Abstract

Parkinson’s disease is a prevalent neurodegenerative disorder that is clinically identified by motor deficits. Its pathogenesis remains unclear and current treatments cannot reverse disease progression. Mounting evidence suggests that cellular senescence plays a crucial part in the development of Parkinson’s disease. The analysis of genes related to aging in Parkinson’s samples using bioinformatics has not been conducted so far. This study identified differentially expressed senescence genes using bioinformatics approaches and found genes RASL11B and PRRG1 to be highly correlated with Parkinson’s, suggesting their potential as diagnostic and therapeutic targets. The miR-20 family of miRNAs may participate in Parkinson’s pathogenesis by regulating these genes. Examining senescence genes within a senescence network framework, this study pioneers the investigation of their involvement in Parkinson’s disease. It establishes the theoretical groundwork and identifies potential targets for the development of innovative diagnostic and therapeutic approaches focused on senescence. The present research reveals the important function of aging processes in the development of Parkinson’s disease, enabling the advancement of novel diagnostic and therapeutic approaches for Parkinson’s that focus on mechanisms related to aging.

Introduction

Parkinson’s disease (PD) is a degenerative disorder of the nervous system that is identified by the degeneration of dopaminergic neurons in the substantia nigra region of the brain. This degeneration causes motor symptoms including tremors, stiffness, and slow movement. The condition affects approximately 2%–۳% of individuals who are 65 years old and above (Poewe et al., 2017), with an annual incidence rate ranging from 10 to 18 per 100,000 people (Van Den Eeden et al., 2003). Moreover, the prevalence rate among individuals in my country who are 65 years old and above is recorded at 1.7% (Chen et al., 2016). The development of PD is intricate, encompassing interactions among hereditary and ecological elements (Bellou et al., 2016; Hernandez et al., 2016). Nonetheless, recent studies suggest that the pathogenic mechanisms of PD could be associated with cellular senescence (Sahu et al., 2022). Cellular senescence refers to a state of irreversible cell cycle arrest that is activated by different stressors, including telomere shortening (Hunt et al., 2015; Kuszel et al., 2015), DNA damage (Bai, 2015; Bai et al., 2015), and oxidative stress (Blacker and Duchen, 2016). Senescent cells display various phenotypic alterations, such as modified gene expression, release of proinflammatory substances, and reduced sensitivity to growth factors (Hayflick, 1965). The buildup of aging cells has been linked to diseases associated with aging, including cancer, heart disease, and neurodegenerative disorders. Recent studies suggest that the buildup of aging cells might contribute to the development of PD. In the brains of individuals with PD, researchers have observed aging dopaminergic neurons, while aging astrocytes were discovered to release proinflammatory cytokines that contribute to neuroinflammation (Franceschi et al., 2007; Chung et al., 2009). Furthermore, it has been demonstrated that aging cells can hinder the functioning of mitochondria (Reeve et al., 2013), which is believed to have a crucial impact on the development of PD. Hence, focusing on aged cells is seen as a hopeful path that may hinder or ease the beginning and advancement of PD by targeting the aged characteristic. The advancements in microarray technology and bioinformatics have significantly assisted the progress of biomedicine. At present, these bioinformatics examinations offer insightful hints to comprehend the origin of PD from various perspectives. Nevertheless, the precise molecular mechanisms connecting cellular senescence to PD pathology are still unknown. Consequently, our objective in this research is to examine PD-related GEO datasets from the standpoint of SAGs. The identification of SADEGs involved limma testing, WGCNA, and the intersection of 1302 genes related to senescence and senescence-associated genes. GO and GSEA were used to determine the possible biological roles and pathways of SADEGs. Next, the identification of hub genes linked to the progression of PD was carried out using SVM-RFE, LASSO logistic regression, and RF. Furthermore, we investigated the associations between the central genes and Parkinson’s disease. In the end, the validation set was used to confirm the expression levels of four central genes.

Conclusion

To summarize, we have discovered and confirmed two genes (RASL11B and PRRG1) that are strongly linked to PD and are involved in the process of senescence. The results of our study indicate that these genes could have significant functions in the onset and advancement of PD pathology. Our study reveals the participation of RASL11B and PRRG1 in PD, offering fresh perspectives on their potential as biomarkers for early detection and monitoring of the disease. Additional investigation is necessary to explore the mechanisms by which these genes contribute to the development of PD, as it has the potential to unveil novel therapeutic targets. In general, our discovery of the connections between genes related to aging and PD signifies a significant progress in comprehending the causes and development of PD.

There are some limitations to our study:Although we validated our results with other datasets, there may be some bias in our interpretation due to the relatively small sample size and data from mRNA expression. In future work, we intend to perform ELISA experiments (at the protein level) to validate the diagnostic role of these two SADEGs in larger sample sizes.