Weighted Gene Correlation Network Analysis (WGCNA) Reveals Novel Transcription Factors Associated With Bisphenol A Dose-Response

Maertens, Alexandra; Tran, Vy; Kleensang, André; Hartung, Thomas

doi:10.3389/fgene.2018.00508

Cited by 37 publications

(41 citation statements)

References 43 publications

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“…In this study, we used the weighted gene co-expression network analysis (WGCNA) to construct a weighted gene co-expression network in LrGG samples from the TCGA database. Due to several advantages such as reflecting the continuous nature of the underlying co-expression information, providing systems-level insights, and high sensitivity to low abundance and small fold-changes in genes without any information loss, WGCNA has been widely applied in various cancers (Pei et al, 2017; Maertens et al, 2018;Wan et al, 2018). Through principal components analysis, we initially screened out the co-expression modules that significantly correlated with the immune score and stromal score as calculated by the ESTIMATE algorithm.…”

Section: Discussionmentioning

confidence: 99%

Identification of a Tumor Microenvironment-Related Eight-Gene Signature for Predicting Prognosis in Lower-Grade Gliomas

Long

et al. 2019

Front. Genet.

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Lower-grade gliomas (LrGG), characterized by invasiveness and heterogeneity, remain incurable with current therapies. Clinicopathological features provide insufficient information to guide optimal individual treatment and cannot predict prognosis completely. Recently, an increasing amount of studies indicate that the tumor microenvironment plays a pivotal role in tumor malignancy and treatment responses. However, studies focusing on the tumor microenvironment (TME) of LrGG are still limited. In this study, taking advantage of the currently popular computational methods for estimating the infiltration of tumor-associated normal cells in tumor samples and using weighted gene co-expression network analysis, we screened the co-expressed gene modules associated with the TME and further identified the prognostic hub genes in these modules. Furthermore, eight prognostic hub genes (ARHGDIB, CLIC1, OAS3, PDIA4, PARP9, STAT1, TAP2, and TAGLN2) were selected to construct a prognostic risk score model using the least absolute shrinkage and selection operator method. Univariate and multivariate Cox regression analysis demonstrated that the risk score was an independent prognostic factor for LrGG. Moreover, time-dependent ROC curves indicated that our model had favorable efficiency in predicting both short- and long-term prognosis in LrGG patients, and the stratified survival analysis demonstrated that our model had prognostic value for different subgroups of LrGG patients. Additionally, our model had potential value for predicting the sensitivity of LrGG patients to radio- and chemotherapy. Besides, differential expression analysis showed that the eight genes were aberrantly expressed in LrGG compared to normal brain tissue. Correlation analysis revealed that the expression of the eight genes was significantly associated with the infiltration levels of six types of immune cells in LrGG. In summary, the TME-related eight-gene signature was significantly associated with the prognosis of LrGG patients. They might act as potential prognostic biomarkers for LrGG patients, offer better stratification for future clinical trials, and be candidate targets for immunotherapy, thus deserving further investigation.

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Section: Discussionmentioning

confidence: 99%

Identification of a Tumor Microenvironment-Related Eight-Gene Signature for Predicting Prognosis in Lower-Grade Gliomas

Long

et al. 2019

Front. Genet.

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“…By hierarchical clustering and dynamic tree cutting (Langfelder et al, 2007), genes clustered into distinct modules were assigned to a color. The hybrid dynamic tree cutting method was used to cut branches using a minimum module size of 30, which is the default and commonly used value (Maertens et al, 2018). Furthermore, module eigengene (ME) (Zhao et al, 2010), representing the module expressions of each module, was calculated by the first principal component of the expression matrix.…”

Section: Weighted Gene Coexpression Network Analysismentioning

confidence: 99%

Muscle Transcriptome Analysis Reveals Potential Candidate Genes and Pathways Affecting Intramuscular Fat Content in Pigs

Zhao

Lin

et al. 2020

Front. Genet.

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Intramuscular fat (IMF) content plays an essential role in meat quality. For identifying potential candidate genes and pathways regulating IMF content, the IMF content and the longissimus dorsi transcriptomes of 28 purebred Duroc pigs were measured. As a result, the transcriptome analysis of four high-and four low-IMF individuals revealed a total of 309 differentially expressed genes (DEGs) using edgeR and DESeq2 (p < 0.05, |log 2 (fold change)| ≥ 1). Functional enrichment analysis of the DEGs revealed 19 hub genes significantly enriched in the Gene Ontology (GO) terms and pathways (q < 0.05) related to lipid metabolism and fat cell differentiation. The weighted gene coexpression network analysis (WGCNA) of the 28 pigs identified the most relevant module with 43 hub genes. The combined results of DEGs, WGCNA, and proteinprotein interactions revealed ADIPOQ, PPARG, LIPE, CIDEC, PLIN1, CIDEA, and FABP4 to be potential candidate genes affecting IMF. Furthermore, the regulation of lipolysis in adipocytes and the peroxisome proliferator-activated receptor (PPAR) signaling pathway were significantly enriched for both the DEGs and genes in the most relevant module. Some DEGs and pathways detected in our study play essential roles and are potential candidate genes and pathways that affect IMF content in pigs. This study provides crucial information for understanding the molecular mechanism of IMF content and would be helpful in improving pork quality.

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“…Finally, the project defined new AOPs to seamlessly integrate data generated by alternative methods or in vivo testing in a mechanistic and quantitative manner (Maertens et al 2018 ; Terron et al 2018 ; Zgheib et al 2019 ).…”

Section: The Repository Of Nams Available For Nam-enhanced Raxmentioning

confidence: 99%

Setting the stage for next-generation risk assessment with non-animal approaches: the EU-ToxRisk project experience

et al. 2020

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In 2016, the European Commission launched the EU-ToxRisk research project to develop and promote animal-free approaches in toxicology. The 36 partners of this consortium used in vitro and in silico methods in the context of case studies (CSs). These CSs included both compounds with a highly defined target (e.g. mitochondrial respiratory chain inhibitors) as well as compounds with poorly defined molecular initiation events (e.g. short-chain branched carboxylic acids). The initial project focus was on developing a science-based strategy for read-across (RAx) as an animal-free approach in chemical risk assessment. Moreover, seamless incorporation of new approach method (NAM) data into this process (= NAM-enhanced RAx) was explored. Here, the EU-ToxRisk consortium has collated its scientific and regulatory learnings from this particular project objective. For all CSs, a mechanistic hypothesis (in the form of an adverse outcome pathway) guided the safety evaluation. ADME data were generated from NAMs and used for comprehensive physiological-based kinetic modelling. Quality assurance and data management were optimized in parallel. Scientific and Regulatory Advisory Boards played a vital role in assessing the practical applicability of the new approaches. In a next step, external stakeholders evaluated the usefulness of NAMs in the context of RAx CSs for regulatory acceptance. For instance, the CSs were included in the OECD CS portfolio for the Integrated Approach to Testing and Assessment project. Feedback from regulators and other stakeholders was collected at several stages. Future chemical safety science projects can draw from this experience to implement systems toxicology-guided, animal-free next-generation risk assessment.

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Weighted Gene Correlation Network Analysis (WGCNA) Reveals Novel Transcription Factors Associated With Bisphenol A Dose-Response

Cited by 37 publications

References 43 publications

Identification of a Tumor Microenvironment-Related Eight-Gene Signature for Predicting Prognosis in Lower-Grade Gliomas

Identification of a Tumor Microenvironment-Related Eight-Gene Signature for Predicting Prognosis in Lower-Grade Gliomas

Muscle Transcriptome Analysis Reveals Potential Candidate Genes and Pathways Affecting Intramuscular Fat Content in Pigs

Setting the stage for next-generation risk assessment with non-animal approaches: the EU-ToxRisk project experience

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