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BMC Pulmonary Medicine
Published in: BMC Pulmonary Medicine 1/2024

Open Access 01-12-2024 | COVID-19 | Research

Identification of biomarkers and pathways for the SARS-CoV-2 infections in obstructive sleep apnea patients based on machine learning and proteomic analysis

Authors: Hong Luo, Jisong Yan, Rui Gong, Dingyu Zhang, Xia Zhou, Xianguang Wang

Published in: BMC Pulmonary Medicine | Issue 1/2024

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Abstract

Background

The prevalence of obstructive sleep apnea (OSA) was found to be higher in individuals following COVID-19 infection. However, the intricate mechanisms that underscore this concomitance remain partially elucidated. The aim of this study was to delve deeper into the molecular mechanisms that underpin this comorbidity.

Methods

We acquired gene expression profiles for COVID-19 (GSE157103) and OSA (GSE75097) from the Gene Expression Omnibus (GEO) database. Upon identifying shared feature genes between OSA and COVID-19 utilizing LASSO, Random forest and Support vector machines algorithms, we advanced to functional annotation, analysis of protein–protein interaction networks, module construction, and identification of pivotal genes. Furthermore, we established regulatory networks encompassing transcription factor (TF)-gene and TF-miRNA interactions, and searched for promising drug targets. Subsequently, the expression levels of pivotal genes were validated through proteomics data from COVID-19 cases.

Results

Fourteen feature genes shared between OSA and COVID-19 were selected for further investigation. Through functional annotation, it was indicated that metabolic pathways play a role in the pathogenesis of both disorders. Subsequently, employing the cytoHubba plugin, ten hub genes were recognized, namely TP53, CCND1, MDM2, RB1, HIF1A, EP300, STAT3, CDK2, HSP90AA1, and PPARG. The finding of proteomics unveiled a substantial augmentation in the expression level of HSP90AA1 in COVID-19 patient samples, especially in severe conditions.

Conclusions

Our investigation illuminate a mutual pathogenic mechanism that underlies both OSA and COVID-19, which may provide novel perspectives for future investigations into the underlying mechanisms.
Appendix
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Metadata
Title
Identification of biomarkers and pathways for the SARS-CoV-2 infections in obstructive sleep apnea patients based on machine learning and proteomic analysis
Authors
Hong Luo
Jisong Yan
Rui Gong
Dingyu Zhang
Xia Zhou
Xianguang Wang
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Pulmonary Medicine / Issue 1/2024
Electronic ISSN: 1471-2466
DOI
https://doi.org/10.1186/s12890-024-02921-1

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WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

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Prof. Florian Limbourg
Prof. Anoop Chauhan
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