Spotlight on CYP4B1

Röder, Annika; Hüsken, Saskia; Hutter, Michael C.; Rettie, Allan E.; Wiek, Constanze; Girhard, Marco

doi:10.3390/ijms24032038

Cited by 8 publications

(1 citation statement)

References 132 publications

(232 reference statements)

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“…CYP4B1 is a special member of the CYP4 family which is responsible for the metabolism of endosymbionts (fatty acids) and xenobiotics (perilla ketone and valproic acid, etc.) [55]. Although the effects on humans are unknown, the ability of CYP4B1 to bioactivate 4-ipomeanol (IPO) in rabbits to convert it into a cytotoxic substance implies the potential of CYP4B1 for targeted cancer therapy [56].…”

Section: Prediction and Validation Of Candidate Drugsmentioning

confidence: 99%

TRIM2, S100A14, CYP4B1, LMO7, and SFN are signature genes in the progression of idiopathic pulmonary fibrosis epithelial cells to their lung adenocarcinoma comorbidities: a comprehensive analysis based on single-cell RNA sequencing data and RNA transcriptome data

Zhou

Tan

Zhang

2023

Preprint

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Background Patients with idiopathic pulmonary fibrosis (IPF) have a significantly higher prevalence of lung adenocarcinoma (LUAD) than normal subjects, but the underlying association is unclear. This study aims to reveal the potential mechanisms by which IPF progresses to LUAD comorbidity. Methods The raw data involved in the study were obtained from the Gene Expression Omnibus (GEO) database. Sc-RNA seq data were applied to identify specific cell types for disease. Differential expression analysis and Weighted Gene Co-Expression Network Analysis (WGCNA) were used to screen for differentially expressed genes (DEGs) and modular signature genes (MSGs) shared by IPF and LUAD. Genes intersecting DEGs and MSGs were considered hub genes. After performing 3 kinds of machine learning algorithms on epithelial cell-derived hub genes, we captured epithelial cell-derived signature genes (EDSGs) shared. External cohort data were exploited to validate the robustness of EDSGs. We adopted immunohistochemical staining to reveal the protein expression levels of EDSGs. K-M plots denoted the survival prognosis of EDSGs in LUAD patients. Based on EDSGs, we constructed a TF-Gene-miRNA regulatory network and predicted the candidate drugs. Molecular docking can validate the strength of action between candidate drugs and EDSGs. Results Epithelial cells were a specific cell type shared by IPF and LUAD. And then we obtained 650 DEGs and 1773 MSGs, which were shared by IPF and LUAD. As for 379 hub genes, we performed pathway and functional enrichment analysis, aiming to explore the molecular mechanisms shared. By analyzing sc-RNA seq data, we identified 1234 marker genes of IPF epithelial cell-derived and 1481 of LUAD. And these epithelial cell-derived marker genes shared 8 genes with 379 hub genes. Furthermore, through the machine learning algorithm, we got 5 EDSGs, including TRIM2, S100A14, CYP4B1, LMO7, and SFN. The ROC curves validated that EDSGs have high accuracy in predicting the onset of LUAD comorbidity in IPF patients. The TF-Gene-miRNA network revealed potential regulatory mechanisms of EDSGs. And molecular docking suggested strong binding activity between Thalidomide, Docetaxel, and Imatinib and their corresponding targets. Conclusion Our study preliminarily identified potential mechanisms for the progression of IPF to its LUAD comorbidity, which will inform subsequent studies.

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Section: Prediction and Validation Of Candidate Drugsmentioning

confidence: 99%