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

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.

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Identification of Key Genes and miRNAs in Osteosarcoma Patients with Chemoresistance by Bioinformatics Analysis

BioMed Research International ◽

10.1155/2018/4761064 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 9

Author(s):

Binbin Xie ◽

Yiran Li ◽

Rongjie Zhao ◽

Yuzi Xu ◽

Yuhui Wu ◽

...

Keyword(s):

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Regulation Of Transcription ◽

Functional Annotations ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Search Tool ◽

Poor Outcomes

Chemoresistance is a significant factor associated with poor outcomes of osteosarcoma patients. The present study aims to identify Chemoresistance-regulated gene signatures and microRNAs (miRNAs) in Gene Expression Omnibus (GEO) database. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) included positive regulation of transcription, DNA-templated, tryptophan metabolism, and the like. Then differentially expressed genes (DEGs) were uploaded to Search Tool for the Retrieval of Interacting Genes (STRING) to construct protein-protein interaction (PPI) networks, and 9 hub genes were screened, such as fucosyltransferase 3 (Lewis blood group) (FUT3) whose expression in chemoresistant samples was high, but with a better prognosis in osteosarcoma patients. Furthermore, the connection between DEGs and differentially expressed miRNAs (DEMs) was explored. GEO2R was utilized to screen out DEGs and DEMs. A total of 668 DEGs and 5 DEMs were extracted from GSE7437 and GSE30934 differentiating samples of poor and good chemotherapy reaction patients. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to perform GO and KEGG pathway enrichment analysis to identify potential pathways and functional annotations linked with osteosarcoma chemoresistance. The present study may provide a deeper understanding about regulatory genes of osteosarcoma chemoresistance and identify potential therapeutic targets for osteosarcoma.

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Identification of Therapeutic Targets and Prognostic Biomarkers Among Integrin Subunits in the Skin Cutaneous Melanoma Microenvironment

Frontiers in Oncology ◽

10.3389/fonc.2021.751875 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yeltai Nurzat ◽

Weijie Su ◽

Peiru Min ◽

Ke Li ◽

Heng Xu ◽

...

Keyword(s):

T Cells ◽

Cutaneous Melanoma ◽

Regulatory Networks ◽

Disease Free Survival ◽

Enrichment Analysis ◽

Expression Levels ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Go Enrichment ◽

Underlying Mechanisms

The roles of different integrin alpha/beta (ITGA/ITGB) subunits in skin cutaneous melanoma (SKCM) and their underlying mechanisms of action remain unclear. Oncomine, UALCAN, GEPIA, STRING, GeneMANIA, cBioPortal, TIMER, TRRUST, and Webgestalt analysis tools were used. The expression levels of ITGA3, ITGA4, ITGA6, ITGA10, ITGB1, ITGB2, ITGB3, ITGB4, and ITGB7 were significantly increased in SKCM tissues. The expression levels of ITGA1, ITGA4, ITGA5, ITGA8, ITGA9, ITGA10, ITGB1, ITGB2, ITGB3, ITGB5, ITGB6 and ITGB7 were closely associated with SKCM metastasis. The expression levels of ITGA1, ITGA4, ITGB1, ITGB2, ITGB6, and ITGB7 were closely associated with the pathological stage of SKCM. The expression levels of ITGA6 and ITGB7 were closely associated with disease-free survival time in SKCM, and the expression levels of ITGA6, ITGA10, ITGB2, ITGB3, ITGB6, ITGB7, and ITGB8 were markedly associated with overall survival in SKCM. We also found significant correlations between the expression of integrin subunits and the infiltration of six types of immune cells (B cells, CD8+ T cells, CD4+T cells, macrophages, neutrophils, and dendritic cells). Finally, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed, and protein-protein interaction (PPI) networks were constructed. We have identified abnormally-expressed genes and gene regulatory networks associated with SKCM, improving understanding of the underlying pathogenesis of SKCM.

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Ageing Transcriptome Meta-Analysis Reveals Similarities Between Key Mammalian Tissues

10.1101/815381 ◽

2019 ◽

Cited By ~ 4

Author(s):

Daniel Palmer ◽

Fabio Fabris ◽

Aoife Doherty ◽

Alex A. Freitas ◽

João Pedro de Magalhães

Keyword(s):

Meta Analysis ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Sources Of Information ◽

Specific Expression ◽

Functional Changes ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Muscle Tissues ◽

Specific Expression Pattern

1AbstractUnderstanding the expression changes that come with age is an important step in understanding the ageing process as a whole. By combining such transcriptomic data with other sources of information, for instance protein-protein interaction (PPI) data, it is possible to make inferences about the functional changes that occur with age. To address this, we conducted a meta-analysis on 127 publicly available microarray and RNA-Seq datasets from mice, rats and humans, to identify genes that are commonly differentially expressed with age in mammals. We also conducted analyses on subsets of these datasets, to produce transcriptomic signatures for brain, heart and muscle tissues, all of which are important tissues in the pathophysiology of ageing. This approach identified the transcriptomic signatures of the ageing system, as well as brain, heart and muscle tissues. We then applied enrichment analysis and machine learning to functionally describe those signatures. This revealed a typical ageing signature including the overexpression of immune and stress response genes and the underexpression of metabolic and developmental genes. Further analysis of the ageing expression signatures revealed that genes differentially expressed with age tend to be broadly expressed across tissues, rather than be tissue-specific, and that the ageing expression signatures (particularly the overexpressed signatures) of the whole system, brain and muscle tend to include genes that are central in PPI networks. We also show that genes underexpressed in the brain are highly central in a co-expression map, suggesting that underexpression of these genes may play a part in cognitive ageing. In sum, we show numerous functional similarities between the ageing transcriptomes of these important tissues, a broad non-specific expression pattern in genes differentially expressed with age, along with altered network properties of these genes in both a PPI and co-expression network.

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

10.21203/rs.3.rs-34924/v1 ◽

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.

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Bioinformatics analysis of common key genes and pathways of intracranial, abdominal, and thoracic aneurysms

BMC Cardiovascular Disorders ◽

10.1186/s12872-020-01838-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Siwei Bi ◽

Ruiqi Liu ◽

Linfeng He ◽

Jingyi Li ◽

Jun Gu

Keyword(s):

Aortic Aneurysm ◽

Enrichment Analysis ◽

Cytosolic Calcium ◽

Gene Expression Omnibus ◽

Ion Concentration ◽

Hub Genes ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Thoracic Aneurysms ◽

Calcium Ion Concentration

Abstract Background Aneurysm is a severe and fatal disease. This study aims to comprehensively identify the highly conservative co-expression modules and hub genes in the abdominal aortic aneurysm (AAA), thoracic aortic aneurysm (TAA) and intracranial aneurysm (ICA) and facilitate the discovery of pathogenesis for aneurysm. Methods GSE57691, GSE122897, and GSE5180 microarray datasets were downloaded from the Gene Expression Omnibus database. We selected highly conservative modules using weighted gene co‑expression network analysis before performing the Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway and Reactome enrichment analysis. The protein–protein interaction (PPI) network and the miRNA-hub genes network were constructed. Furtherly, we validated the preservation of hub genes in three other datasets. Results Two modules with 193 genes and 159 genes were identified as well preserved in AAA, TAA, and ICA. The enrichment analysis identified that these genes were involved in several biological processes such as positive regulation of cytosolic calcium ion concentration, hemostasis, and regulation of secretion by cells. Ten highly connected PPI networks were constructed, and 55 hub genes were identified. In the miRNA-hub genes network, CCR7 was the most connected gene, followed by TNF and CXCR4. The most connected miRNAs were hsa-mir-26b-5p and hsa-mir-335-5p. The hub gene module was proved to be preserved in all three datasets. Conclusions Our study highlighted and validated two highly conservative co-expression modules and miRNA-hub genes network in three kinds of aneurysms, which may promote understanding of the aneurysm and provide potential therapeutic targets and biomarkers of aneurysm.

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Bioinformatics Analysis to Identify Key Genes and Pathways Associated with Sex Differences in Rheumatoid Arthritis

10.21203/rs.3.rs-687988/v1 ◽

2021 ◽

Author(s):

Siwei Su ◽

Wenjun Jiang ◽

Xiaoying Wang ◽

Sen Du ◽

Lu Zhou ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Gene Expression ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Hub Genes ◽

Protein Protein Interaction ◽

Kegg Pathways ◽

Ppi Networks ◽

Males And Females ◽

Key Genes

Abstract ObjectiveThis study aims to explore the key genes and investigated the different signaling pathways of rheumatoid arthritis (RA) between males and females.Data and MethodsThe gene expression data of GSE55457, GSE55584, and GSE12021 were obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using R software. Then, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis of DEGs were conducted via Database for Annotation, Visualization, and Integrated Discovery (DAVID). The protein-protein interaction (PPI) networks of DEGs were constructed by Cytoscape 3.6.0. ResultsA total of 416 upregulated DEGs and 336 downregulated DEGs were identified in males, and 744 upregulated DEGs and 309 downregulated DEGs were identified in females.IL6, MYC, EGFR, FOS and JUN were considered as hub genes in RA pathogenesis in males, while IL6, ALB, PTPRC, CXCL8 and CCR5 were considered as hub genes in RA pathogenesis in females. ConclusionIdentified DEG may be involved in the different mechanisms of RA disease progression between males and females, and they are treated as prognostic markers or therapeutic targets for males and females. The pathogenesis mechanism of RA is sex-dependent.

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KRT8 and KRT19, associated with EMT, are hypomethylated and overexpressed in lung adenocarcinoma and link to unfavorable prognosis

Bioscience Reports ◽

10.1042/bsr20193468 ◽

2020 ◽

Vol 40 (7) ◽

Cited By ~ 3

Author(s):

Wenlong Wang ◽

Junhong He ◽

Hongda Lu ◽

Qingzhi Kong ◽

Shengyou Lin

Keyword(s):

Lung Adenocarcinoma ◽

Epithelial To Mesenchymal Transition ◽

Methylation Status ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Cxcl12 Expression ◽

Mesenchymal Transition ◽

Gene Set ◽

Protein Protein Interaction ◽

Ppi Networks

Abstract Background: Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer. To date, the prognosis of patients with LUAD remains dismal. Methods: Three datasets were downloaded from the GEO database. Differentially expressed genes (DEGs) were obtained. FunRich was used to perform pathway enrichment analysis. Protein–protein interaction (PPI) networks were established and hub genes were obtained by Cytoscape software. GEPIA was utilized to conduct correlation and survival analysis. Upstream miRNAs of DEGs were predicted via miRNet database, and methylation status of promoters of DEGs was determined through UALCAN database. Results: A total of 375 DEGs, including 105 and 270 up-regulated and down-regulated genes in LUAD, were commonly appeared in three datasets. These DEGs were significantly enriched in mesenchymal-to-epithelial transition (MET) and epithelial-to-mesenchymal transition (EMT). About 8 up-regulated and 5 down-regulated DEGs were commonly appeared in EMT/MET-related gene set and the top 50 hub gene set. Among the 13 genes, increased expression of KRT8 and KRT19 indicated unfavorable prognosis whereas high expression of DCN and CXCL12 suggested favorable prognosis in LUAD. Correlation analysis showed that KRT8 (DCN) expression was linked to KRT19 (CXCL12) expression. Further analysis displayed that KRT8 and KRT19 could jointly forecast poor prognosis in LUAD. About 42 and 2 potential miRNAs were predicted to target KRT8 and KRT19, respectively. Moreover, methylation level analysis demonstrated that KRT8 and KRT19 were significantly hypomethylated in LUAD compared with normal controls. Conclusions: All these findings suggest that KRT8 and KRT19 are hypomethylated and overexpressed in LUAD and associated with unfavorable prognosis.

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Temporal Identification of Dysregulated Genes and Pathways in Clear Cell Renal Cell Carcinoma Based on Systematic Tracking of Disrupted Modules

Computational and Mathematical Methods in Medicine ◽

10.1155/2015/313740 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Shao-Mei Wang ◽

Ze-Qiang Sun ◽

Hong-Yun Li ◽

Jin Wang ◽

Qing-Yong Liu

Keyword(s):

Pearson Correlation ◽

Enrichment Analysis ◽

Glutathione Metabolism ◽

Pathway Enrichment Analysis ◽

Cell Renal Cell Carcinoma ◽

Bipartite Matching ◽

Pathway Enrichment ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Metabolism Of Xenobiotics

Objective. The objective of this work is to identify dysregulated genes and pathways of ccRCC temporally according to systematic tracking of the dysregulated modules of reweighted Protein-Protein Interaction (PPI) networks.Methods. Firstly, normal and ccRCC PPI network were inferred and reweighted based on Pearson correlation coefficient (PCC). Then, we identified altered modules using maximum weight bipartite matching and ranked them in nonincreasing order. Finally, gene compositions of altered modules were analyzed, and pathways enrichment analyses of genes in altered modules were carried out based on Expression Analysis Systematic Explored (EASE) test.Results. We obtained 136, 576, 693, and 531 disrupted modules of ccRCC stages I, II, III, and IV, respectively. Gene composition analyses of altered modules revealed that there were 56 common genes (such asMAPK1,CCNA2, andGSTM3) existing in the four stages. Besides pathway enrichment analysis identified 5 common pathways (glutathione metabolism, cell cycle, alanine, aspartate, and glutamate metabolism, arginine and proline metabolism, and metabolism of xenobiotics by cytochrome P450) across stages I, II, III, and IV.Conclusions. We successfully identified dysregulated genes and pathways of ccRCC in different stages, and these might be potential biological markers and processes for treatment and etiology mechanism in ccRCC.

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Bufalin: A Systematic Review of Research Hotspots and Antitumor Mechanisms by Text Mining and Bioinformatics

The American Journal of Chinese Medicine ◽

10.1142/s0192415x20500810 ◽

2020 ◽

Vol 48 (07) ◽

pp. 1633-1650

Author(s):

Xian Zhang ◽

Xiaoxuan Zhao ◽

Kaili Liu ◽

Yuxuan Che ◽

Xun Qiu ◽

...

Keyword(s):

Network Analysis ◽

Signaling Pathway ◽

Enrichment Analysis ◽

Ppi Network ◽

Protein Protein Interaction ◽

Kegg Pathways ◽

Mesh Terms ◽

Go Enrichment ◽

Pubmed Database ◽

Pathways Analysis

Bufalin is an anticancer drug extract from traditional Chinese medicine. Several articles about bufalin have been published. However, the literature on bufalin has not yet been systematically studied. This study aimed to identify the study status and knowledge structures of bufalin and to summarize the antitumor mechanism. Data were retrieved and downloaded from the PubMed database. The softwares of BICOMB, gCLUTO, Ucinet 6.0, and NetDraw2.084 were used to analyze these publications. The bufalin related genes were recognized and tagged by ABNER software. Then these BF-related genes were performed by Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis, and protein-protein interaction (PPI) network analysis. A total of 474 papers met the search criteria from 2000 to 2019. By biclustering clustering analysis, the 50 high-frequency main MeSH terms/subheadings were classified into 5 clusters. The clusters of drug therapy and the mechanism of bufalin were hotspot topics. A total of 50 genes were identified as BF-related genes. PPI network analysis showed that inducing apoptosis was the main effect of bufalin, and apoptosis-related gene Caspase 3 was the most reported by people. Bufalin could inhibit the proliferation, invasion, and metastasis of cancer cells through multiple signaling pathways, such as PI3K/AKT, Hedgehog, MAPK/JNK, Wnt/[Formula: see text]-catenin, TGF-[Formula: see text]/Smad, Integrin signaling pathway, and NF-KB signaling pathway via KEGG analysis. Through the quantitative analysis of bufalin literature, we revealed the research status and hot spots in this field and provided some guidance for further research.

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

10.21203/rs.3.rs-34924/v2 ◽

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.

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