Coexpression network analysis identified Krüppel‐like factor 6 (<i>KLF6</i>) association with chemosensitivity in ovarian cancer

Chen, Ying; Bi, Fangfang; An, Yuanyuan; Yang, Qing

doi:10.1002/jcb.27567

Cited by 23 publications

(18 citation statements)

References 46 publications

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“…SE activity is associated with KLF6 expression in all OC histotypes, but the SE is only mutated in HGSOC (P = 8.2 × 10 −8 ). KLF6 is a Krüppel-like transcription factor with tumor suppressor functions, and is associated with chemoresponse and prognosis in OC patients 30,31 .…”

Section: Resultsmentioning

confidence: 99%

Non-coding somatic mutations converge on the PAX8 pathway in ovarian cancer

et al. 2020

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The functional consequences of somatic non-coding mutations in ovarian cancer (OC) are unknown. To identify regulatory elements (RE) and genes perturbed by acquired non-coding variants, here we establish epigenomic and transcriptomic landscapes of primary OCs using H3K27ac ChIP-seq and RNA-seq, and then integrate these with whole genome sequencing data from 232 OCs. We identify 25 frequently mutated regulatory elements, including an enhancer at 6p22.1 which associates with differential expression of ZSCAN16 (P = 6.6 × 10-4) and ZSCAN12 (P = 0.02). CRISPR/Cas9 knockout of this enhancer induces downregulation of both genes. Globally, there is an enrichment of single nucleotide variants in active binding sites for TEAD4 (P = 6 × 10-11) and its binding partner PAX8 (P = 2×10-10), a known lineagespecific transcription factor in OC. In addition, the collection of cis REs associated with PAX8 comprise the most frequently mutated set of enhancers in OC (P = 0.003). These data indicate that non-coding somatic mutations disrupt the PAX8 transcriptional network during OC development.

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Section: Resultsmentioning

confidence: 99%

Non-coding somatic mutations converge on the PAX8 pathway in ovarian cancer

et al. 2020

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“…DNA, RNA, etc.) play an important role in regulating biological functions, 9 network-based approaches, including WGCNA, have been widely used in kidney research and other biomedical studies [12][13][14]28 to capture altered molecular networks and pathways. Once driver modules or pathways are identified, it is crucial to pinpoint key genes for in-depth pathophysiological study and intervention target identification.…”

Section: Discussionmentioning

confidence: 99%

“…8 In addition, variable selection approaches may detect meaningful phenotype-genotype relationships. 11 Therefore, the combined use of WGCNA and variable selection may help to detect novel genes associated with a number of diseases [12][13][14] and construct predictive models.…”

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confidence: 99%

A network‐based variable selection approach for identification of modules and biomarker genes associated with end‐stage kidney disease

Zeng

et al. 2019

Nephrology

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Aims Intervention for end‐stage kidney disease (ESKD), which is associated with adverse prognoses and major economic burdens, is challenging due to its complex pathogenesis. The study was performed to identify biomarker genes and molecular mechanisms for ESKD by bioinformatics approach. Methods Using the Gene Expression Omnibus dataset GSE37171, this study identified pathways and genomic biomarkers associated with ESKD via a multi‐stage knowledge discovery process, including identification of modules of genes by weighted gene co‐expression network analysis, discovery of important involved pathways by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses, selection of differentially expressed genes by the empirical Bayes method, and screening biomarker genes by the least absolute shrinkage and selection operator (Lasso) logistic regression. The results were validated using GSE70528, an independent testing dataset. Results Three clinically important gene modules associated with ESKD, were identified by weighted gene co‐expression network analysis. Within these modules, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed important biological pathways involved in ESKD, including transforming growth factor‐β and Wnt signalling, RNA‐splicing, autophagy and chromatin and histone modification. Furthermore, Lasso logistic regression was conducted to identify five final genes, namely, CNOT8, MST4, PPP2CB, PCSK7 and RBBP4 that are differentially expressed and associated with ESKD. The accuracy of the final model in distinguishing the ESKD cases and controls was 96.8% and 91.7% in the training and validation datasets, respectively. Conclusion Network‐based variable selection approaches can identify biological pathways and biomarker genes associated with ESKD. The findings may inform more in‐depth follow‐up research and effective therapy.

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“…It helpsunravel the interactions between genes in different modulesand hence can be used foridenti cation of candidate biomarkers or therapeutic targets [18,19]. In addition,WGCNA links microarray data directly to clinical traits thus revealing mechanisms of drug resistance [20].Further, WGCNAis used for identi cation of factors for predicting pathological stage and prognosis of disease [21].…”

Section: Introductionmentioning

confidence: 99%

Hub Genes of Stroke Identified by Weighted Gene Co-expression Network Analysis

Zhai

et al. 2020

Preprint

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BACKGROUD: Microarray-based gene expression profiling is widely used in biomedical research. Weighted gene co-expression network analysis (WGCNA) links microarray data directly to clinical traits and identifies rules for predicting pathological stage and prognosis of disease.WGCNA is useful in understandingmany biological processes. Stroke is a common disease worldwide, however, molecular mechanisms of its pathogenesis are largely unknown. The aim of this study was to construct gene co-expression networks for identification of key modules and hub genes associated with stroke pathogenesis.METHODS: Gene microarray expression profiles of stroke samples were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened by the limma package in R software. WGCNA was used to construct free-scale gene co-expression networks to explore the associations between gene sets and clinical features, and to identify key modules and hub genes. Subsequently, functional enrichment analyses were performed. Further, receiver operating characteristic (ROC) curve analysis was carried out to validate expression of hub genes and literature validation was performed as well.RESULTS: A total of 11,747 most variant genes were used for co-expression network construction. Pink and yellow modules were significantly correlated to stroke pathogenesis. Functional enrichment analysis showed that the pink module was mainly involved in regulation of neuron regeneration, and repair of DNA damage.On the other hand, yellow module was mainly enriched in ion transport system dysfunction which was correlated with neuron death. A total of eight hub genes (PRR11, NEDD9, Notch2, RUNX1-IT1, ANP32A-IT1, ASTN2, SAMHD1 and STIM1) were identified and validated at transcriptional levels and through existing literature.CONCLUSION: The eight hub genes (PRR11, NEDD9, Notch2, RUNX1-IT1, ANP32A-IT1, ASTN2, SAMHD1 and STIM1) identified in the study are potentialbiomarkers and therapeutic targets for effective diagnosis and treatment of stroke.

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Coexpression network analysis identified Krüppel‐like factor 6 (KLF6) association with chemosensitivity in ovarian cancer

Cited by 23 publications

References 46 publications

Non-coding somatic mutations converge on the PAX8 pathway in ovarian cancer

Non-coding somatic mutations converge on the PAX8 pathway in ovarian cancer

A network‐based variable selection approach for identification of modules and biomarker genes associated with end‐stage kidney disease

Hub Genes of Stroke Identified by Weighted Gene Co-expression Network Analysis

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