Identification of gene expression profiles and key genes in subchondral bone of osteoarthritis using weighted gene coexpression network analysis

2018 ◽  
Vol 119 (9) ◽  
pp. 7687-7695 ◽  
Author(s):  
Sheng-Min Guo ◽  
Jian-Xiong Wang ◽  
Jin Li ◽  
Fang-Yuan Xu ◽  
Quan Wei ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Subchondral Bone ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Gene Coexpression ◽  
Key Genes ◽  
Coexpression Network Analysis

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

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.

scholarly journals Identification of Potential Biomarkers Associated with Acute Myocardial Infarction by Weighted Gene Coexpression Network Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yan Wang ◽  
Xiangyang Zhang ◽  
Min Duan ◽  
Chenguang Zhang ◽  
Ke Wang ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Network Analysis ◽  
Interaction Network ◽  
Coexpression Network ◽  
Pathway Enrichment Analysis ◽  
Gene Coexpression Network ◽  
Gene Coexpression ◽  
Key Genes ◽  
Coexpression Network Analysis

Background. In the general population, acute myocardial infarction (AMI) represents a significant cause of mortality. This study is aimed at identifying novel diagnostic biomarkers to aid in treating and diagnosing AMI. Methods. The Gene Expression Omnibus (GEO) database was explored to extract two microarray datasets, GSE66360 and GSE48060, which were subsequently merged into a single cohort. Both AMI and control samples were analyzed for differentially expressed genes (DEGs), which were subsequently subjected to weighed gene coexpression network analysis (WGCNA) to identify the most significant module. Gene Ontology (GO) and pathway analyses subsequently carried out the most significant gene modules along with construction of a protein-protein interaction network (PPI). Cytoscape plugin cytoHubba allowed for the prediction of the top 4 key genes according to the network maximal clique centrality (MCC) algorithm. The expression levels and diagnostic value of the four key genes were additionally verified in the GSE62646 dataset. Results. A WCGNA analysis revealed 878 DEGs which were clustered into 6 modules. The module with the most significance in AMI was colored blue. Subsequent GO and KEGG pathway enrichment analysis on blue module genes revealed that they were primarily enriched in the inflammation-related pathways. These findings, in combination with PPI and coexpression networks, resulted in the identification of the top four genes by cytoHubba, which included leukocyte immunoglobulin-like receptor B2 (LILRB2), toll-like receptor 2 (TLR2), neutrophil cytosolic factor 2 (NCF2), and S100A9. Among them, LILRB2, NCF2, and S100A9 were validated in the GSE62646 dataset. Conclusions. The results suggested that LILRB2, NCF2, and S100A9 could be potential gene biomarkers for AMI.

scholarly journals Identification of Hub Gene GRIN1 Correlated with Histological Grade and Prognosis of Glioma by Weighted Gene Coexpression Network Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Aoran Yang ◽  
Xinhuan Wang ◽  
Yaofeng Hu ◽  
Chao Shang ◽  
Yang Hong
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Molecular Mechanisms ◽  
The Cancer Genome Atlas ◽  
Coexpression Network ◽  
Normal Brain ◽  
Gene Coexpression Network ◽  
Gene Coexpression ◽  
Coexpression Network Analysis ◽  
Core Genes

The function of glutamate ionotropic receptor NMDA type subunit 1 (GRIN1) in neurodegenerative diseases has been widely reported; however, its role in the occurrence of glioma remains less explored. We obtained clinical data and transcriptome data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Hub gene’s expression differential analysis and survival analysis were conducted by browsing the Gene Expression Profiling Interactive Analysis (GEPIA) database, Human Protein Atlas database, and LOGpc database. We conducted a variation analysis of datasets obtained from GEO and TCGA and performed a weighted gene coexpression network analysis (WGCNA) using the R programming language (3.6.3). Kaplan-Meier (KM) analysis was used to calculate the prognostic value of GRIN1. Finally, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Using STRING, we constructed a protein–protein interaction (PPI) network. Cytoscape software, a prerequisite of visualizing core genes, was installed, and CytoHubba detected the 10 most tumor-related core genes. We identified 185 differentially expressed genes (DEGs). GO and KEGG enrichment analyses illustrated that the identified DEGs are imperative in different biological functions and ascertained the potential pathways in which the DEGs may be enriched. The overall survival calculated by KM analysis showed that patients with lower expression of GRIN1 had worse prognoses than patients with higher expression of GRIN1 ( p = 0.004 ). The GEPIA and LOGpc databases were used to verify the expression difference of GRIN1 among GBM, LGG, and normal brain tissues. Ultimately, immunohistochemical assay results showed that GRIN1 was detected in normal tissue and not in the tumor specimens. Our results highlight a potential target for glioma treatment and will further our understanding of the molecular mechanisms underlying the treatment of glioma.

scholarly journals Identification of Prognostic Candidate Genes in Breast Cancer by Integrated Bioinformatic Analysis

2019 ◽  
Vol 8 (8) ◽  
pp. 1160 ◽  
Author(s):  
Wang ◽  
Li ◽  
Cai ◽  
Sheu ◽  
Tsai ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Network Analysis ◽  
Survival Time ◽  
Expression Profiles ◽  
Coexpression Network ◽  
Marker Genes ◽  
Gene Coexpression Network ◽  
Gene Coexpression ◽  
Significant Difference

Breast cancer is one of the most common malignancies. However, the molecular mechanisms underlying its pathogenesis remain to be elucidated. The present study aimed to identify the potential prognostic marker genes associated with the progression of breast cancer. Weighted gene coexpression network analysis was used to construct free-scale gene coexpression networks, evaluate the associations between the gene sets and clinical features, and identify candidate biomarkers. The gene expression profiles of GSE48213 were selected from the Gene Expression Omnibus database. RNA-seq data and clinical information on breast cancer from The Cancer Genome Atlas were used for validation. Four modules were identified from the gene coexpression network, one of which was found to be significantly associated with patient survival time. The expression status of 28 genes formed the black module (basal); 18 genes, dark red module (claudin-low); nine genes, brown module (luminal), and seven genes, midnight blue module (nonmalignant). These modules were clustered into two groups according to significant difference in survival time between the groups. Therefore, based on betweenness centrality, we identified TXN and ANXA2 in the nonmalignant module, TPM4 and LOXL2 in the luminal module, TPRN and ADCY6 in the claudin-low module, and TUBA1C and CMIP in the basal module as the genes with the highest betweenness, suggesting that they play a central role in information transfer in the network. In the present study, eight candidate biomarkers were identified for further basic and advanced understanding of the molecular pathogenesis of breast cancer by using co-expression network analysis.

scholarly journals Comprehensive Analysis of the Control of Cancer Stem Cell Characteristics in Endometrial Cancer by Network Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yun Liu ◽  
Peigen Chen ◽  
Mengxiong Li ◽  
Hui Fei ◽  
Jinfeng Huang ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Network Analysis ◽  
Immune Cell ◽  
Interaction Analysis ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Relationship Analysis ◽  
Gene Coexpression ◽  
Key Genes ◽  
Coexpression Network Analysis

Background. Cancer stem cells play an important role in endometrial cancer (EC). It is closely related to self-renewal and therapeutic resistance of EC. Methods. In this study, WGCNA (weighted gene coexpression network analysis) was used to analyze the relationship between genes and clinical features. We also performed immune cell infiltration analysis of a key module by using ImmuCellAI (Immune Cell Abundance Identifier). Then, key genes were verified in the GEO database. Finally, causal relationship analysis and protein-protein interaction analysis were performed in DisNor tool and STRING. Result. The mRNA expression-based stemness index (mRNAsi) is significantly lower in normal tissues and is significantly higher in individuals with stage IV or high-grade cancer and those who are obese or postmenopausal. Nineteen key genes (ORC6, C1orf112, RAD54L, SGO2, BUB1, PLK4, KIF18B, BUB1B, TTK, NCAPG, XRCC2, CENPF, KIF15, RACGAP1, ARHGAP11A, TPX2, KIF14, KIF4A, and NCAPH) that were enriched mainly in terms related to the cell cycle and DNA replication were selected by weighted gene coexpression network analysis (WGCNA). Based on the key modules, the numbers of NKT cells, NK cells, and neutrophils in the normal group were significantly higher than those in the cancer group. PLK1, CDK1, and MAD2L1, which were correlated with upstream genes, may be an regulated upstream of key genes. Conclusion. PLK1, CDK1, and MAD2L1 which were strongly correlated with upstream genes may be a regulated upstream of key genes.

scholarly journals Weighted Gene Coexpression Network Analysis in Mouse Livers following Ischemia-Reperfusion and Extensive Hepatectomy

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wei Liu ◽  
Yongquan Shi ◽  
Tao Cheng ◽  
Ruixue Jia ◽  
Ming-Zhong Sun ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Partial Hepatectomy ◽  
Ischemia Reperfusion ◽  
Expression Patterns ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Gene Coexpression ◽  
Extensive Hepatectomy ◽  
Coexpression Network Analysis

In mouse models, the recovery of liver volume is mainly mediated by the proliferation of hepatocytes after partial hepatectomy that is commonly accompanied with ischemia-reperfusion. The identification of differently expressed genes in liver following partial hepatectomy benefits the better understanding of the molecular mechanisms during liver regeneration (LR) with appliable clinical significance. Briefly, studying different gene expression patterns in liver tissues collected from the mice group that survived through extensive hepatectomy will be of huge critical importance in LR than those collected from the mice group that survived through appropriate hepatectomy. In this study, we performed the weighted gene coexpression network analysis (WGCNA) to address the central candidate genes and to construct the free-scale gene coexpression networks using the identified dynamic different expressive genes in liver specimens from the mice with 85% hepatectomy (20% for seven-day survial rate) and 50% hepatectomy (100% for seven-day survial rate under ischemia-reperfusion condition compared with the sham group control mice). The WGCNA combined with Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses pinpointed out the apparent distinguished importance of three gene expression modules: the blue module for apoptotic process, the turquoise module for lipid metabolism, and the green module for fatty acid metabolic process in LR following extensive hepatectomy. WGCNA analysis and protein-protein interaction (PPI) network construction highlighted FAM175B, OGT, and PDE3B were the potential three hub genes in the previously mentioned three modules. This work may help to provide new clues to the future fundamental study and treatment strategy for LR following liver injury and hepatectomy.

scholarly journals Identification of key genes and regulators associated with carotenoid metabolism in apricot (Prunus armeniaca) fruit using weighted gene coexpression network analysis

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Lina Zhang ◽  
Qiuyun Zhang ◽  
Wenhui Li ◽  
Shikui Zhang ◽  
Wanpeng Xi
Keyword(s):  
Network Analysis ◽  
Coexpression Network ◽  
Carotenoid Content ◽  
Pcr Analysis ◽  
Gene Coexpression Network ◽  
Gene Coexpression ◽  
Carotenoid Metabolism ◽  
Total Carotenoid Content ◽  
Key Genes ◽  
Coexpression Network Analysis

Abstract Background Carotenoids are a class of terpenoid pigments that contribute to the color and nutritional value of many fruits. Their biosynthetic pathways have been well established in a number of plant species; however, many details of the regulatory mechanism controlling carotenoid metabolism remain to be elucidated. Apricot is one of the most carotenoid-rich fruits, making it a valuable system for investigating carotenoid metabolism. The purpose of this study was to identify key genes and regulators associated with carotenoid metabolism in apricot fruit based on transcriptome sequencing. Results During fruit ripening in the apricot cultivar ‘Luntaixiaobaixing’ (LT), the total carotenoid content of the fruit decreased significantly, as did the levels of the carotenoids β-carotene, lutein and violaxanthin (p < 0.01). RNA sequencing (RNA-Seq) analysis of the fruit resulted in the identification of 44,754 unigenes and 6916 differentially expressed genes (DEGs) during ripening. Among these genes, 33,498 unigenes were annotated using public protein databases. Weighted gene coexpression network analysis (WGCNA) showed that two of the 13 identified modules (‘blue’ and ‘turquoise’) were highly correlated with carotenoid metabolism, and 33 structural genes from the carotenoid biosynthetic pathway were identified. Network visualization revealed 35 intramodular hub genes that putatively control carotenoid metabolism. The expression levels of these candidate genes were determined by quantitative real-time PCR analysis, which showed ripening-associated carotenoid accumulation. This analysis revealed that a range of genes (NCED1, CCD1/4, PIF3/4, HY5, ERF003/5/12, RAP2–12, AP2, AP2-like, BZR1, MADS14, NAC2/25, MYB1R1/44, GLK1/2 and WRKY6/31/69) potentially affect apricot carotenoid metabolism during ripening. Based on deciphering the molecular mechanism involved in ripening, a network model of carotenoid metabolism in apricot fruit was proposed. Conclusions Overall, our work provides new insights into the carotenoid metabolism of apricot and other species, which will facilitate future apricot functional studies and quality breeding through molecular design.

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