Associating transcriptional modules with colon cancer survival through weighted gene co-expression network analysis

Liu, Rong; Zhang, Wei; Liu, Zhao‐Qian; Zhou, Hong‐Hao

doi:10.1186/s12864-017-3761-z

Cited by 53 publications

(49 citation statements)

References 34 publications

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“…We then confirmed our findings in four independently validating datasets. WGCNA has many obvious advantages over other methods, because the analysis focus on the relationship between co-expression gene modules and clinical outcomes, and the results have much higher dependability and biological significance [22], moreover, the consistency among all of samples could be maintained by this method [6]. The genes that are related in function were assigned into the same module.…”

Section: Discussionmentioning

confidence: 99%

“…The hub genes were identified through GS together k.in. In our study, hub genes were selected by the following norm: (1) top 15 genes with the largest k.in in the module they belonging and (2) GS is larger than 2 (the p value of the association test < 0.01) [6].…”

Section: Hub Genes Definitionmentioning

confidence: 99%

“…The genes, with a consistent expression profile and concordant biological role, were assigned to the same module [5]. WGCNA also has been used to detect co-expression gene modules and biomarkers, which usually be applied to cancer diagnosis [6][7][8][9], prognosis [10] and treatment [11,12]. Similarly, it has been successfully applied in other diseases to identify the genetic determinants, such as osteoporosis [13,14], obesity [14] and neurodegenerative disease [15].…”

Section: Introductionmentioning

confidence: 99%

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Identification of gene modules associated with survival of diffuse large B-cell lymphoma treated with CHOP-based chemotherapy

Gao¹,

Sun²,

Hu³

et al. 2020

Pharmacogenomics J

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Diffuse Large B-cell Lymphoma (DLBCL), a heterogeneous disease, is influenced by complex network of gene interactions. Most previous studies focused on individual genes, but ignored the importance of intergenic correlations. In current study, we aimed to explore the association between gene networks and overall survival (OS) of DLBCL patients treated with CHOP-based chemotherapy (cyclophosphamide combination with doxorubicin, vincristine and prednisone). Weighted gene co-expression network analysis was conducted to obtain insights into the molecular characteristics of DLBCL. Ten coexpression gene networks (modules) were identified in training dataset (n = 470), and their associations with patients' OS after chemotherapy were tested. The results were validated in four independent datasets (n = 802). Gene ontology (GO) biological function enrichment analysis was conducted with Metascape. Three modules (purple, brown and red), which were enriched in T-cell immune, cell-cell adhesion and extracellular matrix (ECM), respectively, were found to be related to longer OS. Higher expression of several hub genes within these three co-expression modules, for example, LCP2 (HR = 0.77, p = 5.40 × 10 −2 ), CD2 (HR = 0.87, p = 6.31 × 10 −2 ), CD3D (HR = 0.83, p = 6.94 × 10 −3 ), FYB (HR = 0.82, p = 1.40 × 10 −2 ), GZMK (HR = 0.92, p = 1.19 × 10 −1 ), FN1 (HR = 0.88, p = 7.06 × 10 −2 ), SPARC (HR = 0.82, p = 2.06 × 10 −2 ), were found to be associated with favourable survival. Moreover, the associations of the modules and hub genes with OS in different molecular subtypes and different chemotherapy groups were also revealed. In general, our research revealed the key gene modules and several hub genes were upregulated correlated with good survival of DLBCL patients, which might provide potential therapeutic targets for future clinical research.

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Section: Discussionmentioning

confidence: 99%

Section: Hub Genes Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identification of gene modules associated with survival of diffuse large B-cell lymphoma treated with CHOP-based chemotherapy

Gao¹,

Sun²,

Hu³

et al. 2020

Pharmacogenomics J

Self Cite

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“…The 871 strain is susceptible to BmNPV infection, and the 871C strain is highly resistant. Here, we identified antiviral genes via weighted gene co‐expression network analysis (WGCNA), which has been used widely in disease research and to identify trait correlation genes (Chen et al ., ; Deist et al ., ; Huang et al ., ; Liu et al ., ; Wang et al ., ; ; Liu et al ., ). We identified a total of 2899 DEGs, including genes potentially related to BmNPV resistance in the 871C strains.…”

Section: Introductionmentioning

confidence: 99%

Specific genes related to nucleopolyhedrovirus in Bombyx mori susceptible and near‐isogenic resistant strains through comparative transcriptome analysis

Chen

Tan

et al. 2019

Insect Molecular Biology

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Bombyx mori nucleopolyhedrovirus (BmNPV) is one of the primary pathogens that causes severe economic losses to sericulture. Comparative transcriptomics analysis has been widely applied to explore the antiviral mechanism in resistant strains. Here, to identify genes involved in BmNPV infection, we identified differentially expressed genes (DEGs) and performed weighted gene co‐expression network analysis (WGCNA) between two Bombyx mori strains: strain 871 (susceptible to BmNPV infection) and the near‐isogenic strain 871C (resistant to BmNPV). Our results showed that 400 genes were associated with resistance in strain 871C, and 76 genes were related to susceptibility in strain 871. In addition, the correlation analysis of DEGs and WGCNA showed that 40 genes related to resistance were highly expressed in the resistant strain. Among them, gene BGIBMGA004291 was the most noticeable. We further identified the effect of gene BGIBMGA004291, which encoded a multiprotein bridge factor 2 (MBF2) family member (MBF2‐10), on viral infection in cells. Our data suggested that MBF2‐10 inhibited viral infection. Taken together, this study showed specific module trait correlations related to viral infection in strains 871 and 871C, and we identified a resistance‐related gene. These findings suggested promising candidate genes with antiviral activity, aiding in the analysis of the antiviral molecular mechanisms in resistant strains.

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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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Associating transcriptional modules with colon cancer survival through weighted gene co-expression network analysis

Cited by 53 publications

References 34 publications

Identification of gene modules associated with survival of diffuse large B-cell lymphoma treated with CHOP-based chemotherapy

Identification of gene modules associated with survival of diffuse large B-cell lymphoma treated with CHOP-based chemotherapy

Specific genes related to nucleopolyhedrovirus in Bombyx mori susceptible and near‐isogenic resistant strains through comparative transcriptome analysis

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

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