Identification of key gene modules and pathways of human glioma through coexpression network

Shi, Tiantian; Jianchun, Chen; Li, Jing; Yang, Bingyin; Zhang, Qiao‐Lin

doi:10.1002/jcp.27059

Cited by 6 publications

(3 citation statements)

References 21 publications

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“…WGCNA was used to de ne modules, network nodes, and intramodular hubs to determine the relationship between co-expressed modules and to compare the topology of different networks to screen for signi cant trait genes associated with clinical traits [12]. Currently, WGCNA has been widely used to analyze genomics and metabolomics data, including microarray data, single-cell RNA-Seq data, DNA methylation data, and noncoding RNA data [13][14][15][16]. In this study, we explored differential genes in adipose and muscle tissues after fasting to reveal the potential biological alteration process of fasting.…”

Section: Introductionmentioning

confidence: 99%

Identification of significant modules and hub genes involved in fasting using WGCNA

Qiu

et al. 2022

Preprint

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Background: Dietary interventions are one of the most common health promotion tools. Regular intermittent fasting is thought to reduce body weight and ameliorate adverse cardiovascular disease factors significantly. However, there is growing evidence that fasting positively affects body composition and biochemical parameters, but very few studies related to its mechanisms. In this study, bioinformatics network analysis was performed to investigate the effects of fasting on adipose and muscle tissues and further explore the potential mechanisms and targets of action. Methods: We downloaded the adipose tissue and muscle tissue gene expression datasets before and after fasting from the Gene Expression Omnibus (GEO) database and constructed co-expression networks by Weighted correlation network analysis (WGCNA) to identify key modules. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the differential genes in adipose tissue and muscle tissue-related modules, respectively. Then, we constructed protein-protein interaction (PPI) networks using the STRING database and detected the central genes in the networks. Results: Functional enrichment analysis showed that AMPK pathway and neurodegenerative disease-related pathways might be involved in the regulation of fasting in humans. PPI network construction indicated that the regulation of fasting in humans, both in adipose and muscle tissues, may be associated with two central genes, TXNIP and DLAT, and that this regulation is likely to act on human metabolism. Conclusion: Our work indicates that a total of 15 key genes, including TXNIP, DLAT, PDK4, DDIT3, and PFKFB3, may receive regulation by fasting interventions, especially TXNIP and DLAT are the basis of fasting mechanisms in adipose and muscle tissues. The pathways regulated by these key genes may provide new targets for further studies on the mechanism of fasting and the treatment of metabolic diseases.

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Section: Introductionmentioning

confidence: 99%

Identification of significant modules and hub genes involved in fasting using WGCNA

Qiu

et al. 2022

Preprint

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“…In recent research, differentially expressed genes (DEGs) in tumor samples compared with normal samples can be identified using gene expression profiling arrays [9,10]. Some key molecules have also been reported in oligodendroglial tumor using bioinformatics analysis [11][12][13]. However, the number of the identified functional genes is far from sufficient to explain the mechanisms underlying the pathogenesis of oligodendroglial tumor.…”

Section: Introductionmentioning

confidence: 99%

Screening and Authentication of Key Genes and Prognostic Value Analysis in Oligodendroglial Tumors

Bao

Peng

2020

Preprint

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Background: Emerging evidence indicates that various functional genes with altered expression are involved in the human tumor progression. This study is aimed at identifying novel key genes that may be used for oligodendroglial tumor diagnosis, prognosis, and targeted therapy. Methods: This study included three expression proﬁles (GSE15824, GSE29796 and GSE108474) obtained from the Gene Expression Omnibus (GEO). GEO2R was used to analyze the diﬀerentially expressed genes (DEGs) between normal samples and oligodendroglial tumor, including oligodendroglioma and anaplastic oligodendroglioma. The functional and pathway enrichment analysis was performed by the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network of the identiﬁed DEGs was constructed using the Search Tool for the Retrieval of Interacting Gene, and hub genes were identiﬁed. ONCOMINE and The Cancer Genome Atlas (TCGA) databases were used to verify the expression of the hub genes in oligodendroglial tumor tissues and the hub genes on the overall survival of oligodendroglial tumor patients. Results: A total of 128 DEGs were identiﬁed from the three expression proﬁles. These DEGs were enriched with functional processes and pathways related to oligodendroglial tumor pathogenesis. From the PPI network, five hub genes were identiﬁed. The expression of the five hub genes was all upregulated in oligodendroglial tumor tissues compared with the control tissues. Kaplan-Meier survival curves indicated that high expression of cullin 3 (CUL3), cop9 signalosome subunit 8 (COPS8), cullin associated and neddylation dissociated 1 (CAND1), F-box protein 22 (FBXO22), and leucine rich repeat containing 41 (LRRC41) predicted poor overall survival in oligodendroglial tumor patients (all log-rank P < 0.01). Conclusions: These results revealed that the DEGs may serve as candidate key genes during oligodendroglial tumor pathogenesis. The five hub genes, including CUL3, COPS8, CAND1, FBXO22, and LRRC41, may serve as promising prognostic biomarkers in oligodendroglial tumor.

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“…[13][14][15] WGCNA is used to define modules, intramodular hubs, and network nodes with regard to module membership to determine the relationships between co-expression modules and compare the topology of different networks, thereby defining the significant eigengenes related to clinical traits. 14 WGCNA has been extensively applied for analyzing genomics and metabolomics data, including microarray data, 16,17 single-cell RNA-Seq data, 18 DNA methylation, data 19 and non-coding RNA data, 20,21 peptide counts, 22,23 and microbiota 16sRNA data, 24 and shown to be suitable for investigating integrated coexpression networks obtained with large-scale samples.…”

Section: Introductionmentioning

confidence: 99%

<p>Super-Enhancer-Associated Hub Genes In Chronic Myeloid Leukemia Identified Using Weighted Gene Co-Expression Network Analysis</p>

Ma¹,

Qu²,

Luo³

et al. 2019

CMAR

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Purpose: Super-enhancer (SE)-associated oncogenes extensively potentiate the uncontrolled proliferation capacity of cancer cells. In this study, we aimed to identify the SE-associated hub genes associated with the clinical characteristics of chronic myeloid leukemia (CML). Methods: Eigengenes from CML clinical modules were determined using weighted gene co-expression network analysis (WGCNA). Overlapping genes between eigengenes and SEassociated genes were used to construct protein-protein interaction (PPI) networks and annotate for pathway enrichment analysis. Expression patterns of the top-ranked SEassociated hub genes were further determined in CML patients and healthy controls via realtime PCR. After treatment of K562 cells with the BRD4 inhibitor, JQ1, for 24 hrs, mRNA and protein levels of SE-associated hub genes were evaluated using real-time PCR and Western blotting, respectively. H3K27ac, H3K4me1 and BRD4 ChIP-seq signal peaks were used to predict and identify SEs visualized by the Integrative Genomics Viewer. Results: The yellow module was significantly related to the status and pathological phase of CML. SE-associated hub candidate genes were mainly enriched in the cell cycle pathway. Based on the PPI networks of hub genes and the top rank of degree, five SE-associated genes were identified: specifically, BUB1, CENPO, KIF2C, ORC1, and RRM2. Elevated expression of these five genes was not only related to CML status and phase but also positively regulated by SE and suppressed by the BRD4 inhibitor, JQ1, in K562 cells. Strong signal peaks of H3K27ac, H3K4me1 and BRD4 ChIP-seq of the five genes were additionally observed close to the predicted SE regions. Conclusion: This is the first study to characterize SE-associated genes linked to clinical characteristics of CML via weighted gene co-expression network analysis. Our results support a novel mechanism involving aberrant expression of hub SE-associated genes in CML patients and K562 cells, and these genes will be potential new therapeutic targets for human leukemia.

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Identification of key gene modules and pathways of human glioma through coexpression network

Cited by 6 publications

References 21 publications

Identification of significant modules and hub genes involved in fasting using WGCNA

Identification of significant modules and hub genes involved in fasting using WGCNA

Screening and Authentication of Key Genes and Prognostic Value Analysis in Oligodendroglial Tumors

<p>Super-Enhancer-Associated Hub Genes In Chronic Myeloid Leukemia Identified Using Weighted Gene Co-Expression Network Analysis</p>

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