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

Shan-Han Huang, Ying-Chi Lin and Chun-Wei Tung
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Shan-Han Huang: Ph. D. Program in Toxicology, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
Ying-Chi Lin: Ph. D. Program in Toxicology, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
Chun-Wei Tung: Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei 11031, Taiwan

IJERPH, 2020, vol. 17, issue 12, 1-14

Abstract: 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.

Keywords: time-invariant biomarkers; non-genotoxic hepatocarcinogens; toxicogenomics; machine learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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