Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

BackgroundThe recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.MethodsRNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.ResultsBased on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19’s main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.ConclusionThis study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.

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CENPL, ISG20L2, LSM4 and MRPL3 Are Four Novel Hub Genes Involved in Breast Cancer Development and Progression

10.21203/rs.3.rs-96226/v1 ◽

2020 ◽

Author(s):

Jinbao Yin ◽

Chen Lin ◽

Meng jiang ◽

Xinbing Tang ◽

Danlin Xie ◽

...

Keyword(s):

Breast Cancer ◽

Network Analysis ◽

Candidate Genes ◽

Molecular Mechanism ◽

Molecular Mechanisms ◽

Rank Aggregation ◽

Functional Enrichment ◽

Hub Genes ◽

Ppi Networks ◽

Kaplan Meier

Abstract BackgroundAs a highly prevalent tumor disease worldwide, Further elucidation of the molecular mechanisms of the occurrence, development and prognosis of breast cancer remain an urgent need. Identifying hub genes involved in these pathogenesis and progression can potentially help to unveil its mechanism and provide novel diagnostic and prognostic markers for breast cancer.MethodsIn this study, we systematically integrated multiple bioinformatic methods, including robust rank aggregation (RRA), functional enrichment analysis, protein-protein interaction (PPI) networks construction and analysis, weighted gene co-expression network analysis (WGCNA), ROC and Kaplan-Meier analyses, DNA methylation analyses and genomic mutation analyses, GSEA and GSVA, based on ten mRNA datasets to identify and investigate novel hub genes involved in breast cancer. In parallel, RNA in situ detection technology was applied to validate those novel hub gene.ResultsEZH2 was recognized as a key gene by PPI network analysis. CENPL, ISG20L2, LSM4 and MRPL3 were identified as four novel hub genes through the WGCNA analysis and literate search. Among those five hub genes, many studies on EZH2 gene in breast cancer have been reported, but no studies are related to the roles of CENPL, ISG20L2, MRPL3 and LSM4 in breast cancer. These novel four hub genes were up-regulated in breast cancer tissues and associated with tumor progression. ROC and Kaplan-Meier indicated these four hub genes all showed good diagnostic performance and prognostic values for breast cancer. The preliminary analysis revealed those novel hub genes are four potentially candidate genes for further exploring the molecular mechanism of breast cancer.ConclusionWe identify four novel hub genes (CENPL, ISG20L2, MRPL3, and LSM4) that are likely playing key roles in the molecular mechanism of occurrence and development of breast cancer. Those hub genes are four potentially candidate genes served as promising candidate diagnostic biomarkers and prognosis predictors for breast cancer, and their exact functional mechanisms in breast cancer deserve further in-depth study.

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Identification of Biomarkers and Pathogenesis in Severe Asthma by Co-expression Network Analysis

10.21203/rs.3.rs-123118/v1 ◽

2020 ◽

Author(s):

Zeyi Zhang ◽

Ou Chen ◽

Jingjing Wang

Keyword(s):

Network Analysis ◽

Severe Asthma ◽

Gene Networks ◽

Molecular Mechanisms ◽

Area Under The Curve ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Asthma Severity ◽

Functional Enrichment ◽

Hub Genes

Abstract BackgroundSevere asthma is a heterogeneous inflammatory disease. The rise of precise immunotherapy for severe asthmatics underlines more understanding of molecular mechanisms and biomarkers. In this study, we aim to identify underlying mechanisms and hub genes that define asthma severity.MethodsDifferentially expressed genes were screened out based on bronchial epithelial brushings from mild and severe asthmatics. Then, the weighted gene co-expression network analysis was adopted to identify gene networks and the most significant module associated with asthma severity. Meanwhile, hub genes screening and functional enrichment analysis was performed. Receiver operating characteristic was conducted to validate the hub genes.ResultsWeighted gene co-expression network analysis identified 6 modules associated with asthma severity. Three modules were positively correlated (P < 0.001) with asthma severity, containing genes upregulated in severe asthmatics. Functional enrichment analysis found genes in the highlighted module mainly enriched in neutrophil degranulation and activation, leukocyte migration and chemotaxis. Hub genes identified in the module were CXCR1, CXCR2, CCR1, CCR7, TLR2, FPR1, FCGR3B, FCGR2A, ITGAM, and PLEK. Combining these hub genes possessed a moderate ability for discriminating between severe asthmatics and mild-moderate asthmatics with an area under the curve of 0.75.ConclusionOur results identified biomarkers and potential pathogenesis of severe asthma, which provides sight into treatment targets and prognostic markers.

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Identification of Four Key Biomarkers and Small Molecule Drugs Innasopharyngeal Carcinoma by Weighted Gene Co-expression Network Analysis

10.21203/rs.3.rs-49643/v1 ◽

2020 ◽

Author(s):

Xi Pan ◽

Jian-Hao Liu

Keyword(s):

Gene Expression ◽

Network Analysis ◽

Small Molecule ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Tumor Stage ◽

Functional Enrichment ◽

Hub Genes ◽

Therapeutic Goals ◽

Small Molecule Drugs

Abstract Background Nasopharyngeal carcinoma (NPC) is a heterogeneous carcinoma that the underlying molecular mechanisms involved in the tumor initiation, progression, and migration are largely unclear. The purpose of the present study was to identify key biomarkers and small-molecule drugs for NPC screening, diagnosis, and therapy via gene expression profile analysis. Methods Raw microarray data of NPC were retrieved from the Gene Expression Omnibus (GEO) database and analyzed to screen out the potential differentially expressed genes (DEGs). The key modules associated with histology grade and tumor stage was identified by using weighted correlation network analysis (WGCNA). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of genes in the key module were performed to identify potential mechanisms. Candidate hub genes were obtained, which based on the criteria of module membership (MM) and high connectivity. Then we used receiver operating characteristic (ROC) curve to evaluate the diagnostic value of hub genes. The Connectivity map database was further used to screen out small-molecule drugs of hub genes. Results A total of 430 DEGs were identified based on two GEO datasets. The green gene module was considered as key module for the tumor stage of NPC via WGCNA analysis. The results of functional enrichment analysis revealed that genes in the green module were enriched in regulation of cell cycle, p53 signaling pathway, cell part morphogenesis. Furthermore, four DEGs-related hub genes in the green module were considered as the final hub genes. Then ROC revealed that the final four hub genes presented with high areas under the curve, suggesting these hub genes may be diagnostic biomarkers for NPC. Meanwhile, we screened out several small-molecule drugs that have provided potentially therapeutic goals for NPC. Conclusions Our research identified four potential prognostic biomarkers and several candidate small-molecule drugs for NPC, which may contribute to the new insights for NPC therapy.

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Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning

PLoS ONE ◽

10.1371/journal.pone.0257343 ◽

2021 ◽

Vol 16 (9) ◽

pp. e0257343

Author(s):

Shaoshuo Li ◽

Baixing Chen ◽

Hao Chen ◽

Zhen Hua ◽

Yang Shao ◽

...

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Network Analysis ◽

Postmenopausal Osteoporosis ◽

Molecular Mechanism ◽

Molecular Mechanisms ◽

Functional Enrichment ◽

Support Vector ◽

Hub Genes ◽

Genetic Biomarkers

Objectives Smoking is a significant independent risk factor for postmenopausal osteoporosis, leading to genome variations in postmenopausal smokers. This study investigates potential biomarkers and molecular mechanisms of smoking-related postmenopausal osteoporosis (SRPO). Materials and methods The GSE13850 microarray dataset was downloaded from Gene Expression Omnibus (GEO). Gene modules associated with SRPO were identified using weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, and pathway and functional enrichment analyses. Feature genes were selected using two machine learning methods: support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF). The diagnostic efficiency of the selected genes was assessed by gene expression analysis and receiver operating characteristic curve. Results Eight highly conserved modules were detected in the WGCNA network, and the genes in the module that was strongly correlated with SRPO were used for constructing the PPI network. A total of 113 hub genes were identified in the core network using topological network analysis. Enrichment analysis results showed that hub genes were closely associated with the regulation of RNA transcription and translation, ATPase activity, and immune-related signaling. Six genes (HNRNPC, PFDN2, PSMC5, RPS16, TCEB2, and UBE2V2) were selected as genetic biomarkers for SRPO by integrating the feature selection of SVM-RFE and RF. Conclusion The present study identified potential genetic biomarkers and provided a novel insight into the underlying molecular mechanism of SRPO.

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Hub Genes of Stroke Identified by Weighted Gene Co-expression Network Analysis

10.21203/rs.3.rs-38160/v1 ◽

2020 ◽

Author(s):

Junhong Li ◽

Yang Zhai ◽

Peng Wu ◽

Yueqiang Hu ◽

Wei Chen ◽

...

Keyword(s):

Gene Expression ◽

Network Analysis ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Common Disease ◽

Hub Genes ◽

Neuron Death ◽

Roc Curve Analysis

Abstract BACKGROUD: Microarray-based gene expression profiling is widely used in biomedical research. Weighted gene co-expression network analysis (WGCNA) links microarray data directly to clinical traits and identifies rules for predicting pathological stage and prognosis of disease.WGCNA is useful in understandingmany biological processes. Stroke is a common disease worldwide, however, molecular mechanisms of its pathogenesis are largely unknown. The aim of this study was to construct gene co-expression networks for identification of key modules and hub genes associated with stroke pathogenesis.METHODS: Gene microarray expression profiles of stroke samples were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened by the limma package in R software. WGCNA was used to construct free-scale gene co-expression networks to explore the associations between gene sets and clinical features, and to identify key modules and hub genes. Subsequently, functional enrichment analyses were performed. Further, receiver operating characteristic (ROC) curve analysis was carried out to validate expression of hub genes and literature validation was performed as well.RESULTS: A total of 11,747 most variant genes were used for co-expression network construction. Pink and yellow modules were significantly correlated to stroke pathogenesis. Functional enrichment analysis showed that the pink module was mainly involved in regulation of neuron regeneration, and repair of DNA damage.On the other hand, yellow module was mainly enriched in ion transport system dysfunction which was correlated with neuron death. A total of eight hub genes (PRR11, NEDD9, Notch2, RUNX1-IT1, ANP32A-IT1, ASTN2, SAMHD1 and STIM1) were identified and validated at transcriptional levels and through existing literature.CONCLUSION: The eight hub genes (PRR11, NEDD9, Notch2, RUNX1-IT1, ANP32A-IT1, ASTN2, SAMHD1 and STIM1) identified in the study are potentialbiomarkers and therapeutic targets for effective diagnosis and treatment of stroke.

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Identification of Four Hub Genes Involved in Breast Cancer Based on Robust Rank Aggregation and WGCNA Methods

10.21203/rs.3.rs-70354/v1 ◽

2020 ◽

Author(s):

Rongqin Ke ◽

Jinbao Yin

Keyword(s):

Breast Cancer ◽

Therapeutic Targets ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Rank Aggregation ◽

Functional Enrichment ◽

Hub Genes ◽

Ppi Networks ◽

Genomic Mutation ◽

Mutation Analyses

Abstract Background: Further elucidation of the molecular mechanisms of the occurrence, development and prognosis of breast cancer remains an urgent need. Identifying hub genes involved in these pathogenesis and progression can potentially help to unveil these mechanisms and provide novel therapeutic targets for breast cancer. Methods: In this study, we systematically integrated robust rank aggregation (RRA), functional enrichment analysis, protein-protein interaction (PPI) networks construction and analysis, weighted gene co-expression network analysis (WGCNA), DNA methylation analyses and genomic mutation analyses, GSEA and GSVA to identify potential hub genes that are highly associated with breast cancer. Results: We identified a total of 512 robust DEGs that were significantly associated with breast cancer based on RRA analysis and functional enrichment analysis. CENPL, ISG20L2, MRPL3 and LSM4 were identified as four potential hub genes for breast cancer through the WGCNA analysis and literate search. These four hub genes were upregulated in breast cancer tissues and associated with tumor progression. ROC and Kaplan-Meier indicated these four hub genes all showed good diagnostic performance and prognostic values for breast cancer. Methylation analyses and genomic mutation analyses suggested that the abnormal up-regulation of these genes are likelyresulted from hypomethylation and gene mutations. Moreover, GSEA and GSVA for single potential hub genes revealed they were all tightly related to the proliferation of tumor cells. Conclusion: We identify four genes (CENPL, ISG20L2, MRPL3, and LSM4) that are likely playing key roles in the molecular mechanism of occurrence and development of breast cancer. They may become potential therapeutic targets for breast cancer patients with further studies. Keywords: breast cancer, RRA, WGCNA, hub genes

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Hub genes of stroke identified by weighted gene co-expression network analysis

10.21203/rs.2.14681/v1 ◽

2019 ◽

Author(s):

Junhong Li ◽

Yang Zhai ◽

Peng Wu ◽

Yueqiang Hu ◽

Wei Chen ◽

...

Keyword(s):

Network Analysis ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Hub Genes ◽

Neuron Death ◽

Roc Curve Analysis ◽

Precise Diagnosis

Abstract Background Microarray-based gene expression profiling has been widely used in biomedical research. Weighted gene co-expression network analysis (WGCNA) can link microarray data directly to clinical traits and to identify rules for predicting pathological stage and prognosis of disease, it has been found useful in many biological processes. Stroke is one of the most common diseases worldwide, yet molecular mechanisms of its pathogenesis are largely unknown. We aimed to construct gene co-expression networks to identify key modules and hub genes associated with the pathogenesis of stroke.Results In this study, we screened out the differentially expressed genes from gene microarray expression profiles, then constructed the free-scale gene co-expression networks to explore the associations between gene sets and clinical features, and to identify key modules and hub genes. Subsequently, functional enrichment and the receiver operating characteristic (ROC) curve analysis were performed. And the results show that a total of 11,747 most variant genes were used for co-expression network construction. Pink and yellow modules were found to be the most significantly related to stroke. Functional enrichment analysis showed that the pink module was mainly involved in regulation of neuron regeneration, and the repair of DNA damage, while the yellow module was mainly enriched in ion transport system dysfunction which were correlated with neuron death. A total of 8 hub genes (PRR11, NEDD9, Notch2, RUNX1-IT1, ANP32A-IT1, ASTN2, SAMHD1 and STIM1) were identified and validated at transcriptional levels (other datasets) and by existing literatures.Conclusions Eight hub genes (PRR11, NEDD9, Notch2, RUNX1-IT1, ANP32A-IT1, ASTN2, SAMHD1 and STIM1) may serve as biomarkers and therapeutic targets for precise diagnosis and treatment of stroke in the future.

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Gene and lncRNA co-expression network analysis reveals novel ceRNA network for triple-negative breast cancer

Scientific Reports ◽

10.1038/s41598-019-51626-7 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 6

Author(s):

Kehao Le ◽

Hui Guo ◽

Qiulei Zhang ◽

Xiaojuan Huang ◽

Ming Xu ◽

...

Keyword(s):

Breast Cancer ◽

Network Analysis ◽

Triple Negative Breast Cancer ◽

Molecular Mechanisms ◽

Triple Negative ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Hub Genes ◽

Protein Coding ◽

Cerna Network

Abstract Breast cancer is the most frequently diagnosed malignancy among women, and triple-negative breast cancer (TNBC) is a highly aggressive subtype. Increasing evidence has shown that lncRNAs are involved in tumor growth, cell-cycle, and apoptosis through interactions with miRNAs or mRNAs. However, there is still limited data on ceRNAs involved in the molecular mechanisms underlying TNBC. In this study, we applied the weighted gene co-expression network analysis to the existing microarray mRNA and lncRNA expression data obtained from the breast tissues of TNBC patients to find the hub genes and lncRNAs involved in TNBC. Functional enrichment was performed on the module that correlated with Ki-67 status the most (Turquoise module). The hub genes in the Turquoise module were found to be associated with DNA repair, cell proliferation, and the p53 signaling pathway. We performed co-expression analysis of the protein-coding and lncRNA hub genes in the Turquoise module. Analysis of the RNA-seq data obtained from The Cancer Genome Atlas database revealed that the protein-coding genes and lncRNAs that were co-expressed were also differentially expressed in the TNBC tissues compared with the normal mammary tissues. On the basis of establishing the ceRNA network, two mRNAs (RAD51AP1 and TYMS) were found to be correlated with overall survival in TNBC. These results suggest that TNBC-specific mRNA and lncRNAs may participate in a complex ceRNA network, which represents a potential therapeutic target for the treatment of TNBC.

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Identification and Validation of Four Novel Biomarkers Involved in Docetaxel Resistant Prostate Cancer

10.21203/rs.3.rs-298854/v1 ◽

2021 ◽

Author(s):

Ruiji Liu ◽

Shuying Li ◽

Liquan Liu ◽

Bin Xu ◽

Ming Chen

Keyword(s):

Prostate Cancer ◽

Pathway Analysis ◽

Cellular Response ◽

Molecular Mechanisms ◽

Bioinformatic Analysis ◽

Functional Enrichment ◽

Castration Resistant Prostate Cancer ◽

Hub Genes ◽

Ppi Networks ◽

Docetaxel Chemotherapy

Abstract Background: Prostate cancer (PCa) is one of the most common malignancies affecting men’s health. Docetaxel chemotherapy has been utilized in castration resistant prostate cancer (CRPC). However, docetaxel resistance further reduces the survival of PCa patients. Thus, it is necessary to explore effective biomarkers for docetaxel chemotherapy resistant PCa (DCRPC).Methods: A comprehensive bioinformatic analysis was performed to identify differentially expressed genes (DEGs) between DCRPC and docetaxel chemotherapy sensitive PCa (DCSPC) cell. DEGs were selected between GSE33455 and GSE36135 obtained from the Gene Expression Omnibus (GEO) database. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were applied for functional enrichment and pathway analysis. The STRING database were used to construct protein-protein interaction (PPI) networks. Diseases free survival (DFS) was used for prognostic analysis via GEPIA.Results: A total of 89 DEGs from GSE33455 and GSE36135 were screened. GO functional analysis showed that these DEGs are mainly enriched in the extracellular exosome, cell adhesion molecule binding, ATP binding and cellular response to tumor necrosis factor. KEGG pathway analysis showed that these DEGs are mainly involved in the TNF signaling pathway, chemokine pathway, and nicotinate and nicotinamide metabolism. 30 closely related DEGs were used for PPI network construction. Finally, four prognostic relative genes (RHOF, ADCY7, DOCK2, LMO7) were identified. Conclusion: The present study identified prognostic hub genes and related signaling pathways, which may help improve understanding of the molecular mechanisms of DCRPC. These hub genes can potentially serve as therapeutic and prognostic biomarkers for DCRPC.

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Comprehensive Analysis to Identify Key Genes Involved in Advanced Atherosclerosis

Disease Markers ◽

10.1155/2021/4026604 ◽

2021 ◽

Vol 2021 ◽

pp. 1-25

Author(s):

Tian-ming Huo ◽

Zhi-wei Wang

Keyword(s):

Gene Expression ◽

Network Analysis ◽

Lupus Erythematosus ◽

Inflammatory Responses ◽

Therapeutic Targets ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Hub Genes ◽

Key Genes

Background. The study was aimed at finding accurate and effective therapeutic targets and deepening our understanding of the mechanisms of advanced atherosclerosis (AA). Methods. We downloaded the gene expression datasets GSE28829, GSE120521, and GSE43292 from Gene Expression Omnibus. Weighted gene coexpression network analysis (WGCNA) was performed for GSE28829, and functional enrichment analysis and protein–protein interaction network analysis were conducted on the key module. Significant genes in the key module were analyzed by molecular complex detection, and genes in the most important subnetwork were defined as hub genes. Multiple dataset analyses for hub genes were conducted. Genes that overlapped between hub genes and differentially expressed genes (DEGs) of GSE28829 and GSE120521 were defined as key genes. Further validation for key genes was performed using GSE28829 and GSE43292. Gene set enrichment analysis (GSEA) was applied to key genes. Results. A total of 77 significant genes in the key module of GSE28829 were screened out that were mainly associated with inflammation and immunity. The subnetwork was obtained from significant genes, and 18 genes in this module were defined as hub genes, which were related to immunity and expressed in multiple diseases, particularly systemic lupus erythematosus. Some hub genes were regulated by SPI1 and associated with the blood, spleen, and lung. After overlapping with DEGs of GSE28829 and GSE120521, a total of 10 genes (HCK, ITGAM, CTSS, TYROBP, LAPTM5, FCER1G, ITGB2, NCF2, AIF1, and CD86) were identified as key genes. All key genes were validated and evaluated successfully and were related to immune response pathways. Conclusion. Our study suggests that the key genes related to immune and inflammatory responses are involved in the development of AA. This may deepen our understanding of the mechanisms of and provide valuable therapeutic targets for AA.

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