Identification of aging-related biomarkers for intervertebral disc degeneration in whole blood samples based on bioinformatics and machine learning

Identification of aging-related biomarkers for intervertebral disc degeneration in whole blood samples based on bioinformatics and machine learning

IntroductionAging is characterized by gradual structural and functional changes in the body over time, with intervertebral disc degeneration (IVDD) representing a key manifestation of spinal aging and a major contributor to low back pain (LBP).MethodsThis study utilized bioinformatics and machine le...

Full description

Saved in:
Bibliographic Details
Main Authors: Zi-hang Li, Shi-pian Li, Ya-hao Li, Yu-cheng Wang, Zhen-yu Tang, Kai-yang Xu, Xiao-rong Li, Zhen Tan, Jiao-yi Pan, Jin-tao Liu, Hong Jiang, Zhi-jia Ma, Yu-xiang Dai, Peng-fei Yu
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-04-01
Series:Frontiers in Immunology
Subjects:
intervertebral disc degeneration
aging-related biomarkers
bioinformatics
machine learning
immune infiltration analysis
whole blood samples
Online Access:https://www.frontiersin.org/articles/10.3389/fimmu.2025.1565945/full
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:IntroductionAging is characterized by gradual structural and functional changes in the body over time, with intervertebral disc degeneration (IVDD) representing a key manifestation of spinal aging and a major contributor to low back pain (LBP).MethodsThis study utilized bioinformatics and machine learning approaches to identify aging-related biomarkers associated with IVDD in whole blood samples. By analyzing GEO datasets alongside aging-related databases such as GeneCards, HAGR, and AgeAnno, we identified 15 aging-related differentially expressed genes (AIDEGs). Correlation and immune infiltration analyses were conducted on these AIDEGs, and diagnostic models were developed using WGCNA, logistic regression, random forest, support vector machine, k-nearest neighbors, and LASSO regression to identify key genes.ResultsAmong these, FCGR1A, CBS, and FASLG emerged as significant biomarkers with strong predictive capabilities for IVDD. Further exploration of biological pathways involving AIDEGs provided insights into their potential roles in IVDD pathogenesis. To further validate these findings, we collected human blood specimens and conducted in vitro experiments. ELISA assays confirmed that CBS and FASLG are crucial biomarkers of IVDD, with distinct expression patterns in patients with moderate versus severe degeneration.DiscussionThese results highlight the diagnostic potential of AIDEGs and provide a new perspective for early intervention and treatment strategies in IVDD.
ISSN:1664-3224

Similar Items