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Hu Y, Jin L, Wang Z. Genome-wide association study of dilated cardiomyopathy-induced heart failure associated with renal insufficiency in a Chinese population. BMC Cardiovasc Disord 2023; 23:335. [PMID: 37391705 PMCID: PMC10314512 DOI: 10.1186/s12872-023-03370-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 06/28/2023] [Indexed: 07/02/2023] Open
Abstract
BACKGROUND As it is unclear whether there is genetic susceptibility to cardiorenal syndrome (CRS), we conducted a genome-wide association study of dilated cardiomyopathy (DCM)-induced heart failure (HF) associated with renal insufficiency (RI) in a Chinese population to identify putative susceptibility variants and culprit genes. METHODS A total of 99 Han Chinese patients with DCM-induced chronic HF were selected and divided into one of three groups, namely, HF with normal renal function (Group 1), HF with mild RI (Group 2) and HF with moderate to severe RI (Group 3). Genomic DNA was extracted from each subject for genotyping. RESULTS According to Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, top 10 lists of molecular function, cell composition and biological process of differential target genes and 15 signalling pathways were discriminated among the three groups. Additionally, sequencing results identified 26 significantly different single-nucleotide polymorphisms (SNPs) in the 15 signalling pathways, including three SNPs (rs57938337, rs6683225 and rs6692782) in ryanodine receptor 2 (RYR2) and two SNPs (rs12439006 and rs16958069) in RYR3. The genotype and allele frequencies of the five SNPs in RYR2 and RYR3 were significantly differential between HF (Group 1) and CRS (Group 2 + 3) patients. CONCLUSION Twenty-six significantly different SNP loci in 17 genes of the 15 KEGG pathways were found in the three patient groups. Among these variants, rs57938337, rs6683225 and rs6692782 in RYR2 and rs12439006 and rs16958069 in RYR3 are associated with RI in Han Chinese patients with heart failure, suggesting that these variants may be used to identify patients susceptible to CRS in the future.
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Affiliation(s)
- Yuexin Hu
- Department of Cardiovascular Medicine, Affiliated Nanjing Brain Hospital, Nanjing Medical University, No. 246 Guangzhou Road, Nanjing, Jiangsu, 210008, China
- Department of Cardiovascular Medicine, Nanjing Chest Hospital, Nanjing, China
| | - Liangli Jin
- Department of Cardiovascular Medicine, Affiliated Nanjing Brain Hospital, Nanjing Medical University, No. 246 Guangzhou Road, Nanjing, Jiangsu, 210008, China
- Department of Cardiovascular Medicine, Nanjing Chest Hospital, Nanjing, China
| | - Zhi Wang
- Department of Cardiovascular Medicine, Affiliated Nanjing Brain Hospital, Nanjing Medical University, No. 246 Guangzhou Road, Nanjing, Jiangsu, 210008, China.
- Department of Cardiovascular Medicine, Nanjing Chest Hospital, Nanjing, China.
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Ye LF, Weng JY, Wu LD. Integrated genomic analysis defines molecular subgroups in dilated cardiomyopathy and identifies novel biomarkers based on machine learning methods. Front Genet 2023; 14:1050696. [PMID: 36824437 PMCID: PMC9941670 DOI: 10.3389/fgene.2023.1050696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 01/20/2023] [Indexed: 02/09/2023] Open
Abstract
Aim: As the most common cardiomyopathy, dilated cardiomyopathy (DCM) often leads to progressive heart failure and sudden cardiac death. This study was designed to investigate the molecular subgroups of DCM. Methods: Three datasets of DCM were downloaded from GEO database (GSE17800, GSE79962 and GSE3585). After log2-transformation and background correction with "limma" package in R software, the three datasets were merged into a metadata cohort. The consensus clustering was conducted by the "Consensus Cluster Plus" package to uncover the molecular subgroups of DCM. Moreover, clinical characteristics of different molecular subgroups were compared in detail. We also adopted Weighted gene co-expression network analysis (WGCNA) analysis based on subgroup-specific signatures of gene expression profiles to further explore the specific gene modules of each molecular subgroup and its biological function. Two machine learning methods of LASSO regression algorithm and SVM-RFE algorithm was used to screen out the genetic biomarkers, of which the discriminative ability of molecular subgroups was evaluated by receiver operating characteristic (ROC) curve. Results: Based on the gene expression profiles, heart tissue samples from patients with DCM were clustered into three molecular subgroups. No statistical difference was found in age, body mass index (BMI) and left ventricular internal diameter at end-diastole (LVIDD) among three molecular subgroups. However, the results of left ventricular ejection fraction (LVEF) statistics showed that patients from subgroup 2 had a worse condition than the other group. We found that some of the gene modules (pink, black and grey) in WGCNA analysis were significantly related to cardiac function, and each molecular subgroup had its specific gene modules functions in modulating occurrence and progression of DCM. LASSO regression algorithm and SVM-RFE algorithm was used to further screen out genetic biomarkers of molecular subgroup 2, including TCEAL4, ISG15, RWDD1, ALG5, MRPL20, JTB and LITAF. The results of ROC curves showed that all of the genetic biomarkers had favorable discriminative effectiveness. Conclusion: Patients from different molecular subgroups have their unique gene expression patterns and different clinical characteristics. More personalized treatment under the guidance of gene expression patterns should be realized.
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Affiliation(s)
- Ling-Fang Ye
- Changzhi People’s Hospital, Changzhi, Shanxi, China
| | - Jia-Yi Weng
- Department of Cardiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University,Suzhou, China,*Correspondence: Li-Da Wu, ; Jia-Yi Weng,
| | - Li-Da Wu
- Nanjing Medical University, Nanjing, China,*Correspondence: Li-Da Wu, ; Jia-Yi Weng,
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Vadgama N, Ameen M, Sundaram L, Gaddam S, Gifford C, Nasir J, Karakikes I. De novo and inherited variants in coding and regulatory regions in genetic cardiomyopathies. Hum Genomics 2022; 16:55. [PMID: 36357925 PMCID: PMC9647983 DOI: 10.1186/s40246-022-00420-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 09/24/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Cardiomyopathies are a leading cause of progressive heart failure and sudden cardiac death; however, their genetic aetiology remains poorly understood. We hypothesised that variants in noncoding regulatory regions and oligogenic inheritance mechanisms may help close the diagnostic gap. METHODS We first analysed whole-genome sequencing data of 143 parent-offspring trios from Genomics England 100,000 Genomes Project. We used gene panel testing and a phenotype-based, variant prioritisation framework called Exomiser to identify candidate genes in trios. To assess the contribution of noncoding DNVs to cardiomyopathies, we intersected DNVs with open chromatin sequences from single-cell ATAC-seq data of cardiomyocytes. We also performed a case-control analysis in an exome-negative cohort, including 843 probands and 19,467 controls, to assess the association between noncoding variants in known cardiomyopathy genes and disease. RESULTS In the trio analysis, a definite or probable genetic diagnosis was identified in 21 probands according to the American College of Medical Genetics guidelines. We identified novel DNVs in diagnostic-grade genes (RYR2, TNNT2, PTPN11, MYH7, LZR1, NKX2-5), and five cases harbouring a combination of prioritised variants, suggesting that oligogenic inheritance and genetic modifiers contribute to cardiomyopathies. Phenotype-based ranking of candidate genes identified in noncoding DNV analysis revealed JPH2 as the top candidate. Moreover, a case-control analysis revealed an enrichment of rare noncoding variants in regulatory elements of cardiomyopathy genes (p = .035, OR = 1.43, 95% Cl = 1.095-1.767) versus controls. Of the 25 variants associated with disease (p< 0.5), 23 are novel and nine are predicted to disrupt transcription factor binding motifs. CONCLUSION Our results highlight complex genetic mechanisms in cardiomyopathies and reveal novel genes for future investigations.
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Affiliation(s)
- Nirmal Vadgama
- Department of Cardiothoracic Surgery and Cardiovascular Institute, Stanford University, 240 Pasteur Drive, Palo Alto, CA, 943054, USA
- Department of Pediatrics, Division of Cardiology, Stanford School of Medicine, Lucile Packard Children's Hospital, Palo Alto, CA, USA
- Department of Genetics, Stanford School of Medicine, Lucile Packard Children's Hospital, Palo Alto, CA, USA
- BASE Initiative, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Palo Alto, CA, USA
| | - Mohamed Ameen
- Department of Cancer Biology, Stanford University, Stanford, CA, USA
| | | | - Sadhana Gaddam
- Program in Epithelial Biology and Department of Dermatology, Stanford University School of Medicine, Stanford, CA, USA
| | - Casey Gifford
- Department of Pediatrics, Division of Cardiology, Stanford School of Medicine, Lucile Packard Children's Hospital, Palo Alto, CA, USA
- Department of Genetics, Stanford School of Medicine, Lucile Packard Children's Hospital, Palo Alto, CA, USA
- BASE Initiative, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Palo Alto, CA, USA
| | - Jamal Nasir
- Division of Life Sciences, University of Northampton, Waterside Campus, University Drive, Northampton, NN1 5PH, UK.
| | - Ioannis Karakikes
- Department of Cardiothoracic Surgery and Cardiovascular Institute, Stanford University, 240 Pasteur Drive, Palo Alto, CA, 943054, USA.
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Croon M, Szczepanowska K, Popovic M, Lienkamp C, Senft K, Brandscheid CP, Bock T, Gnatzy-Feik L, Ashurov A, Acton RJ, Kaul H, Pujol C, Rosenkranz S, Krüger M, Trifunovic A. FGF21 modulates mitochondrial stress response in cardiomyocytes only under mild mitochondrial dysfunction. SCIENCE ADVANCES 2022; 8:eabn7105. [PMID: 35385313 PMCID: PMC8986112 DOI: 10.1126/sciadv.abn7105] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 02/11/2022] [Indexed: 05/10/2023]
Abstract
The mitochondrial integrated stress response (mitoISR) has emerged as a major adaptive pathway to respiratory chain deficiency, but both the tissue specificity of its regulation, and how mitoISR adapts to different levels of mitochondrial dysfunction are largely unknown. Here, we report that diverse levels of mitochondrial cardiomyopathy activate mitoISR, including high production of FGF21, a cytokine with both paracrine and endocrine function, shown to be induced by respiratory chain dysfunction. Although being fully dispensable for the cell-autonomous and systemic responses to severe mitochondrial cardiomyopathy, in the conditions of mild-to-moderate cardiac OXPHOS dysfunction, FGF21 regulates a portion of mitoISR. In the absence of FGF21, a large part of the metabolic adaptation to mitochondrial dysfunction (one-carbon metabolism, transsulfuration, and serine and proline biosynthesis) is strongly blunted, independent of the primary mitoISR activator ATF4. Collectively, our work highlights the complexity of mitochondrial stress responses by revealing the importance of the tissue specificity and dose dependency of mitoISR.
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Affiliation(s)
- Marijana Croon
- Institute for Mitochondrial Diseases and Aging, Medical Faculty, University of Cologne, D-50931 Cologne, Germany
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931 Cologne, Germany
| | - Karolina Szczepanowska
- Institute for Mitochondrial Diseases and Aging, Medical Faculty, University of Cologne, D-50931 Cologne, Germany
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931 Cologne, Germany
- Center for Molecular Medicine (CMMC), University of Cologne, 50931 Cologne, Germany
- ReMedy International Research Agenda Unit, IMol Polish Academy of Sciences, Warsaw, Poland
| | - Milica Popovic
- Institute for Mitochondrial Diseases and Aging, Medical Faculty, University of Cologne, D-50931 Cologne, Germany
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931 Cologne, Germany
- Cologne Cardiovascular Research Center (CCRC), University of Cologne, 50931 Cologne, Germany
| | - Christina Lienkamp
- Max Planck Institute for Biology of Ageing, Joseph-Stelzmann-Str. 9b, 50931 Cologne, Germany
| | - Katharina Senft
- Institute for Mitochondrial Diseases and Aging, Medical Faculty, University of Cologne, D-50931 Cologne, Germany
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931 Cologne, Germany
| | - Christoph Paul Brandscheid
- Institute for Mitochondrial Diseases and Aging, Medical Faculty, University of Cologne, D-50931 Cologne, Germany
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931 Cologne, Germany
| | - Theresa Bock
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931 Cologne, Germany
- Institute of Genetics, University of Cologne, 50931 Cologne, Germany
| | - Leoni Gnatzy-Feik
- Cologne Cardiovascular Research Center (CCRC), University of Cologne, 50931 Cologne, Germany
- Klinik III für Innere Medizin, Herzzentrum, University of Cologne, Kerpener Str, 62, 50937 Cologne, Germany
| | - Artem Ashurov
- Institute for Mitochondrial Diseases and Aging, Medical Faculty, University of Cologne, D-50931 Cologne, Germany
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931 Cologne, Germany
| | - Richard James Acton
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931 Cologne, Germany
| | - Harshita Kaul
- Institute for Mitochondrial Diseases and Aging, Medical Faculty, University of Cologne, D-50931 Cologne, Germany
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931 Cologne, Germany
| | - Claire Pujol
- Institut Pasteur, UMR3691 CNRS, Université de Paris, 75015 Paris, France
| | - Stephan Rosenkranz
- Center for Molecular Medicine (CMMC), University of Cologne, 50931 Cologne, Germany
- Cologne Cardiovascular Research Center (CCRC), University of Cologne, 50931 Cologne, Germany
- Klinik III für Innere Medizin, Herzzentrum, University of Cologne, Kerpener Str, 62, 50937 Cologne, Germany
| | - Marcus Krüger
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931 Cologne, Germany
- Center for Molecular Medicine (CMMC), University of Cologne, 50931 Cologne, Germany
- Institute of Genetics, University of Cologne, 50931 Cologne, Germany
| | - Aleksandra Trifunovic
- Institute for Mitochondrial Diseases and Aging, Medical Faculty, University of Cologne, D-50931 Cologne, Germany
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931 Cologne, Germany
- Center for Molecular Medicine (CMMC), University of Cologne, 50931 Cologne, Germany
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Ma X, Mo C, Huang L, Cao P, Shen L, Gui C. An Robust Rank Aggregation and Least Absolute Shrinkage and Selection Operator Analysis of Novel Gene Signatures in Dilated Cardiomyopathy. Front Cardiovasc Med 2022; 8:747803. [PMID: 34970603 PMCID: PMC8713643 DOI: 10.3389/fcvm.2021.747803] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Accepted: 11/17/2021] [Indexed: 12/15/2022] Open
Abstract
Objective: Dilated cardiomyopathy (DCM) is a heart disease with high mortality characterized by progressive cardiac dilation and myocardial contractility reduction. The molecular signature of dilated cardiomyopathy remains to be defined. Hence, seeking potential biomarkers and therapeutic of DCM is urgent and necessary. Methods: In this study, we utilized the Robust Rank Aggregation (RRA) method to integrate four eligible DCM microarray datasets from the GEO and identified a set of significant differentially expressed genes (DEGs) between dilated cardiomyopathy and non-heart failure. Moreover, LASSO analysis was carried out to clarify the diagnostic and DCM clinical features of these genes and identify dilated cardiomyopathy derived diagnostic signatures (DCMDDS). Results: A total of 117 DEGs were identified across the four microarrays. Furthermore, GO analysis demonstrated that these DEGs were mainly enriched in the regulation of inflammatory response, the humoral immune response, the regulation of blood pressure and collagen–containing extracellular matrix. In addition, KEGG analysis revealed that DEGs were mainly enriched in diverse infected signaling pathways. Moreover, Gene set enrichment analysis revealed that immune and inflammatory biological processes such as adaptive immune response, cellular response to interferon and cardiac muscle contraction, dilated cardiomyopathy are significantly enriched in DCM. Moreover, Least absolute shrinkage and selection operator (LASSO) analyses of the 18 DCM-related genes developed a 7-gene signature predictive of DCM. This signature included ANKRD1, COL1A1, MYH6, PERELP, PRKACA, CDKN1A, and OMD. Interestingly, five of these seven genes have a correlation with left ventricular ejection fraction (LVEF) in DCM patients. Conclusion: Our present study demonstrated that the signatures could be robust tools for predicting DCM in clinical practice. And may also be potential treatment targets for clinical implication in the future.
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Affiliation(s)
- Xiao Ma
- Department of Cardiology, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Changhua Mo
- Department of Cardiology, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Liangzhao Huang
- Department of Cardiology, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Peidong Cao
- Department of Cardiology, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Louyi Shen
- Department of Cardiology, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Chun Gui
- Department of Cardiology, First Affiliated Hospital of Guangxi Medical University, Nanning, China
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Integrated Analysis of Hub Genes and miRNAs in Dilated Cardiomyopathy. BIOMED RESEARCH INTERNATIONAL 2020; 2020:8925420. [PMID: 33015184 PMCID: PMC7512046 DOI: 10.1155/2020/8925420] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 08/10/2020] [Accepted: 08/17/2020] [Indexed: 12/15/2022]
Abstract
Purpose The aim of this study is to identify hub genes and miRNAs by the miRNA-mRNA interaction network in dilated cardiomyopathy (DCM) disease. Methods The differentially expressed miRNAs (DEMis) and mRNAs (DEMs) were selected using data of DCM patients downloaded from the GEO database (GSE112556 and GSE3585). Gene Ontology (GO) pathway analysis and transcription factor enrichment analysis were used for selecting DEMis, and the target mRNAs of DEMis were filtered by using miRDB, miRTarBase, and TargetScan. Cytoscape software was used to visualize the network between miRNAs and mRNAs and calculate the hub genes. GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to analyze the mRNAs in the regulatory network. Results A total of 9 DEMis and 281 DEMs were selected, from which we reconstructed the miRNA-mRNA network consisting of 7 miRNAs and 51 mRNAs. The top 10 nodes, miR-144-3p, miR-363-3p, miR-9-3p, miR-21-3p, miR-144-5p, miR-338-3p, ID4 (inhibitor of DNA binding/differentiation 4), miR-770-5p, PIK3R1 (p85α regulatory subunit of phosphoinositide 3-kinase (PI3K)), and FN1 (fibronectin 1), were identified as important regulators. Conclusions The study uncovered several important hub genes and miRNAs involved in the pathogenesis of DCM, among which, the miR-144-3p/FN1 and miR-9-3p/FN1 pathways may play an important role in myocardial fibrosis, which can help identify the etiology of DCM, and provide potential therapeutic targets.
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Mollanoori H, Rahmati Y, Hassani B, Esmaeili S, Amini K, Teimourian S. Screening the underlying molecular mechanisms involved in the development of heart failure. Meta Gene 2020. [DOI: 10.1016/j.mgene.2020.100743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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Zhang H, Huo J, Jiang W, Shan Q. Integrated microarray analysis to identify potential biomarkers and therapeutic targets in dilated cardiomyopathy. Mol Med Rep 2020; 22:915-925. [PMID: 32626989 PMCID: PMC7339620 DOI: 10.3892/mmr.2020.11145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 04/09/2020] [Indexed: 01/20/2023] Open
Abstract
Dilated cardiomyopathy (DCM) is a primary cardiomyopathy with high mortality. The aim of the present study was to identify the related genes in DCM. The four expression profiles (GSE17800, GSE21610, GSE42955 and GSE79962) downloaded from the Gene Expression Omnibus (GEO) database were analyzed using RankProd and metaMA R packages to identify differentially expressed genes (DEGs). DEGs were uploaded to the Database for Annotation, Visualization and Integrated Discovery (DAVID), for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. A protein-protein interaction (PPI) network of the DEGs was constructed using the STRING database. In addition, hub genes were identified using the Cytoscape plugin cytoHubba. A mouse DCM model, which established via intraperitoneal injection with doxorubicin (DOX), was used to validate the hub genes. A total of 898 DEGs were identified across the four microarrays. Furthermore, GO analysis demonstrated that these DEGs were mainly enriched in cell adhesion, negative regulation of cell proliferation, negative regulation of apoptotic process and potassium ion transport. In addition, KEGG analysis revealed that DEGs were mainly enriched in the ECM-receptor interaction, the p53 signaling pathway, cardiac muscle contraction and the hypoxia-inducible factor signaling pathway. Proenkephalin (PENK), chordin like 1 (CHRDL1), calumenin (CALU), apolipoprotein L1, insulin-like growth factor binding protein 3 (IGFBP3) and ceruloplasmin (CP) were identified as hub genes in the PPI network. Furthermore, the expression levels of PENK, CHRDL1, IGFBP3, CP and CALU in hearts with DCM were validated using a mouse model. In conclusion, the present study identified six hub genes related to DCM. Therefore, the present results may provide a potential mechanism for DCM involving these hub genes, which may serve as biomarkers for screening and diagnosis in the clinic.
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Affiliation(s)
- Hao Zhang
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, P.R. China
| | - Junyu Huo
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, P.R. China
| | - Wanying Jiang
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, P.R. China
| | - Qijun Shan
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, P.R. China
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Zhang Q, Wang Y, Liang J, Tian Y, Zhang Y, Tao K. Bioinformatics analysis to identify the critical genes, microRNAs and long noncoding RNAs in melanoma. Medicine (Baltimore) 2017; 96:e7497. [PMID: 28723760 PMCID: PMC5521900 DOI: 10.1097/md.0000000000007497] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Melanoma, which is usually induced by ultraviolet light exposure and the following DNA damage, is the most dangerous skin cancer. The purpose of the present study was to screen key molecules involved in melanoma.Microarray data of E-MTAB-1862 were downloaded from the ArrayExpress database, which included 21 primary melanoma samples and 11 benign nevus samples. In addition, the RNASeq version 2 and microRNA (miRNA) sequencing data of cutaneous melanoma were downloaded from The Cancer Genome Atlas database. After identifying the differentially expressed genes (DEGs) using Limma package, enrichment analysis and protein-protein interaction (PPI) network analysis were performed separately for them using DAVID software and Cytoscape software. In addition, survival analysis and regulatory network analysis were further performed by log-rank test and Cytoscape software, respectively. Moreover, real-time reverse transcription polymerase chain reaction (RT-PCR) was performed to further verify the expression patterns of several selected DEGs.A total of 382 DEGs were identified in primary melanoma samples, including 206 upregulated genes and 176 downregulated genes. Functional enrichment analysis showed that COL17A1 was enriched in epidermis development. In the PPI network, CXCL8 (degree = 29) and STAT1 (degree = 28) had higher degrees and could interact with each other. Survival analysis showed that 21 DEGs, 55 long noncoding RNAs (lncRNAs) and 32 miRNAs were found to be associated with prognosis. Furthermore, several regulatory relationships were found in the lncRNA-gene regulatory network (such as RP11-361L15.4 targeting COL17A1) and the miRNA-gene regulatory network (such as hsa-miR-375 targeting CCL27 and hsa-miR-375 targeting insulin-like growth factor 1 receptor [IGF1R]). Real-time RT-PCR results showed that the overall direction of differential expression was consistent except COL17A1.CXCL8 interacted with STAT1, CCL27, and IGF1R targeted by hsa-miR-375, and COL17A1 targeted by RP11-361L15.4 might function in the development and progression of melanoma, which should be verified by more detailed experiments.
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