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.
目的： 旨在深入探索慢性鼻窦炎（CRS）的分子机制，识别关键细胞亚群和基因，构建有效的诊断模型，并筛选潜在的治疗药物。 方法： 通过单细胞转录组测序数据鉴定CRS中的关键细胞亚群。通过高维加权基因共表达网络分析（high dimensional weighted gene co-expression network analysis，hdWGCNA）和多种机器学习算法的联合应用，筛选CRS的关键基因并构建CRS的诊断模型。通过孟德尔随机化和共定位分析进行因果推断分析。使用分子对接技术进行靶点药物的鉴定，并通过免疫荧光染色对生信分析的结果进行验证。采用Graphpad Prism、R、python和Adobe Illustrator软件进行数据及图像处理。 结果： CRS尤其是嗜酸性慢性鼻窦炎伴鼻息肉（ECRSwNP）中基底细胞和基上皮细胞占比增加（P=0.001）。hdWGCNA显示“黄色模块”与CRS中基底细胞和基上皮细胞密切相关。采用单因素逻辑回归和最小绝对值收敛和选择算法（least absolute shrinkage and selection operator，LASSO）筛选出13个关键基因（CTSC、LAMB3、CYP2S1、TRPV4、ARHGAP21、PTHLH、CDH26、MRPS6、TENM4、FAM110C、NCKAP5、SAMD3和PTCHD4）。基于这13个基因，使用多种机器学习算法构建出有效的CRS诊断模型（AUC=0.958）。孟德尔随机化分析显示组织蛋白酶C（cathepsin C，CTSC）与CRS具有因果关系（逆方差加权：OR=1.06，P=0.006），共定位分析证实CTSC与CRS具有共享的遗传变异（PPH4/PPH3>2）。分子对接结果显示，对乙酰氨基酚与CTSC具有较好的结合能力（结合能：-5.638 kcal/mol）。免疫荧光染色实验表明CRS中CTSC+细胞增多。 结论： 本研究综合运用多种生物信息学方法，识别了CRS中的关键细胞类型和基因，构建了有效的诊断模型，强调了CTSC在CRS中的关键作用，为CRS的治疗提供了新的靶点。.
目的： 旨在深入探索慢性鼻窦炎（CRS）的分子机制，识别关键细胞亚群和基因，构建有效的诊断模型，并筛选潜在的治疗药物。 方法： 通过单细胞转录组测序数据鉴定CRS中的关键细胞亚群。通过高维加权基因共表达网络分析（high dimensional weighted gene co-expression network analysis，hdWGCNA）和多种机器学习算法的联合应用，筛选CRS的关键基因并构建CRS的诊断模型。通过孟德尔随机化和共定位分析进行因果推断分析。使用分子对接技术进行靶点药物的鉴定，并通过免疫荧光染色对生信分析的结果进行验证。采用Graphpad Prism、R、python和Adobe Illustrator软件进行数据及图像处理。 结果： CRS尤其是嗜酸性慢性鼻窦炎伴鼻息肉（ECRSwNP）中基底细胞和基上皮细胞占比增加（P=0.001）。hdWGCNA显示“黄色模块”与CRS中基底细胞和基上皮细胞密切相关。采用单因素逻辑回归和最小绝对值收敛和选择算法（least absolute shrinkage and selection operator，LASSO）筛选出13个关键基因（CTSC、LAMB3、CYP2S1、TRPV4、ARHGAP21、PTHLH、CDH26、MRPS6、TENM4、FAM110C、NCKAP5、SAMD3和PTCHD4）。基于这13个基因，使用多种机器学习算法构建出有效的CRS诊断模型（AUC=0.958）。孟德尔随机化分析显示组织蛋白酶C（cathepsin C，CTSC）与CRS具有因果关系（逆方差加权：OR=1.06，P=0.006），共定位分析证实CTSC与CRS具有共享的遗传变异（PPH4/PPH3>2）。分子对接结果显示，对乙酰氨基酚与CTSC具有较好的结合能力（结合能：-5.638 kcal/mol）。免疫荧光染色实验表明CRS中CTSC+细胞增多。 结论： 本研究综合运用多种生物信息学方法，识别了CRS中的关键细胞类型和基因，构建了有效的诊断模型，强调了CTSC在CRS中的关键作用，为CRS的治疗提供了新的靶点。.
摘要：
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