Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis
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Le résumé fourni par la source
Renal biopsy is the gold standard for defining renal fibrosis which causes calcium deposits in the kidneys. Persistent calcium deposition leads to kidney inflammation, cell necrosis, and is related to serious kidney diseases. However, it is invasive and involves the risk of complications such as bleeding, especially in patients with end-stage renal diseases. Therefore, it is necessary to identify specific diagnostic biomarkers for renal fibrosis. This study aimed to develop a predictive drug target signature to diagnose renal fibrosis based on m6A subtypes. We then performed an unsupervised consensus clustering analysis to identify three different m6A subtypes of renal fibrosis based on the expressions of 21 m6A regulators. We evaluated the immune infiltration characteristics and expression of canonical immune checkpoints and immune-related genes with distinct m6A modification patterns. Subsequently, we performed the WGCNA analysis using the expression data of 1,611 drug targets to identify 474 genes associated with the m6A modification. 92 overlapping drug targets between WGCNA and DEGs (renal fibrosis vs. normal samples) were defined as key drug targets. A five target gene predictive model was developed through the combination of LASSO regression and stepwise logistic regression (LASSO-SLR) to diagnose renal fibrosis. We further performed drug sensitivity analysis and extracellular matrix analysis on model genes. The ROC curve showed that the risk score (AUC = 0.863) performed well in diagnosing renal fibrosis in the training dataset. In addition, the external validation dataset further confirmed the outstanding predictive performance of the risk score (AUC = 0.755). These results indicate that the risk model has an excellent predictive performance for diagnosing the disease. Furthermore, our results show that this 5-target gene model is significantly associated with many drugs and extracellular matrix activities. Finally, the expression levels of both predictive signature genes EGR1 and PLA2G4A were validated in renal fibrosis and adjacent normal tissues by using qRT-PCR and Western blot method.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis
- Date Crossref
- 31/08/2022
- Éditeur
- Frontiers Media SA
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Guangdong Academy of Medical Sciences pays non établi dans la noticeÉtablissement de santé
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Guangdong Provincial People's Hospital Department of Urology pays non établi dans la noticeÉtablissement de santé
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Wuhan No.1 Hospital pays non établi dans la noticeÉtablissement de santé
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Huazhong University of Science and Technology pays non établi dans la noticeUniversité ou école supérieure
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Tongji Hospital pays non établi dans la noticeÉtablissement de santé
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Southwest Jiaotong University pays non établi dans la noticeUniversité ou école supérieure
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Tongji Medical College Department of Urology pays non établi dans la noticeUniversité ou école supérieure
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Wuhan No. 1 Hospital Department of Urology pays non établi dans la noticeÉtablissement de santé
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School of Life Science and Engineering pays non établi dans la noticeUniversité ou école supérieure
Guangdong Academy of Medical Sciences, Department of Urology — Guangdong Provincial People's Hospital et Wuhan No.1 Hospital, avec 6 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.