Research Paper Volume 13, Issue 19 pp 22792—22801
Identification of hub genes and key pathways of paraquat-induced human embryonic pulmonary fibrosis by bioinformatics analysis and in vitro studies
- 1 Department of Respiration, First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, Guangdong, China
- 2 Department of General Medicine, The First People’s Hospital of Foshan, The Affiliated Foshan Hospital of Sun Yat-Sen University, Foshan 528000, Guangdong, China
- 3 The Poison Treatment Centre of Foshan, Foshan 528000, Guangdong, China
- 4 Department of Emergency Medicine, The First People’s Hospital of Foshan, The Affiliated Foshan Hospital of Sun Yat-Sen University, Foshan 528000, Guangdong, China
Received: April 26, 2021 Accepted: September 10, 2021 Published: September 27, 2021https://doi.org/10.18632/aging.203570
How to Cite
Copyright: © 2021 Zeng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Objective: Paraquat (N,N0-dimethyl-4,40-bipyridinium dichloride;PQ) is a highly toxic pesticide, which usually leads to acute lung injury and subsequent development of pulmonary fibrosis. The exact mechanism underlying PQ-induced lung fibrosis remain largely unclear and as yet, no specific treatment drugs have been approved. Our study aimed to identify its potential mechanisms of PQ-induced fibrosis through a modeling study in vitro studies and bioinformatics analysis.
Methods: Gene expression datasets associated with PQ-induced lung fibrosis were obtained from the Gene Expression Omnibus, wherefrom differentially expressed genes (DEGs) were identified using GEO2R. Functional enrichment analyses were performed using the Database for Annotation Visualization and Integrated Discovery. The DEGs analyzed by a protein–protein interaction network was constructed with the Search Tool for the Retrieval of Interacting Genes database. MCODE, a Cytoscape plugin, was subsequently used to identify the most significant modules. The expression of the key genes in PQ-induced pulmonary fibrotic tissues was verified by reverse transcription-quantitative PCR (RT-qPCR).
Results: Two datasets were analyzed and revealed 92 overlapping DEGs. Functional analysis demonstrated that these 92 DEGs were enriched in the ‘TNF signaling pathway’, ‘CXCR chemokine receptor binding’, and ‘core promoter binding’. Moreover, nine hub genes were identified from the protein–protein interaction network formed from the DEGs. These results suggested that the TNF signaling pathway and nine hub genes are possibly involved in PQ-induced lung fibrosis progression.
Conclusions: This integrative analysis identified candidate genes and pathways potentially involved in PQ-induced lung fibrosis, and could benefit future development of novel approaches for controlling and treating this disease.