Weighted gene co-expression network analysis revealed T cell differentiation associated with the age-related phenotypes in COVID-19 patients
© 2023. The Author(s)..
The risk of severe condition caused by Corona Virus Disease 2019 (COVID-19) increases with age. However, the underlying mechanisms have not been clearly understood. The dataset GSE157103 was used to perform weighted gene co-expression network analysis on 100 COVID-19 patients in our analysis. Through weighted gene co-expression network analysis, we identified a key module which was significantly related with age. This age-related module could predict Intensive Care Unit status and mechanical-ventilation usage, and enriched with positive regulation of T cell receptor signaling pathway biological progress. Moreover, 10 hub genes were identified as crucial gene of the age-related module. Protein-protein interaction network and transcription factors-gene interactions were established. Lastly, independent data sets and RT-qPCR were used to validate the key module and hub genes. Our conclusion revealed that key genes were associated with the age-related phenotypes in COVID-19 patients, and it would be beneficial for clinical doctors to develop reasonable therapeutic strategies in elderly COVID-19 patients.
Medienart: |
E-Artikel |
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Erscheinungsjahr: |
2023 |
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Erschienen: |
2023 |
Enthalten in: |
Zur Gesamtaufnahme - volume:16 |
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Enthalten in: |
BMC medical genomics - 16(2023), 1 vom: 25. März, Seite 59 |
Sprache: |
Englisch |
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Beteiligte Personen: |
Lin, Yao [VerfasserIn] |
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Links: |
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Themen: |
Aging |
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Anmerkungen: |
Date Completed 28.03.2023 Date Revised 10.04.2023 published: Electronic Citation Status MEDLINE |
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doi: |
10.1186/s12920-023-01490-2 |
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funding: |
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Förderinstitution / Projekttitel: |
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PPN (Katalog-ID): |
NLM35471368X |
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520 | |a © 2023. The Author(s). | ||
520 | |a The risk of severe condition caused by Corona Virus Disease 2019 (COVID-19) increases with age. However, the underlying mechanisms have not been clearly understood. The dataset GSE157103 was used to perform weighted gene co-expression network analysis on 100 COVID-19 patients in our analysis. Through weighted gene co-expression network analysis, we identified a key module which was significantly related with age. This age-related module could predict Intensive Care Unit status and mechanical-ventilation usage, and enriched with positive regulation of T cell receptor signaling pathway biological progress. Moreover, 10 hub genes were identified as crucial gene of the age-related module. Protein-protein interaction network and transcription factors-gene interactions were established. Lastly, independent data sets and RT-qPCR were used to validate the key module and hub genes. Our conclusion revealed that key genes were associated with the age-related phenotypes in COVID-19 patients, and it would be beneficial for clinical doctors to develop reasonable therapeutic strategies in elderly COVID-19 patients | ||
650 | 4 | |a Journal Article | |
650 | 4 | |a Research Support, Non-U.S. Gov't | |
650 | 4 | |a Aging | |
650 | 4 | |a COVID-19 | |
650 | 4 | |a T-cell immunity | |
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700 | 1 | |a Meng, Jun |e verfasserin |4 aut | |
700 | 1 | |a Li, Jingyi |e verfasserin |4 aut | |
700 | 1 | |a Chu, Jiemei |e verfasserin |4 aut | |
700 | 1 | |a Zheng, Ruili |e verfasserin |4 aut | |
700 | 1 | |a Wang, Hailong |e verfasserin |4 aut | |
700 | 1 | |a Pan, Peijiang |e verfasserin |4 aut | |
700 | 1 | |a Su, Jinming |e verfasserin |4 aut | |
700 | 1 | |a Jiang, Junjun |e verfasserin |4 aut | |
700 | 1 | |a Ye, Li |e verfasserin |4 aut | |
700 | 1 | |a Liang, Hao |e verfasserin |4 aut | |
700 | 1 | |a An, Sanqi |e verfasserin |4 aut | |
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