scholarly journals Identification of Unique Transcriptomic Signatures and Hub Genes Through RNA Sequencing and Integrated WGCNA and PPI Network Analysis in Nonerosive Reflux Disease

2021 ◽  
Vol Volume 14 ◽  
pp. 6143-6156
Author(s):  
Ye Zhao ◽  
Teng Ma ◽  
Duowu Zou
Keyword(s):  
Network Analysis ◽  
Rna Sequencing ◽  
Reflux Disease ◽  
Ppi Network ◽  
Hub Genes ◽  
Nonerosive Reflux Disease

scholarly journals Co-Expression Network Analysis and Hub Gene Selection for High-Quality Fiber in Upland Cotton (Gossypium hirsutum) Using RNA Sequencing Analysis

Genes ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 119 ◽  
Author(s):  
Xianyan Zou ◽  
Aiying Liu ◽  
Zhen Zhang ◽  
Qun Ge ◽  
Senmiao Fan ◽  
...  
Keyword(s):  
Network Analysis ◽  
Gossypium Hirsutum ◽  
Rna Sequencing ◽  
Differentially Expressed Genes ◽  
Upland Cotton ◽  
Fiber Quality ◽  
Fiber Development ◽  
Hub Genes ◽  
High Quality ◽  
Development Stages

Upland cotton (Gossypium hirsutum) is grown for its elite fiber. Understanding differential gene expression patterns during fiber development will help to identify genes associated with fiber quality. In this study, we used two recombinant inbred lines (RILs) differing in fiber quality derived from an intra-hirsutum population to explore expression profiling differences and identify genes associated with high-quality fiber or specific fiber-development stages using RNA sequencing. Overall, 72/27, 1137/1584, 437/393, 1019/184, and 2555/1479 differentially expressed genes were up-/down-regulated in an elite fiber line (L1) relative to a poor-quality fiber line (L2) at 10, 15, 20, 25, and 30 days post-anthesis, respectively. Three-hundred sixty-three differentially expressed genes (DEGs) between two lines were colocalized in fiber strength (FS) quantitative trait loci (QTL). Short Time-series Expression Miner (STEM) analysis discriminated seven expression profiles; gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation were performed to identify difference in function between genes unique to L1 and L2. Co-expression network analysis detected five modules highly associated with specific fiber-development stages, especially for high-quality fiber tissues. The hub genes in each module were identified by weighted gene co-expression network analysis. Hub genes encoding actin 1, Rho GTPase-activating protein with PAK-box, TPX2 protein, bHLH transcription factor, and leucine-rich repeat receptor-like protein kinase were identified. Correlation networks revealed considerable interaction among the hub genes, transcription factors, and other genes.

scholarly journals Identification of miRNA Regulatory Networks and Candidate Markers for Fracture Healing in Mice

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xianglu Li ◽  
Zhaohua Zhong ◽  
Enguang Ma ◽  
Xiaowei Wu
Keyword(s):  
Network Analysis ◽  
Fracture Healing ◽  
Collagen Fibril ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Healing Process ◽  
Differentially Expressed ◽  
Differential Analysis ◽  
Ppi Network ◽  
Hub Genes

Background. It is important to improve the understanding of the fracture healing process at the molecular levels, then to discover potential miRNA regulatory mechanisms and candidate markers. Methods. Expression profiles of mRNA and miRNA were obtained from the Gene Expression Omnibus database. We performed differential analysis, enrichment analysis, protein-protein interaction (PPI) network analysis. The miRNA-mRNA network analysis was also performed. Results. We identified 499 differentially expressed mRNAs (DEmRs) that were upregulated and 534 downregulated DEmRs during fracture healing. They were mainly enriched in collagen fibril organization and immune response. Using the PPI network, we screened 10 hub genes that were upregulated and 10 hub genes downregulated with the largest connectivity. We further constructed the miRNA regulatory network for hub genes and identified 13 differentially expressed miRNAs (DEmiRs) regulators. Cd19 and Col6a1 were identified as key candidate mRNAs with the largest fold change, and their DEmiR regulators were key candidate regulators. Conclusion. Cd19 and Col6a1 might serve as candidate markers for fracture healing in subsequent studies. Their expression is regulated by miRNAs and is involved in collagen fibril organization and immune responses.

scholarly journals Identification of immune-infiltrating cell-related biomarkers in hepatocellular carcinoma based on gene co-expression network analysis

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Yinghui Hou ◽  
Guizhi Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Network Analysis ◽  
Early Diagnosis ◽  
The Cancer Genome Atlas ◽  
Ppi Network ◽  
Sequencing Data ◽  
Hub Genes ◽  
Survival Prognosis ◽  
Expression Levels ◽  
Cancer Tissues

Abstract Background Hepatocellular carcinoma (HCC) is often caused by chronic liver infection or inflammation. Searching for potential immunotherapy targets will aid the early diagnosis and treatment of HCC. Methods Firstly, detailed HCC data were downloaded from The Cancer Genome Atlas database. GDCRNATools was used for the comprehensive analysis of RNA sequencing data. Subsequently, the CIBERSORT package was used to estimate infiltration scores of 22 types of immune cells in complex samples. Furthermore, hub genes were identified via weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis. In addition, multiple databases were used to validate the expression of hub gene in the tumor tissue. Finally, prognostic, diagnostic and immunohistochemical analysis of key hub genes was performed. Results In the present study, 9 hub genes were identified using WGCNA and PPI network analysis. Furthermore, the expression levels of 9 genes were positively correlated with the infiltration levels of CD8-positive T (CD8+ T) cells. In multiple dataset validations, the expression levels of CCL5, CXCR6, CD3E, and LCK were decreased in cancer tissues. In addition, survival analysis revealed that patients with LCK low expression had a poor survival prognosis (P < 0.05). Immunohistochemistry results demonstrated that CCL5, CD3E and LCK were expressed at low levels in HCC cancer tissues. Conclusion The identification of CCL5, CXCR6, CD3E and LCK may be helpful in the development of early diagnosis and therapy of HCC. LCK may be a potential prognostic biomarker for immunotherapy for HCC.

scholarly journals Transmembrane signaling molecules play a key role in the pathogenesis of IgA nephropathy: a weighted gene co-expression network analysis study

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Alieh Gholaminejad ◽  
Amir Roointan ◽  
Yousof Gheisari
Keyword(s):  
Network Analysis ◽  
Molecular Mechanisms ◽  
Immunoglobulin A ◽  
Cell Communication ◽  
Signaling Molecules ◽  
Health Concern ◽  
Transmembrane Signaling ◽  
Ppi Network ◽  
Hub Genes ◽  
Efficient Treatment

Abstract Background Immunoglobulin A nephropathy (IgAN) is one of the most common primary glomerulonephritis and a serious health concern worldwide; though still the underlying molecular mechanisms of IgAN are yet to be known and there is no efficient treatment for this disease. The main goal of this study was to explore the IgAN underlying pathogenic pathways, plus identifying the disease correlated modules and genes using the weighted gene co-expression network analysis (WGCNA) algorithm. Results GSE104948 dataset (the expression data from glomerular tissue of IgAN patients) was analyzed and the identified differentially expressed genes (DEGs) were introduced to the WGCNA algorithm for building co-expression modules. Genes were classified into six co-expression modules. Genes of the disease’s most correlated module were mainly enriched in the immune system, cell–cell communication and transmembrane cell signaling pathways. The PPI network was constructed by genes in all the modules and after hub-gene identification and validation steps, 11 genes, mostly transmembrane proteins (CD44, TLR1, TLR2, GNG11, CSF1R, TYROBP, ITGB2, PECAM1), as well as DNMT1, CYBB and PSMB9 were identified as potentially key players in the pathogenesis of IgAN. In the constructed regulatory network, hsa-miR-129-2-3p, hsa-miR-34a-5p and hsa-miR-27a-3p, as well as STAT3 were spotted as top molecules orchestrating the regulation of the hub genes. Conclusions The excavated hub genes from the hearts of co-expressed modules and the PPI network were mostly transmembrane signaling molecules. These genes and their upstream regulators could deepen our understanding of IgAN and be considered as potential targets for hindering its progression.

scholarly journals Gene Correlation Network Analysis To Identify Regulatory Factors In Sciatic Nerve Injury

2021 ◽  
Author(s):  
Liuxun Li ◽  
Xiaokang Du ◽  
Haiqian Ling ◽  
Yuhang Li ◽  
Xuemin Wu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Sciatic Nerve ◽  
Nerve Injury ◽  
Enrichment Analysis ◽  
Sciatic Nerve Injury ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Adult Group ◽  
Hub Genes

Abstract Background: Sciatic nerve injury (SNI), which frequently occurs under the traumatic hip and hip fracture dislocation, induces serious complications such as motor and sensory loss, muscle atrophy, or even disabling. The present work aimed to determine the regulating factors and gene network related to the SNI pathology.Methods: Sciatic nerve injury dataset GSE18803 with 24 samples was randomly divided into adult group and neonate group. We performed weighted gene co-expression network analysis (WGCNA) to identify modules associated with SNI in the two groups. Moreover, differentially expressed genes (DEGs) were determined from every group, separately. Subsequently, co-expression network, protein-protein interaction (PPI) network, enrichment analysis and gene set enrichment analysis (GSEA) were integrated to identify hub genes and associated pathways. GSE30165 was used as the test set for investigating the hub gene involvement within SNI. Finally, we employed DGIdb for predicting the possible therapeutic agents leading to the abnormal up-regulation of hub genes.Results: 14 SNI status modules and 97 DEGs were identified in adult group, while 15 modules and 21 DEGs in neonate group. A total of 12 hub genes was overlapping from co-expression and PPI network. After the results from both test and training sets were overlapped, we verified that the ten real hub genes showed remarkably up-regulation within SNI. According to functional enrichment of DEGs, the above genes participated in the immune effector process, inflammatory responses, the antigen processing and presentation, and the phagocytosis. GSEA also supported that gene sets with the highest significance was mostly related to the cytokine-cytokine receptor interaction.Conclusions: The gene expression network is determined in the present work based on the related regulating factors within SNI, which sheds more lights on SNI pathology and offers the possible biomarkers and therapeutic targets in subsequent research.

scholarly journals Identification of key genes and pathways in endometriosis by integrated expression profiles analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10171
Author(s):  
Ding Cui ◽  
Yang Liu ◽  
Junyan Ma ◽  
Kaiqing Lin ◽  
Kaihong Xu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Differentially Expressed Genes ◽  
Western Blotting ◽  
Expression Profiles ◽  
Hippo Pathway ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Normal Endometrium

The purpose of this study was to integrate the existing expression profile data on endometriosis (EM)-related tissues in order to identify the differentially expressed genes. In this study, three series of raw expression data were downloaded from GEO database. Differentially expressed genes (DEGs) in three tissue types were screened. GO, KEGG pathway enrichment analysis, core differential genes (CDGs) protein–protein interaction (PPI) network and weighted gene co-expression network analysis (WGCNA) were performed, finally, the dysregulation of Hippo pathway in ectopic endometrium (EC) was detected by Western blotting. A total of 1,811 DEGs between eutopic (EU) and normal endometrium (NE), 5,947 DEGs between EC and EU, and 3,192 DEGs between EC and NE datasets were identified. After screening, 394 CDGs were obtained, and 5 hub genes identified in the PPI network. CDGs enrichment and WGCNA network analysis revealed cell proliferation, differentiation, migration and other biological processes, Hippo and Wnt signaling pathways, and a variety of tumor-related pathways. Western blotting results showed that YAP/TAZ was upregulated, and MOB1, pMOB1, SAV1, LATS1 and LATS2 were downregulated in EC. Moreover, CDGs, especially the hub genes, are potential biomarkers and therapeutic targets. Finally, the Hippo pathway might play a key role in the development of endometriosis.

scholarly journals Weighted gene co-expression network analysis reveals specific modules and hub genes related to neuropathic pain in dorsal root ganglions

2019 ◽  
Vol 39 (11) ◽  
Author(s):  
Nan Cheng ◽  
Zheng Zhang ◽  
Yue Guo ◽  
Zhuo-Lin Qiu ◽  
Jing-Yi Du ◽  
...  
Keyword(s):  
Neuropathic Pain ◽  
Network Analysis ◽  
Dorsal Root ◽  
Molecular Mechanisms ◽  
Tissue Inhibitor Of Metalloproteinase ◽  
In Vivo Studies ◽  
Ppi Network ◽  
Hub Genes ◽  
Clinical Issue

Abstract Neuropathic pain is a common, debilitating clinical issue. Here, the weighted gene co-expression network analysis (WGCNA) was used to identify the specific modules and hub genes that are related to neuropathic pain. The microarray dataset of a neuropathic rat model induced by tibial nerve transection (TNT), including dorsal root ganglion (DRG) tissues from TNT model (n=7) and sham (n=8) rats, was downloaded from the ArrayExpress database (E-MTAB-2260). The co-expression network modules were identified by the WGCNA package. The protein–protein interaction (PPI) network was constructed, and the node with highest level of connectivity in the network were identified as the hub gene. A total of 1739 genes and seven modules were identified. The most significant module was the brown module, which contained 215 genes that were primarily associated with the biological process (BP) of the defense response and molecular function of calcium ion binding. Furthermore, C–C motif chemokine ligand 2 (Ccl2), Fos and tissue inhibitor of metalloproteinase 1 (Timp1) which were identified as the hub genes in the PPI network and two subnetworks separately. The in vivo studies validated that mRNA and protein levels of Ccl2, Fos and Timp1 were up-regulated in DRG and spinal cord tissues after TNT. The present study offers novel insights into the molecular mechanisms of neuropathic pain in the context of peripheral nerve injury.

scholarly journals Transmembrane Signaling Molecules Play a Key Role in Progression of IgA Nephropathy: A Weighted Gene Co-expression Network Analysis Study

2021 ◽  
Author(s):  
Alieh Gholaminejad ◽  
Amir Roointan ◽  
Yousof Gheisari
Keyword(s):  
Network Analysis ◽  
Molecular Mechanisms ◽  
Immunoglobulin A ◽  
Cell Communication ◽  
Signaling Molecules ◽  
Health Concern ◽  
Transmembrane Signaling ◽  
Ppi Network ◽  
Hub Genes ◽  
Efficient Treatment

Abstract Background: Immunoglobulin A nephropathy (IgAN) is one of the most common primary glomerulonephritis and a serious health concern worldwide; though, still the underlying molecular mechanisms of IgAN are yet to be known and there is no efficient treatment for this disease. The main goal of this study was to explore the IgAN underlying pathogenic pathways, plus identifying the disease correlated modules and genes using weighted gene co-expression network analysis (WGCNA) algorithm. Results: GSE104948 dataset was analyzed and the identified differentially expressed genes (DEGs) were introduced to the WGCNA algorithm for building co-expression modules. Genes were classified into six co-expression modules. Genes of the disease most correlated module were mainly enriched in the immune system, cell-cell communication and transmembrane cell signaling pathways. The PPI network was constructed by genes in all the modules and after hub-gene identification and validation steps, 11 genes, mostly transmembrane proteins (CD44, TLR1, TLR2, GNG11, CSF1R, TYROBP, ITGB2, PECAM1), as well as DNMT1, CYBB and PSMB9 were identified as potential key players in the IgAN progression. In the constructed regulatory network, miR-129-2, miR-34a and miR-27a, as well as STAT3 were spotted as top molecules orchestrating the regulation of the hub genes.Conclusions: The excavated hub genes from the hearts of co-expressed modules and PPI network were mostly transmembrane signaling molecules. These genes and their upstream regulators could deepen our understanding of IgAN and be considered as potential targets for hindering its progression.

scholarly journals Integrated Weighted Gene Co-expression Network Analysis Identified That TLR2 and CD40 Are Related to Coronary Artery Disease

2021 ◽  
Vol 11 ◽  
Author(s):  
Bin Qi ◽  
Jian-Hong Chen ◽  
Lin Tao ◽  
Chuan-Meng Zhu ◽  
Yong Wang ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Heart Failure ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Network Analysis ◽  
Kegg Pathway ◽  
Ppi Network ◽  
Hub Genes ◽  
Plaque Instability ◽  
Artery Disease

The current research attempted to identify possible hub genes and pathways of coronary artery disease (CAD) and to detect the possible mechanisms. Array data from GSE90074 were downloaded from the Gene Expression Omnibus (GEO) database. Integrated weighted gene co-expression network analysis (WGCNA) was performed to analyze the gene module and clinical characteristics. Gene Ontology annotation (GO), Disease Ontology (DO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by clusterProfiler and the DOSE package in R. A protein-protein interaction (PPI) network was established using Cytoscape software, and significant modules were analyzed using Molecular Complex Detection (MCODE) to identify hub genes. Then, further functional validation of hub genes in other microarrays and population samples was performed, and survival analysis was performed to investigate the prognosis. A total of 660 genes were located in three modules and associated with CAD. GO functions identified 484 biological processes, 39 cellular components, and 22 molecular functions with an adjusted P &lt; 0.05. In total, 38 pathways were enriched in KEGG pathway analysis, and 147 DO items were identified with an adjusted P &lt; 0.05 (false discovery rate, FDR set at &lt; 0.05). There was a total of four modules with a score &gt; 10 after PPI network analysis using the MCODE app, and two hub genes (TLR2 and CD14) were identified. Then, we validated the information from the GSE60993 dataset using the GSE59867 dataset and population samples, and we found that these two genes were associated with plaque vulnerability. These two genes varied at different time points after myocardial infarction, and both of them had the lowest prognosis of heart failure when they were expressed at low levels. We performed an integrated WGCNA and validated that TLR2 and CD14 were closely associated with the severity of coronary artery disease, plaque instability and the prognosis of heart failure after myocardial infarction.

scholarly journals Construction of Gene Modules and Analysis of Prognostic Biomarkers for Cervical Cancer by Weighted Gene Co-Expression Network Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Jiamei Liu ◽  
Shengye Liu ◽  
Xianghong Yang
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Network Analysis ◽  
Cancer Progression ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Gene Modules

BackgroundDespite advances in the understanding of neoplasm, patients with cervical cancer still have a poor prognosis. Identifying prognostic markers of cervical cancer may enable early detection of recurrence and more effective treatment.MethodsGene expression profiling data were acquired from the Gene Expression Omnibus database. After data normalization, genes with large variation were screened out. Next, we built co-expression modules by using weighted gene co-expression network analysis to investigate the relationship between the modules and clinical traits related to cervical cancer progression. Functional enrichment analysis was also applied on these co-expressed genes. We integrated the genes into a human protein-protein interaction (PPI) network to expand seed genes and build a co-expression network. For further analysis of the dataset, the Cancer Genome Atlas (TCGA) database was used to identify seed genes and their correlation to cervical cancer prognosis. Verification was further conducted by qPCR and the Human Protein Atlas (HPA) database to measure the expression of hub genes.ResultsUsing WGCNA, we identified 25 co-expression modules from 10,016 genes in 128 human cervical cancer samples. After functional enrichment analysis, the magenta, brown, and darkred modules were selected as the three most correlated modules for cancer progression. Additionally, seed genes in the three modules were combined with a PPI network to identify 31 tumor-specific genes. Hierarchical clustering and Gepia results indicated that the expression quantity of hub genes NDC80, TIPIN, MCM3, MCM6, POLA1, and PRC1 may determine the prognosis of cervical cancer. Finally, TIPIN and POLA1 were further filtered by a LASSO model. In addition, their expression was identified by immunohistochemistry in HPA database as well as a biological experiment.ConclusionOur research provides a co-expression network of gene modules and identifies TIPIN and POLA1 as stable potential prognostic biomarkers for cervical cancer.

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