Predicting Treatment Outcomes in Patients with Low Back Pain Using Gene Signature-Based Machine Learning Models.

ABSTRACT:

Introduction

Low back pain (LBP) is a significant global health burden, with variable treatment outcomes and an unclear underlying molecular mechanism. Effective prediction of treatment responses remains a challenge. In this study, we aimed to develop gene signature-based machine learning models using transcriptomic data from peripheral immune cells to predict treatment outcomes in patients with LBP.

Methods

The transcriptomic data of patients with LBP from peripheral immune cells were retrieved from the GEO database. Patients with LBP were recruited, and treatment outcomes were assessed after 3 months. Patients were classified into two groups: those with resolved pain and those with persistent pain. Differentially expressed genes (DEGs) between the two groups were identifie

SUBMITTER: Lian Y 

PROVIDER: S-EPMC11751268 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature