# Construction and Identification of Inflammation-Related TF–mRNA–miRNA Coexpression Network and Immune Infiltration in Parkinson's Disease

## TL;DR

This study identifies four inflammation-related genes and builds a regulatory network to better understand Parkinson's disease mechanisms.

## Contribution

The paper introduces a novel TF–mRNA–miRNA coexpression network and validates inflammation-related biomarkers in Parkinson's disease.

## Key findings

- Four key inflammation-related differential expression genes (CXCR4, LEP, SLC18A2, TAC1) were identified and validated.
- Key pathways and coexpression networks related to Parkinson's disease were uncovered through biological function analysis.
- Increased CD4 T-cell infiltration was found to be potentially linked to the onset of Parkinson's disease.

## Abstract

Background: Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide. Inflammation, marked by the infiltration of inflammatory mediators and the proliferation of inflammatory cells, is closely linked to PD. This study aims to identify and validate inflammation-related biomarkers in PD and construct a TF–mRNA–miRNA coexpression network through bioinformatics analysis.

Methods: The PD-associated dataset GSE7621 and inflammation-related genes were downloaded from the GEO Database and GeneCards platform to obtain inflammation-related differential expression genes (IRDEGs). The key IRDEGs were generated by PPI network analysis. The gene expression levels of the key IRDEGs were validated by blood samples from PD patients using QPCR analysis. We utilized the ENCODE, hTFtarget, CHEA, miRWALK, and miRDB databases to obtain upstream and downstream molecular network models for constructing the TF–mRNA–miRNA interaction network of the key IRDEGs. Finally, based on CIBERSORT algorithm, the associations between IRDEs and immune cell infiltration were investigated.

Results: A total of four key IRDEGs (CXCR4, LEP, SLC18A2, and TAC1) were screened and validated. Through biological function analysis, key-related pathways and coexpression networks of PD were identified. These genes may be closely related to the onset of PD. Additionally, we found that increased CD4 T-cell infiltration might be associated with the occurrence of PD.

Conclusions: We identified four potential inflammation-related treatment target and constructed a TF–mRNA–miRNA regulatory network. This information provides an initial basis for understanding the complex PD regulatory mechanisms.