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Long non-coding RNA pairs to assist in diagnosing sepsis
BACKGROUND: Sepsis is the major cause of death in Intensive Care Unit (ICU) globally. Molecular detection enables rapid diagnosis that allows early intervention to minimize the death rate. Recent studies showed that long non-coding RNAs (lncRNAs) regulate proinflammatory genes and are related to the...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8050902/ https://www.ncbi.nlm.nih.gov/pubmed/33863291 http://dx.doi.org/10.1186/s12864-021-07576-4 |
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author | Zheng, Xubin Leung, Kwong-Sak Wong, Man-Hon Cheng, Lixin |
author_facet | Zheng, Xubin Leung, Kwong-Sak Wong, Man-Hon Cheng, Lixin |
author_sort | Zheng, Xubin |
collection | PubMed |
description | BACKGROUND: Sepsis is the major cause of death in Intensive Care Unit (ICU) globally. Molecular detection enables rapid diagnosis that allows early intervention to minimize the death rate. Recent studies showed that long non-coding RNAs (lncRNAs) regulate proinflammatory genes and are related to the dysfunction of organs in sepsis. Identifying lncRNA signature with absolute abundance is challenging because of the technical variation and the systematic experimental bias. RESULTS: Cohorts (n = 768) containing whole blood lncRNA profiling of sepsis patients in the Gene Expression Omnibus (GEO) database were included. We proposed a novel diagnostic strategy that made use of the relative expressions of lncRNA pairs, which are reversed between sepsis patients and normal controls (eg. lncRNA(i) > lncRNA(j) in sepsis patients and lncRNA(i) < lncRNA(j) in normal controls), to identify 14 lncRNA pairs as a sepsis diagnostic signature. The signature was then applied to independent cohorts (n = 644) to evaluate its predictive performance across different ages and normalization methods. Comparing to common machine learning models and existing signatures, SepSigLnc consistently attains better performance on the validation cohorts from the same age group (AUC = 0.990 & 0.995 in two cohorts) and across different groups (AUC = 0.878 on average), as well as cohorts processed by an alternative normalization method (AUC = 0.953 on average). Functional analysis demonstrates that the lncRNA pairs in SepsigLnc are functionally similar and tend to implicate in the same biological processes including cell fate commitment and cellular response to steroid hormone stimulus. CONCLUSION: Our study identified 14 lncRNA pairs as signature that can facilitate the diagnosis of septic patients at an intervenable point when clinical manifestations are not dramatic. Also, the computational procedure can be generalized to a standard procedure for discovering diagnostic molecule signatures. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-021-07576-4. |
format | Online Article Text |
id | pubmed-8050902 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-80509022021-04-19 Long non-coding RNA pairs to assist in diagnosing sepsis Zheng, Xubin Leung, Kwong-Sak Wong, Man-Hon Cheng, Lixin BMC Genomics Research Article BACKGROUND: Sepsis is the major cause of death in Intensive Care Unit (ICU) globally. Molecular detection enables rapid diagnosis that allows early intervention to minimize the death rate. Recent studies showed that long non-coding RNAs (lncRNAs) regulate proinflammatory genes and are related to the dysfunction of organs in sepsis. Identifying lncRNA signature with absolute abundance is challenging because of the technical variation and the systematic experimental bias. RESULTS: Cohorts (n = 768) containing whole blood lncRNA profiling of sepsis patients in the Gene Expression Omnibus (GEO) database were included. We proposed a novel diagnostic strategy that made use of the relative expressions of lncRNA pairs, which are reversed between sepsis patients and normal controls (eg. lncRNA(i) > lncRNA(j) in sepsis patients and lncRNA(i) < lncRNA(j) in normal controls), to identify 14 lncRNA pairs as a sepsis diagnostic signature. The signature was then applied to independent cohorts (n = 644) to evaluate its predictive performance across different ages and normalization methods. Comparing to common machine learning models and existing signatures, SepSigLnc consistently attains better performance on the validation cohorts from the same age group (AUC = 0.990 & 0.995 in two cohorts) and across different groups (AUC = 0.878 on average), as well as cohorts processed by an alternative normalization method (AUC = 0.953 on average). Functional analysis demonstrates that the lncRNA pairs in SepsigLnc are functionally similar and tend to implicate in the same biological processes including cell fate commitment and cellular response to steroid hormone stimulus. CONCLUSION: Our study identified 14 lncRNA pairs as signature that can facilitate the diagnosis of septic patients at an intervenable point when clinical manifestations are not dramatic. Also, the computational procedure can be generalized to a standard procedure for discovering diagnostic molecule signatures. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-021-07576-4. BioMed Central 2021-04-16 /pmc/articles/PMC8050902/ /pubmed/33863291 http://dx.doi.org/10.1186/s12864-021-07576-4 Text en © The Author(s) 2021 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 Article Zheng, Xubin Leung, Kwong-Sak Wong, Man-Hon Cheng, Lixin Long non-coding RNA pairs to assist in diagnosing sepsis |
title | Long non-coding RNA pairs to assist in diagnosing sepsis |
title_full | Long non-coding RNA pairs to assist in diagnosing sepsis |
title_fullStr | Long non-coding RNA pairs to assist in diagnosing sepsis |
title_full_unstemmed | Long non-coding RNA pairs to assist in diagnosing sepsis |
title_short | Long non-coding RNA pairs to assist in diagnosing sepsis |
title_sort | long non-coding rna pairs to assist in diagnosing sepsis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8050902/ https://www.ncbi.nlm.nih.gov/pubmed/33863291 http://dx.doi.org/10.1186/s12864-021-07576-4 |
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