Biological features, gene expression profile, and mechanisms of drug resistance of two- and three-dimensional hepatocellular carcinoma cell cultures

Abnormal expression of serum miR-4746-5p in liver cancer patients after interventional chemotherapy and its possible mechanism

Interventional chemotherapy is a common operation in the clinical treatment of liver cancer. The aim of this study was to investigate the expression and molecular mechanism of serum miR-4746-5p in liver cancer patients before and after interventional chemotherapy. The levels of miR-4746-5p and CDKN1C in serum samples from liver cancer patients were detected using real-time fluorescence quantitative polymerase chain reaction. Receiver operating characteristic curves revealed the diagnostic value of miR-4746-5p in tumors. Differences in clinical indicators between liver cancer patients and healthy controls were assessed using Pearson correlation analysis. Luciferase reporter gene assays confirmed the targeted interaction between miR-4746-5p and CDKN1C. In vitro cellular assays were validated by Cell Counting Kit-8, Transwell assay, and chemoresistance assay. Serum miR-4746-5p levels were increased in liver cancer patients but were downregulated after chemotherapy intervention. CDKN1C expression showed the opposite trend. Low levels of miR-4746-5p mediated cell growth and metastasis by targeting and negatively regulating CDKN1C expression, while silencing CDKN1C restored cell activity. Inhibition of miR-4746-5p reduced chemoresistance, while downregulation of CDKN1C affected cell sensitivity. miR-4746-5p may be a potential therapeutic factor for liver cancer diagnosis and interventional chemotherapy.

A programmed cell death-related gene signature to predict prognosis and therapeutic responses in liver hepatocellular carcinoma

BACKGROUND

Programmed cell death (PCD) functions critically in cancers and PCD-related genes are associated with tumor microenvironment (TME), prognosis and therapeutic responses of cancer patients. This study stratified hepatocellular carcinoma (HCC) patients and develop a prognostic model for predicting prognosis and therapeutic responses.

METHODS

Consensus clustering analysis was performed to subtype HCC patients in The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) among the subtypes were filtered and subjected to the least absolute shrinkage and selection operator (LASSO) regression analysis and univariate Cox regression analysis to filter prognostic genes. A PCD-related prognostic gene signature in TCGA was constructed and validated in ICGC-LIRI-JP and GSE14520 datasets. TME was analyzed using CIBERSORT, MCP-counter, TIMER and EPIC algorithms. Drug sensitivity was predicted by oncoPredict package. Spearman analysis was used to detect correlation.

RESULTS

Four molecular subtypes were categorized based on PCD-related genes. Subtype C1 showed the poorest prognosis, the most infiltration of Fibroblasts, dentritic cell (DC) and cancer-associated fibroblasts (CAFs), and the highest TIDE score. C4 had a better prognosis survival outcome, and lowest immune cell infiltration. The survival outcomes of C2 and C3 were intermediate. Next, a total of 69 co-DEGs were screened among the four subtypes and subsequently we identified five prognostic genes (MCM2, SPP1, S100A9, MSC and EPO) for developing the prognostic model. High-risk patients not only had unfavorable prognosis, higher clinical stage and grade, and more inflammatory pathway enrichment, but also possessed higher possibility of immune escape and were more sensitive to Cisplatin and 5. Fluorouracil. The robustness of the prognostic model was validated in external datasets.

CONCLUSION

This study provides new insights into clinical subtyping and the PCD-related prognostic signature may serve as a useful tool to predict prognosis and guide treatments for patients with HCC.

A 3D Biomimetic System for Testing Anticancer Drug Sensitivity

3D cultures of cancer cells enable better mimicking of physiological conditions compared to traditional monolayer 2D cultures. Here we describe alginate scaffold-based model that can be used in both static and biomimetic conditions for studying drug sensitivity in cancer cells and multidrug resistance (MDR) mechanisms. This 3D culture model resembles in vivo conditions and provides relevant and reproducible results. It is easy to set up and allows for facile manipulation for downstream analyses. All these remarkable features make this 3D culture model a promising tool in drug discovery and cancer cell biology research.