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Background As a prodrug of 5-fluorouracil (5-FU), orally administrated capecitabine (CAP) undergoes preliminary conversion into active metabolites in the liver and then releases 5-FU in the gut to exert the anti-tumor activity. Since metabolic changes of CAP play a key role in its activation, a single kind of intestinal or hepatic cell can never be used in vitro to evaluate the pharmacokinetics (PK) and pharmacodynamics (PD) nature. Hence, we aimed to establish a novel in vitro system to effectively assess the PK and PD of these kinds of prodrugs. Methods Co-culture cellular models were established by simultaneously using colorectal cancer (CRC) and hepatocarcinoma cell lines in one system. Cell Counting Kit-8 (CCK-8) and flow cytometric analysis were used to evaluate cell viability and apoptosis, respectively. Apoptosis-related protein expression levels were measured using western blot analysis. A selective liquid chromatography-tandem mass spectrometry (LC–MS/MS) method was developed for cellular PK in co-culture models. Results CAP had little anti-proliferative effect on the five monolayer CRC cell lines (SW480, LoVo, HCT-8, HCT-116 and SW620) or the hepatocarcinoma cell line (HepG2). However, CAP exerted marked anti-tumor activities on each of the CRC cell lines in the co-culture models containing both CRC and hepatocarcinoma cell lines, although its effect on the five CRC cell lines varied. Moreover, after pre-incubation of CAP with HepG2 cells, the culture media containing the active metabolites of CAP also showed an anti-tumor effect on the five CRC cell lines, indicating the crucial role of hepatic cells in the activation of CAP. Conclusion The simple and cost‑effective co-culture models with both CRC and hepatocarcinoma cells could mimic the in vivo process of a prodrug dependent on metabolic conversion to active metabolites in the liver, providing a valuable strategy for evaluating the PK and PD characteristics of CAP-like prodrugs in vitro at the early stage of drug development.

Mounting evidence has revealed the key role of cancer stem cells in hepatocellular carcinoma (HCC) metastasis and therapy resistance, yet the genes maintaining HCC stem cell features remain to be explored. This study aimed to identify and validate the key biomarkers associated with HCC stemness. mRNA expression-based stemness index (mRNAsi) was calculating using one-class logistic regression algorithm. RNA-sequencing data and clinical information of HCC samples were downloaded from the cancer genome atlas (TCGA) and merged with the corresponding mRNAsi. We investigated the correlation between mRNAsi and HCC clinical characteristics, including tumor grades, pathologic stages, vascular invasion, and survival outcomes. Significant genes associated HCC stemness features were screened through weighted gene co-expression network analysis (WGCNA) and were functionally annotated using enrichment analysis. Protein-protein interaction network was constructed among significant genes and the key biomarkers were finally identified based on the maximal clique centrality (MCC) method. The expression of key biomarkers and its correlation with HCC clinical outcomes were validated using oncomine and gene expression omnibus (GEO) database. mR-NAsi was significantly higher in HCC tissues and gradually increased according to tumor grades and pathologic stages. Patients with vascular invasion or poor survival exhibited higher mRNAsi. Forty-four highly-correlated significant gens were screened through WGCNA and functionally related to cell cycle, cellular senescence, p53 signaling pathway, DNA replication, and mismatch repair. Four different GEO datasets confirmed that the expression levels of these 44 genes were notably higher in HCC tissues. We finally identified 15 key biomarkers (KIF4A, TTK, CCNB1, CDC20, NCAPG, CCNB2, CDC45, UBE2C, CENPA, AURKB, RRM2, CDCA8, BIRC5, TPX2, and KIF2C) through MCC method. The expression of these biomarkers was up-regulated in multiple types of cancers and showed a gradually increasing trend with HCC tumor grades. Furthermore, high expression levels of these biomarkers were also correlated with HCC metastasis, recurrence, sorafenib resistance, and poor overall survival. We identified 15 key biomarkers associated with HCC stemness features and these genes might serve as promising therapeutic targets for HCC.