Construction of a Prognostic Nomogram Based on Autophagy-Related Genes for Children With Neuroblastoma. (August 2022) - Ye, Guogang; Wang, Yue
Neuroblastoma (NB) is the most common solid malignancy in children. MYCN gene amplification is the most relevant genetic alteration in patients with NB and is associated with poor prognosis. Autophagy plays specific roles in the occurrence, development, and progression of NB. Here, we aimed to identify and assess the prognostic effects of autophagy-related genes (ARGs) in patients with NB and MYCN gene amplification. Differentially expressed ARGs were identified in patients with NB with and without MYCN gene amplification, and the ARG expression patterns and related clinical data from the Therapeutically Applicable Research to Generate Effective Treatments database were used as the training cohort. Least absolute shrinkage and selection operator analyses were used to identify prognostic ARGs associated with event-free survival (EFS), and a prognostic risk score model was developed. Model performance was assessed using the Kaplan–Meier method and receiver operating characteristic (ROC) curves. The prognostic ARG mode l was verified using the validation cohort dataset, GSE49710. Finally, a nomogram was constructed by combining the ARGbased risk score with clinicopathological factors. Three ARGs (GABARAPL1, NBR1, and PINK1) were selected to build a prognostic risk score model. The EFS in the low-risk group was significantly better than that in the high-risk group in both the training and validation cohorts. A nomogram incorporating the prognostic risk score, age, and International Neuroblastoma Staging System stage showed a favorable predictive ability for EFS rates according to the area under the ROC curve at 3 years (AUC = 0.787) and 5 years (AUC = 0.787). The nomogram demonstrated good discrimination and calibration. Our risk score model for the 3 ARGs can be used as an independent prognostic factor in patients with NB and MYCN gene amplification. The model can accurately predict the 3- and 5-year survival rates.