Immunological Significance of Prognostic DNA Methylation Sites in Hepatocellular Carcinoma - Xu, Qianhui; Hu, Yuanbo; Chen, Shaohuai; Zhu, Yulun; Li, Siwei; Shen, Feng; Guo, Yifan; Sun, Tao; Chen, Xiaoyu; Jiang, Jinpeng; Huang, Wen (Frontiers in Molecular Biosciences, 2021)
Abstract Background: Hepatocellular carcinoma (HCC) is a tumor with high morbidity and high mortality worldwide. DNA methylation, one of the most common of epigenetic changes might serve a vital regulatory role in cancer. Methods: To identify categories based on DNA methylation data, consensus clustering was employed. Risk signature was yielded by systematical bioinformatics analyses based on the remarkably methylated CpG sites of cluster 1. Kaplan-Meier analysis, variable regression analysis, and ROC analysis were further conducted to validate prognosis predictive ability of risk signature. Gene set enrichment analysis (GSEA) was performed for functional annotation. To uncover the context of tumor immune microenvironment (TIME) of HCC, we employed ssGSEA algorithm and CIBERSORT method, TIMER database exploration and single-cell RNA sequencing analysis were performed. Additionally, quantitative real-time polymerase chain reaction to determine LRRC41 expression and preliminarily explored the latent role of LRRC41 in prognostic prediction. Finally, mutation data was analyzed by employing “maftools” package to delineate the tumor mutation load (TMB). Results: HCC samples were assigned into seven subtypes with different overall survival and methylation level based on 5'-cytosine-phosphate-guanine-3' (CpG) sites. Risk prognostic signature including two candidate genes (LRRC41 and KIAA1429) exhibited robust prognostic predictive accuracy, which was validated in external testing cohort. Then, risk score was significantly correlated with TIME and immune checkpoint blockade (ICB)-related genes. Besides, prognostic nomogram based on risk score and clinical stage presented powerful prognostic ability. Additionally, LRRC41 with prognostic value was corroborated to be closely associated with TIME characterization in both expression and methylation level. Subsequently, the correlation regulatory network uncovered the potential targets of LRRC41 and KIAA1429. Finally, methylation level of KIAA1429 was correlated with genes mutation status. Conclusion: In summary, this is the first to identify HCC samples into distinct clusters according to DNA methylation and yield CpG-based prognostic signature and quantitative nomogram to precisely predict prognosis. And the pivotal player of DNA methylation of gene in TIME and TMB status was explored, contributing to clinical decision-making and personalized prognosis monitoring of HCC.