Developing the novel diagnostic model and potential drugs by integrating bioinformatics and machine learning for aldosterone-producing adenomas
Background: Aldosterone-producing adenomas (APA) are a common cause of primary aldosteronism (PA), a clinical syndrome characterized by hypertension and electrolyte disturbances. If untreated, it may lead to serious cardiovascular complications. Therefore, there is an urgent need for potential bioma...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-01-01
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| Series: | Frontiers in Molecular Biosciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2023.1308754/full |
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| author | Deshui Yu Deshui Yu Jinxuan Zhang Jinxuan Zhang Xintao Li Shuwei Xiao Jizhang Xing Jianye Li Jianye Li |
| author_facet | Deshui Yu Deshui Yu Jinxuan Zhang Jinxuan Zhang Xintao Li Shuwei Xiao Jizhang Xing Jianye Li Jianye Li |
| author_sort | Deshui Yu |
| collection | DOAJ |
| description | Background: Aldosterone-producing adenomas (APA) are a common cause of primary aldosteronism (PA), a clinical syndrome characterized by hypertension and electrolyte disturbances. If untreated, it may lead to serious cardiovascular complications. Therefore, there is an urgent need for potential biomarkers and targeted drugs for the diagnosis and treatment of aldosteronism.Methods: We downloaded two datasets (GSE156931 and GSE60042) from the GEO database and merged them by de-batch effect, then screened the top50 of differential genes using PPI and enriched them, followed by screening the Aldosterone adenoma-related genes (ARGs) in the top50 using three machine learning algorithms. We performed GSEA analysis on the ARGs separately and constructed artificial neural networks based on the ARGs. Finally, the Enrich platform was utilized to identify drugs with potential therapeutic effects on APA by tARGseting the ARGs.Results: We identified 190 differential genes by differential analysis, and then identified the top50 genes by PPI, and the enrichment analysis showed that they were mainly enriched in amino acid metabolic pathways. Then three machine learning algorithms identified five ARGs, namely, SST, RAB3C, PPY, CYP3A4, CDH10, and the ANN constructed on the basis of these five ARGs had better diagnostic effect on APA, in which the AUC of the training set is 1 and the AUC of the validation set is 0.755. And then the Enrich platform identified drugs tARGseting the ARGs with potential therapeutic effects on APA.Conclusion: We identified five ARGs for APA through bioinformatic analysis and constructed Artificial neural network (ANN) based on them with better diagnostic effects, and identified drugs with potential therapeutic effects on APA by tARGseting these ARGs. Our study provides more options for the diagnosis and treatment of APA. |
| first_indexed | 2024-03-08T17:06:23Z |
| format | Article |
| id | doaj.art-e8936ab368964a1f8d65e4d663219e31 |
| institution | Directory Open Access Journal |
| issn | 2296-889X |
| language | English |
| last_indexed | 2024-03-08T17:06:23Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Molecular Biosciences |
| spelling | doaj.art-e8936ab368964a1f8d65e4d663219e312024-01-04T04:59:51ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2024-01-011010.3389/fmolb.2023.13087541308754Developing the novel diagnostic model and potential drugs by integrating bioinformatics and machine learning for aldosterone-producing adenomasDeshui Yu0Deshui Yu1Jinxuan Zhang2Jinxuan Zhang3Xintao Li4Shuwei Xiao5Jizhang Xing6Jianye Li7Jianye Li8Department of Urology, Air Force Medical Center, Beijing, ChinaChina Medical University, Shenyang, ChinaDepartment of Urology, Air Force Medical Center, Beijing, ChinaChina Medical University, Shenyang, ChinaDepartment of Urology, Air Force Medical Center, Beijing, ChinaDepartment of Urology, Air Force Medical Center, Beijing, ChinaDepartment of Urology, Air Force Medical Center, Beijing, ChinaDepartment of Urology, Air Force Medical Center, Beijing, ChinaChina Medical University, Shenyang, ChinaBackground: Aldosterone-producing adenomas (APA) are a common cause of primary aldosteronism (PA), a clinical syndrome characterized by hypertension and electrolyte disturbances. If untreated, it may lead to serious cardiovascular complications. Therefore, there is an urgent need for potential biomarkers and targeted drugs for the diagnosis and treatment of aldosteronism.Methods: We downloaded two datasets (GSE156931 and GSE60042) from the GEO database and merged them by de-batch effect, then screened the top50 of differential genes using PPI and enriched them, followed by screening the Aldosterone adenoma-related genes (ARGs) in the top50 using three machine learning algorithms. We performed GSEA analysis on the ARGs separately and constructed artificial neural networks based on the ARGs. Finally, the Enrich platform was utilized to identify drugs with potential therapeutic effects on APA by tARGseting the ARGs.Results: We identified 190 differential genes by differential analysis, and then identified the top50 genes by PPI, and the enrichment analysis showed that they were mainly enriched in amino acid metabolic pathways. Then three machine learning algorithms identified five ARGs, namely, SST, RAB3C, PPY, CYP3A4, CDH10, and the ANN constructed on the basis of these five ARGs had better diagnostic effect on APA, in which the AUC of the training set is 1 and the AUC of the validation set is 0.755. And then the Enrich platform identified drugs tARGseting the ARGs with potential therapeutic effects on APA.Conclusion: We identified five ARGs for APA through bioinformatic analysis and constructed Artificial neural network (ANN) based on them with better diagnostic effects, and identified drugs with potential therapeutic effects on APA by tARGseting these ARGs. Our study provides more options for the diagnosis and treatment of APA.https://www.frontiersin.org/articles/10.3389/fmolb.2023.1308754/fullaldosterone-producing adenomasprimary aldosteronismartificial neural networkmachine learning algorithmpotential targeted drugs |
| spellingShingle | Deshui Yu Deshui Yu Jinxuan Zhang Jinxuan Zhang Xintao Li Shuwei Xiao Jizhang Xing Jianye Li Jianye Li Developing the novel diagnostic model and potential drugs by integrating bioinformatics and machine learning for aldosterone-producing adenomas Frontiers in Molecular Biosciences aldosterone-producing adenomas primary aldosteronism artificial neural network machine learning algorithm potential targeted drugs |
| title | Developing the novel diagnostic model and potential drugs by integrating bioinformatics and machine learning for aldosterone-producing adenomas |
| title_full | Developing the novel diagnostic model and potential drugs by integrating bioinformatics and machine learning for aldosterone-producing adenomas |
| title_fullStr | Developing the novel diagnostic model and potential drugs by integrating bioinformatics and machine learning for aldosterone-producing adenomas |
| title_full_unstemmed | Developing the novel diagnostic model and potential drugs by integrating bioinformatics and machine learning for aldosterone-producing adenomas |
| title_short | Developing the novel diagnostic model and potential drugs by integrating bioinformatics and machine learning for aldosterone-producing adenomas |
| title_sort | developing the novel diagnostic model and potential drugs by integrating bioinformatics and machine learning for aldosterone producing adenomas |
| topic | aldosterone-producing adenomas primary aldosteronism artificial neural network machine learning algorithm potential targeted drugs |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2023.1308754/full |
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