scholarly journals Weighted Gene Coexpression Network Analysis of Key Genes Controlling Cancer Stem Cell Characteristics by Mrnasi in Colon Adenocarcinoma

2021 ◽  
Author(s):  
Jie Zhang ◽  
Honghao Yin ◽  
Zhongsheng Tong
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Stem Cells ◽  
Network Analysis ◽  
Signaling Pathway ◽  
Colon Adenocarcinoma ◽  
Gene Coexpression Network ◽  
Gene Coexpression ◽  
Key Genes ◽  
Coexpression Network Analysis

Abstract Background: Cancer stem cells are hidden tumor cells with the potential for self-renewal and multi-differentiation, which are the principal cause of tumor heterogeneity, metastasis and drug resistance. However, the key genes that maintain cell stemness of colon adenocarcinoma (COAD) are still unknown. Our study was designed to identify key genes associated with the stemness of COAD cell. Methods: RNA sequencing data and clinical information were downloaded from The Cancer Genome Atlas (TCGA). Weighted gene coexpression network analysis (WGCNA) was used to recognize key genes based on mRNAsi. Next, the gene expression data downloaded from Gene Expression Omnibus (GEO), Oncomine, and Gene Expression Profiling Integrative Analysis (GEPIA) were used to demonstrate the expression of key genes.Results: The mRNAsi score of COAD tissue was obviously superior than that of normal tissue. N stages, M stages and overall survival were highly related to mRNAsi. WGCNA showed that the green module was positively related to mRNAsi. We obtained 27 genes closely related to each other at the transcriptional and protein levels. The main biological functions of key genes were organelle fission, chromosomal region, and protein serine/threonine kinase activity. The enriched signaling pathway were cell cycle, progesterone−mediated oocyte maturation, human T−cell leukemia virus 1 infection, oocyte meiosis, DNA replication, cellular senescence, viral carcinogenesis, and p53 signaling pathway. The key genes were highly expressed in COAD and various cancers, which was validated multiple databases. Conclusions: 27 key genes were well correlated with maintaining stemness of COAD, which were significantly related to cell cycle events. The key genes may be therapeutic targets for inhibiting the properties of COAD stem cells.

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 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 Characteristic Genes in Whole Blood of Intervertebral Disc Degeneration Patients by Weighted Gene Coexpression Network Analysis (WGCNA)

2022 ◽  
Vol 2022 ◽  
pp. 1-17
Author(s):  
Xiaobo Ma ◽  
Junqiang Su ◽  
Bo Wang ◽  
Xiasheng Jin
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Intervertebral Disc ◽  
Disc Degeneration ◽  
Intervertebral Disc Degeneration ◽  
Regulatory Mechanisms ◽  
Gene Coexpression Network ◽  
Cerna Network ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

Intervertebral disc degeneration (IDD) is a major cause of lower back pain. However, to date, the molecular mechanism of the IDD remains unclear. Gene expression profiles and clinical traits were downloaded from the Gene Expression Omnibus (GEO) database. Firstly, weighted gene coexpression network analysis (WGCNA) was used to screen IDD-related genes. Moreover, least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine (SVM) algorithms were used to identify characteristic genes. Furthermore, we further investigated the immune landscape by the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm and the correlations between key characteristic genes and infiltrating immune cells. Finally, a competing endogenous RNA (ceRNA) network was established to show the regulatory mechanisms of characteristic genes. A total of 2458 genes were identified by WGCNA, and 48 of them were disordered. After overlapping the genes obtained by LASSO and SVM-RFE algorithms, genes including LINC01347, ASAP1-IT1, lnc-SEPT7L-1, B3GNT8, CHRNB3, CLEC4F, LOC102724000, SERINC2, and LOC102723649 were identified as characteristic genes of IDD. Moreover, differential analysis further identified ASAP1-IT1 and SERINC2 as key characteristic genes. Furthermore, we found that the expression of both ASAP1-IT1 and SERINC2 was related to the proportions of T cells gamma delta and Neutrophils. Finally, a ceRNA network was established to show the regulatory mechanisms of ASAP1-IT1 and SERINC2. In conclusion, the present study identified ASAP1-IT1 and SERINC2 as the key characteristic genes of IDD through integrative bioinformatic analyses, which may contribute to the diagnosis and treatment of IDD.

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.

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