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Development and Validation of a Five-RNA–Based Signature and Identification of Candidate Drugs for Neuroblastoma

ABSTRACT: Neuroblastoma (NBL) originating from the sympathetic nervous system is the most prevalent solid tumor in infancy. Although there is sufficient variability in prognosis among different age pyramids, age-related gene expression profiles and biomarkers remain poorly explored. The present study aimed to construct a signature based on differentially expressed genes (DEGs) between two age groups in NBL. Univariate Cox regression, multivariate Cox regression, and LASSO analyses were used to identify the optimal prognostic factors. The prediction ability of the model was assessed using the receiver operating characteristic (ROC) curve and C-index. Functional enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes and gene ontology databases. A total of 1,160 DEGs were identified between the two groups, and 204 DEGs impacted the survival of NBL. Functional enrichment analysis revealed that the DEGs were involved in retinol metabolism, cholesterol metabolism, and glycolysis/gluconeogenesis pathways. Five RNAs, namely F8A3, PDF, ANKRD24, FAXDC2, and TMEM160 were recruited into the signature. They were correlated with COG risk classification, INSS stage, and histology. MYCN amplification was linked to FAXDC2, TMEM160, PDF, and F8A3. The expression levels of ANKRD24, PDF, and TMEM160 were lower in the hyperdiploid groups. Only FAXDC2 levels were different in the different MKI grades. The ROC curve showed that the five-RNA–based signatures effectively predicted the OS of NBL (3-years AUC = 0.791, 5-years AUC = 0.816) in the TARGET cohort. The predictive capability was also validated by the GSE49711 cohort (3-years AUC = 0.851, 5-years AUC = 0.848). The C-index in the TARGET and GSE49711 cohorts was 0.749 and 0.809, respectively. The potential mechanisms of the five RNAs were also explored via gene set enrichment analysis, and candidate drugs targeting the five genes, including dabrafenib, vemurafenib, and bafetinib, were screened. In conclusion, we constructed a five-RNA–based signature to predict the survival of NBL and screened candidate agents against NBL.

SUBMITTER: Zhang P

PROVIDER: S-EPMC8564070 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Objective: The stratification of neuroblastoma (NBL) prognosis remains difficult. RNA-based signatures might be able to predict prognosis, but independent cross-platform validation is still rare. Methods: RNA-Seq-based profiles from NBL patients were acquired and then analyzed. The RNA-Seq prognostic index (RPI) and the clinically adjusted RPI (RCPI) were successively established in the training cohort (TARGET-NBL) and then verified in the validation cohort (GSE62564). Survival prediction was assessed using a time-dependent receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). Functional enrichment analysis of the genes was conducted using bioinformatics methods. Results: In the training cohort, 10 gene pairs were eventually integrated into the RPI. In both cohorts, the high-risk group had poor overall survival (OS) (P < 0.001 and P < 0.001, respectively) and favorable event-free survival (EFS) (P = 0.00032 and P = 0.06, respectively). ROC curve analysis also showed that the RPI predicted OS (60 month AUC values of 0.718 and 0.593, respectively) and EFS (60 month AUC values of 0.627 and 0.852, respectively) well in both the training and validation cohorts. Clinicopathological indicators associated with prognosis in the univariate and multivariate regression analyses were identified and added to the RPI to form the RCPI. The RCPI was also used to divide populations into different risk groups, and the high-risk group had poor OS (P < 0.001 and P < 0.001, respectively) and EFS (P < 0.05 and P < 0.05, respectively). Finally, the RCPI had higher accuracy than the RPI for the prediction of OS (60 month AUC values of 0.730 and 0.852, respectively) and EFS (60 month AUC values of 0.663 and 0.763, respectively) in both the training and validation cohorts. Moreover, these differentially expressed genes may be involved in certain NBL-related events. Conclusions: The RCPI could reliably categorize NBL patients based on different risks of death.

Project description:Breast cancer (BC), the most common malignancy in women, has a high cancer-related mortality. Endoplasmic reticulum stress (ERS), a response to the accumulation of unfolded proteins, has emerging roles in tumorigenesis, including invasion, metastasis, immune escape, etc. However, few studies have focused on the correlation between ERS with long non-coding RNAs (lncRNAs) in BC. We attempted to construct an ERS-related lncRNA prognostic signature and study its value in BC from tumor mutational burden (TMB), tumor immune microenvironment (TIME), cluster, clinical treatment, and so on. In the present study, transcriptomic and clinical data of BC patients were extracted from The Cancer Genome Atlas (TCGA) database. Correlation test, Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) method were performed to determine an ERS-related lncRNA prognostic signature. Survival and predictive performance were analyzed according to Kaplan-Meier curves and receiver operating characteristic (ROC) curves, while nomograms and calibration curves were established. Then, an enrichment analysis was performed to study the functions and biological processes of ERS-related lncRNAs. TMB and TIME were also analyzed to assess the mutational status and immune status. Additionally, by using consensus cluster analysis, we compared differences among tumor subtypes. Drug sensitivity analysis and immunologic efficacy evaluations were performed together for further exploration. We identified a novel prognostic signature consisting of 9 ERS-related lncRNAs. High-risk patients had worse prognoses. The signature had a good predictive performance as an independent prognostic indicator and was significantly associated with clinicopathological characteristics. Enrichment analysis showed that metabolic pathways were enriched in high-risk patients, while immune pathways were more active in low-risk patients. Low-risk patients had lower TMB, higher immune scores, and stronger immune functions. Cluster analysis clarified that cluster 2 had the most active immune functions and was sensitive to more drugs, which may have the best clinical immunological efficacy. A clinical efficacy evaluation revealed that patients in the low-risk group may benefit more from chemotherapy, targeted therapy, and immunotherapy. The novel signature has significant clinical implications in prognosis prediction for BC. Our study clarifies that there is a potential connection between the ERS-related lncRNAs and BC, which may provide new treatment guidelines for BC.

Project description:Background This study aimed to identify prognostic signatures to predict the prognosis of breast cancer (BRCA) patients based on a series of comprehensive analyses of gene expression data. Methods The RNA-sequencing expression data and corresponding BRCA patient clinical data were collected from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets. Firstly, the differently expressed genes (DEGs) related to prognosis between tumor tissues and normal tissues were ascertained by performing R package “limma”. Secondly, the DEGs were used to construct a polygenic risk scoring model by the weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator Cox regression (Lasso-cox) analysis method. Thirdly, survival analysis was performed to investigate the risk score values in the TCGA cohort. And the enrichment analysis, immune cell infiltration levels analysis, and protein-protein internet (PPI) analysis were performed. Simultaneously, the GEO cohort was used to validate the model. Lastly, we constructed a nomogram to explore the influence of polygenic risk score and other clinical factors on the survival probability of patients with BRCA. Results A total of 1000 DEGs including 396 upregulated genes and 604 downregulated genes were identified from the TCGA-BRCA dataset. We obtained 5 prognosis-related genes, as the key biomarkers by Lasso-cox analysis (FBXL19, HAGHL, PHKG2, PKMYT1, and TXNDC17), all of which were significantly upregulated in breast tumors. The prognostic prediction of the 5 genes model was great in training and validation cohorts. Moreover, the high-risk group had a poorer prognosis. The Cox regression analysis showed that the comprehensive risk score for 5 genes was an independent prognosis factor. Conclusion The 5 genes risk model constructed in this study had an independent predictive ability to distinguish patients with a high risk of death from those with a low-risk score, and it can be used as a practical and reliable prognostic tool for BRCA.

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Development and Validation of a Five-RNA–Based Signature and Identification of Candidate Drugs for Neuroblastoma

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