Identification of consensus biomarkers for predicting non-genotoxic hepatocarcinogens

Huang, Shan Han; Tung, Chun Wei

doi:10.1038/srep41176

Cited by 11 publications

(6 citation statements)

References 58 publications

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“…EnrichR is a prominent tool developed in the Ma'ayan lab for inferring knowledge about an input gene set by comparing it to annotated gene sets from over 160 libraries [26]. Decision trees have been extensively applied for the identification of biomarkers [27,28]. Decision trees are transparent and interpretable predictive models but they require considerable programming skills [29].…”

Section: Introductionmentioning

confidence: 99%

DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

Ghannoum

Netto

Fantini

et al. 2021

IJMS

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The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a user-friendly computational pipeline using Jupyter notebooks. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation dataset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations.

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Section: Introductionmentioning

confidence: 99%

DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

Ghannoum

Netto

Fantini

et al. 2021

IJMS

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“…F19 achieved a median AUC of 0.809 for all exposure styles; however, its IQR (0.085) and C.V.d (6.47%) were both much higher than the time-invariant biomarker set. Please note that the time-invariant biomarker set further improved the median AUC value by 9% compared to our previously published consensus biomarkers obtained from the 1-day exposure style (median AUC of 0.733) [16]. The results indicated that the time-invariant biomarkers can also be applied to the long-term exposure style and still provide good prediction.…”

Section: Time-invariant Biomarkers and Machine Learning Classifiersmentioning

confidence: 61%

“…Three common exposure styles of the referenced datasets, namely the 1-day, 3-day, and 1-week high-dose exposures, were considered for the identification of the time-invariant biomarkers. First, each consensus biomarker set was identified as the overlapped differential expressed genes (DEGs) based on a t-test (p < 0.05), and a 1.5-fold change [16], which were derived from each common exposure style of these datasets. Subsequently, each consensus biomarker set was cross-checked with the other two exposure styles.…”

Section: Identification Of the Time-invariant Biomarker Setsmentioning

confidence: 99%

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Identification of Time-Invariant Biomarkers for Non-Genotoxic Hepatocarcinogen Assessment

Huang

Lin

Tung

2020

IJERPH

Self Cite

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Non-genotoxic hepatocarcinogens (NGHCs) can only be confirmed by 2-year rodent studies. Toxicogenomics (TGx) approaches using gene expression profiles from short-term animal studies could enable early assessment of NGHCs. However, high variance in the modulation of the genes had been noted among exposure styles and datasets. Expanding from our previous strategy in identifying consensus biomarkers in multiple experiments, we aimed to identify time-invariant biomarkers for NGHCs in short-term exposure styles and validate their applicability to long-term exposure styles. In this study, nine time-invariant biomarkers, namely A2m, Akr7a3, Aqp7, Ca3, Cdc2a, Cdkn3, Cyp2c11, Ntf3, and Sds, were identified from four large-scale microarray datasets. Machine learning techniques were subsequently employed to assess the prediction performance of the biomarkers. The biomarker set along with the Random Forest models gave the highest median area under the receiver operating characteristic curve (AUC) of 0.824 and a low interquartile range (IQR) variance of 0.036 based on a leave-one-out cross-validation. The application of the models to the external validation datasets achieved high AUC values of greater than or equal to 0.857. Enrichment analysis of the biomarkers inferred the involvement of chronic inflammatory diseases such as liver cirrhosis, fibrosis, and hepatocellular carcinoma in NGHCs. The time-invariant biomarkers provided a robust alternative for NGHC prediction.

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“…Support vector machines (SVMs) have been shown to yield reliable and efficient classification performances, while limiting overfitting, particularly for cases where the number of features is higher than the number of samples (as often seen with toxicogenomic data). 29 Although a few isolated biomarkers have been used for genotoxicity detection in both environmental and human health applications, such as CYP1A1 and CYP1B1, 38 RAD54, 39 CYP-R, 38 A2m, Ca3, Cxcl1, and Cyp8b1, 40 their correlation with phenotypic genotoxicity endpoints or carcinogenicity has not been quantified. Furthermore, the temporal dependencies of toxicogenomics responses have also not been considered in most cases, since most studies record a snapshot of the responses.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-based Biomarkers Identification and Validation from Toxicogenomics - Bridging to Regulatory Relevant Phenotypic Endpoints

Rahman

Lan

Kaeli

et al. 2020

Preprint

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High-throughput in vitro assays and AOP-based approach is promising for the assessment of health and ecotoxicological risks from exposure to pollutants and their mixtures. However, one of the major challenges in realization and implementations of the Tox21 vision is the urgent need to establish quantitative link between in-vitro assay molecular endpoint and in-vivo phenotypic toxicity endpoint. Here, we demonstrated that, using time series toxicomics in-vitro assay along with machine learning-based feature selection (MRMR) and classification method (SVM), an “optimal” number of biomarkers with minimum redundancy can be identified for prediction of phenotypic endpoints with good accuracy. We included two case studies for in-vivo carcinogenicity and Ames genotoxicity prediction with 20 selected chemicals including model genotoxic chemicals and negative controls, respectively, using an in-vitro toxicogenomic assay that captures real-time proteomic response data of 38 GFP-fused proteins of S. cerevisiae strains covering biomarkers indicative of all known DNA damage and repair pathways in yeast. The results suggested that, employing the adverse outcome pathway (AOP) concept, molecular endpoints based on a relatively small number of properly selected biomarker-ensemble involved in the conserved DNA-damage and repair pathways among eukaryotes, were able to predict both in-vivo carcinogenicity in rats and Ames genotoxicity endpoints. The specific biomarkers identified are different for the two different phenotypic genotoxicity assays. The top-ranked five biomarkers for the in-vivo carcinogenicity prediction mainly focused on double strand break repair and DNA recombination, whereas the selected top-ranked biomarkers for Ames genotoxicity prediction are associated with base- and nucleotide-excision repair. Current toxicomics approach still mostly rely on large number of redundant markers without pre-selection or ranking, therefore, selection of relevant biomarkers with minimal redundancy would reduce the number of markers to be monitored and reduce the cost, time, and complexity of the toxicity screening and risk monitoring. The method developed in this study will help to fill in the knowledge gap in phenotypic anchoring and predictive toxicology, and contribute to the progress in the implementation of tox 21 vision for environmental and health applications.TOC Art

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Identification of consensus biomarkers for predicting non-genotoxic hepatocarcinogens

Cited by 11 publications

References 58 publications

DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

Identification of Time-Invariant Biomarkers for Non-Genotoxic Hepatocarcinogen Assessment

Machine Learning-based Biomarkers Identification and Validation from Toxicogenomics - Bridging to Regulatory Relevant Phenotypic Endpoints

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