Bioinformatics-Based Analysis of lncRNA-mRNA Interaction Network of Mild Hepatic Encephalopathy in Cirrhosis

Backgrounds. Serum long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) interaction network was discovered to exert an important role in liver cirrhosis while little is known in mild hepatic encephalopathy (MHE). Therefore, we aim to systematically evaluate the serum lncRNA-mRNA network and its regulatory mechanism in MHE. Methods. The data of serum mRNAs and lncRNAs were derived from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were calculated between 11 cirrhotic patients with and without MHE. Next, the biological functions and underlined pathways of DEGs were determined through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Finally, an interactive network between lncRNAs and mRNAs was built, and hub genes were identified, respectively. Results. A total of 64 differentially expressed lncRNAs (dif-lncRNAs) were found between patients with and without MHE, including 30 up- and 34 downregulated genes. 187 differentially expressed mRNAs (dif-mRNAs) were identified, including 84 up- and 103 downregulated genes. Functional enrichment analysis suggested that the regulatory pathways involved in MHE mainly consisted of a series of immune and inflammatory responses. Several hub mRNAs involved in regulatory network were identified, including CCL5, CCR5, CXCR3, CD274, STAT1, CXCR6, and EOMES. In addition, lnc-FAM84B-8 and lnc-SAMD3-1 were found to regulate these above hub genes through building a lncRNA-mRNA network. Conclusion. This is the first study to construct the serum lncRNA-mRNA network in MHE, demonstrating the critical role of lncRNAs in regulating inflammatory and immunological profiles in the developing of MHE, suggesting a latent mechanism in this pathophysiological process.

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Based on bioinformatics analysis, NF1, BTRC and MAPK14 are the potential biomarkers in the progression of osteoarthritis synovitis

10.21203/rs.3.rs-349764/v1 ◽

2021 ◽

Author(s):

Mingyi Yang ◽

Yani Su ◽

Yao Ma ◽

Yirixiati Aihaiti ◽

Peng Xu

Keyword(s):

Differentially Expressed Genes ◽

Interaction Network ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

Differentially Expressed ◽

Hub Genes ◽

Hub Gene ◽

Potential Biomarkers

Abstract Objective: To study the potential biomarkers and related pathways in osteoarthritis (OA) synovial lesions, and to provide theoretical basis and research directions for the pathogenesis and treatment of OA. Methods: Download the microarray data sets GSE12021 and GSE82107 from Gene Expression Omnibus. GEO2R recognizes differentially expressed genes. Perform functional enrichment analysis of differentially expressed genes and construct protein-protein interaction network. Cytoscape performs module analysis and enrichment analysis of top-level modules. Further identify the Hub gene and perform functional enrichment analysis. TargetScan, miRDB and miRWalk three databases predict the target miRNAs of Hub gene and identify key miRNAs. Results: Finally, 10 Hub genes and 17 key miRNAs related to the progression of OA synovitis were identified. NF1, BTRC and MAPK14 may play a vital role in OA synovial disease. Conclusion: The Hub genes and key miRNAs discovered in this study may be potential biomarkers in the development of OA synovitis, and provide research methods and target basis for the pathogenesis and treatment of OA.

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Bioinformatics-Based Study to Investigate Potential Differentially Expressed Genes and miRNAs in Hashimoto’s Thyroiditis

10.21203/rs.3.rs-1023446/v1 ◽

2021 ◽

Author(s):

Li Guoquan ◽

Du Junwei ◽

He Qi ◽

Fu Xinghao ◽

Ji Feihong ◽

...

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Inflammatory Responses ◽

Expression Profiles ◽

Treatment Options ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Chronic Lymphocytic Thyroiditis ◽

Hub Genes

Abstract BackgroundHashimoto's thyroiditis (HT), also known as chronic lymphocytic thyroiditis, is a common autoimmune disease, which mainly occurs in women. The early manifestation was hyperthyroidism, however, hypothyroidism may occur if HT was not controlled for a long time. Numerous studies have shown that multiple factors, including genetic, environmental, and autoimmune factors, were involved in the pathogenesis of the disease, but the exact mechanisms were not yet clear. The aim of this study was to identify differentially expressed genes (DEGs) by comprehensive analysis and to provide specific insights into HT. MethodsTwo gene expression profiles (GSE6339, GSE138198) about HT were downloaded from the Gene Expression Omnibus (GEO) database. The DEGs were assessed between the HT and normal groups using the GEO2R. The DEGs were then sent to the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The hub genes were discovered using Cytoscape and CytoHubba. Finally, NetworkAnalyst was utilized to create the hub genes' targeted microRNAs (miRNAs). ResultsA total of 62 DEGs were discovered, including 60 up-regulated and 2 down-regulated DEGs. The signaling pathways were mainly engaged in cytokine interaction and cytotoxicity, and the DEGs were mostly enriched in immunological and inflammatory responses. IL2RA, CXCL9, IL10RA, CCL3, CCL4, CCL2, STAT1, CD4, CSF1R, and ITGAX were chosen as hub genes based on the results of the protein-protein interaction (PPI) network and CytoHubba. Five miRNAs, including mir-24-3p, mir-223-3p, mir-155-5p, mir-34a-5p, mir-26b-5p, and mir-6499-3p, were suggested as likely important miRNAs in HT. ConclusionsThese hub genes, pathways and miRNAs contribute to a better understanding of the pathophysiology of HT and offer potential treatment options for HT.

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System Analysis of MIRNAs in Maize Internode Elongation

Biomolecules ◽

10.3390/biom9090417 ◽

2019 ◽

Vol 9 (9) ◽

pp. 417

Author(s):

Chuanxi Peng ◽

Xing Wang ◽

Tianyu Feng ◽

Rui He ◽

Mingcai Zhang ◽

...

Keyword(s):

Target Genes ◽

System Analysis ◽

Interaction Network ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Internode Elongation ◽

Differentially Expressed Mirnas ◽

Post Transcriptional Regulation

MicroRNAs (miRNAs), the post-transcriptional gene regulators, are known to play an important role in plant development. The identification of differentially expressed miRNAs could better help us understand the post-transcriptional regulation that occurs during maize internode elongation. Accordingly, we compared the expression of MIRNAs between fixed internode and elongation internode samples and classified six differentially expressed MIRNAs as internode elongation-responsive miRNAs including zma-MIR160c, zma-MIR164b, zma-MIR164c, zma-MIR168a, zma-MIR396f, and zma-MIR398b, which target mRNAs supported by transcriptome sequencing. Functional enrichment analysis for predictive target genes showed that these miRNAs were involved in the development of internode elongation by regulating the genes respond to hormone signaling. To further reveal how miRNA affects internode elongation by affecting target genes, the miRNA–mRNA–PPI (protein and protein interaction) network was constructed to summarize the interaction of miRNAs and these target genes. Our results indicate that miRNAs regulate internode elongation in maize by targeting genes related to cell expansion, cell wall synthesis, transcription, and regulatory factors.

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Identification of nine key genes by bioinformatics analysis for predicting poor prognosis in smoking-induced lung adenocarcinoma

Lung Cancer Management ◽

10.2217/lmt-2020-0009 ◽

2020 ◽

Vol 9 (2) ◽

pp. LMT30

Author(s):

Chuanli Ren ◽

Weixiu Sun ◽

Xu Lian ◽

Chongxu Han

Keyword(s):

Lung Adenocarcinoma ◽

Differentially Expressed Genes ◽

Interaction Network ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Kaplan Meier ◽

Key Genes ◽

Core Genes

Aim: To screen and identify key genes related to the development of smoking-induced lung adenocarcinoma (LUAD). Materials & methods: We obtained data from the GEO chip dataset GSE31210. The differentially expressed genes were screened by GEO2R. The protein interaction network of differentially expressed genes was constructed by STRING and Cytoscape. Finally, core genes were screened. The overall survival time of patients with the core genes was analyzed by Kaplan–Meier method. Gene ontology and Kyoto encyclopedia of genes and genomes bioaccumulation was calculated by DAVID. Results: Functional enrichment analysis indicated that nine key genes were actively involved in the biological process of smoking-related LUAD. Conclusion: 23 core genes and nine key genes among them were correlated with adverse prognosis of LUAD induced by smoking.

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Identification of novel genes and pathways in carotid atheroma using integrated bioinformatic methods

Scientific Reports ◽

10.1038/srep18764 ◽

2016 ◽

Vol 6 (1) ◽

Cited By ~ 10

Author(s):

Wenqing Nai ◽

Diane Threapleton ◽

Jingbo Lu ◽

Kewei Zhang ◽

Hongyuan Wu ◽

...

Keyword(s):

Signaling Pathway ◽

Transforming Growth Factor ◽

Interaction Network ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Transforming Growth Factor Β ◽

Functional Enrichment ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Atheromatous Plaques

Abstract Atherosclerosis is the primary cause of cardiovascular events and its molecular mechanism urgently needs to be clarified. In our study, atheromatous plaques (ATH) and macroscopically intact tissue (MIT) sampled from 32 patients were compared and an integrated series of bioinformatic microarray analyses were used to identify altered genes and pathways. Our work showed 816 genes were differentially expressed between ATH and MIT, including 443 that were up-regulated and 373 that were down-regulated in ATH tissues. GO functional-enrichment analysis for differentially expressed genes (DEGs) indicated that genes related to the “immune response” and “muscle contraction” were altered in ATHs. KEGG pathway-enrichment analysis showed that up-regulated DEGs were significantly enriched in the “FcεRI-mediated signaling pathway”, while down-regulated genes were significantly enriched in the “transforming growth factor-β signaling pathway”. Protein-protein interaction network and module analysis demonstrated that VAV1, SYK, LYN and PTPN6 may play critical roles in the network. Additionally, similar observations were seen in a validation study where SYK, LYN and PTPN6 were markedly elevated in ATH. All in all, identification of these genes and pathways not only provides new insights into the pathogenesis of atherosclerosis, but may also aid in the development of prognostic and therapeutic biomarkers for advanced atheroma.

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Multiple-Tissue and Multilevel Analysis on Differentially Expressed Genes and Differentially Correlated Gene Pairs for HFpEF

Frontiers in Genetics ◽

10.3389/fgene.2021.668702 ◽

2021 ◽

Vol 12 ◽

Author(s):

Guofeng Zhou ◽

Shaoyan Sun ◽

Qiuyue Yuan ◽

Run Zhang ◽

Ping Jiang ◽

...

Keyword(s):

Adipose Tissue ◽

Differentially Expressed Genes ◽

Complex Disease ◽

Cerebral Arteries ◽

Interaction Network ◽

Functional Enrichment ◽

Differentially Expressed ◽

Hub Genes ◽

Tissue Specific ◽

Gene Pairs

Heart failure with preserved ejection fraction (HFpEF) is a complex disease characterized by dysfunctions in the heart, adipose tissue, and cerebral arteries. The elucidation of the interactions between these three tissues in HFpEF will improve our understanding of the mechanism of HFpEF. In this study, we propose a multilevel comparative framework based on differentially expressed genes (DEGs) and differentially correlated gene pairs (DCGs) to investigate the shared and unique pathological features among the three tissues in HFpEF. At the network level, functional enrichment analysis revealed that the networks of the heart, adipose tissue, and cerebral arteries were enriched in the cell cycle and immune response. The networks of the heart and adipose tissues were enriched in hemostasis, G-protein coupled receptor (GPCR) ligand, and cancer-related pathway. The heart-specific networks were enriched in the inflammatory response and cardiac hypertrophy, while the adipose-tissue-specific networks were enriched in the response to peptides and regulation of cell adhesion. The cerebral-artery-specific networks were enriched in gene expression (transcription). At the module and gene levels, 5 housekeeping DEGs, 2 housekeeping DCGs, 6 modules of merged protein–protein interaction network, 5 tissue-specific hub genes, and 20 shared hub genes were identified through comparative analysis of tissue pairs. Furthermore, the therapeutic drugs for HFpEF-targeting these genes were examined using molecular docking. The combination of multitissue and multilevel comparative frameworks is a potential strategy for the discovery of effective therapy and personalized medicine for HFpEF.

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Co-expression network analysis identifies specific hub genes in association with developmental neuronal remodeling in Drosophila melanogaster

10.7287/peerj.preprints.27586v1 ◽

2019 ◽

Author(s):

Yunze Liu ◽

Xiaojie Sun ◽

Aijun Qu

Keyword(s):

Drosophila Melanogaster ◽

Interaction Network ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

Protein Protein Interaction ◽

Neuronal Remodeling ◽

Gene Modules

As an evolutionarily conserved mechanism, developmental neuronal remodeling is needed for the proper wiring of the nervous system and is critical for understanding the neurodevelopment mechanisms. Previous studies have shown that during metamorphosis lots of Drosophila melanogaster mushroom body neurons experience stereotypic remodeling. However, the related regulators and downstream executors of pathways are yet unclear, especially studies of transcriptional gene co-expression analysis of nervous systems remain insufficient. In this study, we develop a weighted gene co-expression network (WGCNA) to classify gene modules associated with neuronal remodeling. Moreover, functional and pathway enrichment analysis with protein-protein network construction is applied to detect high informative hub genes in the targeted gene module. Thus, we select a total of five hub genes that play critical roles in neuronal remodeling and identify them with functional enrichment analysis and protein-protein interaction network. Overall, this study provides insight into the underlying molecular mechanism of developmental neuronal remodeling in Drosophila melanogaster.

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Construction of Circular RNA–MicroRNA–Messenger RNA Regulatory Network of Recurrent Implantation Failure to Explore Its Potential Pathogenesis

Frontiers in Genetics ◽

10.3389/fgene.2020.627459 ◽

2021 ◽

Vol 11 ◽

Author(s):

Jiahuan Luo ◽

Li Zhu ◽

Ning Zhou ◽

Yuanyuan Zhang ◽

Lirong Zhang ◽

...

Keyword(s):

Messenger Rna ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Implantation Failure ◽

Ppi Network ◽

Recurrent Implantation Failure ◽

Hub Genes ◽

Hub Gene ◽

Gene Regulatory

Background: Many studies on circular RNAs (circRNAs) have recently been published. However, the function of circRNAs in recurrent implantation failure (RIF) is unknown and remains to be explored. This study aims to determine the regulatory mechanisms of circRNAs in RIF.Methods: Microarray data of RIF circRNA (GSE147442), microRNA (miRNA; GSE71332), and messenger RNA (mRNA; GSE103465) were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed circRNA, miRNA, and mRNA. The circRNA–miRNA–mRNA network was constructed by Cytoscape 3.8.0 software, then the protein–protein interaction (PPI) network was constructed by STRING database, and the hub genes were identified by cytoHubba plug-in. The circRNA–miRNA–hub gene regulatory subnetwork was formed to understand the regulatory axis of hub genes in RIF. Finally, the Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the hub genes were performed by clusterProfiler package of Rstudio software, and Reactome Functional Interaction (FI) plug-in was used for reactome analysis to comprehensively analyze the mechanism of hub genes in RIF.Results: A total of eight upregulated differentially expressed circRNAs (DECs), five downregulated DECs, 56 downregulated differentially expressed miRNAs (DEmiRs), 104 upregulated DEmiRs, 429 upregulated differentially expressed genes (DEGs), and 1,067 downregulated DEGs were identified regarding RIF. The miRNA response elements of 13 DECs were then predicted. Seven overlapping miRNAs were obtained by intersecting the predicted miRNA and DEmiRs. Then, 56 overlapping mRNAs were obtained by intersecting the predicted target mRNAs of seven miRNAs with 1,496 DEGs. The circRNA–miRNA–mRNA network and PPI network were constructed through six circRNAs, seven miRNAs, and 56 mRNAs; and four hub genes (YWHAZ, JAK2, MYH9, and RAP2C) were identified. The circRNA–miRNA–hub gene regulatory subnetwork with nine regulatory axes was formed in RIF. Functional enrichment analysis and reactome analysis showed that these four hub genes were closely related to the biological functions and pathways of RIF.Conclusion: The results of this study provide further understanding of the potential pathogenesis from the perspective of circRNA-related competitive endogenous RNA network in RIF.

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Comprehensive Analysis of Urinary lncRNA And mRNA Expression Profiles In Patients With lupus Nephritis

10.21203/rs.3.rs-823763/v1 ◽

2021 ◽

Author(s):

Huiqiong Zeng ◽

Zhongyu Xie ◽

Baiwei Luo ◽

Chenghui Liao ◽

Haofei Hu ◽

...

Keyword(s):

Network Analysis ◽

Lupus Nephritis ◽

Expression Profiles ◽

Critical Role ◽

Interaction Network ◽

Enrichment Analysis ◽

Specific Gene ◽

Messenger Rnas ◽

Clinical Value ◽

P53 Signaling

Abstract Background:To explore long-non-coding RNA (lncRNAs),messenger RNAs (mRNAs) expression profiles and biological functions in the urine samples in lupus nephritis (LN) patients.Methods:Three LN patients and three healthy controls(con) were recruited,whose midstream morning urine was collected.A microarray of mRNA and lncRNA was applied to explore total RNA expression variation.Then venn analysis was applied to screen out specific gene expression in the LN group compared to the con group.Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to reveal the gene functions of the dysregulated lncRNA-associated with LN;STRING online website and Cytoscape software network analysis were applied to construct protein interaction network (PPI) and screen out the Hub Gene.Drug online websites were conducted as drug prediction websites.Results:A total of 273 mRNAs and 549 lncRNAs were differentially expressed in LN patients' urine compared with the con group.GO analysis of LN revealed that mRNAs from the lncRNA-mRNA network were enriched in terms of antigen-negative regulation of immune response.KEGG analysis of hub genes related to the LN lncRNA-mRNA network highlights their critical role in protein processing in the endoplasmic reticulum,P53 signaling pathway.Co-expression and PPI network analysis suggested that high-degree nodes are clustered in apoptosis,autophagy, reactive oxygen species pathway.Drug prediction indicated that the targeted drugs mainly included antioxidant activity,phosphorylative mechanism.Conclusion:Our findings indicated that the differential expressed urinary lncRNAs(DELs) possessed considerable clinical value in the diagnosis and potential therapeutic applications of drugs in LN patients.

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Identification of Key Genes and miRNA-mRNA Regulatory Pathways in Bronchopulmonary Dysplasia in Preterm Infants by Bioinformatics Methods

10.21203/rs.3.rs-50763/v1 ◽

2020 ◽

Author(s):

Tong Sun ◽

Haiyang Yu ◽

Jianhua Fu

Keyword(s):

Gene Expression ◽

Preterm Infants ◽

Bronchopulmonary Dysplasia ◽

Regulatory Networks ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Regulatory Pathways ◽

Ppi Networks ◽

Key Genes

Abstract Background: Bronchopulmonary dysplasia (BPD) remains a severe respiratory complication of preterm infants in neonatal intensive care units (NICUs). However, its pathogenesis has been unclear. Bioinformatics analysis, which can help us explore genetic alternations and recognize latent diagnostic biomarkers, has recently promoted the comprehension of the molecular mechanisms underlying disease occurrence and development. Methods: In this study, we identified key genes and miRNA-mRNA regulatory networks in BPD in preterm infants to elucidate the pathogenesis of BPD. We downloaded and analyzed miRNA and gene expression microarray datasets from the Gene Expression Omnibus database (GEO). Differentially expressed miRNA (DEMs) and differentially expressed genes (DEGs) were obtained through NetworkAnalyst. We performed pathway enrichment analysis using the Database for Annotation, Visualization and Integrated Discovery program (DAVID), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Then we used the STRING to establish protein–protein interactions and the Cytoscape tool to establish miRNA–mRNA regulatory networks. Results: We identified 19 significant DEMs and 140 and 33 significantly upregulated and downregulated DEGs, respectively. Functional enrichment analysis indicated that significant DEGs were associated with the antigen processing and presentation, and B-cell receptor signaling pathways in BPD. Key DEGs, such as CD19, CD79B, MS4A1, and FCGR2B were selected as hub genes in PPI networks. Conclusions: In this study, we screened out 19 DEMs that might play important roles in the regulatory networks of BPD. Higher expression of miRNAs such as miR-15b-5p, hsa-miR-32-5p, miR-3613-3p, and miR-33a-5p and lower expression of miRNAs such as miR-3960, miR-425-5p, and miR-3202 might be correlated with the process of BPD.

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