基于机器学习对慢性鼻窦炎伴鼻息肉预后模型的初探
Exploration of prognostic models for chronic rhinosinusitis with nasal polyps based on machine learning
蒋思洁 1谢邵兵 1章华 1谢志海 1蒋卫红1
作者信息
- 1. 中南大学湘雅医院耳鼻咽喉头颈外科 耳鼻喉重大疾病湖南省重点实验室 老年疾病国家临床研究中心 临床解剖中心鼻颅底解剖实验室,长沙 410008
- 折叠
摘要
目的 分析慢性鼻窦炎伴鼻息肉(CRSwNP)的分子特征,揭示其病理生理机制,并构建能有效预测术后复发的预后模型.方法 整合3个数据集GSE198950、GSE179265及GSE136825,其中对照组39例,慢性鼻窦炎不伴鼻息肉组16例,CRSwNP组89例,提取校正P值＜0.05且Log2FC＞1的差异基因,随后进行KEGG和GO富集分析及蛋白互作评分.采用随机森林和最小绝对值收敛和选择算法(least absolute shrinkage and selection operator,LASSO)进行变量筛选,交集分析后得到关键节点.回顾性分析二代RNA测序数据(对照4例,CRSwNP 8例),利用Mann-Whitney U检验对前述关键节点进行比较,将P＜0.05的变量纳入模型.利用既往测序数据(原发CRSwNP 16例,复发CRSwNP 15例)通过Logistic回归构建CRSwNP的预后模型,绘制列线图,利用校准曲线和受试者工作特征曲线(ROC),并计算曲线下面积(AUC)以验证模型可信性.结果 对CRSwNP组中上调和下调的差异基因进行分析,其中神经活性配体-受体相互作用、白细胞介素17信号通路被激活,而钙信号通路及间隙连接被抑制.利用随机森林和LASSO鉴定出关键节点,其中G蛋白γ亚基4(U=3.00,P=0.028)、胆囊收缩素(U=0.50,P=0.006)、表皮生长因子(U=1.00,P=0.008)和神经突触细胞黏附分子1(U=0.00,P=0.004)在Whitney U检验中具有统计学意义,将其纳入回归模型.将预后模型制成列线图,验证模型校准曲线和ROC表明其高度可信(C-index=0.875,AUC=0.866),而测试集中的ROC曲线显示其可有效预测CRSwNP的术后复发(AUC=0.859).结论 本研究利用CRSwNP相关数据集,全面描述了该疾病的分子特征.通过随机森林和LASSO回归筛选出的关键节点构建的预后模型在验证中表现出高准确性,为推进CRSwNP的个体化诊疗提供了有力支持.
Abstract
Objective To analysis the molecular characteristics of chronic rhinosinusitis with nasal polyps(CRSwNP),to unravel its pathophysiological mechanisms,and to develop a prognostic model capable of effectively predicting postoperative recurrence.Methods The data from three datasets(GSE198950,GSE179265,and GSE136825)were integrated,comprising 39 control cases,16 cases of chronic rhinosinusitis without nasal polyps,and 89 cases of CRSwNP.Differential expression genes(DEGs)were identified based on adjusted P＜0.05 and Log2FC＞1.KEGG and GO enrichment analyses,as well as STRING node scoring,were conducted.Variable selection was performed using random forest and least absolute shrinkage and selection operator regression(LASSO),with key nodes identified through intersection analysis.Mann-Whitney U test was applied,and variables with P＜0.05 were included in the model.A prognostic model for CRSwNP was constructed using logistic regression,externally validated using RNA-seq data,and evaluated with receiver operating characteristic(ROC)curve analysis to calculate the area under the curve(AUC).Results This research illustrated both upregulated and downregulated DEGs in CRSwNP,activating pathways like neuroactive ligand-receptor interaction and IL-17 signaling,while inhibiting calcium signaling and gap junctions.Key nodes identified through random forest and LASSO,including G protein subunit γ4(U=3.00 P=0.028),Cholecystokinin(U=0.50,P=0.006),Epidermal growth factor(U=1.00 P=0.008),and Neurexin-1(U=0.00,P=0.004),showing statistical significance in external validation.The prognostic model,visualized in a line graph,exhibited high reliability(C-index=0.875,AUC=0.866).The ROC curve in external validation indicated its effectiveness in predicting postoperative recurrence(AUC=0.859).Conclusions This study integrates multiple datasets on CRSwNP to provide a comprehensive description of its molecular features.The prognostic model,built upon key nodes identified through random forest and LASSO analyses,demonstrates high accuracy in both internal and external validations,thus providing robust support for the development of personalized treatment strategies for CRSwNP.
关键词
鼻窦炎/鼻息肉/复发/预后模型/机器学习Key words
Sinusitis/Nasal polyp/Recurrence/Prognostic model/Machine learning引用本文复制引用
基金项目
国家自然科学基金(82371126)
国家自然科学基金(82371127)
国家自然科学基金(82301289)
国家自然科学基金(82171118)
湖南省自然科学基金(2022JJ30986)
湖南省自然科学基金(2023JJ30953)
湖南省自然科学基金(2023JJ41004)
长沙市自然科学基金(kq2208391)
中国博士后科学基金(2023M743960)
出版年
2024
NETL
NSTL
万方数据