A two-dimensional multiwell cell culture method for the production of CYP3A4-expressing hepatocyte-like cells from HepaRG cells

Transgenic HepaRG cells expressing CYP2D6 as an improved model of primary human hepatocytes

CYP2D6 and CYP3A4, which are members of the cytochrome P450 superfamily of metabolic enzymes, play major roles in the metabolism of commonly available drugs. CYP3A4 is involved in the metabolism of 50% of drugs on the market, whereas CYP2D6 is involved in the metabolism of 25% of them. CYP2D6 exhibits a high degree of polymorphic nature in the human population, causing individual differences in CYP2D6 expression and enzymatic activity. Therefore, accurate prediction of drug metabolism and toxicity require a human adult hepatocyte cell model that mimics individual responses in the average population. HepaRG cells, a human hepatocellular carcinoma cell line, is the only cell line that can differentiate into hepatocyte-like cells with high expression of CYP3A4 but poor expression of CYP2D6. To solve this problem, we developed transgenic HepaRG cell clones expressing either full-length or spliced CYP2D6 at various levels with an easy monitoring system for CYP2D6 expression in living cells under a fluorescent microscope. As CYP2D6 mRNA, protein, and fluorescence intensity were closely correlated among transgenic HepaRG clones, fluorescence levels will provide a simple tool for quality assurance of CYP2D6-expressing HepaRG cells. Thus, the package of transgenic HepaRG cell clones expressing CYP2D6 at various levels will provide an improved hepatocyte model that reflects the average or individual reactions in the human population for in vitro studies of drug metabolism and toxicity involving CYP2D6 and CYP3A4.

Integrated Proteomics and Bioinformatics to Identify Potential Prognostic Biomarkers in Hepatocellular Carcinoma

BACKGROUND

Liver hepatocellular carcinoma (HCC) is the third most common cause of death by cancer and has a high mortality world-widely. Approximately 75-85% of primary liver cancers are caused by HCC. Uncovering novel genes with prognostic significance would shed light on improving the HCC patient's outcome.

OBJECTIVE

In this research, we aim to identify novel prognostic biomarkers in hepatocellular carcinoma.

METHODS

Integrated proteomics and bioinformatics analysis were performed to investigate the expression landscape of prognostic biomarkers in 24 paired HCC patients.

RESULTS

As a result, eight key genes related to prognosis, including ACADS, HSD17B13, PON3, AMDHD1, CYP2C8, CYP4A11, SLC27A5, CYP2E1, were identified by comparing the weighted gene co-expression network analysis (WGCNA), proteomic differentially expressed genes (DEGs), proteomic turquoise module, The Cancer Genome Atlas (TCGA) cohort DEGs of HCC. Furthermore, we trained and validated eight pivotal genes integrating these independent clinical variables into a nomogram with superior accuracy in predicting progression events, and their lower expression was associated with a higher stage/risk score. The Gene Set Enrichment Analysis (GSEA) further revealed that these key genes showed enrichment in the HCC regulatory pathway.

CONCLUSION

All in all, we found that these eight genes might be the novel potential prognostic biomarkers for HCC and also provide promising insights into the pathogenesis of HCC at the molecular level.