Uncovering ferroptosis in Parkinson’s disease via bioinformatics and machine learning, and reversed deducing potential therapeutic natural products
Objective: Ferroptosis, a novel form of cell death, is closely associated with excessive iron accumulated within the substantia nigra in Parkinson’s disease (PD). Despite extensive research, the underlying molecular mechanisms driving ferroptosis in PD remain elusive. Here, we employed a bioinformat...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1231707/full |
| _version_ | 1797785651465158656 |
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| author | Peng Wang Peng Wang Qi Chen Zhuqian Tang Liang Wang Bizhen Gong Bizhen Gong Min Li Shaodan Li Minghui Yang |
| author_facet | Peng Wang Peng Wang Qi Chen Zhuqian Tang Liang Wang Bizhen Gong Bizhen Gong Min Li Shaodan Li Minghui Yang |
| author_sort | Peng Wang |
| collection | DOAJ |
| description | Objective: Ferroptosis, a novel form of cell death, is closely associated with excessive iron accumulated within the substantia nigra in Parkinson’s disease (PD). Despite extensive research, the underlying molecular mechanisms driving ferroptosis in PD remain elusive. Here, we employed a bioinformatics and machine learning approach to predict the genes associated with ferroptosis in PD and investigate the interactions between natural products and their active ingredients with these genes.Methods: We comprehensively analyzed differentially expressed genes (DEGs) for ferroptosis associated with PD (PDFerDEGs) by pairing 3 datasets (GSE7621, GSE20146, and GSE202665) from the NCBI GEO database and the FerrDb V2 database. A machine learning approach was then used to screen PDFerDEGs for signature genes. We mined the interacted natural product components based on screened signature genes. Finally, we mapped a network combined with ingredients and signature genes, then carried out molecular docking validation of core ingredients and targets to uncover potential therapeutic targets and ingredients for PD.Results: We identified 109 PDFerDEGs that were significantly enriched in biological processes and KEGG pathways associated with ferroptosis (including iron ion homeostasis, iron ion transport and ferroptosis, etc.). We obtained 29 overlapping genes and identified 6 hub genes (TLR4, IL6, ADIPOQ, PTGS2, ATG7, and FADS2) by screening with two machine learning algorithms. Based on this, we screened 263 natural product components and subsequently mapped the “Overlapping Genes-Ingredients” network. According to the network, top 5 core active ingredients (quercetin, 17-beta-estradiol, glycerin, trans-resveratrol, and tocopherol) were molecularly docked to hub genes to reveal their potential role in the treatment of ferroptosis in PD.Conclusion: Our findings suggested that PDFerDEGs are associated with ferroptosis and play a role in the progression of PD. Taken together, core ingredients (quercetin, 17-beta-estradiol, glycerin, trans-resveratrol, and tocopherol) bind well to hub genes (TLR4, IL6, ADIPOQ, PTGS2, ATG7, and FADS2), highlighting novel biomarkers for PD. |
| first_indexed | 2024-03-13T00:57:59Z |
| format | Article |
| id | doaj.art-4c3932c68eb042a799bfe033ac0eed00 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-03-13T00:57:59Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-4c3932c68eb042a799bfe033ac0eed002023-07-06T15:22:43ZengFrontiers Media S.A.Frontiers in Genetics1664-80212023-07-011410.3389/fgene.2023.12317071231707Uncovering ferroptosis in Parkinson’s disease via bioinformatics and machine learning, and reversed deducing potential therapeutic natural productsPeng Wang0Peng Wang1Qi Chen2Zhuqian Tang3Liang Wang4Bizhen Gong5Bizhen Gong6Min Li7Shaodan Li8Minghui Yang9Postgraduate School, Medical School of Chinese PLA, Beijing, ChinaDepartment of Traditional Chinese Medicine, The Sixth Medical Center, Chinese PLA General Hospital, Beijing, ChinaDepartment of Traditional Chinese Medicine, The Sixth Medical Center, Chinese PLA General Hospital, Beijing, ChinaSchool of Pharmacy, Key Laboratory for Modern Research of Traditional Chinese Medicine of Jiangsu, Nanjing University of Chinese Medicine, Nan Jing, Jiangsu, ChinaDepartment of Gastroenterology and Hepatology, The First Medical Center, Chinese PLA General Hospital, Beijing, ChinaPostgraduate School, Medical School of Chinese PLA, Beijing, ChinaDepartment of Traditional Chinese Medicine, The Sixth Medical Center, Chinese PLA General Hospital, Beijing, ChinaDepartment of Traditional Chinese Medicine, The Sixth Medical Center, Chinese PLA General Hospital, Beijing, ChinaDepartment of Traditional Chinese Medicine, The Sixth Medical Center, Chinese PLA General Hospital, Beijing, ChinaDepartment of Traditional Chinese Medicine, The Sixth Medical Center, Chinese PLA General Hospital, Beijing, ChinaObjective: Ferroptosis, a novel form of cell death, is closely associated with excessive iron accumulated within the substantia nigra in Parkinson’s disease (PD). Despite extensive research, the underlying molecular mechanisms driving ferroptosis in PD remain elusive. Here, we employed a bioinformatics and machine learning approach to predict the genes associated with ferroptosis in PD and investigate the interactions between natural products and their active ingredients with these genes.Methods: We comprehensively analyzed differentially expressed genes (DEGs) for ferroptosis associated with PD (PDFerDEGs) by pairing 3 datasets (GSE7621, GSE20146, and GSE202665) from the NCBI GEO database and the FerrDb V2 database. A machine learning approach was then used to screen PDFerDEGs for signature genes. We mined the interacted natural product components based on screened signature genes. Finally, we mapped a network combined with ingredients and signature genes, then carried out molecular docking validation of core ingredients and targets to uncover potential therapeutic targets and ingredients for PD.Results: We identified 109 PDFerDEGs that were significantly enriched in biological processes and KEGG pathways associated with ferroptosis (including iron ion homeostasis, iron ion transport and ferroptosis, etc.). We obtained 29 overlapping genes and identified 6 hub genes (TLR4, IL6, ADIPOQ, PTGS2, ATG7, and FADS2) by screening with two machine learning algorithms. Based on this, we screened 263 natural product components and subsequently mapped the “Overlapping Genes-Ingredients” network. According to the network, top 5 core active ingredients (quercetin, 17-beta-estradiol, glycerin, trans-resveratrol, and tocopherol) were molecularly docked to hub genes to reveal their potential role in the treatment of ferroptosis in PD.Conclusion: Our findings suggested that PDFerDEGs are associated with ferroptosis and play a role in the progression of PD. Taken together, core ingredients (quercetin, 17-beta-estradiol, glycerin, trans-resveratrol, and tocopherol) bind well to hub genes (TLR4, IL6, ADIPOQ, PTGS2, ATG7, and FADS2), highlighting novel biomarkers for PD.https://www.frontiersin.org/articles/10.3389/fgene.2023.1231707/fullParkinson’s diseaseferroptosistranscriptomicsmachine learningnatural productingredient |
| spellingShingle | Peng Wang Peng Wang Qi Chen Zhuqian Tang Liang Wang Bizhen Gong Bizhen Gong Min Li Shaodan Li Minghui Yang Uncovering ferroptosis in Parkinson’s disease via bioinformatics and machine learning, and reversed deducing potential therapeutic natural products Frontiers in Genetics Parkinson’s disease ferroptosis transcriptomics machine learning natural product ingredient |
| title | Uncovering ferroptosis in Parkinson’s disease via bioinformatics and machine learning, and reversed deducing potential therapeutic natural products |
| title_full | Uncovering ferroptosis in Parkinson’s disease via bioinformatics and machine learning, and reversed deducing potential therapeutic natural products |
| title_fullStr | Uncovering ferroptosis in Parkinson’s disease via bioinformatics and machine learning, and reversed deducing potential therapeutic natural products |
| title_full_unstemmed | Uncovering ferroptosis in Parkinson’s disease via bioinformatics and machine learning, and reversed deducing potential therapeutic natural products |
| title_short | Uncovering ferroptosis in Parkinson’s disease via bioinformatics and machine learning, and reversed deducing potential therapeutic natural products |
| title_sort | uncovering ferroptosis in parkinson s disease via bioinformatics and machine learning and reversed deducing potential therapeutic natural products |
| topic | Parkinson’s disease ferroptosis transcriptomics machine learning natural product ingredient |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2023.1231707/full |
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