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ORIGINAL RESEARCH article
Front. Immunol.
Sec. Inflammation
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1565945
This article is part of the Research Topic Regulation of intervertebral disc homeostasis and the pathological or pathophysiological alterations under various harmful stimuli during aging process View all 3 articles
Identification of Aging-Related Biomarkers for Intervertebral Disc Degeneration in Whole Blood Samples Based on Bioinformatics and Machine Learning
Provisionally accepted
Zi-hang Li 1
Shi-pian Li 2
Ya-hao Li 1
Yu-cheng Wang 1
Zhen-yu Tang 1
Kai-yang Xu 1
Xiao-rong Li 1
Zhen Tan 1
Jiao-yi Pan 3
Jin-tao Liu 1
Hong Jiang 1
Zhi-jia Ma 1
Yu-xiang Dai 1,2*
Peng-fei Yu 1*- 1 Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, China
- 2 Spinal Disease Research Institute, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, Shanghai Municipality, China
- 3 Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Zhejiang Chinese Medical University, Wenzhou, Zhejiang Province, China
Aging 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). This 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. Among these, FCGR1A, CBS, and FASLG emerged as significant biomarkers with strong predictive capabilities for IVDD. Further exploration of biological , 215009
Keywords: Intervertebral Disc Degeneration, Aging-related biomarkers, bioinformatics, machine learning, immune infiltration analysis, whole blood samples 200032, 325000
Received: 24 Jan 2025; Accepted: 27 Mar 2025.
Copyright: © 2025 Li, Li, Li, Wang, Tang, Xu, Li, Tan, Pan, Liu, Jiang, Ma, Dai and Yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yu-xiang Dai, Spinal Disease Research Institute, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, Shanghai Municipality, China
Peng-fei Yu, Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, China
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