Single-cell transcriptomic sequencing coupled with Mendelian randomization analysis elucidates the pivotal role of CTSC in chronic rhinosinusitis
10.3760/cma.j.cn115330-20231211-00285
- VernacularTitle:单细胞转录组测序联合孟德尔随机化分析揭示慢性鼻窦炎中 CTSC的关键作用
- Author:
Shican ZHOU
1
;
Ju LAI
;
Kai FAN
;
Jingwen LI
;
Xiayue XU
;
Chunyan YAO
;
Bojin LONG
;
Chuanliang ZHAO
;
Na CHE
;
Yawen GAO
;
Shaoqing YU
Author Information
1. 同济大学同济医院耳鼻咽喉头颈外科 变态反应科，上海 200065
- Keywords:
Sinusitis;
Single-cell RNA sequencing;
Mendelian randomization;
CTSC
- From:
Chinese Journal of Otorhinolaryngology Head and Neck Surgery
2024;59(6):551-559
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the molecular mechanisms of chronic rhinosinusitis (CRS), to identify key cell subgroups and genes, to construct effective diagnostic models, and to screen for potential therapeutic drugs.Methods:Key cell subgroups in CRS were identified through single-cell transcriptomic sequencing data. Essential genes associated with CRS were selected and diagnostic models were constructed by hdWGCNA (high dimensional weighted gene co-expression network analysis) and various machine learning algorithms. Causal inference analysis was performed using Mendelian randomization and colocalization analysis. Potential therapeutic drugs were identified using molecular docking technology, and the results of bioinformatics analysis were validated by immunofluorescence staining. Graphpad Prism, R, Python, and Adobe Illustrator software were used for data and image processing.Results:An increased proportion of basal and suprabasal cells was observed in CRS, especially in eosinophilic CRS with nasal polyps (ECRSwNP), with P=0.001. hdWGCNA revealed that the "yellow module" was closely related to basal and suprabasal cells in CRS. Univariate logistic regression and LASSO algorithm selected 13 key genes ( CTSC, LAMB3, CYP2S1, TRPV4, ARHGAP21, PTHLH, CDH26, MRPS6, TENM4, FAM110C, NCKAP5, SAMD3, and PTCHD4). Based on these 13 genes, an effective CRS diagnostic model was developed using various machine learning algorithms (AUC=0.958). Mendelian randomization analysis indicated a causal relationship between CTSC and CRS (inverse variance weighted: OR=1.06, P=0.006), and colocalization analysis confirmed shared genetic variants between CTSC and CRS (PPH4/PPH3>2). Molecular docking results showed that acetaminophen binded well with CTSC (binding energy:-5.638 kcal/mol). Immunofluorescence staining experiments indicated an increase in CTSC +cells in CRS. Conclusion:This study integrates various bioinformatics methods to identify key cell types and genes in CRS, constructs an effective diagnostic model, underscores the critical role of the CTSC gene in CRS pathogenesis, and provides new targets for the treatment of CRS.
