Exploring common biomarkers of ischemic stroke and obstructive sleep apnea through bioinformatics analysis

Abstract

Background: Clinical observations have shown that many patients with ischemic stroke (IS) have a history of obstructive sleep apnea (OSA) both before and after the stroke's onset, suggesting potential underlying connections and shared comorbid mechanisms between the two conditions. The aim of this study is to identify the genetic characteristics of OSA patients who develop IS and to establish a reliable disease diagnostic model to assess the risk of IS in OSA patients.

Methods: We selected IS and OSA datasets from the Gene Expression Omnibus (GEO) database as training sets. Core genes were identified using the Limma package, Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning algorithms. Gene Set Variation Analysis (GSVA) was conducted for pathway enrichment analysis, while single-sample gene set enrichment analysis (ssGSEA) was employed for immune infiltration analysis. Finally, a diagnostic model was developed using Least Absolute Shrinkage and Selection Operator (LASSO) regression, with its diagnostic efficacy validated using receiver operating characteristic (ROC) curves across two independent validation sets.

Results: The results revealed that differential analysis and machine learning identified two common genes, TM9SF2 and CCL8, shared between IS and OSA. Additionally, seven signaling pathways were found to be commonly upregulated in both conditions. Immune infiltration analysis demonstrated a significant decrease in monocyte levels, with TM9SF2 showing a negative correlation and CCL8 showing a positive correlation with monocytes. The diagnostic model we developed exhibited excellent predictive value in the validation set.

Conclusions: In summary, two immune-related core genes, TM9SF2 and CCL8, were identified as common to both IS and OSA. The diagnostic model developed based on these genes may be used to predict the risk of IS in OSA patients.

Fig 1. Workflow diagram.

DEGs, differentially expressed genes; WGCNA, weighted gene co-expression network analysis; GSVA, gene set variation analysis; LASSO, least absolute shrinkage and selection operator; ssGSEA, single-sample gene set enrichment analysis; ROC, receiver operating characteristic.

Fig 2. Limma differential analysis results for IS and OSA.

(A, C) Heatmaps displaying the top 25 upregulated and downregulated DEGs for IS and OSA. The red module represents upregulation, and the blue module represents downregulation. (B, D) Volcano plots illustrating the DEGs for IS and OSA. Red points indicate upregulation, while green points indicate downregulation. IS, ischemic stroke; OSA, obstructive sleep apnea; DEGs, Differentially Expressed Genes.

Fig 3. Co-expression network of IS and OSA.

(A, E) Identification of outlier samples for IS and OSA. (B, F) Different soft-thresholds and corresponding network fitting metrics for IS and OSA. (C, G) Gene co-expression networks constructed through hierarchical clustering, revealing different color modules. (D, H) Correlation of modules in IS and OSA with diseases, represented by correlation coefficient values. IS, ischemic stroke; OSA, obstructive sleep apnea.

Fig 4. GSVA pathway scores for IS and OSA.

(A, B) GSVA scores for IS and OSA, where blue represents upregulated pathways, green represents downregulated pathways, with the x-axis indicating scores and the y-axis indicating pathway names. IS, ischemic stroke; OSA, obstructive sleep apnea; GSVA, Gene Set Variation Analysis.

Fig 5. LASSO regression for screening candidate diagnostic biomarkers.

(A, B) Venn diagram of the intersection between DEGs and hub genes. (C) Results of cross-validation in LASSO regression, including cross-validation error curves at different penalty levels. (D) Chart of L1 norm in LASSO regression, observing the sparsity of coefficients at different lambda values. IS, ischemic stroke; OSA, obstructive sleep apnea; DEGs, Differentially Expressed Genes; WGCNA, Weighted Gene Co-expression Network Analysis; LASSO, least absolute shrinkage and selection operator.

Fig 6. ssGSEA is employed to analyze immune infiltration in IS and OSA.

(A, D) ssGSEA heatmaps for IS and OSA, where red represents upregulated immune cells and blue represents downregulated immune cells. (B, E) ssGSEA box plots for IS and OSA, with the x-axis representing 28 types of immune cells and the y-axis representing immune cell scores. Blue indicates the disease group, and red represents the control group. ns, p > 0.05; *, p < 0.05; **, p < 0.01; ***, p < 0.001. (C, F) Correlation heatmaps between core genes in IS and OSA and immune cells. Red indicates positive correlation, blue indicates negative correlation, and numbers represent correlation coefficients. IS, ischemic stroke; OSA, obstructive sleep apnea; ssGSEA, single-sample gene set enrichment analysis.

Fig 7. The ROC curves and AUC values of the diagnostic model.

(A, B) ROC curves and AUC values of the diagnostic model in the IS and OSA training sets. (C, D) ROC curves and AUC values of the diagnostic model in the IS and OSA validation sets.