Neuroblastoma is a common tumor in infants and young children. Neuroblastoma is a group of heterogeneous tumors with different prognosis. Clear clinical risk grouping is important for making treatment plan and evaluating prognosis. Precision medicine is a medical model that customizes treatment plans for patients according to their characteristics in genome, phenotype, and biomarkers. Nomogram is a statistical model that can be used for clinical events predictive analysis. Compared with other predictive statistical methods, nomogram can provide better prognostic risk assessment in a visual way. To date, some nomograms have been constructed to predict the prognosis of patients with cancer. Base on the cancer genome atlas database, some researchers developed a nomogram that incorporate an autophagy-related signature for predicting the survival of glioblastoma patients. Another previous study has constructed a nomogram that could be used to predict the prognosis of uterine corpus endometrial carcinoma . In an effort to guide risk-based treatment for patients with neuroblastoma, we developed and validated nomograms that were based on combinations of clinical and molecular prognostic markers. The nomogram may help perform risk stratification and provide more individualized clinical advice for each patient.
Recent studies have shown that LncRNA can regulate many important life activities, and it is also closely related to the occurrence and development of tumor . However, the prognostic value of lncRNAs in neuroblastoma is not entirely clear. In this research, we constructed and validated a 16-lncRNA-based LASSO risk score to predict overall survival for patients with neuroblastoma. Patients in the low-risk group had significantly better survival than those in the high-risk group. Our research highlighted the prognostic value of the 16-lncRNAs and suggested practical applications in prognostic prediction of neuroblastoma. The lncRNA FOXD1-AS1 functions as an important oncogenic lncRNA in glioma and affected biological processes via protein eIF5a.In breast cancer, higher lncRNA ZFHX4-AS1 expression is associated with worse prognosis, and lncRNA ZFHX4-AS1 acts as an oncogene through the Hippo signaling pathway .In non-small cell lung cancer, lncRNA PCAT7 can promote cell proliferation and induce epithelial-mesenchymal transition, it has been well documented as a key regulator of tumour growth and development . The biological function of the remaining lncRNAs in our study has not been investigated in previous studies. Further researches are required to explore the underlying molecular mechanisms of these lncRNAs. These data might improve the development of a cheap molecular test. In our study, stratified analysis were performed to further evaluate whether the 16-lncRNA-based LASSO risk score exhibit predictive effect within same clinical characteristics. We stratified patients into different group based on age, INSS stage and MYCN status. These results showed that the 16-lncRNA-based LASSO risk score can still separate neuroblastoma patients into high-risk or low-risk group, and patients in high-risk group have shorter overall survival and poor prognosis than those in low-risk group. These results demonstrated that this 16-lncRNA-based LASSO risk score was independent risk factors for survival prediction of neuroblastoma patients and could stratify patients from different group into subtypes with different prognosis. Thus, the ability of our 16-lncRNAs in identifying subgroups of neuroblastoma patients implies that the 16-lncRNAs may be used to refining the current prognostic model and promoting further stratification of patients in the future clinical research.
Most of these dysregulated lncRNAs are not yet functionally annotated. However, we can infer the underlying regulatory function of the lncRNAs using the mRNA expression data of the same group of patients. In order to identify a group of pathways significantly enriched in high-risk group with respect to low-risk group, we performed gene set enrichment analysis (GSEA) using the Java GSEA implementation. The neuroblastoma patients in GSE49710 were assigned into two groups (high risk vs. low risk) according to the optimal cut-off of LASSO-risk-score. Here, the 16-lncRNAs identified in our study are involved in biological pathway known to be crucial to the relapse of neuroblastoma, namely KRAS signalling pathway. A study involving 32 samples have reported that KRAS mutations have been found in some cases of relapse neuroblastomas. E2F-mediated associated pathways were significantly associated with the recurrence of cancer. Abnormal copy number of G1-cell cycle related genes is frequently found in neuroblastoma and increased expression of E2F target genes . Metastasis is a hallmark of malignant neuroblastoma and is the main reason for therapeutic failure. TNF-α is a pro-inflammatory cytokine that activates the nuclear factor-κB signaling pathway, leading to CXCR4 overexpression, fostering neuroblastoma cell metastasis. Thus, it is a plausible inference for the 16-lncRNAs associated with survival of patients with neuroblastoma. The potential molecular function may put forward the direction for the further study on the mechanism of neuroblastoma progression.
There are some limitations in our study. We cannot exclude the possibility of residual confounding after internal validation as a result of possible overfitting from variable and threshold selection for these models. So, internal validation with bootstrapping and external validation were used to address these concerns. In addition, the underlying mechanisms of these lncRNAs in our risk score remain unclear. Further cell and animal studies are needed to confirm the exact molecular mechanisms of these lncRNAs.
In conclusion, this study identified a novel and robust lncRNA-based LASSO risk score for prognostic prediction of neuroblastoma by mining currently available microarray data. This risk score may contribute to personalize prediction of neuroblastoma prognosis and acted as potential biomarkers. A nomogram combining the molecular signature and clinical factors was constructed. The nomogram that contain the 16-lncRNAs allows for accurate risk assessment and guides future clinical planning regarding patient surveillance.