Using machine learning methods to study the tumour microenvironment and its biomarkers in osteosarcoma metastasis.

ABSTRACT:

Background

The long-term prognosis for patients with osteosarcoma (OS) metastasis remains unfavourable, highlighting the urgent need for research that explores potential biomarkers using innovative methodologies.

Methods

This study explored potential biomarkers for OS metastasis by analysing data from the Cancer Genome Atlas Program (TCGA) and Gene Expression Omnibus (GEO) databases. The synthetic minority oversampling technique (SMOTE) was employed to tackle class imbalances, while genes were selected using four feature selection algorithms (Monte Carlo feature selection [MCFS], Borota, minimum-redundancy maximum-relevance [mRMR], and light gradient-boosting machine [LightGBM]) based on the gene expression matrix. Four machine learning (ML) algorithms (support vector machin

SUBMITTER: Liu G 

PROVIDER: S-EPMC11016722 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature