整合转录组学与机器学习探索鼻息肉中的炎症特征及关键氧化应激相关分子

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中文摘要

背景： 慢性鼻窦炎伴鼻息肉（CRSwNP）是一种由上气道多种炎症细胞和介质驱动的慢性炎症性疾病。本研究旨在表征CRSwNP的炎症景观，并识别和验证CRSwNP中氧化应激相关的关键基因。
方法： 对22例受试者的鼻黏膜组织进行RNA测序，包括8例对照、5例非嗜酸性CRSwNP（neCRSwNP）患者和9例eCRSwNP患者。使用DESeq2 R包进行差异表达分析。进行了富集分析、Ingenuity通路分析（IPA）、加权基因共表达网络分析（WGCNA）和CIBERSORT。将差异表达基因（DEGs）、嗜酸性炎症相关模块基因和氧化应激相关基因取交集以生成候选基因，随后进行蛋白质-蛋白质相互作用（PPI）网络分析。使用候选基因构建了六种机器学习模型。使用SHapley加法解释（SHAP）解释特征贡献，并使用受试者工作特征（ROC）曲线评估区分性能。使用外部单细胞转录组数据集和鼻组织样本进行验证。
结果： 富集分析显示，eCRSwNP与免疫炎症反应和氧化应激相关通路显著相关。WGCNA识别出四个与嗜酸性炎症密切相关的模块，最终选择了五个氧化应激相关的核心特征基因：HIF1A、RAC2、SELP、NCF2和NCF4。ROC分析显示所有五个基因均具有良好的区分能力。SHAP分析表明各算法之间的效应方向和特征优先排序模式一致。外部验证和鼻组织样本实验证实了这些基因在eCRSwNP中总体上调。细胞类型定位分析表明，NCF2、NCF4和RAC2主要来源于髓系细胞，SELP主要定位于血管内皮细胞，HIF1A在炎症细胞浸润区域广泛表达。
结论： 通过整合转录组分析与多算法机器学习，我们识别并验证了HIF1A、RAC2、SELP、NCF2和NCF4作为eCRSwNP中氧化应激相关的核心特征基因，并证实了它们在eCRSwNP组织中的表达升高和细胞类型特异性定位。这些基因可能参与活性氧相关的炎症过程，并代表eCRSwNP内型分型和进一步机制研究的候选分子特征。

英文摘要

BACKGROUND: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a chronic inflammatory disease of the upper airway driven by diverse inflammatory cells and mediators. This study aimed to characterize the inflammatory landscape of CRSwNP and to identify and validate oxidative stress-related key genes in CRSwNP.
METHODS: RNA sequencing of nasal mucosal tissues was performed in 22 subjects, including 8 controls, 5 patients with non-eosinophilic CRSwNP (neCRSwNP), and 9 patients with eCRSwNP. Differential expression analysis was conducted using DESeq2 R package. Enrichment analysis, Ingenuity Pathway Analysis (IPA),weighted gene co-expression network analysis (WGCNA) and CIBERSORT was performed. Differentially expressed genes (DEGs), eosinophilic inflammation-associated module genes, and oxidative stress-related genes were intersected to generate candidate genes, followed by protein-protein interaction (PPI) network analysis. Six machine learning models were constructed using the candidate genes. SHapley Additive exPlanations (SHAP) were used to interpret feature contributions, and receiver operating characteristic (ROC) curves were used to assess discriminatory performance. External single-cell transcriptomic datasets and nasal tissue samples were used for validation.
RESULTS: Enrichment analyses showed that eCRSwNP was prominently associated with immune-inflammatory responses and oxidative stress-related pathways. WGCNA identified four modules closely related to eosinophilic inflammation, and five oxidative stress-related core signature genes were ultimately selected: HIF1A, RAC2, SELP, NCF2, and NCF4. ROC analysis demonstrated good discriminatory ability for all five genes. SHAP analyses indicated consistent directions of effects and feature-prioritization patterns across algorithms. External validation and nasal tissue sample experiments confirmed the overall upregulation of these genes in eCRSwNP. Cell-type localization analyses indicated that NCF2, NCF4, and RAC2 were mainly derived from myeloid cells, and SELP was predominantly localized to vascular endothelial cells, and HIF1A was broadly expressed within inflammatory cell infiltration area.
CONCLUSION: By integrating transcriptomic analysis with multi-algorithm machine learning, we identified and validated HIF1A, RAC2, SELP, NCF2, and NCF4 as oxidative stress-related core signature genes in eCRSwNP and confirmed their elevated expression and cell-type-specific localization in eCRSwNP tissues. These genes may contribute to reactive oxygen species-associated inflammatory processes and represent candidate molecular signatures for eCRSwNP endotyping and further mechanistic investigation.