Purpose: Post-hepatectomy liver failure (PHLF) is a severe complication in hepatocellular carcinoma (HCC) patients who have undergone hepatectomy. This research aimed to investigate the combination of albumin–bilirubin (ALBI) score and aspartate aminotransferase-platelet ratio index (APRI) as a novel approach in predicting PHLF risk in hepatitis B virus (HBV)-related HCC patients. Patients and methods: HBV-related HCC patients who underwent hepatectomy from January 2006 to October 2013 were enrolled in this study. A novel model was constructed using a combination of ALBI and APRI scores to predict PHLF risk, and the prognostic value of the model was evaluated and compared with Child-Pugh (C-P) grade, ALBI score and APRI score. Results: A total of 1,055 HCC patients were retrospectively studied, which included 151 experienced PHLF. Univariable and multivariate analyses showed that the ALBI and APRI scores were independent predictors of PHLF. The area under the ROC curve (AUC) of the ALBI score, APRI score, and C-P grade was 0.717, 0.720, and 0.602, respectively, with AUC (ALBI) > AUC (C-P) (P AUC (C-P) (P AUC (ALBI) (P AUC (APRI) (P =0.047). Our results indicated that ALBI and APRI scores had higher discriminatory abilities than C-P grade in predicting the risk of PHLF, and the ALBI-APRI model could enhance the capability of predicting PHLF compared to ALBI or APRI alone. Conclusion: ALBI-APRI score is a novel and effective predictive model of PHLF for HBV-related HCC patients, and its accuracy in predicting the risk of PHLF is better than that of C-P, ALBI and APRI scores.

RNA-binding motif protein 8A (RBM8A) is abnormally overexpressed in hepatocellular carcinoma (HCC) and involved in the epithelial-mesenchymal transition (EMT). The EMT plays an important role in the development of drug resistance, suggesting that RBM8A may be involved in the regulation of oxaliplatin (OXA) resistance in HCC. Here we examined the potential involvement of RBM8A and its downstream pathways in OXA resistance using in vitro and in vivo models. RBM8A overexpression induced the EMT in OXA-resistant HCC cells, altering cell proliferation, apoptosis, migration, and invasion. Moreover, whole-genome microarrays combined with bioinformatics analysis revealed that RBM8A has a wide range of transcriptional regulatory capabilities in OXA-resistant HCC, including the ability to regulate several important tumor-related signaling pathways. In particular, histone deacetylase 9 (HDAC9) emerged as an important mediator of RBM8A activity related to OXA resistance. These data suggest that RBM8A and its related regulatory pathways represent potential markers of OXA resistance and therapeutic targets in HCC.

Introduction: Hepatocellular carcinoma (HCC) is a high-grade malignant disease with unfavorable prognosis, and although surgical therapy is necessary, not all patients with HCC are suitable candidates for surgery. Downstaging as preoperative therapeutic strategy, which can convert unresectable HCC into resectable HCC, intends to increase the resection rate and improve prognosis.Methods: We searched multiple databases updated to December 30, 2020, for studies on transcatheter arterial chemoembolization (TACE), Yttrium 90 microsphere selective internal radiation (SIR)/transcatheter radioembolization (TARE), hepatic arterial infusion (HAI), and systemic treatment as downstaging treatment before resection for patients with unresectable HCC.Results: A total of 20 comparative and non-comparative studies were finally included in the meta-analysis. The pooled downstaging rate of hepatic resection (HR) was 14% [95% confidence interval (CI) 0.10–0.17] with significant heterogeneity (I² = 94.51%). The chemotherapy, combination, and non-cirrhosis groups exhibit higher rates of downstaging, but these differences were not significant. For comparative studies, the overall survival (OS) rates of resection after downstaging were far better than those inpatients who received locoregional therapy (LRT) or systemic treatment alone at 1 year (RR 1.87, 95% CI 1.48–2.38), 3 years (RR 5.56, 95% CI 2.55–12.10), and 5 years (RR 5.47, 95% CI 2.22–13.49). In addition, the pooled disease-free survival (DFS) rates in patients undergoing HR after successful downstaging were 78% (95% CI 0.62–0.93) at 1 year, 47% (95% CI 0.25–0.68) at 3 years, and 46% (95% CI 0.32–0.59) at 5 years. The pooled OS rates were 88% (95% CI 0.82–0.95) at 1 year, 64% (95% CI 0.59–0.69) at 3 years, and 42% (95% CI 0.29–0.54) at 5 years.Conclusions: Downstaging may serve as a screening tool to identify patients who might benefit from surgery. Resection after successful downstaging can improve prognosis.

RNA-binding proteins (RBPs) act as a key factor in gene regulation by governing RNA metabolism. They contribute to the expression and functions of most RNAs by binding to them and forming complexes. RNA-binding motif protein 38 (RBM38), a member of the RBP family, alters the stability and translation of targeted mRNAs to affect various biological processes, such as cell proliferation, cell cycle arrest, and myogenic differentiation. RBM38 contains a highly conserved RNA recognition motif (RRM) consisting of two subunits, RNP1 and RNP2, which specifically bind to RNAs. Recent studies have revealed that RBM38 regulates the mRNA stability of several tumor-related genes, such as p53, mdm2, p63, p73, p21, and c-Myc, by binding to their 3′ untranslated regions (3′ UTRs); thus, RBM38 modulates targeted gene expression and affects the biological processes of tumors. In addition, abnormal RBM38 expression in some malignant tumors and its correlation with prognosis have been documented in many studies, indicating its value for potential clinical applications. In this review, we present an overview of RBM38, specifically highlighting its relationship with tumor manifestation and development. A brief overview of the potential use of RBM38 in cancer therapy is also included to provide ideas for further research on RBM38.

Objectives: The occurrence of hepatocellular carcinoma (HCC) is a complex process involving genetic mutations, epigenetic variation, and abnormal gene expression. However, a comprehensive multiomics investigation of HCC is lacking, and the available multiomics evidence has not led to improvements in clinical practice. Therefore, we explored the molecular mechanism underlying the development of HCC through an integrative analysis of multiomics data obtained at multiple levels to provide innovative perspectives and a new theoretical basis for the early diagnosis, personalized treatment and medical guidance of HCC. Methods: In this study, we collected whole-exome sequencing data, RNA (mRNA and miRNA) sequencing data, DNA methylation array data, and single nucleotide polymorphism (SNP) array data from The Cancer Genome Atlas (TCGA). We analyzed the copy number variation (CNV) in HCC using GISTIC2. MutSigCV was applied to identify significantly mutated genes (SMGs). Functional enrichment analyses were performed using the clusterProfiler package in R software. The prognostic values of discrete variables were estimated using Kaplan–Meier survival curves. Results: By analyzing the HCC data in TCGA, we constructed a comprehensive multiomics map of HCC. Through copy number analysis, we identified significant amplification at 29 loci and significant deletions at 33 loci. A total of 13 significant mutant genes were identified. In addition, we also identified three HCC-related mutant signatures, and among these, signature 22 was closely related to exposure to aristolochic acids. Subsequently, we analyzed the methylation level of HCC samples and identified 51 epigenetically silenced genes that were significantly associated with methylation. The differential expression analysis identified differentially expressed mRNAs and miRNAs in HCC samples. Based on the above-described results, we identified a total of 93 possible HCC driver genes, which are driven by mutations, methylation, and CNVs and have prognostic value. Conclusion: Our study reveals variations in different dimensions of HCC. We performed an integrative analysis of genomic signatures, single nucleotide variants (SNVs), CNVs, methylation, and gene expression in HCC. Based on the results, we identified HCC possible driver genes that might facilitate prognostic prediction and support decision making with regard to the choice of therapy.