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

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

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

Scientific Reports ◽

10.1038/s41598-021-97319-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Negin Sheybani ◽

Mohammad Reza Bakhtiarizadeh ◽

Abdolreza Salehi

Keyword(s):

Network Analysis ◽

Protein Interactions ◽

Regulatory Networks ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Biological Pathways ◽

Functional Enrichment ◽

Integrated Analysis ◽

Rna Seq

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

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Identification of Key Genes and Pathways Associated with Differences of Subchondral Bone in Osteoarthritis

10.21203/rs.3.rs-51799/v1 ◽

2020 ◽

Author(s):

Yiyuan Zhang ◽

Rongguo Yu ◽

Jiayu Zhang ◽

Eryou Feng ◽

Haiyang Wang ◽

...

Keyword(s):

Subchondral Bone ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Hub Genes ◽

Common Chronic Disease ◽

Drug Candidates ◽

Therapeutic Modalities ◽

Pathogenic Genes

Abstract BackgroundOsteoarthritis (OA) is a common chronic disease worldwide. Subchondral bone is an important pathological change in OA and responds more rapidly to adverse loading and events compared to cartilage. However, the pathogenic genes and pathways of subchondral bone are largely unclear.ObjectiveThis study aimed to identify signature differences in genes involved in knee lateral tibial (LT) and medial tibial (MT) plateaus of subchondral bone tissue while exploring their potential molecular mechanisms via bioinformatics analysis.MethodsFirst, the gene expression data of GSE51588 was downloaded from the GEO database. Differentially expressed genes (DEGs) between knee LT and MT were identified, and functional enrichment analyses were performed. Then, a protein-protein interactive network was constructed in order to acquire the hub genes, and modules analysis was conducted using STRING and Cytoscape for further analysis. The enriched hub genes were queried in DGIdb database to find suitable drug candidates in OA.ResultsA total of 202 DEGs (112 upregulated genes and 84 downregulated genes) were determined. In the PPI network, ten hub genes were identified. Five significant modules were identified using the MCODE plugin unit. Functional enrichment analysis revealed the most important signaling pathways. Six of the ten hub genes were targetable by a total of 35 drugs, suggesting their possible therapeutic use for OA .ConclusionsThe identified hub genes and functional enrichment pathways were implicated in the development and progression of subchondral bone in OA, thus improving our understanding of OA and offering molecular targets for future therapeutic modalities.

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Identification of Four Key Biomarkers and Small Molecule Drugs Innasopharyngeal Carcinoma by Weighted Gene Co-expression Network Analysis

10.21203/rs.3.rs-49643/v1 ◽

2020 ◽

Author(s):

Xi Pan ◽

Jian-Hao Liu

Keyword(s):

Gene Expression ◽

Network Analysis ◽

Small Molecule ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Tumor Stage ◽

Functional Enrichment ◽

Hub Genes ◽

Therapeutic Goals ◽

Small Molecule Drugs

Abstract Background Nasopharyngeal carcinoma (NPC) is a heterogeneous carcinoma that the underlying molecular mechanisms involved in the tumor initiation, progression, and migration are largely unclear. The purpose of the present study was to identify key biomarkers and small-molecule drugs for NPC screening, diagnosis, and therapy via gene expression profile analysis. Methods Raw microarray data of NPC were retrieved from the Gene Expression Omnibus (GEO) database and analyzed to screen out the potential differentially expressed genes (DEGs). The key modules associated with histology grade and tumor stage was identified by using weighted correlation network analysis (WGCNA). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of genes in the key module were performed to identify potential mechanisms. Candidate hub genes were obtained, which based on the criteria of module membership (MM) and high connectivity. Then we used receiver operating characteristic (ROC) curve to evaluate the diagnostic value of hub genes. The Connectivity map database was further used to screen out small-molecule drugs of hub genes. Results A total of 430 DEGs were identified based on two GEO datasets. The green gene module was considered as key module for the tumor stage of NPC via WGCNA analysis. The results of functional enrichment analysis revealed that genes in the green module were enriched in regulation of cell cycle, p53 signaling pathway, cell part morphogenesis. Furthermore, four DEGs-related hub genes in the green module were considered as the final hub genes. Then ROC revealed that the final four hub genes presented with high areas under the curve, suggesting these hub genes may be diagnostic biomarkers for NPC. Meanwhile, we screened out several small-molecule drugs that have provided potentially therapeutic goals for NPC. Conclusions Our research identified four potential prognostic biomarkers and several candidate small-molecule drugs for NPC, which may contribute to the new insights for NPC therapy.

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Integrated analysis identifies a pathway-related competing endogenous RNA network in the progression of pancreatic cancer

BMC Cancer ◽

10.1186/s12885-020-07470-4 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Fuqiang Zu ◽

Peng Liu ◽

Huaitao Wang ◽

Ting Zhu ◽

Jian Sun ◽

...

Keyword(s):

Pancreatic Cancer ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Tumor Formation ◽

Functional Enrichment ◽

Integrated Analysis ◽

Competing Endogenous Rna ◽

Hub Genes ◽

Cerna Network ◽

Competing Endogenous Rna Network

Abstract Background It is well acknowledged that cancer-related pathways play pivotal roles in the progression of pancreatic cancer (PC). Employing Integrated analysis, we aim to identify the pathway-related ceRNA network associated with PC progression. Methods We divided eight GEO datasets into three groups according to their platform, and combined TCGA and GTEx databases as a group. Additionally, we screened out the differentially expressed genes (DEGs) and performed functional enrichment analysis in each group, and recognized the top hub genes in the most enriched pathway. Furthermore, the upstream of miRNAs and lncRNAs were predicted and validated according to their expression and prognostic roles. Finally, the co-expression analysis was applied to identify a pathway-related ceRNA network in the progression of PC. Results A total of 51 significant pathways that common enriched in all groups were spotted. Enrichment analysis indicated that pathway in cancer was greatly linked with tumor formation and progression. Next, the top 20 hug genes in this pathway were recognized, and stepwise prediction and validation from mRNA to lncRNA, including 11 hub genes, 4 key miRNAs, and 2 key lncRNAs, were applied to identify a meaningful ceRNA network according to ceRNA rules. Ultimately, we identified the PVT1/miR-20b/CCND1 axis as a promising pathway-related ceRNA axis in the progression of PC. Conclusion Overall, we elucidate the pathway-related ceRNA regulatory network of PVT1/miR-20b/CCND1 in the progression of PC, which can be considered as therapeutic targets and encouraging prognostic biomarkers for PC.

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Integrated analysis identifies a pathway-related competing endogenous RNA network in the progression of Pancreatic cancer

10.21203/rs.3.rs-36637/v3 ◽

2020 ◽

Author(s):

Fuqiang Zu ◽

Peng Liu ◽

Huaitao Wang ◽

Ting Zhu ◽

Jian Sun ◽

...

Keyword(s):

Pancreatic Cancer ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Tumor Formation ◽

Functional Enrichment ◽

Integrated Analysis ◽

Competing Endogenous Rna ◽

Hub Genes ◽

Cerna Network ◽

Competing Endogenous Rna Network

Abstract Background: It is well acknowledged that cancer-related pathways play pivotal roles in the progression of pancreatic cancer (PC). Employing Integrated analysis, we aim to identify the pathway-related ceRNA network associated with PC progression.Methods: We divided eight GEO datasets into three groups according to their platform, and combined TCGA and GTEx databases as a group. Additionally, we screened out the differentially expressed genes (DEGs) and performed functional enrichment analysis in each group, and recognized the top hub genes in the most enriched pathway. Furthermore, the upstream of miRNAs and lncRNAs were predicted and validated according to their expression and prognostic roles. Finally, the co-expression analysis was applied to identify a pathway-related ceRNA network in the progression of PC.Results: A total of 51 significant pathways that common enriched in all groups were spotted. Enrichment analysis indicated that pathway in cancer was greatly linked with tumor formation and progression. Next, the top 20 hug genes in this pathway were recognized, and stepwise prediction and validation from mRNA to lncRNA, including 11 hub genes, 4 key miRNAs, and 2 key lncRNAs, were applied to identify a meaningful ceRNA network according to ceRNA rules. Ultimately, we identified the PVT1/miR-20b-/CCND1 axis as a promising pathway-related ceRNA axis in the progression of PC.Conclusion: Overall, we elucidate the pathway-related ceRNA regulatory network of PVT1/miR-20b-/CCND1 in the progression of PC, which can be considered as therapeutic targets and encouraging prognostic biomarkers for PC.

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Identification of potential prognostic markers for osteosarcoma by integrated analysis

10.21203/rs.3.rs-52760/v2 ◽

2020 ◽

Author(s):

Yuxiang Ge ◽

Wang Ding ◽

Chong Bian ◽

Huijie Gu ◽

Jun Xu ◽

...

Keyword(s):

Prognostic Markers ◽

Bone Development ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Great Promise ◽

Integrated Analysis ◽

Hub Genes ◽

Hub Gene ◽

Gene Modules

Abstract Background: Osteosarcoma (OS), one of the utmost common and malignant cancer, accounts for over 30% among skeletal sarcomas. Although great efforts have been made, the mechanism of OS still remains largely unknown. Here, we intend to identify gene modules and candidate biomarkers for clinical diagnosis of patients with OS, and reveal the mechanisms of OS progression.Methods: Weighted gene co-expression network analysis (WGCNA) was conducted to build a co-expression network and investigate the relationship between modules and clinical traits. Functional enrichment analysis was performed on module genes. Protein-protein interaction (PPI) network was constructed to identify the hub gene and the expression level of hub genes was validated based on another dataset.Results: A total of 9854 genes were included in WGCNA, and 17 gene modules were constructed. Gene module related with OS in sacrum was mainly enriched in skeletal system development, bone development and extracellular structure organization. Furthermore, we screened the top 10 hub genes and further validated 5 of the 10 (MMP13, DCN, GNG2, PCOLCE and RUNX2), the expression of which were upregulated as compared with normal tissues.Conclusion: The hub gene we identified show great promise as prognostic markers for the management of OS and our findings also provide new insight for molecular mechanism of OS.

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

European Neurology ◽

10.1159/000521390 ◽

2022 ◽

pp. 1-12

Author(s):

Zhengfei Ma ◽

Ping Zhong ◽

Peidong Yue ◽

Zhongwu Sun

Keyword(s):

Network Analysis ◽

Intracranial Aneurysm ◽

Differentially Expressed Genes ◽

Regulatory Networks ◽

Molecular Mechanisms ◽

Area Under The Curve ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed

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

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Screening a prognosis-related target gene in patients with HER-2 positive breast cancer by bioinformatics analysis

Medical Principles and Practice ◽

10.1159/000516322 ◽

2021 ◽

Author(s):

Song Wang ◽

Yi Quan

Keyword(s):

Breast Cancer ◽

Target Gene ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Hub Genes ◽

Study Objective ◽

Her 2 ◽

Positive Breast Cancer

Objective: HER-2 positive breast cancer has a high risk of for relapse, metastasis and drug resistance, and is related to a poor prognosis. Thus, the study objective was to determine a target gene and explore the associated molecular mechanisms in HER-2 positive breast cancer. Methods: Three RNA expression profiles were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), and were used to identify differentially expressed genes (DEGs) using R software. A Protein-Protein Interaction (PPI) network was constructed and hub genes were determined. Subsequently, the relationship between clinical parameters and hub genes was examined to screen target gene. Next, DNA methylation and genomic alterations of the target gene were evaluated. To further explore potential molecular mechanisms, genes co-expressed with the target gene were performed functional enrichment analysis Results: The differential expression analysis revealed 217 DEGs in HER-2 positive breast cancer tissues compared to normal breast tissues. RRM2 was the only hub gene closely associated with lymphatic metastasis and prognosis in HER-2 positive breast cancer. Additionally, RRM2 was frequently often amplified and negatively associated with the methylation level. Functional enrichment analysis showed that the co-expression genes were mainly involved in cell cycle. Conclusions: The present study identified RRM2 as a target gene associated with the initiation, progression and prognosis of HER-2 positive breast cancer, which may contribute to provide a new biomarker and therapeutic target.

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Low expression of CHRDL1 and SPARCL1 predicts poor prognosis of lung adenocarcinoma based on comprehensive analysis and immunohistochemical validation

Cancer Cell International ◽

10.1186/s12935-021-01933-9 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Huan Deng ◽

Qingqing Hang ◽

Dijian Shen ◽

Yibi Zhang ◽

Ming Chen

Keyword(s):

Lung Adenocarcinoma ◽

Molecular Mechanisms ◽

Transforming Growth Factor ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Prognostic Indicators ◽

Hub Genes ◽

Coexpressed Genes

Abstract Purpose Exploring the molecular mechanisms of lung adenocarcinoma (LUAD) is beneficial for developing new therapeutic strategies and predicting prognosis. This study was performed to select core genes related to LUAD and to analyze their prognostic value. Methods Microarray datasets from the GEO (GSE75037) and TCGA-LUAD datasets were analyzed to identify differentially coexpressed genes in LUAD using weighted gene coexpression network analysis (WGCNA) and differential gene expression analysis. Functional enrichment analysis was conducted, and a protein–protein interaction (PPI) network was established. Subsequently, hub genes were identified using the CytoHubba plug-in. Overall survival (OS) analyses of hub genes were performed. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Human Protein Atlas (THPA) databases were used to validate our findings. Gene set enrichment analysis (GSEA) of survival-related hub genes were conducted. Immunohistochemistry (IHC) was carried out to validate our findings. Results We identified 486 differentially coexpressed genes. Functional enrichment analysis suggested these genes were primarily enriched in the regulation of epithelial cell proliferation, collagen-containing extracellular matrix, transforming growth factor beta binding, and signaling pathways regulating the pluripotency of stem cells. Ten hub genes were detected using the maximal clique centrality (MCC) algorithm, and four genes were closely associated with OS. The CPTAC and THPA databases revealed that CHRDL1 and SPARCL1 were downregulated at the mRNA and protein expression levels in LUAD, whereas SPP1 was upregulated. GSEA demonstrated that DNA-dependent DNA replication and catalytic activity acting on RNA were correlated with CHRDL1 and SPARCL1 expression, respectively. The IHC results suggested that CHRDL1 and SPARCL1 were significantly downregulated in LUAD. Conclusions Our study revealed that survival-related hub genes closely correlated with the initiation and progression of LUAD. Furthermore, CHRDL1 and SPARCL1 are potential therapeutic and prognostic indicators of LUAD.

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Integrated Analysis of lncRNA-miRNA-mRNA ceRNA Network in Human Diarrhea Irritable Bowel Syndrome

10.21203/rs.3.rs-1117471/v1 ◽

2021 ◽

Author(s):

Jun-wei LIANG ◽

Wen-jun BAI ◽

Xiao-yan WANG ◽

Li-li CHI

Keyword(s):

Irritable Bowel Syndrome ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Integrated Analysis ◽

Down Regulation ◽

Tissue Samples ◽

Cerna Network ◽

Irritable Bowel

Abstract Background:Many studies on long chain non-coding RNAs (lncRNAs) are published in recent years. But the roles of lncRNAs in diarrhea irritable bowel syndrome (IBS-D) are still unclear and should be further examined. The present work focused on determining the molecular mechanisms underlying lncRNAs regulation in IBS-D on the basis of the lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) network.Methods:This study collected the mRNAs (GSE36701) expression data within human tissue samples with IBS-D group and normal group based on Gene Expression Omnibus (GEO) database and collected the differentially expressed lncRNAs (DELs) and differentially expressed miRNAs (DEmiRs) based on PubMed.Functional enrichment analysis of DEGs was performed on the DAVID database. Then the interaction network was constructed and visualized using STRING database and Cytoscape.Results: This study identified 3192 DEmRNAs (1437 with up-regulation and 1755 with down-regulation),29 DEmiRs (18 upregulated and 11 downregulated)and 2 DELs(one upregulated and one downregulated) between IBS-D and control samples.Furthermore,we constructed a lncRNA-miRNA-mRNA network through two DELs (lncRNA TUG1 with up-regulation and lncRNA H19 with down-regulation), four DemiRs (hsa-miR-148a-3p,hsa-miR-342-3p,hsa-miR-149-5p with up-regulation and hsa-miR-219a-5p with down-regulation)and 24 DEGs (4 with up-regulation and 20 with down-regulation) with 42 axes. Simultaneously, we conducted functional enrichment and pathway analyses on genes within the as-constructed ceRNA network. According to our PPI/ceRNA network and functional enrichment analysis results, two critical genes were found (BCL2L11 and QKI). Conclusion:In conclusion, the ceRNA interaction axis we identified is a potentially critical target for treating IBS-D.BCL2L11 axis(LncH19-hsa-miR-148a-3p-BCL2L11) may via interaction with PI3K/AKT pathways in IBS-D.Our results shed more lights on the possible pathogenic mechanism in IBS-D using a lncRNA-associated ceRNA network.

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