Weighted gene co-expression network analysis revealed T cell differentiation associated with the age-related phenotypes in COVID-19 patients
Abstract 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 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lin, Yao [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
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| Anmerkungen: |
© The Author(s) 2023 |
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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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SPR04983570X |
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| 520 | |a Abstract 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. | ||
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| 700 | 1 | |a Chen, Hubin |4 aut | |
| 700 | 1 | |a Meng, Jun |4 aut | |
| 700 | 1 | |a Li, Jingyi |4 aut | |
| 700 | 1 | |a Chu, Jiemei |4 aut | |
| 700 | 1 | |a Zheng, Ruili |4 aut | |
| 700 | 1 | |a Wang, Hailong |4 aut | |
| 700 | 1 | |a Pan, Peijiang |4 aut | |
| 700 | 1 | |a Su, Jinming |4 aut | |
| 700 | 1 | |a Jiang, Junjun |4 aut | |
| 700 | 1 | |a Ye, Li |4 aut | |
| 700 | 1 | |a Liang, Hao |4 aut | |
| 700 | 1 | |a An, Sanqi |4 aut | |
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