Coexpression Network Analysis of lncRNA Associated with Overexpression of DNMT1 in Esophageal Epithelial Cells

Screening and preliminary identification of high DNMT1 expression-related lncRNA, which is involved in various interrelated signaling pathways, has led to the development of a theoretical basis for various types of disease mechanisms. Differential expression profiles of lncRNA and mRNA were identified in a microarray. Ten lncRNAs with high levels of variation were identified by qRT-PCR. KEGG and GO analyses were used to identify differentially expressed mRNAs. Six signaling pathways were selected based on the KEGG results of the lncRNA-mRNA expression network analysis. From the microarrays in the experimental and control groups, we found a total of 6987 differentially expressed lncRNAs, and 7421 differentially expressed mRNAs were obtained ( P < 0.05 ; fold change > 2.0 x ). GO analysis and KEGG pathway analysis showed high expression of DNMT1 in esophageal epithelial cells. Nine pathways were involved in mRNA upregulation, including natural killer cell-mediated cytotoxicity and many other prominent biochemical pathways. Forty-six pathways were associated with downregulated mRNAs and ribosomes involving multiple biological pathways. Coexpression network analysis showed that 8 mRNAs and 16 lncRNAs were linked to the p53 signaling pathway. In Helicobacter pylori infections, interactions occurred between 22 lncRNAs and 11 mRNAs in the ErbB signaling pathway and between 19 lncRNAs and 8 mRNAs in epithelial cell signal transduction. Interactions were present between 19 lncRNAs and 5 mRNAs in the sphingolipid signaling pathway, along with interactions between 21 lncRNAs and 12 mRNAs in the PI3K-Akt signaling pathway. Cytotoxicity interactions occurred between 22 lncRNAs and 9 mRNAs in natural killer cells.

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Co-expression Network analysis of LncRNA associated with overexpression of DNMT1 in esophageal epithelial cells

10.21203/rs.2.12818/v1 ◽

2019 ◽

Author(s):

Dong Yin ◽

Yi XU ◽

Xu-feng LI ◽

Yan CHEN

Keyword(s):

Network Analysis ◽

Epithelial Cells ◽

Natural Killer ◽

Signaling Pathway ◽

Expression Profiles ◽

Killer Cell ◽

Differentially Expressed ◽

High Expression ◽

Signal Pathways ◽

Esophageal Epithelial Cells

Abstract Screening and preliminary identification of DNMT1 high expression related LncRNA, which is involved in various inter-related signaling pathways, has revealed a theoretical basis for various types of disease mechanisms. LncRNA and mRNA differential expression profiles were identified on a microarray. The 10 LncRNAs with high levels of variation were identified by qRT-PCR. KEGG and GO analyses were used to dissect out differentially expressed mRNAs. Six signal pathways were selected based on the KEGG results of the LncRNA-mRNAs expression network analysis.We found a total of 6987 differentially expressed LncRNAs and 7421 differentially expressed mRNAs were obtained from the microarrays in experimental and control groups (P<0.05; Fold Change >2.0x). GO analysis KEGG pathway analysis showed high expression of DNMT1 in esophageal epithelial cells. Nine pathways involved mRNA up-regulation including natural killer cell mediated cytotoxicity and many other prominent biochemical pathways. Forty six pathways were associated with down regulated mRNAs and ribosomes involving multiple biological pathways. Co-expression network analysis showed that the 8 mRNA and 16 LncRNA were linked to the P53 signaling pathway. Interactions occurred between 22 LncRNA and 11 mRNA in the ErbB signaling pathway, between 19 LncRNA and 8 mRNA in epithelial cell signal transduction of Helicobacter pylori infection. Interactions were present in 19 LncRNAs and 5 mRNAs in the sphingolipid signaling pathway along with 21 with LncRNA and 12 with mRNAs in the PI3K-Akt signaling pathway. Cytotoxicity interactions occurred with 22 LncRNAs and 9 mRNAs in natural killer cells.

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Co-expression Network analysis of LncRNA associated with overexpression of DNMT1 in esophageal epithelial cells

10.21203/rs.3.rs-92861/v1 ◽

2020 ◽

Author(s):

Yi LEI ◽

Chen ZHANG ◽

Dong YIN ◽

Yi XU ◽

Xu-feng LI ◽

...

Keyword(s):

Network Analysis ◽

Epithelial Cells ◽

Natural Killer ◽

Signaling Pathway ◽

Expression Profiles ◽

Killer Cell ◽

Differentially Expressed ◽

High Expression ◽

Signal Pathways ◽

Esophageal Epithelial Cells

Abstract Background:Screening and preliminary identification of DNMT1 high expression related LncRNA, which is involved in various inter-related signaling pathways, has revealed a theoretical basis for various types of disease mechanisms. Methods:LncRNA and mRNA differential expression profiles were identified on a microarray. The 10 LncRNAs with high levels of variation were identified by qRT-PCR. KEGG and GO analyses were used to dissect out differentially expressed mRNAs. Results and Conclusions:Six signal pathways were selected based on the KEGG results of the LncRNA-mRNAs expression network analysis.We found a total of 6987 differentially expressed LncRNAs and 7421 differentially expressed mRNAs were obtained from the microarrays in experimental and control groups (P<0.05; Fold Change >2.0x). GO analysis KEGG pathway analysis showed high expression of DNMT1 in esophageal epithelial cells. Nine pathways involved mRNA up-regulation including natural killer cell mediated cytotoxicity and many other prominent biochemical pathways. Forty six pathways were associated with down regulated mRNAs and ribosomes involving multiple biological pathways. Co-expression network analysis showed that the 8 mRNA and 16 LncRNA were linked to the P53 signaling pathway. Interactions occurred between 22 LncRNA and 11 mRNA in the ErbB signaling pathway, between 19 LncRNA and 8 mRNA in epithelial cell signal transduction of Helicobacter pylori infection. Interactions were present in 19 LncRNAs and 5 mRNAs in the sphingolipid signaling pathway along with 21 with LncRNA and 12 with mRNAs in the PI3K-Akt signaling pathway. Cytotoxicity interactions occurred with 22 LncRNAs and 9 mRNAs in natural killer cells.

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Identification of Candidate Genes and MicroRNAs for Acute Myocardial Infarction by Weighted Gene Coexpression Network Analysis

BioMed Research International ◽

10.1155/2019/5742608 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 4

Author(s):

Yan Li ◽

Xiao_nan He ◽

Chao Li ◽

Ling Gong ◽

Min Liu

Keyword(s):

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Network Analysis ◽

Differentially Expressed Genes ◽

Microarray Data ◽

Differentially Expressed ◽

Coexpression Network ◽

Gene Coexpression Network ◽

Gene Coexpression ◽

Coexpression Network Analysis

Background. Identification of potential molecular targets of acute myocardial infarction is crucial to our comprehensive understanding of the disease mechanism. However, studies of gene coexpression analysis via jointing multiple microarray data of acute myocardial infarction still remain restricted. Methods. Microarray data of acute myocardial infarction (GSE48060, GSE66360, GSE97320, and GSE19339) were downloaded from Gene Expression Omnibus database. Three data sets without heterogeneity (GSE48060, GSE66360, and GSE97320) were subjected to differential expression analysis using MetaDE package. Differentially expressed genes having upper 25% variation across samples were imported in weighted gene coexpression network analysis. Functional and pathway enrichment analyses were conducted for genes in the most significant module using DAVID. The predicted microRNAs to regulate target genes in the most significant module were identified using TargetScan. Moreover, subpathway analyses using iSubpathwayMiner package and GenCLiP 2.0 were performed on hub genes with high connective weight in the most significant module. Results. A total of 1027 differentially expressed genes and 33 specific modules were screened out between acute myocardial infarction patients and control samples. Ficolin (collagen/fibrinogen domain containing) 1 (FCN1), CD14 molecule (CD14), S100 calcium binding protein A9 (S100A9), and mitochondrial aldehyde dehydrogenase 2 (ALDH2) were identified as critical target molecules; hsa-let-7d, hsa-let-7b, hsa-miR-124-3, and hsa-miR-9-1 were identified as potential regulators of the expression of the key genes in the two biggest modules. Conclusions. FCN1, CD14, S100A9, ALDH2, hsa-let-7d, hsa-let-7b, hsa-miR-124-3, and hsa-miR-9-1 were identified as potential candidate regulators in acute myocardial infarction. These findings might provide new comprehension into the underlying molecular mechanism of disease.

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

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/8183420 ◽

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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Weighted gene coexpression network analysis-based identification of key modules and hub genes associated with drought sensitivity in rice

10.21203/rs.3.rs-56040/v4 ◽

2020 ◽

Author(s):

Baiyang Yu ◽

Jianbin Liu ◽

Di Wu ◽

Ying Liu ◽

Weijian Cen ◽

...

Keyword(s):

Network Analysis ◽

Drought Resistance ◽

Differentially Expressed ◽

Chromosome 1 ◽

Coexpression Network ◽

Gene Coexpression Network ◽

Hub Genes ◽

Gene Coexpression ◽

Go Terms ◽

Coexpression Network Analysis

Abstract Background: Drought stress is an adverse factor with deleterious effects on several aspects of rice growth. However, the mechanism underlying drought resistance in rice remains unclear. To understand the molecular mechanism of the drought response in rice, drought-sensitive CSSL (Chromosome Single-substitution Segment Line) PY6 was used to map QTLs of sensitive phenotypes and to reveal the impact of the QTLs on transcriptional profiling.Results: The QTL dss-1 was mapped onto the short arm of chromosome 1 of rice. According to transcriptomic analysis, the identified differentially expressed genes (DEGs) exhibited a downregulated pattern and were mainly enriched in photosynthesis-related GO terms, indicating that photosynthesis was greatly inhibited under drought. Further, according to weighted gene coexpression network analysis (WGCNA), specific gene modules (designating a group of genes with a similar expression pattern) were strongly correlated with H2O2 (4 modules) and MDA (3 modules), respectively. Likewise, GO analysis revealed that the photosynthesis-related GO terms were consistently overrepresented in H2O2-correlated modules. Functional annotation of the differentially expressed hub genes (DEHGs) in the H2O2 and MDA-correlated modules revealed cross-talk between abiotic and biotic stress responses for these genes, which were annotated as encoding WRKYs and PR family proteins, were notably differentially expressed between PY6 and PR403.Conclusions: We speculated that drought-induced photosynthetic inhibition leads to H2O2 and MDA accumulation, which can then trigger the reprogramming of the rice transcriptome, including the hub genes involved in ROS scavenging, to prevent oxidative stress damage. Our results shed light on and provide deep insight into the drought resistance mechanism in rice.

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Integrated weighted gene coexpression network analysis identifies Frizzled 2 (FZD2) as a key gene in invasive malignant pleomorphic adenoma

Journal of Translational Medicine ◽

10.1186/s12967-021-03204-7 ◽

2022 ◽

Vol 20 (1) ◽

Author(s):

Zhenyuan Han ◽

Huiping Ren ◽

Jingjing Sun ◽

Lihui Jin ◽

Qin Wang ◽

...

Keyword(s):

Gene Expression ◽

Network Analysis ◽

Pleomorphic Adenoma ◽

Expression Profiles ◽

Functional Enrichment ◽

Coexpression Network ◽

Low Grade ◽

Gene Coexpression Network ◽

Gene Coexpression ◽

Coexpression Network Analysis

Abstract Background Invasive malignant pleomorphic adenoma (IMPA) is a highly malignant neoplasm of the oral salivary glands with a poor prognosis and a considerable risk of recurrence. Many disease-causing genes of IMPA have been identified in recent decades (e.g., P53, PCNA and HMGA2), but many of these genes remain to be explored. Weighted gene coexpression network analysis (WGCNA) is a newly emerged algorithm that can cluster genes and form modules based on similar gene expression patterns. This study constructed a gene coexpression network of IMPA via WGCNA and then carried out multifaceted analysis to identify novel disease-causing genes. Methods RNA sequencing (RNA-seq) was performed for 10 pairs of IMPA and normal tissues to acquire the gene expression profiles. Differentially expressed genes (DEGs) were screened out with the cutoff criteria of |log2 Fold change (FC)|> 1 and adjusted p value < 0.05. Then, WGCNA was applied to systematically identify the hidden diagnostic hub genes of IMPA. Results In this research, a total of 1970 DEGs were screened out in IMPA tissues, including 1056 upregulated DEGs and 914 downregulated DEGs. Functional enrichment analysis was performed for identified DEGs and revealed an enrichment of tumor-associated GO terms and KEGG pathways. We used WGCNA to identify gene module most relevant with the histological grade of IMPA. The gene FZD2 was then recognized as the hub gene of the selected module with the highest module membership (MM) value and intramodule connectivity in protein–protein interaction (PPI) network. According to immunohistochemistry (IHC) staining, the expression level of FZD2 was higher in low-grade IMPA than in high-grade IMPA. Conclusion FZD2 shows an expression dynamic that is negatively correlated with the clinical malignancy of IMPA and it plays a central role in the transcription network of IMPA. Thus, FZD2 serves as a promising histological indicator for the precise prediction of IMPA histological stages.

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miRNA Regulation Network Analysis in Qianliening Capsule Treatment of Benign Prostatic Hyperplasia

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2015/365484 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Liya Liu ◽

Yun Wan ◽

Aling Shen ◽

Jinyan Zhao ◽

Jiumao Lin ◽

...

Keyword(s):

Gene Expression ◽

Cell Proliferation ◽

Benign Prostatic Hyperplasia ◽

Network Analysis ◽

Signaling Pathways ◽

Expression Profiles ◽

Prostatic Hyperplasia ◽

Differentially Expressed ◽

Epithelial Cell Line ◽

Mirna Regulation

Objective. The objective of this study was to evaluate the molecular mechanism by which Qianliening capsule (QC) treats benign prostatic hyperplasia (BPH).Methods. Benign prostatic hyperplasia epithelial cell line BPH-1 was treated with 0, 1.25, 2.5, and 5 mg/mL QC for 48 h, respectively. Evaluation of cell viability and observation of morphologic changes of BPH-1 cell gene expression and miRNA expression profiles were analyzed. Real-time quantitative PCR was used to confirm changes in miRNA and gene expression. GO and KEGG pathway-based approaches were used to investigate biological functions and signaling pathways affected by differentially expressed mRNAs.Results. QC inhibited BPH-1 cell proliferation. Differential expression of 19 upregulated and 2 downregulated miRNAs was observed in QC-treated BPH-1 cells compared to untreated control cells. 107 upregulated and 71 downregulated genes were identified between the two groups. Significantly enriched signaling pathways based on deregulated mRNAs were mainly involved in regulation of cell proliferation, apoptosis, and so on. Additionally, miRNA-mRNA network analysis integrated these miRNAs and genes by outlining interactions of miRNA and related genes.Conclusion. The study was the first report of differentially expressed miRNA and mRNA in QC-treated BPH-1 cells.

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Comparative Transcriptome Profiling of Human and Pig Intestinal Epithelial Cells after Porcine Deltacoronavirus Infection

Viruses ◽

10.3390/v13020292 ◽

2021 ◽

Vol 13 (2) ◽

pp. 292

Author(s):

Diana Cruz-Pulido ◽

Patricia A. Boley ◽

Wilberforce Zachary Ouma ◽

Moyasar A. Alhamo ◽

Linda J. Saif ◽

...

Keyword(s):

Epithelial Cells ◽

Signaling Pathways ◽

Signaling Pathway ◽

Cell Lines ◽

Intestinal Epithelial Cells ◽

Expression Profiles ◽

Human Cells ◽

Host Cells ◽

Transcriptional Level ◽

Intestinal Epithelial

Porcine deltacoronavirus (PDCoV) is an emerging infectious disease of swine with zoonotic potential. Phylogenetic analysis suggests that PDCoV originated recently from a host-switching event between birds and mammals. Little is known about how PDCoV interacts with its differing hosts. Human-derived cell lines are susceptible to PDCoV infection. Herein, we compare the gene expression profiles of an established host swine cells to potential emerging host human cells after infection with PDCoV. Cell lines derived from intestinal lineages were used to reproduce the primary sites of viral infection in the host. Porcine intestinal epithelial cells (IPEC-J2) and human intestinal epithelial cells (HIEC) were infected with PDCoV. RNA-sequencing was performed on total RNA extracted from infected cells. Human cells exhibited a more pronounced response to PDCoV infection in comparison to porcine cells with more differentially expressed genes (DEGs) in human, 7486, in comparison to pig cells, 1134. On the transcriptional level, the adoptive host human cells exhibited more DEGs in response to PDCoV infection in comparison to the primary pig host cells, where different types of cytokines can control PDCoV replication and virus production. Key immune-associated DEGs and signaling pathways are shared between human and pig cells during PDCoV infection. These included genes related to the NF-kappa-B transcription factor family, the interferon (IFN) family, the protein-kinase family, and signaling pathways such as the apoptosis signaling pathway, JAK-STAT signaling pathway, inflammation/cytokine–cytokine receptor signaling pathway. MAP4K4 was unique in up-regulated DEGs in humans in the apoptosis signaling pathway. While similarities exist between human and pig cells in many pathways, our research suggests that the adaptation of PDCoV to the porcine host required the ability to down-regulate many response pathways including the interferon pathway. Our findings provide an important foundation that contributes to an understanding of the mechanisms of PDCoV infection across different hosts. To our knowledge, this is the first report of transcriptome analysis of human cells infected by PDCoV.

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Weighted gene coexpression network analysis-based identification of key modules and hub genes associated with drought sensitivity in rice

10.21203/rs.3.rs-56040/v3 ◽

2020 ◽

Author(s):

Baiyang Yu ◽

Jianbin Liu ◽

Di Wu ◽

Ying Liu ◽

Weijian Cen ◽

...

Keyword(s):

Network Analysis ◽

Drought Resistance ◽

Differentially Expressed ◽

Chromosome 1 ◽

Coexpression Network ◽

Gene Coexpression Network ◽

Hub Genes ◽

Gene Coexpression ◽

Go Terms ◽

Coexpression Network Analysis

Abstract Background: Drought stress is an adverse factor with deleterious effects on several aspects of rice growth. However, the mechanism underlying drought resistance in rice remains unclear. To understand the molecular mechanism of the drought response in rice, drought-sensitive CSSL (Chromosome Single-substitution Segment Line) PY6 was used to map QTLs of sensitive phenotypes and to reveal the impact of the QTLs on transcriptional profiling. Results: The QTL dss-1 was mapped onto the short arm of chromosome 1 of rice. According to transcriptomic analysis, the identified differentially expressed genes (DEGs) exhibited a downregulated pattern and were mainly enriched in photosynthesis-related GO terms, indicating that photosynthesis was greatly inhibited under drought. Further, according to weighted gene coexpression network analysis (WGCNA), specific gene modules (designating a group of genes with a similar expression pattern) were strongly correlated with H2O2 (4 modules) and MDA (3 modules), respectively. Likewise, GO analysis revealed that the photosynthesis-related GO terms were consistently overrepresented in H2O2-correlated modules. Functional annotation of the differentially expressed hub genes (DEHGs) in the H2O2 and MDA-correlated modules revealed cross-talk between abiotic and biotic stress responses for these genes, which were annotated as encoding WRKYs and PR family proteins, were notably differentially expressed between PY6 and PR403.Conclusions: We speculated that drought-induced photosynthetic inhibition leads to H2O2 and MDA accumulation, which can then trigger the reprogramming of the rice transcriptome, including the hub genes involved in ROS scavenging, to prevent oxidative stress damage. Our results shed light on and provide deep insight into the drought resistance mechanism in rice.

Download Full-text