Uncovering the Independent Role of Lipid Peroxidation in Osteoporosis Through an Integrated Bibliometric and Bioinformatics Analysis.

This study, utilising bibliometric analysis combined with bioinformatics approaches, systematically analysed research trends in the fields of osteoporosis (OP) and autophagy (ATG) over the past two decades, with a focus on the emerging frontier of lipid peroxidation (LP). The aim was to reveal its independent role in the OP network, distinct from the ferroptosis framework. CiteSpace.6.4.R1 was utilised to perform visualisation analysis on 588 relevant articles from the Web of Science Core Collection, examining countries, institutions, authors, and keywords. Common targets between OP and key burst terms were screened via the Gene Expression Omnibus (GEO) database, followed by the construction of a protein-protein interaction (PPI) network and gene ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Subsequently, we constructed 113 models using 12 machine learning algorithms to screen for feature genes, and the diagnostic value of key targets was validated using receiver operating characteristic (ROC) curves. Bibliometric analysis indicated that the field entered a period of rapid development from 2018, with China dominating in terms of publication volume and the United States leading in academic influence. Keyword burst detection identified 'LP' as an emerging frontier since 2023. Bioinformatics analysis identified 127 common OP-LP targets, which are enriched in pathways such as NF-κB, eestrogen signalling, and mitophagy. Through machine learning and MCODE module analysis, five key targets were ultimately screened: Amyloid beta precursor protein (APP), Forkhead Box O1 (FOXO1), Forkhead Box O3 (FOXO3), Jun Proto-Oncogene (JUN), and Synuclein Alpha (SNCA). ROC curves demonstrated their good diagnostic efficacy. This study is the first to integrate bibliometric and bioinformatics methods, revealing the macro-level trends in OP-ATG research and the molecular mechanisms underlying OP-LP crossover. It successfully identified five key OP-LP targets, providing a new perspective for understanding OP mechanisms and developing targeted therapies.