[Identification of oxidative stress-related biomarkers in chronic rhinosinusitis with nasal polyps using WGCNA combined with machine learning algorithms].

Y Yuan, X Y Shi, X Y Ma, X Y Xie, C H Wu, L Q Zhang, X Z Li, P Wang, X Feng
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引用次数: 0

Abstract

Objective: 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. Methods: 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. Results: Through data analysis and clinical validation, CP, SERPINF1 and GSTO2 were identified among 4 138 DEGs as oxidative stress markers related to CRSwNP. Specifically, the expression of CP and SERPINF1 increased in CRSwNP, whereas that of GSTO2 decreased, with statistically significant differences (P<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 SERPINF1 was mainly distributed in epithelial cells, stromal cells, and fibroblasts, while CP was primarily located in epithelial cells, and GSTO2 was minimally present in the epithelial cells and fibroblasts of nasal polyps. Consequently, a CeRNA regulatory network was constructed for the genes CP and GSTO2. This construction allowed for the prediction of potential drugs that could target CP. Conclusion: This study successfully identifies CP, SERPINF1 and GSTO2 as diagnostic and therapeutic markers related to oxidative stress in CRSwNP.

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[利用 WGCNA 结合机器学习算法识别慢性鼻窦炎伴鼻息肉患者的氧化应激相关生物标记物]。
目的通过分析转录组测序数据,确定慢性鼻炎伴鼻息肉(CRSwNP)中与氧化应激相关的诊断标记物,并研究它们在 CRSwNP 中的作用。方法本研究利用四个 CRSwNP 测序数据集,进行了差异表达基因(DEGs)分析、加权基因共表达网络分析(WGCNA)和三种用于 Hub 基因选择的机器学习方法。随后,利用外部数据集、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了验证。此外,还通过接收者操作特征曲线(ROC)评估了基因的诊断效果,随后进行了功能和通路富集分析、免疫相关分析和细胞群定位。此外,还构建了竞争性内源性 RNA(CeRNA)网络,以预测潜在的药物靶点。统计分析和绘图使用 SPSS 26.0 和 Graphpad Prism9 软件进行。结果通过数据分析和临床验证,在 4 138 个 DEGs 中,CP、SERPINF1 和 GSTO2 被确定为与 CRSwNP 相关的氧化应激标志物。具体而言,CP 和 SERPINF1 在 CRSwNP 中的表达量增加,而 GSTO2 的表达量减少,差异有统计学意义(P0.7),表明它们是有效的诊断指标。重要的是,功能分析表明这些基因主要与脂质代谢、细胞粘附迁移和免疫有关。单细胞数据分析显示,SERPINF1 主要分布在上皮细胞、基质细胞和成纤维细胞中,CP 主要分布在上皮细胞中,而 GSTO2 在鼻息肉的上皮细胞和成纤维细胞中含量极少。因此,为 CP 和 GSTO2 基因构建了 CeRNA 调控网络。该网络的构建有助于预测针对 CP 的潜在药物。结论本研究成功地将 CP、SERPINF1 和 GSTO2 鉴定为与 CRSwNP 氧化应激有关的诊断和治疗标志物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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