Background Hepatocarcinogenesis is reportedly correlated with abnormal m6A modifications; however, it is unknown whether m6A RNA methylation regulators facilitate the occurrence of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). Thus, we constructed an m6A-related model that may enhance HBV-related HCC prognosis. Methods Gene signatures of HNRNPA2B1 and RBM15 were generated by univariate and Lasso Cox regression analyses using the gene set and clinical information from The Cancer Genome Atlas (TCGA) database. High-risk and low-risk groups were confirmed based on the gene signature model. Furthermore, we validated the predictive roles of the two genes for overall survival (OS) in the GSE14520 dataset. The relative expression of 22 paired mRNAs was measured using quantitative real-time polymerase chain reaction (qRT-PCR) analysis to determine whether the two genes had a predictive role in our Guilin cohort. Results The differences in OS between the high-risk and low-risk groups were statistically significant in the TCGA (p = 0.003) and GSE14520 (p = 0.045) datasets, but not in the Guilin cohort, owing to differences in clinical information among the three cohorts (mainly the TNM stage and survival state). Stratified analysis of TNM stages showed that the two-gene signature acted as a prognostic indicator of HBV-related HCC patients in the early TNM stage; both TCGA and GSE14520 cohorts showed statistical significance. Moreover, multivariate Cox regression analysis indicated that the two-gene signature was an independent factor for predicting prognosis (HR = 1.087, 95% CI: 1.007–1.172). Correlation analysis between the gene signature and clinical features revealed that the risk stratification was significantly correlated with grade and survival state. Finally, Gene Set Enrichment Analysis (GSEA) revealed that the KEGG pathways associated with the cell cycle, DNA replication, the spliceosome, repair, and metabolism-related processes were all significantly enriched in the high-risk group. Among the enriched genes, the expression levels of the replication protein RPA1 and the pre-mRNA splicing factor SF3B1 were significantly upregulated in the high-risk group. These results might help in elucidating the underlying molecular mechanisms of HBV-related HCC. Conclusions Our data may provide new predictive signatures and potential therapeutic targets to identify and treat HBV-related HCC patients in the early disease stage.

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BackgroundHepatocellular carcinoma (HCC) is the seventh most common malignancy and the second most common cause of cancer-related deaths. Autophagy plays a crucial role in the development and progression of HCC.MethodsUnivariate and Lasso Cox regression analyses were performed to determine a gene model that was optimal for overall survival (OS) prediction. Patients in the GSE14520 and GSE54236 datasets of the Cancer Genome Atlas (TCGA) were divided into the high-risk and low-risk groups according to established ATG models. Univariate and multivariate Cox regression analyses were used to identify risk factors for OS for the purpose of constructing nomograms. Calibration and receiver operating characteristic (ROC) curves were used to evaluate model performance. Real-time PCR was used to validate the effects of the presence or absence of an autophagy inhibitor on gene expression in HepG2 and Huh7 cell lines.ResultsOS in the high-risk group was significantly shorter than that in the low-risk group. Gene set enrichment analysis (GSEA) indicated that the association between the low-risk group and autophagy- as well as immune-related pathways was significant. ULK2, PPP3CC, and NAFTC1 may play vital roles in preventing HCC progression. Furthermore, tumor environment analysis via ESTIMATION indicated that the low-risk group was associated with high immune and stromal scores. Based on EPIC prediction, CD8+ T and B cell fractions in the TCGA and GSE54236 datasets were significantly higher in the low-risk group than those in the high-risk group. Finally, based on the results of univariate and multivariate analyses three variables were selected for nomogram development. The calibration plots showed good agreement between nomogram prediction and actual observations. Inhibition of autophagy resulted in the overexpression of genes constituting the gene model in HepG2 and Huh7 cells.ConclusionsThe current study determined the role played by autophagy-related genes (ATGs) in the progression of HCC and constructed a novel nomogram that predicts OS in HCC patients, through a combined analysis of TCGA and gene expression omnibus (GEO) databases.

In this study, we investigated the molecular epidemiology and evolution of influenza viruses from patients infected during the 2013-2014 influenza season in Beijing. A phylogenetic analysis of the hemagglutinin (HA) and neuraminidase (NA) sequences of influenza A and B viruses from 18 patients (6 A(H1N1)pdm09, 4 H3N2, and 8 influenza B virus) was performed. Among the influenza A viruses, A(H1N1)pdm09 was the dominant subtype, whereas the B/Yamagata lineage was predominant for influenza B. The influenza B HA and NA strains in Beijing were dominated by reassortants derived from the Yamagata lineage and the Victoria lineage, respectively. All six A(H1N1)pdm09 strains fell into the 6B genetic group with amino acid substitutions D97N, S185T, K163Q, and A256T; the four H3N2 strains fell into genetic group 3C.3 with substitutions T128A, R142G, N145S, and V186G, and the eight influenza B strains were categorized into subgroup 3.1 and harbored an N217S mutation. Two new mutations (K180Q and G187E at the Sa and Ca antigenic sites of the H1 segment, respectively), which were not detected during the preceding influenza season, were identified. Mutations N131K, S165I, N181Y, and D212N in HA of influenza B mapped to the 120-loop, 150-loop, 160-loop, and 190-helix, respectively. Our results reveal the molecular epidemiology and phylogenetic characteristics of influenza viruses within a single geographic location and can have implications for vaccination selection in northern China.

Tumor-specific neoantigens, which are expressed on tumor cells, can induce an effective antitumor cytotoxic T-cell response and mediate tumor regression. Among tumor immunotherapies, neoantigen vaccines are in early human clinical trials and have demonstrated substantial efficiency. Compared with more neoantigens in melanoma, the paucity and inefficient identification of effective neoantigens in hepatocellular carcinoma (HCC) remain enormous challenges in effectively treating this malignancy. In this review, we highlight the current development of HCC neoantigens in its generation, screening, and identification. We also discuss the possibility that there are more effective neoantigens in hepatitis B virus (HBV)-related HCC than in non-HBV-related HCC. In addition, since HCC is an immunosuppressive tumor, strategies that reverse immunosuppression and enhance the immune response should be considered for the practical exploitation of HCC neoantigens. In summary, this review offers some strategies to solve existing problems in HCC neoantigen research and provide further insights for immunotherapy.