Identification of potential Parkinson's disease biomarkers using computational biology approaches

Parkinson's disease (PD) is one of the most common neurodegenerative disorders. This aging-related disease occurs due to the degenerative loss of tissue or cellular functions in the brain and due to genetic and epigenetic effects. This study was conducted on an RNA-seq dataset of PD collected from BA9 tissues to get insights to PD. A few RNA-seq based transcriptomics studies on PD are available. However, most of these studies are limited to differential expression analysis, i.e., individual gene-based analysis that ignores interactions and associations among genes to establish the association with the disease. Here, we initially identify differentially expressed genes and then construct a co-expression network on detected genes to identify modules. Module preservation analysis is carried out to find the non-preserved modules. We identify a non-preserved module with 73 (70 are annotated) genes. Differential connectivity analysis, topological analysis, and functional enrichment analysis are performed to find the initial set of interesting genes. Our finding is that 42 (60%) genes are significantly enriched in pathways, biological processes, or molecular functions, and they are topologically interesting. Among these genes, 19 can be linked to the PD based on evidence from literature. They are considered as biomarkers for PD. From the remaining 23 genes, 11 are expressed in brain region. Therefore, these genes may be further explored to understand their roles in PD and can be considered as potential biomarkers.