Co-expression analysis identifies long noncoding RNA SNHG1 as a novel predictor for event-free survival in neuroblastoma

Neuroblastoma (NB) is a commonly occurring cancer among infants and young children. Recently, long non-coding RNAs (lncRNAs) have been using as prognostic biomarkers for therapeutics and interventions in various cancers. Considering the poor survival of NB, the lncRNA-based therapeutic strategies must be improved. This work proposes an overall survival time estimator called SVR-NB to identify the lncRNA signature that is associated with the overall survival of patients with NB. SVR-NB is an optimized support vector regression (SVR)-based method that uses an inheritable bi-objective combinatorial genetic algorithm for feature selection. The dataset of 231 NB patients that contains overall survival information and expression profiles of 783 lncRNAs was used to design and evaluate SVR-NB from the database of gene expression omnibus accession GSE62564. SVR-NB identified a signature of 35 lncRNAs and achieved a mean squared correlation coefficient of 0.85 and a mean absolute error of 0.56 year between the actual and estimated overall survival time using 10-fold cross-validation. Further, we ranked and characterized the 35 lncRNAs according to their contribution towards the estimation accuracy. Functional annotations and co-expression gene analysis of LOC440896, LINC00632, and IGF2-AS revealed the association of co-expressed genes in Kyoto Encyclopedia of Genes and Genomes pathways.

Current clinical risk factors stratify patients with neuroblastoma (NB) for appropriate treatments, yet patients with similar clinical behaviors evoke variable responses. MYCN amplification is one of the established drivers of NB and, when combined with high-risk displays, worsens outcomes. Growing high-throughput transcriptomics studies suggest long noncoding RNA (lncRNA) dysregulation in cancers, including NB. However, expression-based lncRNA signatures are altered by MYCN amplification, which is associated with high-risk, and patient prognosis remains limited. We investigated RNA-seq-based expression profiles of lncRNAs in MYCN status and risk status in a discovery cohort (n = 493) and validated them in three independent cohorts. In the discovery cohort, a prognostic association of lncRNAs was determined by univariate Cox regression and integrated into a signature using the risk score method. A novel risk score threshold selection criterion was developed to stratify patients into risk groups. Outcomes by risk group and clinical subgroup were assessed using Kaplan-Meier survival curves and multivariable Cox regression. The performance of lncRNA signatures was evaluated by receiver operating characteristic curve. All statistical tests were two-sided. In the discovery cohort, 16 lncRNAs that were differentially expressed (fold change ≥ 2 and adjusted P ≤ 0.01) integrated into a prognostic signature. A high risk score group of lncRNA signature had poor event-free survival (EFS; P < 1E-16). Notably, lncRNA signature was independent of other clinical risk factors when predicting EFS (hazard ratio = 3.21, P = 5.95E–07). The findings were confirmed in independent cohorts (P = 2.86E-02, P = 6.18E-03, P = 9.39E-03, respectively). Finally, the lncRNA signature had higher accuracy for EFS prediction (area under the curve = 0.788, 95% confidence interval = 0.746 to 0.831). Here, we report the first (to our knowledge) RNA-seq 16-lncRNA prognostic signature for NB that may contribute to precise clinical stratification and EFS prediction.

Background: Esophageal adenocarcinoma is a lethal disease. For locally advanced patients, neoadjuvant chemoradiotherapy followed by surgery is the standard of care. Risk stratification relies heavily on clinicopathologic features, particularly pathologic response, which is inadequate, therefore establishing the need for new and reliable biomarkers for risk stratification. Methods: Thirty four patients with locally advanced esophageal adenocarcinoma were analyzed, of which 21 received a CROSS regimen with carboplatin, paclitaxel, and radiation. Capture-based targeted sequencing was performed on the paired baseline and post-treatment samples. Differentially mutated gene analysis between responders and non-responders of treatment was performed to determine predictors of response. A univariate Cox proportional hazard regression was used to examine associations between gene mutation status and overall survival. Results: A 3-gene signature, based on mutations in EPHA5, BCL6, and ERBB2, was identified that robustly predicts response to the CROSS regimen. For this model, sensitivity was 84.6% and specificity was 100%. Independently, a 9 gene signature was created using APC, MAP3K6, ETS1, CSF3R, PDGFRB, GATA2, ARID1A, PML, and FGF6, which significantly stratifies patients into risk categories, prognosticating for improved relapse-free (p = 4.73E-03) and overall survival (p = 3.325E-06). The sensitivity for this model was 73.33% and the specificity was 94.74%. Conclusion: We have identified a 3-gene signature (EPHA5, BCL6, and ERBB2) that is predictive of response to neoadjuvant chemoradiotherapy and a separate prognostic 9-gene classifier that predicts survival outcomes. These panels provide significant potential for personalized management of locally advanced esophageal cancer.