Decoding the Genetic Links Between Substance Use Disorder and Cancer Vulnerability

Decoding the Genetic Links Between Substance Use Disorder and Cancer Vulnerability | 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 Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Decoding the Genetic Links Between Substance Use Disorder and Cancer Vulnerability Xin Su, Xiaoyan Mo, Jun Kan, Bei Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3463220/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Cancer is a leading cause of mortality and morbidity globally and burdens public health heavily. Cannabis and opioids are promising applications for cancer pain management. However, due to their widespread abuse and addiction potential, they have become the focus of public health attention. They may have critical long-term health effects, raising concerns about their possible association with cancer risk. However, their relationship with cancer vulnerability is highly controversial. This Mendelian randomization (MR) study aimed to investigate the causal relationship of cannabis use disorder (CUD) and opioids use disorder (OUD) on cancer vulnerability. Methods Two-sample MR study using summary statistics from genome-wide association studies (GWAS), FinnGen, and UK Biobank. The primary method was inverse-variance weighted (IVW), and we included a range of sensitivity analyses to assess the robustness of the findings. Findings: We found the IVW results showed a causal association between OUD and bladder cancer (OR = 1.040, 95% CI 1.004–1.078, P = 0.029, adj. P = 0.125), acute myeloid leukemia (OR = 0.931, 95% CI 0.885–0.978, P = 0.005, adj. P = 0.061) and ovarian cancer (OR = 0.937, 95% CI 0.891–0.984, P = 0.010, adj. P = 0.064). Sensitivity analysis is directionally consistent with IVW. In the reverse MR analysis, none of the methods produced statistically significant proof of a connection between OUD and three cancers (all P > 0.05). However, OUD did not prove a genetic causal relationship with other cancers ( P > 0.05). We found no relevant evidence of a statistically significant potential causal effect of CUD on cancers ( P > 0.05). Summary: This study suggests that OUD may be causally linked to bladder, AML, and ovarian cancer, which needs to be further evaluated in extensive population studies. Cannabis use disorder opioids use disorder cancer Mendelian randomization carcinogenicity Figures Figure 1 Figure 2 1. Introduction Cancer, a formidable adversary to human health and well-being, stands as a leading cause of mortality and morbidity globally, imposing a profound burden on individuals, families, and healthcare systems[ 1 , 2 ]. The etiology of cancer is complex and multifaceted, with many factors contributing to its development, including genetic predispositions, environmental exposures, lifestyle choices, and various carcinogens[ 3 , 4 ]. Among the well-established risk factors are tobacco use, excessive alcohol consumption, exposure to environmental pollutants, and dietary habits[ 5 – 7 ]. Substance use disorders (SUDs) encompass a diverse array of conditions characterized by the recurrent and compulsive use of substances, leading to adverse consequences in various aspects of an individual's life[ 8 ]. The numbers for SUDs are large, and we need to pay attention to them. Within the context of SUD, two essential substances have been the subject of much research and attention - cannabis and opioids. Data from the 2018 National Survey on Drug Use and Health estimate that 4.4 million people have a cannabis use disorder (CUD), and 2 million people have opioids use disorder (OUD). The main active constituents of cannabis are delta 9-tetrahydrocannabinol (THC) and cannabidiol (CBD), which have unique physiological and psychological effects. THC is known for its psychoactive properties, inducing euphoria and altering sensory perception. At the same time, CBD has various potential medicinal properties, including possible soothing effects on a wide range of disorders such as anxiety, depression, and epilepsy, and its products are primarily mediated by affecting the endocannabinoid system[ 9 , 10 ]. Understanding the impact of cannabis use on cancer vulnerability is of increasing interest and importance, especially given the changing legal landscape and the growing popularity of cannabis consumption. Notably, cannabis and its active compounds, THC and CBD, are being explored for their potential in controlling cancer-related symptoms, including pain, nausea, and loss of appetite, making them promising candidates for cancer pain treatment[ 11 , 12 ]. However, there is an ongoing controversy between cannabis use and cancer vulnerability due to inconsistency and complexity in research[ 13 ]. On the other hand, opioids, including prescription painkillers such as morphine and oxycodone, as well as illicit substances such as heroin, have come to the forefront of public health concerns due to their widespread abuse and addiction potential. These drugs are highly effective in treating severe pain (especially in cancer patients)[ 14 ]. The opioid crisis has highlighted critical questions about the potential long-term health consequences of opioid use, raising concerns about their plausible association with cancer vulnerability[ 15 ]. The significance of investigating cannabis and opioids as exposure factors in this study lies in their prevalent use and promising role in cancer pain management. However, their controversial association with cancer vulnerability underscores the urgent need to thoroughly investigate and analyze their potential impact, making them an essential focus for understanding the complex relationship between substance use and cancer outcomes. Mendelian Randomization (MR) is an analytical approach that utilizes genetic data to depict causal relationships[ 16 ]. Through meticulous employment of genetic tools and advanced statistical methods, this study aims to assess the genetic determinants of cannabis use and opioid consumption from a genetic perspective. We utilize the MR framework to disentangle the intricate relationship between these substances and cancer vulnerability. This innovative methodology allows us to explore the potential causal effects of cannabis and opioids on susceptibility to cancer. This unique perspective offers valuable scientific insights into the ongoing discussion surrounding substance use and its potential impact on vulnerability to cancer, providing nuanced input for public health strategies. 2. Methods 2.1 Study design We systematically evaluated the causal relationship between CUD or OUD and the risk of 20 types of cancer using a two-sample MR design. MR is a novel statistical method for causal inference widely used to study risk factors for human disease-related phenotypes[ 17 ]. As is widely acknowledged, randomized controlled trials (RCTs) are a recognized method for establishing cause-and-effect relationships[ 18 ]. However, initiating RCTs is often challenging for various reasons, including substantial time and labor investments, ethical compliance, etc. In the absence of RCTs, MR serves as an alternative approach for causal inference[ 19 ]. In the MR framework, single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS) are used as instrumental variables (IVs) to proxy the exposure of interest[ 20 ]. The random allocation of genetic variants during meiosis makes the MR design a natural mimicry of RCT, reducing the potential for confounding[ 21 ]. The inherent advantages of the MR method make it less susceptible to reverse causation and residual confounding. A sound MR design should adhere to three core assumptions: (1) a close association between the IVs and the exposure; (2) the IVs are unrelated to confounding factors; (3) the IVs affect the outcome solely through the exposure of interest. Among these, the second and third assumptions are collectively known as the independence of pleiotropy and can be tested using various statistical methods. An overview of the study is presented in Fig. 1 . This study used publicly available de-identified data from participant studies that an ethical standards committee concerning human experimentation approved. No separate ethical approval was required in this study. 2.1 Data sources for substance use disorder To thoroughly investigate the causal relationship between CUD and cancer, this study acquired the most extensive summary statistics from a GWAS on cannabis use published to date, focusing on a European ancestry population[ 22 ]. This large sample GWAS analysis included 357,806 participants (14,080 cases and 343,726 controls) and a total of 18 samples: 16 samples from the Psychiatric Genomics Consortium Substance Use Disorders working group, one iPSYCH9 sample, and one deCODE sample. GWAS summary statistics for OUD were obtained from the latest UK Biobank data available in March 2023 ( http://www.nealelab.is/uk-biobank ). The UK Biobank is a prospective, population-based cohort with deep phenotyping and genomic data. Most genome-wide analyses of this dataset used only individuals of European ancestry and included more than 500,000 UK residents. It collects much information about lifestyle, body measurement data, and diseases[ 23 ]. Details of all exposure data included in the study are represented in Table 1 . Table 1 The detailed information on GWAS studies in the Mendelian randomization Traits Definition Participants SNPs Year Source Cannabis use disorder Binary phenotype (DSM-IV, DSM-5 or ICD- 10) 357,806 8,851,634 2020 A meta-analysis of GWAS Opioids use disorder Binary phenotype (ICD-10) 420,531 28,987,534 2023 UK Biobank AML Binary phenotype (ICD-10, ICD-9, ICD-8 or ICD-O-3) 287,367 20,167,489 2022 FinnGen Bladder cancer 289,190 20,167,610 2022 Breast cancer 172,982 20,158,910 2022 Brain cancer 287,901 20,167,595 2022 Cervical carcinoma 167,558 20,156,086 2022 CLL 287,737 20,167,519 2022 Colorectal cancer 293,646 20,167,784 2022 Corpus uteri cancer 169,156 20,156,299 2022 Esophageal cancer 287,703 20,167,516 2022 MNHE 289,268 20,167,585 2022 Hepatocellular carcinoma 287,690 20,167,509 2022 NSCLC 292,038 20,167,658 2022 NHL 288,065 20,167,533 2022 MNOA 287,916 20,167,518 2022 Ovarian cancer 168,214 20,156,158 2022 Prostatic cancer 133,164 20,145,564 2022 Pancreatic cancer 288,137 20,167,596 2022 Gastric cancer 288,444 20,167,627 2022 Testicular cancer 120,355 20,139,656 2022 Thyroid cancer 288,920 20,167,556 2022 SNPs, single nucleotide polymorphisms; GWAS, genome-wide association studies; EUR, European; AML, acute myeloid leukemia; CLL, chronic lymphocytic leukemia; NSCLC, Non-small cell lung cancer; NHL, Non-Hodgkin lymphoma; MNHE, Malignant neoplasm of head and neck; MNOA, Malignant neoplasm of oral cavity. Table 3 The heterogeneity and pleiotropy analysis of the causal association between cannabis use disorder and the risk of several types of cancer Cancer SNPs Egger Intercept test Cochran Q test P for MR-PRESSO Intercept P Q P AML 24 -0.023 0.696 16.269 0.844 0.865 Bladder cancer 24 0.010 0.622 19.376 0.679 0.691 Breast cancer 24 -0.003 0.697 24.323 0.386 0.416 Brain cancer 24 -0.006 0.853 20.410 0.618 0.628 Cervical carcinoma 24 0.027 0.567 16.644 0.826 0.833 CLL 24 -0.060 0.140 29.303 0.170 0.180 Colorectal cancer 23 0.003 0.840 12.180 0.954 0.957 Corpus uteri cancer 24 0.004 0.816 13.093 0.950 0.958 Esophageal cancer 24 0.013 0.766 28.701 0.190 0.180 MNHE 24 0.009 0.631 13.445 0.942 0.945 Hepatocellular carcinoma 24 -0.019 0.698 30.520 0.135 0.158 NSCLC 23 0.015 0.351 30.212 0.113 0.136 NHL 23 0.021 0.657 24.564 0.318 0.329 MNOA 24 0.010 0.752 10.212 0.990 0.991 Ovarian cancer 24 0.022 0.439 14.028 0.926 0.932 Pancreatic cancer 23 -0.043 0.237 23.114 0.395 0.405 Prostatic cancer 24 -0.002 0.880 29.986 0.150 0.169 Gastric cancer 24 0.010 0.683 21.597 0.545 0.525 Testicular cancer 23 -0.010 0.823 20.552 0.549 0.586 Thyroid cancer 24 0.024 0.260 20.488 0.612 0.652 2.2 Data sources for cancers This study encompassed 20 types of cancers as outcome data, namely acute myeloid leukemia (AML), bladder cancer, breast cancer, brain cancer, cervical carcinoma, chronic lymphocytic leukemia (CLL), colorectal cancer, corpus uteri cancer, esophageal cancer, malignant neoplasm of head and neck, hepatocellular carcinoma, non-small cell lung cancer, non-Hodgkin lymphoma, malignant neoplasm of the oral cavity, ovarian cancer, prostatic cancer, pancreatic cancer, gastric cancer, testicular cancer, thyroid cancer. The GWAS summary statistics of multiple types of cancers were publicly downloaded from the FinnGen study ( https://r9.finngen.fi/ ). The FinnGen study is a population-based prospective cohort study with over 300000 residents. FinnGen GWAS results (freeze 9; September 2023) have collected the number of disease endpoints (phenotypes) available for 2722 endpoints[ 24 ]. The two-sample MR analysis is based on two independent populations of the same ancestry[ 25 ]. If the cancer GWAS participants did not originate from European ancestry, such GWAS dataset will be eliminated. The above GWAS studies included the population of European origin only. This study has no duplicate populations for the exposure and outcome data. Details of all cancers are shown in Table 1 . 2.3 Selection of genetic instruments A series of steps were performed to select eligible genetic variants associated with metabolites. First, given the limited number of SNPs that reached genome-wide significance, and to obtain a more significant number of SNPs for subsequent MR analyses, a relatively loose P -value cutoff of 5E-06 was used to identify significant SNPs[ 26 ]. we set the clumping method using pairwise linkage disequilibrium (LD) R2 < 0.001 within a 10000 kb distance to obtain top independent SNPs[ 27 ]. To determine that IVs are not associated with confounding factors, we have conducted a study to identify pleiotropic SNPs in PhenoScanner V2 ( http://www.phenoscanner.medschl.cam.ac.uk/ ), excluding SNPs related to potential risk factors for cancers (e.g., smoking, alcohol consumption). The data of CUD cleared five SNPs (rs12975781, rs7783012, rs12122743, rs7459111, rs1178309). Also, to avoid bias due to weak instruments, F statistics were calculated for each SNP to measure statistical strength (F = beta 2 /se 2 )[ 28 ]. If F statistics were less than 10, SNPs were removed to ensure a strong correlation between SNPs and exposure[ 29 ]. We then extracted exposure SNPs from the outcome data and excluded SNPs correlated with the outcome data ( P < 5E-08). Coordination was then performed to align the alleles of the exposure and outcome SNPs and discard SNPs with incompatible alleles (e.g., A/G vs. A/C). Proxy-SNPs were not used for MR analysis. We used these selected SNPs as gene IVs for MR analysis. 2.4 Mendelian randomization analyses MR analysis can be performed when exposure and outcome data are harmonized. The inverse-variance weighting (IVW) method is the main analytical method used as the primary method for inferring causality because it takes into account heterogeneity in variant-specific causal estimates and can combine the effect values of multiple IVs into one estimate[ 30 ]. Other sensitivity analyses, including simple model, weighted model, weighted median, and MR-Egger regression, were further conducted to assess the robustness of the findings. These sensitivity analyses can provide more robust estimates on a broader range of situations, albeit with a wider confidence interval[ 31 , 32 ]. For each pair of “exposure-outcome”, MR-PRESSO and leave-one-out analysis were used to identify outliers and assess the effect of individual differences on the observed associations. In addition, we performed MR post-hoc analyses, including Cochran's Q test for heterogeneity and the MR Egger Intercept test for horizontal multidimensionality. This study conducted reverse MR analysis for meaningful data to verify whether the observed causal relationships were biased due to change causal relationships. A false-discovery rate was used to adjust the significance level thresholds to account for multiple testing. Thus, substantial evidence was suggested for adj. P < 0.05 and suggestive evidence of P < 0.05. The location of SNPs is based on the Genome Reference Consortium Human Build 37 (GRCh37). The statistical analyses were performed using “TwoSampleMR” (v.0.5.7) and “fdrtool” (v 1.2.17) in the R software package (v.4.3.1). The study was performed according to the STROBE MR guidelines[ 33 ]. This analysis was not pre-registered, and the results should be considered exploratory. 3. Results 3.1 Causal effects of CUD on cancer vulnerability Supplementary Table S1 -20 listed details of instrumental SNPs for CUD and their association with multiple cancers. Table 2 showed the main result of MR analysis of CUD on various types of cancers. The F-statistics of IVs varied from 21.03 to 29.42. Using IVs associated with CUD, we found no relevant evidence of CUD's statistically significant potential causal effect on cancers (all P > 0.05). Meanwhile, similar risk estimates were obtained using four other sensitivity analyses (simple model, weighted model, weighted median, and MR-Egger regression), confirming that the strength of the heritability association between the two was not statistically significant (all P > 0.05), as shown in Supplementary Table 42. For the robustness of the study, a post-MR analysis was also performed. All P values of the MR-Egger intercept tests were > 0.05, indicating that no horizontal pleiotropy existed. However, heterogeneity was observed in the Q test analysis of CUD and non-small cell lung cancer (Q = 38.728, P = 0.021). Although heterogeneity was detected in a single outcome, which did not affect the failure of the MR estimates as a random effects IVW in the study, which might balance the pooled heterogeneity[ 34 ]. MR-PRESSO and leave-one-out analysis ruled out the possibility that specific SNPs influenced the results. 3.2 Causal effect from OUD to cancers Supplementary Table 21–40 presented detailed information on IVs of OUD and their association with multiple cancers. Six to eight SNPs comprising the IVs were used for MR analysis with 20 cancers with f-statistics ranging from 20.88 to 29.73. Table 4 showed the results of IVW for OUD for various cancers. The primary MR analysis showed that OUD had a suggestive causal relationship with the risk of bladder cancer (IVW: OR = 1.040, 95% CI 1.004–1.078, P = 0.029, adj. P = 0.125). And a negative causal association between OUD and AML (IVW: OR = 0.931, 95% CI 0.885–0.978, P = 0.005, adj. P = 0.061) and ovarian cancer (IVW: OR = 0.937, 95% CI 0.891–0.984, P = 0.010, adj. P = 0.064). As shown in Fig. 2 , the other four sensitivity analyses' direction was consistent with that of IVW. In the reverse MR analysis, none of the MR analytics produced statistically significant proof of a connection between OUD and the susceptibility of three cancers (all P > 0.05). However, OUD did not prove a genetic causal relationship with other cancers, P > 0.05. As shown in Supplementary Table 43, similar results were obtained by applying the sensitivity analyses. MR-PRESSO and leave-one-out analysis ruled out the possibility that specific SNPs influenced this study. The stability of the results was likewise tested, including the Cochran Q test for heterogeneity and MR-egger regression for multiplicity. The Cochran Q test for the IVW model showed no heterogeneity in all MR results (Table 5 ). In addition, the results of our MR analyses did not show horizontal diversity due to the lack of statistical evidence for MR-egger regression (Table 5 ). Table 5 The heterogeneity and pleiotropy analysis of the causal association between opioids use disorder and the risk of several types of cancer Cancer SNPs Egger Intercept test Cochran Q test P for MR-PRESSO Intercept P Q P AML 8 -0.005 0.971 4.001 0.780 0.838 Bladder cancer 7 -0.030 0.635 2.670 0.856 0.870 Breast cancer 8 0.018 0.451 11.569 0.116 0.361 Brain cancer 8 0.020 0.844 11.581 0.115 0.338 Cervical carcinoma 7 0.129 0.390 4.203 0.649 0.968 CLL 7 -0.030 0.789 1.541 0.957 0.953 Colorectal cancer 8 -0.012 0.639 3.313 0.855 0.882 Corpus uteri cancer 7 -0.040 0.608 7.880 0.247 0.309 Esophageal cancer 8 -0.064 0.552 9.524 0.217 0.445 MNHE 8 0.038 0.409 6.429 0.491 0.659 Hepatocellular carcinoma 8 -0.111 0.280 7.002 0.429 0.574 NSCLC 7 0.027 0.539 6.126 0.409 0.476 NHL 7 0.026 0.778 2.395 0.880 0.901 MNOA 7 -0.027 0.843 9.293 0.158 0.205 Ovarian cancer 7 0.003 0.976 2.012 0.918 0.948 Pancreatic cancer 7 -0.014 0.857 5.611 0.468 0.511 Prostatic cancer 8 0.014 0.575 9.776 0.202 0.399 Gastric cancer 6 0.201 0.212 9.754 0.082 0.130 Testicular cancer 8 0.444 0.667 3.799 0.803 0.877 Thyroid cancer 7 0.031 0.642 3.181 0.786 0.835 4. Discussion The 20 cancers selected as outcomes in this Mendelian randomization study were chosen based on several criteria. Firstly, we considered cancers with notable prevalence and incidence in the population to ensure robust analysis. Based on the GLOBOCAN 2020 global cancer burden estimates compiled by the International Agency for Research on Cancer, we selected cancers with relatively high prevalence and incidence in the population, facilitating reliable analyses and meaningful interpretations of potential causal relationships[ 35 ]. Second, considering cannabis and opioid components, we included cancers reported to be biologically likely to be associated with exposure[ 36 – 38 ]. Then, several exhaustive literature reviews supported our selection, highlighting existing associations between substance abuse and these specific cancers[ 39 – 41 ]. In addition, we sought diversity, covering a variety of organ systems, such as the reproductive system and thyroid gland, to fully assess potential associations. The selected cancers represent a range of risk factors, allowing for consideration of confounding variables and genetic predisposition. Ultimately, these selections enable a comprehensive exploration of the potential causal relationship between SUD and cancer. We applied the two-sample MR method to comprehensively evaluate the causal effect of CUD on susceptibility to a wide range of cancers using a two-sample MR method. Although no clear evidence was found to support a causal development of genetically predicted CUD on cancer susceptibility, we found a causal relationship between OUD and bladder cancer, AML, and ovarian cancer. As the scientific and public communities grapple with the evolving understanding of cancer causation, recent decades have witnessed a shift in societal attitudes towards cannabis, and cannabis use has been viewed as a promising alternative treatment for chronic cancer pain[ 42 , 43 ]. A randomized, placebo-controlled phase II clinical trial confirmed that THC: CBD cannabis extract significantly increased the complete remission rate of refractory chemotherapy-induced nausea and vomiting after chemotherapy compared with standard antiemetics[ 44 ]. Despite evidence supporting the use of cannabis for symptom management in cancer, there is a lack of rigorous evidence on the safety of cannabis[ 43 , 45 ]. Whether cannabis use is associated with cancer susceptibility, especially in a CUD state when access is being facilitated, should be explored in depth. Extensive epidemiological studies have elucidated the association between cannabis use and cancer vulnerability. Reece et al. used U.S. cancer data, drug exposure data, and congenital malformation data to explore the association of cannabis use with hereditary cancers. They found an association between marijuana exposure and five cancers (thyroid, liver, breast, pancreatic, and AML)[ 46 ]. The data from our study did not formally associate CUD with these cancers, which seemed to suggest that cannabis use was genotoxic and would likely have significant effects over multiple generations and across generations. A meta-analysis reported low-strength evidence that cannabis use was associated with developing testicular germ cell tumors; its association with other cancers was unknown[ 47 ]. For their part, Volkow et al. suggested that the effect of long-term cannabis use (the equivalent of 30 or more joint years) on the risk of developing cancer is ambiguous. Long-term cannabis use was associated with an increased incidence of several upper gastrointestinal cancers and lung cancer; however, this association disappeared after adjusting for potential confounders such as smoking[ 48 ]. This is inconsistent with our MR analysis report, and we have also observed conflicting findings in reproductive system tumors and cancers. According to population data analysis from the United States, limited statistical evidence links cannabis use to developing testicular germ cell tumors in the U.S. without solid support for causation[ 49 ]. Another study targeting the European population found no evidence to suggest a significant relationship between the lifetime use of marijuana and the subsequent development of testicular germ cell tumors[ 50 ], and our research results also support this conclusion. These investigations have yielded a discordant landscape of findings, leaving critical questions unanswered. The inconsistency between MR analysis and epidemiologic studies may stem from potential confounding factors and selective bias in real-world settings. Additionally, regulatory restrictions and legal discrepancies regarding the use, possession, and research of cannabis impede comprehensive studies on the benefits of cannabis for cancer treatment. These factors limit the accumulation of reliable standardized data. The relationship between opioids and cancer susceptibility is a complex and growing area of research. Several clinical studies have reported the possible impact of long-term opioid use on cancer vulnerability and progression. One study found that opioid use may be an undetermined risk factor for the increased incidence of cancer in the United States[ 51 ]. This causal link was not observed in the European population in the study. Alcala's team[ 52 ] assessed the burden of opium use and its combination with smoking on cancer in areas where opioid consumption is prevalent. They conducted a prospective cohort study involving more than 50,000 Iranian adults and followed participants for approximately 14 years. The study found that opioid use alone was associated with an increased risk of developing all combined cancers, especially bladder cancers. A working group of the International Agency for Research on Cancer (IARC) evaluated the carcinogenicity of opium and found it to be carcinogenic to bladder cancer. This finding was similarly confirmed by an additional meta-analysis based on this work[ 53 , 54 ]. Our study also validated this finding based on a European population. It had also been demonstrated in in vitro and in vivo experiments that opioids increased the migratory activity of bladder cancer cells by a mechanism that was related to the rearrangement of the actin cytoskeleton in a non-opioid receptor-mediated manner[ 55 ]. This study observed a suggestive negative correlation between OUD and AML and ovarian cancer. Some preclinical studies have proposed multiple molecular mechanisms by which opioids fight cancer. An early study confirmed that the therapeutic opioid methadone inhibits the proliferation of leukemia cells and induces cell death by inducing apoptosis. In addition, methadone overcomes doxorubicin resistance in leukemia cells by activating mitochondria[ 56 ]. A study reported that fentanyl inhibited the growth and colony formation of AML-differentiated cells and directed progenitor cells without affecting their survival. Fentanyl may inhibit AML cells via opioid receptor-independent suppression of Ras and STAT5 pathways and is synergistic with the chemotherapeutic agent cytarabine[ 57 ]. Kim et al. found that opioid growth factor receptors are negative regulators of cell proliferation and are binding sites for morphine, participating in morphine-induced inhibition of adenocarcinoma growth[ 58 ]. Our study also demonstrated the multifaceted nature of cancer and the multilevel pharmacologic effects of opioids, making them present opposed effects in different cancer contexts. It is worth noting that while various hypotheses and associations exist, establishing a clear causal relationship between opioid use and cancer susceptibility is challenging. 5. Advantages and limitations There are several advantages to this study. First, using an MR design, our analysis can simulate a randomized controlled trial in an observational setting. Randomized contrast features are widely recognized in causality but are costly to implement in practice. However, MR studies can effectively avoid the confounding bias of randomly assigned SNPs at conception. Second, our findings may influence treatment strategies for cancer pain. Given the elevated prevalence of SUD and cancer in the general population, revealing the causal relationship between SUD and cancer could influence current prevention and intervention strategies for cancer pain. Our findings suggest enhanced cancer screening in patients with genetically predicted OUD may be useful. More attention should be paid to revealing the association of environmentally determined CUD with cancer and its prognosis. However, our study suffers from several limitations. First, this study utilized a pooled analysis of publicly available GWAS datasets. More in-depth subgroup analyses were not possible due to the unavailability of detailed raw data on participants. Second, to minimize potential population-related bias, this study was limited to GWAS datasets specific to the European population, and the consistency of our described findings in other populations remains to be investigated. Finally, eliminating the effect of horizontal pleiotropy in MR studies is challenging. Although analytical methods such as PhenoScanner and leave-one-out analysis were used, which suggests that our findings are robust to horizontal pleiotropy, the possibility of bias cannot be ignored entirely. 6. Conclusion This is the first MR study to explore the causality of SUD on cancer vulnerability. This study suggests a suggestive causal relationship between OUD and the vulnerability of bladder cancer. In addition, the results of this study show a suggestive negative correlation between OUD and AML and ovarian cancer, respectively. Our MR analysis does not support the hypothesis that CUD could increase cancer vulnerability. These outcomes underscore the need for further investigation and a nuanced understanding of the relationships between substance use and specific cancer types. Declarations Availability of data and materials The original contributions presented in the study are included in the article/Additional file, further inquiries can be directed to the corresponding author. Acknowledgements This work was supported by the Key Research and Development Program of Guangzhou Science and Technology Bureau (Grant No. 202206080012) and National Natural Science Foundation of China (Grant No. 82104951). Declaration of interests None to declare. Author contributions Xin Su: Data curation; formal analysis; investigation; writing-original draft and editing. Jun Kan: methodology; visualization. FanYang: Conceptualization; resources and writing-review. Xiaoyan Mo: Resources; soft-ware; supervision; validation. Bei Zhang: Supervision; investigation and editing. Xin Su and Xiaoyan Mo contributed equally to this work. 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Paice JA, Bohlke K, Barton D, Craig DS, El-Jawahri A, Hershman DL, Kong LR, Kurita GP, LeBlanc TW, Mercadante S, et al: Use of Opioids for Adults With Pain From Cancer or Cancer Treatment: ASCO Guideline. J Clin Oncol 2023, 41: 914-930. Tripolt S, Neubauer HA, Knab VM, Elmer DP, Aberger F, Moriggl R, Fux DA: Opioids drive breast cancer metastasis through the δ-opioid receptor and oncogenic STAT3. Neoplasia 2021, 23: 270-279. Yavorska OO, Burgess S: MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol 2017, 46: 1734-1739. Birney E: Mendelian Randomization. Cold Spring Harb Perspect Med 2022, 12 . Hariton E, Locascio JJ: Randomised controlled trials - the gold standard for effectiveness research: Study design: randomised controlled trials. Bjog 2018, 125: 1716. Sanderson E, Glymour MM, Holmes MV, Kang H, Morrison J, Munafò MR, Palmer T, Schooling CM, Wallace C, Zhao Q, Smith GD: Mendelian randomization. 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Kurki MI, Karjalainen J, Palta P, Sipilä TP, Kristiansson K, Donner KM, Reeve MP, Laivuori H, Aavikko M, Kaunisto MA, et al: FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 2023, 613: 508-518. Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G: Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med 2008, 27: 1133-1163. Zeng R, Wang J, Zheng C, Jiang R, Tong S, Wu H, Zhuo Z, Yang Q, Leung FW, Sha W, Chen H: Lack of Causal Associations of Inflammatory Bowel Disease with Parkinson's Disease and Other Neurodegenerative Disorders. Mov Disord 2023, 38: 1082-1088. Wang X, Zhao C, Feng H, Li G, He L, Yang L, Liang Y, Tan X, Xu Y, Cui R, et al: Associations of Insomnia With Insulin Resistance Traits: A Cross-sectional and Mendelian Randomization Study. J Clin Endocrinol Metab 2023, 108: e574-e582. Wang Z, Li S, Tan D, Abudourexiti W, Yu Z, Zhang T, Ding C, Gong J: Association between inflammatory bowel disease and periodontitis: A bidirectional two-sample Mendelian randomization study. J Clin Periodontol 2023, 50: 736-743. Chen L, Yang H, Li H, He C, Yang L, Lv G: Insights into modifiable risk factors of cholelithiasis: A Mendelian randomization study. Hepatology 2022, 75: 785-796. Xiang Y, Zhang C, Wang J, Cheng Y, Wang L, Tong Y, Yan D: Identification of host gene-microbiome associations in colorectal cancer patients using mendelian randomization. J Transl Med 2023, 21: 535. Burgess S, Thompson SG: Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol 2017, 32: 377-389. Bowden J, Davey Smith G, Haycock PC, Burgess S: Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol 2016, 40: 304-314. Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, VanderWeele TJ, Higgins JPT, Timpson NJ, Dimou N, et al: Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. Jama 2021, 326: 1614-1621. Chen X, Hong X, Gao W, Luo S, Cai J, Liu G, Huang Y: Causal relationship between physical activity, leisure sedentary behaviors and COVID-19 risk: a Mendelian randomization study. J Transl Med 2022, 20: 216. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021, 71: 209-249. Hart S, Fischer OM, Ullrich A: Cannabinoids induce cancer cell proliferation via tumor necrosis factor alpha-converting enzyme (TACE/ADAM17)-mediated transactivation of the epidermal growth factor receptor. Cancer Res 2004, 64: 1943-1950. Zagon IS, McLaughlin PJ: Duration of opiate receptor blockade determines tumorigenic response in mice with neuroblastoma: a role for endogenous opioid systems in cancer. Life Sci 1984, 35: 409-416. Mathew B, Lennon FE, Siegler J, Mirzapoiazova T, Mambetsariev N, Sammani S, Gerhold LM, LaRiviere PJ, Chen CT, Garcia JG, et al: The novel role of the mu opioid receptor in lung cancer progression: a laboratory investigation. Anesth Analg 2011, 112: 558-567. Irrera N, Bitto A, Sant'Antonio E, Lauro R, Musolino C, Allegra A: Pros and Cons of the Cannabinoid System in Cancer: Focus on Hematological Malignancies. Molecules 2021, 26 . Creanga-Murariu I, Filipiuc LE, Cuciureanu M, Tamba BI, Alexa-Stratulat T: Should oncologists trust cannabinoids? Front Pharmacol 2023, 14: 1211506. Lennon FE, Moss J, Singleton PA: The μ-opioid receptor in cancer progression: is there a direct effect? Anesthesiology 2012, 116: 940-945. Elser H, Humphreys K, Kiang MV, Mehta S, Yoon JH, Faustman WO, Matthay EC: State Cannabis Legalization and Psychosis-Related Health Care Utilization. JAMA Netw Open 2023, 6: e2252689. Wang L, Hong PJ, May C, Rehman Y, Oparin Y, Hong CJ, Hong BY, AminiLari M, Gallo L, Kaushal A, et al: Medical cannabis or cannabinoids for chronic non-cancer and cancer related pain: a systematic review and meta-analysis of randomised clinical trials. Bmj 2021, 374: n1034. Grimison P, Mersiades A, Kirby A, Lintzeris N, Morton R, Haber P, Olver I, Walsh A, McGregor I, Cheung Y, et al: Oral THC:CBD cannabis extract for refractory chemotherapy-induced nausea and vomiting: a randomised, placebo-controlled, phase II crossover trial. Ann Oncol 2020, 31: 1553-1560. Chhabra M, Ben-Eltriki M, Paul A, Lê ML, Herbert A, Oberoi S, Bradford N, Bowers A, Rassekh SR, Kelly LE: Cannabinoids for symptom management in children with cancer: A systematic review and meta-analysis. Cancer 2023. Reece AS, Hulse GK: Epidemiological overview of multidimensional chromosomal and genome toxicity of cannabis exposure in congenital anomalies and cancer development. Sci Rep 2021, 11: 13892. Ghasemiesfe M, Barrow B, Leonard S, Keyhani S, Korenstein D: Association Between Marijuana Use and Risk of Cancer: A Systematic Review and Meta-analysis. JAMA Netw Open 2019, 2: e1916318. Volkow ND, Baler RD, Compton WM, Weiss SR: Adverse health effects of marijuana use. N Engl J Med 2014, 370: 2219-2227. Lacson JC, Carroll JD, Tuazon E, Castelao EJ, Bernstein L, Cortessis VK: Population-based case-control study of recreational drug use and testis cancer risk confirms an association between marijuana use and nonseminoma risk. Cancer 2012, 118: 5374-5383. Callaghan RC, Allebeck P, Akre O, McGlynn KA, Sidorchuk A: Cannabis Use and Incidence of Testicular Cancer: A 42-Year Follow-up of Swedish Men between 1970 and 2011. Cancer Epidemiol Biomarkers Prev 2017, 26: 1644-1652. Barlass U, Deshmukh A, Beck T, Bishehsari F: Opioid use as a potential risk factor for pancreatic cancer in the United States: An analysis of state and national level databases. PLoS One 2021, 16: e0244285. Alcala K, Poustchi H, Viallon V, Islami F, Pourshams A, Sadjadi A, Nemati S, Khoshnia M, Gharavi A, Roshandel G, et al: Incident cancers attributable to using opium and smoking cigarettes in the Golestan cohort study. EClinicalMedicine 2023, 64: 102229. M. Filho A, Turner MC, Warnakulasuriya S, Richardson DB, Hosseini B, Kamangar F, Pourshams A, Sewram V, Cronin-Fenton D, Etemadi A: The carcinogenicity of opium consumption: a systematic review and meta-analysis. European Journal of Epidemiology 2023, 38: 373-389. A MF, Turner MC, Warnakulasuriya S, Richardson DB, Hosseini B, Kamangar F, Pourshams A, Sewram V, Cronin-Fenton D, Etemadi A, et al: The carcinogenicity of opium consumption: a systematic review and meta-analysis. Eur J Epidemiol 2023, 38: 373-389. Vassou D, Notas G, Hatzoglou A, Castanas E, Kampa M: Opioids increase bladder cancer cell migration via bradykinin B2 receptors. Int J Oncol 2011, 39: 697-707. Friesen C, Roscher M, Alt A, Miltner E: Methadone, commonly used as maintenance medication for outpatient treatment of opioid dependence, kills leukemia cells and overcomes chemoresistance. Cancer Res 2008, 68: 6059-6064. Dai S, Zhang X, Zhang P, Zheng X, Pang Q: Fentanyl inhibits acute myeloid leukemia differentiated cells and committed progenitors via opioid receptor-independent suppression of Ras and STAT5 pathways. Fundam Clin Pharmacol 2021, 35: 174-183. Kim JY, Ahn HJ, Kim JK, Kim J, Lee SH, Chae HB: Morphine Suppresses Lung Cancer Cell Proliferation Through the Interaction with Opioid Growth Factor Receptor: An In Vitro and Human Lung Tissue Study. Anesth Analg 2016, 123: 1429-1436. Table 2 and 4 Table 2 and 4 are available in the Supplementary Files section. Supplementary Files TableS1.docx Additional file 1: Table S1 STROBE-MR checklist of recommended items to address in reports of Mendelian randomization study TableS221.xlsx Additional file 2: Table S2-21 Association of the instrumental SNPs of CUD on cancers. AML, acute myeloid leukemia;CLL, chronic lymphocytic leukemia; NHL, non-Hodgkin’s lymphoma. TableS2241.xlsx Additional file 3: Table S22-41 Association of the instrumental SNPs of OUD on cancers. TableS4243.docx Additional file 3: Table S42-43 The sensitivity analyses between SUD and several types of cancer. Table2and4.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board 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-3463220","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":242004974,"identity":"dc72c326-183a-44ef-b588-701131b63b7a","order_by":0,"name":"Xin Su","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACNmb+D4Yf/9TI2bc3HyBOCx97g0GxZMMxYwOeYwnEaZHjOWDwgbeBOXGDRI4BkQ6TSEjcILmDjXE7z5mPN94w2MnpNhDWctig8IwMs2V772bLOQzJxmYHCGpJbDOQYGNjYzhzdps0D8OBxG2EtSSz/+BhY+ZhuJHzjEgtPMcYDHjbmCUMbuSwEamFvYfBWOLMMQPJnmPGlnMMiPCLfDMPg+GHipr6fvbmhzfeVNjJEdSCAiR4iIwaZC2k6hgFo2AUjIIRAQBEOkAxrVtjnwAAAABJRU5ErkJggg==","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Su","suffix":""},{"id":242004975,"identity":"83f96db6-1a8f-47fe-a410-372c732c089f","order_by":1,"name":"Xiaoyan Mo","email":"","orcid":"","institution":"Sun Yat-sen University Affiliated Tumor Hospital: Sun Yat-sen University Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Mo","suffix":""},{"id":242004976,"identity":"d02e781f-9a19-4d71-b8fd-4309f2a1a676","order_by":2,"name":"Jun Kan","email":"","orcid":"","institution":"Sun Yat-Sen University Cancer Prevention and Treatment Center: Sun Yat-sen University Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Kan","suffix":""},{"id":242004977,"identity":"e1cee28f-d497-4022-a61c-d9783d8d8446","order_by":3,"name":"Bei Zhang","email":"","orcid":"","institution":"Zhongshan University Cancer Prevention and Treatment Center: Sun Yat-sen University Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bei","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2023-10-18 17:19:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3463220/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3463220/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":45333671,"identity":"1c64b746-68d8-42e3-a66d-88c65902f5b4","added_by":"auto","created_at":"2023-10-27 17:33:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":404463,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of the current Mendelian randomization (MR) study.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMR, Mendelian randomization; MR‐PRESSO, Mendelian randomization Pleiotropy RESidual Sum and Outlier; SNPs, single nucleotide polymorphisms; LD, linkage disequilibrium. Created with BioRender.com\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3463220/v1/311ba46543d74ea2f67bcf19.png"},{"id":45333676,"identity":"8fd1f378-4613-40b6-9133-53eca4c54445","added_by":"auto","created_at":"2023-10-27 17:33:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":178553,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot and Forest plot for the significant Mendelian randomization association. (A) scatterplot for the significant association between OUD and bladder cancer; (B) scatterplot for the significant association between OUD and AML; (C) scatterplot for the significant association between OUD and ovarian cancer; (D) forest plot of SNPs associated with OUD and their risk of bladder cancer after outliers removal; (E) forest plot of SNPs associated with OUD and their risk of AML after outliers removal; (F) forest plot of SNPs associated with OUD and their risk of ovarian cancer after outliers removal. AML, acute myeloid leukemia; OUD, opioids use disorder.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3463220/v1/3887b9c2cd196975adb22cfa.png"},{"id":48899456,"identity":"072a358d-9bf8-4d51-8382-731dc2ef4250","added_by":"auto","created_at":"2023-12-28 09:45:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2142592,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3463220/v1/bd22f782-236c-4b8b-bd9e-6a5a52ce7105.pdf"},{"id":45333674,"identity":"5df2aabc-a4fc-4ebd-94fb-5d52f48ace42","added_by":"auto","created_at":"2023-10-27 17:33:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":31611,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1: Table S1\u003c/p\u003e\n\u003cp\u003eSTROBE-MR checklist of recommended items to address in reports of Mendelian randomization study\u003c/p\u003e","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3463220/v1/66918b6098461e56b76a25ee.docx"},{"id":45333675,"identity":"4f9a2ebf-044e-4f42-8aae-0a6c11b6cb4c","added_by":"auto","created_at":"2023-10-27 17:33:45","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":84428,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2: Table S2-21\u003c/p\u003e\n\u003cp\u003eAssociation of the instrumental SNPs of CUD on cancers.\u003c/p\u003e\n\u003cp\u003eAML, acute myeloid leukemia;CLL, chronic lymphocytic leukemia; NHL, non-Hodgkin’s lymphoma.\u003c/p\u003e","description":"","filename":"TableS221.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3463220/v1/e7cba7dfa6170631ddc3355e.xlsx"},{"id":45333672,"identity":"c72e6798-8ed2-49ac-ab4b-b38155cc13ae","added_by":"auto","created_at":"2023-10-27 17:33:45","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":46069,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 3: Table S22-41\u003c/p\u003e\n\u003cp\u003eAssociation of the instrumental SNPs of OUD on cancers.\u003c/p\u003e","description":"","filename":"TableS2241.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3463220/v1/0e7ba482004d631ce09d8794.xlsx"},{"id":45333673,"identity":"3f1faaa8-ebf1-4594-a260-6991b88c7234","added_by":"auto","created_at":"2023-10-27 17:33:45","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":20520,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 3: Table S42-43\u003c/p\u003e\n\u003cp\u003eThe sensitivity analyses between SUD and several types of cancer.\u003c/p\u003e","description":"","filename":"TableS4243.docx","url":"https://assets-eu.researchsquare.com/files/rs-3463220/v1/16f0daaad9c3586e0a365b8c.docx"},{"id":45333677,"identity":"d8156130-70e5-4793-b1cd-57f6bd1600f6","added_by":"auto","created_at":"2023-10-27 17:33:45","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":205936,"visible":true,"origin":"","legend":"","description":"","filename":"Table2and4.docx","url":"https://assets-eu.researchsquare.com/files/rs-3463220/v1/547c27c3a4389cf3018697dd.docx"}],"financialInterests":"","formattedTitle":"Decoding the Genetic Links Between Substance Use Disorder and Cancer Vulnerability","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCancer, a formidable adversary to human health and well-being, stands as a leading cause of mortality and morbidity globally, imposing a profound burden on individuals, families, and healthcare systems[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The etiology of cancer is complex and multifaceted, with many factors contributing to its development, including genetic predispositions, environmental exposures, lifestyle choices, and various carcinogens[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Among the well-established risk factors are tobacco use, excessive alcohol consumption, exposure to environmental pollutants, and dietary habits[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSubstance use disorders (SUDs) encompass a diverse array of conditions characterized by the recurrent and compulsive use of substances, leading to adverse consequences in various aspects of an individual's life[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The numbers for SUDs are large, and we need to pay attention to them. Within the context of SUD, two essential substances have been the subject of much research and attention - cannabis and opioids. Data from the 2018 National Survey on Drug Use and Health estimate that 4.4\u0026nbsp;million people have a cannabis use disorder (CUD), and 2\u0026nbsp;million people have opioids use disorder (OUD). The main active constituents of cannabis are delta 9-tetrahydrocannabinol (THC) and cannabidiol (CBD), which have unique physiological and psychological effects. THC is known for its psychoactive properties, inducing euphoria and altering sensory perception. At the same time, CBD has various potential medicinal properties, including possible soothing effects on a wide range of disorders such as anxiety, depression, and epilepsy, and its products are primarily mediated by affecting the endocannabinoid system[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Understanding the impact of cannabis use on cancer vulnerability is of increasing interest and importance, especially given the changing legal landscape and the growing popularity of cannabis consumption. Notably, cannabis and its active compounds, THC and CBD, are being explored for their potential in controlling cancer-related symptoms, including pain, nausea, and loss of appetite, making them promising candidates for cancer pain treatment[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, there is an ongoing controversy between cannabis use and cancer vulnerability due to inconsistency and complexity in research[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. On the other hand, opioids, including prescription painkillers such as morphine and oxycodone, as well as illicit substances such as heroin, have come to the forefront of public health concerns due to their widespread abuse and addiction potential. These drugs are highly effective in treating severe pain (especially in cancer patients)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The opioid crisis has highlighted critical questions about the potential long-term health consequences of opioid use, raising concerns about their plausible association with cancer vulnerability[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe significance of investigating cannabis and opioids as exposure factors in this study lies in their prevalent use and promising role in cancer pain management. However, their controversial association with cancer vulnerability underscores the urgent need to thoroughly investigate and analyze their potential impact, making them an essential focus for understanding the complex relationship between substance use and cancer outcomes.\u003c/p\u003e \u003cp\u003eMendelian Randomization (MR) is an analytical approach that utilizes genetic data to depict causal relationships[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Through meticulous employment of genetic tools and advanced statistical methods, this study aims to assess the genetic determinants of cannabis use and opioid consumption from a genetic perspective. We utilize the MR framework to disentangle the intricate relationship between these substances and cancer vulnerability. This innovative methodology allows us to explore the potential causal effects of cannabis and opioids on susceptibility to cancer. This unique perspective offers valuable scientific insights into the ongoing discussion surrounding substance use and its potential impact on vulnerability to cancer, providing nuanced input for public health strategies.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Study design\u003c/h2\u003e\n \u003cp\u003eWe systematically evaluated the causal relationship between CUD or OUD and the risk of 20 types of cancer using a two-sample MR design. MR is a novel statistical method for causal inference widely used to study risk factors for human disease-related phenotypes[\u003cspan\u003e17\u003c/span\u003e]. As is widely acknowledged, randomized controlled trials (RCTs) are a recognized method for establishing cause-and-effect relationships[\u003cspan\u003e18\u003c/span\u003e]. However, initiating RCTs is often challenging for various reasons, including substantial time and labor investments, ethical compliance, etc. In the absence of RCTs, MR serves as an alternative approach for causal inference[\u003cspan\u003e19\u003c/span\u003e]. In the MR framework, single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS) are used as instrumental variables (IVs) to proxy the exposure of interest[\u003cspan\u003e20\u003c/span\u003e]. The random allocation of genetic variants during meiosis makes the MR design a natural mimicry of RCT, reducing the potential for confounding[\u003cspan\u003e21\u003c/span\u003e]. The inherent advantages of the MR method make it less susceptible to reverse causation and residual confounding. A sound MR design should adhere to three core assumptions: (1) a close association between the IVs and the exposure; (2) the IVs are unrelated to confounding factors; (3) the IVs affect the outcome solely through the exposure of interest. Among these, the second and third assumptions are collectively known as the independence of pleiotropy and can be tested using various statistical methods. An overview of the study is presented in Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e. This study used publicly available de-identified data from participant studies that an ethical standards committee concerning human experimentation approved. No separate ethical approval was required in this study.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.1 Data sources for substance use disorder\u003c/h2\u003e\n \u003cp\u003eTo thoroughly investigate the causal relationship between CUD and cancer, this study acquired the most extensive summary statistics from a GWAS on cannabis use published to date, focusing on a European ancestry population[\u003cspan\u003e22\u003c/span\u003e]. This large sample GWAS analysis included 357,806 participants (14,080 cases and 343,726 controls) and a total of 18 samples: 16 samples from the Psychiatric Genomics Consortium Substance Use Disorders working group, one iPSYCH9 sample, and one deCODE sample. GWAS summary statistics for OUD were obtained from the latest UK Biobank data available in March 2023 (\u003cspan\u003e\u003cspan\u003ehttp://www.nealelab.is/uk-biobank\u003c/span\u003e\u003c/span\u003e). The UK Biobank is a prospective, population-based cohort with deep phenotyping and genomic data. Most genome-wide analyses of this dataset used only individuals of European ancestry and included more than 500,000 UK residents. It collects much information about lifestyle, body measurement data, and diseases[\u003cspan\u003e23\u003c/span\u003e]. Details of all exposure data included in the study are represented in Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe detailed information on GWAS studies in the Mendelian randomization\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTraits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDefinition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParticipants\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSNPs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCannabis use disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBinary phenotype\u003c/p\u003e\n \u003cp\u003e(DSM-IV, DSM-5 or ICD- 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e357,806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,851,634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA meta-analysis of GWAS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOpioids use disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBinary phenotype\u003c/p\u003e\n \u003cp\u003e(ICD-10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e420,531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28,987,534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUK Biobank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAML\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"20\"\u003e\n \u003cp\u003eBinary phenotype (ICD-10, ICD-9, ICD-8 or ICD-O-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e287,367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20,167,489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"20\"\u003e\n \u003cp\u003eFinnGen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBladder cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e289,190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20,167,610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e172,982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20,158,910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrain cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e287,901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20,167,595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCervical carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e167,558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20,156,086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCLL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e287,737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20,167,519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eColorectal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e293,646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20,167,784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCorpus uteri cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e169,156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20,156,299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEsophageal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e287,703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20,167,516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMNHE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e289,268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20,167,585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHepatocellular carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e287,690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20,167,509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNSCLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e292,038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20,167,658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNHL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e288,065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20,167,533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMNOA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e287,916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20,167,518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOvarian cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e168,214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20,156,158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProstatic cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e133,164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20,145,564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePancreatic cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e288,137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20,167,596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGastric cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e288,444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20,167,627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTesticular cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120,355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20,139,656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThyroid cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e288,920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20,167,556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eSNPs, single nucleotide polymorphisms; GWAS, genome-wide association studies; EUR, European; AML, acute myeloid leukemia; CLL, chronic lymphocytic leukemia; NSCLC, Non-small cell lung cancer; NHL, Non-Hodgkin lymphoma; MNHE, Malignant neoplasm of head and neck; MNOA, Malignant neoplasm of oral cavity.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe heterogeneity and pleiotropy analysis of the causal association between cannabis use disorder and the risk of several types of cancer\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSNPs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEgger Intercept test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCochran Q test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for MR-PRESSO\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAML\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBladder cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.691\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrain cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.628\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCervical carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCLL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eColorectal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.957\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCorpus uteri cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEsophageal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMNHE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHepatocellular carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNSCLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNHL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMNOA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOvarian cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePancreatic cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProstatic cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGastric cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTesticular cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThyroid cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.652\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.2 Data sources for cancers\u003c/h2\u003e\n \u003cp\u003eThis study encompassed 20 types of cancers as outcome data, namely acute myeloid leukemia (AML), bladder cancer, breast cancer, brain cancer, cervical carcinoma, chronic lymphocytic leukemia (CLL), colorectal cancer, corpus uteri cancer, esophageal cancer, malignant neoplasm of head and neck, hepatocellular carcinoma, non-small cell lung cancer, non-Hodgkin lymphoma, malignant neoplasm of the oral cavity, ovarian cancer, prostatic cancer, pancreatic cancer, gastric cancer, testicular cancer, thyroid cancer. The GWAS summary statistics of multiple types of cancers were publicly downloaded from the FinnGen study (\u003cspan\u003e\u003cspan\u003ehttps://r9.finngen.fi/\u003c/span\u003e\u003c/span\u003e). The FinnGen study is a population-based prospective cohort study with over 300000 residents. FinnGen GWAS results (freeze 9; September 2023) have collected the number of disease endpoints (phenotypes) available for 2722 endpoints[\u003cspan\u003e24\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe two-sample MR analysis is based on two independent populations of the same ancestry[\u003cspan\u003e25\u003c/span\u003e]. If the cancer GWAS participants did not originate from European ancestry, such GWAS dataset will be eliminated. The above GWAS studies included the population of European origin only. This study has no duplicate populations for the exposure and outcome data. Details of all cancers are shown in Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.3 Selection of genetic instruments\u003c/h2\u003e\n \u003cp\u003eA series of steps were performed to select eligible genetic variants associated with metabolites. First, given the limited number of SNPs that reached genome-wide significance, and to obtain a more significant number of SNPs for subsequent MR analyses, a relatively loose \u003cem\u003eP\u003c/em\u003e-value cutoff of 5E-06 was used to identify significant SNPs[\u003cspan\u003e26\u003c/span\u003e]. we set the clumping method using pairwise linkage disequilibrium (LD) R2\u0026thinsp;\u0026lt;\u0026thinsp;0.001 within a 10000 kb distance to obtain top independent SNPs[\u003cspan\u003e27\u003c/span\u003e]. To determine that IVs are not associated with confounding factors, we have conducted a study to identify pleiotropic SNPs in PhenoScanner V2 (\u003cspan\u003e\u003cspan\u003ehttp://www.phenoscanner.medschl.cam.ac.uk/\u003c/span\u003e\u003c/span\u003e), excluding SNPs related to potential risk factors for cancers (e.g., smoking, alcohol consumption). The data of CUD cleared five SNPs (rs12975781, rs7783012, rs12122743, rs7459111, rs1178309). Also, to avoid bias due to weak instruments, F statistics were calculated for each SNP to measure statistical strength (F\u0026thinsp;=\u0026thinsp;beta\u003csup\u003e2\u003c/sup\u003e/se\u003csup\u003e2\u003c/sup\u003e)[\u003cspan\u003e28\u003c/span\u003e]. If F statistics were less than 10, SNPs were removed to ensure a strong correlation between SNPs and exposure[\u003cspan\u003e29\u003c/span\u003e]. We then extracted exposure SNPs from the outcome data and excluded SNPs correlated with the outcome data (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5E-08). Coordination was then performed to align the alleles of the exposure and outcome SNPs and discard SNPs with incompatible alleles (e.g., A/G vs. A/C). Proxy-SNPs were not used for MR analysis. We used these selected SNPs as gene IVs for MR analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e2.4 Mendelian randomization analyses\u003c/h2\u003e\n \u003cp\u003eMR analysis can be performed when exposure and outcome data are harmonized. The inverse-variance weighting (IVW) method is the main analytical method used as the primary method for inferring causality because it takes into account heterogeneity in variant-specific causal estimates and can combine the effect values of multiple IVs into one estimate[\u003cspan\u003e30\u003c/span\u003e]. Other sensitivity analyses, including simple model, weighted model, weighted median, and MR-Egger regression, were further conducted to assess the robustness of the findings. These sensitivity analyses can provide more robust estimates on a broader range of situations, albeit with a wider confidence interval[\u003cspan\u003e31\u003c/span\u003e, \u003cspan\u003e32\u003c/span\u003e]. For each pair of \u0026ldquo;exposure-outcome\u0026rdquo;, MR-PRESSO and leave-one-out analysis were used to identify outliers and assess the effect of individual differences on the observed associations. In addition, we performed MR post-hoc analyses, including Cochran\u0026apos;s Q test for heterogeneity and the MR Egger Intercept test for horizontal multidimensionality. This study conducted reverse MR analysis for meaningful data to verify whether the observed causal relationships were biased due to change causal relationships. A false-discovery rate was used to adjust the significance level thresholds to account for multiple testing. Thus, substantial evidence was suggested for adj. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and suggestive evidence of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The location of SNPs is based on the Genome Reference Consortium Human Build 37 (GRCh37). The statistical analyses were performed using \u0026ldquo;TwoSampleMR\u0026rdquo; (v.0.5.7) and \u0026ldquo;fdrtool\u0026rdquo; (v 1.2.17) in the R software package (v.4.3.1). The study was performed according to the STROBE MR guidelines[\u003cspan\u003e33\u003c/span\u003e]. This analysis was not pre-registered, and the results should be considered exploratory.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Causal effects of CUD on cancer vulnerability\u003c/h2\u003e \u003cp\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-20 listed details of instrumental SNPs for CUD and their association with multiple cancers. Table\u0026nbsp;2 showed the main result of MR analysis of CUD on various types of cancers. The F-statistics of IVs varied from 21.03 to 29.42. Using IVs associated with CUD, we found no relevant evidence of CUD's statistically significant potential causal effect on cancers (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Meanwhile, similar risk estimates were obtained using four other sensitivity analyses (simple model, weighted model, weighted median, and MR-Egger regression), confirming that the strength of the heritability association between the two was not statistically significant (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), as shown in Supplementary Table\u0026nbsp;42. For the robustness of the study, a post-MR analysis was also performed. All \u003cem\u003eP\u003c/em\u003e values of the MR-Egger intercept tests were \u0026gt;\u0026thinsp;0.05, indicating that no horizontal pleiotropy existed. However, heterogeneity was observed in the Q test analysis of CUD and non-small cell lung cancer (Q\u0026thinsp;=\u0026thinsp;38.728, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021). Although heterogeneity was detected in a single outcome, which did not affect the failure of the MR estimates as a random effects IVW in the study, which might balance the pooled heterogeneity[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. MR-PRESSO and leave-one-out analysis ruled out the possibility that specific SNPs influenced the results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Causal effect from OUD to cancers\u003c/h2\u003e \u003cp\u003eSupplementary Table\u0026nbsp;21\u0026ndash;40 presented detailed information on IVs of OUD and their association with multiple cancers. Six to eight SNPs comprising the IVs were used for MR analysis with 20 cancers with f-statistics ranging from 20.88 to 29.73. Table\u0026nbsp;4 showed the results of IVW for OUD for various cancers. The primary MR analysis showed that OUD had a suggestive causal relationship with the risk of bladder cancer (IVW: OR\u0026thinsp;=\u0026thinsp;1.040, 95% CI 1.004\u0026ndash;1.078, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029, adj. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.125). And a negative causal association between OUD and AML (IVW: OR\u0026thinsp;=\u0026thinsp;0.931, 95% CI 0.885\u0026ndash;0.978, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005, adj. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.061) and ovarian cancer (IVW: OR\u0026thinsp;=\u0026thinsp;0.937, 95% CI 0.891\u0026ndash;0.984, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010, adj. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.064). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the other four sensitivity analyses' direction was consistent with that of IVW. In the reverse MR analysis, none of the MR analytics produced statistically significant proof of a connection between OUD and the susceptibility of three cancers (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, OUD did not prove a genetic causal relationship with other cancers, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05. As shown in Supplementary Table\u0026nbsp;43, similar results were obtained by applying the sensitivity analyses. MR-PRESSO and leave-one-out analysis ruled out the possibility that specific SNPs influenced this study. The stability of the results was likewise tested, including the Cochran Q test for heterogeneity and MR-egger regression for multiplicity. The Cochran Q test for the IVW model showed no heterogeneity in all MR results (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In addition, the results of our MR analyses did not show horizontal diversity due to the lack of statistical evidence for MR-egger regression (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \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 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe heterogeneity and pleiotropy analysis of the causal association between opioids use disorder and the risk of several types of cancer\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eEgger Intercept test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eCochran Q test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for MR-PRESSO\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBladder cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCervical carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCLL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColorectal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorpus uteri cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEsophageal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMNHE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatocellular carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMNOA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOvarian cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePancreatic cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProstatic cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastric cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTesticular cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroid cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe 20 cancers selected as outcomes in this Mendelian randomization study were chosen based on several criteria. Firstly, we considered cancers with notable prevalence and incidence in the population to ensure robust analysis. Based on the GLOBOCAN 2020 global cancer burden estimates compiled by the International Agency for Research on Cancer, we selected cancers with relatively high prevalence and incidence in the population, facilitating reliable analyses and meaningful interpretations of potential causal relationships[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Second, considering cannabis and opioid components, we included cancers reported to be biologically likely to be associated with exposure[\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Then, several exhaustive literature reviews supported our selection, highlighting existing associations between substance abuse and these specific cancers[\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In addition, we sought diversity, covering a variety of organ systems, such as the reproductive system and thyroid gland, to fully assess potential associations. The selected cancers represent a range of risk factors, allowing for consideration of confounding variables and genetic predisposition. Ultimately, these selections enable a comprehensive exploration of the potential causal relationship between SUD and cancer.\u003c/p\u003e \u003cp\u003eWe applied the two-sample MR method to comprehensively evaluate the causal effect of CUD on susceptibility to a wide range of cancers using a two-sample MR method. Although no clear evidence was found to support a causal development of genetically predicted CUD on cancer susceptibility, we found a causal relationship between OUD and bladder cancer, AML, and ovarian cancer.\u003c/p\u003e \u003cp\u003eAs the scientific and public communities grapple with the evolving understanding of cancer causation, recent decades have witnessed a shift in societal attitudes towards cannabis, and cannabis use has been viewed as a promising alternative treatment for chronic cancer pain[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. A randomized, placebo-controlled phase II clinical trial confirmed that THC: CBD cannabis extract significantly increased the complete remission rate of refractory chemotherapy-induced nausea and vomiting after chemotherapy compared with standard antiemetics[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Despite evidence supporting the use of cannabis for symptom management in cancer, there is a lack of rigorous evidence on the safety of cannabis[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Whether cannabis use is associated with cancer susceptibility, especially in a CUD state when access is being facilitated, should be explored in depth. Extensive epidemiological studies have elucidated the association between cannabis use and cancer vulnerability. Reece et al. used U.S. cancer data, drug exposure data, and congenital malformation data to explore the association of cannabis use with hereditary cancers. They found an association between marijuana exposure and five cancers (thyroid, liver, breast, pancreatic, and AML)[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The data from our study did not formally associate CUD with these cancers, which seemed to suggest that cannabis use was genotoxic and would likely have significant effects over multiple generations and across generations. A meta-analysis reported low-strength evidence that cannabis use was associated with developing testicular germ cell tumors; its association with other cancers was unknown[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. For their part, Volkow et al. suggested that the effect of long-term cannabis use (the equivalent of 30 or more joint years) on the risk of developing cancer is ambiguous. Long-term cannabis use was associated with an increased incidence of several upper gastrointestinal cancers and lung cancer; however, this association disappeared after adjusting for potential confounders such as smoking[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This is inconsistent with our MR analysis report, and we have also observed conflicting findings in reproductive system tumors and cancers. According to population data analysis from the United States, limited statistical evidence links cannabis use to developing testicular germ cell tumors in the U.S. without solid support for causation[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Another study targeting the European population found no evidence to suggest a significant relationship between the lifetime use of marijuana and the subsequent development of testicular germ cell tumors[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], and our research results also support this conclusion. These investigations have yielded a discordant landscape of findings, leaving critical questions unanswered. The inconsistency between MR analysis and epidemiologic studies may stem from potential confounding factors and selective bias in real-world settings. Additionally, regulatory restrictions and legal discrepancies regarding the use, possession, and research of cannabis impede comprehensive studies on the benefits of cannabis for cancer treatment. These factors limit the accumulation of reliable standardized data.\u003c/p\u003e \u003cp\u003eThe relationship between opioids and cancer susceptibility is a complex and growing area of research. Several clinical studies have reported the possible impact of long-term opioid use on cancer vulnerability and progression. One study found that opioid use may be an undetermined risk factor for the increased incidence of cancer in the United States[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. This causal link was not observed in the European population in the study. Alcala's team[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] assessed the burden of opium use and its combination with smoking on cancer in areas where opioid consumption is prevalent. They conducted a prospective cohort study involving more than 50,000 Iranian adults and followed participants for approximately 14 years. The study found that opioid use alone was associated with an increased risk of developing all combined cancers, especially bladder cancers. A working group of the International Agency for Research on Cancer (IARC) evaluated the carcinogenicity of opium and found it to be carcinogenic to bladder cancer. This finding was similarly confirmed by an additional meta-analysis based on this work[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Our study also validated this finding based on a European population. It had also been demonstrated in \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e experiments that opioids increased the migratory activity of bladder cancer cells by a mechanism that was related to the rearrangement of the actin cytoskeleton in a non-opioid receptor-mediated manner[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. This study observed a suggestive negative correlation between OUD and AML and ovarian cancer.\u003c/p\u003e \u003cp\u003eSome preclinical studies have proposed multiple molecular mechanisms by which opioids fight cancer. An early study confirmed that the therapeutic opioid methadone inhibits the proliferation of leukemia cells and induces cell death by inducing apoptosis. In addition, methadone overcomes doxorubicin resistance in leukemia cells by activating mitochondria[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. A study reported that fentanyl inhibited the growth and colony formation of AML-differentiated cells and directed progenitor cells without affecting their survival. Fentanyl may inhibit AML cells via opioid receptor-independent suppression of Ras and STAT5 pathways and is synergistic with the chemotherapeutic agent cytarabine[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Kim et al. found that opioid growth factor receptors are negative regulators of cell proliferation and are binding sites for morphine, participating in morphine-induced inhibition of adenocarcinoma growth[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Our study also demonstrated the multifaceted nature of cancer and the multilevel pharmacologic effects of opioids, making them present opposed effects in different cancer contexts. It is worth noting that while various hypotheses and associations exist, establishing a clear causal relationship between opioid use and cancer susceptibility is challenging.\u003c/p\u003e"},{"header":"5. Advantages and limitations","content":"\u003cp\u003eThere are several advantages to this study. First, using an MR design, our analysis can simulate a randomized controlled trial in an observational setting. Randomized contrast features are widely recognized in causality but are costly to implement in practice. However, MR studies can effectively avoid the confounding bias of randomly assigned SNPs at conception. Second, our findings may influence treatment strategies for cancer pain. Given the elevated prevalence of SUD and cancer in the general population, revealing the causal relationship between SUD and cancer could influence current prevention and intervention strategies for cancer pain. Our findings suggest enhanced cancer screening in patients with genetically predicted OUD may be useful. More attention should be paid to revealing the association of environmentally determined CUD with cancer and its prognosis. However, our study suffers from several limitations. First, this study utilized a pooled analysis of publicly available GWAS datasets. More in-depth subgroup analyses were not possible due to the unavailability of detailed raw data on participants. Second, to minimize potential population-related bias, this study was limited to GWAS datasets specific to the European population, and the consistency of our described findings in other populations remains to be investigated. Finally, eliminating the effect of horizontal pleiotropy in MR studies is challenging. Although analytical methods such as PhenoScanner and leave-one-out analysis were used, which suggests that our findings are robust to horizontal pleiotropy, the possibility of bias cannot be ignored entirely.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis is the first MR study to explore the causality of SUD on cancer vulnerability. This study suggests a suggestive causal relationship between OUD and the vulnerability of bladder cancer. In addition, the results of this study show a suggestive negative correlation between OUD and AML and ovarian cancer, respectively. Our MR analysis does not support the hypothesis that CUD could increase cancer vulnerability. These outcomes underscore the need for further investigation and a nuanced understanding of the relationships between substance use and specific cancer types.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/Additional file, further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Key Research and Development Program of Guangzhou Science and Technology Bureau (Grant No. 202206080012) and National Natural Science Foundation of China (Grant No. 82104951).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXin Su:\u003c/strong\u003e Data curation; formal analysis; investigation; writing-original draft and editing. \u003cstrong\u003eJun Kan:\u003c/strong\u003e methodology; visualization. \u003cstrong\u003eFanYang:\u003c/strong\u003e Conceptualization; resources and writing-review. \u003cstrong\u003eXiaoyan Mo:\u003c/strong\u003e Resources; soft-ware; supervision; validation. \u003cstrong\u003eBei Zhang:\u003c/strong\u003e Supervision; investigation and editing.\u0026nbsp;Xin Su and Xiaoyan Mo\u0026nbsp;contributed equally to this work.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRitter J, Allen S, Cohen PD, Fajardo AF, Marx K, Loggetto P, Auste C, Lewis H, de S\u0026aacute; Rodrigues KE, Hussain S, et al: \u003cstrong\u003eFinancial hardship in families of children or adolescents with cancer: a systematic literature review.\u003c/strong\u003e \u003cem\u003eLancet Oncol 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Filho A, Turner MC, Warnakulasuriya S, Richardson DB, Hosseini B, Kamangar F, Pourshams A, Sewram V, Cronin-Fenton D, Etemadi A: \u003cstrong\u003eThe carcinogenicity of opium consumption: a systematic review and meta-analysis.\u003c/strong\u003e \u003cem\u003eEuropean Journal of Epidemiology \u003c/em\u003e2023, \u003cstrong\u003e38:\u003c/strong\u003e373-389.\u003c/li\u003e\n\u003cli\u003eA MF, Turner MC, Warnakulasuriya S, Richardson DB, Hosseini B, Kamangar F, Pourshams A, Sewram V, Cronin-Fenton D, Etemadi A, et al: \u003cstrong\u003eThe carcinogenicity of opium consumption: a systematic review and meta-analysis.\u003c/strong\u003e \u003cem\u003eEur J Epidemiol \u003c/em\u003e2023, \u003cstrong\u003e38:\u003c/strong\u003e373-389.\u003c/li\u003e\n\u003cli\u003eVassou D, Notas G, Hatzoglou A, Castanas E, Kampa M: \u003cstrong\u003eOpioids increase bladder cancer cell migration via bradykinin B2 receptors.\u003c/strong\u003e \u003cem\u003eInt J Oncol \u003c/em\u003e2011, \u003cstrong\u003e39:\u003c/strong\u003e697-707.\u003c/li\u003e\n\u003cli\u003eFriesen C, Roscher M, Alt A, Miltner E: \u003cstrong\u003eMethadone, commonly used as maintenance medication for outpatient treatment of opioid dependence, kills leukemia cells and overcomes chemoresistance.\u003c/strong\u003e \u003cem\u003eCancer Res \u003c/em\u003e2008, \u003cstrong\u003e68:\u003c/strong\u003e6059-6064.\u003c/li\u003e\n\u003cli\u003eDai S, Zhang X, Zhang P, Zheng X, Pang Q: \u003cstrong\u003eFentanyl inhibits acute myeloid leukemia differentiated cells and committed progenitors via opioid receptor-independent suppression of Ras and STAT5 pathways.\u003c/strong\u003e \u003cem\u003eFundam Clin Pharmacol \u003c/em\u003e2021, \u003cstrong\u003e35:\u003c/strong\u003e174-183.\u003c/li\u003e\n\u003cli\u003eKim JY, Ahn HJ, Kim JK, Kim J, Lee SH, Chae HB: \u003cstrong\u003eMorphine Suppresses Lung Cancer Cell Proliferation Through the Interaction with Opioid Growth Factor Receptor: An In Vitro and Human Lung Tissue Study.\u003c/strong\u003e \u003cem\u003eAnesth Analg \u003c/em\u003e2016, \u003cstrong\u003e123:\u003c/strong\u003e1429-1436.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 2 and 4","content":"\u003cp\u003eTable 2 and 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cannabis use disorder, opioids use disorder, cancer, Mendelian randomization, carcinogenicity","lastPublishedDoi":"10.21203/rs.3.rs-3463220/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3463220/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCancer is a leading cause of mortality and morbidity globally and burdens public health heavily. Cannabis and opioids are promising applications for cancer pain management. However, due to their widespread abuse and addiction potential, they have become the focus of public health attention. They may have critical long-term health effects, raising concerns about their possible association with cancer risk. However, their relationship with cancer vulnerability is highly controversial. This Mendelian randomization (MR) study aimed to investigate the causal relationship of cannabis use disorder (CUD) and opioids use disorder (OUD) on cancer vulnerability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo-sample MR study using summary statistics from genome-wide association studies (GWAS), FinnGen, and UK Biobank. The primary method was inverse-variance weighted (IVW), and we included a range of sensitivity analyses to assess the robustness of the findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe found the IVW results showed a causal association between OUD and bladder cancer (OR = 1.040, 95% CI 1.004–1.078, \u003cem\u003eP\u003c/em\u003e = 0.029, adj. \u003cem\u003eP\u003c/em\u003e = 0.125), acute myeloid leukemia (OR = 0.931, 95% CI 0.885–0.978, \u003cem\u003eP\u003c/em\u003e = 0.005, adj. \u003cem\u003eP\u003c/em\u003e = 0.061) and ovarian cancer (OR = 0.937, 95% CI 0.891–0.984, \u003cem\u003eP\u003c/em\u003e = 0.010, adj. \u003cem\u003eP\u003c/em\u003e = 0.064). Sensitivity analysis is directionally consistent with IVW. In the reverse MR analysis, none of the methods produced statistically significant proof of a connection between OUD and three cancers (all \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). However, OUD did not prove a genetic causal relationship with other cancers (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). We found no relevant evidence of a statistically significant potential causal effect of CUD on cancers (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSummary:\u003c/strong\u003e This study suggests that OUD may be causally linked to bladder, AML, and ovarian cancer, which needs to be further evaluated in extensive population studies.\u003c/p\u003e","manuscriptTitle":"Decoding the Genetic Links Between Substance Use Disorder and Cancer Vulnerability","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-27 17:33:40","doi":"10.21203/rs.3.rs-3463220/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"372ce2dc-3f0f-412c-8113-e5b4d85c0824","owner":[],"postedDate":"October 27th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-12-28T09:37:46+00:00","versionOfRecord":[],"versionCreatedAt":"2023-10-27 17:33:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3463220","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3463220","identity":"rs-3463220","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}