Review of Patient Gene Profiles Obtained through a Non-Negative Matrix Factorization-Based Framework to Determine the Role Inflammation Plays in Neuroblastoma Pathogenesis

Int J Mol Sci. 2024 Apr 17;25(8):4406. doi: 10.3390/ijms25084406.

Abstract

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 (PIK3CG, NFATC2, PIK3R2, VAV1, RAC2, COL6A2, COL6A3, COL12A1, COL14A1, ITGAL, ITGB7, FOS, PTGS2, PTPRC, ITPR3) 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.

Keywords: biomarkers; gene profiling; inflammatory phenotype; neuroblastoma.

MeSH terms

  • Gene Expression Profiling / methods
  • Gene Expression Regulation, Neoplastic
  • Humans
  • Inflammation* / genetics
  • Neuroblastoma* / genetics
  • Neuroblastoma* / pathology
  • Transcriptome

Grants and funding

This work was supported by: Piano Nazionale di Ripresa e Resilienza (PNRR), Missione 4 “Istruzione e Ricerca”—Componente C2 Investimento 1.1, “Fondo per il Programma Nazionale di Ricerca e Progetti di Rilevante Interesse Nazionale”, Progetto PRIN-2022 PNRR, P2022BLN38, Computational approaches for the integration of multi-omics data. CUP: H53D23008870001.