Identification of PLOD3 and LRRN3 as potential biomarkers for Parkinson's disease based on integrative analysis

Abstract

Parkinson's disease (PD) is one of the most prevalent movement disorders and its diagnosis relies heavily on the typical clinical manifestations in the late stages. This study aims to screen and identify biomarkers of PD for earlier intervention. We performed a differential analysis of postmortem brain transcriptome studies. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify biomarkers related to Braak stage. We found 58 genes with significantly different expression in both PD brain tissue and blood samples. PD gene signature and risk score model consisting of nine genes were constructed using least absolute shrinkage and selection operator regression (LASSO) and logistic regression. PLOD3 and LRRN3 in gene signature were identified to serve as key genes as well as potential risk factors in PD. Gene function enrichment analysis and evaluation of immune cell infiltration revealed that PLOD3 was implicated in suppression of cellular metabolic function and inflammatory cell infiltration, whereas LRRN3 exhibited an inverse trend. The cellular subpopulation expression of the PLOD3 and LRRN3 has significant distributional variability. The expression of PLOD3 was more enriched in inflammatory cell subpopulations, such as microglia, whereas LRRN3 was more enriched in neurons and oligodendrocyte progenitor cells clusters (OPC). Additionally, the expression of PLOD3 and LRRN3 in Qilu cohort was verified to be consistent with previous results. Collectively, we screened and identified the functions of PLOD3 and LRRN3 based the integrated study. The combined detection of PLOD3 and LRRN3 expression in blood samples can improve the early detection of PD.

Fig. 1. Flow chart of our research.

The flow chart showing the collection of multiple data sets, dimensionality reduction, gene signature model construction and evaluation, and validation of key biomarkers.

Fig. 2. Integrated analysis of tissue and blood samples from PD and control patients.

a Heat map visualization of 921 differentially expressed genes (DEGs) between PD and control nigrostriatal (SN) RNA. b The weighted gene co-expression network analysis (WGCNA) was performed with the DEGs expression data to identify the Braak stage related models. c Volcano map visualization of 1001 DEGs between PD and control blood samples of GSE99039. d, e Venn diagrams and scatter plots show the distribution of the overlapping 58 genes.

Fig. 3. Identification of a PD gene signature consisting 9 biomarker genes.

a Least absolute shrinkage and selection operator (LASSO) coefficient profiles (y-axis) of the 58 overlapping genes (left panel). The dashed line on the left represents the optimal value of λ, which corresponds to the number of genes on the x-axis (right panel). b Multivariate logistic regression model analysis, which included the nine genes in the training set of GSE99039. The forest plot displays the odds ratio (OR) values and their 95% confidence intervals from various genes. Each square represents a gene, with the position of the square indicating the OR value and the horizontal line representing the 95% confidence interval. c The differences of risk score between PD and control groups for training set and test set of GSE99039. d The differences of risk score between different braak stage PD patients of GSE49036 e Receiver operating characteristic (ROC) curves for the risk score model both in the training and test sets. f The calibration curves of risk score predictions for the training and test sets are close to the ideal performance (45 degrees line). g, h The differences of PLOD3 and LRRN3 expression between PD and control groups of GSE99039 (g) and merged SN tissue samples (h), respectively. i The differences of PLOD3 and LRRN3 expression between different Braak stages groups of GSE49036. Boxplots summarize the distribution of the data. The box represents the interquartile range, with the horizontal line inside the box indicating the median. The whiskers extend to the minimum and maximum values within 90% of the data range and the shape of the violin provides insights into the data distribution.

Fig. 4. Single-cell RNA analysis reveals the functions of PLOD3 and LRRN3.

a UMAPs of control and different Braak stages PD brain samples. b Cluster composition of myeloid, oligodendrocyte progenitor cells (OPC), endothelial cells (End), neurons (Neu), astrocytes (Ast), oligodendrocytes (Oli), and microglia (Mic) from snRNA sequencing. c, d Single-cell RNA sequencing of GSE184950 visualizing UMAP cell clusters, PLOD3 and LRRN3 expression. The cellular subpopulation expression of the PLOD3 and LRRN3 has significant distributional variability. The expression of PLOD3 was more enriched in astrocytes, microglia and oligodendrocytes cell clusters, whereas LRRN3 was more enriched in neurons and oligodendrocyte progenitor cells clusters. e–h The combined GSEA curves using GO-BP and KEGG analysis visualize the functions of PLOD3 and LRRN3.

Fig. 5. Validation the expression of PLOD3 an LRRN3 in blood samples.

a, c The bar chart showing the relative expression of PLOD3 and LRRN3 in PD and control blood samples. b, d Pie charts showing the distribution of PLOD3-Low/High (b) and LRRN3-Low/High (d) expression in control and PD blood samples. e, f ROC curves and calibration curves for the expression of PLOD3 and LRRN3 in Qilu cohort. g Nomogram based on the expression of PLOD3, LRRN3 and age for predicting the incidence of PD in Qilu cohort. h The differences of nomoRisk between control and PD. i The differences in PLOD3 and LRRN3 expression between PD patients with high and low UPDRS improvement rates.