BMC Medical Genomics (Mar 2023)

Weighted gene co-expression network analysis revealed T cell differentiation associated with the age-related phenotypes in COVID-19 patients

  • 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

DOI
https://doi.org/10.1186/s12920-023-01490-2
Journal volume & issue
Vol. 16, no. 1
pp. 1 – 14

Abstract

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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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