Title |
Weighted gene co-expression network analysis revealed T cell differentiation associated with the age-related phenotypes in COVID-19 patients
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Published in |
BMC Medical Genomics, March 2023
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DOI | 10.1186/s12920-023-01490-2 |
Pubmed ID | |
Authors |
Yao Lin, Yueqi Li, Hubin Chen, Jun Meng, Jingyi Li, Jiemei Chu, Ruili Zheng, Hailong Wang, Peijiang Pan, Jinming Su, Junjun Jiang, Li Ye, Hao Liang, Sanqi An |
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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