scholarly journals An integrated analysis of mRNAs, lncRNAs, and miRNAs based on weighted gene co-expression network analysis involved in bovine endometritis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Negin Sheybani ◽  
Mohammad Reza Bakhtiarizadeh ◽  
Abdolreza Salehi
Keyword(s):  
Network Analysis ◽  
Protein Interactions ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Biological Pathways ◽  
Functional Enrichment ◽  
Integrated Analysis ◽  
Rna Seq

AbstractIn dairy cattle, endometritis is a severe infectious disease that occurs following parturition. It is clear that genetic factors are involved in the etiology of endometritis, however, the molecular pathogenesis of endometritis is not entirely understood. In this study, a system biology approach was used to better understand the molecular mechanisms underlying the development of endometritis. Forty transcriptomic datasets comprising of 20 RNA-Seq (GSE66825) and 20 miRNA-Seq (GSE66826) were obtained from the GEO database. Next, the co-expressed modules were constructed based on RNA-Seq (Rb-modules) and miRNA-Seq (mb-modules) data, separately, using a weighted gene co-expression network analysis (WGCNA) approach. Preservation analysis was used to find the non-preserved Rb-modules in endometritis samples. Afterward, the non-preserved Rb-modules were assigned to the mb-modules to construct the integrated regulatory networks. Just highly connected genes (hubs) in the networks were considered and functional enrichment analysis was used to identify the biological pathways associated with the development of the disease. Furthermore, additional bioinformatic analysis including protein–protein interactions network and miRNA target prediction were applied to enhance the reliability of the results. Thirty-five Rb-modules and 10 mb-modules were identified and 19 and 10 modules were non-preserved, respectively, which were enriched in biological pathways related to endometritis like inflammation and ciliogenesis. Two non-preserved Rb-modules were significantly assigned to three mb-modules and three and two important sub-networks in the Rb-modules were identified, respectively, including important mRNAs, lncRNAs and miRNAs genes like IRAK1, CASP3, CCDC40, CCDC39, ZMYND10, FOXJ1, TLR4, IL10, STAT3, FN1, AKT1, CD68, ENSBTAG00000049936, ENSBTAG00000050527, ENSBTAG00000051242, ENSBTAG00000049287, bta-miR-449, bta-miR-484, bta-miR-149, bta-miR-30b and bta-miR-423. The potential roles of these genes have been previously demonstrated in endometritis or related pathways, which reinforced putative functions of the suggested integrated regulatory networks in the endometritis pathogenesis. These findings may help further elucidate the underlying mechanisms of bovine endometritis.

scholarly journals Integrated Analysis of lncRNAs, mRNAs, and TFs to Identify Regulatory Networks Underlying MAP Infection in Cattle

2021 ◽  
Vol 12 ◽  
Author(s):  
Maryam Heidari ◽  
Abbas Pakdel ◽  
Mohammad Reza Bakhtiarizadeh ◽  
Fariba Dehghanian
Keyword(s):  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Johne's Disease ◽  
Johne’S Disease ◽  
Functional Enrichment ◽  
Integrated Analysis ◽  
Complex Nature ◽  
Hub Genes ◽  
Cis And Trans

Johne’s disease is a chronic infection of ruminants that burdens dairy herds with a significant economic loss. The pathogenesis of the disease has not been revealed clearly due to its complex nature. In order to achieve deeper biological insights into molecular mechanisms involved in MAP infection resulting in Johne’s disease, a system biology approach was used. As far as is known, this is the first study that considers lncRNAs, TFs, and mRNAs, simultaneously, to construct an integrated gene regulatory network involved in MAP infection. Weighted gene coexpression network analysis (WGCNA) and functional enrichment analysis were conducted to explore coexpression modules from which nonpreserved modules had altered connectivity patterns. After identification of hub and hub-hub genes as well as TFs and lncRNAs in the nonpreserved modules, integrated networks of lncRNA-mRNA-TF were constructed, and cis and trans targets of lncRNAs were identified. Both cis and trans targets of lncRNAs were found in eight nonpreserved modules. Twenty-one of 47 nonpreserved modules showed significant biological processes related to the immune system and MAP infection. Some of the MAP infection’s related pathways in the most important nonpreserved modules comprise “positive regulation of cytokine-mediated signaling pathway,” “negative regulation of leukocyte migration,” “T-cell differentiation,” “neutrophil activation,” and “defense response.” Furthermore, several genes were identified in these modules, including SLC11A1, MAPK8IP1, HMGCR, IFNGR1, CMPK2, CORO1A, IRF1, LDLR, BOLA-DMB, and BOLA-DMA, which are potentially associated with MAP pathogenesis. This study not only enhanced our knowledge of molecular mechanisms behind MAP infection but also highlighted several promising hub and hub-hub genes involved in macrophage-pathogen interaction.

Uncovering of Key Pathways and miRNAs for Intracranial Aneurysm Based on Weighted Gene Co-Expression Network Analysis

2022 ◽  
pp. 1-12
Author(s):  
Zhengfei Ma ◽  
Ping Zhong ◽  
Peidong Yue ◽  
Zhongwu Sun
Keyword(s):  
Network Analysis ◽  
Intracranial Aneurysm ◽  
Differentially Expressed Genes ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Area Under The Curve ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed

<b><i>Background:</i></b> Intracranial aneurysm (IA) is a serious cerebrovascular disease. The identification of key regulatory genes can provide research directions for early diagnosis and treatment of IA. <b><i>Methods:</i></b> Initially, the miRNA and mRNA data were downloaded from the Gene Expression Omnibus database. Subsequently, the limma package in R was used to screen for differentially expressed genes. In order to investigate the function of the differentially expressed genes, a functional enrichment analysis was performed. Moreover, weighted gene co-expression network analysis (WGCNA) was performed to identify the hub module and hub miRNAs. The correlations between miRNAs and mRNAs were assessed by constructing miRNA-mRNA regulatory networks. In addition, in vitro validation was performed. Finally, diagnostic analysis and electronic expression verification were performed on the GSE122897 dataset. <b><i>Results:</i></b> In the present study, 955 differentially expressed mRNAs (DEmRNAs, 480 with increased and 475 with decreased expression) and 46 differentially expressed miRNAs (DEmiRNAs, 36 with increased and 10 with decreased expression) were identified. WGCNA demonstrated that the yellow module was the hub module. Moreover, 16 hub miRNAs were identified. A total of 1,124 negatively regulated miRNA-mRNA relationship pairs were identified. Functional analysis demonstrated that DEmRNAs in the targeted network were enriched in vascular smooth muscle contraction and focal adhesion pathways. In addition, the area under the curve of 16 hub miRNAs was &#x3e;0.8. It is implied that 16 hub miRNAs may be used as potential diagnostic biomarkers of IA. <b><i>Conclusion:</i></b> Hub miRNAs and key signaling pathways were identified by bioinformatics analysis. This evidence lays the foundation for understanding the underlying molecular mechanisms of IA and provided potential therapeutic targets for the treatment of this disease.

Integrated analysis of the expression, involved functions, and regulatory network of RUNX3 in melanoma

2021 ◽  
Vol 24 ◽  
Author(s):  
Yanxin Liu ◽  
Zhang Feng ◽  
Huaxia Chen
Keyword(s):  
Protein Interactions ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Protein Protein Interactions ◽  
Nf Kappab ◽  
Microarray Datasets

Background: As a tumor suppressor or oncogenic gene, abnormal expression of RUNX family transcription factor 3 (RUNX3) has been reported in various cancers. Introduction: This study aimed to investigate the role of RUNX3 in melanoma. Methods: The expression level of RUNX3 in melanoma tissues was analyzed by immunohistochemistry and the Oncomine database. Based on microarray datasets GSE3189 and GSE7553, differentially expressed genes (DEGs) in melanoma samples were screened, followed by functional enrichment analysis. Gene Set Enrichment Analysis (GSEA) was performed for RUNX3. DEGs that co-expressed with RUNX3 were analyzed, and the transcription factors (TFs) of RUNX3 and its co-expressed genes were predicted. The protein-protein interactions (PPIs) for RUNX3 were analyzed utilizing the GeneMANIA database. MicroRNAs (miRNAs) that could target RUNX3 expression, were predicted. Results : RUNX3 expression was significantly up-regulated in melanoma tissues. GSEA showed that RUNX3 expression was positively correlated with melanogenesis and melanoma pathways. Eleven DEGs showed significant co-expression with RUNX3 in melanoma, for example, TLE4 was negatively co-expressed with RUNX3. RUNX3 was identified as a TF that regulated the expression of both itself and its co-expressed genes. PPI analysis showed that 20 protein-encoding genes interacted with RUNX3, among which 9 genes were differentially expressed in melanoma, such as CBFB and SMAD3. These genes were significantly enriched in transcriptional regulation by RUNX3, RUNX3 regulates BCL2L11 (BIM) transcription, regulation of I-kappaB kinase/NF-kappaB signaling, and signaling by NOTCH. A total of 31 miRNAs could target RUNX3, such as miR-326, miR-330-5p, and miR-373-3p. Conclusion: RUNX3 expression was up-regulated in melanoma and was implicated in the development of melanoma.

scholarly journals Integrated Analysis of lncRNA and mRNA Reveals Novel Insights into Wool Bending in Zhongwei Goat

Animals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 3326
Author(s):  
Xiaobo Li ◽  
Zhanfa Liu ◽  
Shaohui Ye ◽  
Yue Liu ◽  
Qian Chen ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Target Genes ◽  
Economic Value ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Hair Follicles ◽  
Functional Enrichment ◽  
Integrated Analysis ◽  
Rna Seq ◽  
Camp Signaling Pathway

Chinese Zhongwei goat is a rare and precious fur breed as its lamb fur is a well-known fur product. Wool bending of lamb fur of the Zhongwei goat is its most striking feature. However, the curvature of the wool decreases gradually with growth, which significantly affects its quality and economic value. The mechanism regulating the phenotypic changes of hair bending is still unclear. In the present study, the skin tissues of Zhongwei goats at 45 days (curving wool) and 108 days (slight-curving wool) after birth were taken as the research objects, and the expression profiling of long non-coding RNAs (lncRNAs) and mRNAs were analyzed based on the Ribo Zero RNA sequencing (RNA-seq) method. In total, 46,013 mRNAs and 13,549 lncRNAs were identified, of which 352 were differentially expressed mRNAs and 60 were. lncRNAs. Functional enrichment analysis of the target genes of lncRNAs were mainly enriched in PI3K-Akt, Arachidonic acid metabolic, cAMP, Wnt, and other signaling pathways. The qRT-PCR results of eight selected lncRNAs and target genes were consistent with the sequencing result, which indicated our data were reliable. Through the analysis of the weighted gene co-expression network, 13 co-expression modules were identified. The turquoise module contained a large number of differential expressed lncRNAs, which were mainly enriched in the PI3K-Akt signaling pathway and cAMP signaling pathway. The predicted LOC102172600 and LOC102191729 might affect the development of hair follicles and the curvature of wool by regulating the target genes. Our study provides novel insights into the potential roles of lncRNAs in the regulation of wool bending. In addition, the study offers a theoretical basis for further study of goat wool growth, so as to be a guidance and reference for breeding and improvement in the future.

scholarly journals UbiNet 2.0: a verified, classified, annotated and updated database of E3 ubiquitin ligase–substrate interactions

Database ◽  
2021 ◽  
Vol 2021 ◽  
Author(s):  
Zhongyan Li ◽  
Siyu Chen ◽  
Jhih-Hua Jhong ◽  
Yuxuan Pang ◽  
Kai-Yao Huang ◽  
...  
Keyword(s):  
Substrate Specificity ◽  
Protein Interactions ◽  
Regulatory Networks ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Ubiquitin Proteasome System ◽  
Functional Enrichment ◽  
Post Translational Modification ◽  
Substrate Interactions ◽  
E3 Ligases

Abstract Ubiquitination is an important post-translational modification, which controls protein turnover by labeling malfunctional and redundant proteins for proteasomal degradation, and also serves intriguing non-proteolytic regulatory functions. E3 ubiquitin ligases, whose substrate specificity determines the recognition of target proteins of ubiquitination, play crucial roles in ubiquitin–proteasome system. UbiNet 2.0 is an updated version of the database UbiNet. It contains 3332 experimentally verified E3–substrate interactions (ESIs) in 54 organisms and rich annotations useful for investigating the regulation of ubiquitination and the substrate specificity of E3 ligases. Based on the accumulated ESIs data, the recognition motifs in substrates for each E3 were also identified and a functional enrichment analysis was conducted on the collected substrates. To facilitate the research on ESIs with different categories of E3 ligases, UbiNet 2.0 performed strictly evidence-based classification of the E3 ligases in the database based on their mechanisms of ubiquitin transfer and substrate specificity. The platform also provides users with an interactive tool that can visualize the ubiquitination network of a group of self-defined proteins, displaying ESIs and protein–protein interactions in a graphical manner. The tool can facilitate the exploration of inner regulatory relationships mediated by ubiquitination among proteins of interest. In summary, UbiNet 2.0 is a user-friendly web-based platform that provides comprehensive as well as updated information about experimentally validated ESIs and a visualized tool for the construction of ubiquitination regulatory networks available at http://awi.cuhk.edu.cn/~ubinet/index.php.

scholarly journals LncRNA SNHG8 is identified as a key regulator of acute myocardial infarction by RNA-seq analysis

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Liu-An Zhuo ◽  
Yi-Tao Wen ◽  
Yong Wang ◽  
Zhi-Fang Liang ◽  
Gang Wu ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Roc Curve ◽  
Regulatory Networks ◽  
Pairwise Comparison ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Diagnostic Value ◽  
Functional Enrichment ◽  
Rna Seq

Abstract Background Long noncoding RNAs (lncRNAs) are involved in numerous physiological functions. However, their mechanisms in acute myocardial infarction (AMI) are not well understood. Methods We performed an RNA-seq analysis to explore the molecular mechanism of AMI by constructing a lncRNA-miRNA-mRNA axis based on the ceRNA hypothesis. The target microRNA data were used to design a global AMI triple network. Thereafter, a functional enrichment analysis and clustering topological analyses were conducted by using the triple network. The expression of lncRNA SNHG8, SOCS3 and ICAM1 was measured by qRT-PCR. The prognostic values of lncRNA SNHG8, SOCS3 and ICAM1 were evaluated using a receiver operating characteristic (ROC) curve. Results An AMI lncRNA-miRNA-mRNA network was constructed that included two mRNAs, one miRNA and one lncRNA. After RT-PCR validation of lncRNA SNHG8, SOCS3 and ICAM1 between the AMI and normal samples, only lncRNA SNHG8 had significant diagnostic value for further analysis. The ROC curve showed that SNHG8 presented an AUC of 0.850, while the AUC of SOCS3 was 0.633 and that of ICAM1 was 0.594. After a pairwise comparison, we found that SNHG8 was statistically significant (PSNHG8-ICAM1 = 0.002; PSNHG8-SOCS3 = 0.031). The results of a functional enrichment analysis of the interacting genes and microRNAs showed that the shared lncRNA SNHG8 may be a new factor in AMI. Conclusions Our investigation of the lncRNA-miRNA-mRNA regulatory networks in AMI revealed a novel lncRNA, lncRNA SNHG8, as a risk factor for AMI and expanded our understanding of the mechanisms involved in the pathogenesis of AMI.

scholarly journals Integrated analysis of lncRNA-miRNA-mRNA ceRNA network in human aortic dissection

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Hao Zhang ◽  
Ce Bian ◽  
Simei Tu ◽  
Fanxing Yin ◽  
Panpan Guo ◽  
...  
Keyword(s):  
Aortic Dissection ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Down Regulation ◽  
Tissue Samples ◽  
Cerna Network

Abstract Background Many studies on long chain non-coding RNAs (lncRNAs) are published in recent years. But the roles of lncRNAs in aortic dissection (AD) are still unclear and should be further examined. The present work focused on determining the molecular mechanisms underlying lncRNAs regulation in aortic dissection on the basis of the lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) network. Methods This study collected the lncRNAs (GSE52093), mRNAs (GSE52093) and miRNAs (GSE92427) expression data within human tissue samples with aortic dissection group and normal group based on Gene Expression Omnibus (GEO) database. Results This study identified three differentially expressed lncRNAs (DELs), 19 differentially expressed miRNAs (DEmiRs) and 1046 differentially expressed mRNAs (DEGs) identified regarding aortic dissection. Furthermore, we constructed a lncRNA-miRNA-mRNA network through three lncRNAs (including two with up-regulation and one with down-regulation), five miRNAs (five with up-regulation), as well as 211 mRNAs (including 103 with up-regulation and 108 with down-regulation). Simultaneously, we conducted functional enrichment and pathway analyses on genes within the as-constructed ceRNA network. According to our PPI/ceRNA network and functional enrichment analysis results, four critical genes were found (E2F2, IGF1R, BDNF and PPP2R1B). In addition, E2F2 level was possibly modulated via lncRNA FAM87A-hsa-miR-31-5p/hsa-miR-7-5p or lncRNA C9orf106-hsa-miR-7-5p. The expression of IGF1R may be regulated by lncRNA FAM87A-hsa-miR-16-5p/hsa-miR-7-5p or lncRNA C9orf106-hsa-miR-7-5p. Conclusion In conclusion, the ceRNA interaction axis we identified is a potentially critical target for treating AD. Our results shed more lights on the possible pathogenic mechanism in AD using a lncRNA-associated ceRNA network.

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

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.

scholarly journals Identification of Potential Key Genes and Pathways in Early-Onset Colorectal Cancer Through Bioinformatics Analysis

2019 ◽  
Vol 26 (1) ◽  
pp. 107327481983126 ◽  
Author(s):  
Bin Zhao ◽  
Zulqarnain Baloch ◽  
Yunhan Ma ◽  
Zheng Wan ◽  
Yani Huo ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Muscle Contraction ◽  
Protein Interactions ◽  
Early Onset ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Smooth Muscle Contraction ◽  
Functional Enrichment ◽  
Early Onset Colorectal Cancer

This study was designed to identify the potential key protein interaction networks, genes, and correlated pathways in early-onset colorectal cancer (CRC) via bioinformatics methods. We selected microarray data GSE4107 consisting 12 patient’s colonic mucosa and 10 healthy control mucosa; initially, the GSE4107 were downloaded and analyzed using limma package to identify differentially expressed genes (DEGs). A total of 131 DEGs consisting of 108 upregulated genes and 23 downregulated genes of patients in early-onset CRC were selected by the criteria of adjusted P values <.01 and |log2 fold change (FC)| ≥ 2. The gene ontology functional enrichment analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were accomplished to view the biological process, cellular components, molecular function, and the KEGG pathways of DEGs. Finally, protein–protein interactions (PPIs) were constructed, and the hub protein module was identified. Genes such as ACTA2, ACTG2, MYH11, CALD1, MYL9, TPM2, and LMOD1 were strongly implicated in CRC. In summary, in this study, we indicated that molecular mechanisms were involved in muscle contraction and vascular smooth muscle contraction signaling pathway, which improve our understanding of CRC and could be used as new therapeutic targets for CRC.

scholarly journals Identification of Key Modules and Hub Genes in Hypertrophic Cardiomyopathy Based on Integrative Weighted Gene Co-expression Network Analysis

2021 ◽  
Author(s):  
Jun Jiang ◽  
Delong Chen ◽  
Siyuan Xie ◽  
Qichao Dong ◽  
Yi Yu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Hedgehog Signaling ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Remodeling Of Extracellular Matrix

Abstract BackgroundHypertrophic cardiomyopathy (HCM) is a heterogeneously inherited cardiac disorder with unclear biological pathogenesis. This study aims to identify the key modules and genes involved in the development of HCM.MethodsUsing weighted gene co-expression network analysis (WGCNA) algorithm, we constructed integrative co-expression networks for the two large sample HCM datasets separately. After selecting clinically significant modules with the same clinical trait, functional enrichment analysis was performed to detect their common pathways. Based on the intramodular connectivity (IC), the shared hub genes were generated, validated, and further explored in gene set enrichment analysis (GSEA).ResultsThe orange and pink modules in GSE141910, the green and brown modules in GSE36961 were mostly related to HCM. Functional enrichment analysis suggested that HCM might exhibit enhanced processes including remodeling of extracellular matrix, activation of abnormal protein signaling, aggregation of calcium ion, and organization of cytoskeleton. SMOC2, COL16A1, RASL11B, TUBA3D, IL18R1 were defined as real hub genes due to their top IC values, significantly different expression levels, and excellent diagnostic performance in both datasets. Moreover, GSEA analysis demonstrated that pathways of the five hub genes were mainly involved in neuroactive ligand-receptor interaction, ECM-receptor interaction, Hedgehog signaling pathway.ConclusionOur study provides more comprehensive insights into the molecular mechanisms of HCM, identifies five hub genes as candidate biomarkers for HCM, which might be theoretically feasible for targeted therapy against HCM.

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