Developing the novel diagnostic model and potential drugs by integrating bioinformatics and machine learning for aldosterone-producing adenomas.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38239411", "kind": "science-record", "revision": 3, "content_hash": "sha256:95aa8c12bc8fe95752fef6e7cfd7033f9ff0ee760724f8372accd90e60ab2a65", "created_at": "2026-11-01T19:20:09Z", "updated_at": "2026-11-01T22:11:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Developing the novel diagnostic model and potential drugs by integrating bioinformatics and machine learning for aldosterone-producing adenomas.", "doi": "10.3389/fmolb.2023.1308754", "pmid": "38239411", "authors": ["Deshui Yu", "Jinxuan Zhang", "Xintao Li", "Shuwei Xiao", "Jizhang Xing", "Jianye Li"], "journal": "Frontiers in molecular biosciences", "year": 2023, "abstract": "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.", "claims": [{"id": "claim-method", "statement": "We performed ROC analysis using the R package “pROC” to obtain AUC, specifically, we obtained 5 hub genes expression data and disease status grouping data from APA sets, performed ROC analysis using the “roc” function of “pROC” package and using the “ci” function of “pROC” to obtain the final AUC results.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38239411", "locator": "sections[10] \"Evaluation of the diagnostic value of the selected ARGs in APA\" paragraph 2", "quote": "We performed ROC analysis using the R package “pROC” to obtain AUC, specifically, we obtained 5 hub genes expression data and disease status grouping data from APA sets, performed ROC analysis using the “roc” function of “pROC” package and using the “ci” function of “pROC” to obtain the final AUC results."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38239411", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Five aldosterone adenoma-related genes were identified using three machine-learning algorithms, and an ANN based on these genes achieved an AUC of 1 in the training set and 0.755 in the validation set.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38239411", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "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."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Five aldosterone adenoma-related genes were identified using three machine-learning algorithms, and an ANN based on these genes achieved an AUC of 1 in the training set and 0.755 in the validation set.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:38239411"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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