The results of the differential expression analysis indicate that MYH7 and FH are considered true hub genes. In addition, the ROC curves revealed their high diagnostic value as biomarkers for HCM. Finally, in the results of the GSEA analysis, MYH7 and FH highly expressed genes were enriched with the "proteasome" and a "PPAR signaling pathway," respectively.Conclusions: The MYH7 and FH genes may be the true hub genes of HCM. Their respective enriched pathways, namely the "proteasome" and the "PPAR signaling pathway," may play an important role in the development of HCM.

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Weighted Gene Co-Expression Network Analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathy

10.21203/rs.3.rs-34084/v2 ◽

2020 ◽

Author(s):

Xin Li ◽

Chenxin Wang ◽

Xiaoqing Zhang ◽

Jiali Liu ◽

Yu Wang ◽

...

Keyword(s):

Signaling Pathway ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Roc Curves ◽

R Package ◽

Diagnostic Value ◽

Wilcoxon Test ◽

Hub Genes ◽

Kegg Pathways ◽

Ppar Signaling

Abstract Objective: To reveal the molecular mechanism underlying the pathogenesis of HCM and find new effective therapeutic strategies using a systematic biological approach.Methods: The WGCNA algorithm was applied to building the co-expression network of HCM samples. A sample cluster analysis was performed using the hclust tool and a co-expression module was constructed. The WGCNA algorithm was used to study the interactive connection between co-expression modules and draw a heat map to show the strength of interactions between modules. The genetic information of the respective modules was mapped to the associated GO terms and KEGG pathways, and the Hub Genes with the highest connectivity in each module were identified. The Wilcoxon test was used to verify the expression level of hub genes between HCM and normal samples, and the "pROC" R package was used to verify the possibility of hub genes as biomarkers. Finally, the potential functions of hub genes were analyzed by GSEA software. Results: Seven co-expression modules were constructed using sample clustering analysis. GO and KEGG enrichment analysis judged that the turquoise module is an important module. The hub genes of each module are RPL35A for module Black, FH for module Blue, PREI3 for module Brown, CREB1 for module Green, LOC641848 for module Pink, MYH7 for module Turquoise and MYL6 for module Yellow. The results of the differential expression analysis indicate that MYH7 and FH are considered true hub genes. In addition, the ROC curves revealed their high diagnostic value as biomarkers for HCM. Finally, in the results of the GSEA analysis, MYH7 and FH highly expressed genes were enriched with the "proteasome" and a "PPAR signaling pathway," respectively.Conclusions: The MYH7 and FH genes may be the true hub genes of HCM. Their respective enriched pathways, namely the "proteasome" and the "PPAR signaling pathway," may play an important role in the development of HCM.

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Co-expression Analysis Revealed Key Biomarkers Associated With the Diagnosis and Progression of Hypertrophic Cardiomyopathy

10.21203/rs.3.rs-34084/v1 ◽

2020 ◽

Author(s):

Xin Li ◽

Xiaoqing Zhang ◽

Jiali Liu ◽

Yu Wang ◽

Chenxin Wang ◽

...

Keyword(s):

Signaling Pathway ◽

Expression Analysis ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Roc Curves ◽

R Package ◽

Diagnostic Value ◽

Wilcoxon Test ◽

Hub Genes ◽

Ppar Signaling

Abstract Objective: To reveal the molecular mechanism underlying the pathogenesis of HCM and find new effective therapeutic strategies using a systematic biological approach.Methods: The WGCNA algorithm was applied to building the co-expression network of HCM samples. A sample cluster analysis was performed using the hclust tool and a co-expression module was constructed. The WGCNA algorithm was used to study the interactive connection between co-expression modules and draw a heat map to show the strength of interactions between modules. The genetic information of the respective modules was mapped to the associated GO terms and KEGG pathways, and the Hub Genes with the highest connectivity in each module were identified. The Wilcoxon test was used to verify the expression level of hub genes between HCM and normal samples, and the "pROC" R package was used to verify the possibility of hub genes as biomarkers. Finally, the potential functions of hub genes were analyzed by GSEA software. Results: Seven co-expression modules were constructed using sample clustering analysis. GO and KEGG enrichment analysis judged that the turquoise module is an important module. The hub genes of each module are RPL35A for module Black, FH for module Blue, PREI3 for module Brown, CREB1 for module Green, LOC641848 for module Pink, MYL7 for module Turquoise and MYL6 for module Yellow. The results of the differential expression analysis indicate that MYL7 and FH are considered true hub genes. In addition, the ROC curves revealed their high diagnostic value as biomarkers for HCM. Finally, in the results of the GSEA analysis, MYL7 and FH highly expressed genes were enriched with the "proteasome" and a "PPAR signaling pathway," respectively.Conclusions: The MYL7 and FH genes may be the true hub genes of HCM. Their respective enriched pathways, namely the "proteasome" and the "PPAR signaling pathway," may play an important role in the development of HCM.

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Differential Expression Analysis Revealing CLCA1 to Be a Prognostic and Diagnostic Biomarker for Colorectal Cancer

Frontiers in Oncology ◽

10.3389/fonc.2020.573295 ◽

2020 ◽

Vol 10 ◽

Author(s):

Fang-Ze Wei ◽

Shi-Wen Mei ◽

Zhi-Jie Wang ◽

Jia-Nan Chen ◽

Hai-Yu Shen ◽

...

Keyword(s):

Colorectal Cancer ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Diagnostic Value ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Hub Genes ◽

Ncbi Gene Expression Omnibus ◽

Public Datasets

Colorectal cancer (CRC) is a common malignant tumor of the digestive tract and lacks specific diagnostic markers. In this study, we utilized 10 public datasets from the NCBI Gene Expression Omnibus (NCBI-GEO) database to identify a set of significantly differentially expressed genes (DEGs) between tumor and control samples and WGCNA (Weighted Gene Co-Expression Network Analysis) to construct gene co-expression networks incorporating the DEGs from The Cancer Genome Atlas (TCGA) and then identify genes shared between the GEO datasets and key modules. Then, these genes were screened via MCC to identify 20 hub genes. We utilized regression analyses to develop a prognostic model and utilized the random forest method to validate. All hub genes had good diagnostic value for CRC, but only CLCA1 was related to prognosis. Thus, we explored the potential biological value of CLCA1. The results of gene set enrichment analysis (GSEA) and immune infiltration analysis showed that CLCA1 was closely related to tumor metabolism and immune invasion of CRC. These analysis results revealed that CLCA1 may be a candidate diagnostic and prognostic biomarker for CRC.

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Identification of Candidate Genetic Markers and a Novel 4-genes Diagnostic Model in Osteoarthritis through Integrating Multiple Microarray Data

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207323666200428120310 ◽

2020 ◽

Vol 23 (8) ◽

pp. 805-813

Author(s):

Ai Jiang ◽

Peng Xu ◽

Zhenda Zhao ◽

Qizhao Tan ◽

Shang Sun ◽

...

Keyword(s):

Signaling Pathway ◽

Microarray Data ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Mapk Signaling ◽

Functional Enrichment ◽

Joint Disease ◽

Support Vector ◽

Diagnostic Model ◽

Data Sets

Background: Osteoarthritis (OA) is a joint disease that leads to a high disability rate and a low quality of life. With the development of modern molecular biology techniques, some key genes and diagnostic markers have been reported. However, the etiology and pathogenesis of OA are still unknown. Objective: To develop a gene signature in OA. Method: In this study, five microarray data sets were integrated to conduct a comprehensive network and pathway analysis of the biological functions of OA related genes, which can provide valuable information and further explore the etiology and pathogenesis of OA. Results and Discussion: Differential expression analysis identified 180 genes with significantly expressed expression in OA. Functional enrichment analysis showed that the up-regulated genes were associated with rheumatoid arthritis (p < 0.01). Down-regulated genes regulate the biological processes of negative regulation of kinase activity and some signaling pathways such as MAPK signaling pathway (p < 0.001) and IL-17 signaling pathway (p < 0.001). In addition, the OA specific protein-protein interaction (PPI) network was constructed based on the differentially expressed genes. The analysis of network topological attributes showed that differentially upregulated VEGFA, MYC, ATF3 and JUN genes were hub genes of the network, which may influence the occurrence and development of OA through regulating cell cycle or apoptosis, and were potential biomarkers of OA. Finally, the support vector machine (SVM) method was used to establish the diagnosis model of OA, which not only had excellent predictive power in internal and external data sets (AUC > 0.9), but also had high predictive performance in different chip platforms (AUC > 0.9) and also had effective ability in blood samples (AUC > 0.8). Conclusion: The 4-genes diagnostic model may be of great help to the early diagnosis and prediction of OA.

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HEMGN and SLC2A1 might be potential diagnostic biomarkers of steroid-induced osteonecrosis of femoral head: study based on WGCNA and DEGs screening

BMC Musculoskeletal Disorders ◽

10.1186/s12891-021-03958-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Zhixin Wu ◽

Yinxian Wen ◽

Guanlan Fan ◽

Hangyuan He ◽

Siqi Zhou ◽

...

Keyword(s):

Femoral Head ◽

Roc Curve ◽

Enrichment Analysis ◽

Diagnostic Value ◽

Functional Enrichment ◽

Pathway Enrichment Analysis ◽

Overlapping Genes ◽

Hub Genes ◽

Diagnostic Biomarkers ◽

Key Genes

Abstract Background Steroid-induced osteonecrosis of the femoral head (SONFH) is a chronic and crippling bone disease. This study aims to reveal novel diagnostic biomarkers of SONFH. Methods The GSE123568 dataset based on peripheral blood samples from 10 healthy individuals and 30 SONFH patients was used for weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) screening. The genes in the module related to SONFH and the DEGs were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Genes with |gene significance| > 0.7 and |module membership| > 0.8 were selected as hub genes in modules. The DEGs with the degree of connectivity ≥5 were chosen as hub genes in DEGs. Subsequently, the overlapping genes of hub genes in modules and hub genes in DEGs were selected as key genes for SONFH. And then, the key genes were verified in another dataset, and the diagnostic value of key genes was evaluated by receiver operating characteristic (ROC) curve. Results Nine gene co-expression modules were constructed via WGCNA. The brown module with 1258 genes was most significantly correlated with SONFH and was identified as the key module for SONFH. The results of functional enrichment analysis showed that the genes in the key module were mainly enriched in the inflammatory response, apoptotic process and osteoclast differentiation. A total of 91 genes were identified as hub genes in the key module. Besides, 145 DEGs were identified by DEGs screening and 26 genes were identified as hub genes of DEGs. Overlapping genes of hub genes in the key module and hub genes in DEGs, including RHAG, RNF14, HEMGN, and SLC2A1, were further selected as key genes for SONFH. The diagnostic value of these key genes for SONFH was confirmed by ROC curve. The validation results of these key genes in GSE26316 dataset showed that only HEMGN and SLC2A1 were downregulated in the SONFH group, suggesting that they were more likely to be diagnostic biomarkers of SOFNH than RHAG and RNF14. Conclusions Our study identified that two key genes, HEMGN and SLC2A1, might be potential diagnostic biomarkers of SONFH.

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Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

BMC Cancer ◽

10.1186/s12885-021-08412-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Baojie Wu ◽

Shuyi Xi

Keyword(s):

Gene Expression ◽

Cervical Cancer ◽

Differentially Expressed Genes ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Molecular Targets ◽

Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Kegg Pathways

Abstract Background This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. Methods Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets. Results A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained. Conclusions These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.

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The mRNA landscape profiling reveals potential biomarkers associated with acute kidney injury AKI after kidney transplantation

PeerJ ◽

10.7717/peerj.10441 ◽

2020 ◽

Vol 8 ◽

pp. e10441

Author(s):

Hui Bi ◽

Min Zhang ◽

Jialin Wang ◽

Gang Long

Keyword(s):

Acute Kidney Injury ◽

Kidney Transplantation ◽

Expression Profiles ◽

Differential Expression Analysis ◽

Graft Function ◽

Kidney Injury ◽

Diagnostic Value ◽

Hub Genes ◽

Key Genes ◽

Potential Biomarkers

Background This study aims to identify potential biomarkers associated with acute kidney injury (AKI) post kidney transplantation. Material and Methods Two mRNA expression profiles from Gene Expression Omnibus repertory were downloaded, including 20 delayed graft function (DGF) and 68 immediate graft function (IGF) samples. Differentially expressed genes (DEGs) were identified between DGF and IGF group. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis of DEGs were performed. Then, a protein-protein interaction analysis was performed to extract hub genes. The key genes were searched by literature retrieval and cross-validated based on the training dataset. An external dataset was used to validate the expression levels of key genes. Receiver operating characteristic curve analyses were performed to evaluate diagnostic performance of key genes for AKI. Results A total of 330 DEGs were identified between DGF and IGF samples, including 179 up-regulated and 151 down-regulated genes. Of these, OLIG3, EBF3 and ETV1 were transcription factor genes. Moreover, LEP, EIF4A3, WDR3, MC4R, PPP2CB, DDX21 and GPT served as hub genes in PPI network. EBF3 was significantly up-regulated in validation GSE139061 dataset, which was consistently with our initial gene differential expression analysis. Finally, we found that LEP had a great diagnostic value for AKI (AUC = 0.740). Conclusion EBF3 may be associated with the development of AKI following kidney transplantation. Furthermore, LEP had a good diagnostic value for AKI. These findings provide deeper insights into the diagnosis and management of AKI post renal transplantation.

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MetENP/MetENPWeb: An R package and web application for metabolomics enrichment and pathway analysis in Metabolomics Workbench

10.1101/2020.11.20.391912 ◽

2020 ◽

Author(s):

Kumari Sonal Choudhary ◽

Eoin Fahy ◽

Kevin Coakley ◽

Manish Sud ◽

Mano R Maurya ◽

...

Keyword(s):

Pathway Analysis ◽

Web Application ◽

Enrichment Analysis ◽

R Package ◽

Pathway Enrichment Analysis ◽

Pathway Enrichment ◽

Kegg Pathways ◽

Link Type ◽

Species Specific ◽

User Friendly

ABSTRACTWith the advent of high throughput mass spectrometric methods, metabolomics has emerged as an essential area of research in biomedicine with the potential to provide deep biological insights into normal and diseased functions in physiology. However, to achieve the potential offered by metabolomics measures, there is a need for biologist-friendly integrative analysis tools that can transform data into mechanisms that relate to phenotypes. Here, we describe MetENP, an R package, and a user-friendly web application deployed at the Metabolomics Workbench site extending the metabolomics enrichment analysis to include species-specific pathway analysis, pathway enrichment scores, gene-enzyme information, and enzymatic activities of the significantly altered metabolites. MetENP provides a highly customizable workflow through various user-specified options and includes support for all metabolite species with available KEGG pathways. MetENPweb is a web application for calculating metabolite and pathway enrichment analysis.Availability and ImplementationThe MetENP package is freely available from Metabolomics Workbench GitHub: (https://github.com/metabolomicsworkbench/MetENP), the web application, is freely available at (https://www.metabolomicsworkbench.org/data/analyze.php)

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