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A predictive model for preterm infants with bronchopulmonary dysplasia based on ferroptosis-related lncRNAs
BACKGROUND: Bronchopulmonary dysplasia (BPD) is the most challenging chronic lung disease for prematurity, with difficulties in early identification. Given lncRNA emerging as a novel biomarker and the regulator of ferroptosis, this study aims to develop a BPD predictive model based on ferroptosis-re...
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/PMC10544375/ https://www.ncbi.nlm.nih.gov/pubmed/37784105 http://dx.doi.org/10.1186/s12890-023-02670-7 |
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author | Zhang, Ziming Chen, Kewei Pan, Dandan Liu, Tieshuai Hang, Chengcheng Ying, Yuhan He, Jia Lv, Ying Ma, Xiaolu Chen, Zheng Liu, Ling Zhu, Jiajun Du, Lizhong |
author_facet | Zhang, Ziming Chen, Kewei Pan, Dandan Liu, Tieshuai Hang, Chengcheng Ying, Yuhan He, Jia Lv, Ying Ma, Xiaolu Chen, Zheng Liu, Ling Zhu, Jiajun Du, Lizhong |
author_sort | Zhang, Ziming |
collection | PubMed |
description | BACKGROUND: Bronchopulmonary dysplasia (BPD) is the most challenging chronic lung disease for prematurity, with difficulties in early identification. Given lncRNA emerging as a novel biomarker and the regulator of ferroptosis, this study aims to develop a BPD predictive model based on ferroptosis-related lncRNAs (FRLs). METHODS: Using a rat model, we firstly explored mRNA levels of ferroptosis-related genes and ferrous iron accumulation in BPD rat lungs. Subsequently, a microarray dataset of umbilical cord tissue from 20 preterm infants with BPD and 34 preterm infants without BPD were downloaded from the Gene Expression Omnibus databases. Random forest and LASSO regression were conducted to identify diagnostic FRLs. Nomogram was used to construct a predictive BPD model based on the FRLs. Finally, umbilical cord blood lymphocytes of preterm infants born before 32 weeks gestational age and term infants were collected and determined the expression level of diagnostic FRLs by RT-qPCR. RESULTS: Increased iron accumulation and several dysregulated ferroptosis-associated genes were found in BPD rat lung tissues, indicating that ferroptosis was participating in the development of BPD. By exploring the microarray dataset of preterm infants with BPD, 6 FRLs, namely LINC00348, POT1-AS1, LINC01103, TTTY8, PACRG-AS1, LINC00691, were determined as diagnostic FRLs for modeling. The area under the receiver operator characteristic curve of the model was 0.932, showing good discrimination of BPD. In accordance with our analysis of microarray dataset, the mRNA levels of FRLs were significantly upregulated in umbilical cord blood lymphocytes from preterm infants who had high risk of BPD. CONCLUSION: The incorporation of FRLs into a predictive model offers a non-invasive approach to show promise in improving early detection and management of this challenging chronic lung disease in premature infant, enabling timely intervention and personalized treatment strategies. |
format | Online Article Text |
id | pubmed-10544375 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-105443752023-10-03 A predictive model for preterm infants with bronchopulmonary dysplasia based on ferroptosis-related lncRNAs Zhang, Ziming Chen, Kewei Pan, Dandan Liu, Tieshuai Hang, Chengcheng Ying, Yuhan He, Jia Lv, Ying Ma, Xiaolu Chen, Zheng Liu, Ling Zhu, Jiajun Du, Lizhong BMC Pulm Med Research BACKGROUND: Bronchopulmonary dysplasia (BPD) is the most challenging chronic lung disease for prematurity, with difficulties in early identification. Given lncRNA emerging as a novel biomarker and the regulator of ferroptosis, this study aims to develop a BPD predictive model based on ferroptosis-related lncRNAs (FRLs). METHODS: Using a rat model, we firstly explored mRNA levels of ferroptosis-related genes and ferrous iron accumulation in BPD rat lungs. Subsequently, a microarray dataset of umbilical cord tissue from 20 preterm infants with BPD and 34 preterm infants without BPD were downloaded from the Gene Expression Omnibus databases. Random forest and LASSO regression were conducted to identify diagnostic FRLs. Nomogram was used to construct a predictive BPD model based on the FRLs. Finally, umbilical cord blood lymphocytes of preterm infants born before 32 weeks gestational age and term infants were collected and determined the expression level of diagnostic FRLs by RT-qPCR. RESULTS: Increased iron accumulation and several dysregulated ferroptosis-associated genes were found in BPD rat lung tissues, indicating that ferroptosis was participating in the development of BPD. By exploring the microarray dataset of preterm infants with BPD, 6 FRLs, namely LINC00348, POT1-AS1, LINC01103, TTTY8, PACRG-AS1, LINC00691, were determined as diagnostic FRLs for modeling. The area under the receiver operator characteristic curve of the model was 0.932, showing good discrimination of BPD. In accordance with our analysis of microarray dataset, the mRNA levels of FRLs were significantly upregulated in umbilical cord blood lymphocytes from preterm infants who had high risk of BPD. CONCLUSION: The incorporation of FRLs into a predictive model offers a non-invasive approach to show promise in improving early detection and management of this challenging chronic lung disease in premature infant, enabling timely intervention and personalized treatment strategies. BioMed Central 2023-10-02 /pmc/articles/PMC10544375/ /pubmed/37784105 http://dx.doi.org/10.1186/s12890-023-02670-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Zhang, Ziming Chen, Kewei Pan, Dandan Liu, Tieshuai Hang, Chengcheng Ying, Yuhan He, Jia Lv, Ying Ma, Xiaolu Chen, Zheng Liu, Ling Zhu, Jiajun Du, Lizhong A predictive model for preterm infants with bronchopulmonary dysplasia based on ferroptosis-related lncRNAs |
title | A predictive model for preterm infants with bronchopulmonary dysplasia based on ferroptosis-related lncRNAs |
title_full | A predictive model for preterm infants with bronchopulmonary dysplasia based on ferroptosis-related lncRNAs |
title_fullStr | A predictive model for preterm infants with bronchopulmonary dysplasia based on ferroptosis-related lncRNAs |
title_full_unstemmed | A predictive model for preterm infants with bronchopulmonary dysplasia based on ferroptosis-related lncRNAs |
title_short | A predictive model for preterm infants with bronchopulmonary dysplasia based on ferroptosis-related lncRNAs |
title_sort | predictive model for preterm infants with bronchopulmonary dysplasia based on ferroptosis-related lncrnas |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10544375/ https://www.ncbi.nlm.nih.gov/pubmed/37784105 http://dx.doi.org/10.1186/s12890-023-02670-7 |
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