Hepatocellular carcinoma (HCC) is the global leading cause of cancer-related deaths due to the deficiency of targets for precision therapy. A new modality of epigenetic regulation has emerged involving RNA–RNA crosstalk networks where two or more competing endogenous RNAs (ceRNAs) bind to the same microRNAs. However, the contribution of such mechanisms in HCC has not been well studied. Herein, potential HMGB1-driven RNA–RNA crosstalk networks were evaluated at different HCC stages, identifying the mTORC2 component RICTOR as a potential HMGB1 ceRNA in HBV+ early stage HCC. Indeed, elevated HMGB1 mRNA was found to promote the expression of RICTOR mRNA through competitively binding with the miR-200 family, especially miR-429. Functional assays employing overexpression or interference strategies demonstrated that the HMGB1 and RICTOR 3′untranslated regions (UTR) epigenetically promoted the malignant proliferation, self-renewal, and tumorigenesis in HCC cells. Intriguingly, interference against HMGB1 and RICTOR in HCC cells promoted a stronger anti-PD-L1 immunotherapy response, which appeared to associate with the production of PD-L1+ exosomes. Mechanistically, the HMGB1-driven RNA-RNA crosstalk network facilitated HCC cell glutamine metabolism via dual mechanisms, activating a positive feedback loop involving mTORC2-AKT-C-MYC to upregulate glutamine synthetase (GS) expression, and inducing mTORC1 signaling to derepress SIRT4 on glutamate dehydrogenase (GDH). Meanwhile, this crosstalk network could impede the efficacy of immunotherapy through mTORC1-P70S6K dependent PD-L1 production and PD-L1+ exosomes activity. In conclusion, our study highlights the non-coding regulatory role of HMGB1 with implications for RNA-based therapeutic targeting together with a prediction of anti-PD-L1 immunotherapy in HCC.

Hepatocellular carcinoma (HCC) is a fatal disease with increasing morbidity and poor prognosis due to surgical recurrence and metastasis. Moreover, the molecular mechanism of HCC progression remains unclear. Although the role of p120-catenin (p120ctn) in liver cancer is well studied, the effects of secreted p120ctn transported by exosomes are less understood. Here, we show that p120ctn in exosomes secreted from liver cancer cells suppresses HCC cell proliferation and metastasis and expansion of liver cancer stem cells (CSCs). Mechanically, exosome p120ctn inhibits HCC cell progression via the STAT3 pathway, and the STAT3 inhibitor S3I-201 abolishes the observed effects on growth, metastasis, and self-renewal ability between exosome p120ctn-treated HCC cells and control cells. Taken together, we propose that p120ctn-containing exosomes derived from cancer cells inhibit the progression of liver cancer and may offer a new therapeutic strategy. K E Y W O R D S exosome, hepatocellular carcinoma, p120-catenin, STAT3 Molecular Carcinogenesis. 2019;58:1389-1399.wileyonlinelibrary.com/journal/mc

Objective: To validate and compare the predictive ability of albumin-bilirubin model (ALBI) with other 5 liver functional reserve models (APRI, FIB4, MELD, PALBI, King's score) for posthepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC) who underwent major hepatectomy. Methods: Data of patients undergoing major hepatectomy for HCC from 4 hospitals between January 01, 2008 and December 31, 2019 were retrospectively analyzed. PHLF was evaluated according to the definition of the 50-50 criteria. Performances of six liver functional reserve models were determined by the area under the receiver operating characteristic curve (AUC), calibration plot and decision curve analysis. Results: A total of 745 patients with 103 (13.8%) experienced PHLF were finally included in this study. Among six liver functional reserve models, ALBI showed the highest AUC (0.64, 95% CI: 0.58-0.69) for PHLF. All models showed good calibration and greater net benefit than treating all patients at a limit range of threshold probabilities, but the ALBI demonstrated net benefit across the largest range of threshold probabilities. Subgroup analysis also showed ALBI had good predictive performance in cirrhotic (AUC=0.63) or non-cirrhotic (AUC=0.62) patients. Conclusion: Among the six models, the ALBI model shows more accurate predictive ability for PHLF in HCC patients undergoing major hepatectomy, regardless of having cirrhosis or not.

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