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Updated: Sep 6, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
A nomogram based on A-to-I RNA editing predicting overall survival of patients with lung squamous carcinoma
Li Liu1, Jun Liu2, Xiaoliang Deng1
1The State Key Lab of Respiratory Disease, Institute of Public Health, Guangzhou Medical University, Xinzao, Panyu District, Guangzhou, 511436, China.
Background:
Adenosine-to-inosine RNA editing (ATIRE) is characterized as non-mutational epigenetic reprogramming hallmark of cancer, while little is known about its predictive role in cancer survival.
Methods:
To explore survival-related ATIRE events in lung squamous cell carcinoma (LUSC), ATIRE profile, gene expression data, and corresponding clinical information of LUSC patients were downloaded from the TCGA database. Patients were randomly divided into a training (n = 134) and validation cohort (n = 94). Cox proportional hazards regression followed by least absolute shrinkage and selection operator algorithm were performed to identify survival-related ATIRE sites and to generate ATIRE risk score. Then a nomogram was constructed to predict overall survival (OS) of LUSC patients. The correlation of ATIRE level and host gene expression and ATIREs' effect on transcriptome expression were analyzed.
Results:
Seven ATIRE sites that were TMEM120B chr12:122215052A > I, HMOX2 chr16:4533713A > I, CALCOCO2 chr17:46941503A > I, LONP2 chr16:48388244A > I, ZNF440 chr19:11945758A > I, CLCC1 chr1:109474650A > I, and CHMP3 chr2:86754288A > I were identified to generate the risk score, of which high levers were significantly associated with worse OS and progression-free survival in both the training and validation sets. High risk-score was also associated with advanced T stages and worse clinical stages. The nomogram performed well in predicting OS probability of LUSC. Moreover, the editing of ATIRE sites exerted a significant association with expression of host genes and affected several cancer-related pathways.
Conclusions:
This is the first comprehensive study to analyze the role of ATIRE events in predicting LUSC survival. The AITRE-based model might serve as a novel tool for LUSC survival prediction.
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