A computational approach based on weighted gene co-expression network analysis for biomarkers analysis of Parkinson's disease and construction of diagnostic model

Abstract

Background: Parkinson's disease (PD) is a common age-related chronic neurodegenerative disease. There is currently no affordable, effective, and less invasive test for PD diagnosis. Metabolite profiling in blood and blood-based gene transcripts is thought to be an ideal method for diagnosing PD.

Aim: In this study, the objective is to identify the potential diagnostic biomarkers of PD by analyzing microarray gene expression data of samples from PD patients.

Methods: A computational approach, namely, Weighted Gene Co-expression Network Analysis (WGCNA) was used to construct co-expression gene networks and identify the key modules that were highly correlated with PD from the GSE99039 dataset. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was performed to identify the hub genes in the key modules with strong association with PD. The selected hub genes were then used to construct a diagnostic model based on logistic regression analysis, and the Receiver Operating Characteristic (ROC) curves were used to evaluate the efficacy of the model using the GSE99039 dataset. Finally, Reverse Transcription-Polymerase Chain Reaction (RT-PCR) was used to validate the hub genes.

Results: WGCNA identified two key modules associated with inflammation and immune response. Seven hub genes, LILRB1, LSP1, SIPA1, SLC15A3, MBOAT7, RNF24, and TLE3 were identified from the two modules and used to construct diagnostic models. ROC analysis showed that the diagnostic model had a good diagnostic performance for PD in the training and testing datasets. Results of the RT-PCR experiments showed that there were significant differences in the mRNA expression of LILRB1, LSP1, and MBOAT7 among the seven hub genes.

Conclusion: The 7-gene panel (LILRB1, LSP1, SIPA1, SLC15A3, MBOAT7, RNF24, and TLE3) will serve as a potential diagnostic signature for PD.

Keywords: LASSO regression; Parkinson’s disease; WGCNA; computational approach; potential diagnostic predictor.

FIGURE 1

Construction of WGCNA co-expression modules. Analysis of the scale-free fit index (A) and the mean connectivity (B) for various soft-thresholding powers. (C) Dendrogram of all differentially expressed genes clustered based on a dissimilarity measure (1-TOM).

FIGURE 2

Correlation analysis of modules from weighted gene co-expression network analysis and identification of PD-associated modules. (A) Heatmap of correlation between clinical traits (x-axis) and modules (y-axis). The abscissa represents the clinical information (PD, NDD, Normal) contained in the dataset, and the ordinate represents different modules. The histogram on the right is the color scale. Numbers inside the heatmap signify correlation values and p-values (in parenthesis). (B) Heatmap depicting the interaction of co-expressed genes. Different colors in both axes represent different modules, and the brightness of red in the middle of heatmap indicates the connectivity degree of the corresponding modules. (C) Hierarchical clustering dendrogram displaying the similarity of each module eigengenes value. (D) Heatmap showing correlation between each module, labeled by their corresponding color.

FIGURE 3

Functional enrichment analyses of magenta and yellow modules. (A) GO analysis of genes in magenta module. (B) KEGG pathway analysis of genes in magenta module. (C) GO analysis of genes in yellow module. (D) KEGG pathway analysis of genes in yellow module.

FIGURE 4

The process of screening genes most associated with prognosis in the training set by Lasso regression. (A) Processes of LASSO regression model fitting. (B) The misclassification error in the jackknife rates analysis.

FIGURE 5

ROC curves were used to evaluate the accuracy of logistic regression model. (A) Analysis of the GES9903 training set. (B) Analysis of the GES9903 test set.

FIGURE 6

Reverse Transcription-Polymerase Chain Reaction validation of the hub gene between PD and normal controls. (A–G) Relative expression levels of LILRB1, LSP1, MBOAT7, RNF24, SIPA1, SLC15A3, and TLE3. *p < 0.05.