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Construct of qualitative diagnostic biomarkers specific for glioma by pairing serum microRNAs

BACKGROUND: Serum microRNAs (miRNAs) are promising non-invasive biomarkers for diagnosing glioma. However, most reported predictive models are constructed without a large enough sample size, and quantitative expression levels of their constituent serum miRNAs are susceptible to batch effects, decrea...

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Autores principales: Li, Hongdong, Ma, Liyuan, Luo, Fengyuan, Liu, Wenkai, Li, Na, Hu, Tao, Zhong, Haijian, Guo, You, Hong, Guini
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9983174/
https://www.ncbi.nlm.nih.gov/pubmed/36864382
http://dx.doi.org/10.1186/s12864-023-09203-w
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author Li, Hongdong
Ma, Liyuan
Luo, Fengyuan
Liu, Wenkai
Li, Na
Hu, Tao
Zhong, Haijian
Guo, You
Hong, Guini
author_facet Li, Hongdong
Ma, Liyuan
Luo, Fengyuan
Liu, Wenkai
Li, Na
Hu, Tao
Zhong, Haijian
Guo, You
Hong, Guini
author_sort Li, Hongdong
collection PubMed
description BACKGROUND: Serum microRNAs (miRNAs) are promising non-invasive biomarkers for diagnosing glioma. However, most reported predictive models are constructed without a large enough sample size, and quantitative expression levels of their constituent serum miRNAs are susceptible to batch effects, decreasing their clinical applicability. METHODS: We propose a general method for detecting qualitative serum predictive biomarkers using a large cohort of miRNA-profiled serum samples (n = 15,460) based on the within-sample relative expression orderings of miRNAs. RESULTS: Two panels of miRNA pairs (miRPairs) were developed. The first was composed of five serum miRPairs (5-miRPairs), reaching 100% diagnostic accuracy in three validation sets for distinguishing glioma and non-cancer controls (n = 436: glioma = 236, non-cancers = 200). An additional validation set without glioma samples (non-cancers = 2611) showed a predictive accuracy of 95.9%. The second panel included 32 serum miRPairs (32-miRPairs), reaching 100% diagnostic performance in training set on specifically discriminating glioma from other cancer types (sensitivity = 100%, specificity = 100%, accuracy = 100%), which was reproducible in five validation datasets (n = 3387: glioma = 236, non-glioma cancers = 3151, sensitivity> 97.9%, specificity> 99.5%, accuracy> 95.7%). In other brain diseases, the 5-miRPairs classified all non-neoplastic samples as non-cancer, including stroke (n = 165), Alzheimer’s disease (n = 973), and healthy samples (n = 1820), and all neoplastic samples as cancer, including meningioma (n = 16), and primary central nervous system lymphoma samples (n = 39). The 32-miRPairs predicted 82.2 and 92.3% of the two kinds of neoplastic samples as positive, respectively. Based on the Human miRNA tissue atlas database, the glioma-specific 32-miRPairs were significantly enriched in the spinal cord (p = 0.013) and brain (p = 0.015). CONCLUSIONS: The identified 5-miRPairs and 32-miRPairs provide potential population screening and cancer-specific biomarkers for glioma clinical practice. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-023-09203-w.
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spelling pubmed-99831742023-03-04 Construct of qualitative diagnostic biomarkers specific for glioma by pairing serum microRNAs Li, Hongdong Ma, Liyuan Luo, Fengyuan Liu, Wenkai Li, Na Hu, Tao Zhong, Haijian Guo, You Hong, Guini BMC Genomics Research BACKGROUND: Serum microRNAs (miRNAs) are promising non-invasive biomarkers for diagnosing glioma. However, most reported predictive models are constructed without a large enough sample size, and quantitative expression levels of their constituent serum miRNAs are susceptible to batch effects, decreasing their clinical applicability. METHODS: We propose a general method for detecting qualitative serum predictive biomarkers using a large cohort of miRNA-profiled serum samples (n = 15,460) based on the within-sample relative expression orderings of miRNAs. RESULTS: Two panels of miRNA pairs (miRPairs) were developed. The first was composed of five serum miRPairs (5-miRPairs), reaching 100% diagnostic accuracy in three validation sets for distinguishing glioma and non-cancer controls (n = 436: glioma = 236, non-cancers = 200). An additional validation set without glioma samples (non-cancers = 2611) showed a predictive accuracy of 95.9%. The second panel included 32 serum miRPairs (32-miRPairs), reaching 100% diagnostic performance in training set on specifically discriminating glioma from other cancer types (sensitivity = 100%, specificity = 100%, accuracy = 100%), which was reproducible in five validation datasets (n = 3387: glioma = 236, non-glioma cancers = 3151, sensitivity> 97.9%, specificity> 99.5%, accuracy> 95.7%). In other brain diseases, the 5-miRPairs classified all non-neoplastic samples as non-cancer, including stroke (n = 165), Alzheimer’s disease (n = 973), and healthy samples (n = 1820), and all neoplastic samples as cancer, including meningioma (n = 16), and primary central nervous system lymphoma samples (n = 39). The 32-miRPairs predicted 82.2 and 92.3% of the two kinds of neoplastic samples as positive, respectively. Based on the Human miRNA tissue atlas database, the glioma-specific 32-miRPairs were significantly enriched in the spinal cord (p = 0.013) and brain (p = 0.015). CONCLUSIONS: The identified 5-miRPairs and 32-miRPairs provide potential population screening and cancer-specific biomarkers for glioma clinical practice. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-023-09203-w. BioMed Central 2023-03-02 /pmc/articles/PMC9983174/ /pubmed/36864382 http://dx.doi.org/10.1186/s12864-023-09203-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Li, Hongdong
Ma, Liyuan
Luo, Fengyuan
Liu, Wenkai
Li, Na
Hu, Tao
Zhong, Haijian
Guo, You
Hong, Guini
Construct of qualitative diagnostic biomarkers specific for glioma by pairing serum microRNAs
title Construct of qualitative diagnostic biomarkers specific for glioma by pairing serum microRNAs
title_full Construct of qualitative diagnostic biomarkers specific for glioma by pairing serum microRNAs
title_fullStr Construct of qualitative diagnostic biomarkers specific for glioma by pairing serum microRNAs
title_full_unstemmed Construct of qualitative diagnostic biomarkers specific for glioma by pairing serum microRNAs
title_short Construct of qualitative diagnostic biomarkers specific for glioma by pairing serum microRNAs
title_sort construct of qualitative diagnostic biomarkers specific for glioma by pairing serum micrornas
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9983174/
https://www.ncbi.nlm.nih.gov/pubmed/36864382
http://dx.doi.org/10.1186/s12864-023-09203-w
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