Weighted Gene Coexpression Network Analysis of Specific Modules of Parkinson's Disease and Construction of Diagnostic Models

Abstract

Aim: In this study, microarray gene expression data in patients with Parkinson's disease(PD) were analyzed to identify potential diagnostic biomarkers.

Methods: Firstly, we used Weighted Correlation Network Analysis (WGCNA) to analyze the key modules with high correlation with PD from GSE99039 data set. Then, The hub genes in the key modules were subjected to the Least Absolute Shrinkage and Selection Operator(LASSO) regression analysis, Logistic regression model was constructed, and Receiver Operating Characteristic(ROC) was used to evaluate the test data set. Finally, an external dataset GSE99039 was used for ROC verification.

Results: WGCNA identified two key modules closely related to inflammation and immune response, screened 7 genes LILRB1, LSP1, SIPA1, SLC15A3, MBOAT7, RNF24 and TLE3, and constructed diagnostic models. ROC analysis showed that the diagnostic model had moderate diagnostic performance for PD in training, validation set and external validation data set.

Conclusion: A 7-gene panel (LILRB1, LSP1, SIPA1, SLC15A3, MBOAT7, RNF24,TLE3) serves as a potential diagnostic predictor for PD.

Note:
Funding Information: This study was supported by the National Natural Science Foundation of China (No. 81974213).

Declaration of Interests: None.

Ethics Approval Statement: Ethical approval for this study was not required, because the data were downloaded directly from public database.

Keywords: Parkinson's disease, WGCNA, LASSO regression, diagnostic predictor