Yuan Y, Shi X Y, Ma X Y, Xie X Y, Wu C H, Zhang L Q, Li X Z, Wang P, Feng X
Department of Otorhinolaryngology, Qilu Hospital of Shandong University, National Health Commission Key Laboratory of Otorhinolaryngology (Shandong University), Jinan 250012, China.
Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi. 2024 Jun 7;59(6):560-572. doi: 10.3760/cma.j.cn115330-20240202-00076.
To identify diagnostic markers related to oxidative stress in chronic rhinosinusitis with nasal polyps (CRSwNP) by analyzing transcriptome sequencing data, and to investigate their roles in CRSwNP. Utilizing four CRSwNP sequencing datasets, differentially expressed genes (DEGs) analysis, weighted gene co-expression network analysis (WGCNA), and three machine learning methods for Hub gene selection were performed in this study. Subsequent validation was carried out using external datasets, as well as real-time quantitative polymerase chain reaction (Real-time qPCR), and immunofluorescence staining of clinical samples. Moreover, the diagnostic efficacy of the genes was assessed by receiver operating characteristic (ROC) curve, followed by functional and pathway enrichment analysis, immune-related analysis, and cell population localization. Additionally, a competing endogenous RNA (CeRNA) network was constructed to predict potential drug targets. Statistical analysis and plotting were conducted using SPSS 26.0 and Graphpad Prism9 software. Through data analysis and clinical validation, , and were identified among 4 138 DEGs as oxidative stress markers related to CRSwNP. Specifically, the expression of and increased in CRSwNP, whereas that of decreased, with statistically significant differences (<0.05). Additionally, an area under the curve (AUC)>0.7 indicated their effectiveness as diagnostic indicators. Importantly, functional analysis indicated that these genes were mainly related to lipid metabolism, cell adhesion migration, and immunity. Single-cell data analysis revealed that was mainly distributed in epithelial cells, stromal cells, and fibroblasts, while was primarily located in epithelial cells, and was minimally present in the epithelial cells and fibroblasts of nasal polyps. Consequently, a CeRNA regulatory network was constructed for the genes and . This construction allowed for the prediction of potential drugs that could target . This study successfully identifies , and as diagnostic and therapeutic markers related to oxidative stress in CRSwNP.
通过分析转录组测序数据，鉴定与鼻息肉慢性鼻-鼻窦炎（CRSwNP）中氧化应激相关的诊断标志物，并研究它们在CRSwNP中的作用。本研究利用四个CRSwNP测序数据集，进行差异表达基因（DEG）分析、加权基因共表达网络分析（WGCNA）以及三种用于选择枢纽基因的机器学习方法。随后使用外部数据集以及实时定量聚合酶链反应（Real-time qPCR）和临床样本的免疫荧光染色进行验证。此外，通过受试者工作特征（ROC）曲线评估基因的诊断效能，随后进行功能和通路富集分析、免疫相关分析以及细胞群体定位。另外，构建竞争性内源性RNA（CeRNA）网络以预测潜在的药物靶点。使用SPSS 26.0和Graphpad Prism9软件进行统计分析和绘图。通过数据分析和临床验证，在4138个DEG中鉴定出 、 和 作为与CRSwNP相关的氧化应激标志物。具体而言， 和 在CRSwNP中的表达增加，而 的表达降低，差异具有统计学意义（<0.05）。此外，曲线下面积（AUC）>0.7表明它们作为诊断指标的有效性。重要的是，功能分析表明这些基因主要与脂质代谢、细胞黏附迁移和免疫相关。单细胞数据分析显示， 主要分布在上皮细胞、基质细胞和成纤维细胞中，而 主要位于上皮细胞中， 在鼻息肉的上皮细胞和成纤维细胞中含量最少。因此，为基因 和 构建了CeRNA调控网络。这一构建使得能够预测可能靶向 的潜在药物。本研究成功鉴定出 、 和 作为与CRSwNP中氧化应激相关的诊断和治疗标志物。
