{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T16:33:23Z","timestamp":1788539603991,"version":"build-2803163510"},"reference-count":60,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T00:00:00Z","timestamp":1758499200000},"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. Mol. Neurosci."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Intervertebral disc degeneration (IVDD) and non-alcoholic fatty liver disease (NAFLD) represent major global health burdens. Although recent evidence points to a potential association between these two conditions, the underlying molecular mechanisms remain poorly understood. This study aims to elucidate their shared molecular landscape using integrated bioinformatics approaches.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Three IVDD and two NAFLD datasets were acquired from the Gene Expression Omnibus (GEO). We performed differential expression analysis (DEGs), weighted gene co-expression network analysis (WGCNA), and machine learning to identify shared hub genes. The diagnostic relevance of these genes was further assessed using ROC curves and nomograms. Single-cell sequencing analysis was employed to examine gene expression patterns across cell clusters in intervertebral disk and liver tissues. <jats:italic>In vivo<\/jats:italic> experiments were conducted to evaluate the influence of NAFLD on IVDD progression and the therapeutic potential of exercise intervention.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Six shared genes were identified between IVDD and NAFLD. Among these, ME1, HAS2, and ADRB2 were highlighted as potential biomarkers. Validation confirmed consistent expression patterns and strong predictive performance for both diseases. KEGG pathway and immune infiltration analyses indicated significant involvement of these biomarkers in disease-related pathways and immune cell interactions. Single-cell sequencing revealed distinct expression profiles and functional roles of ME1, HAS2, and ADRB2 across relevant cell types. <jats:italic>In vivo<\/jats:italic> studies demonstrated that NAFLD exacerbates IVDD progression, and intervention through swimming exercise ameliorated NAFLD and exerted protective effects on IVDD under high-fat diet conditions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>This study identifies ME1, HAS2, and ADRB2 as pivotal shared biomarkers for IVDD and NAFLD, providing new insights into their molecular interconnection. The findings enhance our understanding of the comorbid mechanisms and highlight the potential of exercise as a therapeutic strategy for both conditions. These results pave the way for further mechanistic and clinical research into common pathways and integrated treatment approaches.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnmol.2025.1639705","type":"journal-article","created":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T05:29:58Z","timestamp":1758518998000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Cross-disease biomarker identification reveals shared diagnostic biomarkers for IVDD and NAFLD via bulk and single-cell RNA sequencing"],"prefix":"10.3389","volume":"18","author":[{"given":"Jiasen","family":"Wei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenglong","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lina","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linhui","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbo","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ximing","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaiqiang","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,9,22]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"107289","DOI":"10.1016\/j.compbiomed.2023.107289","article-title":"Machine learning algorithms to predict major adverse cardiovascular events in patients with diabetes","volume":"164","author":"Abegaz","year":"2023","journal-title":"Comput. 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