Development and Validation of a Four Adenosine-to-Inosine RNA Editing Site-Relevant Prognostic Signature for Assessing Survival in Breast Cancer Patients

BackgroundAdenosine-to-inosine RNA editing (ATIRE) is increasingly being used to characterize cancer. However, no studies have been conducted to identify an ATIRE signature for predicting cancer survival. MethodsBreast cancer (BRCA) samples with ATIRE profiles from The Cancer Genome Atlas were divided into training (n = 452) and internal validation cohorts (n = 311), and 197 additional BRCA patients were recruited as an external validation cohort. The ATIRE signature for BRCA overall survival (OS) and disease-free survival (DFS) were identified using forest algorithm analysis and experimentally verified by direct sequencing. An ATIRE-based risk score (AIRS) was established with these selected ATIRE sites. Significantly prognostic factors were incorporated to generate a nomogram that was evaluated using Harrell's C-index and calibration plot for all cohorts. ResultsSeven ATIRE sites were revealed to be associated with both BRCA OS and DFS, of which four sites were experimentally confirmed. Patients with high AIRS displayed a higher risk of death than those with low AIRS in the training (hazard ratio (HR) = 3.142, 95%CI = 1.932-5.111), internal validation (HR = 2.097, 95%CI = 1.123-3.914), and external validation cohorts (HR = 2.680, 95%CI = 1.000-7.194). A similar hazard effect of high AIRS on DFS was also observed. The nomogram yielded Harrell's C-indexes of 0.816 (95%CI = 0.784-0.847), 0.742 (95%CI = 0.684-0.799), and 0.869 (95%CI = 0.835-0.902) for predicting OS and 0.767 (95%CI = 0.708-0.826), 0.684 (95%CI = 0.605-0.763), and 0.635 (95%CI = 0.566-0.705) for predicting DFS in the three cohorts. ConclusionAIRS nomogram could help to predict OS and DFS of patients with BRCA.

Automated summary

This study identified an ATIRE signature associated with the overall survival and disease-free survival of breast cancer patients. A risk score based on these ATIRE sites was established and a nomogram was generated to predict patient outcomes. Experimental confirmation showed that high risk scores were associated with increased risk of death. The nomogram showed good predictive accuracy for breast cancer patient survival.