Potential Therapeutic Drugs for Parkinson’s Disease Based on Data Mining and Bioinformatics Analysis

Abstract

The objective is to search potential therapeutic drugs for Parkinson's disease based on data mining and bioinformatics analysis and providing new ideas for research studies on “new application of conventional drugs.” Method differential gene candidates were obtained based on data mining of genes of PD brain tissue, original gene data analysis, differential gene crossover, pathway enrichment analysis, and protein interaction, and potential therapeutic drugs for Parkinson's disease were obtained through drug-gene relationship. Result. 250 common differential genes were obtained from 3 research studies, and 31 differential gene candidates were obtained through gene enrichment analysis and protein interaction. 10 drugs such as metformin hydrochloride were directly or indirectly correlated to differential gene candidates. Conclusion. Potential therapeutic drugs that may be used for prevention and treatment of Parkinson's disease were discovered through data mining and bioinformatics analysis, which provided new ideas for research and development of drugs. Results showed that metformin hydrochloride and other drugs had certain therapeutical effect on Parkinson's disease, and melbine (DMBG) can be used for treatment of Parkinson's disease and type 2 diabetes patients.

Bacterial, viral, and fungal infection‐related risk of Parkinson's disease: Meta‐analysis of cohort and case–control studies

“…The main pathological change of PD is degeneration and death of dopaminergic neurons in the substantia nigra due to unclear etiology and pathogenesis (Li et al, 2019;Salamon, Zádori, Szpisjak, Klivényi, & Vécsei, 2019). Similar to other neurodegenerative diseases, multiple factors, including gene, neuroinflammation, trauma, drugs, and toxicity, appear to play important roles in the development of PD (Dick et al, 2007;Mcgeer, Yasojima, & Mcgeer, 2003;Park et al, 2019;Xu, Chen, Xu, Zhang, & Li, 2018;Xu et al, 2014). Recently, infection is increasingly recognized as a risk factor for PD (Liu, Gao, & Hong, 2003;Mattson, 2004) because it may trigger chronic inflammation of the microglia (Alam et al, 2016) and, thus, may promote the onset of PD.…”

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“…Several studies have been carried out by numerous researchers on the early diagnosis of PD based on machine learning methods [8][9][10][11][12][13][14][15][16][17][18][19][20]. Little and his co-researchers conducted an important study to detect PD by introducing a new measure of dysphonia [8].…”

“…Then they performed clustering using the KStar and NNge classifiers [19]. Recently, with the help of data mining and bioinformatics analysis, several potential therapeutic drugs that may be used for prevention and treatment of Parkinson's disease were discovered [20]. This certainly open new perspectives for drugs development using AI techniques.…”