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Vol. 60. Issue 3.
Pages 191-194 (March 2024)
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Vol. 60. Issue 3.
Pages 191-194 (March 2024)
Scientific Letter
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Unveiling the Link between Asthma and Cancer Risk: Shedding New Light through Mendelian Randomization
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632
Wenjie Lia,1, Peixin Dongb,1, Wei Wanga,
Corresponding author
wwei9500@smu.edu.cn

Corresponding author.
a Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, China
b Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China
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Table 1. Details of GWASs Analyzed in the Present MR Analyses.
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Table 2. Causality of the Risk for Asthma and Cancers.
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To the Director,

Asthma is the most common chronic respiratory disease, affecting an estimated 262 million people worldwide and associated with several comorbidities whose prevalence have increased in recent years.1 Although the broader implications of asthma in the development of cancer have been of interest for many years, evidence regarding the risk of cancer development after asthma diagnosis is controversial and inconclusive. For instance, a Korean population-based cohort showed that asthma increased the risk of several cancer,2 but other epidemiological studies suggested that asthma decreased the overall risk of cancer.3 Conflicting observations may be from small sample sizes, inadequate adjustment for confounding factors that could affect cancer development, use of cross-sectional or case–control designs, or focus on specific cancer types. For example, traffic-related air pollution has been related to the development of a wide variety of cancers in adults and also has been found to be a risk factor for the development of adult-onset asthma. Pollutants vary by geographic location, and individuals living closer to major roadways may be at a higher risk for both asthma and cancer. Due to the limitation of observational design, it remains unclear if there is a true causal relationship between asthma itself and cancer risks. Mendelian randomization (MR) has emerged as a powerful tool to evaluate causal relationships between exposures and outcomes using genetic variants as instrumental variables. MR leverages the principle of genetic inheritance and random assortment of alleles during gamete formation to help identify causal effects. This method can provide valuable insights into the potential causal relationship between asthma and cancer risk, minimizing biases commonly encountered in observational studies. Thus, the present study aimed to explore the relationship between asthma and cancer risk using the MR method, identifying potential areas of future research.

To meticulously adhere to these principles, we employed four criteria for instrumental variables (IVs) selection. Firstly, we obtained SNPs from existing genome-wide association studies (GWASs) that demonstrated a substantial and consistent correlation with asthma and cancers (p<5×10−8). Subsequently, we performed linkage disequilibrium (LD) clumping analysis at an R2<0.001 and 10,000kb window to preserve the SNPs that were most robustly related to asthma. Thirdly, to address potential pleiotropic effects, we performed a comprehensive analysis utilizing the PhenoScanner database (http://www.phenoscanner.medschl.cam.ac.uk/). Lastly, we eliminated palindromic SNPs to mitigate potential pleiotropic effects. We extracted genetic variants regarding childhood-onset and adult-onset asthma from a study enrolling 314,633 and 327,253 participants of European descent from the UK Biobank, respectively (Table 1). Other large-scale GWASs were used to obtain 18 specific-site cancer-related SNPs (Table 1). The main MR results were obtained through the utilization of the inverse variance weighted (IVW) method. To ensure the reliability of the findings, the weighted median and MR-Egger approaches were employed, each offering distinct advantages and assumptions. The results of the MR analysis were reported as odds ratios (ORs) with corresponding 95% confidence intervals (CIs). To assess potential pleiotropic effects, we conducted the MR-PRESSO test. Considering the extensive scope of our MR analyses, which encompassed 22 unique cancer subtypes, we deemed the statistical significance of potential associations to be contingent upon the p-value adjustment through the Bonferroni correction, falling within the range of 0.05–0.0022 (calculated as 0.05 divided by 22). Therefore, a p-value below 0.05 was considered indicative of a significant causal association, while results within the range of p-values from 0.05 to 0.0022 suggested a suggestive causality. All statistical analyses were performed using R (Version 4.2.0) with the TwosampleMR package.

Table 1.

Details of GWASs Analyzed in the Present MR Analyses.

Phenotype  Sample Size  Number of Patients  Number of Controls  Number of Variants  Ethnicity  Trait ID in MR Base  PubMed ID  Year 
Exposure
Adult-onset asthma  327253  26582  300671  8949308  European  ebi-a-GCST00779  30929738  2019 
Childhood-onset asthma  314633  13962  300671  8984776  European  ebi-a-GCST007800  30929738  2019 
Outcome
Basal cell carcinoma  392871  17416  375455  7244167  European  ebi-a-GCST90013410  33549134  2021 
Bladder cancer  373295  1279  372016  9904926  European  ieu-b-4874  NA  2021 
Breast cancer  228951  122977  105974  10680257  European  ieu-a-1126  29059683  2017 
Breast cancer (ER+)  175475  69501  105974  10680257  European  ieu-a-1127  29059683  2017 
Breast cancer (ER−)  127442  21468  105974  10680257  European  ieu-a-1135  29059683  2017 
Cervical cancer  462933  1889  461044  9851867  European  ukb-b-8777  NA  2018 
Colorectal cancer  32072  19948  12124  38356021  European  ebi-a-GCST012879  30510241  2018 
Endometrial cancer  12906  108979  121885  9470555  European  ebi-a-GCST006464  30093612  2018 
Gastric cancer  476116  1029  475087  24188662  European  ebi-a-GCST90018849  34594039  2021 
Kidney cancer  463010  1114  461896  9851867  European  ukb-b-1316  NA  2018 
Liver cancer  463010  304  218488  16380466  European  finn-b-C3_LIVER_INTRAHEPATIC_BILE_DUCTS  NA  2021 
Lung adenocarcinoma  66756  11273  55483  8881354  European  ieu-a-965  24880342  2014 
Lung cancer  85716  29266  56450  8945893  European  ieu-a-966  24880342  2014 
Lymphoma  361194  1752  359442  361194  European  ukb-d-C_LYMPHOMA  NA  2018 
Melanoma  337159  2677  334482  10855955  European  ukb-d-C3_MELANOMA_SKIN  NA  2018 
Mesothelioma  174139  133  174006  16380303  European  finn-b-C3_MESOTHELIOMA_EXALLC  NA  2021 
Oesophageal cancer  372756  740  372016  8970465  European  ieu-b-4960  NA  2021 
Ovarian cancer  25509  40941  66450  NA  European  ieu-a-1120  28346442  2017 
Pancreatic cancer  463010  233  462777  521863  European  ieu-a-822  19648918  2009 
Prostate cancer  140254  79148  61106  19733911  European  ebi-a-GCST006085  29892016  2018 
Squamous cell lung cancer  63053  7426  55627  8893750  European  ieu-a-967  24880342  2014 
Thyroid cancer  174995  989  174006  16380316  European  finn-b-C3_THYROID_GLAND_EXALLC  NA  2021 

MR: Mendelian randomization; GWAS: genome-wide association studies; NA: not available.

Table 2 illustrates the results of our IV estimation in the present study. The results of our MR analyses revealed a positive association between asthma (childhood onset) and pancreatic cancer (ORIVW: 1.0242, 95% CI: 1.0067–1.0421, p=0.0064) (Table 2). Besides, the MR-PRESSO Global tests (p=0.455) further validated the IVW results and revealed no obvious heterogeneity. However, no causality between asthma (childhood onset) and other cancers was observed (Table 2). Besides, the risks of lymphomas were higher in asthma (adult onset) patients when compared to the general population (ORIVW: 1.0001, 95% CI: 1.00001–1.0002, p=0.033) (Table 2). The MR-PRESSO Global tests (p=0.715) further validated the IVW results and revealed no obvious heterogeneity. However, no causality between asthma (adult onset) and other cancers was observed (Table 2).

Table 2.

Causality of the Risk for Asthma and Cancers.

Exposures  Outcomes  IVWWeighted MedianMR-Egger
    OR (95% CI)  p-Value  OR (95% CI)  p-Value  OR (95% CI)  p-Value 
Childhood-onset asthmaBasal cell carcinoma  1.0001 (0.9965–1.0037)  0.9652  0.9917 (0.9611–1.0232)  0.6025  0.9971 (0.9929–1.0014)  0.1905 
Bladder cancer  1 (1–1)  0.3249  0.9999 (0.9997–1.0002)  0.6454  1 (0.9999–1)  0.5038 
Breast cancer  1.0004 (0.9988–1.002)  0.6181  1.0245 (1.0096–1.0397)  0.0018  1.0013 (0.999–1.0037)  0.2628 
Breast cancer (ER−)  1.0019 (0.998–1.0058)  0.3379  1.0177 (0.9843–1.0523)  0.3053  1.003 (0.9972–1.0089)  0.3072 
Breast cancer (ER+)  1.0002 (0.9983–1.002)  0.8686  1.0141 (0.9967–1.0317)  0.1170  1.0005 (0.9976–1.0034)  0.7271 
Cervical cancer  1 (1–1)  0.5453  0.9998 (0.9995–1.0001)  0.2344  1 (0.9999–1)  0.4331 
Colorectal cancer  1 (0.9999–1.0000)  0.2084  0.9997 (0.9991–1.0003)  0.2989  0.9999 (0.9998–1)  0.0706 
Endometrial cancer  1.0012 (0.997–1.0054)  0.5845  1.0295 (0.9936–1.0667)  0.1113  1.0017 (0.9962–1.0073)  0.5490 
Glioma  1.0035 (0.9857–1.0217)  0.7024  1.0645 (0.9285–1.2204)  0.3783  1.0094 (0.9838–1.0357)  0.4758 
Lung adenocarcinoma  1.0054 (0.9986–1.0122)  0.1180  1.0057 (0.9453–1.0698)  0.8587  1.0071 (0.9972–1.0171)  0.1589 
Lung cancer  1.0031 (0.9986–1.0075)  0.1748  0.992 (0.9507–1.0351)  0.7130  1.0034 (0.9972–1.0096)  0.2853 
Lymphomas  1 (1–1)  0.8903  0.9998 (0.9995–1.0002)  0.3386  1 (0.9999–1.0001)  0.7623 
Malignant melanoma of skin  1 (1–1)  0.9258  0.9997 (0.9993–1.0002)  0.2193  1 (1–1.0001)  0.4152 
Malignant neoplasm of kidney  1 (1–1)  0.5524  1.0001 (0.9998–1.0004)  0.6399  1 (1–1.0001)  0.3518 
Malignant neoplasm of liver and intrahepatic bile ducts  1.0004 (0.9815–1.0197)  0.9647  1.0734 (0.9104–1.2657)  0.4015  1.0042 (0.9772–1.0319)  0.7633 
Malignant neoplasm of stomach  0.9892 (0.9761–1.0024)  0.1078  1.0624 (0.9407–1.1999)  0.3320  0.9921 (0.9727–1.012)  0.4345 
Malignant neoplasm of thyroid gland  0.9917 (0.981–1.0024)  0.1291  0.924 (0.8402–1.0161)  0.1063  0.9908 (0.9753–1.0064)  0.2458 
Oesophageal cancer  1 (1–1)  0.3301  1.0002 (1.0000–1.0004)  0.0954  1 (1–1)  0.3717 
Ovarian cancer  1.0021 (0.9988–1.0053)  0.2113  0.9985 (0.9699–1.0279)  0.9178  1.0016 (0.9968–1.0064)  0.5190 
Pancreatic cancer  1.0242 (1.0067–1.0421)  0.0065*  1.073 (0.9253–1.2443)  0.3577  1.0185 (0.9943–1.0434)  0.1357 
Prostate cancer  0.9994 (0.9975–1.0014)  0.5828  1.0136 (0.9957–1.032)  0.1416  0.9985 (0.9954–1.0015)  0.3192 
Squamous cell lung cancer  1.0002 (0.9934–1.007)  0.9615  0.9987 (0.941–1.0599)  0.9651  0.9965 (0.987–1.0061)  0.4767 
Adult-onset asthmaBasal cell carcinoma  1.0006 (0.9949–1.0062)  0.8433  0.9226 (0.8573–0.9929)  0.0397  1.0005 (0.9937–1.0074)  0.8780 
Bladder cancer  1.0000 (1.0000–1.0000)  0.8873  0.9999 (0.9992–1.0005)  0.7374  1 (0.9999–1.0000)  0.3075 
Breast cancer  1.0013 (0.9983–1.0042)  0.4041  1.0294 (0.9877–1.0729)  0.1782  1.0025 (0.999–1.0059)  0.1596 
Breast cancer (ER−)  0.9986 (0.993–1.0042)  0.6182  1.0132 (0.9337–1.0993)  0.7554  0.9991 (0.9908–1.0076)  0.8378 
Breast cancer (ER+)  0.9997 (0.9958–1.0035)  0.8596  1.0067 (0.954–1.0624)  0.8090  1.0009 (0.9969–1.0049)  0.6598 
Cervical cancer  1.0000 (0.9999–1.0000)  0.6402  0.9997 (0.9989–1.0005)  0.5293  1 (0.9999–1.0001)  0.5522 
Colorectal cancer  1.0000 (0.9999–1.0001)  0.9097  0.9993 (0.9977–1.0009)  0.4096  1 (0.9998–1.0001)  0.5865 
Endometrial cancer  1.0021 (0.9961–1.0082)  0.4877  1.0413 (0.9566–1.1335)  0.3555  1.0026 (0.995–1.0103)  0.4980 
Glioma  0.9817 (0.9473–1.0173)  0.3094  1.6059 (0.9772–2.639)  0.0862  1.0062 (0.9707–1.043)  0.7359 
Lung adenocarcinoma  1.005 (0.9952–1.015)  0.3179  1.0588 (0.9057–1.2378)  0.4782  0.9947 (0.9809–1.0087)  0.4549 
Lung cancer  0.9994 (0.9927–1.0061)  0.8504  0.9566 (0.8665–1.0561)  0.3860  0.9961 (0.9867–1.0055)  0.4161 
Lymphomas  1.0001 (1.00001–1.0002)  0.0328*  1.0000 (0.9993–1.0007)  0.9739  1.0001 (1.0000–1.0001)  0.1876 
Malignant melanoma of skin  1.0000 (0.9999–1.0001)  0.9491  0.9986 (0.9977–0.9995)  0.0053  1 (0.9999–1.0001)  0.5266 
Malignant neoplasm of kidney  1.0000 (1.0000–1.0001)  0.1138  1.0002 (0.9995–1.0009)  0.5714  1.0001 (1–1.0001)  0.1058 
Malignant neoplasm of liver and intrahepatic bile ducts  0.9918 (0.9642–1.0203)  0.5708  1.0492 (0.6827–1.6126)  0.8277  0.985 (0.9461–1.0254)  0.4614 
Malignant neoplasm of stomach  0.9996 (0.9801–1.0194)  0.9664  0.9419 (0.7045–1.2592)  0.6882  1.0135 (0.9856–1.0422)  0.3462 
Malignant neoplasm of thyroid gland  0.9958 (0.9799–1.0121)  0.6134  0.8632 (0.6864–1.0855)  0.2159  1.0014 (0.9793–1.024)  0.9016 
Oesophageal cancer  1.0000 (0.9999–1.0000)  0.2308  1.0002 (0.9997–1.0007)  0.4477  1 (0.9999–1.0000)  0.8095 
Ovarian cancer  0.9988 (0.9943–1.0034  0.6177  0.9632 (0.8963–1.035)  0.3128  0.9973 (0.9904–1.0042)  0.4468 
Pancreatic cancer  0.9829 (0.9594–1.007)  0.1629  0.9375 (0.6562–1.3394)  0.7279  0.9795 (0.9485–1.0114)  0.2054 
Prostate cancer  0.9984 (0.9956–1.0013)  0.2801  0.9752 (0.9339–1.0184)  0.2627  0.9957 (0.9916–0.9997)  0.0375 
Squamous cell lung cancer  1.0035 (0.9936–1.0135)  0.4941  0.9474 (0.8219–1.0921)  0.4613  0.9992 (0.9856–1.013)  0.9089 
*

and bold values mean statistical significance.

Asthma, being a chronic inflammatory ailment, is integral in the process of cancer development as inflammation takes precedence. Previous MR studies have endeavored to explore the underlying biological mechanisms linking intrinsic immunity and cancer, but have yielded no evidence suggesting a causal relationship between asthma and the risk of breast,4 prostate,4 or lung cancer.5 It has been observed that airway inflammation in asthmatic individuals may exert an influence on the overall inflammatory response in other bodily regions, thereby being potentially linked to pancreatic cancer development. Consequently, the prevalence of pancreatic cancer is expected to be higher in asthma cases that manifest during childhood due to the prolonged impact of inflammation. Our research findings further corroborate this notion. Regarding the cellular mechanisms involved, STAT6, a constituent of a protein family encompassing seven analogous proteins, assumes a principal role.6 Activation of STAT6 is primarily prompted by interleukins IL-4 and IL-13, thereby playing a vital role in the initiation of T helper type 2 immune response. The involvement of STAT6 has been established in various allergic conditions, including asthma, while also playing a part in regulating the tumor microenvironment. Perturbations in the STAT6 pathway have been associated with the progress and onset of lymphomas. In parallel, our results indicate that asthmatic individuals are more prone to developing lymphomas when compared to the general population lacking asthma, thereby aligning with our preceding findings. In summary, our investigation demonstrates a positive association between childhood-onset asthma and the incidence of pancreatic cancer, along with a heightened risk of lymphomas among asthma patients, particularly those with adult-onset asthma, when compared to the broader population. However, it is crucial to highlight the limitations of the present study. Despite both the current investigation and previous MR research5 indicating no causal link between asthma and the risk of lung cancer, further studies are warranted due to the significant implications for public health.7 Moreover, several pertinent confounding factors related to asthma were not accounted for in the current MR study. Specifically, the treatment of asthma, such as corticosteroids, may confer an impact on the onset of lung cancer.8 Additionally, chronic activation pathways in the lung microenvironment induced by house dust mites, an etiological factor of asthma, may exert a tumorigenic effect.9 Therefore, additional research is necessary to establish causality and elucidate potential underlying mechanisms.

Ethical Approval

No ethical approval and written consent were needed for the secondary analysis of public data.

Authors’ Contributions

Wenjie Li and Peixin Dong: conceptualization, methodology, data curation, software and writing – review & editing. Wei Wang: funding acquisition, project administration, supervision and validation. The work reported in the paper has been performed by the authors, unless clearly specified in the text.

).

Funding

This work was supported by the National Natural Science Foundation of China Grant (82172642 to W. Wang) and Natural Science Foundation of Guangdong Province (2021A1515011683 to W. Wang).

Conflicts of Interest

None declared.

Acknowledgements

We appreciate the work of the open GWAS project (https://gwas.mrcieu.ac.uk/).

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These authors contributed equally.

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