Constructing and validating of m7G-related genes prognostic signature for hepatocellular carcinoma and immune infiltration: potential biomarkers for predicting the overall survival

RNA modification-mediated mRNA translation regulation in liver cancer: mechanisms and clinical perspectives

Malignant liver cancer is characterized by rapid tumour progression and a high mortality rate, whereas the molecular mechanisms underlying liver cancer initiation and progression are still poorly understood. The dynamic and reversible RNA modifications have crucial functions in gene expression regulation by modulating RNA processing and mRNA translation. Emerging evidence has revealed that alterations in RNA modifications facilitate the selective translation of oncogenic transcripts and promote the diverse tumorigenic processes of liver cancer. In this Review, we first highlight the current progress on the functions and mechanisms underlying RNA modifications in the regulation of mRNA translation and then summarize the exciting discoveries on aberrant RNA modification-mediated mRNA translation in the regulation of tumour initiation, metastasis, metabolism, tumour microenvironment, and drug and radiotherapy resistance in liver cancer. Finally, we discuss the diagnostic and therapeutic potentials of targeting RNA modifications and mRNA translation for the clinical management of liver cancer.

Sensitivity Analysis for Survival Prognostic Prediction with Gene Selection: A Copula Method for Dependent Censoring

Prognostic analysis for patient survival often employs gene expressions obtained from high-throughput screening for tumor tissues from patients. When dealing with survival data, a dependent censoring phenomenon arises, and thus the traditional Cox model may not correctly identify the effect of each gene. A copula-based gene selection model can effectively adjust for dependent censoring, yielding a multi-gene predictor for survival prognosis. However, methods to assess the impact of various types of dependent censoring on the multi-gene predictor have not been developed. In this article, we propose a sensitivity analysis method using the copula-graphic estimator under dependent censoring, and implement relevant methods in the R package “compound.Cox”. The purpose of the proposed method is to investigate the sensitivity of the multi-gene predictor to a variety of dependent censoring mechanisms. In order to make the proposed sensitivity analysis practical, we develop a web application. We apply the proposed method and the web application to a lung cancer dataset. We provide a template file so that developers can modify the template to establish their own web applications.