scholarly journals Co-expression network analysis identified novel potential Signature Genes Associated with human left ventricle cardiomyopathies arises from different etiologies

2020 ◽  
Author(s):  
Jiao Tian ◽  
Zhengyuan Wu ◽  
Yingying He ◽  
SHUBAI LIU
Keyword(s):  
Network Analysis ◽  
Ischemic Cardiomyopathy ◽  
Post Partum ◽  
Mapk Signaling ◽  
Clinical Diagnostics ◽  
Pathological Feature ◽  
Signature Genes ◽  
Ppi Networks ◽  
Idiopathic Cardiomyopathy ◽  
Disease Signature

Abstract Background: Heart disease is global pandemic and causes huge healthcare burden to society. However, it is still illusive that the whole transcription disorder pattern of cardiomyopathies arises from different etiologies. The Weighted Gene Co-Expression Network Analysis (WGCNA) was applied to construct and screen functional gene that be significantly related to different cardiomyopathies pathological feature. Through co-expression and protein-protein interaction (PPI) networks enrichment analysis, the hub genes and key pathways were screened, which were correlated to cardiomyopathy traits. To discover the novel disease signature genes, cardiovascular disease bioportal database and were employed to blast and validate, which contained independently investigations of clinical cardiomyopathies cases. Results: The potential disease signature genes were identified and assorted into three common axes shared among five subtype of cardiomyopathies. Four genes (MDM4, CFLAR, RPS6KB1, PKD1L2) were shared by ischemic and ischemic cardiomyopathy group. The secondary axe contained eight signature genes (MAPK1, MAPK11, MAPK14, LMNA, RAC1, PECAM1, XIAP, CREB1) and was overlapped by Ischemic Cardiomyopathy, Post. Partum Cardiomyopathy, Familiar Cardiomyopathy and Idiopathic Cardiomyopathy. The third axe consisted of two common signature genes (TFAM, RHEB) that shared among the subgroups of Viral Cardiomyopathy, Post. Partum Cardiomyopathy, Familiar Cardiomyopathy and Idiopathic Cardiomyopathy. The majority of disorder functions and pathways were enriched in metabolic processes and pathways of MAPK signaling, protein processing in endoplasmic reticulum, regulation of actin cytoskeleton. Conclusion: These results strongly suggest that expression disorder of signature genes contribute to the cardiac dysregulation and functional relapse into cardiomyopathies. Taken together, these novel signature genes could be utilized as potential diagnostic biomarkers or therapy targets. It will be benefit the cardiomyopathy precise clinical diagnostics with better outcome. In summary, this study will attract great interest of clinical research scientists as well as medical scientists that work on heart diseases.

scholarly journals Co-expression network analysis identified novel potential Signature Genes Associated with human left ventricle cardiomyopathies arises from different etiologies

2020 ◽  
Author(s):  
Jiao Tian ◽  
Zhengyuan Wu ◽  
Yingying He ◽  
SHUBAI LIU
Keyword(s):  
Network Analysis ◽  
Ischemic Cardiomyopathy ◽  
Heart Diseases ◽  
Post Partum ◽  
Mapk Signaling ◽  
Clinical Diagnostics ◽  
Signature Genes ◽  
Ppi Networks ◽  
Idiopathic Cardiomyopathy ◽  
Disease Signature

Abstract Background: Heat disease is worldwide pandemic and brings huge healthcare burden to global society. However, it is still illusive that the whole transcription disorder pattern of cardiomyopathies arises from different etiologies. We applied Weighted Gene Co-Expression Network Analysis (WGCNA) to construct and screen functional gene significantly related to 8 subtypes of cardiomyopathies. Through co-expression and protein-protein interaction (PPI) networks enrichment analysis, the hub genes and key pathways were identified, which highly correlated with pathologic traits. Compared with expression profile of healthy group, potential disease signature genes were validated through another independently investigations of cardiomyopathies via cardiovascular disease bioportal. Results: The novel potential disease signature genes were identified and assembled into three axes that shared among five cardiomyopathies groups, including idiopathic cardiomyopathy, familial cardiomyopathy, post-partum cardiomyopathy, Ischemic cardiomyopathy and viral cardiomyopathy. Four disease signature genes (MDM4, CFLAR, RPS6KB1, PKD1L2) were shared by ischemic and ischemic cardiomyopathy group. Eight signature genes (MAPK1, MAPK11, MAPK14, LMNA, RAC1, PECAM1, XIAP, CREB1) were overlapped by Ischemic Cardiomyopathy with Post. Partum/Familiar/Idiopathic Cardiomyopathy groups. The signature genes (TFAM, RHEB) were common genes among Viral Cardiomyopathy and Post. Partum / Familiar /Idiopathic Cardiomyopathy groups. These some novel signature genes were highlighted as potential biomarkers for cardiomyopathies. The majority disorder functions and pathways enriched in metabolic processes and concentrated on MAPK signaling pathway, protein processing in endoplasmic reticulum, regulation of actin cytoskeleton pathway. Conclusion: It strongly suggests that expression disorder of these signature genes may contribute the cardiac dysregulation and relapse into cardiomyopathies. Taken together, these novel signature genes could be utilized as diagnostic biomarkers or therapy targets and benefit the precise clinical diagnostics with better outcome. In summary, this study will attract great interest of clinical research scientists as well as medical scientists that work on heart diseases.

scholarly journals Novel Signature Genes for Human Left Ventricle Cardiomyopathies Identified by Weighted Co-expression Network Analysis (WGCNA)

2020 ◽  
Author(s):  
Jiao Tian ◽  
Zhengyuan Wu ◽  
Yaqi Zhang ◽  
Yingying He ◽  
SHUBAI LIU
Keyword(s):  
Post Partum ◽  
Enrichment Analysis ◽  
Mapk Signaling ◽  
Clinical Diagnostics ◽  
Hub Genes ◽  
Signature Genes ◽  
Ppi Networks ◽  
The Common ◽  
Human Left Ventricle ◽  
Key Pathways

Abstract BackgroundCardiomyopathy, a heart disease that arises from different etiologies, brings a huge healthcare burden to the global society. Identification of biomarkers can be very useful for early diagnosis of cardiomyopathy, interruption of the disease procession to heart failure, and decrement of the mortality. MethodsClinical cases of cardiomyopathy were screened out of independently investigations from the genomic database. Exploration of its whole transcription disorder pattern by WGCNA (Weighted Gene Co-expression Network Analysis) to discover the signature genes for different subtypes of cardiomyopathy. The hub genes and key pathways, which are correlated to cardiomyopathy traits, were identified through co-expression and protein-protein interaction (PPI) networks enrichment analysis. Discovered hub genes were blast through the Cardiovascular Disease Portal to verify functions related to human cardiomyopathies.ResultsThree common axes of signature genes were discovered for five subtypes of cardiomyopathy: 1) Four genes (MDM4, CFLAR, RPS6KB1, PKD1L2) were common for ischemic and ischemic cardiomyopathy subgroups; 2) Subtypes of cardiomyopathy (ischemic, post. partum, familiar and idiopathic) shared eight genes (MAPK1, MAPK11, MAPK14, LMNA, RAC1, PECAM1, XIAP, CREB1); 3) TFAM and RHEB were the common signature genes for subtypes of cardiomyopathy (viral, post. partum, familiar, and idiopathic). Major pathways enriched were including MAPK signaling pathway, the pathway of protein processing in endoplasmic reticulum, and pathway of regulatory actin cytoskeleton. Aberrant in these pathways caused disorders metabolic process and cellular malfunctions that generally contributes to cardiac dysregulation and functional relapse into cardiomyopathies.ConclusionThis study identified the key signaling pathways, functions and biological process related to cardiomyopathies and will give a light to better understand the molecular mechanism of processes of cardiomyopathies and figure out the rational clinical interference way to cure the patients. Therein, these novel signature genes may work as potential promising biomarkers for cardiomyopathy diagnosis, and will benefit for the better clinical diagnostics and outcome for patients with cardiomyopathies. 


scholarly journals Identified Three Interferon Induced Proteins as Novel Biomarkers of Human Ischemic Cardiomyopathy

2021 ◽  
Vol 22 (23) ◽  
pp. 13116
Author(s):  
Cheng Chen ◽  
Jiao Tian ◽  
Zhicheng He ◽  
Wenyong Xiong ◽  
Yingying He ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Ischemic Cardiomyopathy ◽  
Enrichment Analysis ◽  
Cardiovascular Disorder ◽  
Diagnosis And Treatment ◽  
Protein Protein Interaction ◽  
Signature Genes ◽  
Therapy Targets ◽  
Ppi Networks ◽  
Pubmed Database

Ischemic cardiomyopathy is the most frequent type of heart disease, and it is a major cause of myocardial infarction (MI) and heart failure (HF), both of which require expensive medical treatment. Precise biomarkers and therapy targets must be developed to enhance improve diagnosis and treatment. In this study, the transcriptional profiles of 313 patients’ left ventricle biopsies were obtained from the PubMed database, and functional genes that were significantly related to ischemic cardiomyopathy were screened using the Weighted Gene Co-Expression Network Analysis and protein–protein interaction (PPI) networks enrichment analysis. The rat myocardial infarction model was developed to validate these findings. Finally, the putative signature genes were blasted through the common Cardiovascular Disease Knowledge Portal to explore if they were associated with cardiovascular disorder. Three interferon stimulated genes (IFIT2, IFIT3 and IFI44L), as well as key pathways, have been identified as potential biomarkers and therapeutic targets for ischemic cardiomyopathy, and their alternations or mutations have been proven to be strongly linked to cardiac disorders. These novel signature genes could be utilized as bio-markers or potential therapeutic objectives in precise clinical diagnosis and treatment of ischemic cardiomyopathy.

scholarly journals Bioinformatics Analysis and Identification of Genes and Pathways in Ischemic Cardiomyopathy

2021 ◽  
Author(s):  
Jing Cao ◽  
Zhaoya Liu ◽  
Jie Liu ◽  
Chan Li ◽  
Guogang Zhang ◽  
...  
Keyword(s):  
Network Analysis ◽  
Ischemic Cardiomyopathy ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Ppi Networks ◽  
Regulated Expression ◽  
Limma Package ◽  
Pathway Analyses

Abstract BackgroundIschemic cardiomyopathy (ICM) is considered to be the common cause of heart failure, which has high prevalence and mortality. This study aimed to investigate the different expressed genes (DEGs) and pathways in the pathogenesis of ICM using bioinformatics analysis.MethodsThe control and ICM datasets GSE116250，GSE46224 and GSE5406 were collected from the gene expression omnibus (GEO) database. DEGs were identified using limma package of R software and co-expressed genes were identified with Venn diagrams. Then, the gene otology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to explored the biological functions and signaling pathways. Protein-protein interaction (PPI) networks were assembled with Cytoscape software to identify hub genes related to the pathogenesis of ICM.ResultsA total of 844 DEGs were screened from GSE116250, of which 447 up-regulated and 397 down-regulated genes respectively. A total of 99 DEGs were singled out from GSE46224, of which 58 up-regulated and 41 down-regulated genes respectively. 30 DEGs were screened from GSE5406, including 10 genes with up-regulated expression and 20 genes with down-regulated expression. 5 up-regulated and 3 down-regulated co-expressed DEGs were intersected in three datasets. GO and KEGG pathway analyses revealed that DEGs mainly enriched in collagen fibril organization, protein digestion and absorption, AGE-RAGE signaling pathway and other related pathways. Collagen alpha-1(III) chain (COL3A1), collagen alpha-2(I) chain (COL1A2) and lumican (LUM) are the three hub genes in all three datasets through PPI network analysis. The expression of 5 DEGs (SERPINA3, FCN3, COL3A1, HBB, MXRA5) in heart tissues by qRT-PCR results were consistent with our GEO analysis, while expression of 3 DEGs (ASPN, LUM, COL1A2) were opposite with GEO analysis.ConclusionsThese findings from this bioinformatics network analysis investigated key hub genes, which contributed to better understand the mechanism and new therapeutic targets of ICM.

Identification of key mRNAs and lncRNAs involved in the effects of anti-TWEAK on Osteosarcoma

2020 ◽  
Vol 15 ◽  
Author(s):  
Mingxuan Yang ◽  
Liangtao Zhao ◽  
Xuchang Hu ◽  
Haijun Feng ◽  
Xuewen Kang
Keyword(s):  
Dna Replication ◽  
Signaling Pathway ◽  
Bioinformatics Analysis ◽  
Mapk Signaling ◽  
Migration And Invasion ◽  
Type I ◽  
Interferon Signaling ◽  
Nf Kappa B ◽  
Ppi Networks ◽  
Coexpression Networks

Background: Osteosarcoma (OS) is one of the most common primary malignant bone tumors in teenagers. Emerging studies demonstrated TWEAK and Fn14 were involved in regulating cancer cell differentiation, proliferation, apoptosis, migration and invasion. Objective: The present study identified differently expressed mRNAs and lncRNAs after anti-TWEAK treatment in OS cells using GSE41828. Methods: We identified 922 up-regulated mRNAs, 863 downregulated mRNAs, 29 up-regulated lncRNAs, and 58 down-regulated lncRNAs after anti-TWEAK treatment in OS cells. By constructing PPI networks, we identified several key proteins involved in anti-TWEAK treatment in OS cells, including MYC, IL6, CD44, ITGAM, STAT1, CCL5, FN1, PTEN, SPP1, TOP2A, and NCAM1. By constructing lncRNAs coexpression networks, we identified several key lncRNAs, including LINC00623, LINC00944, PSMB8-AS1, LOC101929787. Result: Bioinformatics analysis revealed DEGs after anti-TWEAK treatment in OS were involved in regulating type I interferon signaling pathway, immune response related pathways, telomere organization, chromatin silencing at rDNA, and DNA replication. Bioinformatics analysis revealed differently expressed lncRNAs after antiTWEAK treatment in OS were related to telomere organization, protein heterotetramerization, DNA replication, response to hypoxia, TNF signaling pathway, PI3K-Akt signaling pathway, Focal adhesion, Apoptosis, NF-kappa B signaling pathway, MAPK signaling pathway, FoxO signaling pathway. Conclusion: : This study provided useful information for understanding the mechanisms of TWEAK underlying OS progression and identifying novel therapeutic markers for OS.

scholarly journals Transcriptional profiling and network analysis of the murine angiotensin II-induced abdominal aortic aneurysm

2011 ◽  
Vol 43 (17) ◽  
pp. 993-1003 ◽  
Author(s):  
Joshua M. Spin ◽  
Mark Hsu ◽  
Junya Azuma ◽  
Maureen M. Tedesco ◽  
Alicia Deng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Abdominal Aortic Aneurysm ◽  
Angiotensin Ii ◽  
Network Analysis ◽  
Aortic Aneurysm ◽  
Mapk Signaling ◽  
Aortic Dilatation ◽  
Ang Ii ◽  
Abdominal Aortic ◽  
Aneurysm Development

We sought to characterize temporal gene expression changes in the murine angiotensin II (ANG II)-ApoE−/− model of abdominal aortic aneurysm (AAA). Aortic ultrasound measurements were obtained over the 28-day time-course. Harvested suprarenal aortic segments were evaluated with whole genome expression profiling at 7, 14, and 28 days using the Agilent Whole Mouse Genome microarray platform and Statistical Analysis of Microarrays at a false discovery rate of <1%. A group of angiotensin-treated mice experienced contained rupture (CR) within 7 days and were analyzed separately. Progressive aortic dilatation occurred throughout the treatment period. However, the numerous early expression differences between ANG II-treated and control were not sustained over time. Ontologic analysis revealed widespread upregulation of inflammatory, immune, and matrix remodeling genes with ANG II treatment, among other pathways such as apoptosis, cell cycling, angiogenesis, and p53 signaling. CR aneurysms displayed significant decreases in TGF-β/BMP-pathway signaling, MAPK signaling, and ErbB signaling genes vs. non-CR/ANG II-treated samples. We also performed literature-based network analysis, extracting numerous highly interconnected genes associated with aneurysm development such as Spp1, Myd88, Adam17 and Lox. 1) ANG II treatment induces extensive early differential expression changes involving abundant signaling pathways in the suprarenal abdominal aorta, particularly wide-ranging increases in inflammatory genes with aneurysm development. 2) These gene expression changes appear to dissipate with time despite continued growth, suggesting that early changes in gene expression influence disease progression in this AAA model, and that the aortic tissue adapts to prolonged ANG II infusion. 3) Network analysis identified nexus genes that may constitute aneurysm biomarkers or therapeutic targets.

scholarly journals Identification of Hub Genes and Potential ceRNA Networks of Diabetic Nephropathy by Weighted Gene Co-Expression Network Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Guoqing Li ◽  
Jun Zhang ◽  
Dechen Liu ◽  
Qiong Wei ◽  
Hui Wang ◽  
...  
Keyword(s):  
Diabetic Nephropathy ◽  
Network Analysis ◽  
Disease Progression ◽  
Signaling Pathway ◽  
Microvascular Complications ◽  
Pearson Correlation ◽  
Mapk Signaling ◽  
Diabetic Patients ◽  
Hub Genes ◽  
Regulatory Pathways

Diabetic nephropathy (DN) is one of the most common microvascular complications in diabetic patients, and is the main cause of end-stage renal disease. The exact molecular mechanism of DN is not fully understood. The aim of this study was to identify novel biomarkers and mechanisms for DN disease progression by weighted gene co-expression network analysis (WGCNA). From the GSE142153 dataset based on the peripheral blood monouclear cells (PBMC) of DN, we identified 234 genes through WGCNA and differential expression analysis. Gene Ontology (GO) annotations mainly included inflammatory response, leukocyte cell-cell adhesion, and positive regulation of proteolysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways mostly included IL-17 signaling pathway, MAPK signaling pathway, and PPAR signaling pathway in DN. A total of four hub genes (IL6, CXCL8, MMP9 and ATF3) were identified by cytoscape, and the relative expression levels of hub genes were also confirmed by RT-qPCR. ROC curve analysis determined that the expression of the four genes could distinguish DN from controls (the area under the curve is all greater than 0.8), and Pearson correlation coefficient analysis suggested that the expression of the four genes was related to estimated glomerular filtration rate (eGFR) of DN. Finally, through database prediction and literature screening, we constructed lncRNA-miRNA-mRNA network. We propose that NEAT1/XIST/KCNQ1T1-let-7b-5p-IL6, NEAT1/XIST-miR-93-5p-CXCL8 and NEAT1/XIST/KCNQ1T1-miR-27a-3p/miR-16-5p-ATF3 might be potential RNA regulatory pathways to regulate the disease progression of early DN. In conclusion, we identified four hub genes, namely, IL6, CXCL8, MMP9, and ATF3, as markers for early diagnosis of DN, and provided insight into the mechanisms of disease development in DN at the transcriptome level.

Uncovering the Key miRNAs and Targets of the Liuwei Dihuang Pill in Diabetic Nephropathy-Related Osteoporosis based on Weighted Gene Co-Expression Network and Network Pharmacology Analysis

2021 ◽  
Vol 21 ◽  
Author(s):  
Ming Ming Liu ◽  
Nan Ning Lv ◽  
Rui Geng ◽  
Zhen Hua ◽  
Yong Ma ◽  
...  
Keyword(s):  
Diabetic Nephropathy ◽  
Network Analysis ◽  
Signaling Pathway ◽  
Diabetic Complications ◽  
Cellular Response ◽  
Mapk Signaling ◽  
Functional Enrichment ◽  
Network Pharmacology ◽  
Integrated Analysis ◽  
Therapeutic Effects

Background: Diabetic nephropathy-related osteoporosis (DNOP) is the most common comorbid bone metabolic disorder associated with diabetes mellitus (DM). The Liuwei Dihuang Pill (LWD) is a traditional Chinese herbal medicine widely used to treat diabetic complications, including diabetic nephropathy (DN). This study aimed to identify the biomarkers of the mechanisms of DNOP in LWD with systems biology approaches. Methods: Herein, we performed an integrated analysis of the GSE51674 and GSE63446 datasets from the GEO database via weighted gene co-expression network and network pharmacology (WGCNA) analysis. In addition, a network pharmacology approach, including bioactive compounds, was used with oral bioavailability (OB) and drug-likeness (DL) evaluation. Next, target prediction, functional enrichment analysis, network analysis, and virtual docking were used to investigate the mechanisms of LWD in DNOP. Results : WGCNA successfully identified 63 DNOP-related miRNAs. Among them, miR-574 was significantly upregulated in DN and OP samples. A total of 117 targets of 22 components associated with LWD in DNOP were obtained. The cellular response to nitrogen compounds, the AGE-RAGE signaling pathway in diabetic complications, and the MAPK signaling pathway were related to the main targets. Network analysis showed that kaempferol and quercetin were the most significant components. MAPK1 was identified as a potential target of miR-574 and the hub genes in the protein-protein interaction (PPI) network. The docking models demonstrated that kaempferol and quercetin had a strong binding affinity for Asp 167 of MAPK1. Conclusion: This study demonstrated that miR-574 may play important roles in DNOP, and the therapeutic effects of kaempferol and quercetin on LWD in DNOP might be mediated by miR-574 by targeting MAPK1. Our results provide new perspectives for further studies on the anti-DNOP mechanism of LWD.

scholarly journals Circulating miR-1246 Targeting UBE2C, TNNI3, TRAIP, UCHL1 Genes and Key Pathways as a Potential Biomarker for Lung Adenocarcinoma: Integrated Biological Network Analysis

2020 ◽  
Vol 10 (4) ◽  
pp. 162
Author(s):  
Siyuan Huang ◽  
Yong-Kai Wei ◽  
Satyavani Kaliamurthi ◽  
Yanghui Cao ◽  
Asma Sindhoo Nangraj ◽  
...  
Keyword(s):  
Network Analysis ◽  
Lung Adenocarcinoma ◽  
Signaling Pathway ◽  
Target Genes ◽  
Biological Network ◽  
Mapk Signaling ◽  
Surgical Operation ◽  
Biological Network Analysis ◽  
Potential Biomarker ◽  
Key Pathways

Analysis of circulating miRNAs (cmiRNAs) before surgical operation (BSO) and after the surgical operation (ASO) has been informative for lung adenocarcinoma (LUAD) diagnosis, progression, and outcomes of treatment. Thus, we performed a biological network analysis to identify the potential target genes (PTGs) of the overexpressed cmiRNA signatures from LUAD samples that had undergone surgical therapy. Differential expression (DE) analysis of microarray datasets, including cmiRNAs (GSE137140) and cmRNAs (GSE69732), was conducted using the Limma package. cmiR-1246 was predicted as a significantly upregulated cmiRNA of LUAD samples BSO and ASO. Then, 9802 miR-1246 target genes (TGs) were predicted using 12 TG prediction platforms (MiRWalk, miRDB, and TargetScan). Briefly, 425 highly expressed overlapping miRNA-1246 TGs were observed between the prediction platform and the cmiRNA dataset. ClueGO predicted cell projection morphogenesis, chemosensory behavior, and glycosaminoglycan binding, and the PI3K–Akt signaling pathways were enriched metabolic interactions regulating miRNA-1245 overlapping TGs in LUAD. Using 425 overlapping miR-1246 TGs, a protein–protein interaction network was constructed. Then, 12 PTGs of three different Walktrap modules were identified; among them, ubiquitin-conjugating enzyme E2C (UBE2C), troponin T1(TNNT1), T-cell receptor alpha locus interacting protein (TRAIP), and ubiquitin c-terminal hydrolase L1(UCHL1) were positively correlated with miR-1246, and the high expression of these genes was associated with better overall survival of LUAD. We conclude that PTGs of cmiRNA-1246 and key pathways, namely, ubiquitin-mediated proteolysis, glycosaminoglycan binding, the DNA metabolic process, and the PI3K–Akt–mTOR signaling pathway, the neurotrophin and cardiomyopathy signaling pathway, and the MAPK signaling pathway provide new insights on a noninvasive prognostic biomarker for LUAD.

scholarly journals Weighed Gene Coexpression Network Analysis Screens the Potential Long Noncoding RNAs and Genes Associated with Progression of Coronary Artery Disease

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Lang Wang ◽  
Jun Hu ◽  
Jiali Zhou ◽  
Fan Guo ◽  
Tan Yao ◽  
...  
Keyword(s):  
Network Analysis ◽  
Signaling Pathway ◽  
Noncoding Rnas ◽  
Mapk Signaling ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Signaling System ◽  
Pathway Network ◽  
Artery Disease ◽  
Coexpression Network Analysis

Background. Coronary artery disease (CAD) is a type of heart disease with a high morbidity rate. This study is aimed at identifying potential biomarkers closely related to the progression of CAD. Materials and Methods. A microarray dataset of GSE59867 was downloaded from a public database, Gene Expression Omnibus, which included 46 cases of stable CAD without a history of myocardial infarction (MI), 30 cases of MI without heart failure (HF), and 34 cases of MI with HF. Differentially expressed long noncoding RNAs (DElncRNAs) and mRNAs (DEmRNAs) were identified by the limma package, and functions of DEmRNAs were annotated by Gene Ontology and KEGG pathways. In addition, weighed gene coexpression network analysis (WGCNA) was used to construct a coexpression network of DEmRNAs, and a disease-related lncRNAs-mRNAs-pathway network was constructed. Finally, the datasets of GSE61145 and GSE57338 were used to verify the expression levels of the above highly correlated candidates. Results. A total of 2362 upregulated mRNAs and 2816 downregulated mRNAs, as well as 235 upregulated lncRNAs and 113 downregulated lncRNAs were screened. These genes were significantly enriched in “cytokine-cytokine receptor interaction,” “RIG-I-like receptor signaling pathway,” and “natural killer cell-mediated cytotoxicity.” Five modules including 1201 DEmRNAs were enriched in WGCNA. A coexpression network including 19 DElncRNAs and 413 DEmRNAs was constructed. These genes were significantly enriched in “phosphatidylinositol signaling system,” “insulin signaling pathway,” and “MAPK signaling pathway”. Disease-related gene-pathway network suggested FASN in “insulin signaling pathway,” DGKZ in “phosphatidylinositol signaling system,” and TNFRSF1A in “MAPK signaling pathway” were involved in MI. Conclusion. FASN, DGKZ, and TNFRSF1A were revealed to be CAD progression-associated genes by WGCNA coexpression network analysis.

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