A Prognostic Model for Predicting Liver Cancer Patients Based On Immune Checkpoint Gene-Related Basement Membrane Genes, And Analyzing Immunity and Potential Drug Candidates
Keywords:immune checkpoint gene, basement membrane, liver cancer, immunity
Objective: Hepatocellular carcinoma is one of the most common malignant tumors in the world. The expression of immune checkpoint genes in tumor cells prevents the immune system from eliminating tumors. Basement membrane-related genes are genes that are closely related to human diseases obtained in the latest research.
Methods: First, basement membrane-related genes were extracted from immune checkpoint genes, and a prognosis model of immune checkpoint-related basement membrane genes was constructed. C-index curves and ROC were drawn by survival analysis, progression-free survival analysis, and independent prognostic analysis. Curves, principal component analysis, and validation of the clinical grouping model were performed to verify its accuracy, enrichment analysis, immune analysis, and tumor mutation burden survival analysis were performed to further explore the potential functions of the model, and finally, potential drugs targeting the model were discussed.
Results: A prognostic model for predicting the survival time of liver cancer patients was constructed, and the predictive ability of the model was verified. GO and KEGG enrichment analysis revealed differences in the functions and pathways of differential genes. Four differentially expressed immune functions were found. The top 4 genes mutated in the high and low risk groups were compared. Twenty-five drugs with significant differences in drug sensitivity between high- and low-risk groups were explored.
Conclusion: The risk-prognostic model based on the association of basement membrane genes and immune checkpoint genes in this study may be promising for clinical prediction of prognosis and immunotherapy response in patients with liver cancer.
Annals of Abbasi Shaheed Hospital and Karachi Medical and Dental College acquires copyright ownership of the content. The articles are distributed under a Creative Commons (CC) Attribution-Non-Commercial 4.0 License (http://creativecommons.org/licenses/by-nc/4.0/). This license permit uses, distribution and reproduction in any medium; provided the original work is properly cited and initial publication in this journal.