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      Nucleolin expression has prognostic value in neuroblastoma patients

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          Summary

          Background

          Neuroblastoma (NB) represents the most frequent form of extra-cranial solid tumour of infants, responsible for 15% of childhood cancer deaths. Nucleolin (NCL) prognostic value in NB was investigated.

          Methods

          NCL protein expression was retrospectively evaluated in tumour samples of NB patients at diagnosis and after chemotherapy. NCL prognostic value at mRNA level was assessed in a cohort of 20 patients with stage 4 NB (qPCR20, n=20, discovery dataset) and in the MultiPlatform786 including 786 patients of all stages (validation dataset). Overall and event-free survival curves were plotted by Kaplan-Meier method and compared by log-rank test.

          Findings

          NCL protein, down-modulated after chemotherapy in association with features of neuroblastic differentiation,resulted statistically significantly overexpressed in NB tumours and higher in stage 4 compared to stage 1,2,3 patients. In the stage 4 patients cohort qPCR20, patients with high NCLmRNA expression revealed a statisticallysignificant lower survival probability than those with low NCL expression (OS: HR 4.1 95%CI 1.2–13.8; p=0.0215[Log-rank test], EFS: HR 4.1 95%CI 1.2–14.0, p=0.0197[Log-rank test]). In the MultiPlatform786 ( n=786), multivariate analysis suggested that NCL expression has a statistically significant prognostic value even in the model adjusted for established prognostic markers. NCL expression significantly stratified also patients with >18 months and stage 4 tumour(OS: HR 1.8 95%CI 1.2–2.7, p=0.0009[Log-rank test]; EFS: HR 1.7 95%CI 1.1–2.5, p=0.002[Log-rank test]), patients with>18 months stage 4 with MYCN non amplified tumour[EFS: HR 2.3 95%CI 1.2–4.7, p=0.01[Log-rank test]), and patients with MYCN non amplified and MYC high [OS: HR 11.9 95%CI 2.3–62.4, p=0.003[Log-rank test]; EFS: HR 7.2 95%CI 1.6–33.4, p=0.01[Log-rank test]) . A statistically significant correlation between NCL and MYCN, MYC, and TERT was found in independent datasets (MultiPlatform786 ( n=786) and Agilent394 ( n=394). Gene set enrichment analysis revealed a statisticallysignificant positive enrichment of MYC target genes and genes involved in telomerase maintenance.

          Interpretation

          NCL is a novel and independent (adjusting for age, INSS stage, and MYCN status) prognostic marker for NB.

          Funding

          IMH-EuroNanoMed II-2015 and AIRC-IG.

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          Most cited references47

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            The Molecular Signatures Database (MSigDB) hallmark gene set collection.

            The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB. Each hallmark in this collection consists of a "refined" gene set, derived from multiple "founder" sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.
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              The International Neuroblastoma Risk Group (INRG) classification system: an INRG Task Force report.

              Because current approaches to risk classification and treatment stratification for children with neuroblastoma (NB) vary greatly throughout the world, it is difficult to directly compare risk-based clinical trials. The International Neuroblastoma Risk Group (INRG) classification system was developed to establish a consensus approach for pretreatment risk stratification. The statistical and clinical significance of 13 potential prognostic factors were analyzed in a cohort of 8,800 children diagnosed with NB between 1990 and 2002 from North America and Australia (Children's Oncology Group), Europe (International Society of Pediatric Oncology Europe Neuroblastoma Group and German Pediatric Oncology and Hematology Group), and Japan. Survival tree regression analyses using event-free survival (EFS) as the primary end point were performed to test the prognostic significance of the 13 factors. Stage, age, histologic category, grade of tumor differentiation, the status of the MYCN oncogene, chromosome 11q status, and DNA ploidy were the most highly statistically significant and clinically relevant factors. A new staging system (INRG Staging System) based on clinical criteria and tumor imaging was developed for the INRG Classification System. The optimal age cutoff was determined to be between 15 and 19 months, and 18 months was selected for the classification system. Sixteen pretreatment groups were defined on the basis of clinical criteria and statistically significantly different EFS of the cohort stratified by the INRG criteria. Patients with 5-year EFS more than 85%, more than 75% to or = 50% to < or = 75%, or less than 50% were classified as very low risk, low risk, intermediate risk, or high risk, respectively. By defining homogenous pretreatment patient cohorts, the INRG classification system will greatly facilitate the comparison of risk-based clinical trials conducted in different regions of the world and the development of international collaborative studies.
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                Author and article information

                Contributors
                Journal
                eBioMedicine
                EBioMedicine
                eBioMedicine
                Elsevier
                2352-3964
                06 October 2022
                November 2022
                06 October 2022
                : 85
                : 104300
                Affiliations
                [a ]Laboratory of Molecular Biology, IRCCS Istituto Giannina Gaslini, Genova, Italy
                [b ]Laboratory of Experimental Therapies in Oncology, IRCCS Istituto G. Gaslini, Genoa, Italy
                [c ]Department of Basic Medical Sciences, Neurosciences, and Sensory Organs, University of Bari Medical School, Bari, Italy
                [d ]Department of Pathology, IRCCS IstitutoGianninaGaslini, Genoa, Italy
                [e ]CNC – Center for Neurosciences and Cell Biology, Center for Innovative Biomedicine and Biotechnology (CIBB), University of Coimbra, Faculty of Medicine (Polo 1), Coimbra, Portugal
                [f ]Univ Coimbra – University of Coimbra, CIBB, Faculty of Pharmacy, Pólo das Ciências da Saúde, Azinhaga de Santa Comba, Coimbra, Portugal
                [g ]UOC Oncologia, IRCCS IstitutoGiannina Gaslini, Genova, Italy
                Author notes
                [1]

                Sharing first authorship.

                [2]

                Present address: Clinical Bioinformatics Unit, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini 5, 16147 Genova, Italy.

                Article
                S2352-3964(22)00482-0 104300
                10.1016/j.ebiom.2022.104300
                9547201
                1b53d747-1f90-4edc-8472-55542215bd65
                © 2022 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 24 January 2022
                : 12 September 2022
                : 16 September 2022
                Categories
                Articles

                neuroblastoma,nucleolin,biomarker,prognostic value
                neuroblastoma, nucleolin, biomarker, prognostic value

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