Boccarelli Angelina, Del Buono Nicoletta, Esposito Flavia
Department of Precision and Regenerative Medicine and Polo Jonico, School of Medicine, University of Bari Aldo Moro, Piazza Giulio Cesare 11, 70121 Bari, Italy.
Department of Mathematics, University of Bari Aldo Moro, Via Edoardo Orabona 4, 70125 Bari, Italy.
Int J Mol Sci. 2024 Apr 17;25(8):4406. doi: 10.3390/ijms25084406.
Neuroblastoma is the most common extracranial solid tumor in children. It is a highly heterogeneous tumor consisting of different subcellular types and genetic abnormalities. Literature data confirm the biological and clinical complexity of this cancer, which requires a wider availability of gene targets for the implementation of personalized therapy. This paper presents a study of neuroblastoma samples from primary tumors of untreated patients. The focus of this analysis is to evaluate the impact that the inflammatory process may have on the pathogenesis of neuroblastoma. Eighty-eight gene profiles were selected and analyzed using a non-negative matrix factorization framework to extract a subset of genes relevant to the identification of an inflammatory phenotype, whose targets (, , , , , , , , , , , , , , ) allow further investigation. Based on the genetic signals automatically derived from the data used, neuroblastoma could be classified according to stage rather than as a "cold" or "poorly immunogenic" tumor.
神经母细胞瘤是儿童最常见的颅外实体瘤。它是一种高度异质性肿瘤,由不同的亚细胞类型和基因异常组成。文献数据证实了这种癌症的生物学和临床复杂性,这需要有更多的基因靶点以实施个性化治疗。本文介绍了一项对未经治疗患者原发性肿瘤的神经母细胞瘤样本的研究。该分析的重点是评估炎症过程可能对神经母细胞瘤发病机制产生的影响。选择了88个基因谱,并使用非负矩阵分解框架进行分析,以提取与识别炎症表型相关的基因子集,其靶点(,,,,,,,,,,,,,,)可供进一步研究。根据从所用数据自动得出的基因信号,神经母细胞瘤可根据分期进行分类,而不是被归类为“冷”肿瘤或“免疫原性差”的肿瘤。