The application of weighted gene co-expression network analysis and support vector machine learning in the screening of Parkinson's disease biomarkers and construction of diagnostic models.

Background: This study aims to utilize Weighted Gene Co-expression Network Analysis (WGCNA) and Support Vector Machine (SVM) algorithm for screening biomarkers and constructing a diagnostic model for Parkinson's disease. Methods: Firstly, we conducted WGCNA analysis on gene expression data from Parkinson's disease patients and control group using three GEO datasets (GSE8397, GSE20163, and GSE20164) to identify gene modules associated with Parkinson's disease. Then, key genes with significantly differential expression from these gene modules were selected as candidate biomarkers and validated using the GSE7621 dataset. Further functional analysis revealed the important roles of these genes in processes such as immune regulation, inflammatory response, and cell apoptosis. Based on these findings, we constructed a diagnostic model by using the expression data of FLT1, ATP6V0E1, ATP6V0E2, and H2BC12 as inputs and training and validating the model using SVM algorithm. Results: The prediction model demonstrated an AUC greater than 0.8 in the training, test, and validation sets, thereby validating its performance through SMOTE analysis. These findings provide strong support for early diagnosis of Parkinson's disease and offer new opportunities for personalized treatment and disease management. Conclusion: In conclusion, the combination of WGCNA and SVM holds potential in biomarker screening and diagnostic model construction for Parkinson's disease.