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

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.

Integration of Epigenetic Mechanisms into Non-Genotoxic Carcinogenicity Hazard Assessment: Focus on DNA Methylation and Histone Modifications

“…A transcriptomic biomarker (or signature) is a set of transcripts induced by a specific event that can be useful in chemical hazard assessment, as well as clinical diagnostic, prognostic, and therapeutic purposes [427,428]. Transcriptomic analyses are being improved to identify signatures associated with specific chemical classes and modes of action and may eventually assist in the identification of NGTxC using in vitro [429], and short-term in vivo rodent data [430]. Transcriptomic approaches [431] are data rich, and they offer the possibility of using the same sample to assess multiple signatures in parallel in a screening strategy.…”

“…Efforts for the identification of signatures of NGTxC based on rodent short-term in vivo experiments using genes with less dynamic expression changes are also progressing. Using machine learning and publicly available hepatic datasets Huang et al [430] exposed male Sprague Dawley rats to chemicals for 1 to 28 days and identified a signature of 9 genes (upregulation of Akr7a3, Aqp7, Cdc2a, and Cdkn3, and downregulation of A2m, Ca3, Cyp2c11, Ntf3, and Sds) for which expression levels remain unaltered through time and that can be measured in 3-day exposure experiments to predict NGTxC and other hepatic diseases. By combining gene expression and DNAm analyses, Ito et al [436] identified four genes (Ldlrad4, Proc, Cdh17, and Nfia) silenced and hypermethylated after 28 days of exposure to non-genotoxic hepatocarcinogens (carbon tetrachloride, thioacetamide, methapyrilene hydrochloride) in rats, that remained silenced until the end of the 90-day experiment.…”

Development of a Prognostic Signature Based on Single-Cell RNA Sequencing Data of Immune Cells in Intrahepatic Cholangiocarcinoma

“…CEBPB is associated with the development of various tumors, including osteosarcoma, gastric cancer, HCC, glioblastoma, and human acute myeloid leukemia (Lu et al, 2019 ; Jinesh et al, 2020 ). A2M was identified as a key gene associated with various tumors, including non–small cell lung cancer, bladder cancer, osteosarcoma, and HCC (Ma et al, 2019 ; Huang et al, 2020 ). TUBA1B was related to breast cancer, HCC, and Wilms tumor (Lou et al, 2020 ; Tian et al, 2020 ).…”

Big Data, Decision Models, and Public Health

“…This theme investigates the potential risks and benefits associated with disease control [ 33 , 34 , 35 , 36 , 37 ]. Data mining is a technique that allows for the analysis of large data sets, enabling the identification of previously unknown relationships and new structures.…”