Background: Hepatocellular carcinoma (HCC) originates from the hepatocytes and accounts for 90% of liver cancer. The study intends to identify novel prognostic biomarkers for predicting the prognosis of HCC patients based on TCGA and GSE14520 cohorts. Methods: Differential analysis was employed to obtain the DEGs (Differentially Expressed Genes) of the TCGA-LIHC-TPM cohort. The lasso regression analysis was applied to build the prognosis model through using the TCGA cohort as the training group and the GSE14520 cohort as the testing group. Next, based on the prognosis model, we performed the following analyses: the survival analysis, the independent prognosis analysis, the clinical feature analysis, the mutation analysis, the immune cell infiltration analysis, the tumor microenvironment analysis, and the drug sensitivity analysis. Finally, the survival time of HCC patients was predicted by constructing nomograms. Results: Through the lasso regression analysis, we obtained a prognosis model of ten genes including BIRC5 (baculoviral IAP repeat containing 5), CDK4 (cyclin-dependent kinase 4), DCK (deoxycytidine kinase), HSPA4 (heat shock protein family A member 4), HSP90AA1 (heat shock protein 90 α family class A member 1), PSMD2 (Proteasome 26S Subunit Ubiquitin Receptor, Non-ATPase 2), IL1RN (interleukin 1 receptor antagonist), PGF (placental growth factor), SPP1 (secreted phosphoprotein 1), and STC2 (stanniocalcin 2). First, we found that the risk score is an independent prognosis factor and is related to the clinical features of HCC patients, covering AFP (α-fetoprotein) and stage. Second, we observed that the p53 mutation was the most obvious mutation between the high-risk and low-risk groups. Third, we also discovered that the risk score is related to some immune cells, covering B cells, T cells, dendritic, macrophages, neutrophils, etc. Fourth, the high-risk group possesses a lower TIDE score, a higher expression of immune checkpoints, and higher ESTIMATE score. Finally, nomograms include the clinical features and risk signatures, displaying the clinical utility of the signature in the survival prediction of HCC patients. Conclusions: Through the comprehensive analysis, we constructed an immune-related prognosis model to predict the survival of HCC patients. In addition to predicting the survival time of HCC patients, this model significantly correlates with the tumor microenvironment. Furthermore, we concluded that these ten immune-related genes (BIRC5, CDK4, DCK, HSPA4, HSP90AA1, PSMD2, IL1RN, PGF, SPP1, and STC2) serve as novel targets for antitumor immunity. Therefore, this study plays a significant role in exploring the clinical application of immune-related genes.
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.
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.
Pancreatic cancer (PC) ranked fourth among cancer-related death worldwide with a survival rate less than 5%. The abnormal proliferation and distant metastasis are major obstacles for the diagnosis and treatment of pancreatic cancer, therefore, it is urgent for researchers to uncover the molecular mechanisms underlying the PC proliferation and metastasis. In current study, we found that USP33, a member of deubiquitinating enzyme family, was upregulated among PC samples and cells, meanwhile, the high expression of USP33 correlated with poor prognosis of patients. Function experiments revealed that USP33 overexpression promoted the proliferation, migration and invasion of PC cells while the inhibition of USP33 expression in PC cells exhibited the opposite effect. The mass spectrum and luciferase complementation assay screened TGFBR2 as the potential binding protein of USP33. Mechanistically, USP33 triggered the deubiquitination of TGFBR2 and prevented its degradation by lysosome, therefore promoted TGFBR2 accumulation in cell membrane and eventually contributed to the sustained activation of TGF-β signaling. Moreover, our results revealed that the activation of TGF-β targeted gene ZEB1 promoted the transcription of USP33. In conclusion, our study found that USP33 contributed to the proliferation and metastasis of pancreatic cancer through a positive feedback loop with TGF-β signaling pathway. Moreover, this study suggested that USP33 may serve as a potential prognostic and therapeutic target in PC.
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.
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