Background. N6-methyladenosine (m6A) modification plays an essential role in diverse key biological processes and may take part in the development and progression of hepatocellular carcinoma (HCC). Here, we systematically analyzed the expression profiles and prognostic values of 13 widely reported m6A modification-related genes in HCC. Methods. The mRNA expression of 13 m6A modification-related genes and clinical parameters of HCC patients were downloaded from TCGA, ICGC, GSE109211, and GSE78220. Univariate and LASSO analyses were used to develop risk signature. Time-dependent ROC was performed to assess the predictive accuracy and sensitivity of risk signature. Results. FTO, YTHDC1, YTHDC2, ALKBH5, KIAA1429, HNRNPC, METTL3, RBM15, YTHDF2, YTHDF1, and WTAP were significantly overexpressed in HCC patients. YTHDF1, HNRNPC, RBM15, METTL3, and YTHDF2 were independent prognostic factors for OS and DFS in HCC patients. Next, a risk signature was also developed and validated with five m6A modification-related genes in TCGA and ICGC HCC cohort. It could effectively stratify HCC patients into high-risk patients with shorter OS and DFS and low-risk patients with longer OS and DFS and showed good predictive efficiency in predicting OS and DFS. Moreover, significantly higher proportions of macrophages M0 cells, neutrophils, and Tregs were found to be enriched in HCC patients with high risk scores, while significantly higher proportions of memory CD4 T cells, gamma delta T cells, and naive B cells were found to be enriched in HCC patients with low scores. Finally, significantly lower risk scores were found at sorafenib treatment responders and anti-PD-1 immunotherapy responders compared to that in nonresponders, and anti-PD-1 immunotherapy-treated patients with lower risk scores had better OS than patients with higher risk scores. Conclusion. A risk signature developed with the expression of 5 m6A-related genes could improve the prediction of prognosis of HCC and correlated with sorafenib treatment and anti-PD-1 immunotherapy response.

Background. Our study aims to develop a lncRNA-based classifier and a nomogram incorporating the genomic signature and clinicopathologic factors to help to improve the accuracy of recurrence prediction for hepatocellular carcinoma (HCC) patients. Methods. The lncRNA profiling data of 374 HCC patients and 50 normal healthy controls were downloaded from The Cancer Genome Atlas (TCGA). Using univariable Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, we developed a 15-lncRNA-based classifier and compared our classifier to the existing six-lncRNA signature. Besides, a nomogram incorporating the genomic classifier and clinicopathologic factors was also developed. The predictive accuracy and discriminative ability of the genomic-clinicopathologic nomogram were determined by a concordance index (C-index) and calibration curve and were compared with the TNM staging system by the C-index and receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was performed to estimate the clinical value of our nomogram. Results. Fifteen relapse-free survival (RFS-) related lncRNAs were identified, and the classifier, consisting of the identified 15 lncRNAs, could effectively classify patients into the high-risk and low-risk subgroups. The prediction accuracy of the 15-lncRNA-based classifier for predicting 2-year and 5-year RFS was 0.791 and 0.834 in the training set and 0.684 and 0.747 in the validation set, respectively, which was better than the existing six-lncRNA signature. Moreover, the AUC of genomic-clinicopathologic nomogram in predicting RFS were 0.837 in the training set and 0.753 in the validation set, and the C-index of the genomic-clinicopathologic nomogram was 0.78 (0.72-0.83) in the training set and 0.71 (0.65-0.76) in the validation set, which was better than the traditional TNM stage and 15-lncRNA-based classifier. The decision curve analysis further demonstrated that our nomogram had a larger net benefit than the TNM stage and 15-lncRNA-based classifier. The results were confirmed externally. Conclusion. Compared to the TNM stage, the 15-lncRNAs-based classifier-clinicopathologic nomogram is a more effective and valuable tool to identify HCC recurrence and may aid in clinical decision-making.

Background. CpG island methylator phenotype (CIMP), featured with concurrent and widespread hypermethylation of a cluster of CpGs, has been reported to play an important role in carcinogenesis. Limited studies have investigated the role of CIMP in pancreatic cancer (PC). The aim of this study was to explore the CIMP in PC patients and its impact on the immune response of the tumor microenvironment and prognosis. Methods. DNA methylation, somatic mutation, mRNA, and corresponding clinical data of PC patients were downloaded from TCGA (184 patients) and the ICGC (264 patients). Univariate and multivariate regression analyses were used to identify prognosis-related CpGs. Consensus clustering analysis was used for identification of the CIMP in PC patients. ESTIMATE and CIBORORT were used for estimation of the tumor microenvironment (TME) in PC patients. Results. In the TCGA PC cohort, 22,450 differential CpGs, including 12,937 hypermethylated CpGs and 9,513 hypomethylated CpGs, were identified between 184 PC patients and 10 normal controls. Univariate and multivariate Cox analysis further screened out 72 OS-related CpGs, and three distinct CIMP groups with distinctly different prognosis and molecular features, including the CIMP-L subgroup, CIMP-M subgroup, and CIMP-H subgroup, were identified based on unsupervised consensus clustering analysis of these CpGs. Patients of the CIMP-H subgroup had poorer OS and RFS, while patients of the CIMP-L subgroup had better OS and RFS. The CIMP status was also an independent prognostic factor for OS and PFS. In molecular features, significantly higher somatic mutation burden and tumor mutational burden were found in patients of the CIMP-H subgroup compared to those of the CIMP-L subgroup. Besides, lower stromal score, immune score, and higher cancer stemness indices and tumor purity were also found in patients of the CIMP-H subgroup compared to those of the CIMP-L subgroup. Correspondingly, significant total T cells, total B cells, CD8 T cells, memory CD4 T cells, and higher regulatory T cells were found in patients of the CIMP-H subgroup. Moreover, significantly lower expression of immune checkpoint genes, such as PD-1, CTLA4, CD86, VTCN1, and LAG-3, was also found in patients of the CIMP-H subgroup compared to those of the CIMP-L subgroup. In the end, we validated the CIMP status in PC patients of the ICGC dataset. Conclusion. The CIMP may modulate the immune response of the tumor microenvironment and influence the prognosis of pancreatic cancer patients, which may help to make an assertion to provide specific and efficient treatment options for patients of different subtypes.

Background. Liver cirrhosis is one of the major drivers of hepatocellular carcinoma (HCC). In the present study, we aimed to identify and validate new biomarker for early prediction of HCC development in early-stage cirrhosis patients. Methods. mRNA expression and clinical parameters of GSE63898, GSE89377, GSE15654, GSE14520, and TCGA-HCC cohort and ICGC-HCC cohort were downloaded for analysis. Wilcoxon test was performed to identify DEGs. Univariate and multivariate Cox regression analysis were used to develop the risk signature, and ROC analysis was performed to analyze the predictive accuracy and sensitivity of the risk signature. Results. There were 42 DEGs (including 28 upregulated genes and 14 downregulated genes) found in early-stage liver cirrhosis patients before developing HCC from GSE1565442. Then, a risk signature consisting of 8 DEGs could effectively classify early-stage cirrhosis patients into high-risk group with shorter HCC development time and low-risk group with longer HCC development time from GSE15654. Multivariate Cox analysis indicated that the risk signature was an independent prognostic factor for the prediction of HCC development and ROC analysis showed that the signature exhibited good predictive efficiency in predicting 2-, 5-, and 10-year HCC development. Mechanistically, significantly higher proportions of CD8 T cells were found to be enriched in cirrhosis patients with low risk score, and higher CD8 T cells were associated with longer HCC development time. Besides, the signature was an independent prognostic factor for poorer prognosis of early-stage liver cirrhosis patients of GSE15654. Moreover, the signature could also separate HCC patients from healthy controls and was also associated with the poorer prognosis of HCC patients from three HCC cohorts. Finally, we also identified HDAC inhibitors, such as trichostatin A, to be a potential chemopreventive treatment for the prevention of HCC development by targeting risk signature based on CMap analysis. Conclusion. A risk signature was developed and validated for early prediction of HCC development, which may be a useful tool to set up individualized follow-up interval schedules.

Background: N6-methyladenosine (m6A) modification plays an essential role in diverse key biological processes and may take part in the development and progression of hepatocellular carcinoma (HCC). Here, we systematically analyzed expression profiles and prognostic values of 13 widely reported m6A modification related genes in HCC.Methods: mRNA expression of 13 m6A modification related genes and clinical parameters of HCC patients were downloaded from TCGA, ICGC, GSE109211 and GSE78220. Univariate and LASSO analysis was used to develop risk signature. Time-dependent ROC was performed to assess the predictive accuracy and sensitivity of risk signature.Results: FTO, YTHDC1, YTHDC2, ALKBH5, KIAA1429, HNRNPC, METTL3, RBM15, YTHDF2, YTHDF1 and WTAP were significantly overexpressed in HCC patients. YTHDF1, HNRNPC, RBM15, METTL3, YTHDF2 were independent prognostic factors for OS and DFS in HCC patients. Next, a risk signature was also developed and validated with five m6A modification related genes in TCGA and ICGC HCC cohort. It could effectively stratify HCC patients into high risk patients with shorter OS and DFS and low risk patients with longer OS and DFS and showed good predictive efficiency in predicting OS and DFS. Moreover, significantly higher proportions of macrophages M0 cells, neutrophils and Tregs were found to be enriched in HCC patients with high risk score, while significantly higher proportions of memory CD4 T cells, gamma delta T cells and naive B cells were found to be enriched in HCC patients with high low score. Finally, significantly lower risk scores were found at sorafenib treatment responders and anti-PD-1 immunotherapy responders compared to that in non-responders, and anti-PD-1 immunotherapy treated patients with lower risk score had better OS than patients with higher risk score. Conclusion: A risk signature developed with the expression of 5 m6A related genes could improve the prediction of prognosis of HCC and correlate with sorafenib treatment and anti-PD-1 immunotherapy response.