Transcriptomic analysis of human bronchial epithelium reveals unique immune landscape in severe asthma

DOI:10.4121/19537258.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/19537258
Datacite citation style:
Qian Yan (2022): Transcriptomic analysis of human bronchial epithelium reveals unique immune landscape in severe asthma. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/19537258.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

Airway epithelial transcriptome analysis of severe asthma (SA) patients was used to identify the immune infiltration mechanisms that affect asthma exacerbation, which may contribute to improving asthma therapy. A serises of bioinformatics methods were utilized to identify the hub genes and signaling pathways in SA. 

History

  • 2022-04-13 first online, published, posted

Publisher

4TU.ResearchData

Format

.zip

Funding

  • This work was supported by the Science and Technology Program of Guangzhou, China (Grant no. 201904010235), and the National Natural Science Foundation of Guangdong, China (Grant no. 2020A1515010589 and Grant no. 2021A1515010146). This work was also supported by the “Double First-Class” and High-level University Discipline Collaborative Innovation Team Project of Guangzhou University of Chinese Medicine (Grant No.2021XK16), Guangdong Provincial Department of Education Innovation Team Project (Grant No: 2018KCXTD007), the Key-Area Research and Development Program of Guangdong Province (Grant No. 2020B1111100002), the National Natural Science Foundation of China (Grant No. 81973814 and No. 81904132), and the Technology Research of COVID-19 Treatment and Prevention and Special Project of Traditional Chinese Medicine Application-Research on the platform construction for the prevention and treatment of viral infectious diseases with traditional Chinese medicine (Grant No. 2020KJCX-KTYJ-130).

Organizations

Guangzhou University of Chinese Medicine

DATA

Files (1)

  • 31,466,129 bytesMD5:7381f065f4f08087b079e5d998274fd1R code.rar