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. 2024 Apr 27;10(9):e30335.
doi: 10.1016/j.heliyon.2024.e30335. eCollection 2024 May 15.

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

Ping Hu  1   2 , Beining Li  1   2 , Zhenyu Yin  1   2 , Peng Peng  1   2 , Jiangang Cao  3 , Wanyu Xie  1   2 , Liang Liu  4 , Fujiang Cao  1   2 , Bin Zhang  1   2
Affiliations

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

Ping Hu et al. Heliyon. .

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: Bioinformatics; Machine learning; Macrophage polarization; Osteoarthritis; WGCNA.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Flowchart for research.
Fig. 2
Fig. 2
Identification of DEGs in synovial membranes of patients with osteoarthritis. (A): Expression levels before correction; (B): Expression levels before correction; (C): Volcanic map of DEGs associated with OA. The abscissa is log2FoldChange and the ordinate is -log 10 (P value).; Red nodes, blue nodes and gray nodes represent up-regulated DEGs, downregulated DEGs, and genes that are not notably differentially expressed,individually; (D): Heat map of osteoarthritis-related DEG expression levels: Blue bars：normal control samples; Red bars：disease samples, red denotes high gene expression, while blue shows low gene expression.
Fig. 3
Fig. 3
Immune infiltration analysis (A)：Immune infiltration analysis via xcell algorithm，the enrichment fraction diagram of infiltrating immunocytes; (B): Heatmap of inter-immune cell correlations; (C)：Box diagram of immune infiltrating cells in Control and OA samples. p < 0.05,"*"; p < 0.01,"**"; p < 0.001,"***".
Fig. 4
Fig. 4
WGCNA analysis to appraise osteoarthritis immune hub module. (A): Clinical sample trait clustering; (B)： Analyze optimal soft thresholds power; (C)：Modules discovered by combining modules with feature factors larger than 0.25; (D)：Heatmap of correlation between WGCNA module and immune cells; (E): Scatter plot of key modules (left: blue module, right: pink module).
Fig. 5
Fig. 5
Functional analysis of DEGs-WGCNA-macrophage polarization genes (A): Venn diagram of genes associated with polarized macrophages; (B):GO analysis bar graph of polarized macrophages; (C):KEGG analyzes chordal diagrams of polarized macrophages; (D):DO analyzes circle diagram of polarized macrophages; (E), results of up-regulated pathway in GSEA analysis; (F):results of down-regulated pathway in GSEA analysis; (G):Ridge map showing GSEA enrichment pathway.
Fig. 6
Fig. 6
Machine learning-based screening of possible biomarkers (A-B)：Optimal accuracy and error rate of SVM model based on 19 feature genes; (C)： Logarithmic (Lambda) values of the three genes in the LASSO model (left), the most suitable logarithmic (Lambda) values in the LASSO model (right); (D)：Results of random forest analysis. x-axis shows genetic variables, y-axis shows decrease in mean accuracy; (E): Venn diagrams of three types of machine learning, showing their overlapping genes.
Fig. 7
Fig. 7
Interaction networks of 71 target genes with hub genes and GO entries.
Fig. 8
Fig. 8
Single genes GSEA analysis of hub gene. (A:CSF1R; B: CX3CR1; C：CEBPB; D：TLR7).
Fig. 9
Fig. 9
Diagram of the relationship between immune infiltration and macrophage regulation for (A:CEBPB; B:CSF1R; C：CX3CR1; D：TLR7).
Fig. 10
Fig. 10
Scatter plot of the expression of hub genes CEBPB and CSF1R with different polarization states of macrophages. (A): Scatter plot of CEBPB and Macrophages; (B): Scatter plot of CEBPB and Macrophages M1; (C): Scatter plot of CSF1R versus Macrophages M1; (D): Scatter plot of CSF1R versus Macrophages M2.
Fig. 11
Fig. 11
ROC curves and box plots showing potential biomarkers for the analyzed data set GSE55235, (A–D): ROC curves of CEBPB, CSF1R, CX3CR1, and TLR7 for the analyzed data set GSE55235; (E): box plots of CEBPB, CSF1R, CX3CR1, and TLR7 for the analyzed data set GSE55235.
Fig. 12
Fig. 12
ROC curves and box plots showing potential biomarkers for the Validation dataset GSE55457. (A–D): ROC curves of CEBPB, CSF1R, CX3CR1, and TLR7 for the Validation dataset GSE55457; (E): box plots of CEBPB, CSF1R, CX3CR1, and TLR7 for the Validation dataset GSE55457.
Fig. 13
Fig. 13
TF—hub—miRNA sankitu.
Fig. 14
Fig. 14
Visualization of single-cell results. A:Scatter plot and correlation coefficient of the four features in the dataset B:violin plot showing the distribution of the four features in the dataset C:umap plot showing the clustering results and distribution of the cells in the dataset D:hub gene of the cells separately E:heat map showing the expression levels of cell clusters on the five genes with the largest mean log fold change in each cluster F:bubble plot showing the distribution of hub genes in cells as well as inside the sample.
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