Dataset Information

Nicotinamide N-Methyl Transferase as a Predictive Marker of Tubular Fibrosis in CKD.

ABSTRACT:

Purpose

Chronic kidney disease (CKD) progression is complex. There are not standardized methods for predicting the prognosis of CKD. Nicotinamide N-methyltransferase (NNMT) has been shown to be associated with renal fibrosis. This study aimed to validate NNMT as a prognostic biomarker of progressive CKD.

Patients and methods

We explored the relationship between NNMT expression and CKD-related outcome variables using the NephroseqV5 and GEO databases. Additionally, a validation set of 37 CKD patients was enrolled to measure the correlation between NNMT expression levels and CKD outcomes. Furthermore, single-cell RNA sequencing data and the Human Protein Atlas were reanalyzed to investigate the expression specificity of NNMT in the kidney. Finally, to detect the status of NNMT expression with tubular fibrosis in vivo, we constructed a unilateral ureteral obstruction (UUO) mouse treated with an NNMT inhibitor.

Results

Analyzing the datasets showed that NNMT was expressed mainly in proximal tubule compartments. And patients with high NNMT expression levels had a significantly lower overall survival rate compared to those with low NNMT expression levels (P = 0.013). NNMT was independent of prognosis factors in the multivariate Cox regression model, and the AUCs for CKD progression at 1, 3, and 5 years were 0.849, 0.775, and 0.877, respectively. Pathway enrichment analysis indicated that NNMT regulates the biological processes of tubulointerstitial fibrosis (TIF). In the validation group, NNMT levels were significantly higher in the CKD group combined with interstitial fibrosis. In vivo, NNMT was a high expression in the UUO group, peaking at postoperative day 21. Treatment with an NNMT inhibitor improved renal tubular interstitial fibrosis, and expression levels of FN, α-SMA, VIM, and TGF-β1 were decreased compared with UUO (P < 0.05).

Conclusion

NNMT was expressed mainly in tubular renal compartments, and associated with CKD prognosis. It holds potential as a diagnostic biomarker for tubular fibrosis in CKD.

SUBMITTER: Ye Q

PROVIDER: S-EPMC10417815 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Nicotinamide N-Methyl Transferase as a Predictive Marker of Tubular Fibrosis in CKD.

Ye Qinglin Q Xu Guiling G Huang Haizhen H Pang Shuting S Xie Boji B Feng Bingmei B Liang Peng P Qin Yijie Y Li Siji S Luo Yin Y Xue Chao C Li Wei W

International journal of general medicine 20230807

<h4>Purpose</h4>Chronic kidney disease (CKD) progression is complex. There are not standardized methods for predicting the prognosis of CKD. Nicotinamide N-methyltransferase (NNMT) has been shown to be associated with renal fibrosis. This study aimed to validate NNMT as a prognostic biomarker of progressive CKD.<h4>Patients and methods</h4>We explored the relationship between NNMT expression and CKD-related outcome variables using the NephroseqV5 and GEO databases. Additionally, a validation set ...[more]

PMID: 37576910

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Project description:Background: Interstitial fibrosis and tubular atrophy (IFTA) are the histopathological manifestations of chronic kidney disease (CKD) and one of the causes of long-term renal loss in transplanted kidneys. Necroptosis as a type of programmed death plays an important role in the development of IFTA, and in the late functional decline and even loss of grafts. In this study, 13 machine learning algorithms were used to construct IFTA diagnostic models based on necroptosis-related genes. Methods: We screened all 162 "kidney transplant"-related cohorts in the GEO database and obtained five data sets (training sets: GSE98320 and GSE76882, validation sets: GSE22459 and GSE53605, and survival set: GSE21374). The training set was constructed after removing batch effects of GSE98320 and GSE76882 by using the SVA package. The differentially expressed gene (DEG) analysis was used to identify necroptosis-related DEGs. A total of 13 machine learning algorithms-LASSO, Ridge, Enet, Stepglm, SVM, glmboost, LDA, plsRglm, random forest, GBM, XGBoost, Naive Bayes, and ANNs-were used to construct 114 IFTA diagnostic models, and the optimal models were screened by the AUC values. Post-transplantation patients were then grouped using consensus clustering, and the different subgroups were further explored using PCA, Kaplan-Meier (KM) survival analysis, functional enrichment analysis, CIBERSOFT, and single-sample Gene Set Enrichment Analysis. Results: A total of 55 necroptosis-related DEGs were identified by taking the intersection of the DEGs and necroptosis-related gene sets. Stepglm[both]+RF is the optimal model with an average AUC of 0.822. A total of four molecular subgroups of renal transplantation patients were obtained by clustering, and significant upregulation of fibrosis-related pathways and upregulation of immune response-related pathways were found in the C4 group, which had poor prognosis. Conclusion: Based on the combination of the 13 machine learning algorithms, we developed 114 IFTA classification models. Furthermore, we tested the top model using two independent data sets from GEO.

Dataset Information

Nicotinamide N-Methyl Transferase as a Predictive Marker of Tubular Fibrosis in CKD.

Purpose

Patients and methods

Results

Conclusion

Publications

Nicotinamide N-Methyl Transferase as a Predictive Marker of Tubular Fibrosis in CKD.

Similar Datasets

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