Multi-omics characterization of macrophage polarization-related features in osteoarthritis based on a machine learning computational framework

• Published in: Heliyon Vol. 10; no. 9; p. e30335
• Main Authors: Hu, Ping, Li, Beining, Yin, Zhenyu, Peng, Peng, Cao, Jiangang, Xie, Wanyu, Liu, Liang, Cao, Fujiang, Zhang, Bin
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 15.05.2024; Elsevier
• Subjects: Bioinformatics; Machine learning; Macrophage polarization; Osteoarthritis; WGCNA; Macrophage polarization; Osteoarthritis; Bioinformatics; WGCNA; Machine learning
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2024.e30335

OA imposes a heavy burden on patients and society in that its mechanism is still unclear, and there is a lack of effective targeted therapy other than surgery. The osteoarthritis dataset GSE55235 was downloaded from the GEO database and analyzed for differential genes by limma package, followed by analysis of immune-related modules by xcell immune infiltration combined with the WGCNA method, and macrophage polarization-related genes were downloaded according to the Genecard database, and VennDiagram was used to determine their intersection. These genes were also subjected to gene ontology (GO), disease ontology (DO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. Using machine learning, the key osteoarthritis genes were finally screened. Using single gene GSEA and GSVA, we examined the significance of these key gene functions in immune cell and macrophage pathways. Next, we confirmed the correctness of the hub gene expression profile using the GSE55457 dataset and the ROC curve. Finally, we projected TF, miRNA, and possible therapeutic drugs using the miRNet, TargetScanHuman, ENCOR, and NetworkAnalyst databases, as well as Enrichr. VennDiagram obtained 71 crossover genes for DEGs, WGCNA-immune modules, and Genecards; functional enrichment demonstrated NF-κB, IL-17 signaling pathway play an important role in osteoarthritis-macrophage polarization genes; machine learning finally identified CSF1R, CX3CR1, CEBPB, and TLR7 as hub genes; GSVA analysis showed that CSF1R, CEBPB play essential roles in immune infiltration and macrophage pathway; validation dataset GSE55457 analyzed hub genes were statistically different between osteoarthritis and healthy controls, and the AUC values of ROC for CSF1R, CX3CR1, CEBPB and TLR7 were more outstanding than 0.65. CSF1R, CEBPB, CX3CR1, and TLR7 are potential diagnostic biomarkers for osteoarthritis, and CSF1R and CEBPB play an important role in regulating macrophage polarization in osteoarthritis progression and are expected to be new drug targets. •Pioneering bioinformatics approach to study macrophage genes in osteoarthritis.•Breakthrough in analyzing GSE55235 dataset for osteoarthritis.•Identified CSF1R, CX3CR1, CEBPB, TLR7 as diagnostic biomarkers.•Validated hub genes and predicted drugs for osteoarthritis treatment.•Integrated GO, DO, KEGG, and GSEA in osteoarthritis research.