Identification of N6-methylandenosine-related lncRNA for tuberculosis diagnosis in person living with human immunodeficiency virus

Development of a robust diagnostic test for patients co-infected with human immunodeficiency virus and tuberculosis (HIV/TB) is urgently needed. We believe N6-methyladenosine (m6A)- related long non-coding RNA (lncRNAs) from the host blood could be utilized to diagnose patients co-infected with HIV/...

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Published inTuberculosis (Edinburgh, Scotland) Vol. 140; p. 102337
Main Authors Xu, Shaohua, Yuan, Huicheng, Li, Ling, Yang, Kai, Zhao, Liangcun
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LanguageEnglish
Published Scotland Elsevier Ltd 01.05.2023
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Abstract Development of a robust diagnostic test for patients co-infected with human immunodeficiency virus and tuberculosis (HIV/TB) is urgently needed. We believe N6-methyladenosine (m6A)- related long non-coding RNA (lncRNAs) from the host blood could be utilized to diagnose patients co-infected with HIV/TB. In this study, differentially expressed analysis, correlation analysis, univariate logistic regression, and logistic regression with least absolute shrinkage and selection operator (LASSO) were performed in RNA-Seq dataset containing of 14 HIV/TB co-infected subjects and 11 HIV mono-infected subjects. In total, five m6A related-lncRNAs with powerful diagnostic significance for HIV/TB co-infection were identified. We then built a deep learning model based on the five m6A related-lncRNAs for accurately discriminating the HIV/TB co-infected patients from HIV mono-infected patients with an accuracy of 92.0%, a sensitivity of 92.9%, a specificity of 90.9%, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.935. Moreover, the diagnostic performance was validated in an external cohort containing 15 HIV/TB co-infected subjects and 16 HIV mono-infected subjects of whole blood. Overall, the findings showed that deep learning model based on five m6A-related lncRNAs had a worthy diagnostic performance for HIV/TB co-infection, and these diagnostic lncRNAs associated with m6A regulator genes could play a potential role in the pathogenesis of HIV/TB co-infection.
AbstractList Development of a robust diagnostic test for patients co-infected with human immunodeficiency virus and tuberculosis (HIV/TB) is urgently needed. We believe N6-methyladenosine (m6A)- related long non-coding RNA (lncRNAs) from the host blood could be utilized to diagnose patients co-infected with HIV/TB. In this study, differentially expressed analysis, correlation analysis, univariate logistic regression, and logistic regression with least absolute shrinkage and selection operator (LASSO) were performed in RNA-Seq dataset containing of 14 HIV/TB co-infected subjects and 11 HIV mono-infected subjects. In total, five m6A related-lncRNAs with powerful diagnostic significance for HIV/TB co-infection were identified. We then built a deep learning model based on the five m6A related-lncRNAs for accurately discriminating the HIV/TB co-infected patients from HIV mono-infected patients with an accuracy of 92.0%, a sensitivity of 92.9%, a specificity of 90.9%, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.935. Moreover, the diagnostic performance was validated in an external cohort containing 15 HIV/TB co-infected subjects and 16 HIV mono-infected subjects of whole blood. Overall, the findings showed that deep learning model based on five m6A-related lncRNAs had a worthy diagnostic performance for HIV/TB co-infection, and these diagnostic lncRNAs associated with m6A regulator genes could play a potential role in the pathogenesis of HIV/TB co-infection.
Development of a robust diagnostic test for patients co-infected with human immunodeficiency virus and tuberculosis (HIV/TB) is urgently needed. We believe N6-methyladenosine (m6A)- related long non-coding RNA (lncRNAs) from the host blood could be utilized to diagnose patients co-infected with HIV/TB. In this study, differentially expressed analysis, correlation analysis, univariate logistic regression, and logistic regression with least absolute shrinkage and selection operator (LASSO) were performed in RNA-Seq dataset containing of 14 HIV/TB co-infected subjects and 11 HIV mono-infected subjects. In total, five m6A related-lncRNAs with powerful diagnostic significance for HIV/TB co-infection were identified. We then built a deep learning model based on the five m6A related-lncRNAs for accurately discriminating the HIV/TB co-infected patients from HIV mono-infected patients with an accuracy of 92.0%, a sensitivity of 92.9%, a specificity of 90.9%, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.935. Moreover, the diagnostic performance was validated in an external cohort containing 15 HIV/TB co-infected subjects and 16 HIV mono-infected subjects of whole blood. Overall, the findings showed that deep learning model based on five m6A-related lncRNAs had a worthy diagnostic performance for HIV/TB co-infection, and these diagnostic lncRNAs associated with m6A regulator genes could play a potential role in the pathogenesis of HIV/TB co-infection.Development of a robust diagnostic test for patients co-infected with human immunodeficiency virus and tuberculosis (HIV/TB) is urgently needed. We believe N6-methyladenosine (m6A)- related long non-coding RNA (lncRNAs) from the host blood could be utilized to diagnose patients co-infected with HIV/TB. In this study, differentially expressed analysis, correlation analysis, univariate logistic regression, and logistic regression with least absolute shrinkage and selection operator (LASSO) were performed in RNA-Seq dataset containing of 14 HIV/TB co-infected subjects and 11 HIV mono-infected subjects. In total, five m6A related-lncRNAs with powerful diagnostic significance for HIV/TB co-infection were identified. We then built a deep learning model based on the five m6A related-lncRNAs for accurately discriminating the HIV/TB co-infected patients from HIV mono-infected patients with an accuracy of 92.0%, a sensitivity of 92.9%, a specificity of 90.9%, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.935. Moreover, the diagnostic performance was validated in an external cohort containing 15 HIV/TB co-infected subjects and 16 HIV mono-infected subjects of whole blood. Overall, the findings showed that deep learning model based on five m6A-related lncRNAs had a worthy diagnostic performance for HIV/TB co-infection, and these diagnostic lncRNAs associated with m6A regulator genes could play a potential role in the pathogenesis of HIV/TB co-infection.
ArticleNumber 102337
Author Zhao, Liangcun
Li, Ling
Yang, Kai
Xu, Shaohua
Yuan, Huicheng
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Keywords Biomarker
N6-methyladenosine
Tuberculosis
Co-infection
HIV
Long non-coding RNA
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Snippet Development of a robust diagnostic test for patients co-infected with human immunodeficiency virus and tuberculosis (HIV/TB) is urgently needed. We believe...
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SubjectTerms Biomarker
Co-infection
Coinfection - diagnosis
HIV
Humans
Long non-coding RNA
Mycobacterium tuberculosis - genetics
N6-methyladenosine
RNA, Long Noncoding - genetics
Tuberculosis
Tuberculosis - diagnosis
Tuberculosis - genetics
Title Identification of N6-methylandenosine-related lncRNA for tuberculosis diagnosis in person living with human immunodeficiency virus
URI https://www.clinicalkey.com/#!/content/1-s2.0-S1472979223000355
https://dx.doi.org/10.1016/j.tube.2023.102337
https://www.ncbi.nlm.nih.gov/pubmed/36965224
https://www.proquest.com/docview/2791381783
Volume 140
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