Cargando…
Identification and validation of an angiogenesis-related signature associated with preeclampsia by bioinformatic analysis
Preeclampsia (PE) is a pregnancy disorder with high morbidity and mortality rates for both mothers and newborns. This study explores potential diagnostic indicators of PE. We downloaded the messenger ribonucleic acid profiles of the GSE75010 dataset from the Gene Expression Omnibus database, and use...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9902003/ https://www.ncbi.nlm.nih.gov/pubmed/36749240 http://dx.doi.org/10.1097/MD.0000000000032741 |
_version_ | 1784883152282451968 |
---|---|
author | Ma, Jiancai Wu, Hong Yang, Xiaofang Zheng, Lulu Feng, Haiqin Yang, Liping |
author_facet | Ma, Jiancai Wu, Hong Yang, Xiaofang Zheng, Lulu Feng, Haiqin Yang, Liping |
author_sort | Ma, Jiancai |
collection | PubMed |
description | Preeclampsia (PE) is a pregnancy disorder with high morbidity and mortality rates for both mothers and newborns. This study explores potential diagnostic indicators of PE. We downloaded the messenger ribonucleic acid profiles of the GSE75010 dataset from the Gene Expression Omnibus database, and used placenta samples to carry out different analyses including differential expression, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analyses. Least absolute shrinkage and selection operator regression was constructed and the receiver operating characteristic curve was drawn to evaluate the accuracy of the model. An external validation was conducted to prove the stability of the risk model. We found 140 angiogenesis-related genes and identified 29 angiogenesis-related genes between the 2 groups, including 12 upregulated genes and 17 downregulated genes. In addition, we established a 12-gene risk signature, which has a high accuracy in predicting PE during pregnancy (area under curve = 0.90). The immune infiltration characteristics are differentially distributed in the 2 groups, which may be the cause of hypertension during pregnancy. The external validation with the GSE25906 dataset confirmed the high accuracy of our model (area under curve = 0.87). Our results outline the characteristics of a set of genes potentially involved in PE and its subgroups, contributing to a better understanding of the molecular mechanisms of PE. |
format | Online Article Text |
id | pubmed-9902003 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-99020032023-02-08 Identification and validation of an angiogenesis-related signature associated with preeclampsia by bioinformatic analysis Ma, Jiancai Wu, Hong Yang, Xiaofang Zheng, Lulu Feng, Haiqin Yang, Liping Medicine (Baltimore) 5600 Preeclampsia (PE) is a pregnancy disorder with high morbidity and mortality rates for both mothers and newborns. This study explores potential diagnostic indicators of PE. We downloaded the messenger ribonucleic acid profiles of the GSE75010 dataset from the Gene Expression Omnibus database, and used placenta samples to carry out different analyses including differential expression, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analyses. Least absolute shrinkage and selection operator regression was constructed and the receiver operating characteristic curve was drawn to evaluate the accuracy of the model. An external validation was conducted to prove the stability of the risk model. We found 140 angiogenesis-related genes and identified 29 angiogenesis-related genes between the 2 groups, including 12 upregulated genes and 17 downregulated genes. In addition, we established a 12-gene risk signature, which has a high accuracy in predicting PE during pregnancy (area under curve = 0.90). The immune infiltration characteristics are differentially distributed in the 2 groups, which may be the cause of hypertension during pregnancy. The external validation with the GSE25906 dataset confirmed the high accuracy of our model (area under curve = 0.87). Our results outline the characteristics of a set of genes potentially involved in PE and its subgroups, contributing to a better understanding of the molecular mechanisms of PE. Lippincott Williams & Wilkins 2023-02-03 /pmc/articles/PMC9902003/ /pubmed/36749240 http://dx.doi.org/10.1097/MD.0000000000032741 Text en Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY) (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | 5600 Ma, Jiancai Wu, Hong Yang, Xiaofang Zheng, Lulu Feng, Haiqin Yang, Liping Identification and validation of an angiogenesis-related signature associated with preeclampsia by bioinformatic analysis |
title | Identification and validation of an angiogenesis-related signature associated with preeclampsia by bioinformatic analysis |
title_full | Identification and validation of an angiogenesis-related signature associated with preeclampsia by bioinformatic analysis |
title_fullStr | Identification and validation of an angiogenesis-related signature associated with preeclampsia by bioinformatic analysis |
title_full_unstemmed | Identification and validation of an angiogenesis-related signature associated with preeclampsia by bioinformatic analysis |
title_short | Identification and validation of an angiogenesis-related signature associated with preeclampsia by bioinformatic analysis |
title_sort | identification and validation of an angiogenesis-related signature associated with preeclampsia by bioinformatic analysis |
topic | 5600 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9902003/ https://www.ncbi.nlm.nih.gov/pubmed/36749240 http://dx.doi.org/10.1097/MD.0000000000032741 |
work_keys_str_mv | AT majiancai identificationandvalidationofanangiogenesisrelatedsignatureassociatedwithpreeclampsiabybioinformaticanalysis AT wuhong identificationandvalidationofanangiogenesisrelatedsignatureassociatedwithpreeclampsiabybioinformaticanalysis AT yangxiaofang identificationandvalidationofanangiogenesisrelatedsignatureassociatedwithpreeclampsiabybioinformaticanalysis AT zhenglulu identificationandvalidationofanangiogenesisrelatedsignatureassociatedwithpreeclampsiabybioinformaticanalysis AT fenghaiqin identificationandvalidationofanangiogenesisrelatedsignatureassociatedwithpreeclampsiabybioinformaticanalysis AT yangliping identificationandvalidationofanangiogenesisrelatedsignatureassociatedwithpreeclampsiabybioinformaticanalysis |