{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,5]],"date-time":"2026-11-05T01:58:43Z","timestamp":1793843923678,"version":"4.6.1"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Lumbar disc degeneration, a primary cause of chronic low back pain, is closely linked to inflammatory responses and the immune microenvironment; however, its underlying mechanisms remain poorly understood.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study integrated scRNA-seq and bulk RNA-seq data to identify macrophage subpopulations in degenerative tissues and constructed co-expression modules using hdWGCNA. Functional enrichment was explored through GO, KEGG, and GSEA analyses. A panel of 101 machine learning algorithms was employed to screen diagnostic genes, with ROC curves used for validation. A combined diagnostic model for LDD risk was developed based on the expression profiles of the diagnostic genes. Additionally, immune infiltration was assessed via CIBERSORT, potential therapeutic compounds were identified and validated through molecular docking, and animal experiments were performed to verify the reliability of the results.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Single-cell analysis identified a pro-inflammatory macrophage subpopulation enriched in degenerative tissues. hdWGCNA revealed highly correlated black and blue modules, which were primarily associated with \u201cimmune signaling\u2013matrix remodeling,\u201d as indicated by enrichment analysis. Machine learning approaches screened key genes, including CDK1 and COL4A2, from these modules. ROC analysis confirmed the strong diagnostic performance of these genes, and the combined diagnostic model based on them demonstrated excellent predictive capability for LDD risk. Immune infiltration analysis highlighted a close association between the key genes and the \u03b3\u03b4T cell\u2013neutrophil axis. Molecular docking suggested that RO 3306 and AR234960 may serve as potential therapeutic agents. qPCR and Western blot experiments validated the expression of the key genes and the possible effects of these compounds.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study elucidates the genetic signatures associated with macrophages and their immune regulatory mechanisms in LDD, identifies potential diagnostic biomarkers and therapeutic targets, and proposes new strategies for precision intervention.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2025.1671961","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T05:14:14Z","timestamp":1764652454000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Integration of single-cell sequencing and machine 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