Gu Zhan, Wu Yonghui, Yu Fengzhi, Sun Jijia, Wang Lixin
Department of Integrative Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Department of Mathematics and Physics, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
BMC Pulm Med. 2024 Dec 18;24(1):618. doi: 10.1186/s12890-024-03444-5.
Chronic obstructive pulmonary disease (COPD) is closely linked to lung cancer (LC) development. The aim of this study is to identify the genetic and clinical risk factors for LC risk in COPD, according to which the prediction model for LC in COPD was constructed.
This is a case-control study in which patientis with COPD + LC as the case group, patientis with only COPD as the control group, and patientis with only LC as the second control group. A panel of clinical variables including demographic, environmental and lifestyle factors were collected. A total of 20 single nucleotide polymorphisms (SNPs) were genotyped. The univariate analysis, candidate gene study and multivariate analysis were applied to identify the independent risk factors, as well as the prediction model was constructed. The ROC analysis was used to evaluate the predictive ability of the model.
A total of 503 patients were finally enrolled in this study, with 188 patients for COPD + LC group, 162 patients for COPD group and 153 patients for LC group. The univariate analysis of clincial data showed compared with the patients with COPD, the patients with COPD + LC tended to have significantly lower BMI, higher smoking pack-years, and higher prevalence of emphysema. The results of the candidate gene study showed the rs1489759 in HHIP and rs56113850 in CYP2A6 demonstrated significant differences between COPD and COPD + LC groups. By using multivariate logistic regression analysis, four variables including BMI, pack-years, emphysema and rs56113850 were identified as independent risk factors for LC in COPD and the prediction model integrating genetic and clinical data was constructed. The AUC of the prediction model for LC in COPD reached 0.712, and the AUC of the model for predicting LC in serious COPD reached up to 0.836.
The rs56113850 (risk allele C) in CYP2A6, decrease in BMI, increase in pack-years and emphysema presence were independent risk factors for LC in COPD. Integrating genetic and clinical data for predicting LC in COPD demonstrated favorable predictive performance.
慢性阻塞性肺疾病(COPD)与肺癌(LC)的发生密切相关。本研究旨在确定COPD患者发生LC的遗传和临床风险因素,并据此构建COPD患者发生LC的预测模型。
这是一项病例对照研究,以COPD合并LC患者为病例组,仅患有COPD的患者为对照组,仅患有LC的患者为第二对照组。收集了包括人口统计学、环境和生活方式因素在内的一系列临床变量。对总共20个单核苷酸多态性(SNP)进行基因分型。应用单因素分析、候选基因研究和多因素分析来确定独立风险因素,并构建预测模型。采用ROC分析评估模型的预测能力。
本研究最终纳入503例患者,其中COPD合并LC组188例,COPD组162例,LC组153例。临床数据的单因素分析显示,与COPD患者相比,COPD合并LC患者的BMI往往显著更低,吸烟包年数更高,肺气肿患病率更高。候选基因研究结果显示,HHIP基因中的rs1489759和CYP2A6基因中的rs56113850在COPD组和COPD合并LC组之间存在显著差异。通过多因素logistic回归分析,确定了BMI、吸烟包年数、肺气肿和rs56113850这四个变量为COPD患者发生LC的独立风险因素,并构建了整合遗传和临床数据的预测模型。COPD患者发生LC的预测模型的AUC达到0.712,严重COPD患者预测LC的模型的AUC高达0.836。
CYP2A6基因中的rs56113850(风险等位基因C)、BMI降低、吸烟包年数增加和存在肺气肿是COPD患者发生LC的独立风险因素。整合遗传和临床数据预测COPD患者发生LC具有良好的预测性能。