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Bioinformatics-based discovery of the urinary BBOX1 mRNA as a potential biomarker of diabetic kidney disease
BACKGROUND: Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease (ESKD) in the world. Emerging evidence has shown that urinary mRNAs may serve as early diagnostic and prognostic biomarkers of DKD. In this article, we aimed to first establish a novel bioinformatics-based met...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6394064/ https://www.ncbi.nlm.nih.gov/pubmed/30819181 http://dx.doi.org/10.1186/s12967-019-1818-2 |
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author | Zhou, Le-Ting Lv, Lin-Li Qiu, Shen Yin, Qing Li, Zuo-Lin Tang, Tao-Tao Ni, Li-Hua Feng, Ye Wang, Bin Ma, Kun-Ling Liu, Bi-Cheng |
author_facet | Zhou, Le-Ting Lv, Lin-Li Qiu, Shen Yin, Qing Li, Zuo-Lin Tang, Tao-Tao Ni, Li-Hua Feng, Ye Wang, Bin Ma, Kun-Ling Liu, Bi-Cheng |
author_sort | Zhou, Le-Ting |
collection | PubMed |
description | BACKGROUND: Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease (ESKD) in the world. Emerging evidence has shown that urinary mRNAs may serve as early diagnostic and prognostic biomarkers of DKD. In this article, we aimed to first establish a novel bioinformatics-based methodology for analyzing the “urinary kidney-specific mRNAs” and verify their potential clinical utility in DKD. METHODS: To select candidate mRNAs, a total of 127 Affymetrix microarray datasets of diabetic kidney tissues and other tissues from humans were compiled and analyzed using an integrative bioinformatics approach. Then, the urinary expression of candidate mRNAs in stage 1 study (n = 82) was verified, and the one with best performance moved on to stage 2 study (n = 80) for validation. To avoid potential detection bias, a one-step Taqman PCR assay was developed for quantification of the interested mRNA in stage 2 study. Lastly, the in situ expression of the selected mRNA was further confirmed using fluorescent in situ hybridization (FISH) assay and bioinformatics analysis. RESULTS: Our bioinformatics analysis identified sixteen mRNAs as candidates, of which urinary BBOX1 (uBBOX1) levels were significantly upregulated in the urine of patients with DKD. The expression of uBBOX1 was also increased in normoalbuminuric diabetes subjects, while remained unchanged in patients with urinary tract infection or bladder cancer. Besides, uBBOX1 levels correlated with glycemic control, albuminuria and urinary tubular injury marker levels. Similar results were obtained in stage 2 study. FISH assay further demonstrated that BBOX1 mRNA was predominantly located in renal tubular epithelial cells, while its expression in podocytes and urothelium was weak. Further bioinformatics analysis also suggested that tubular BBOX1 mRNA expression was quite stable in various types of kidney diseases. CONCLUSIONS: Our study provided a novel methodology to identify and analyze urinary kidney-specific mRNAs. uBBOX1 might serve as a promising biomarker of DKD. The performance of the selected urinary mRNAs in monitoring disease progression needs further validation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12967-019-1818-2) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6394064 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-63940642019-03-11 Bioinformatics-based discovery of the urinary BBOX1 mRNA as a potential biomarker of diabetic kidney disease Zhou, Le-Ting Lv, Lin-Li Qiu, Shen Yin, Qing Li, Zuo-Lin Tang, Tao-Tao Ni, Li-Hua Feng, Ye Wang, Bin Ma, Kun-Ling Liu, Bi-Cheng J Transl Med Research BACKGROUND: Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease (ESKD) in the world. Emerging evidence has shown that urinary mRNAs may serve as early diagnostic and prognostic biomarkers of DKD. In this article, we aimed to first establish a novel bioinformatics-based methodology for analyzing the “urinary kidney-specific mRNAs” and verify their potential clinical utility in DKD. METHODS: To select candidate mRNAs, a total of 127 Affymetrix microarray datasets of diabetic kidney tissues and other tissues from humans were compiled and analyzed using an integrative bioinformatics approach. Then, the urinary expression of candidate mRNAs in stage 1 study (n = 82) was verified, and the one with best performance moved on to stage 2 study (n = 80) for validation. To avoid potential detection bias, a one-step Taqman PCR assay was developed for quantification of the interested mRNA in stage 2 study. Lastly, the in situ expression of the selected mRNA was further confirmed using fluorescent in situ hybridization (FISH) assay and bioinformatics analysis. RESULTS: Our bioinformatics analysis identified sixteen mRNAs as candidates, of which urinary BBOX1 (uBBOX1) levels were significantly upregulated in the urine of patients with DKD. The expression of uBBOX1 was also increased in normoalbuminuric diabetes subjects, while remained unchanged in patients with urinary tract infection or bladder cancer. Besides, uBBOX1 levels correlated with glycemic control, albuminuria and urinary tubular injury marker levels. Similar results were obtained in stage 2 study. FISH assay further demonstrated that BBOX1 mRNA was predominantly located in renal tubular epithelial cells, while its expression in podocytes and urothelium was weak. Further bioinformatics analysis also suggested that tubular BBOX1 mRNA expression was quite stable in various types of kidney diseases. CONCLUSIONS: Our study provided a novel methodology to identify and analyze urinary kidney-specific mRNAs. uBBOX1 might serve as a promising biomarker of DKD. The performance of the selected urinary mRNAs in monitoring disease progression needs further validation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12967-019-1818-2) contains supplementary material, which is available to authorized users. BioMed Central 2019-02-28 /pmc/articles/PMC6394064/ /pubmed/30819181 http://dx.doi.org/10.1186/s12967-019-1818-2 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Zhou, Le-Ting Lv, Lin-Li Qiu, Shen Yin, Qing Li, Zuo-Lin Tang, Tao-Tao Ni, Li-Hua Feng, Ye Wang, Bin Ma, Kun-Ling Liu, Bi-Cheng Bioinformatics-based discovery of the urinary BBOX1 mRNA as a potential biomarker of diabetic kidney disease |
title | Bioinformatics-based discovery of the urinary BBOX1 mRNA as a potential biomarker of diabetic kidney disease |
title_full | Bioinformatics-based discovery of the urinary BBOX1 mRNA as a potential biomarker of diabetic kidney disease |
title_fullStr | Bioinformatics-based discovery of the urinary BBOX1 mRNA as a potential biomarker of diabetic kidney disease |
title_full_unstemmed | Bioinformatics-based discovery of the urinary BBOX1 mRNA as a potential biomarker of diabetic kidney disease |
title_short | Bioinformatics-based discovery of the urinary BBOX1 mRNA as a potential biomarker of diabetic kidney disease |
title_sort | bioinformatics-based discovery of the urinary bbox1 mrna as a potential biomarker of diabetic kidney disease |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6394064/ https://www.ncbi.nlm.nih.gov/pubmed/30819181 http://dx.doi.org/10.1186/s12967-019-1818-2 |
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