Evidence map›Paper›PMID 42890622›Full record

ArticleFrontiers in immunology2026

Integrated transcriptomic, machine learning, and network pharmacology analyses reveal shared inflammatory biomarkers between intervertebral disc degeneration and osteoporosis.

Mengjie Zheng, Guizhi Zhu, Hongde Jiang, Yuzhu Chen, Jinwen Jiang, Min Lin, Yongqi Xu, Bin Zhang

Abstract read
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Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Mengjie Zheng *Department of Hand and Foot Microsurgery, The Affiliated Yangming Hospital of Ningbo University, Ningbo, China.
Guizhi Zhu *Health Science Center, Ningbo University, Ningbo, China.
Hongde Jiang *Health Science Center, Ningbo University, Ningbo, China.
Yuzhu Chen *Department of Oncology, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Jinwen JiangHealth Science Center, Ningbo University, Ningbo, China.
Min LinHealth Science Center, Ningbo University, Ningbo, China.
Yongqi XuHealth Science Center, Ningbo University, Ningbo, China.
Bin ZhangDepartment of Hand and Foot Microsurgery, The Affiliated Yangming Hospital of Ningbo University, Ningbo, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Intervertebral Disc DegenerationMachine LearningOsteoporosisTranscriptomeAgammaglobulinaemia Tyrosine KinaseBiomarkersGene Expression ProfilingHumansInflammationMolecular Docking SimulationNetwork PharmacologyNucleus PulposusOsteoblastsTumor Necrosis Factor-alphaAgammaglobulinaemia Tyrosine KinaseBiomarkersBTK protein, humanTumor Necrosis Factor-alphaintervertebral disc degenerationmachine learningnetwork pharmacologyosteoporosistranscriptomics

Identifiers

PMID42890622
PMCPMC13672032

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.