Association between psoriasis and risk of malignancy: observational and genetic investigations

Association between psoriasis and risk of malignancy: observational and genetic investigations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association between psoriasis and risk of malignancy: observational and genetic investigations Aijun Chen, Ruolin Li, Xiangjun Chen, Qinglian Zeng, Wenjin Luo, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3842779/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Sep, 2024 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract The relationship between psoriasis and site-specific cancers remains unclear. We aimed to investigate whether psoriasis is causally associated with site-specific cancers. We used observational and genetic data from UK Biobank. We obtained genome-wide association study (GWAS) summary data, expression quantitative trait locus (eQTL) analysis data, The Cancer Genome Atlas (TCGA) data and genotype-tissue expression (GTEx) data from public datasets. We used a phenome-wide association study (PheWAS), PRS analysis, and one-sample and two-sample Mendelian randomization (MR) analysis to investigate potential causal associations between psoriasis and cancers. We added gene annotation for potential molecular associations. A total of 13463 patients with psoriasis and 463136 participants without psoriasis were included. In unselected PheWAS analysis, psoriasis was associated with higher risks of 14 types of cancer. In one-sample MR analyses, genetically predicted psoriasis was associated with higher risks of anal canal cancer (hazard ratio [HR] 1.61, 95% CI 1.12–2.32), breast cancer (HR 1.06, 95% CI 1.02–1.11) and nonmelanoma skin cancer (HR 1.07, 95% CI 1.01–1.14) in women and lung cancer (HR 1.17, 95% CI 1.04–1.32) and kidney cancer (HR 1.34, 95% CI 1.13–1.58) in men. Two-sample MR analysis indicated that psoriasis was causally associated with breast cancer (inverse variance weighted [IVW] odds ratio 1.02, 95% CI 1.01–1.03) and lung cancer (IVW odds ratio 1.12, 95% CI 1.02–1.22). Gene annotation revealed that psoriasis-related genes (such as ERAP1 and C6orf3) were significantly changed in lung and breast cancer tissues. Our findings demonstrate psoriasis is causally associated with lung cancer and breast cancer. Regular screening for lung and breast cancer might be relevant for patients with psoriasis. Health sciences/Diseases/Immunological disorders/Inflammatory diseases/Psoriasis Health sciences/Risk factors Health sciences/Health care/Public health/Epidemiology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Psoriasis is an immune-system-mediated inflammatory skin disease that affects approximately 3% of the population 1 . Cancer has been reported to be the leading cause of death in patients with psoriasis 2 , but the association between psoriasis and the risk of site-specific cancers remains unclear. A meta-analysis that included cohort or case‒control studies indicated that psoriasis was associated with increased risk of developing 11 types of site-specific cancers, including colon, colorectal, kidney, laryngeal, liver, lymphoma, keratinocyte, esophageal, oral cavity, and pancreatic cancers and non-Hodgkin lymphoma 3 . However, another meta-analysis including cohort studies found that patients with psoriasis or psoriatic arthritis had higher risks of keratinocyte cancer, lymphoma, lung cancer and bladder cancer 4 . These inconsistent results might be ascribed, at least partially, to limited sample sizes, varied diagnostic criteria of psoriasis and insufficient adjustments for potential confounders. Psoriasis has a significant hereditary predisposition 5 . Genome-wide association studies (GWASs) have identified several susceptibility loci of psoriasis involved in immune-mediated inflammatory disorders and innate/acquired host defense 6 , leading to a higher possibility of developing cancer for patients with psoriasis 1,7,8 . Although integrating genetic data into analyses of the polygenic risk score (PRS) or Mendelian randomization (MR) is an effective approach to explore causal effects on outcomes 9,10 , different MR studies lead to controversial results regarding the causal association between psoriasis and cancer 11–13 . To date, systematic explorations based on a large-scale cohort with genetic data examining the relationship between psoriasis and the incidence of malignancy are lacking. In this study, we systematically explored associations between psoriasis and 89 types of cancers in a large population-based cohort. Then, we used PRS and MR analyses to investigate whether psoriasis is causally associated with site-specific cancers. The biological functions and expression levels of psoriasis-related genetic variants in cancers were further explored to uncover potential molecular correlations. Methods Study Setting In the current study, we conducted five analyses to investigate the causal relationship between psoriasis and site-specific cancers. Analysis 1 was designed as a prospective cohort study, and we used observational data from UK Biobank. Analysis 2 was a PRS study, and we used GWAS summary data from public datasets and genetic data from UK Biobank. Analysis 3 was a one-sample MR analysis, and we used genotype and phenotypic data from UK Biobank. Analysis 4 was a two-sample MR analysis, and we used GWAS summary data from public datasets. For analysis 5, expression quantitative trait locus (eQTL) and differential expression analyses between tumor samples and normal samples were evaluated based on Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) data. We used the phenome-wide association study (PheWAS) method to explore the association between all site-specific cancers and psoriasis (analysis 1) or the PRS of psoriasis (analysis 2). In analysis 3, we performed one-sample MR analysis including the site-specific cancer types with P values < 0.05 in PheWAS of analysis 1 or 2. Additionally, infrequent cancers, defined as those with fewer than 100 cases in the overall population or fewer than 50 cases in sex subgroups, were excluded from analysis 3. Two-sample MR analysis (analysis 4) was performed if the P value of the site-specific cancer type was < 0.05 in one-sample MR analysis. We performed analysis 5 for site-specific cancer types if their P value was < 0.05 in two-sample MR analysis. UK Biobank UK Biobank is a prospective population-based cohort involving more than 500,000 participants aged between 40 and 70 years from the United Kingdom 14 . The National Information Governance Board for Health and Social Care and the National Health Service North West Multicenter Research Ethics Committee (reference 13/NW/0382) approved the UK Biobank ethical application. All participants provided informed consent through electronic signatures at the first assessment. This study was approved by UK Biobank Consortium. Information on psoriasis, cancer status (prevalent and incident cases), relevant confounding factors, and genetic data are available in UK Biobank. The current analysis was approved by UK Biobank in August 2020 with the ID 66536. We set the cohort baseline (2006–2010) as our analysis baseline, and the end of the follow-up period was set as 2019. Study population In the UK Biobank, we included unrelated individuals of European ancestries who had a medical history and genetic data that passed quality control steps described previously 15 . We excluded participants if they were diagnosed with other autoimmune diseases that might potentially influence the association between psoriasis and cancers, including ulcerative colitis, Crohn's disease, multiple sclerosis, lupus erythematosus, systemic lupus erythematosus, dermatomyositis, pemphigus, pemphigoid, Behcet’s disease, rheumatoid arthritis, necrotizing vasculopathies and other systemic connective tissue disorders. We excluded participants who had been diagnosed with any type of cancer at baseline or before the diagnosis of psoriasis in analysis 1 and analysis 2. For PRS and MR analyses, we further excluded participants who were not white British and those who lacked genetic data. We excluded cancers with < 50 cases in the total population or < 25 cases in subgroups of women and men. Diagnoses of psoriasis and cancers Participants from UK Biobank were registered with the National Health Service (NHS) and agreed to link their medical records. UK Biobank tracks participants’ electronic medical or health-related records, including hospital inpatient admissions and death. All disease types (including psoriasis and site-specific cancers) are recorded and analyzed according to the tenth version of the International Classification of Diseases code (ICD-10). Individual codes of psoriasis and site-specific cancers are summarized in Supplementary Table 1 . PRS of psoriasis We extracted the genetic instruments from published GWAS summary data of European ancestry 6 . We selected single-nucleotide polymorphisms (SNPs) based on the following criteria: ( 1 ) SNPs significantly associated with psoriasis (P ≤ 5×10 − 8 ); ( 2 ) SNPs with minor allele frequencies > 0.01; and ( 3 ) no insertion or deletion or ambiguous SNPs. Clump-based linkage disequilibrium pruning was performed with a physical distance threshold of 250 kb and an LD threshold (R2) < 0.001. We identified a total of 74 SNPs and present their information in Supplementary Table 2 . The PRS of psoriasis was calculated as the sum of risk alleles of the 74 significantly associated SNPs. Variants were weighted by their effect size obtained from the primary GWAS. The weighted score was calculated as (BETA 1 ×SNP 1 )+(BETA 2 ×SNP 2 )+…+(BETA n ×SNP n ). MR analysis We used the PRS of psoriasis to conduct one-sample MR analysis using UK Biobank data. For two-sample MR analysis, we obtained summary-level data from public GWAS sources from FinnGen ( https://www.finngen.fi/fi ) via the IEU-OpenGWAS project (inquiry code: fnn-b-L12_PSORIASIS). Individuals from the FinnGen project were genotyped using Illumina and Affymetrix chip arrays. A total of 4510 psoriasis cases identified by ICD-10 code (L40) and 212,242 healthy controls were included. GWAS data for carcinoma in situ of the anus are derived from 107 European ancestry cases and 456,241 European ancestry controls 16 . GWAS data for breast cancer are derived from 76,192 female cases and 63,082 controls of European ancestry (IEU-OpenGWAS project inquiry code: ebi-a-GCST004988), and most SNPs were reported in a previous GWAS 17 . We obtained GWAS summary data for lung and kidney cancer from GWAS Catalog (inquiry code: GCST90011812, GCST90011818), which included 2,485 cases of lung cancer, 1,338 cases of kidney cancer (identified by ICD-9 or ICD-10 codes in UK biobank, and ICD-O-3 codes in Kaiser Permanente Genetic Epidemiology Research on Adult Health and Aging cohorts) and 410,350 healthy controls 18 . We obtained GWAS summary data of nonmelanoma skin cancer (NMSC) from the IEU-OpenGWAS project (inquiry code: ieu-b-4959), with sequencing data sourced from UK Biobank, which comprised 23,694 cases and 372,016 control individuals ( Supplementary Table 3 ). Annotation and enrichment analysis We annotated the 74 SNPs included in PRS analysis by using gProfiler ( http://biit.cs.ut.ee/gprofiler/ ) 19 and Variant Effect Predictor ( https://useast.ensembl.org/info/docs/tools/vep/index.html ) 20 . The 74 SNPs were mapped to 50 genes ( Supplementary Table 17 ). To assess the potential biological functions of the mapped genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed in Metascape 21 and DAVID 22 , respectively. eQTL and TCGA We used the Genotype-Tissue Expression (GTEx) project to conduct single-tissue expression quantitative trait locus (eQTL) analysis. The GTEx project is supported by the Common Fund of the Office of the Director of the National Institutes of Health and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. We acquired detailed data from the GTEx portal on 08/01/2023. We obtained gene expression RNA-seq data and clinical data from The Cancer Genome Atlas (TCGA) and GTEx RNA datasets, as accessed via the UCSC XENA database ( http://xena.ucsc.edu/ ) 23 . The TCGA data were generated using the Illumina HiSeq 2000 RNA sequencing platform. Statistical analysis Categorical variables are presented as percentages, and normally distributed continuous variables are presented as the mean ± SD. We imputed missing data of covariates by multivariate imputation with a chained equations algorithm. A P value < 0.05 was considered statistically significant. Data were analyzed with the use of R software, version 4.0.3. We conducted all statistical analyses on the supercomputer platform (inspur M5). PheWAS for associations between psoriasis and incident site-specific cancers (the observational PheWAS) was estimated by Cox regression while adjusting for age, sex, smoking status, alcohol intake frequency and physical activity as potential confounders. We used PRSice software to calculate the PRS for each participant after summing the risk loci weighted by the effect size of the association for individual SNPs and psoriasis. PheWAS for associations between the PRS of psoriasis and site-specific cancer (the PRS PheWAS) was estimated by Cox regression while adjusting for age, sex, top 10 principal components, and the UK Biobank assessment center. In one-sample MR analysis, we implemented a Wald ratio method to estimate the causal effect of psoriasis on site-specific cancers. We determined the association between the PRS and psoriasis (βX|G), as well as the association between the PRS and site-specific cancers (βY|G), by using logistic regression adjusted for age, sex, and the top 10 principal components of the genetic information. The ratio estimate of the causal effect is calculated as βIV = βY|G/βX|G. We further conducted subgroup analyses by sex with the same methodology. We conducted two-sample MR analysis by using the ‘TwoSampleMR’ package in R to extract instrumental variables with p ≤ 5×10 − 8 , and we then clumped these SNPs on the basis of the European ancestry reference panel with an R 2 < 0.001 within a 10000-kb window. The SNPs used in the two-sample MR analysis are presented in Supplementary Table 12 to Supplementary Table 16 . We used 4 distinct methods to evaluate causal effects, namely, Mendelian randomization-Egger (MR‒Egger), weighted median, inverse variance weighted (IVW) and weighted mode. Each method makes different assumptions regarding the effectiveness of instrumental variables, and the IVW method is more reliable when there is less potential violation in MR assumptions. For sensitivity analysis, we used the MR-pleiotropy residual sum and outlier (MR-PRESSO) method to remove the potential influence of outliers 24 . We used Cochrane's Q test for IVW analyses and Rücker's Q test for MR‒Egger analyses to assess heterogeneity. We used the MR‒Egger intercept method to test the horizontal pleiotropy of instrumental variables. We performed leave-one-out analyses using MR‒Egger or fixed effects inverse variance methods to investigate whether the overall results were driven by any individual variant 25 . In eQTL analysis, cis-eQTLs were defined if the SNP was in a +/- 1 Mb cis window around the transcription start site. Significance was determined using a Q value threshold, and protein-coding genes with a P value < 5×10 − 10 were selected for subsequent analysis. For TCGA analysis, all data were transformed by log2(x + 1), where x represents RNA-Seq by expectation-maximization value. We compared gene expression in normal tissues from GTEx with various cancer types from TCGA, with Welch's t test for test any difference. Results Baseline characteristics For analyses 1 ~ 3, a total of 13463 patients with psoriasis and 463136 participants without psoriasis were included, and their characteristics at baseline are summarized in Table 2 . In analysis 4, we obtained GWAS summary data from public datasets, and the genotype and gene expression data in analysis 5 were collected from TCGA and GTEx datasets. We illustrate the flow chart for the study design in Fig. 1 . The research question, data sources utilized, strengths and limitations of each analysis are presented in Table 1 . Table 1 Summary of the study design, research question, data sources utilized, and strengths and limitations of each methodological approach applied in the present study. Study design Research question Data sources Key strengths Key limitations Analysis 1: Observational PheWAS Are patients with psoriasis associated with a higher risk of site-specific cancers? Individual-level data from UK biobank A large population-based dataset; low heterogeneity; prospective data collection; large availability of confounding data Observational design with unknown confounding factors; cannot assess causality Analysis 2: PRS PheWAS Is genetic susceptibility to psoriasis associated with elevated risk of site-specific cancers? GWAS summary data from public datasets, and individual-level genotype and phenotype data from UK biobank Quantifies the latent genetic predisposition for psoriasis, regardless of clinical diagnosis; potentially mitigating exposure misclassification within the observational study; information on the outcome phenotype which is collected prospectively Horizontal pleiotropy; causal inference is inferior to Mendelian Randomization because of the pleiotropy Analysis 3: One-sample MR Does genetic predisposition to psoriasis have a causal impact on the risk of developing site-specific cancers? Individual-level genotype and phenotype data from UK biobank Causal effects can be determined; akin to a natural randomized controlled trial; minimally affected by measurement error, confounding factors, and reverse causality False-positive results due to weak instruments Analysis 4: Two-sample MR The same as one-sample MR GWAS summary data from public datasets The same as one-sample MR but more powerful and is less prone to false-positive bias Investigation of subset of participants requires new GWAS to be performed Analysis 5: eQTL and expression analysis Do psoriasis-related SNPs potentially influence cancer development? Genotype and gene expression data collected from TCGA and the GTEx datasets. Offering mechanistic insights Lack of causative links and clinical significance Abbreviation: MR, Mendelian randomization. GWAS, Genome Wide Association Study. eQTL, expression quantitative trait loci. TCGA, The Cancer Genome Atlas. GTEx, Genotype-Tissue Expression Table 2 Baseline characteristics of participants after exclusion of other autoimmune diseases. Non-PSO PSO SMD N = 463136 N = 13463 Age(years) 56.9 (8.11) 57.4 (7.98) 0.057 Male(N,%) 212843 (46.0%) 6840 (50.8%) 0.097 BMI(kg/m 2 ) 27.4 (4.76) 28.3 (5.09) 0.178 SBP(mmHg) 138 (18.50) 139 (18.20) 0.052 FBG(mmol/L) 5.12 (1.23) 5.20 (1.39) 0.064 Smoking status(%) 0.228 Never 256841 (55.5%) 6001 (44.6%) Previous 158186 (34.2%) 5428 (40.3%) Current 48109 (10.4%) 2034 (15.1%) Alcohol intake frequency(%) 0.048 Daily or almost daily 94455 (20.4%) 2992 (22.2%) Three or four times a week 107973 (23.3%) 3086 (22.9%) Once or twice a week 120155 (25.9%) 3314 (24.6%) One to three times a month 51509 (11.1%) 1491 (11.1%) Special occasions only 52594 (11.4%) 1520 (11.3%) Never 36450 (7.9%) 1060 (7.9%) Physical activity(%) 0.047 Low intensity 86288 (18.6%) 2759 (20.5%) Moderate intensity 189459 (40.9%) 5420 (40.3%) High intensity 187389 (40.5%) 5284 (39.2%) Abbreviation: PSO, psoriasis. BMI, body mass index. SBP, systolic blood pressure. FBG, fasting blood glucose. SMD, standardized mean difference. Observational PheWAS In observational PheWAS, psoriasis was significantly associated with a higher risk of breast cancer (ICD-10 code C50, hazard ratio [HR] 1.29, 95% CI 1.16 to 1.44), NMSC (C44, HR 1.19, 95% CI 1.09 to 1.29), PMS (malignant neoplasms of independent primary multiple sites, C97, HR 1.43, 95% CI 1.09 to 1.88), SLN (secondary and unspecified malignant neoplasm of lymph nodes, C77, HR 1.15, 95% CI 1.03 to 1.29), mouth cancer (C04, HR 2.73, 95% CI 1.09 to 6.88), and lung cancer (C34, HR 1.16, 95% CI 1.00 to 1.33) (Fig. 2 A). In males, the association of psoriasis with lung cancer and NMSC remained consistent with the overall population. We further observed significantly higher risks of penile cancer (C60, HR 3.16, 95% CI 1.52 to 6.57), liver cancer (C22, HR 1.56, 95% CI, 1.07 to 2.27), bladder cancer (C67, HR 1.24, 95% CI 1.01 to 1.52), and Hodgkin's disease (C81, HR 2.00, 95% CI 1.02 to 3.94) among patients with psoriasis (Fig. 2 B). In females, risk of breast cancer was found to be significantly elevated (C50, HR 1.29, 95% CI 1.16 to 1.44). We further observed a higher risk of developing OIDO (malignant neoplasm of other and ill-defined digestive organs, C26, HR 3.27, 95% CI 1.71 to 6.25), SLN (C77, HR 1.23, 95% CI 1.06 to 1.43), anal canal cancer (C21, HR 2.33, 95% CI 1.26 to 4.30), palate cancer (C05, HR 3.94, 95% CI 1.38 to 11.24), corpus uteri cancer (C54, HR 1.35, 95% CI 1.04 to 1.77) and NMSC (C44, HR 1.15, 95% CI 1.01 to 1.32) among patients with psoriasis (Fig. 2 C). No association between psoriasis and risk of other site-specific cancers (not mentioned above) was observed in the total population or in each sex ( Supplementary Tables 4–6 ). PheWAS of psoriasis PRS A higher PRS of psoriasis was associated with an increased risk of kidney cancer (C64, HR 1.02, 95% CI 1.01 to 1.03), lung cancer (C34, HR 1.01, 95% CI 1.00 to 1.01) and breast cancer (C50, HR 1.01, 95% CI 1.00 to 1.01) ( Fig. 2 D, Supplementary Table 7 ). In males, a higher PRS of psoriasis was associated with an increased risk of kidney cancer (C64, HR 1.01, 95% CI 1.00 to 1.02) and lung cancer (C34, HR 1.02, 95% CI 1.01 to 1.04). In females, a higher PRS of psoriasis was associated with an increased risk of breast cancer in females (C50, HR 1.01, 95% CI 1.00 to 1.01) and a decreased risk of leukemia of unspecified cell type (C95, HR 0.89, 95% CI 0.83 to 0.96) (Fig. 2 E-F, Supplementary Tables 8–9 ). One-sample MR In one-sample MR analysis, genetically predicted psoriasis was significantly associated with anal canal cancer (C21, OR 1.38, 95% CI 1.01 to 1.87), lung cancer (C34, OR 1.12, 95% CI 1.03 to 1.21), and kidney cancer (C64, OR 1.25, 95% CI 1.09 to 1.42) in the total population (Table 3 ). In males, genetically predicted psoriasis was associated with a higher risk of lung cancer (C34, OR 1.17, 95% CI 1.04 to 1.32) and kidney cancer (C64, OR 1.34, 95% CI 1.13 to 1.58). In females, genetically predicted psoriasis was significantly associated with a higher risk of anal canal cancer (C21, OR 1.61, 95% CI 1.12 to 2.32), NMSC (C44, OR 1.07, 95% CI 1.01 to 1.14), and breast cancer (C50, OR 1.06, 95% CI 1.02 to 1.11). No causal relationship was observed between psoriasis and liver cancer, OIDO, corpus uteri cancer, penile cancer, bladder cancer, thyroid gland cancer, SLN, Hodgkin's disease or PMS. Table 3 One-sample Mendelian randomization estimates of psoriasis on the risk for site-specific cancers. Cancer Sex Case/Total OR (95% CI) p value C21 Malignant neoplasm of anus and anal canal Male and Female 369/458587 1.38 (1.01,1.87) 0.041 Male 124/209587 0.97 (0.56,1.68) 0.909 Female 245/249000 1.61 (1.12,2.32) 0.010 C22 Malignant neoplasm of liver and intrahepatic bile ducts Male and Female 973/458587 1.00 (0.83,1.21) 0.970 Male 597/209587 1.10 (0.85,1.41) 0.474 Female 376/249000 0.88 (0.66,1.18) 0.406 C26 Malignant neoplasm of other and ill-defined digestive organs Male and Female 340/458587 1.19 (0.86,1.63) 0.303 Male 195/209587 1.10 (0.71,1.71) 0.688 Female 145/249000 1.30 (0.81,2.09) 0.276 C34 Malignant neoplasm of bronchus and lung Male and Female 5228/458587 1.12 (1.03,1.21) 0.008 Male 2690/209587 1.17 (1.04,1.32) 0.009 Female 2538/249000 1.07 (0.95,1.19) 0.278 C44 Other malignant neoplasms of skin Male and Female 23072/458587 1.04 (0.99,1.08) 0.092 Male 12476/209587 1.00 (0.95,1.06) 0.941 Female 10596/249000 1.07 (1.01,1.14) 0.017 C50 Malignant neoplasm of breast Male and Female NA NA NA Male NA NA NA Female 16144/249000 1.06 (1.02,1.11) 0.010 C54 Malignant neoplasm of corpus uteri Male and Female NA NA NA Male NA NA NA Female 2222/249000 0.96 (0.85,1.08) 0.492 C60 Malignant neoplasm of penis Male and Female NA NA NA Male 114/209587 0.61 (0.34,1.08) 0.090 Female NA NA NA C64 Malignant neoplasm of kidney, except renal pelvis Male and Female 2102/458587 1.25 (1.09,1.42) 0.001 Male 1348/209587 1.34 (1.13,1.58) 0.001 Female 754/249000 1.11 (0.91,1.37) 0.312 C67 Malignant neoplasm of bladder Male and Female 4092/458587 1.00 (0.91,1.09) 0.954 Male 3068/209587 0.98 (0.88,1.10) 0.791 Female 1024/249000 1.03 (0.86,1.24) 0.735 C73 Malignant neoplasm of thyroid gland Male and Female 637/458587 0.88 (0.70,1.11) 0.294 Male 181/209587 1.08 (0.68,1.71) 0.749 Female 456/249000 0.82 (0.63,1.07) 0.145 C77 Secondary and unspecified malignant neoplasm of lymph nodes Male and Female 11433/458587 1.04 (0.98,1.10) 0.182 Male 4587/209587 1.06 (0.96,1.16) 0.257 Female 6846/249000 1.03 (0.96,1.10) 0.432 C81 Hodgkin's disease Male and Female 426/458587 0.95 (0.71,1.27) 0.745 Male 231/209587 0.90 (0.60,1.34) 0.611 Female 195/249000 1.01 (0.67,1.53) 0.951 C97 Malignant neoplasms of primary multiple sites Male and Female 1335/458587 1.02 (0.87,1.20) 0.805 Male 786/209587 1.02 (0.82,1.27) 0.870 Female 549/249000 1.03 (0.80,1.31) 0.845 Abbreviation: MR, Mendelian randomization. OR, Odds Ratio. CI, Confidence Interval. Two-sample MR analysis Considering that two-sample MR is less prone to false-positive bias than one-sample MR analysis 26 , we validated the findings of one-sample MR analysis by using two-sample MR analysis, in which psoriasis was causally associated with breast cancer (IVW OR 1.02, 95% CI 1.01 to 1.03) and lung cancer (IVW OR 1.12, 95% CI 1.02 to 1.22) (Fig. 3 A, B). Pleiotropy robust methods (weighted median and weighted mode) showed results similar to those of the IVW method ( Supplementary Table 10 ). No outliers were identified by the MR-PRESSO method. We found no evidence of directional pleiotropy by using MR‒Egger intercepts (p = 0.512 for breast cancer and p = 0.815 for lung cancer). We observed no evidence of heterogeneity for the association between psoriasis and site-specific cancers ( Supplementary Table 11 ). In leave-one-out analysis, the main results remained robust when we removed individual SNPs from the main two-sample MR analysis ( Supplementary Fig. 1 ). We present additional visualizations of the causal effect of psoriasis on the risk of lung cancer and breast cancer in Supplementary Figs. 2–3 . Gene annotation for the molecular association between psoriasis and cancer We performed gene annotation on the aforementioned 74 SNPs used in the study and subsequently conducted enrichment analysis on the related 50 genes. GO enrichment analysis revealed that the psoriasis-related genes (41 genes in the output) were enriched in ‘Response to type II interferon’, ‘Regulation of immune effector process’, ‘Regulation of response to biotic stimulus’, ‘Tumor necrosis factor-mediated signaling pathway’, and ‘Regulation of double-strand break repair via homologous recombination’ (Fig. 4 A). KEGG pathway analysis (8 genes in the output) indicated that ‘IL-17 signaling pathway’, ‘NF-kappa B signaling pathway’ and ‘cytokine‒cytokine receptor interaction’ are involved in psoriasis (Fig. 4 B). cis-eQTL analysis revealed associations between psoriasis-associated SNPs and multiple genes in normal lung ( Supplementary Table 18 ), breast ( Supplementary Table 19 ) and kidney ( Supplementary Table 20 ) tissues. We then evaluated gene expression of those eQTL-related genes in TCGA and GTEx. Among 287 normal tissues and 1013 lung cancer tissues, we observed a marked increase in expression levels of ERAP1, ZFP57, HLA-DQB2, HLA-H, CCDC122 and CARD14 in lung cancer compared to normal tissues from GTEx (Fig. 4 C). Expression levels of MICB, C6orf3, PSORS1C2, HLA-B, HCP5B, ALDH8A1, HLA-V and SGSH were significantly lower in lung cancer tissues than in normal tissues from GTEx (Fig. 4 C). When comparing breast cancer tissues (n = 1099) to normal tissues (n = 179), we observed significantly higher expression levels of ERAP1, MICB, HBS1L, STK19B, and HLA-DQB2 and significantly lower expression levels of C6orf3, PSORS1C2, HCP5B, ALDH8A1, HLA-V, CCDC122, and SGSH (Fig. 4 D). No discernible difference in expression of C6orf3 between kidney cancer (n = 886) and normal tissues (n = 28) was observed ( Supplementary Table 23 ). Discussion Based on unselected observational PheWAS analysis, we found psoriasis to be associated with higher risks of 14 types of cancer. In PRS PheWAS analysis, genetically predicted psoriasis was associated with breast cancer, kidney cancer and lung cancer. However, MR analysis only verified the causal relationship between psoriasis and lung cancer and breast cancer, and gene annotation indicated that psoriasis-related genes (such as ERAP1 and C6orf3) might be mediators linking psoriasis to lung or breast cancer. This is the first systematic analysis of the association between psoriasis and site-specific cancers based on a large population sample. Our data confirmed that psoriasis is causally associated with lung cancer and breast cancer, which indicates that regular screening for lung and breast cancer might be relevant for patients with psoriasis. Using unselected PheWAS analysis, our observational data not only confirmed previous reports that psoriasis is associated with higher risks of site-specific cancers in lung, kidney, liver, bladder, nonmelanoma skin, oral cavity, lymph nodes and Hodgkin's disease 3,4,7 but also revealed some unreported associations between psoriasis and cancers of the breast, penis, anal canal and corpus uteri. Furthermore, our genetic analysis (including PRS and MR analyses) confirmed the causal relationship between psoriasis and lung cancer/breast cancer. Previous meta-analyses have several shortcomings that may lead to biased results, including significant heterogeneity across the included studies, inconsistent diagnostic criteria for psoriasis, and insufficient adjustment for potential confounders linked to cancer. Hence, previous reports on the multiple sites of malignancy related to psoriasis 3,4 should be interpreted cautiously. In our study, measurement errors, confounding factors, and false-positive bias were gradually controlled from observational PheWAS, PRS PheWAS, and one-sample analysis to two-sample MR analysis 26–28 . Overall, the major concerns of malignancy for individuals with psoriasis are lung cancer and breast cancer. Two studies have performed two-sample MR analysis to investigate the association between psoriasis and lung cancer, but the results were inconsistent. Luo et al., based on data from UK Biobank (3,871 cases and 337,159 total), reported that psoriasis was causally associated with a 6% increased risk of lung cancer 12 . In contrast, Wang et al. showed no significant causal relationship between psoriasis and lung cancer, either with regard to subtypes of squamous cell lung cancer or pulmonary adenocarcinoma 11 . Such conflicting evidence may be attributed to the insufficiencies in methodology in MR analysis. Wang et al. used genetic variants with obvious heterogeneity 11 , and Luo et al. selected genetic datasets for psoriasis and lung cancer both from the same population (UK Biobank) 12 , which may influence the reliability and accuracy of results 29 . The causal association between psoriasis and breast cancer has not been explored, though risk of breast cancer was not reported to be increased among patients with psoriasis 3,4 . Compared to these two meta-analyses, we were able to identify an association between psoriasis and site-specific cancers with a relatively low degree of cohort heterogeneity. Both observational studies and genetic analyses provided consistent results, suggesting that psoriasis is a significant risk factor for breast or lung cancer. Psoriasis is a chronic inflammatory disease, and the link between chronic inflammation and cancers has been reported in many studies 30,31 , which is similar to other immune-mediated inflammatory diseases, such as inflammatory bowel diseases, rheumatoid arthritis and sarcoidosis 32–34 . On the other hand, psoriasis is also closely associated with immune dysfunction, and the proteins encoded by these susceptibility genes play important roles in immune and signaling pathways, especially interferon, tumor necrosis factor, the NF-kB pathway and the IL-23/Th17 axis 6,35 . These upregulated cytokines and activated pathways in psoriasis are also involved in the development of breast cancer. TNF-ɑ is reported to promote the growth and metastasis of breast cancer by activating the NF-kB pathway 36 . Excessive infiltration of Th-17 cells in the breast tumor microenvironment releases large amounts of IL17A and promotes the development of breast cancer 37 . By integrating public datasets of eQTL, TCGA and GTEx, our gene annotation preliminarily uncovered the molecular association between psoriasis and lung or breast cancer, highlighting the roles of chronic inflammation and immune dysfunction in the association. Further studies on the specific mechanisms of tumorigenesis in psoriasis are needed. The strength of our study is its comprehensive study design from PheWAS to MR analyses. Large-scale sample sizes and genomic data from UK Biobank and public datasets ensured that the causal relationship between psoriasis and breast/lung cancer was robust. There are also several limitations of the current study. First, the populations included were exclusively of European ancestry, and our findings may not be generalizable to other populations, including those of Asian or African ancestry. Second, the level of evidence provided by an MR analysis is second to randomized controlled trials. Future interventional studies that confirm a causal relationship between psoriasis and site-specific cancers will provide more compelling evidence. In conclusion, psoriasis is causally associated with lung cancer and breast cancer. However, other previously reported psoriasis-related cancers may not be major concerns of psoriasis patients. Our data support regular screening for lung cancer and breast cancer among patients with psoriasis. Declarations Data availability The individual participant data collected for the current study cannot be shared without UK Biobank’s explicit written approval. Additional data corroborating the findings of this study can be obtained through a reasonable request from the corresponding author. Code availability Data were analyzed using R software, version 4.0.3., with its core packages alongside the following supplementary packages: ggplot2, TwoSampleMR and MR-PRESSO. Author contributions JBH and AJC designed the study. Data analysis was done by RLL, WJL and PW. Data interpretation was done by RLL, WJL, XJC, QLZ, PW, JBH, SMY and AJC. Data was validated by QLZ and XJC. RLL, XJC and JBH wrote the first draft of the manuscript. SMY and AJC reviewed and edited the first draft of the manuscript. This report was approved for publication by all co-authors. The authors affirm the integrity and precision of the data and analyses. They had complete access to the study's data and held ultimate responsibility for the decision to submit for publication. Declaration of interests Authors have disclosed no conflicts of interest. Acknowledgments The computing work in this paper was partly supported by the Supercomputing Center of Chongqing Medical University. The authors thank the participants and staff of the UK Biobank. This research has been conducted using the UK Biobank Resource under Application Number 66536. This work was supported by the National Natural Science Foundation of China (81874238 and 82270878). References Lowes, M. A., Suárez-Fariñas, M. & Krueger, J. G. Immunology of psoriasis. Annu. Rev. Immunol. 32 , 227–255 (2014). Colaco, K. et al. Trends in mortality and cause-specific mortality among patients with psoriasis and psoriatic arthritis in Ontario, Canada. J. Am. Acad. Dermatol. 84 , 1302–1309 (2021). Trafford, A. M., Parisi, R., Kontopantelis, E., Griffiths, C. E. M. & Ashcroft, D. M. Association of Psoriasis with the Risk of Developing or Dying of Cancer: A Systematic Review and Meta-analysis. JAMA Dermatology 155 , 1390–1403 (2019). Vaengebjerg, S., Skov, L., Egeberg, A. & Loft, N. D. Prevalence, Incidence, and Risk of Cancer in Patients with Psoriasis and Psoriatic Arthritis: A Systematic Review and Meta-analysis. JAMA Dermatology 156 , 421–429 (2020). Lønnberg, A. S. et al. Heritability of psoriasis in a large twin sample. Br. J. Dermatol. 169 , 412–416 (2013). Tsoi, L. C. et al. Identification of 15 new psoriasis susceptibility loci highlights the role of innate immunity. Nat. Genet. 44 , 1341–1348 (2012). Balda, A. et al. Psoriasis and skin cancer - Is there a link? Int. Immunopharmacol. 121 , 110464 (2023). Srivastava, A. K. et al. Insights into interplay of immunopathophysiological events and molecular mechanistic cascades in psoriasis and its associated comorbidities. J. Autoimmun. 118 , 102614 (2021). Skrivankova, V. W. et al. Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): Explanation and elaboration. BMJ 375 , (2021). Konuma, T. & Okada, Y. 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Visualizing and interpreting cancer genomics data via the Xena platform. Nature biotechnology vol. 38 675–678 at https://doi.org/10.1038/s41587-020-0546-8 (2020). Verbanck, M., Chen, C. Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50 , 693–698 (2018). Burgess, S., Bowden, J., Fall, T., Ingelsson, E. & Thompson, S. G. Sensitivity analyses for robust causal inference from mendelian randomization analyses with multiple genetic variants. Epidemiology 28 , 30–42 (2017). Hemani, G., Bowden, J. & Davey Smith, G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum. Mol. Genet. 27 , R195–R208 (2018). Lawlor, D. A., Harbord, R. M., Sterne, J. A. C., Timpson, N. & Davey Smith, G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat. Med. 27 , 1133–1163 (2008). Richmond, R. C. et al. Investigating causal relations between sleep traits and risk of breast cancer in women: mendelian randomisation study. BMJ 365 , l2327 (2019). Burgess, S. et al. Guidelines for performing Mendelian randomization investigations. Wellcome Open Res. 4 , 186 (2019). Elinav, E. et al. Inflammation-induced cancer: Crosstalk between tumours, immune cells and microorganisms. Nat. Rev. Cancer 13 , 759–771 (2013). Greten, F. R. & Grivennikov, S. I. Inflammation and Cancer: Triggers, Mechanisms, and Consequences. Immunity 51 , 27–41 (2019). Pedersen, N. et al. Risk of extra-intestinal cancer in inflammatory bowel disease: Meta-analysis of population-based cohort studies. Am. J. Gastroenterol. 105 , 1480–1487 (2010). Smitten, A. L., Simon, T. A., Hochberg, M. C. & Suissa, S. A meta-analysis of the incidence of malignancy in adult patients with rheumatoid arthritis. Arthritis Res. Ther. 10 , 1–8 (2008). Boffetta, P., Rabkin, C. S. & Gridley, G. A cohort study of cancer among sarcoidosis patients. Int. J. Cancer 124 , 2697–2700 (2009). Yin, X. et al. Genome-wide meta-analysis identifies multiple novel associations and ethnic heterogeneity of psoriasis susceptibility. Nat. Commun. 6 , (2015). Wu, Y. & Zhou, B. P. TNF-α/NFκ-B/Snail pathway in cancer cell migration and invasion. Br. J. Cancer 102 , 639–644 (2010). Coffelt, S. B. et al. IL-17-producing γδ T cells and neutrophils conspire to promote breast cancer metastasis. Nature 522 , 345–348 (2015). Additional Declarations There is NO Competing Interest. Supplementary Files Supplement.docx Additional information For more information, please refer to the Supplementary Information section. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3842779","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":266734790,"identity":"0f6eb0a9-1003-487b-b520-91fdece96856","order_by":0,"name":"Aijun Chen","email":"data:image/png;base64,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","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Aijun","middleName":"","lastName":"Chen","suffix":""},{"id":266734791,"identity":"f2c6a08e-4fce-4883-83db-40c3ce02d3f7","order_by":1,"name":"Ruolin Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ruolin","middleName":"","lastName":"Li","suffix":""},{"id":266734792,"identity":"65a22e60-2872-4668-8e46-c92b9f4c2b21","order_by":2,"name":"Xiangjun Chen","email":"","orcid":"","institution":"the First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiangjun","middleName":"","lastName":"Chen","suffix":""},{"id":266734793,"identity":"4003dbff-08ef-4538-87b2-74b6eb343e9d","order_by":3,"name":"Qinglian Zeng","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qinglian","middleName":"","lastName":"Zeng","suffix":""},{"id":266734794,"identity":"986cbddd-42df-42a2-80f1-4f7003e9ea08","order_by":4,"name":"Wenjin Luo","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenjin","middleName":"","lastName":"Luo","suffix":""},{"id":266734795,"identity":"f2088f81-618f-4cdf-8c50-23e40d8b3b5e","order_by":5,"name":"Shumin Yang","email":"","orcid":"","institution":"Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shumin","middleName":"","lastName":"Yang","suffix":""},{"id":266734796,"identity":"29f6ffc0-60dc-4cc8-b5c3-2706bc2993cc","order_by":6,"name":"Ping Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Wang","suffix":""},{"id":266734797,"identity":"2bc790d9-2e9c-46f2-92bb-0debeb6d75a2","order_by":7,"name":"Jinbo Hu","email":"","orcid":"https://orcid.org/0000-0003-2925-0583","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jinbo","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2024-01-07 14:40:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3842779/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3842779/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-024-51824-6","type":"published","date":"2024-09-11T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":49641940,"identity":"e3b93916-89bb-41f1-aed8-ec670f5bbb57","added_by":"auto","created_at":"2024-01-15 19:21:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":118718,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart for study design. \u003c/strong\u003eExcluded 1: Other autoimmune diseases.\u003cstrong\u003e \u003c/strong\u003eExcluded 2: Incidene of cancer at baseline; Incidence of cancer before time of psoriasis.\u003cstrong\u003e \u003c/strong\u003eExcluded 3: Participants without genetic data. Non-white British. Other autoimmune diseases: Ulcerative colitis, Crohn's disease, Multiple sclerosis, Lupus erythematosus, Systemic lupus erythematosus, Dermatomyositis, Pemphigus, Pemphigoid, Behcet disease, Rheumatoid arthritis, Necrotizing vasculopathies, Other systemic connective tissue disorders.\u003cstrong\u003e \u003c/strong\u003eAbbreviation: PheWAS, Phenome-wide association study. PRS, polygenic risk score. MR, Mendelian Randomization. MR-Egger, mendelian randomisation-Egger. IVW, inverse variance-weighted method. MR-PRESSO, MR pleiotropy residual sum and outlier test. GWAS, Genome Wide Association Study. eQTL, expression quantitative trait loci. TCGA, The Cancer Genome Atlas. GTEx, Genotype-Tissue Expression.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3842779/v1/c9ad66eefe3d5b31fe086689.png"},{"id":49641941,"identity":"f546e067-caf3-48b2-81a6-df3b48d30813","added_by":"auto","created_at":"2024-01-15 19:21:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":259102,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eManhattan plot of phenome-wide association studies.\u003c/strong\u003eA-C, The adjusted p value (-log10(p value)) from multi-variable Cox regression models for site-specific cancers, according to psoriasis in all participants (A), male participants (B) and female participants (C). D-F, The adjusted p value (-log10(p value)) from multi-variable Cox regression models for site-specific cancers, according to the PRSs for psoriasis in all participants (D), male participants (E) and female participants (F). Dots represent site-specific cancers, grouped into systemic categories denoted by different colors. The horizontal hatched line indicate the statistically significance (p \u0026lt; 0.05). Abbreviation: ISUS, ill-defined, secondary, unspecified sites.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3842779/v1/39305d130068119d26f496e8.png"},{"id":49642421,"identity":"e4dbf14f-7d31-4edd-a927-9811a0801ae9","added_by":"auto","created_at":"2024-01-15 19:29:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":134697,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScatter plot of individual SNP-psoriasis and SNP-cancer associations with overlay of causal estimate from each MR test in two-sample MR analysis.\u003c/strong\u003e The graphs illustrate the strength of association between SNPs of psoriasis and various types of cancer. The vertical axis depicts the effect of SNPs on various malignancies, whereas the horizontal axis represents the effect of SNPs on psoriasis. Each dot represents one SNP. A non-zero gradient in the lines, with the displayed p value in the top box for the various Mendelian randomization models employed, serves as evidence regarding the causality between psoriasis and cancers. Data are presented for breast cancer (A), lung cancer (B), kidney cancer (C), anal canal cancer (D) and nonmelanoma skin cancer (E). Abbreviation: MR, Mendelian randomization. OR, Odds Ratio. CI, Confidence Interval. MR-Egger, Mendelian Randomisation-Egger\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3842779/v1/6bfa05ff4a7ad8e5de2d93f3.png"},{"id":49641942,"identity":"80d38e75-e7eb-480e-bc69-6280b7f563b9","added_by":"auto","created_at":"2024-01-15 19:21:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":141362,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePsoriasis-related genetic variants on gene biological functions and expression patterns in tumors.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.B. Enriched GO terms and KEGG pathways in genes mapped from psoriasis-related SNPs. Scatter diagram illustrating the distribution of the adjusted \u003cem\u003ep\u003c/em\u003e values (−log10(\u003cem\u003ep\u003c/em\u003e values)) and the enrichment factor (the ratio of enriched genes to the total input genes). The red dots on the diagram indicate associations with the inflammatory response and DNA repair pathways. The size of the dots corresponds to the enrichment factor, with larger dots indicating higher enrichment levels.\u003c/p\u003e\n\u003cp\u003eC.The expression distribution of gene in lung cancer (non-small cell lung cancer) tissues and normal tissues. D. The expression distribution of gene in breast cancer tissues and normal tissues. The abscissa represents different genes, and the ordinate represents the expression distribution of miRNA (log2(normalized count+1)). Different colors represent distinct groups. The statistical difference of two groups was compared through the Welch's t-test, for details. For more detailed information, refer to Supplementary Table 18-19. *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001, ns, not significant. Abbreviation: GO, Gene Ontology. KEGG, Kyoto Encyclopedia of Genes and Genomes.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3842779/v1/cd9bb653c7f35146b969beca.png"},{"id":64361686,"identity":"2958cf67-4b09-4d4b-9327-0440b491b771","added_by":"auto","created_at":"2024-09-12 07:06:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1641932,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3842779/v1/21a933f2-489b-4e7c-8830-9d57acbe29af.pdf"},{"id":49641944,"identity":"d11f2bf0-f54b-4ba2-b3ef-e28b15b56ea8","added_by":"auto","created_at":"2024-01-15 19:21:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":629886,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor more information, please refer to the Supplementary Information section.\u003c/p\u003e","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-3842779/v1/c64e56762f670109ac34584a.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Association between psoriasis and risk of malignancy: observational and genetic investigations","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePsoriasis is an immune-system-mediated inflammatory skin disease that affects approximately 3% of the population\u003csup\u003e1\u003c/sup\u003e. Cancer has been reported to be the leading cause of death in patients with psoriasis\u003csup\u003e2\u003c/sup\u003e, but the association between psoriasis and the risk of site-specific cancers remains unclear. A meta-analysis that included cohort or case‒control studies indicated that psoriasis was associated with increased risk of developing 11 types of site-specific cancers, including colon, colorectal, kidney, laryngeal, liver, lymphoma, keratinocyte, esophageal, oral cavity, and pancreatic cancers and non-Hodgkin lymphoma\u003csup\u003e3\u003c/sup\u003e. However, another meta-analysis including cohort studies found that patients with psoriasis or psoriatic arthritis had higher risks of keratinocyte cancer, lymphoma, lung cancer and bladder cancer\u003csup\u003e4\u003c/sup\u003e. These inconsistent results might be ascribed, at least partially, to limited sample sizes, varied diagnostic criteria of psoriasis and insufficient adjustments for potential confounders.\u003c/p\u003e \u003cp\u003ePsoriasis has a significant hereditary predisposition\u003csup\u003e5\u003c/sup\u003e. Genome-wide association studies (GWASs) have identified several susceptibility loci of psoriasis involved in immune-mediated inflammatory disorders and innate/acquired host defense\u003csup\u003e6\u003c/sup\u003e, leading to a higher possibility of developing cancer for patients with psoriasis\u003csup\u003e1,7,8\u003c/sup\u003e. Although integrating genetic data into analyses of the polygenic risk score (PRS) or Mendelian randomization (MR) is an effective approach to explore causal effects on outcomes\u003csup\u003e9,10\u003c/sup\u003e, different MR studies lead to controversial results regarding the causal association between psoriasis and cancer\u003csup\u003e11\u0026ndash;13\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo date, systematic explorations based on a large-scale cohort with genetic data examining the relationship between psoriasis and the incidence of malignancy are lacking. In this study, we systematically explored associations between psoriasis and 89 types of cancers in a large population-based cohort. Then, we used PRS and MR analyses to investigate whether psoriasis is causally associated with site-specific cancers. The biological functions and expression levels of psoriasis-related genetic variants in cancers were further explored to uncover potential molecular correlations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Setting\u003c/h2\u003e \u003cp\u003eIn the current study, we conducted five analyses to investigate the causal relationship between psoriasis and site-specific cancers. Analysis 1 was designed as a prospective cohort study, and we used observational data from UK Biobank. Analysis 2 was a PRS study, and we used GWAS summary data from public datasets and genetic data from UK Biobank. Analysis 3 was a one-sample MR analysis, and we used genotype and phenotypic data from UK Biobank. Analysis 4 was a two-sample MR analysis, and we used GWAS summary data from public datasets. For analysis 5, expression quantitative trait locus (eQTL) and differential expression analyses between tumor samples and normal samples were evaluated based on Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) data.\u003c/p\u003e \u003cp\u003eWe used the phenome-wide association study (PheWAS) method to explore the association between all site-specific cancers and psoriasis (analysis 1) or the PRS of psoriasis (analysis 2). In analysis 3, we performed one-sample MR analysis including the site-specific cancer types with P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in PheWAS of analysis 1 or 2. Additionally, infrequent cancers, defined as those with fewer than 100 cases in the overall population or fewer than 50 cases in sex subgroups, were excluded from analysis 3. Two-sample MR analysis (analysis 4) was performed if the P value of the site-specific cancer type was \u0026lt;\u0026thinsp;0.05 in one-sample MR analysis. We performed analysis 5 for site-specific cancer types if their P value was \u0026lt;\u0026thinsp;0.05 in two-sample MR analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eUK Biobank\u003c/h2\u003e \u003cp\u003eUK Biobank is a prospective population-based cohort involving more than 500,000 participants aged between 40 and 70 years from the United Kingdom\u003csup\u003e14\u003c/sup\u003e. The National Information Governance Board for Health and Social Care and the National Health Service North West Multicenter Research Ethics Committee (reference 13/NW/0382) approved the UK Biobank ethical application. All participants provided informed consent through electronic signatures at the first assessment. This study was approved by UK Biobank Consortium. Information on psoriasis, cancer status (prevalent and incident cases), relevant confounding factors, and genetic data are available in UK Biobank. The current analysis was approved by UK Biobank in August 2020 with the ID 66536. We set the cohort baseline (2006\u0026ndash;2010) as our analysis baseline, and the end of the follow-up period was set as 2019.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eIn the UK Biobank, we included unrelated individuals of European ancestries who had a medical history and genetic data that passed quality control steps described previously\u003csup\u003e15\u003c/sup\u003e. We excluded participants if they were diagnosed with other autoimmune diseases that might potentially influence the association between psoriasis and cancers, including ulcerative colitis, Crohn's disease, multiple sclerosis, lupus erythematosus, systemic lupus erythematosus, dermatomyositis, pemphigus, pemphigoid, Behcet\u0026rsquo;s disease, rheumatoid arthritis, necrotizing vasculopathies and other systemic connective tissue disorders. We excluded participants who had been diagnosed with any type of cancer at baseline or before the diagnosis of psoriasis in analysis 1 and analysis 2. For PRS and MR analyses, we further excluded participants who were not white British and those who lacked genetic data. We excluded cancers with \u0026lt;\u0026thinsp;50 cases in the total population or \u0026lt;\u0026thinsp;25 cases in subgroups of women and men.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDiagnoses of psoriasis and cancers\u003c/h2\u003e \u003cp\u003eParticipants from UK Biobank were registered with the National Health Service (NHS) and agreed to link their medical records. UK Biobank tracks participants\u0026rsquo; electronic medical or health-related records, including hospital inpatient admissions and death. All disease types (including psoriasis and site-specific cancers) are recorded and analyzed according to the tenth version of the International Classification of Diseases code (ICD-10). Individual codes of psoriasis and site-specific cancers are summarized in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePRS of psoriasis\u003c/h2\u003e \u003cp\u003eWe extracted the genetic instruments from published GWAS summary data of European ancestry\u003csup\u003e6\u003c/sup\u003e. We selected single-nucleotide polymorphisms (SNPs) based on the following criteria: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) SNPs significantly associated with psoriasis (P\u0026thinsp;\u0026le;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) SNPs with minor allele frequencies\u0026thinsp;\u0026gt;\u0026thinsp;0.01; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) no insertion or deletion or ambiguous SNPs. Clump-based linkage disequilibrium pruning was performed with a physical distance threshold of 250 kb and an LD threshold (R2)\u0026thinsp;\u0026lt;\u0026thinsp;0.001. We identified a total of 74 SNPs and present their information in \u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e. The PRS of psoriasis was calculated as the sum of risk alleles of the 74 significantly associated SNPs. Variants were weighted by their effect size obtained from the primary GWAS. The weighted score was calculated as (BETA\u003csub\u003e1\u003c/sub\u003e\u0026times;SNP\u003csub\u003e1\u003c/sub\u003e)+(BETA\u003csub\u003e2\u003c/sub\u003e\u0026times;SNP\u003csub\u003e2\u003c/sub\u003e)+\u0026hellip;+(BETA\u003csub\u003en\u003c/sub\u003e\u0026times;SNP\u003csub\u003en\u003c/sub\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMR analysis\u003c/h2\u003e \u003cp\u003eWe used the PRS of psoriasis to conduct one-sample MR analysis using UK Biobank data. For two-sample MR analysis, we obtained summary-level data from public GWAS sources from FinnGen (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.finngen.fi/fi\u003c/span\u003e\u003cspan address=\"https://www.finngen.fi/fi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) via the IEU-OpenGWAS project (inquiry code: fnn-b-L12_PSORIASIS). Individuals from the FinnGen project were genotyped using Illumina and Affymetrix chip arrays. A total of 4510 psoriasis cases identified by ICD-10 code (L40) and 212,242 healthy controls were included. GWAS data for carcinoma in situ of the anus are derived from 107 European ancestry cases and 456,241 European ancestry controls\u003csup\u003e16\u003c/sup\u003e. GWAS data for breast cancer are derived from 76,192 female cases and 63,082 controls of European ancestry (IEU-OpenGWAS project inquiry code: ebi-a-GCST004988), and most SNPs were reported in a previous GWAS\u003csup\u003e17\u003c/sup\u003e. We obtained GWAS summary data for lung and kidney cancer from GWAS Catalog (inquiry code: GCST90011812, GCST90011818), which included 2,485 cases of lung cancer, 1,338 cases of kidney cancer (identified by ICD-9 or ICD-10 codes in UK biobank, and ICD-O-3 codes in Kaiser Permanente Genetic Epidemiology Research on Adult Health and Aging cohorts) and 410,350 healthy controls\u003csup\u003e18\u003c/sup\u003e. We obtained GWAS summary data of nonmelanoma skin cancer (NMSC) from the IEU-OpenGWAS project (inquiry code: ieu-b-4959), with sequencing data sourced from UK Biobank, which comprised 23,694 cases and 372,016 control individuals (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAnnotation and enrichment analysis\u003c/h2\u003e \u003cp\u003eWe annotated the 74 SNPs included in PRS analysis by using gProfiler (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biit.cs.ut.ee/gprofiler/\u003c/span\u003e\u003cspan address=\"http://biit.cs.ut.ee/gprofiler/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e19\u003c/sup\u003e and Variant Effect Predictor (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://useast.ensembl.org/info/docs/tools/vep/index.html\u003c/span\u003e\u003cspan address=\"https://useast.ensembl.org/info/docs/tools/vep/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e20\u003c/sup\u003e. The 74 SNPs were mapped to 50 genes (\u003cb\u003eSupplementary Table\u0026nbsp;17\u003c/b\u003e). To assess the potential biological functions of the mapped genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed in Metascape\u003csup\u003e21\u003c/sup\u003e and DAVID\u003csup\u003e22\u003c/sup\u003e, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eeQTL and TCGA\u003c/h2\u003e \u003cp\u003eWe used the Genotype-Tissue Expression (GTEx) project to conduct single-tissue expression quantitative trait locus (eQTL) analysis. The GTEx project is supported by the Common Fund of the Office of the Director of the National Institutes of Health and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. We acquired detailed data from the GTEx portal on 08/01/2023. We obtained gene expression RNA-seq data and clinical data from The Cancer Genome Atlas (TCGA) and GTEx RNA datasets, as accessed via the UCSC XENA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://xena.ucsc.edu/\u003c/span\u003e\u003cspan address=\"http://xena.ucsc.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e23\u003c/sup\u003e. The TCGA data were generated using the Illumina HiSeq 2000 RNA sequencing platform.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eCategorical variables are presented as percentages, and normally distributed continuous variables are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. We imputed missing data of covariates by multivariate imputation with a chained equations algorithm. A P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Data were analyzed with the use of R software, version 4.0.3. We conducted all statistical analyses on the supercomputer platform (inspur M5).\u003c/p\u003e \u003cp\u003ePheWAS for associations between psoriasis and incident site-specific cancers (the observational PheWAS) was estimated by Cox regression while adjusting for age, sex, smoking status, alcohol intake frequency and physical activity as potential confounders. We used PRSice software to calculate the PRS for each participant after summing the risk loci weighted by the effect size of the association for individual SNPs and psoriasis. PheWAS for associations between the PRS of psoriasis and site-specific cancer (the PRS PheWAS) was estimated by Cox regression while adjusting for age, sex, top 10 principal components, and the UK Biobank assessment center.\u003c/p\u003e \u003cp\u003eIn one-sample MR analysis, we implemented a Wald ratio method to estimate the causal effect of psoriasis on site-specific cancers. We determined the association between the PRS and psoriasis (βX|G), as well as the association between the PRS and site-specific cancers (βY|G), by using logistic regression adjusted for age, sex, and the top 10 principal components of the genetic information. The ratio estimate of the causal effect is calculated as βIV\u0026thinsp;=\u0026thinsp;βY|G/βX|G. We further conducted subgroup analyses by sex with the same methodology.\u003c/p\u003e \u003cp\u003eWe conducted two-sample MR analysis by using the \u0026lsquo;TwoSampleMR\u0026rsquo; package in R to extract instrumental variables with p\u0026thinsp;\u0026le;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e, and we then clumped these SNPs on the basis of the European ancestry reference panel with an R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 within a 10000-kb window. The SNPs used in the two-sample MR analysis are presented in \u003cb\u003eSupplementary Table\u0026nbsp;12 to Supplementary Table\u0026nbsp;16\u003c/b\u003e. We used 4 distinct methods to evaluate causal effects, namely, Mendelian randomization-Egger (MR‒Egger), weighted median, inverse variance weighted (IVW) and weighted mode. Each method makes different assumptions regarding the effectiveness of instrumental variables, and the IVW method is more reliable when there is less potential violation in MR assumptions. For sensitivity analysis, we used the MR-pleiotropy residual sum and outlier (MR-PRESSO) method to remove the potential influence of outliers\u003csup\u003e24\u003c/sup\u003e. We used Cochrane's Q test for IVW analyses and R\u0026uuml;cker's Q test for MR‒Egger analyses to assess heterogeneity. We used the MR‒Egger intercept method to test the horizontal pleiotropy of instrumental variables. We performed leave-one-out analyses using MR‒Egger or fixed effects inverse variance methods to investigate whether the overall results were driven by any individual variant\u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn eQTL analysis, cis-eQTLs were defined if the SNP was in a +/- 1 Mb cis window around the transcription start site. Significance was determined using a Q value threshold, and protein-coding genes with a P value\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e were selected for subsequent analysis. For TCGA analysis, all data were transformed by log2(x\u0026thinsp;+\u0026thinsp;1), where x represents RNA-Seq by expectation-maximization value. We compared gene expression in normal tissues from GTEx with various cancer types from TCGA, with Welch's t test for test any difference.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eFor analyses 1\u0026thinsp;~\u0026thinsp;3, a total of 13463 patients with psoriasis and 463136 participants without psoriasis were included, and their characteristics at baseline are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In analysis 4, we obtained GWAS summary data from public datasets, and the genotype and gene expression data in analysis 5 were collected from TCGA and GTEx datasets. We illustrate the flow chart for the study design in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The research question, data sources utilized, strengths and limitations of each analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the study design, research question, data sources utilized, and strengths and limitations of each methodological approach applied in the present study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy design\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResearch question\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData sources\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKey strengths\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKey limitations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnalysis 1: Observational PheWAS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAre patients with psoriasis associated with a higher risk of site-specific cancers?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndividual-level data from UK biobank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA large population-based dataset; low heterogeneity; prospective data collection; large availability of confounding data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eObservational design with unknown confounding factors; cannot assess causality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnalysis 2: PRS PheWAS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIs genetic susceptibility to psoriasis associated with elevated risk of site-specific cancers?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGWAS summary data from public datasets, and individual-level genotype and phenotype data from UK biobank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuantifies the latent genetic predisposition for psoriasis, regardless of clinical diagnosis; potentially mitigating exposure misclassification within the observational study; information on the outcome phenotype which is collected prospectively\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHorizontal pleiotropy; causal inference is inferior to Mendelian Randomization because of the pleiotropy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnalysis 3: One-sample MR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoes genetic predisposition to psoriasis have a causal impact on the risk of developing site-specific cancers?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndividual-level genotype and phenotype data from UK biobank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCausal effects can be determined; akin to a natural randomized controlled trial; minimally affected by measurement error, confounding factors, and reverse causality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFalse-positive results due to weak instruments\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnalysis 4: Two-sample MR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe same as one-sample MR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGWAS summary data from public datasets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe same as one-sample MR but more powerful and is less prone to false-positive bias\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInvestigation of subset of participants requires new GWAS to be performed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnalysis 5: eQTL and expression analysis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDo psoriasis-related SNPs potentially influence cancer development?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGenotype and gene expression data collected from TCGA and the GTEx datasets.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOffering mechanistic insights\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLack of causative links and clinical significance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviation: MR, Mendelian randomization. GWAS, Genome Wide Association Study. eQTL, expression quantitative trait loci. TCGA, The Cancer Genome Atlas. GTEx, Genotype-Tissue Expression\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of participants after exclusion of other autoimmune diseases.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-PSO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePSO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSMD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;463136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;13463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.9 (8.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.4 (7.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale(N,%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212843 (46.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6840 (50.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.4 (4.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.3 (5.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138 (18.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (18.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.12 (1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.20 (1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e256841 (55.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6001 (44.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158186 (34.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5428 (40.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48109 (10.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2034 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol intake frequency(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily or almost daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94455 (20.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2992 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThree or four times a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107973 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3086 (22.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce or twice a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120155 (25.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3314 (24.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOne to three times a month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51509 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1491 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecial occasions only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52594 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1520 (11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36450 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1060 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86288 (18.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2759 (20.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189459 (40.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5420 (40.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e187389 (40.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5284 (39.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviation: PSO, psoriasis. BMI, body mass index. SBP, systolic blood pressure. FBG, fasting blood glucose. SMD, standardized mean difference.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eObservational PheWAS\u003c/h2\u003e \u003cp\u003eIn observational PheWAS, psoriasis was significantly associated with a higher risk of breast cancer (ICD-10 code C50, hazard ratio [HR] 1.29, 95% CI 1.16 to 1.44), NMSC (C44, HR 1.19, 95% CI 1.09 to 1.29), PMS (malignant neoplasms of independent primary multiple sites, C97, HR 1.43, 95% CI 1.09 to 1.88), SLN (secondary and unspecified malignant neoplasm of lymph nodes, C77, HR 1.15, 95% CI 1.03 to 1.29), mouth cancer (C04, HR 2.73, 95% CI 1.09 to 6.88), and lung cancer (C34, HR 1.16, 95% CI 1.00 to 1.33) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn males, the association of psoriasis with lung cancer and NMSC remained consistent with the overall population. We further observed significantly higher risks of penile cancer (C60, HR 3.16, 95% CI 1.52 to 6.57), liver cancer (C22, HR 1.56, 95% CI, 1.07 to 2.27), bladder cancer (C67, HR 1.24, 95% CI 1.01 to 1.52), and Hodgkin's disease (C81, HR 2.00, 95% CI 1.02 to 3.94) among patients with psoriasis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). In females, risk of breast cancer was found to be significantly elevated (C50, HR 1.29, 95% CI 1.16 to 1.44). We further observed a higher risk of developing OIDO (malignant neoplasm of other and ill-defined digestive organs, C26, HR 3.27, 95% CI 1.71 to 6.25), SLN (C77, HR 1.23, 95% CI 1.06 to 1.43), anal canal cancer (C21, HR 2.33, 95% CI 1.26 to 4.30), palate cancer (C05, HR 3.94, 95% CI 1.38 to 11.24), corpus uteri cancer (C54, HR 1.35, 95% CI 1.04 to 1.77) and NMSC (C44, HR 1.15, 95% CI 1.01 to 1.32) among patients with psoriasis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). No association between psoriasis and risk of other site-specific cancers (not mentioned above) was observed in the total population or in each sex (\u003cb\u003eSupplementary Tables\u0026nbsp;4\u0026ndash;6\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePheWAS of psoriasis PRS\u003c/h2\u003e \u003cp\u003eA higher PRS of psoriasis was associated with an increased risk of kidney cancer (C64, HR 1.02, 95% CI 1.01 to 1.03), lung cancer (C34, HR 1.01, 95% CI 1.00 to 1.01) and breast cancer (C50, HR 1.01, 95% CI 1.00 to 1.01) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, \u003cb\u003eSupplementary Table\u0026nbsp;7\u003c/b\u003e). In males, a higher PRS of psoriasis was associated with an increased risk of kidney cancer (C64, HR 1.01, 95% CI 1.00 to 1.02) and lung cancer (C34, HR 1.02, 95% CI 1.01 to 1.04). In females, a higher PRS of psoriasis was associated with an increased risk of breast cancer in females (C50, HR 1.01, 95% CI 1.00 to 1.01) and a decreased risk of leukemia of unspecified cell type (C95, HR 0.89, 95% CI 0.83 to 0.96) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-F, \u003cb\u003eSupplementary Tables\u0026nbsp;8\u0026ndash;9\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eOne-sample MR\u003c/h2\u003e \u003cp\u003eIn one-sample MR analysis, genetically predicted psoriasis was significantly associated with anal canal cancer (C21, OR 1.38, 95% CI 1.01 to 1.87), lung cancer (C34, OR 1.12, 95% CI 1.03 to 1.21), and kidney cancer (C64, OR 1.25, 95% CI 1.09 to 1.42) in the total population (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In males, genetically predicted psoriasis was associated with a higher risk of lung cancer (C34, OR 1.17, 95% CI 1.04 to 1.32) and kidney cancer (C64, OR 1.34, 95% CI 1.13 to 1.58). In females, genetically predicted psoriasis was significantly associated with a higher risk of anal canal cancer (C21, OR 1.61, 95% CI 1.12 to 2.32), NMSC (C44, OR 1.07, 95% CI 1.01 to 1.14), and breast cancer (C50, OR 1.06, 95% CI 1.02 to 1.11). No causal relationship was observed between psoriasis and liver cancer, OIDO, corpus uteri cancer, penile cancer, bladder cancer, thyroid gland cancer, SLN, Hodgkin's disease or PMS.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOne-sample Mendelian randomization estimates of psoriasis on the risk for site-specific cancers.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCase/Total\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC21 Malignant neoplasm of anus and anal canal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale and Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e369/458587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.38 (1.01,1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124/209587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.56,1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e245/249000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.61 (1.12,2.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC22 Malignant neoplasm of liver and intrahepatic bile ducts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale and Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e973/458587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.83,1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e597/209587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10 (0.85,1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e376/249000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88 (0.66,1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC26 Malignant neoplasm of other and ill-defined digestive organs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale and Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e340/458587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19 (0.86,1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195/209587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10 (0.71,1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145/249000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30 (0.81,2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC34 Malignant neoplasm of bronchus and lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale and Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5228/458587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12 (1.03,1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2690/209587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17 (1.04,1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2538/249000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07 (0.95,1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC44 Other malignant neoplasms of skin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale and Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23072/458587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04 (0.99,1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12476/209587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.95,1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10596/249000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07 (1.01,1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC50 Malignant neoplasm of breast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale and Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16144/249000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (1.02,1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC54 Malignant neoplasm of corpus uteri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale and Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2222/249000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.85,1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC60 Malignant neoplasm of penis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale and Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114/209587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.61 (0.34,1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC64 Malignant neoplasm of kidney, except renal pelvis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale and Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2102/458587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.25 (1.09,1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1348/209587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34 (1.13,1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e754/249000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11 (0.91,1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC67 Malignant neoplasm of bladder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale and Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4092/458587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.91,1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3068/209587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.88,1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1024/249000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.86,1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC73 Malignant neoplasm of thyroid gland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale and Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e637/458587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88 (0.70,1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181/209587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (0.68,1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e456/249000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82 (0.63,1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC77 Secondary and unspecified malignant neoplasm of lymph nodes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale and Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11433/458587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04 (0.98,1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4587/209587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (0.96,1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6846/249000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.96,1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC81 Hodgkin's disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale and Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e426/458587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95 (0.71,1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e231/209587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90 (0.60,1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195/249000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.67,1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC97 Malignant neoplasms of primary multiple sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale and Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1335/458587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02 (0.87,1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e786/209587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02 (0.82,1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e549/249000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.80,1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviation: MR, Mendelian randomization. OR, Odds Ratio. CI, Confidence Interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTwo-sample MR analysis\u003c/h2\u003e \u003cp\u003eConsidering that two-sample MR is less prone to false-positive bias than one-sample MR analysis\u003csup\u003e26\u003c/sup\u003e, we validated the findings of one-sample MR analysis by using two-sample MR analysis, in which psoriasis was causally associated with breast cancer (IVW OR 1.02, 95% CI 1.01 to 1.03) and lung cancer (IVW OR 1.12, 95% CI 1.02 to 1.22) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). Pleiotropy robust methods (weighted median and weighted mode) showed results similar to those of the IVW method (\u003cb\u003eSupplementary Table\u0026nbsp;10\u003c/b\u003e). No outliers were identified by the MR-PRESSO method. We found no evidence of directional pleiotropy by using MR‒Egger intercepts (p\u0026thinsp;=\u0026thinsp;0.512 for breast cancer and p\u0026thinsp;=\u0026thinsp;0.815 for lung cancer). We observed no evidence of heterogeneity for the association between psoriasis and site-specific cancers (\u003cb\u003eSupplementary Table\u0026nbsp;11\u003c/b\u003e). In leave-one-out analysis, the main results remained robust when we removed individual SNPs from the main two-sample MR analysis (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e). We present additional visualizations of the causal effect of psoriasis on the risk of lung cancer and breast cancer in \u003cb\u003eSupplementary Figs.\u0026nbsp;2\u0026ndash;3\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eGene annotation for the molecular association between psoriasis and cancer\u003c/h2\u003e \u003cp\u003eWe performed gene annotation on the aforementioned 74 SNPs used in the study and subsequently conducted enrichment analysis on the related 50 genes. GO enrichment analysis revealed that the psoriasis-related genes (41 genes in the output) were enriched in \u0026lsquo;Response to type II interferon\u0026rsquo;, \u0026lsquo;Regulation of immune effector process\u0026rsquo;, \u0026lsquo;Regulation of response to biotic stimulus\u0026rsquo;, \u0026lsquo;Tumor necrosis factor-mediated signaling pathway\u0026rsquo;, and \u0026lsquo;Regulation of double-strand break repair via homologous recombination\u0026rsquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). KEGG pathway analysis (8 genes in the output) indicated that \u0026lsquo;IL-17 signaling pathway\u0026rsquo;, \u0026lsquo;NF-kappa B signaling pathway\u0026rsquo; and \u0026lsquo;cytokine‒cytokine receptor interaction\u0026rsquo; are involved in psoriasis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ecis-eQTL analysis revealed associations between psoriasis-associated SNPs and multiple genes in normal lung (\u003cb\u003eSupplementary Table\u0026nbsp;18\u003c/b\u003e), breast (\u003cb\u003eSupplementary Table\u0026nbsp;19\u003c/b\u003e) and kidney (\u003cb\u003eSupplementary Table\u0026nbsp;20\u003c/b\u003e) tissues. We then evaluated gene expression of those eQTL-related genes in TCGA and GTEx. Among 287 normal tissues and 1013 lung cancer tissues, we observed a marked increase in expression levels of ERAP1, ZFP57, HLA-DQB2, HLA-H, CCDC122 and CARD14 in lung cancer compared to normal tissues from GTEx (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Expression levels of MICB, C6orf3, PSORS1C2, HLA-B, HCP5B, ALDH8A1, HLA-V and SGSH were significantly lower in lung cancer tissues than in normal tissues from GTEx (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). When comparing breast cancer tissues (n\u0026thinsp;=\u0026thinsp;1099) to normal tissues (n\u0026thinsp;=\u0026thinsp;179), we observed significantly higher expression levels of ERAP1, MICB, HBS1L, STK19B, and HLA-DQB2 and significantly lower expression levels of C6orf3, PSORS1C2, HCP5B, ALDH8A1, HLA-V, CCDC122, and SGSH (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). No discernible difference in expression of C6orf3 between kidney cancer (n\u0026thinsp;=\u0026thinsp;886) and normal tissues (n\u0026thinsp;=\u0026thinsp;28) was observed (\u003cb\u003eSupplementary Table\u0026nbsp;23\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on unselected observational PheWAS analysis, we found psoriasis to be associated with higher risks of 14 types of cancer. In PRS PheWAS analysis, genetically predicted psoriasis was associated with breast cancer, kidney cancer and lung cancer. However, MR analysis only verified the causal relationship between psoriasis and lung cancer and breast cancer, and gene annotation indicated that psoriasis-related genes (such as ERAP1 and C6orf3) might be mediators linking psoriasis to lung or breast cancer. This is the first systematic analysis of the association between psoriasis and site-specific cancers based on a large population sample. Our data confirmed that psoriasis is causally associated with lung cancer and breast cancer, which indicates that regular screening for lung and breast cancer might be relevant for patients with psoriasis.\u003c/p\u003e\n\u003cp\u003eUsing unselected PheWAS analysis, our observational data not only confirmed previous reports that psoriasis is associated with higher risks of site-specific cancers in lung, kidney, liver, bladder, nonmelanoma skin,\u0026nbsp;oral cavity, lymph nodes and Hodgkin\u0026apos;s disease\u003csup\u003e3,4,7\u003c/sup\u003e but also revealed some unreported associations between psoriasis and cancers of the breast, penis, anal canal and corpus uteri. Furthermore, our genetic analysis (including PRS and MR analyses) confirmed the causal relationship between psoriasis and lung cancer/breast cancer. Previous meta-analyses have several shortcomings that may lead to biased results, including significant heterogeneity across the included studies, inconsistent diagnostic criteria for psoriasis, and insufficient adjustment for potential confounders linked to cancer. Hence, previous reports on the multiple sites of malignancy related to psoriasis\u0026nbsp;\u003csup\u003e3,4\u003c/sup\u003e should be interpreted cautiously. In our study, measurement errors, confounding factors, and false-positive bias were gradually controlled from observational PheWAS, PRS PheWAS, and one-sample analysis to two-sample MR analysis\u003csup\u003e26\u0026ndash;28\u003c/sup\u003e. Overall, the major concerns of malignancy for individuals with psoriasis are lung cancer and breast cancer.\u003c/p\u003e\n\u003cp\u003eTwo studies have performed two-sample MR analysis to investigate the association between psoriasis and lung cancer, but the results were inconsistent. Luo et al., based on data from UK Biobank (3,871 cases and 337,159 total), reported that psoriasis was causally associated with a 6% increased risk of lung cancer\u003csup\u003e12\u003c/sup\u003e. In contrast, Wang et al. showed no significant causal relationship between psoriasis and lung cancer, either with regard to subtypes of squamous cell lung cancer or pulmonary adenocarcinoma\u003csup\u003e11\u003c/sup\u003e. Such conflicting evidence may be attributed to the insufficiencies in methodology in MR analysis. Wang et al. used genetic variants with obvious heterogeneity\u003csup\u003e11\u003c/sup\u003e, and Luo et al. selected genetic datasets for psoriasis and lung cancer both from the same population (UK Biobank)\u003csup\u003e12\u003c/sup\u003e, which may influence the reliability and accuracy of results\u003csup\u003e29\u003c/sup\u003e. The causal association between psoriasis and breast cancer has not been explored, though risk of breast cancer was not reported to be increased among patients with psoriasis\u003csup\u003e3,4\u003c/sup\u003e. Compared to these two meta-analyses, we were able to identify an association between psoriasis and site-specific cancers with a relatively low degree of cohort heterogeneity. Both observational studies and genetic analyses provided consistent results, suggesting that psoriasis is a significant risk factor for breast or lung cancer.\u003c/p\u003e\n\u003cp\u003ePsoriasis is a chronic inflammatory disease, and the link between chronic inflammation and cancers has been reported in many studies\u003csup\u003e30,31\u003c/sup\u003e, which is similar to other immune-mediated inflammatory diseases, such as inflammatory bowel diseases, rheumatoid arthritis and sarcoidosis\u003csup\u003e32\u0026ndash;34\u003c/sup\u003e. On the other hand, psoriasis is also closely associated with immune dysfunction, and the proteins encoded by these susceptibility genes play important roles in immune and signaling pathways, especially interferon, tumor necrosis factor, the NF-kB pathway and the IL-23/Th17 axis\u003csup\u003e6,35\u003c/sup\u003e. These upregulated cytokines and activated pathways in psoriasis are also involved in the development of breast cancer. TNF-ɑ is reported to promote the growth and metastasis of breast cancer by activating the NF-kB pathway\u003csup\u003e36\u003c/sup\u003e. Excessive infiltration of Th-17 cells in the breast tumor microenvironment releases large amounts of IL17A and promotes the development of breast cancer\u003csup\u003e37\u003c/sup\u003e. By integrating public datasets of eQTL, TCGA and GTEx, our gene annotation preliminarily uncovered the molecular association between psoriasis and lung or breast cancer, highlighting the roles of chronic inflammation and immune dysfunction in the association. Further studies on the specific mechanisms of tumorigenesis in psoriasis are needed.\u003c/p\u003e\n\u003cp\u003eThe strength of our study is its comprehensive study design from PheWAS to MR analyses. Large-scale sample sizes and genomic data from UK Biobank and public datasets ensured that the causal relationship between psoriasis and breast/lung cancer was robust. There are also several limitations of the current study. First, the populations included were exclusively of European ancestry, and our findings may not be generalizable to other populations, including those of Asian or African ancestry. Second, the level of evidence provided by an MR analysis is second to randomized controlled trials. Future interventional studies that confirm a causal relationship between psoriasis and site-specific cancers will provide more compelling evidence.\u003c/p\u003e\n\u003cp\u003eIn conclusion, psoriasis is causally associated with lung cancer and breast cancer. However, other previously reported psoriasis-related cancers may not be major concerns of psoriasis patients. Our data support regular screening for lung cancer and breast cancer among patients with psoriasis.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe individual participant data collected for the current study cannot be shared without UK Biobank\u0026rsquo;s explicit written approval. Additional data corroborating the findings of this study can be obtained through a reasonable request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were analyzed using R software, version 4.0.3., with its core packages alongside the following supplementary packages: ggplot2, TwoSampleMR and MR-PRESSO.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJBH and AJC designed the study. Data analysis was done by RLL, WJL and PW. Data interpretation was done by RLL, WJL, XJC, QLZ, PW, JBH, SMY and AJC. Data was validated by QLZ and XJC. RLL, XJC and JBH wrote the first draft of the manuscript. SMY and AJC reviewed and edited the first draft of the manuscript. This report was approved for publication by all co-authors. The authors affirm the integrity and precision of the data and analyses. They had complete access to the study\u0026apos;s data and held ultimate responsibility for the decision to submit for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors have disclosed no conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe computing work in this paper was partly supported by the Supercomputing Center of Chongqing Medical University. The authors thank the participants and staff of the UK Biobank. This research has been conducted using the UK Biobank Resource under Application Number 66536. This work was supported by the National Natural Science Foundation of China (81874238 and 82270878).\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eLowes, M. 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B. \u003cem\u003eet al.\u003c/em\u003e IL-17-producing \u0026gamma;\u0026delta; T cells and neutrophils conspire to promote breast cancer metastasis. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e522\u003c/strong\u003e, 345\u0026ndash;348 (2015).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3842779/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3842779/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe relationship between psoriasis and site-specific cancers remains unclear. We aimed to investigate whether psoriasis is causally associated with site-specific cancers. We used observational and genetic data from UK Biobank. We obtained genome-wide association study (GWAS) summary data, expression quantitative trait locus (eQTL) analysis data, The Cancer Genome Atlas (TCGA) data and genotype-tissue expression (GTEx) data from public datasets. We used a phenome-wide association study (PheWAS), PRS analysis, and one-sample and two-sample Mendelian randomization (MR) analysis to investigate potential causal associations between psoriasis and cancers. We added gene annotation for potential molecular associations. A total of 13463 patients with psoriasis and 463136 participants without psoriasis were included. In unselected PheWAS analysis, psoriasis was associated with higher risks of 14 types of cancer. In one-sample MR analyses, genetically predicted psoriasis was associated with higher risks of anal canal cancer (hazard ratio [HR] 1.61, 95% CI 1.12\u0026ndash;2.32), breast cancer (HR 1.06, 95% CI 1.02\u0026ndash;1.11) and nonmelanoma skin cancer (HR 1.07, 95% CI 1.01\u0026ndash;1.14) in women and lung cancer (HR 1.17, 95% CI 1.04\u0026ndash;1.32) and kidney cancer (HR 1.34, 95% CI 1.13\u0026ndash;1.58) in men. Two-sample MR analysis indicated that psoriasis was causally associated with breast cancer (inverse variance weighted [IVW] odds ratio 1.02, 95% CI 1.01\u0026ndash;1.03) and lung cancer (IVW odds ratio 1.12, 95% CI 1.02\u0026ndash;1.22). Gene annotation revealed that psoriasis-related genes (such as ERAP1 and C6orf3) were significantly changed in lung and breast cancer tissues. Our findings demonstrate psoriasis is causally associated with lung cancer and breast cancer. Regular screening for lung and breast cancer might be relevant for patients with psoriasis.\u003c/p\u003e","manuscriptTitle":"Association between psoriasis and risk of malignancy: observational and genetic investigations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-15 19:21:03","doi":"10.21203/rs.3.rs-3842779/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2c38a178-9a16-4947-97e0-47b099c5d7c3","owner":[],"postedDate":"January 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":28114490,"name":"Health sciences/Diseases/Immunological disorders/Inflammatory diseases/Psoriasis"},{"id":28114491,"name":"Health sciences/Risk factors"},{"id":28114492,"name":"Health sciences/Health care/Public health/Epidemiology"}],"tags":[],"updatedAt":"2024-09-12T07:06:48+00:00","versionOfRecord":{"articleIdentity":"rs-3842779","link":"https://doi.org/10.1038/s41467-024-51824-6","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2024-09-11 04:00:00","publishedOnDateReadable":"September 11th, 2024"},"versionCreatedAt":"2024-01-15 19:21:03","video":"","vorDoi":"10.1038/s41467-024-51824-6","vorDoiUrl":"https://doi.org/10.1038/s41467-024-51824-6","workflowStages":[]},"version":"v1","identity":"rs-3842779","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3842779","identity":"rs-3842779","version":["v1"]},"buildId":"zQwnuV7TCBrMSSSToR1PI","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}