Validity of Klotho, CYR61 and YKL-40 as ideal predictive biomarkers for acute kidney injury: review study

Mosa, Osama F; Skitek, Milan; Jerin, Aleš

doi:10.1590/1516-3180.2016.0099220516

Cited by 5 publications

(1 citation statement)

References 63 publications

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“…Researches have shown that Klotho is not only an anti-aging gene but also a tumor suppressor gene that has the function of protecting kidney and inhibiting RCC. The downregulation of Klotho may be closely related to the poor prognosis of RCC ( 19 , 20 ). DNA methylation is an important nucleic acid modification, which needs the catalysis of DNA methyltransferase ( 21 , 22 ).…”

Section: Introductionmentioning

confidence: 99%

Sohlh2 Inhibits the Malignant Progression of Renal Cell Carcinoma by Upregulating Klotho via DNMT3a

et al. 2022

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BackgroundRenal cell carcinoma is the most common malignant tumor of the kidney. The 5-year survival of renal cell carcinoma with distant metastasis is very low. Sohlh2 is a newly discovered tumor suppressor gene playing inhibitory roles in a variety of tumors, but its role in renal cell carcinoma has not been reported.MethodsTo clarify the role of Sohlh2 in the occurrence and development of renal cell carcinoma, we constructed stably transfected human renal cell carcinoma cell lines with Sohlh2 overexpression and Sohlh2 knockdown, separately. First, we studied the effects of Sohlh2 on proliferation, migration, invasion, and epithelial–mesenchymal transition (EMT) of renal cell carcinoma cells in vitro and in vivo. Then, we detected whether Sohlh2 functions through DNMT3a/Klotho using Western blotting, qPCR, and Cell Counting Kit-8 (CCK-8) assay. Finally, we collected 40 resected renal cell carcinoma samples to study the relevance between Sohlh2, DNMT3a, and Klotho by immunohistochemistry.ResultsOur results showed that Sohlh2 was downregulated in renal cell carcinoma, and its expression level was negatively correlated with tumor staging. Both in vitro and in vivo experiments confirmed that Sohlh2 overexpression inhibited the proliferation, migration, invasion, metastasis, and EMT of renal cell carcinoma. Sohlh2 functions through demethylation of Klotho by downregulating the expression of DNA methyltransferase of DNMT3a. In renal cell carcinoma, Sohlh2 was positively correlated with Klotho and negatively correlated with DNMT3a.ConclusionSohlh2 functions as a tumor suppressor gene in renal cell carcinoma by demethylation of Klotho via DNMT3a. Sohlh2 correlated with Klotho positively and with DNMT3a negatively in renal cell carcinoma. Our study suggests that Sohlh2 and DNMT3a/Klotho can be used as potential targets for the clinical treatment of renal cell carcinoma.

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Section: Introductionmentioning

confidence: 99%

Sohlh2 Inhibits the Malignant Progression of Renal Cell Carcinoma by Upregulating Klotho via DNMT3a

et al. 2022

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Kidney Diseases: The Age of Molecular Markers

Veiga

Alves

Perez

et al. 2021

Advances in Experimental Medicine and Biology

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Machine learning models for predicting acute kidney injury: a systematic review and critical appraisal

Vagliano

Chesnaye

Leopold

et al. 2022

Clinical Kidney Journal

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Background The number of studies applying machine learning (ML) to predict acute kidney injury (AKI) has grown steadily over the last decade. We assess and critically appraise the state of the art in ML models for AKI prediction considering performance, methodological soundness, and applicability. Methods We searched PubMed and ArXiv, extracted data, and critically appraised studies based on the TRIPOD, CHARMS, and PROBAST guidelines. Results Forty-six studies from 3166 titles were included. Thirty-eight studies developed a model, five developed and externally validated one, and three studies externally validated one. Flexible ML methods were used more often than deep learning, although the latter was common with temporal variables and text as predictors. Predictive performance showed an area under receiver operating curves ranging from 0.49 to 0.99. Our critical appraisal identified a high risk of bias in thirty-nine studies. Some studies lacked internal validation, whereas external validation and interpretability of results were rarely considered. Fifteen studies focused on AKI prediction in the intensive care setting, and the USA-derived MIMIC dataset was commonly used. Reproducibility was limited as data and code were usually unavailable. Conclusions Flexible ML methods are popular for the prediction of AKI, although more complex models based on deep learning are emerging. Our critical appraisal identified a high risk of bias in most models: Studies should use calibration measures and external validation more often, improve model interpretability, and share data and code to improve reproducibility.

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Validity of Klotho, CYR61 and YKL-40 as ideal predictive biomarkers for acute kidney injury: review study

Cited by 5 publications

References 63 publications

Sohlh2 Inhibits the Malignant Progression of Renal Cell Carcinoma by Upregulating Klotho via DNMT3a

Sohlh2 Inhibits the Malignant Progression of Renal Cell Carcinoma by Upregulating Klotho via DNMT3a

Kidney Diseases: The Age of Molecular Markers

Machine learning models for predicting acute kidney injury: a systematic review and critical appraisal

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