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Identification of novel cell-free RNAs in maternal plasma as preterm biomarkers in combination with placental RNA profiles
BACKGROUND: Preterm birth (PTB) is the main driver of newborn deaths. The identification of pregnancies at risk of PTB remains challenging, as the incomplete understanding of molecular mechanisms associated with PTB. Although several transcriptome studies have been done on the placenta and plasma fr...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10100253/ https://www.ncbi.nlm.nih.gov/pubmed/37046301 http://dx.doi.org/10.1186/s12967-023-04083-w |
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author | Jin, Heyue Zhang, Yimin Fan, Zhigang Wang, Xianyan Rui, Chen Xing, Shaozhen Dong, Hongmei Wang, Qunan Tao, Fangbiao Zhu, Yumin |
author_facet | Jin, Heyue Zhang, Yimin Fan, Zhigang Wang, Xianyan Rui, Chen Xing, Shaozhen Dong, Hongmei Wang, Qunan Tao, Fangbiao Zhu, Yumin |
author_sort | Jin, Heyue |
collection | PubMed |
description | BACKGROUND: Preterm birth (PTB) is the main driver of newborn deaths. The identification of pregnancies at risk of PTB remains challenging, as the incomplete understanding of molecular mechanisms associated with PTB. Although several transcriptome studies have been done on the placenta and plasma from PTB women, a comprehensive description of the RNA profiles from plasma and placenta associated with PTB remains lacking. METHODS: Candidate markers with consistent trends in the placenta and plasma were identified by implementing differential expression analysis using placental tissue and maternal plasma RNA-seq datasets, and then validated by RT-qPCR in an independent cohort. In combination with bioinformatics analysis tools, we set up two protein–protein interaction networks of the significant PTB-related modules. The support vector machine (SVM) model was used to verify the prediction potential of cell free RNAs (cfRNAs) in plasma for PTB and late PTB. RESULTS: We identified 15 genes with consistent regulatory trends in placenta and plasma of PTB while the full term birth (FTB) acts as a control. Subsequently, we verified seven cfRNAs in an independent cohort by RT-qPCR in maternal plasma. The cfRNA ARHGEF28 showed consistence in the experimental validation and performed excellently in prediction of PTB in the model. The AUC achieved 0.990 for whole PTB and 0.986 for late PTB. CONCLUSIONS: In a comparison of PTB versus FTB, the combined investigation of placental and plasma RNA profiles has shown a further understanding of the mechanism of PTB. Then, the cfRNA identified has the capacity of predicting whole PTB and late PTB. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-023-04083-w. |
format | Online Article Text |
id | pubmed-10100253 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-101002532023-04-14 Identification of novel cell-free RNAs in maternal plasma as preterm biomarkers in combination with placental RNA profiles Jin, Heyue Zhang, Yimin Fan, Zhigang Wang, Xianyan Rui, Chen Xing, Shaozhen Dong, Hongmei Wang, Qunan Tao, Fangbiao Zhu, Yumin J Transl Med Research BACKGROUND: Preterm birth (PTB) is the main driver of newborn deaths. The identification of pregnancies at risk of PTB remains challenging, as the incomplete understanding of molecular mechanisms associated with PTB. Although several transcriptome studies have been done on the placenta and plasma from PTB women, a comprehensive description of the RNA profiles from plasma and placenta associated with PTB remains lacking. METHODS: Candidate markers with consistent trends in the placenta and plasma were identified by implementing differential expression analysis using placental tissue and maternal plasma RNA-seq datasets, and then validated by RT-qPCR in an independent cohort. In combination with bioinformatics analysis tools, we set up two protein–protein interaction networks of the significant PTB-related modules. The support vector machine (SVM) model was used to verify the prediction potential of cell free RNAs (cfRNAs) in plasma for PTB and late PTB. RESULTS: We identified 15 genes with consistent regulatory trends in placenta and plasma of PTB while the full term birth (FTB) acts as a control. Subsequently, we verified seven cfRNAs in an independent cohort by RT-qPCR in maternal plasma. The cfRNA ARHGEF28 showed consistence in the experimental validation and performed excellently in prediction of PTB in the model. The AUC achieved 0.990 for whole PTB and 0.986 for late PTB. CONCLUSIONS: In a comparison of PTB versus FTB, the combined investigation of placental and plasma RNA profiles has shown a further understanding of the mechanism of PTB. Then, the cfRNA identified has the capacity of predicting whole PTB and late PTB. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-023-04083-w. BioMed Central 2023-04-12 /pmc/articles/PMC10100253/ /pubmed/37046301 http://dx.doi.org/10.1186/s12967-023-04083-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 Jin, Heyue Zhang, Yimin Fan, Zhigang Wang, Xianyan Rui, Chen Xing, Shaozhen Dong, Hongmei Wang, Qunan Tao, Fangbiao Zhu, Yumin Identification of novel cell-free RNAs in maternal plasma as preterm biomarkers in combination with placental RNA profiles |
title | Identification of novel cell-free RNAs in maternal plasma as preterm biomarkers in combination with placental RNA profiles |
title_full | Identification of novel cell-free RNAs in maternal plasma as preterm biomarkers in combination with placental RNA profiles |
title_fullStr | Identification of novel cell-free RNAs in maternal plasma as preterm biomarkers in combination with placental RNA profiles |
title_full_unstemmed | Identification of novel cell-free RNAs in maternal plasma as preterm biomarkers in combination with placental RNA profiles |
title_short | Identification of novel cell-free RNAs in maternal plasma as preterm biomarkers in combination with placental RNA profiles |
title_sort | identification of novel cell-free rnas in maternal plasma as preterm biomarkers in combination with placental rna profiles |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10100253/ https://www.ncbi.nlm.nih.gov/pubmed/37046301 http://dx.doi.org/10.1186/s12967-023-04083-w |
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