A 16-miRNA Prognostic Model to Predict Overall Survival in Neuroblastoma - Wang, Jiepin; Xiao, Dong; Wang, Junxiang (Frontiers in Genetics, 2022)
Neuroblastoma is the most malignant childhood tumor. The outcome of neuroblastoma is hard to predict due to the limitation of prognostic markers. In our study, we constructed a 16 miRNAs prognostic model to predict the overall survival of neuroblastoma patients at early diagnosis. A total of 205 DE miRNAs were screened using RNA sequencing data from GSE121513. Lasso-Cox regression analysis generated a 16 miRNAs signature consisting of hsa-let-7c, hsa -miR-135a, hsa -miR-137, hsa -miR-146a, hsa -miR-149, hsa -miR-15a, hsa -miR-195, hsa -miR-197, hsa -miR-200c, hsa -miR-204, hsa -miR-302a, hsa -miR-331, hsa -miR-345, hsa -miR-383, hsa -miR-93, hsa -miR-9star. The Concordance index of multivariate Cox regression analysis was 0.9 and the area under curve (AUC) of 3-year and 5-year survival were 0.92 and 0.943. The mechanism was further investigated using the TCGA dataset and GSE90689 datasets. Two miRNA-gene interaction networks were constructed among DEGs from two datasets. Functional analysis revealed that immune-related processes were involved in the initiation and metastasis of neuroblastoma. CIBERSORT and survival analysis suggested that lower CD8 T cells proportion and higher SPTA1 expression were related to a better prognosis. Our study demonstrated that miRNA signature may be useful in prognosis prediction and management improvement.