Identification and External Validation of a Transcription Factor-Related Prognostic Signature in Pediatric Neuroblastoma
Wang R., Wang Q.
Prospective Study, published in J Oncol (2021) — summary generated from the PubMed abstract.
Early human evidence such as case series or small samples is exploring possible benefits.
- Level A · Stronger Clinical Evidence
- Level B · Emerging clinical evidence with positive signals
- Level C · Early human research exploring benefits
- Level D · Scientific groundwork from lab and animal studies
- Emerging · Emerging topic under active research
This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.
- Study type
- Prospective Study
- Journal
- J Oncol (2021)
- Reported sample size
- —
- Source database
- Europe PMC
- PMID
- 34992653
- PMCID
- PMC8727167
- DOI
- 10.1155/2021/1370451
- Citations
- 6
Abstract (original English)
Background Neuroblastoma is a common solid tumor originating from the sympathetic nervous system, commonly found in children, and it is one of the leading causes of tumor-related deaths in children. In addition to pathological features, molecular-level features, such as how much gene expression is present and the mutational profile, may provide useful information for the precise treatment of neuroblastoma. Transcription factors (TFs) play an important regulatory role in all aspects of cellular life activities. But there are currently no studies on transcription factor-based biomarkers of neuroblastoma prognosis, and this study is much needed. Methods We downloaded RNA transcriptome data and clinical data from the TARGET database to construct a prognostic model. The prognostic model was constructed by using univariate Cox analysis, LASSO, and multivariate Cox regression. We divided the patients into low-risk and high-risk groups using the median value of the risk score as the cut-off. Then, we validated the prognostic model with the dataset GSE49710. Results We constructed a prognostic model consisting of eight genes (SATB1, ZNF564, SOX14, EN1, IKZF2, SLC2A4RG, FOXJ2, and ZNF521). Patients in the high-risk group had a lower survival rate than those in the low-risk group. The area under the 3-year ROC curve of the model reached 0.825, suggesting a good predictive efficacy. We per
What this study does not prove
- • This study does not prove SVF is an approved treatment or a replacement for standard care.
Evidence level
Early human evidence such as case series or small samples is exploring possible benefits.
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