A novel adenosine-to-inosine RNA editing-based nomogram for predicting prognosis of hepatocellular carcinoma.

OBJECTIVE

Although the role of adenosine-to-inosine RNA editing (ATIRE) has gained widespread attention in multiple cancers, its predictive role in hepatocellular carcinoma (HCC) remains little known. We aimed to establish a predicting signature based on ATIRE for the prognosis of HCC.

METHODS

A total of 200 HCC patients with survival data and ATIRE profiles from The Cancer Genome Atlas (TCGA) database were divided into training (n = 140) and validation (n = 60) cohorts. Survival-related ATIRE sites were identified by the least absolute shrinkage and selection operator algorithm. ATIRE-based risk score was then generated with these ATIRE sites. Cox proportional hazards regression was employed to construct the ATIRE-based nomogram signature. The receiver operating characteristic (ROC) curve was used to evaluate the predictive efficacy of the signature. Harrell's C-index and calibration plot was utilized to evaluate the significant prognostic factors.

RESULTS

Nine ATIRE sites were screened to establish the ATIRE risk score, and it was found to be associated with prognosis of HCC. Survival analysis revealed that higher ATIRE-based risk scores were significantly associated with worse overall survival (OS) in both the training dataset ( < 0.001) and the validation dataset ( = 0.011), as well as in the combined dataset ( < 0.001). The ROC curve displayed a good predictive efficiency of the risk score regarding 1-year, 2-year, and 3-year OS. Furthermore, ATIRE sites were significantly correlated with the expression of host genes and were likely to be involved in certain cancer-related pathways.

DISCUSSION

Our findings provided a novel ATIRE-based nomogram, which could serve as a potential tool for predicting HCC prognosis.