Objective Biomarkers associated with liver cancer CD8+T cells identified and prognostic models established by single-cell RNA sequencing and Bulk RNA sequencing.Methods Single-cell datasets of liver cancer were downloaded from the GEO database and differentially expressed genes in CD8+T cells between patients and controls were extracted by single-cell RNA sequencing.Gene expression profiling data and clinical data of liver cancer were downloaded from the TCGA database,and modular genes with relevance to CD8+T cells were screened using the CIBERSORT algorithm and WGCNA technology.Differential genes and modular genes were taken as intersecting genes and subjected to GO and KEGG analyses,and a prognostic model was established by applying univariate COX regression analysis and LASSO algorithm.The predictive effectiveness of the model in both internal and external datasets is validated by K-M and ROC curves.The effectiveness of the model's prediction in both internal and external datasets was validated by K-M and ROC curves.High-and low-risk groups were divided according to the median value of the risk score,and the distribution of infiltrating immune cells and tumor mutations between high-and low-risk groups were analyzed.Results A prognostic model with nine genes was constructed,and the K-M and ROC curves showed that the model had good predictive ability in both the internal and external datasets,and there were significant differences in the distribution of infiltrating immune cells and gene mutations in the high-and low-risk groups.Conclusion In this study,a novel prognostic model based on CD8+T cells was developed using bioinformatic method in combination with single-cell RNA sequencing and Bulk RNA sequencing technologies,which provides a reliable theoretical basis for prognostic improvement and survival prediction of liver cancer patients.

Objective Biomarkers associated with liver cancer CD8+T cells identified and prognostic models established by single-cell RNA sequencing and Bulk RNA sequencing.Methods Single-cell datasets of liver cancer were downloaded from the GEO database and differentially expressed genes in CD8+T cells between patients and controls were extracted by single-cell RNA sequencing.Gene expression profiling data and clinical data of liver cancer were downloaded from the TCGA database,and modular genes with relevance to CD8+T cells were screened using the CIBERSORT algorithm and WGCNA technology.Differential genes and modular genes were taken as intersecting genes and subjected to GO and KEGG analyses,and a prognostic model was established by applying univariate COX regression analysis and LASSO algorithm.The predictive effectiveness of the model in both internal and external datasets is validated by K-M and ROC curves.The effectiveness of the model's prediction in both internal and external datasets was validated by K-M and ROC curves.High-and low-risk groups were divided according to the median value of the risk score,and the distribution of infiltrating immune cells and tumor mutations between high-and low-risk groups were analyzed.Results A prognostic model with nine genes was constructed,and the K-M and ROC curves showed that the model had good predictive ability in both the internal and external datasets,and there were significant differences in the distribution of infiltrating immune cells and gene mutations in the high-and low-risk groups.Conclusion In this study,a novel prognostic model based on CD8+T cells was developed using bioinformatic method in combination with single-cell RNA sequencing and Bulk RNA sequencing technologies,which provides a reliable theoretical basis for prognostic improvement and survival prediction of liver cancer patients.

Cardiovascular disease has been a major threat to human's health and lives for many years. It is of great importance to explore the mechanisms and develop strategies to prevent the pathogenesis. Generally, cardiovascular disease is associated with endothelial dysfunction, which is closely related to the nitric oxide (NO)-mediated vasodilatation. The release of NO is regulated by NOS3 gene in mammals' vascular system. A great deal of evidences have shown that the polymorphism and epigenetic of NOS3 gene play vital roles in the pathological process of cardiovascular disease. To gain insights into the role of NOS3 in the cardiovascular diseases, we reviewed the molecular mechanisms underlying the development of cardiovascular diseases in this paper, including the uncoupling of NOS3 protein, epigenetic and polymorphism of NOS3 gene. The review can also offer possible strategies to prevent and treat cardiovascular diseases.
Animals ; Cardiovascular Diseases ; metabolism ; pathology ; Epigenesis, Genetic ; Humans ; Nitric Oxide ; metabolism ; Nitric Oxide Synthase Type III ; genetics ; metabolism ; Polymorphism, Genetic ; Vasodilation