Characterization of gene expression profiles in Alzheimer's disease and osteoarthritis: A bioinformatics study

Abstract

Background: Alzheimer's disease (AD) and Osteoarthritis (OA) have been shown to have a close association in previous studies, but the pathogenesis of both diseases are unclear. This study explores the potential common molecular mechanisms between AD and OA through bioinformatics analysis, providing new insights for clinical treatment strategies.

Methods: The AD and OA-related datasets were downloaded from the gene expression database GEO. The datasets were analyzed to obtain differentially expressed gene (DEG) datasets for OA and AD, respectively. The intersection of these DEGs was analyzed to identify common DEGs (Co-DEGs). Subsequently, the Co-DEGs were enriched, and a protein-protein interaction network was constructed to identify core genes. The expression of these genes was validated in a separate dataset, and their diagnostic value for the diseases was analyzed. In addition, the core genes were analyzed using gene set enrichment analysis and single-gene genome variation analysis.

Results: Analysis of DEGs on gene chips from OA and AD patients revealed significant changes in gene expression patterns. Notably, EFEMP2 and TSPO, genes associated with inflammatory responses, showed lower expression levels in both AD and OA patients, suggesting a downregulation in the pathological backgrounds of these diseases. Additionally, GABARAPL1, which is crucial for the maturation of autophagosomes, was found to be upregulated in both conditions. These findings suggest the potential of these genes as diagnostic biomarkers and potential therapeutic targets. However, to confirm the effectiveness of these genes as therapeutic targets, more in-depth mechanistic studies are needed in the future, particularly to explore the feasibility and specific mechanisms of combating disease progression by regulating the expression of these genes.

Conclusions: This study suggests that AD and OA shares common molecular mechanisms. The identification of EFEMP2, GABARAPL1, and TSPO as key target genes highlights potential common factors in both diseases. Further investigation into these findings could lead to new candidate targets and treatment directions for AD and OA, offering promising avenues for developing more effective and targeted therapeutic interventions.

Fig 1. Overview of study design.

AD: alzheimer’s disease; OA: osteoarthritis; Co-DEGs: common differentially expressed genes; GO: gene ontology; KEGG: kyoto encyclopedia of genes and genomes; PPI: protein-protein interaction; ROC: receiver operating characteristic; GSEA: gene set enrichment analysis; GSVA: single-gene genome variation analysis.

Fig 2. Sample distribution principal component analysis (PCA) plot.

PCA plot of sample distribution before (A) and after (B) of the AD-related training set. PCA plot of sample distribution before (C) and after (D) of the OA-related training set.

Fig 3. Differential expression analysis.

Gene expression in the AD training group (A) and OA training group (B) between disease samples and normal samples. (C) The overlap of upregulated DEGs in AD and OA training sets. (D) The overlap of downregulated DEGs in AD and OA training sets.

Fig 4. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on common DEGs (Co-DEGs) shared between AD and OA.

(A) GO enrichment analysis of Co-DEGs. The x-axis represents the gene ratio (the proportion of DEGs in the GO term relative to the total), the y-axis represents the GO terms ordered by enrichment significance (with color scale indicating the significance level), and the size of the circles represents the number of enriched genes in each term. (B) KEGG enrichment analysis of Co-DEGs, with the x-axis showing the number of enriched genes and the y-axis represents the different KEGG pathways.

Fig 5. Protein-Protein Interaction (PPI) network.

(A) PPI network of Co-DEGs. (B) Cytoscape v.3.8.0 facilitates the visualization of PPI networks. Green represents down-regulated Co-DEGs, while red represents up-regulated Co-DEGs. (C) PPI network of Co-DEGs in the degree algorithm; the color of the circles in the fig from yellow to red represents the gradual increase in the score.

Fig 6. The top 10 CO-DEGs were identified based on their degree in the dataset.

(A) Expression of the 10 core genes in the validation set of AD. (B) Expression of the 10 core genes in the validation set of OA. *P <0.05, **P < 0.01, ***P< 0.001.

Fig 7

(A-B) Receiver Operating Characteristic (ROC) curves and model performance for significantly differentially expressed core genes (EFEMP2, GABARAPL1, TSPO) in AD validation. (C) Violin plot of EFEMP2, GABARAPL1, TSPO expression in AD validation. (D-E) ROC curves and model performance for EFEMP2, GABARAPL1, TSPO in OA validation. (F) Violin plot of EFEMP2, GABARAPL1, TSPO expression in OA validation.

Fig 8. Gene set enrichment analysis (GSEA) of EFEMP2 in AD.

y-axis: The "Running Enrichment Score" reflects the enrichment level of the EFEMP2, GABARAPL1, and TSPO gene sets in AD. The "Ranked List Metric" displays the ranking of genes within the dataset. x-axis: Represents the position of genes in the sorted dataset.

Fig 9. Gene set enrichment analysis (GSEA) of GABARAPL1 in AD.

y-axis: The "Running Enrichment Score" reflects the enrichment level of the EFEMP2, GABARAPL1, and TSPO gene sets in AD. The "Ranked List Metric" displays the ranking of genes within the dataset. x-axis: Represents the position of genes in the sorted dataset.

Fig 10. Gene set enrichment analysis (GSEA) of TSPO in AD.

y-axis: The "Running Enrichment Score" reflects the enrichment level of the EFEMP2, GABARAPL1, and TSPO gene sets in AD. The "Ranked List Metric" displays the ranking of genes within the dataset. x-axis: Represents the position of genes in the sorted dataset.

Fig 11. GSEA of EFEMP2 in OA.

y-axis: The "Running Enrichment Score" reflects the enrichment level of the EFEMP2, GABARAPL1, and TSPO gene sets in OA. The "Ranked List Metric" displays the ranking of genes within the dataset. x-axis: Represents the position of genes in the sorted dataset.

Fig 12. GSEA of GABARAPL1 in OA.

y-axis: The "Running Enrichment Score" reflects the enrichment level of the EFEMP2, GABARAPL1, and TSPO gene sets in OA. The "Ranked List Metric" displays the ranking of genes within the dataset. x-axis: Represents the position of genes in the sorted dataset.

Fig 13. GSEA of TSPO in OA.

y-axis: The "Running Enrichment Score" reflects the enrichment level of the EFEMP2, GABARAPL1, and TSPO gene sets in OA. The "Ranked List Metric" displays the ranking of genes within the dataset. x-axis: Represents the position of genes in the sorted dataset.