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Liver cancer is the main reason of cancer deaths globally, with an unfavorable prognosis. DNA methylation is one of the epigenetic modifications and maintains the right adjustment of gene expression and steady gene silencing. We aim to explore the novel signatures for prognosis by using DNA methylation-driven genes. To acquire the DNA methylation-driven genes, we perform the difference analysis from the gene expression data and DNA methylation data in TCGA or GEO databases. And we obtain the 31 DNA methylation-driven genes. Subsequently, consensus clustering analysis was utilized to identify the molecular subtypes based on the 31 DNA methylation-driven genes. So, two molecular subtypes were identified to perform those analyses: Survival, immune cell infiltration, and tumor mutation. Results showed that two subtypes were clustered with distinct prognoses, tumor-infiltrating immune cell and tumor mutation burden. Furthermore, the 31 DNA methylation-driven genes were applied to perform the survival analysis to select the 14 survival-related genes. Immediately, a five methylation-driven genes risk model was built, and the patients were divided into high and low-risk groups. The model was established with TCGA as the training cohort and GSE14520 as the validation cohort. According to the risk model, we perform the systematical analysis, including survival, clinical feature, immune cell infiltration, somatic mutation status, underlying mechanisms, and drug sensitivity. Results showed that the high and low groups possessed statistical significance. In addition, the ROC curve was utilized to measure the accuracy of the risk model. AUCs at 1-year, 3-years, and 5-years were respectively 0.770, 0.698, 0.676 in training cohort and 0.717, 0.649, 0.621 in validation cohort. Nomogram was used to provide a better prediction for patients’ survival. Risk score increase the accuracy of survival prediction in HCC patients. In conclusion, this study developed a novel risk model of five methylation-driven genes based on the comprehensive bioinformatics analysis, which accurately predicts the survival of HCC patients and reflects the immune and mutation features of HCC. This study provides novel insights for immunotherapy of HCC patients and promotes medical progress.

Hepatocellular carcinoma (HCC) is one of the malignant tumors with high mortality and a worse prognosis globally. Necroptosis is a programmed death mediated by receptor-interacting Protein 1 (RIP1), receptor-interacting Protein 1 (RIP3), and Mixed Lineage Kinase Domain-Like (MLKL). Our study aimed to create a new Necroptosis-related lncRNAs (NRlncRNAs) risk model that can predict survival and tumor immunity in HCC patients. The RNA expression and clinical data originated from the TCGA database. Pearson correlation analysis was applied to identify the NRlncRNAs. The LASSO-Cox regression analysis was employed to build the risk model. Next, the ROC curve and the area under the Kaplan-Meier curve were utilized to evaluate the accuracy of the risk model. In addition, based on the two groups of risk model, we performed the following analysis: clinical correlation, differential expression, PCA, TMB, GSEA analysis, immune cells infiltration, and clinical drug prediction analysis. Plus, qRT-PCR was applied to test the expression of genes in the risk model. Finally, a prognosis model covering six necroptosis-related lncRNAs was constructed to predict the survival of HCC patients. The ROC curve results showed that the risk model possesses better accuracy. The 1, 3, and 5-years AUC values were 0.746, 0.712, and 0.670, respectively. Of course, we also observed that significant differences exist in the following analysis, such as functional signaling pathways, immunological state, mutation profiles, and medication sensitivity between high-risk and low-risk groups of HCC patients. The result of qRT-PCR confirmed that three NRlncRNAs were more highly expressed in HCC cell lines than in the normal cell line. In conclusion, based on the bioinformatics analysis, we constructed an NRlncRNAs associated risk model, which predicts the prognosis of HCC patients. Although our study has some limitations, it may greatly contribute to the treatment of HCC and medical progression.

Hepatocellular carcinoma (HCC) is regarded as one of the universal cancers in the world. Therefore, our study is based on clinical, molecular mechanism and immunological perspectives to analyze how NAP1L1 affects the progression of HCC. To begin with, the gene expression datasets and clinical data of GSE14520, GSE76427, ICGC, and TCGA are originated from GEO, ICGC, and TCGA databases. Subsequently, DEG screening was performed on data using R studio, and we finally found that 2,145 overlapping DEGs were screened from four datasets at the end. Then, we used R studio to filter the survival-related genes of the GSE76427 and ICGC datasets, and we screened out 101 survival-related genes. Finally, 33 common genes were screened out from 2,145 overlapping DEGs and 101 survival-related genes. Then, NAP1L1 was screened from 33 common genes using the CytoHubba plug-in in Cytoscape software. Furthermore, ground on GEO, ICGC, and TCGA databases, the survival analysis, clinical feature analysis, univariate/multivariate regression analysis, and multiple GSEA were used to study NAP1L1. The Conclusion claimed that HCC patients with higher expression levels of NAP1L1 had a poorer prognosis than those with lower expression levels. Thus, we believe that NAP1L1 is an independent prognostic factor for HCC. In order to shed light on NAP1L1’s molecular mechanism promoting the progression of HCC closely, the GSEA tool was applied to complete the GSEA of the four datasets. Furthermore, the results confirmed that NAP1L1 could promote HCC progression by regulating the G2/M transition of the cell cycle and Wnt signaling pathway. Western blot and flow cytometry were also performed to understand those mechanisms in this study. The result of Western blot showed that NAP1L1 silencing led to downregulation of CDK1 and β-catenin proteins; the result of flow cytometry showed that cell numbers in the G2 phase were significantly increased when NAP1L1 was silenced. Thus, we claimed that NAP1L1 might promote HCC progression by activating the Wnt signaling pathway and promoting cell cycle G2/M transition. In addition, ground on GSE14520 and GSE76427 datasets, and ICGC and TCGA databases, the correlation between NAP1L1 and immune cells was analyzed in HCC patients. At the same time, the TISIDB online database and the TIMER online database were testified to the association between NAP1L1 and immune cells. Hence, the summary shows that NAP1L1 was connected with a certain amount of immune cells. We can speculate that NAP1L1 may influence macrophages to promote HCC progression through some potential mechanisms.