Machine learning approach to screen new diagnostic features of adamantinomatous craniopharyngioma and explore personalised treatment strategies.

ABSTRACT:

Background

Adamantinoma craniopharyngioma (ACP) is a non-malignant tumour of unknown pathogenesis that frequently occurs in children and has malignant potential. The main treatment options are currently surgical resection and radiotherapy. These treatments can lead to serious complications that greatly affect the overall survival and quality of life of patients. It is therefore important to use bioinformatics to explore the mechanisms of ACP development and progression and to identify new molecules.

Methods

Sequencing data of ACP was downloaded from the comprehensive gene expression database for differentially expressed gene identification and visualized by Gene Ontology, Kyoto Gene, and gene set enrichment analyses (GSEAs). Weighted correlation network analysis was used to

SUBMITTER: Wu J 

PROVIDER: S-EPMC10248946 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature