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Integrating genetic and clinical data to predict lung cancer in patients with chronic obstructive pulmonary disease

BMC Pulmonary Medicine volume 24, Article number: 618 (2024) Cite this article

Abstract

Background

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.

Methods

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.

Results

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.

Conclusion

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.

Peer Review reports

Introduction

Lung cancer (LC) is one of the most common cancers worldwide with high prevalence and poor prognosis. As the leading cause of cancer deaths, LC resulted in 1.8 million deaths globally in 2020 [1]. Chronic obstructive pulmonary disease (COPD) is a respiratory illness that is characterized by chronic inflammation and irreversible airway obstruction. The incidence of COPD continues to rise and COPD ranks the third leading cause of mortality globally [2, 3]. LC is a common comorbid disease among patients with COPD, and the coexistence of COPD and LC is now becoming a serious public health concern. COPD is closely linked to LC and is recognized as a main risk factor for LC development, independently of tobacco exposure [4, 5]. Compared to people with normal lung function, patients with COPD are 4–10 times more likely to develop LC [6, 7]. Consequently, early identification and treatment of LC in patients with COPD is urgently neeeded.

COPD and LC are both complex diseases that result from the combined effects of genetic susceptibility and environmental factors, involved multiple pathophysiological mechanisms especially for chronic inflammation. In the past years, increasing numbers of single nucleotide polymorphisms (SNPs) might associated with both COPD and LC have been reported by genome-wide association studies (GWAS) and candidate gene studies [8,9,10,11]. Several of these loci and candidate genes may be involved in the LC development in COPD, including hedgehog interacting protein (HHIP), cholinergic receptor, neuronal nicotinic, α-polypeptide 3 (CHRNA3), CHRNA5, glycophorin A (GYPA), cytochrome P450 2A6 (CYP2A6), CYP1A1, matrix metalloproteinase-1 (MMP-1) among others. Besides genetic predisposition, demographic and environmental factors like advanced age, weight loss, smoking exposure, air pollution, dust exposure, a history of previous lung disease, and underlying inflammatory processes all potentially contribute to an increased susceptibility to developing LC in patients with COPD [12]. Nevertheless, few studies have comprehensively evaluated the synergistic effect of multiple risk factors including genetic and clinical variables on increasing the risk of LC in COPD.

The aim of this study is: (1) to evaluate a panel of clinical variables including demographic, environmental and lifestyle factors among three groups of patients (COPD without LC [COPD group], coexistence of COPD and LC [COPD + LC group], and LC without COPD [LC group]); (2) to perform a candidate gene study to investigate the susceptible gene polymorphisms for LC in COPD, focusing on the comparison between COPD and COPD + LC groups; and (3) to select the risk factors and construct a prediction model for LC in patients with COPD and serious COPD.

Methods

Study design and study subjects

This is a case-control study in which COPD + LC group as the case group, COPD group as the control group, and LC group as the second control group. All study subjects with informed consent were enrolled from January 2020 to December 2022 at Shanghai Pulmonary Hospital in Shanghai, China. This study was performed in accordance with the guidelines of the Helsinki Declaration (as revised in 2013) and was approved by the Ethics Committee of Shanghai Pulmonary Hospital affiliated to Tongji University (K20-036Z).

COPD was defined following the Global Initiative for Obstructive Lung Disease (GOLD) recommendations as the presence of persistent respiratory symptoms and the ratio of a forced expiratory volume in first second (FEV1) and forced vital capacity (FVC) was less than 0.70 after a bronchodilator test [13]. The severity of COPD was classified according to the criteria of GOLD. The diagnosis of LC was made after suggestive radiological findings with pathologic confirmation through histological or cytological specimens [14]. Of note, the patients with LC (COPD + LC group and LC group) enrolled in this study were all newly diagnosed LC cases. In the enrollment of COPD + LC group, once the patients were newly diagnosed with LC, then the history of COPD and lung function were detected to ensure that the LC occurrence was secondary to COPD.

The inclusion criteria included physician-diagnosed COPD or LC, over the age of 40, Chinese Han subjects who were not relatred to each other, and complete data measurements. Subjects who had a history of asthma, tuberculosis and interstitial lung diseases that may affect lung function, or had a history of other malignancies or cardiovascular and cerebrovascular diseases were all excluded (Fig. 1).

Fig. 1
figure 1

Flowchart of the study

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Clinical data collection

All clinical variables including demographic, environmental and lifestyle factors were collected and recorded into the self-developed “iLUNG TCM” health management software system (Registration ID: 2022SR0999226). The collected clinical data included gender, age, body mass index (BMI), dust exposure, alcohol use, smoking status, physical activity, lung function and tumor characteristics. The dust exposure referred to the occupational exposure to any of the four dusts of silica, cement, coal and asbestos. The smoking status were categorized into three groups: non-smoker, former smoker, and active smoker. The smoking pack-years were calculated as well. The physical activity included exercise and pulmonary rehabilitation. The lung function was detected by masterlab spirometers (Jaeger, Wuezburg, Germany) by technicians specialized in respiratory functional tests. Emphysema was determined through computed tomography (CT) assessment by professional radiologists. The tumor characteristics included pathological types and the stage at diagnosis according to the TNM eight edition of LC.

SNPs selection and genotyping

The SNPs might involved in the LC development in COPD were selected from previous published GWAS or candidate gene studies [8,9,10,11, 15,16,17,18,19], and several SNPs associated with activation of inflammatory pathways were added [20,21,22]. A total of 20 SNPs were finally selected in this study and the basic information are listed in Table 1 according to the dbSNP database (https://www.ncbi.nlm.nih.gov/snp) and the 1000 Genomes Project (https://www.internationalgenome.org/).

Genomic DNA was extracted from all subjects from 1.5 mL of peripheral blood samples collected in EDTA collection tubes. DNA was extracted using a commercial extraction kit (Tiangen biotechnology, Beijing, China) for genotyping. All 20 SNPs were successfully genotyped by matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) mass spectrometry using the MassARRAY® analyzer platform (Sequenom, San Diego, CA) by Shanghai Fenglin Clinical Laboratory. Detailed information regarding the primers is available (Supplementary material 1).

Table 1 Basic information of selected SNPs
Full size table

Statistical analysis

All statistical analyses and graphs were performed by SPSS version 23.0 (IBM, Armonk, USA), SHEsis platform (http://analysis.bio-x.cn/myAnalysis.php) (Bio-X Institutes, Shanghai, China) [23] and MedCalc version 22.001 (MedCalc, Ostend, Belgium). A descriptive analysis of all clinical variables among three groups of patients was carried out. Normally distributed data were expressed as mean ± standard deviation (SD) and compared using Student’s t tests. Non-normally distributed data were expressed as median (first quartile and third quartile (Q1, Q3)) and compared using Mann-Whitney U tests. Categorical data were expressed as count (percentage) and compared using Pearson’s χ2 tests. The genotype frequency distribution of SNPs was assessed using Pearson’s χ2 tests. The genetic association analysis was using logistic regression to calculate odds ratios (ORs) and 95% confidence intervals (CIs). Univariate logistic regression and multivariate logistic regression analyses were applied to identify the independent risk factors for LC in COPD, according to which the prediction model for LC in COPD was constructed. To evaluate the predictive ability and robustness of the model, receiver operating characteristic curve (ROC) analysis were used and the areas under ROC (AUCs) were calculated. For all of the above analyses, P value < 0.05 was considered statistically significant.

Results

Baseline information and clinical data of the study subjects

As shown in Fig. 1, 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. Baseline information and clinical data among three groups of patients are presented in Table 2. 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 (P < 0.05). There were no differences in gender, age, dust exposure, alcohol use, physical activity, and respiratory functional parameters between COPD and COPD + LC groups. Compared with the patients with LC, the patients with COPD + LC had significantly lower non-smokers, higher smoking pack-years, and higher prevalence of emphysema (P < 0.001).

The most frequent tumor pathological type in COPD + LC group was squamous (48.94%), followed by adenocarcinoma (34.04%), small cell LC (13.30%) and other types (3.72%). The prevalence of squamous was higher and the prevalence of adenocarcinoma was lower in COPD + LC group than that in LC group (P < 0.05). There were no differences in stage at diagnosis.

Table 2 Baseline information and clinical data of the study subjects
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Genotype frequency distribution and genetic association analysis

The genotyping call rates for each SNP reached 100% and the minor allele frequency (MAF) of each SNP were greater than 0.05. The genotype frequency distribution of each SNP between COPD and COPD + LC groups are given in Table 3. Of all 20 SNPs, the rs1489759 in HHIP and rs56113850 in CYP2A6 demonstrated significant differences in genotype frequency distribution (P < 0.05).

The rs1489759 and rs56113850 were further analyzed. As presented in Table 4, the genetic association analysis showed rs1489759 (risk allele A) increased the risk of LC in COPD in recessive and additive models (P < 0.05), and rs56113850 (risk allele C) increased the risk of LC in COPD in dominant, recessive and additive models (P < 0.05). Additionally, rs56113850 increased the disease risk in COPD + LC group compared with LC group (P < 0.001), whereas there was no significant deviation of rs1489759 between COPD + LC and LC groups.

Table 3 Genotype frequency distribution of each SNP
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Table 4 Genetic association analysis of rs1489759 and rs56113850
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Risk factors and prediction model

Based on the results of the univariate analysis and candidate gene study, BMI, smoking pack-years, emphysema, rs1489759 and rs56113850 were included in the next multivariate logistic regression analysis to explore the risk factors for LC in COPD. Notably, rs1489759 and rs56113850 were analyzed using additive models. Ultimately, four variables including BMI, smoking 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 ORs and 95%CIs of risk factors were shown in Table 5.

Table 5 Multivariate analysis to determine the independent risk factors for LC in COPD
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Predictive ability evaluation

ROC analysis for the model to predict LC in COPD is presented in Fig. 2. The AUC of the prediction model integrating genetic and clinical data reached 0.712 (95%CI: 0.661–0.759, P < 0.001), with the sensitivity was 48.94% and specificity was 84.57%. Additionally, the Hosmer-Lemeshow test demonstrated that the model was a good fit (P value: 0.516 > 0.05). If the prediction model only including clinical data, the AUC was only 0.695 (95%CI: 0.644–0.743, P < 0.001).

For serious COPD patients (FEV1% < 50%), the prediction model including BMI, smoking pack-years, emphysema and rs56113850 was also constructed and ROC analysis is presented in Fig. 3. The AUC of the model for predicting LC in serious COPD reached up to 0.836 (95%CI: 0.723–0.916, P < 0.001), with the sensitivity was 86.84% and specificity was 77.78%.

Fig. 2
figure 2

ROC of the model to predict LC in COPD

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Fig. 3
figure 3

ROC of the model to predict LC in serious COPD

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Discussion

The association between COPD and LC has garnered significant attention from researchers and clinicians in recent years. At a rate of 0.8–1.7% of patients with COPD develop LC per year and LC accounts for 33% of all COPD-related deaths [24]. There is a growing interest in identifying LC risk in patients with COPD to realize effectively management. In this study, we evaluated the association of both genetic susceptibility and clinical variables including demographic, environmental and lifestyle factors with LC in COPD. By using univariate analysis, candidate gene study and multivariate analysis, BMI, smoking pack-years, emphysema and rs56113850 in CYP2A6 were ultimately identified as independent risk factors and applied to predict LC in patients with COPD and serious COPD. Moreover, besides the patients with coexistence of COPD and LC, two control groups (patients with only COPD and patients with only LC) were set in this study to comprehensively assess the association of some variables with COPD + LC. To the best of our knowledge, this is the first study to integrate genetic and clinical data to predict LC in patients with COPD.

The chromosomal region 19q13.2 contains the primary nicotine metabolizing gene, CYP2A6 [25]. The CYP2A6 gene is a highly polymorphic enzyme that metabolizes nicotine to cotinine, then cotinine to trans-3’-hydroxycotinine (3HC), and the nicotine metabolite ratio (3HC/cotinine) means the efficacy of nicotine metabolism through CYP2A6. It is demonstrated that the genetic variants in CYP2A6 are associated with nicotine metabolism, smoking behavior, smoking cessation, tobacco-related LC risk [26]. As slower nicotine metabolism, CYP2A6 activity variation may influence LC risk via tobacco exposure and procarcinogen activation [27]. The rs56113850, the sentinel associated SNP in CYP2A6, was reported the effect allele C was associated with increased nicotine metabolism activity [28, 29]. A GWAS summary statistics analysis showed the results that the effect allele C of rs56113850 was associated with an increased risk of heavier smoking, COPD and LC [16]. A recent single-variant and Mendelian randomization analysis acknowledged the causal pathway connecting rs56113850 in CYP2A6, cigarette consumption, and LC susceptibility in smokers [30]. These results all supported our findings. In the present study, rs56113850 (risk allele C) was firstly discovered increased the risk of LC in COPD in Chinese Han population after adjustment for confounding factors. Compared with COPD group or LC group, rs56113850 all increased the disease risk in COPD + LC group. Our results suggested rs56113850 in CYP2A6 might be a potential genetic biomarker of LC in COPD.

The rs1489759 in HHIP was found associated with LC in COPD in unadjusted models in this study, but this association was no longer significant after adjustment. The HHIP gene locates on chromosome 4q31 locus and acts as a negative regulator of Hedgehog signaling by binding to Hedgehog protein. Hedgehog signaling pathway is involved in epithelial-mesenchymal transition, mediates cigarette induced oncogenic transformation of bronchial epithelial cells and is essential for cellular proliferation of many LC cell lines. In addition to this, the expression of HHIP may cause changes in lung repair mechanisms and then lead to COPD [15, 31]. It has also been reported the rs1489759 might associated with both COPD and LC in several studies [15, 32]. Of note, some of the SNPs identified associated with LC in COPD in other studies were not significant in this study. The differences in the results with other findings may be due to the ethnic and regional distributional differences of the samples.

Regarding clinical data, our results showed decrease in BMI, increase in smoking pack-years and emphysema presence were independent risk factors for LC in COPD. BMI is a measurement of body fat based on height and weight. Mounting evidences have suggested that BMI is inversely associated with the risk of LC [33, 34]. A recent pooled analysis of 10 prospective cohort studies indicated that leanness (BMI < 18.5 kg/m2) was associated with a higher risk of LC and every 5 kg/m2 increase in BMI was associated with a 21% lower risk of LC [35]. Our study focused on patients with COPD and found BMI was inversely associated with LC in COPD. It is well known that COPD and LC are two of the most important smoking-related diseases. Smoking plays a pivotal role in the development of both diseases. Our findings showed that pack-years in patients with COPD + LC were much higher than that in patients with only COPD, indicating the underlying mechanisms of nicotine-induced carcinogenesis. Emphysema is a type of lung damage related to smoking. Previous studies have demonstrated that the degree of emphysematous lesions was associated with LC development in COPD [24, 36]. Similarly, our results found the prevalence of emphysema was much higher in COPD + LC group than that in COPD group.

Those clinical risk factors discovered in this study are similar to the COPD-LUCSS prediction tool for LC in COPD, which included age greater than 60 years, BMI less than 25 kg/m2, pack-years greater than 60, and the presence of emphysema [37]. Given the prominent role of genetic factors, we carried out the candidate gene study, and then identified the rs56113850 in CYP2A6 as an independent genetic risk factor for LC in COPD, and ultimately developed a prediction model integrating genetic and clinical data. The AUC of this prediction model was modest and less than 30% of patients using this model would be misclassified. Moreover, the predictive ability for LC risk of this model would significantly increase for patients with serious COPD and only less than 20% of patients using this model would be misclassified.

There are, however, several limitations of this study. The subjects in this study were all Chinese Han ethnicity and the results of the candidate gene study may not be applicable to other ethnic groups. The predictive ability of the model ought to be validated against other cohorts of patients from other settings. Furthermore, a prospective cohort study with a larger sample size, and more tag SNPs covered the genes, and more comprehensive clinical information (e.g., duration of COPD, family history of LC, history of acute exacerbations of COPD, and diffusing capacity for carbon monoxide) is needed.

Conclusion

In summary, our study identified the rs56113850 (risk allele C) in CYP2A6, decrease in BMI, increase in smoking pack-years and emphysema presence as independent risk factors for LC in COPD. Based on those risk factors, the prediction model integrating genetic and clinical data was constructed for predicting LC risk in COPD. The model demonstrated favorable predictive performance, and could facilitate the early identification and management of the patients with COPD at high risk of LC.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

AUCs:

Areas under receiver operating characteristic curve

BMI:

Body mass index

CHRNA3:

Cholinergic receptor, neuronal nicotinic, α-polypeptide 3

CHRNA5:

Cholinergic receptor, neuronal nicotinic, α-polypeptide 5

CIs:

Confidence intervals

COPD:

Chronic obstructive pulmonary disease

CT:

Computed tomography

CYP1A1:

Cytochrome P450 1A1

CYP2A6:

Cytochrome P450 2A6

FAM13A:

Family with sequence similarity 13 member A

FEV1:

Forced expiratory volume in first second

FLT1:

Vascular endothelial growth factor receptor 1

FVC:

Forced vital capacity

GOLD:

Global Initiative for Obstructive Lung Disease

GWAS:

Genome-wide association studies

GYPA:

Glycophorin A

HHIP:

Hedgehog interacting protein

LC:

Lung cancer

MAF:

Minor allele frequency

MALDI-TOF:

Matrix-assisted laser desorption/ionization time of flight

MMP-1:

Matrix metalloproteinase-1

NFKBIA:

Nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor alpha

NRXN1:

Neurexin 1

ORs:

Odds ratios

ROC:

Receiver operating characteristic curve

SCLC:

Small cell lung cancer

SD:

Standard deviation

SNPs:

Single nucleotide polymorphisms

STAT3:

Signal transducer and activator of transcription 3

TLR5:

Toll like receptor 5

TNF:

Tumor necrosis factor

3HC:

3’-hydroxycotinine

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Acknowledgements

We thank all the partners and staff who helped us in the process of this study.

Funding

This work was supported by the National Natural Science Foundation of China (82004115, 82374345) and the Shanghai Key Discipline of Traditional Chinese Medicine (shzyyzdxk-2024106).

Author information

Authors and Affiliations

  1. Department of Integrative Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China

    Zhan Gu, Yonghui Wu, Fengzhi Yu & Lixin Wang

  2. Department of Mathematics and Physics, Shanghai University of Traditional Chinese Medicine, Shanghai, China

    Jijia Sun

Authors
  1. Zhan Gu
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  2. Yonghui Wu
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  3. Fengzhi Yu
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  4. Jijia Sun
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  5. Lixin Wang
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Contributions

ZG and LW designed the study; ZG, YW and FY collected the data and completed the experiments; ZG and JS analyzed the data; ZG wrote the manuscript; LW revised the manuscript content. All authors have read and approved the final submitted manuscript.

Corresponding author

Correspondence to Lixin Wang.

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Ethics approval and consent to participate

This study was performed in accordance with the guidelines of the Helsinki Declaration (as revised in 2013) and was approved by the Ethics Committee of Shanghai Pulmonary Hospital affiliated to Tongji University (K20-036Z). Written informed consent was obtained from all subjects. This type of research was considered no need for submission of Clinical Trial Number.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Gu, Z., Wu, Y., Yu, F. et al. Integrating genetic and clinical data to predict lung cancer in patients with chronic obstructive pulmonary disease. BMC Pulm Med 24, 618 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12890-024-03444-5

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12890-024-03444-5

Keywords

BMC Pulmonary Medicine

ISSN: 1471-2466

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