Molecular targeted therapy for advanced hepatocellular carcinoma (HCC) has changed markedly. Although sorafenib was used in clinical practice as the first molecular targeted agent in 2007, the SHARPE and Asian-Pacific trials demonstrated that sorafenib only improved overall survival (OS) by approximately 3 months in patients with advanced HCC compared with placebo. Molecular targeted agents were developed during the 10-year period from 2007 to 2016, but every test of these agents from phase II or phase III clinical trial failed due to a low response rate and high toxicity. In the 2 years after, 2017 through 2018, four successful novel drugs emerged from clinical trials for clinical use. As recommended by updated Barcelona Clinical Liver cancer (BCLC) treatment algorithms, lenvatinib is now feasible as an alternative to sorafenib as a first-line treatment for advanced HCC. Regorafenib, cabozantinib, and ramucirumab are appropriate supplements for sorafenib as second-line treatment for patients with advanced HCC who are resistant, show progression or do not tolerate sorafenib. In addition, with promising outcomes in phase II trials, immune PD-1/PD-L1 checkpoint inhibitors nivolumab and pembrolizumab have been applied for HCC treatment. Despite phase III trials for nivolumab and pembrolizumab, the primary endpoints of improved OS were not statistically significant, immune PD-1/PD-L1 checkpoint therapy remains to be further investigated. This review summarizes the development and progression of molecular targeted and immune-based checkpoint therapies in HCC.

As a pleiotropic cytokine, interleukin-6 (IL-6) not only regulates the cellular immune response, but it also promotes tumor development by activating multiple carcinogenic pathways. IL-6 expression is significantly elevated in colorectal cancer (CRC) and is closely related to CRC development and patient prognosis. In CRC, IL-6 activates signal transducers and activators of transduction-3 (STAT3) to promote tumor initiation and tumor growth. IL-6/STAT3 signalling has a profound effect on tumor-infiltrating immune cells in the tumor immune microenvironment in CRC. Additionally, IL-6/STAT3 pathway activates downstream target genes to protect tumor cells from apoptosis; drive tumor cell proliferation, cell cycle progression, invasion and metastasis; promote tumor angiogenesis; and stimulate drug resistance. Therefore, a thorough understanding of the many effects of the IL-6/STAT3 pathway in CRC is needed, which the present review examines.

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.

Background: Testing for the presence of liver cirrhosis (LC) is one of the most critical diagnostic and prognostic assessments for patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). More non-invasive tools are needed to diagnose LC but the predictive abilities of current models are still inconclusive. This study aimed to develop and validate a novel and non-invasive artificial neural network (ANN) model for diagnosing LC in patients with HBV-related HCC using routine laboratory serological indicators. Methods: A total of 1152 HBV-related HCC patients who underwent hepatectomy were included and randomly divided into the training set (n = 864, 75%) and validation set (n = 288, 25%). The ANN model was constructed from the training set using multivariate Logistic regression analysis and then verified in the validation set. Results: The morbidity of LC in the training and validation sets was 41.2% and 46.8%, respectively. Multivariate analysis showed that age, platelet count, prothrombin time and total bilirubin were independent risk factors for LC (P < 0.05). The area under the ROC curve (AUC) analyses revealed that the ANN model had higher predictive accuracy than the Logistic model (ANN: 0.757 vs Logistic: 0.721; P < 0.001), and other scoring systems (ANN: 0.757 vs CP: 0.532, MELD: 0.594, ALBI: 0.575, APRI: 0.621, FIB-4: 0.644, AAR: 0.491, and GPR: 0.604; P < 0.05 for all) in diagnosing LC. Similar results were obtained in the validation set. Conclusion: The ANN model has better diagnostic capabilities than other commonly used models and scoring systems in assessing LC risk in patients with HBV-related HCC.

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.