Integrated transcriptomic, machine learning, and network pharmacology analyses reveal shared inflammatory biomarkers between intervertebral disc degeneration and osteoporosis
- 1Department of Hand and Foot Microsurgery, The Affiliated Yangming Hospital of Ningbo University , ,
- 2Health Science Center, Ningbo University , ,
- 3Department of Oncology, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute , ,
Abstract
Background: Intervertebral disc degeneration (IDD) and osteoporosis (OP) frequently coexist, but their shared inflammatory molecular features remain unclear. We investigated whether the two disorders share an immune-extracellular matrix axis and sought candidate inflammation-associated biomarkers.
Methods: Public transcriptomic datasets GSE124272 and GSE35956 were analyzed separately using differential expression analysis and gene set enrichment analysis. Shared genes were prioritized using LASSO regression, random forest, and support vector machine algorithms, with model performance estimated by nested stratified cross-validation. Exploratory nomograms, network pharmacology, and molecular docking were performed. Expression of NCF2, CRH, and BTK was examined in TNF-α-stimulated immortalized human nucleus pulposus cells and osteoblasts.
Results: Twenty shared differentially expressed genes were identified, with enrichment patterns involving immune, inflammatory, and metabolic processes. The candidate panel comprised BTK, NCF2, CRH, FCGR3A, and SERPINA3. Nested cross-validation yielded AUCs of 0.86 (95% CI, 0.78–0.93) for IDD and 0.84 (95% CI, 0.75–0.91) for OP. The nomograms were retained as theoretical classification models. TNF-α exposure increased NCF2 and decreased CRH expression in both cell models, while BTK fluorescence increased in nucleus pulposus cells. Docking generated exploratory compound–target predictions after relevance and safety screening.
Conclusions: The analyses identified candidate inflammation-associated molecular signatures shared by the two disorders. The cellular experiments demonstrated responsiveness to acute TNF-α exposure but did not establish biomarker function or disease causality. Independent clinical cohorts, primary cells from multiple donors, functional studies, and direct compound–target validation are required.
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BibTeX
@article{zheng2026integrated,
title = {{Integrated transcriptomic, machine learning, and network pharmacology analyses reveal shared inflammatory biomarkers between intervertebral disc degeneration and osteoporosis}},
author = {Mengjie Zheng and Guizhi Zhu and Hongde Jiang and Yuzhu Chen and Jinwen Jiang and Min Lin and Yongqi Xu and Bin Zhang},
journal = {Frontiers in immunology},
year = {2026},
month = oct,
doi = {10.3389/fimmu.2026.1880102},
url = {https://europepmc.org/article/PMC/PMC13672032},
note = {CC BY},
}
RIS
TY - JOUR
TI - Integrated transcriptomic, machine learning, and network pharmacology analyses reveal shared inflammatory biomarkers between intervertebral disc degeneration and osteoporosis
AU - Mengjie Zheng
AU - Guizhi Zhu
AU - Hongde Jiang
AU - Yuzhu Chen
AU - Jinwen Jiang
AU - Min Lin
AU - Yongqi Xu
AU - Bin Zhang
PY - 2026
DA - 2026/10/02
JO - Frontiers in immunology
DO - 10.3389/fimmu.2026.1880102
UR - https://europepmc.org/article/PMC/PMC13672032
AB - Background: Intervertebral disc degeneration (IDD) and osteoporosis (OP) frequently coexist, but their shared inflammatory molecular features remain unclear. We investigated whether the two disorders share an immune-extracellular matrix axis and sought candidate inflammation-associated biomarkers. Methods: Public transcriptomic datasets GSE124272 and GSE35956 were analyzed separately using differential expression analysis and gene set enrichment analysis. Shared genes were prioritized using LASSO regression, random forest, and support vector machine algorithms, with model performance estimated by nested stratified cross-validation. Exploratory nomograms, network pharmacology, and molecular docking were performed. Expression of NCF2, CRH, and BTK was examined in TNF-α-stimulated immortalized human nucleus pulposus cells and osteoblasts. Results: Twenty shared differentially expressed genes were identified, with enrichment patterns involving immune, inflammatory, and metabolic processes. The candidate panel comprised BTK, NCF2, CRH, FCGR3A, and SERPINA3. Nested cross-validation yielded AUCs of 0.86 (95% CI, 0.78–0.93) for IDD and 0.84 (95% CI, 0.75–0.91) for OP. The nomograms were retained as theoretical classification models. TNF-α exposure increased NCF2 and decreased CRH expression in both cell models, while BTK fluorescence increased in nucleus pulposus cells. Docking generated exploratory compound–target predictions after relevance and safety screening. Conclusions: The analyses identified candidate inflammation-associated molecular signatures shared by the two disorders. The cellular experiments demonstrated responsiveness to acute TNF-α exposure but did not establish biomarker function or disease causality. Independent clinical cohorts, primary cells from multiple donors, functional studies, and direct compound–target validation are required.
SN - 1664-3224
ER -