Identification of novel markers for neuroblastoma immunoclustering using machine learning.

ABSTRACT:

Background

Due to the unique heterogeneity of neuroblastoma, its treatment and prognosis are closely related to the biological behavior of the tumor. However, the effect of the tumor immune microenvironment on neuroblastoma needs to be investigated, and there is a lack of biomarkers to reflect the condition of the tumor immune microenvironment.

Methods

The GEO Database was used to download transcriptome data (both training dataset and test dataset) on neuroblastoma. Immunity scores were calculated for each sample using ssGSEA, and hierarchical clustering was used to categorize the samples into high and low immunity groups. Subsequently, the differences in clinicopathological characteristics and treatment between the different groups were examined. Three machine learning algo

SUBMITTER: Zhang L 

PROVIDER: S-EPMC11570813 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature