Mendelian Randomization Analysis of the Relationship between Neurotransmitter-Related Genes and Cancer: Insights from Multi-omics Data

Mendelian Randomization Analysis of the Relationship between Neurotransmitter-Related Genes and Cancer: Insights from Multi-omics Data | 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 Mendelian Randomization Analysis of the Relationship between Neurotransmitter-Related Genes and Cancer: Insights from Multi-omics Data Quan Yuan, Yuli Xi, Hao Yu, Rongjie Ye, Neng Wang, Ge Yu, Ming Niu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6036757/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: Epidemiological evidence indicates a potential association between mental disorders and cancer. However, the specific role of neurotransmitter-related genes (NRG) in cancer development remains unclear. This study employs Mendelian randomization with multi-omics summary data to explore the causal effects and underlying mechanisms of NRG in cancer. Materials and Methods: The causal relationships between 10 mental disorders and 14 cancer types were investigated. NRG was retrieved from the GeneCards database. Transcriptome datasets from breast cancer were gathered from the Gene Expression Omnibus (GEO). The Summary-data-based Mendelian Randomization (SMR) method was utilized for genome-wide association study (GWAS) analysis, incorporating expression quantitative trait loci (eQTLs), DNA methylation QTLs (mQTLs), genetic variants that influence gene expression in the intestines (intestinal eQTLs) and influence the composition of the fecal microbiota (mbQTLs). Colocation analysis was performed to identify potential links between host gene expression and gut microbiota. Sensitivity analyses were conducted using two additional Mendelian randomization techniques. Results: Mendelian randomization analysis established a causal association between mental disorders and breast cancer. A meta-analysis of five breast cancer datasets identified 821 differentially expressed genes (DEGs) among 829 non-redundant genes. KRTCAP2 was identified as a potential causal gene in blood tissues, while SMR analysis highlighted cg24674445 as a significant methylation site. KRTCAP2 expression was inversely correlated with breast cancer, whereas cg24674445 methylation negatively affected KRTCAP2 expression, suggesting a positive influence of cg24674445 on breast cancer progression. Conclusion: This study using multi-omics Mendelian randomization found that DNA methylation regulates the association between NRG and breast cancer. Pan-cancer mental disorders Mendelian randomization multi-omics neurotransmitter Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Cancer is defined as a complex and prevalent disease characterized by the uncontrolled proliferation of abnormal cells, posing a significant threat to human health. On a global scale, the incidence of cancer rose from 13.4 million in 2006 to 17.2 million in 2016, and it is projected to continue increasing significantly in the future[ 1 ]. In the United States alone, over 609,000 cancer-related deaths were recorded in 2022[ 2 ]. Despite strides in the comprehension of cancer development, the underlying mechanisms remain profoundly intricate and not entirely deciphered. Emerging evidence points to genetic variations, environmental influences, immune system dysfunction, and the intricate interplay with the gut microbiome as pivotal elements in cancer initiation and progression[ 3 , 4 ]. Deciphering the complexity of these interactions may yield critical insights into the mechanisms of carcinogenesis and unveil potential targets for therapeutic intervention and disease prevention. Mental disorders (MD), including depression, schizophrenia (SCZ), bipolar disorder (BIP), autism spectrum disorder (ASD), Alzheimer's disease (AD), attention-deficit/hyperactivity disorder (ADHD), and other psychiatric conditions, exhibit an age-standardized prevalence exceeding 12,000 per 100,000 population annually, representing a considerable global health burden[ 5 ]. This indicates a significant global health burden. Mental disorders are responsible for approximately 14.3% of global fatalities, 32.4% of years lived with disability, and 13.0% of disability-adjusted life years[ 6 , 7 ]. Psychiatric disorders frequently coexist with cancer[ 8 – 10 ]. For instance, there is compelling evidence of shared genetic characteristics between schizophrenia and breast cancer[ 11 ]. Studies on the entire set of genes in the human body have found similarities between the genetic characteristics of schizophrenia and breast cancer, such as unique immunological traits and shared genetic factors[ 12 , 13 ]. Scientists have recorded a direct relationship between schizophrenia and breast cancer, both in terms of physical characteristics and genetic makeup. They have shown that there is a 0.14% overlap in the genes associated with these two conditions, specifically at the 19p13 locus known as GATA-D2A. This shared genetic element is considered a substantial risk factor for both diseases[ 13 ]. Moreover, the existence of psychiatric illnesses is associated with a higher likelihood of cancer-related death[ 14 ]. The comorbidity of mental disorders and cancer (multimorbidity) places an enormous strain on healthcare systems. Despite mounting epidemiological evidence indicating a link between mental disorders and cancer risk, a comprehensive and systematic investigation into their potential causal relationships remains lacking. The specific biological mechanisms connecting these conditions remain particularly elusive. Mendelian randomization (MR) is a robust method for inferring causality, leveraging genetic variants as instrumental variables to elucidate the contributions of genetic factors to disease development[ 15 ]. This approach provides avenues to navigate the intricate genetic architectures and regulatory networks that underpin both mental disorders and cancer. Integrating multi-omics data, including eQTL and DNA mQTL, can further enhance our understanding by revealing intricate interactions between genetic variations, gene expression, and the gut microbiome[ 16 , 17 ]. This multi-omics perspective not only provides deeper insights into disease mechanisms but also identifies novel targets for therapeutic intervention, paving the way for more effective prevention and treatment strategies. For instance, SMR analysis has revealed the expression level of mitochondrial-related gene NSUN4 is correlated with breast cancer risk[ 18 ]. Another study demonstrated that genetically predicted epigenetic modifications are associated with an increased risk of breast cancer incidence[ 19 ]. Nevertheless, the causal genes influenced by mental factors in breast cancer tissue and their interactions with the gut microbiome remain largely unknown. This study suggests employing a multi-omics-based MR examination to understand the potential causative effects and molecular mechanisms of mental problems on prevalent malignancies. The study will analyze blood and tissue samples. Through the utilization of the SMR approach, we merge the most extensive GWAS summary statistics for mental illnesses and breast cancer with eQTL and mQTL data in blood. In addition, the analysis incorporates the most recent data on gut eQTL and fecal mbQTL to explore potential connections between genes related to breast cancer in the host and the gut microbiome. In addition, two extra MR methods are used as sensitivity analyses to evaluate heterogeneity. Materials and methods 1.Mindset Design and Data Source The study design is illustrated in Figure 1. In our initial Mendelian randomization analysis, we examined the relationship between mental disorders and multiple types of cancer. Specifically, we investigated the impact of schizophrenia, drug dependence, alcohol dependence, bipolar disorder, generalized epilepsy, depression, anxiety disorder, social anxiety disorder, and Alzheimer's disease on 14 specific types of cancer, including lung, breast, colorectal, and gastric cancer. The GWAS data were acquired from the Open Gwas database (https://gwas.mrcieu.ac.uk/datasets) and are shown in Table 1. We also subsequently validated the expression and methylation of the relevant genes using The Cancer Genome Atlas (TCGA) database. Transcriptome data for breast cancer were acquired from the GEO database, namely from the datasets GSE42568, GSE61304, GSE20711, GSE17907, and GSE65194. NRG were retrieved from Genecard, which may be accessed at https://www.genecards.org. The blood eQTL summary statistics for neurotransmitter-related genes were acquired from eQTLGen, which encompasses genetic information on blood gene expression from 37 datasets comprising 31,684 individuals[20]. The blood mQTL summary data were acquired through a meta-analysis of two European cohorts: the Brisbane Systems Genetics Study (n=614) and the Lothian Birth Cohorts (n=1366)[21]. The eQTL data for breast and adipose tissues were acquired from the Genotype-Tissue Expression (GTEx) project, with a sample size of 860 individuals[22]. The present investigation specifically examines cis-eQTLs and cis-mQTLs that are located within a 1 Mb range from the starting and ending positions of the gene, and are represented by single nucleotide polymorphisms (SNPs). 2. Statistical Analysis 2.1 Analysis of Mental Disorders and Pan-Cancer by MR We chose instrumental variables derived from SNPs associated with exposure. All instrumental variables must meet three assumptions: (1) The single nucleotide polymorphism (SNP) is linked to mental disorders. We select significant SNPs from GWAS summary data based on the criteria of P < 1*10^-5, r^2 500 kb to ensure that the SNPs are independent and to eliminate the influence of genetic correlation on the results. We computed F statistics to measure the magnitude of genetic diversity, and all F statistics for SNPs exceeded 10. (2) SNP is independent of confounding factors that influence the link between the exposure and outcome. (3) SNP exclusively influences the development of cancer by means of mental abnormalities, rather than any other mechanisms (exclusive hypothesis). The study employed the inverse variance-weighted (IVW) method to assess the causal impact of two-sample MR analysis. A random model was utilized in the presence of heterogeneity, while a fixed model was employed in its absence. The IVW approach may reliably assess the causal relationship of exposure, provided that all variables satisfy the three conditions of effective instrumental variables. Subsequently, we employed the MR-Egger approach to conduct sensitivity analysis, which yields reliable estimates that are unaffected by the presence of invalid instruments. MR-PRESSO is a novel MR technique employed to identify and rectify level-multi-directional outliers, hence ensuring precise estimations. The MR-PRESSO global test value determines the presence of heterogeneity. The MR-Egger intercept is employed to evaluate the magnitude of both the multi-effect and the bias resulting from the use of faulty instruments. The Cochran-Q test is employed to assess the presence of heterogeneity among SNPs. A "leave-one-out" sensitivity analysis was done to assess the impact of individual SNPs on the results. Furthermore, we performed Steiger's test to eliminate any potential influence from reverse causality and ascertain if our findings align with our hypothesis. 2.2 Meta-analysis of neurotransmitter-related differential genes We acquired breast cancer transcriptome data from the GEO database and conducted an analysis of differentially expressed genes in each of the four gene expression datasets (GSE42568, GSE61304, GSE20711, GSE17907). Subsequently, we employed the R package metafor to do a fixed-effect meta-analysis. We extracted neurotransmitter-related genes from Genecard, considering only those with correlation scores of 1 or above, and then identified the overlapping genes with differential effects that were confirmed in GSE65194. 2.3 Meta-analysis of Breast cis-eQTLs We performed a meta-analysis of cis-eQTLs (expression quantitative trait loci) in breast tissue and adipose tissue using data from the GTEX database. To account for sample overlap, we employed the MeCS approach to conduct SMR (v1.3.1) classic inverse variance weighted meta-analysis for two tissues. 2.4 SMR and Co-localization Analysis We performed a three-step SMR analysis, consisting of the following steps: (1) Using SNP as an instrument, blood gene expression as the exposure, and breast cancer as the result; (2) Using SNP as an instrument, blood DNA methylation as the exposure, and breast cancer as the outcome; (3) Using SNP as an instrument, blood DNA methylation as the exposure, and gene expression in blood as the outcome. The third stage comprised solely of crucial signals derived from steps 1 and 2. The final candidate signals were determined based on the following criteria: (1) a P-value of less than 0.05 in all three SMR stages; (2) a suggestive level of significance (P < 1×10^-5) throughout the entire genome in all eQTLs, mQTLs, and GWAS; (3) the presence of heterogeneity in the HEIDI test findings with a P-value greater than 0.05. We performed two-sample Mendelian randomization using breast cancer as the outcome and assessed for heterogeneity of each causal impact using Cochran's Q test in MR-Egger and IVW techniques, with SMR-screened genes as exposures. false discovery rate (FDR) less than 0.05 or HEIDI values less than 0.05 for Cochran's Q test show the presence of heterogeneity. 2.5 Cell Type-specific Enrichment and Regulatory Component Annotation The Cell type-specific enrichment analysis databases (CSEA-DB) are utilized to investigate whether there are any cell types that are specifically associated with gut DEGs. The CSEA-DB website (https://bioinfo.uth.edu/CSEADB/) is employed for this purpose. eFORGE (http://eforge.cs.ucl.ac.uk/) is utilized for the assessment of DNAm site regulatory feature enrichment, encompassing various factors. 2.6 Software All experimental results were statistically analyzed using R 4.1.1 software. Results 1.There is a causal relationship between breast cancer and mental illness An investigation was conducted to examine the causal connection between cancer and mental diseases, following the exclusion of aberrant results and null values. A forest plot was constructed to analyze the data, revealing a significant causal association between mental diseases and several types of cancer, such as ovarian, lung, breast, and colorectal cancer. Nevertheless, it is important to note that some mental diseases such as schizophrenia and Alzheimer's disease have been identified as risk factors for breast cancer, as shown by an odds ratio greater than 1 (Figure 2). Afterwards, we chose to analyze genes connected to breast cancer and neurotransmitters. 2. A meta-analysis of differential expression of neurotransmitter-related genes in breast cancer patients. By performing differential analysis on a breast cancer dataset using the limma software (Figure 3a-d), and doing a meta-analysis of DEGs, a total of 4953 genes were identified based on the criteria of meta_FDR 0.5 (Figure 3e). These genes were then confirmed in a separate validation set (Figure 3f). The CSEA-DB analysis revealed that the differentially expressed genes are highly concentrated in tissues such as Adipose and Breast (Figure 3g), and are significantly enriched in cells such as Fibroblast and Macrophage within breast tissue (Figure 3h). Genecard identified a total of 4,271 genes related to neurotransmitters, and by comparing them with the differentially expressed genes, we obtained a set of 829 genes (Figure S1). 3 Explore causal relationships between genes and breast cancer at the eQTL level through SMR and MR. The study employed SMR and MR techniques to investigate the causal associations between genes and breast cancer at the eQTL level in tissues. Using SMR analysis, a total of 18 genes were shown to have significant causal associations with breast cancer (FDR_SMR 0.05, Table 2). Using MR, we discovered that two specific genes are causally linked to breast cancer. This conclusion is based on a Q_pval_IVW value greater than 0.05, as shown in Table 3. At the eQTL level in blood, a total of 42 genes were found to have causal associations with breast cancer using the SMR method (FDR_SMR 0.05, Table 4). Using MR, researchers identified a total of 7 genes that have causal links with breast cancer. These genes were determined based on a Q_pval_IVW value greater than 0.05, as shown in Table 5. 4 Explore causal relationships between genes and breast cancer at the mQTL level through SMR and MR A total of 1190 methylation sites that are causally associated to breast cancer and SMR (FDR_SMR 0.05, Table 6) were identified at the mQTL level. These CpG sites were found to be considerably enriched in the enhancer mark H3K4me1, as determined using the eforge online service (Figure 4). A total of 37 methylation sites that are causally associated to breast cancer were found using MR. These sites have a Q_pval_IVW value greater than 0.05 and are listed in Table 7. 5 The causal relationship between methylation sites and genes After obtaining the methylated sites and genes from above, we conducted further investigation into the causal relationship between them using MR. We identified a single pair of causal CpG-Gene pairs, specifically cg24674445 and KRTCAP2, with a Q_pval_IVW value greater than 0.05 (as shown in Table 8). 6 Visualize the co-localization of genes and methylation sites. Choosing KRTCAP2 and methylation sites for co-localization analysis, specifically those associated with a greater number of methylation sites. The cg24674445 site has consistent genetic effects of KRTCAP2 near BRCA GWAS, cis-mQTL, and cis-eQTL. These effects are shown in the site scaling map, with the top to bottom order being average minimum P < 1e-5, as depicted in Figure 5a. The expression of KRTCAP2 is inversely associated with breast cancer, as shown in Figure 5b. Additionally, the methylation level of cg24674445 has a detrimental effect on the expression of KRTCAP2, as seen in Figure 5c. Similarly, the level of methylation of cg24674445 has a beneficial effect on breast cancer (Figure 5d). 7 The correlation between KRTCAP2 and other genes in the Blood_eQTL A link exists between the KRTCAP2 gene and the Blood_eQTL gene. An analysis has been conducted on the correlation between KRTCAP2 and the other six genes among the seven genes that are causally related to breast cancer through MR in Blood_eQTL. A robust positive connection between KRTCAP2 and HCN3 has been identified (Figure 6). The mRNA expression level (Figure S2) and DNA methylation degree (Figure S3) of KRTCAP2 in breast cancer are both higher than those in normal individuals. Discussion The relationship between mental disorders and neurotransmitters is a complex and profound field, involving multiple disciplines such as neuroscience, psychology, and psychiatry[ 23 ]. Extensive research indicates that abnormalities in neurotransmitter levels are closely associated with various mental disorders. For instance, patients with depression often have lower levels of neurotransmitters like dopamine and serotonin, which may lead to symptoms such as low mood and loss of interest[ 24 ]. Similarly, patients with mania may have excessively high levels of dopamine, resulting in symptoms like elevated mood and impulsive behavior. Despite the close relationship between neurotransmitter abnormalities and mental disorders, this relationship is not a simple one-to-one correspondence[ 25 ]. In fact, the pathogenesis of mental disorders involves the combined effects of multiple factors, including genetics, environment, and psychology. Therefore, when exploring the relationship between neurotransmitters and mental disorders, we need to maintain a cautious and comprehensive approach. To date, the genetic association between pan-cancer and multiple MDs remain largely unclear. Previous research has verified that the SCZ factor would not produce a predominant influence on several cancers risk such as breast cancer[ 26 , 27 ]. Some cohort studies have identified a notable correlation between the risk of SCZ and certain malignancies, which could be linked to specific confounding variables. For instance, women with severe mental illness had a greater likelihood of developing breast cancer compared to the overall population. The use of antipsychotic medications that increase prolactin levels is believed to be a potential cause. According to Solmi et al, there is a connection between long-term use of antipsychotic medications that increase prolactin levels and an increased risk of developing breast cancer in women with severe major depression. However, the exact cause of this relationship is yet uncertain[ 28 ]. The link between schizophrenia and cancer is still a subject of debate, possibly because of differences in research methods and the influence of factors that can distort the results, such as smoking, diet, antipsychotic drugs, and inconsistencies in cancer detection and treatment[ 29 , 30 ]. In addition, cancer primarily affects older individuals, while those with mental illness generally have a life expectancy that is shortened by over 10 years[ 31 , 32 ]. Cancer and mental illnesses significantly affect people of different age groups, but the manifestations and concerns may vary. However, in recent years, an increasing number of young people are also facing the risk of cancer, which may be related to bad living habits, environmental factors, genetics, and various other factors. There are also differences in the types of cancer that are more prevalent among different age groups. For example, among adolescents and young adults, blood system tumors such as lymphoma and leukemia are more common; while in middle-aged and older adults, solid tumors such as lung cancer, breast cancer, and colorectal cancer are more prevalent[ 33 ]. The strength of MR resides in its capacity to mitigate the influence of confounding variables, therefore yielding more dependable and accurate observations regarding the genetic-level association between exposure and outcome. This study examined the causal relationship between genetically predicted 11 mental disorders, such as schizophrenia, drug dependence, alcohol dependence, bipolar disorder, generalized epilepsy, depression, anxiety disorder, bipolar disorder, ADHD, and the risk of developing 14 types of cancer, including lung cancer, gastric cancer, breast cancer, and others. We have found compelling evidence suggesting a genetic link between SCZ (OR = 1.03, 95% CI: 1.010–1.050, P = 0.00031), AZD (OR = 1.04, 95% CI: 1.020–1.050, P = 3.51E-7), and the likelihood of developing breast cancer. This study found that patients with SCZ and AD have a higher likelihood of developing breast cancer. A new study has uncovered that the association between AD and cancer may encompass various processes. Chen et colleagues have confirmed that circular RNAs (circRNAs) associated with AD diagnosis and clinical severity exhibit a negative correlation in several types of cancer[ 34 ]. Notably, the circRNAs that were shown to be expressed differently in temporal lobe epilepsy showed no connections with malignancy. The circRNAs have a key role in regulating genes that are involved in interleukin-12-mediated signaling and viral response. The genes regulated by circRNAs are significantly involved in interleukin-12-mediated signaling and viral response. The inflammation signaling response induced by circ-PICALM, circRTN4, and circMAN2A1 may serve as the shared pathogenesis in both cancer and AD. In the aging female population with breast cancer, estrogen-modulating treatments (EMTs) have been found to have estrogenic agonist activity in neural tissues. This activity is linked to a decreased risk of Alzheimer's disease. Additionally, EMTs operate as potent estrogen receptor antagonists in breast tissue[ 35 ]. The role of estrogen-regulating therapy in breast tissue and its potential impact on the risk of breast cancer is a complex and variable issue. Different EMT protocols, treatment durations, patient individual differences, and other factors can all affect the outcomes. Therefore, when applying EMT, it is necessary to consider the specific conditions and treatment goals of each patient to develop a personalized treatment plan. Additional studies have shown that individuals with a genetic predisposition to schizophrenia have a higher likelihood of developing breast cancer, ovarian cancer, and thyroid cancer[ 36 ].These data confirmed the casual association of MDs and breast cancer, implying the significant influence of mental factor in breast cancer development. Considering the critical role of neurotransmitters in orchestrating neural cell homeostasis, we furthermore chose the overlapping genes between neurotransmitters-associated-genes (NAGs) and DEGs in BC to explore the genetic influence of MD on BC. A total of 2 genes (PADI4 and PRDX1) derived from tissue-specific eQTL and 7 genes (PER3, PPT1, NEGR1, KRTCAP2, DPYD, TOR1AIP1, and HCN3) derived from blood-specific eQTL were identified to have a causal relationship with BC. These potential genes identified by eQTL provide a rationale for them as the therapeutic targets. For example, neuronal peptidyl arginine deiminase 4 (PADI4) has been reported to have the potential to induce the brain-autonomous neural repair by activating the transcription of genes involved in recovery processes through histone citrullination[ 37 ]. The new quinone-fused oxazepine, CM728, can effectively inhibit the growth of triple-negative breast cancer (TNBC) cells by boosting the oxidation of Peroxiredoxin-1 (Prdx1). Targeting drug inhibitors that reduce the function of Prdx1 could be a promising approach for treating refractory TNBC[ 38 ]. A total of 129 methylation sites were identified by mQTL analysis, showing a causal link with BC. These specific CpG sites were shown to be considerably enriched in the enhancer marker H3K4me1 alteration. H3K4me1 is a particular alteration of histones that is controlled by LSD1, an important regulator of chromatin structure. In breast cancer, LSD1 functions as an oncogene by removing methyl groups from H3K4me1/2. The stability of LSD1 is precisely controlled by the mechanisms of ubiquitination and deubiquitylation. FBXO24 is classified as an E3 ligase that has the role of ubiquitinating and degrading LSD1. FBXO24 hinders the development of tumors caused by LSD1, making it a tumor suppressor in breast cancer. This is associated with a negative correlation between FBXO24 and LSD1, and is linked to a more favorable prognosis[ 39 ]. This study highlights the predominant role of DNA CpG methylation in BC progress. Furthermore, KRTCAP2 were found have causal relationships with matching CpG sites (KRTCAP2 vs cg24674445 sites). Colocalization analysis of KRTCAP2 (more methylation sites) indicated that the higher methylation of cg24674445 site of KRTCAP2 CDS region upregulated KRTCAP2 expression, thus increasing BC risk. The protein encoded by the KRTCAP2 gene has specific functions in the organism and may be related to certain functions or characteristics of keratinocytes. Keratinocytes are the main cell type of the skin and are responsible for forming the protective barrier of the skin. Studies have shown that the skin microbiome maintains barrier integrity in keratinocytes through the AHR signaling pathway, which is significant for the management of skin diseases[ 40 ]. There is a synergistic impact shown between antimalarial medications (mefloquine, artesunate, chloroquine) and antineoplastic agents in BC cells. The antimalarial medications mentioned here block KRTCAP2, which in turn disrupts lysosomal metabolism. This disruption leads to a decrease in lysosomal drug accumulation and an increase in the bioavailability of antineoplastic therapies[ 41 ]. KRTCAP2 is associated with autoimmune plasma cell dysplasia, Alzheimer's disease, and various tumor types[ 42 , 43 ]. However, little is known about the role of KRTCAP2 in BC. Here, we identified KRTCAP2 as a novel prompting-cancer gene by genetic layer, provide a rationale for the definition of KRTCAP2 in BC progression. However, little is known about the role of KRTCAP2 in BC and more comprehensive study would be urged to clarify the specific role of KRTCAP2 in BC. Limitations This study has several limitations that should be considered when interpreting the findings. First, the sample size of certain GWAS and eQTL datasets may limit the power to detect weaker causal associations, particularly for less common genetic variants. Second, the SMR approach relies heavily on the quality and comprehensiveness of the eQTL and mQTL datasets, which may not fully capture tissue-specific regulatory mechanisms, potentially leading to missed causal signals. Third, while Mendelian randomization is a robust method for inferring causality, it is not immune to horizontal pleiotropy, where genetic instruments may influence the outcome through pathways other than the exposure of interest. Fourth, the study focuses primarily on blood tissue for eQTL and mQTL analyses, which may not fully represent the regulatory landscape of breast cancer, potentially overlooking critical tissue-specific mechanisms. Additionally, the genetic instruments used in this study may explain only a small proportion of the variance in neurotransmitter-related gene expression, limiting the ability to detect subtler effects. Lastly, the clinical relevance of the identified genes and methylation sites in terms of therapeutic targeting remains to be validated in prospective studies. These limitations highlight the need for further research to address these gaps and to explore the biological mechanisms underlying the observed associations in more detail. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials The dataset(s) supporting the conclusions of this article is(are) included within the article (and its additional file(s)). Competing interests The authors declare that they have no competing interests. Funding This study was funded by grants from Beijing Heart to Heart Foundation (HXXT2021ktyj002/ HXXT2021ktyj001) and Haiyan science Foundation (JJMS2022-08). Authors' contributions All authors contributed with writing–original draft and writing–review and editing. 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Solmi M, Lähteenvuo M, Tanskanen A, Corbeil O, Mittendorfer-Rutz E, Correll CU, Tiihonen J, Taipale H: Antipsychotic Use and Risk of Breast Cancer in Women With Severe Mental Illness: Replication of a Nationwide Nested Case-Control Database Study . Schizophrenia bulletin 2024. O'Neill B, Yusuf A, Lofters A, Huang A, Ekeleme N, Kiran T, Greiver M, Sullivan F, Kurdyak P: Breast Cancer Screening Among Females With and Without Schizophrenia . JAMA network open 2023, 6 (11):e2345530. Drevinskaite M, Kaceniene A, Patasius A, Stukas R, Germanavicius A, Miseikyte E, Urbonas V, Smailyte G: Cancer mortality and morbidity among patients with schizophrenia: A hospital-based cohort study, 1992-2020 . Acta psychiatrica Scandinavica 2024, 149 (3):234-243. Shi J, Wen W, Long J, Gamazon ER, Tao R, Cai Q: Genetic correlation and causal associations between psychiatric disorders and lung cancer risk . Journal of affective disorders 2024, 356 :647-656. Zuber V, Jönsson EG, Frei O, Witoelar A, Thompson WK, Schork AJ, Bettella F, Wang Y, Djurovic S, Smeland OB et al : Identification of shared genetic variants between schizophrenia and lung cancer . Scientific reports 2018, 8 (1):674. Gupta S: Adolescents and young adults with cancer and the risk of subsequent primary neoplasms: not just big children . Lancet Oncol 2019, 20 (4):466-467. Chen D, Hao S, Xu J: Revisiting the Relationship Between Alzheimer's Disease and Cancer With a circRNA Perspective . Front Cell Dev Biol 2021, 9 :647197. Branigan GL, Torrandell-Haro G, Chen S, Shang Y, Perez-Miller S, Mao Z, Padilla-Rodriguez M, Cortes-Flores H, Vitali F, Brinton RD: Breast cancer therapies reduce risk of Alzheimer's disease and promote estrogenic pathways and action in brain . iScience 2023, 26 (11):108316. Yuan K, Song W, Liu Z, Lin GN, Yu S: Mendelian Randomization and GWAS Meta Analysis Revealed the Risk-Increasing Effect of Schizophrenia on Cancers . Biology (Basel) 2022, 11 (9). Nakamura A, Sakai S, Taketomi Y, Tsuyama J, Miki Y, Hara Y, Arai N, Sugiura Y, Kawaji H, Murakami M et al : PLA2G2E-mediated lipid metabolism triggers brain-autonomous neural repair after ischemic stroke . Neuron 2023, 111 (19). Spínola-Lasso E, Montero JC, Jiménez-Monzón R, Estévez F, Quintana J, Guerra B, Elokely KM, León F, Del Rosario H, Fernández-Pérez L et al : Chemical-proteomics Identify Peroxiredoxin-1 as an Actionable Target in Triple-negative Breast Cancer . Int J Biol Sci 2023, 19 (6):1731-1747. Dong B, Song X, Wang X, Dai T, Wang J, Yu Z, Deng J, Evers BM, Wu Y: FBXO24 Suppresses Breast Cancer Tumorigenesis by Targeting LSD1 for Ubiquitination . Mol Cancer Res 2023, 21 (12):1303-1316. Alwan W, Di Meglio P: Guardians of the barrier: Microbiota engage AHR in keratinocytes to mantain skin homeostasis . Cell Host Microbe 2021, 29 (8):1213-1216. Duarte D, Vale N: How Antimalarials and Antineoplastic Drugs can Interact in Combination Therapies: A Perspective on the Role of PPT1 Enzyme . Curr Drug Metab 2021, 22 (13):1009-1016. Kannan K, Kordestani GK, Galagoda A, Coarfa C, Yen L: Aberrant MUC1-TRIM46-KRTCAP2 Chimeric RNAs in High-Grade Serous Ovarian Carcinoma . Cancers 2015, 7 (4):2083-2093. O'Hanlon TP, Rider LG, Gan L, Fannin R, Paules RS, Umbach DM, Weinberg CR, Shah RR, Mav D, Gourley MF et al : Gene expression profiles from discordant monozygotic twins suggest that molecular pathways are shared among multiple systemic autoimmune diseases . Arthritis research & therapy 2011, 13 (2):R69. Tables Tables 1 to 8 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files table1ListofGWASDatasetsforBreastCancer.xlsx table2SMRAnalysisofBreastCancerRelatedGenesattheeQTLLevel.xlsx table3CausalInferenceAnalysisforBreastCancerUsingGeneticData.xlsx table4SMRAnalysisofBreastCancerRelatedGenesattheeQTLLevelBlood.xlsx table5CausalInferenceforBreastCancerUsingGeneticDataIVWMREggerSMR.xlsx table6CausalInferenceforBreastCancerUsingDNAMethylationDataSMR.xlsx table7CausalInferenceforBreastCancerUsingDNAMethylationDataRadialMethods.xlsx table8SMRAnalysisofBreastCancerRelatedGenesUsingMultipleMethods.xlsx FigureS1.png FigureS2.png FigureS3.png 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-6036757","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":447058702,"identity":"0644970c-de5c-4189-997a-6bd9150e346f","order_by":0,"name":"Quan Yuan","email":"","orcid":"","institution":"Harbin Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Quan","middleName":"","lastName":"Yuan","suffix":""},{"id":447058704,"identity":"7a3ef72f-1d2b-4e22-9e0b-011d27502915","order_by":1,"name":"Yuli Xi","email":"","orcid":"","institution":"Hongqi Hospital Affiliated to Mudanjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuli","middleName":"","lastName":"Xi","suffix":""},{"id":447058706,"identity":"487ffb84-7ec5-46b0-a637-67875f8efd49","order_by":2,"name":"Hao Yu","email":"","orcid":"","institution":"the First Affiliated Hospital of Xiamen University, Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Yu","suffix":""},{"id":447058708,"identity":"abce7c6c-43d8-4bd0-9ed4-ad561ebff97c","order_by":3,"name":"Rongjie Ye","email":"","orcid":"","institution":"Quanzhou First Hospital Affiliated to Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rongjie","middleName":"","lastName":"Ye","suffix":""},{"id":447058710,"identity":"c8e22c2b-31ee-4c9a-931d-11a81b2a2b2f","order_by":4,"name":"Neng Wang","email":"","orcid":"","institution":"The First Affliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Neng","middleName":"","lastName":"Wang","suffix":""},{"id":447058711,"identity":"291f171f-6518-4ed7-8aac-429948b29a82","order_by":5,"name":"Ge Yu","email":"","orcid":"","institution":"Harbin Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ge","middleName":"","lastName":"Yu","suffix":""},{"id":447058712,"identity":"869b1c43-7781-4e72-a477-76205406af21","order_by":6,"name":"Ming Niu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYDACZiBOYGCQY2NvSHyQUGFDvBZjfp4Djw0enEkj3rLEmTMSn0k+bDtEWKk5O4/phoc7ahk3HEhOq0hgO8DA396dgFeLZTOP2Y3EM8eZDQ4cS7uRwHOHQeLM2Q14tRgcBmlpO8ZmcLAHqEXiGYOBRC5xWngMDvN/K0gwOEy0lhoJyTaGNIaEBKK0sJUBtRww4OdhSJZIOJDGQ9gv5w9vu/mzra6+Tf5B4sef/2zk+Nt78WuBgsNwFg8xykGgjliFo2AUjIJRMBIBAA+sUHJxIlD1AAAAAElFTkSuQmCC","orcid":"","institution":"Harbin Medical University Cancer Hospital","correspondingAuthor":true,"prefix":"","firstName":"Ming","middleName":"","lastName":"Niu","suffix":""}],"badges":[],"createdAt":"2025-02-15 13:23:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6036757/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6036757/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81280146,"identity":"8675c5a9-51f0-4156-93bf-e1c3a9eed8a4","added_by":"auto","created_at":"2025-04-24 10:01:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":367360,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the study.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6036757/v1/cb86dd7f42adfc75473e2ff7.png"},{"id":81281200,"identity":"934d89b2-9d90-40e8-85c3-0553c3398190","added_by":"auto","created_at":"2025-04-24 10:09:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1795389,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMendelian randomization forest chart.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6036757/v1/ba64e2a3c6cae54f20064625.png"},{"id":81280166,"identity":"c4e9f1d4-4a9c-4836-992a-cfaa8b9538eb","added_by":"auto","created_at":"2025-04-24 10:01:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":556111,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePresents a meta-analysis of genes that are differentially expressed and associated with NRG. \u003c/strong\u003eVolcano plots displaying the differential genes in datasets GSE42568, GSE61304, GSE20711, and GSE17907 are shown in panels a to d. (e) Genes exhibiting consistent differential expression across all four datasets. (f) Assessment of the reliability of differential gene validation in GSE65194. (g) Identification of tissue-specific genes. (h) Identification of differentially expressed genes in distinct cell populations of the breast.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6036757/v1/0ccffaee14247ec331c32935.png"},{"id":81280147,"identity":"b005359b-3dd1-4fe4-a5ad-b18500872f6d","added_by":"auto","created_at":"2025-04-24 10:01:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":305842,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCpG sites were significantly enriched in enhancer mark H3K4me1.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6036757/v1/09dc6fc52fe3396714b91f31.png"},{"id":81280157,"identity":"0b839c39-ed63-4e1e-a8e9-435554545ed5","added_by":"auto","created_at":"2025-04-24 10:01:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":485163,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIllustrates the procedure of three-step SMR analysis utilizing blood tissue to examine the hypothesized causative NRG gene and methylation locations in breast cancer.\u003c/strong\u003e (a) The site scaling diagram illustrates the consistent genetic impacts of breast cancer GWAS, cis-mQTL, and cis-eQTL near KRTCAP2. These impacts are presented in the top to bottom panel, all with a minimum P-value of less than 1 x 10−5. (b-d) The three-step SMR analysis shows a strong causal link between gene expression and the beginning of CD mediated by methylation. All three-step SMR FDR values are less than 0.05, and the HEIDI test p-value is greater than 0.05.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6036757/v1/c0f092f7059ecd35bba4893b.png"},{"id":81280155,"identity":"fcf9792e-6dab-4245-8d97-7aef30385ddf","added_by":"auto","created_at":"2025-04-24 10:01:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":295206,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene correlation.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6036757/v1/b573db33e363bc5a2ce7d949.png"},{"id":88435955,"identity":"b87b6dc5-88b6-43e5-82c4-7ce7a5b15a51","added_by":"auto","created_at":"2025-08-06 11:46:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5893179,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6036757/v1/0efc2705-bec7-48dd-9875-759dac3d720c.pdf"},{"id":81281198,"identity":"032413ef-17a8-4ceb-87f9-02750e4d6e74","added_by":"auto","created_at":"2025-04-24 10:09:00","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10440,"visible":true,"origin":"","legend":"","description":"","filename":"table1ListofGWASDatasetsforBreastCancer.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6036757/v1/d0e9f550d78b59714d06739c.xlsx"},{"id":81280152,"identity":"e4f5130b-5a78-4035-a329-4fd7c382b390","added_by":"auto","created_at":"2025-04-24 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10:09:01","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":149743,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.png","url":"https://assets-eu.researchsquare.com/files/rs-6036757/v1/dc4e0241b9519913957be65e.png"},{"id":81280187,"identity":"c28f3f0f-c3d3-4b10-ad02-7e5a28e94b95","added_by":"auto","created_at":"2025-04-24 10:01:02","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":183231,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.png","url":"https://assets-eu.researchsquare.com/files/rs-6036757/v1/82206f8a675bfb5eb41c5dec.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mendelian Randomization Analysis of the Relationship between Neurotransmitter-Related Genes and Cancer: Insights from Multi-omics Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer is defined as a complex and prevalent disease characterized by the uncontrolled proliferation of abnormal cells, posing a significant threat to human health. On a global scale, the incidence of cancer rose from 13.4\u0026nbsp;million in 2006 to 17.2\u0026nbsp;million in 2016, and it is projected to continue increasing significantly in the future[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In the United States alone, over 609,000 cancer-related deaths were recorded in 2022[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite strides in the comprehension of cancer development, the underlying mechanisms remain profoundly intricate and not entirely deciphered. Emerging evidence points to genetic variations, environmental influences, immune system dysfunction, and the intricate interplay with the gut microbiome as pivotal elements in cancer initiation and progression[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Deciphering the complexity of these interactions may yield critical insights into the mechanisms of carcinogenesis and unveil potential targets for therapeutic intervention and disease prevention.\u003c/p\u003e \u003cp\u003eMental disorders (MD), including depression, schizophrenia (SCZ), bipolar disorder (BIP), autism spectrum disorder (ASD), Alzheimer's disease (AD), attention-deficit/hyperactivity disorder (ADHD), and other psychiatric conditions, exhibit an age-standardized prevalence exceeding 12,000 per 100,000 population annually, representing a considerable global health burden[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This indicates a significant global health burden. Mental disorders are responsible for approximately 14.3% of global fatalities, 32.4% of years lived with disability, and 13.0% of disability-adjusted life years[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Psychiatric disorders frequently coexist with cancer[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For instance, there is compelling evidence of shared genetic characteristics between schizophrenia and breast cancer[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Studies on the entire set of genes in the human body have found similarities between the genetic characteristics of schizophrenia and breast cancer, such as unique immunological traits and shared genetic factors[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Scientists have recorded a direct relationship between schizophrenia and breast cancer, both in terms of physical characteristics and genetic makeup. They have shown that there is a 0.14% overlap in the genes associated with these two conditions, specifically at the 19p13 locus known as GATA-D2A. This shared genetic element is considered a substantial risk factor for both diseases[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Moreover, the existence of psychiatric illnesses is associated with a higher likelihood of cancer-related death[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The comorbidity of mental disorders and cancer (multimorbidity) places an enormous strain on healthcare systems.\u003c/p\u003e \u003cp\u003eDespite mounting epidemiological evidence indicating a link between mental disorders and cancer risk, a comprehensive and systematic investigation into their potential causal relationships remains lacking. The specific biological mechanisms connecting these conditions remain particularly elusive. Mendelian randomization (MR) is a robust method for inferring causality, leveraging genetic variants as instrumental variables to elucidate the contributions of genetic factors to disease development[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This approach provides avenues to navigate the intricate genetic architectures and regulatory networks that underpin both mental disorders and cancer.\u003c/p\u003e \u003cp\u003eIntegrating multi-omics data, including eQTL and DNA mQTL, can further enhance our understanding by revealing intricate interactions between genetic variations, gene expression, and the gut microbiome[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This multi-omics perspective not only provides deeper insights into disease mechanisms but also identifies novel targets for therapeutic intervention, paving the way for more effective prevention and treatment strategies. For instance, SMR analysis has revealed the expression level of mitochondrial-related gene NSUN4 is correlated with breast cancer risk[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Another study demonstrated that genetically predicted epigenetic modifications are associated with an increased risk of breast cancer incidence[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Nevertheless, the causal genes influenced by mental factors in breast cancer tissue and their interactions with the gut microbiome remain largely unknown.\u003c/p\u003e \u003cp\u003eThis study suggests employing a multi-omics-based MR examination to understand the potential causative effects and molecular mechanisms of mental problems on prevalent malignancies. The study will analyze blood and tissue samples. Through the utilization of the SMR approach, we merge the most extensive GWAS summary statistics for mental illnesses and breast cancer with eQTL and mQTL data in blood. In addition, the analysis incorporates the most recent data on gut eQTL and fecal mbQTL to explore potential connections between genes related to breast cancer in the host and the gut microbiome. In addition, two extra MR methods are used as sensitivity analyses to evaluate heterogeneity.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e1.Mindset Design and Data Source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study design is illustrated in Figure 1. In our initial Mendelian randomization analysis, we examined the relationship between mental disorders and multiple types of cancer. Specifically, we investigated the impact of schizophrenia, drug dependence, alcohol dependence, bipolar disorder, generalized epilepsy, depression, anxiety disorder, social anxiety disorder, and Alzheimer\u0026apos;s disease on 14 specific types of cancer, including lung, breast, colorectal, and gastric cancer. The GWAS data were acquired from the Open Gwas database (https://gwas.mrcieu.ac.uk/datasets) and are shown in Table 1. We also subsequently validated the expression and methylation of the relevant genes using The Cancer Genome Atlas (TCGA) database.\u003c/p\u003e\n\u003cp\u003eTranscriptome data for breast cancer were acquired from the GEO database, namely from the datasets GSE42568, GSE61304, GSE20711, GSE17907, and GSE65194. NRG were retrieved from Genecard, which may be accessed at https://www.genecards.org. The blood eQTL summary statistics for neurotransmitter-related genes were acquired from eQTLGen, which encompasses genetic information on blood gene expression from 37 datasets comprising 31,684 individuals[20]. The blood mQTL summary data were acquired through a meta-analysis of two European cohorts: the Brisbane Systems Genetics Study (n=614) and the Lothian Birth Cohorts (n=1366)[21]. The eQTL data for breast and adipose tissues were acquired from the Genotype-Tissue Expression (GTEx) project, with a sample size of 860 individuals[22]. The present investigation specifically examines cis-eQTLs and cis-mQTLs that are located within a 1 Mb range from the starting and ending positions of the gene, and are represented by single nucleotide polymorphisms (SNPs).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1 Analysis of Mental Disorders and Pan-Cancer by MR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe chose instrumental variables derived from SNPs associated with exposure. All instrumental variables must meet three assumptions: (1) The single nucleotide polymorphism (SNP) is linked to mental disorders. We select significant SNPs from GWAS summary data based on the criteria of P \u0026lt; 1*10^-5, r^2 \u0026lt; 0.1, and a linkage disequilibrium width \u0026gt; 500 kb to ensure that the SNPs are independent and to eliminate the influence of genetic correlation on the results. We computed F statistics to measure the magnitude of genetic diversity, and all F statistics for SNPs exceeded 10. (2) SNP is independent of confounding factors that influence the link between the exposure and outcome. (3) SNP exclusively influences the development of cancer by means of mental abnormalities, rather than any other mechanisms (exclusive hypothesis). The study employed the inverse variance-weighted (IVW) method to assess the causal impact of two-sample MR analysis. A random model was utilized in the presence of heterogeneity, while a fixed model was employed in its absence. The IVW approach may reliably assess the causal relationship of exposure, provided that all variables satisfy the three conditions of effective instrumental variables. Subsequently, we employed the MR-Egger approach to conduct sensitivity analysis, which yields reliable estimates that are unaffected by the presence of invalid instruments. MR-PRESSO is a novel MR technique employed to identify and rectify level-multi-directional outliers, hence ensuring precise estimations. The MR-PRESSO global test value determines the presence of heterogeneity. The MR-Egger intercept is employed to evaluate the magnitude of both the multi-effect and the bias resulting from the use of faulty instruments. The Cochran-Q test is employed to assess the presence of heterogeneity among SNPs. A \u0026quot;leave-one-out\u0026quot; sensitivity analysis was done to assess the impact of individual SNPs on the results. Furthermore, we performed Steiger\u0026apos;s test to eliminate any potential influence from reverse causality and ascertain if our findings align with our hypothesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Meta-analysis of neurotransmitter-related differential genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acquired breast cancer transcriptome data from the GEO database and conducted an analysis of differentially expressed genes in each of the four gene expression datasets (GSE42568, GSE61304, GSE20711, GSE17907). Subsequently, we employed the R package metafor to do a fixed-effect meta-analysis. We extracted neurotransmitter-related genes from Genecard, considering only those with correlation scores of 1 or above, and then identified the overlapping genes with differential effects that were confirmed in GSE65194.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Meta-analysis of Breast cis-eQTLs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed a meta-analysis of cis-eQTLs (expression quantitative trait loci) in breast tissue and adipose tissue using data from the GTEX database. To account for sample overlap, we employed the MeCS approach to conduct SMR (v1.3.1) classic inverse variance weighted meta-analysis for two tissues.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 SMR and Co-localization Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed a three-step SMR analysis, consisting of the following steps: (1) Using SNP as an instrument, blood gene expression as the exposure, and breast cancer as the result; (2) Using SNP as an instrument, blood DNA methylation as the exposure, and breast cancer as the outcome; (3) Using SNP as an instrument, blood DNA methylation as the exposure, and gene expression in blood as the outcome. The third stage comprised solely of crucial signals derived from steps 1 and 2. The final candidate signals were determined based on the following criteria: (1) a P-value of less than 0.05 in all three SMR stages; (2) a suggestive level of significance (P \u0026lt; 1\u0026times;10^-5) throughout the entire genome in all eQTLs, mQTLs, and GWAS; (3) the presence of heterogeneity in the HEIDI test findings with a P-value greater than 0.05.\u003c/p\u003e\n\u003cp\u003eWe performed two-sample Mendelian randomization using breast cancer as the outcome and assessed for heterogeneity of each causal impact using Cochran\u0026apos;s Q test in MR-Egger and IVW techniques, with SMR-screened genes as exposures. false discovery rate (FDR) less than 0.05 or HEIDI values less than 0.05 for Cochran\u0026apos;s Q test show the presence of heterogeneity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Cell Type-specific Enrichment and Regulatory Component Annotation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Cell type-specific enrichment analysis databases (CSEA-DB) are utilized to investigate whether there are any cell types that are specifically associated with gut DEGs. The CSEA-DB website (https://bioinfo.uth.edu/CSEADB/) is employed for this purpose. eFORGE (http://eforge.cs.ucl.ac.uk/) is utilized for the assessment of DNAm site regulatory feature enrichment, encompassing various factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Software\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experimental results were statistically analyzed using R 4.1.1 software.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1.There is a causal relationship between breast cancer and mental illness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn investigation was conducted to examine the causal connection between cancer and mental diseases, following the exclusion of aberrant results and null values. A forest plot was constructed to analyze the data, revealing a significant causal association between mental diseases and several types of cancer, such as ovarian, lung, breast, and colorectal cancer. Nevertheless, it is important to note that some mental diseases such as schizophrenia and Alzheimer\u0026apos;s disease have been identified as risk factors for breast cancer, as shown by an odds ratio greater than 1 (Figure 2). Afterwards, we chose to analyze genes connected to breast cancer and neurotransmitters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. A meta-analysis of differential expression of neurotransmitter-related genes in breast cancer patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy performing differential analysis on a breast cancer dataset using the limma software (Figure 3a-d), and doing a meta-analysis of DEGs, a total of 4953 genes were identified based on the criteria of meta_FDR \u0026lt; 0.05 and abs (meta_Hedges) \u0026gt; 0.5 (Figure 3e). These genes were then confirmed in a separate validation set (Figure 3f). The CSEA-DB analysis revealed that the differentially expressed genes are highly concentrated in tissues such as Adipose and Breast (Figure 3g), and are significantly enriched in cells such as Fibroblast and Macrophage within breast tissue (Figure 3h). Genecard identified a total of 4,271 genes related to neurotransmitters, and by comparing them with the differentially expressed genes, we obtained a set of 829 genes (Figure S1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3 Explore causal relationships between genes and breast cancer at the eQTL level through SMR and MR.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study employed SMR and MR techniques to investigate the causal associations between genes and breast cancer at the eQTL level in tissues. Using SMR analysis, a total of 18 genes were shown to have significant causal associations with breast cancer (FDR_SMR \u0026lt; 0.05, p_HEIDI \u0026gt; 0.05, Table 2). Using MR, we discovered that two specific genes are causally linked to breast cancer. This conclusion is based on a Q_pval_IVW value greater than 0.05, as shown in Table 3. At the eQTL level in blood, a total of 42 genes were found to have causal associations with breast cancer using the SMR method (FDR_SMR \u0026lt; 0.05, p_HEIDI \u0026gt; 0.05, Table 4). Using MR, researchers identified a total of 7 genes that have causal links with breast cancer. These genes were determined based on a Q_pval_IVW value greater than 0.05, as shown in Table 5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4 Explore causal relationships between genes and breast cancer at the mQTL level through SMR and MR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 1190 methylation sites that are causally associated to breast cancer and SMR (FDR_SMR \u0026lt; 0.01, p_HEIDI \u0026gt; 0.05, Table 6) were identified at the mQTL level. These CpG sites were found to be considerably enriched in the enhancer mark H3K4me1, as determined using the eforge online service (Figure 4). A total of 37 methylation sites that are causally associated to breast cancer were found using MR. These sites have a Q_pval_IVW value greater than 0.05 and are listed in Table 7.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5 The causal relationship between methylation sites and genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter obtaining the methylated sites and genes from above, we conducted further investigation into the causal relationship between them using MR. We identified a single pair of causal CpG-Gene pairs, specifically cg24674445 and KRTCAP2, with a Q_pval_IVW value greater than 0.05 (as shown in Table 8).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6 Visualize the co-localization of genes and methylation sites.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChoosing KRTCAP2 and methylation sites for co-localization analysis, specifically those associated with a greater number of methylation sites. The cg24674445 site has consistent genetic effects of KRTCAP2 near BRCA GWAS, cis-mQTL, and cis-eQTL. These effects are shown in the site scaling map, with the top to bottom order being average minimum P \u0026lt; 1e-5, as depicted in Figure 5a. The expression of KRTCAP2 is inversely associated with breast cancer, as shown in Figure 5b. Additionally, the methylation level of cg24674445 has a detrimental effect on the expression of KRTCAP2, as seen in Figure 5c. Similarly, the level of methylation of cg24674445 has a beneficial effect on breast cancer (Figure 5d).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7 The correlation between KRTCAP2 and other genes in the Blood_eQTL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA link exists between the KRTCAP2 gene and the Blood_eQTL gene. An analysis has been conducted on the correlation between KRTCAP2 and the other six genes among the seven genes that are causally related to breast cancer through MR in Blood_eQTL. A robust positive connection between KRTCAP2 and HCN3 has been identified (Figure 6). The mRNA expression level (Figure S2) and DNA methylation degree (Figure S3) of KRTCAP2 in breast cancer are both higher than those in normal individuals.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe relationship between mental disorders and neurotransmitters is a complex and profound field, involving multiple disciplines such as neuroscience, psychology, and psychiatry[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Extensive research indicates that abnormalities in neurotransmitter levels are closely associated with various mental disorders. For instance, patients with depression often have lower levels of neurotransmitters like dopamine and serotonin, which may lead to symptoms such as low mood and loss of interest[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Similarly, patients with mania may have excessively high levels of dopamine, resulting in symptoms like elevated mood and impulsive behavior. Despite the close relationship between neurotransmitter abnormalities and mental disorders, this relationship is not a simple one-to-one correspondence[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In fact, the pathogenesis of mental disorders involves the combined effects of multiple factors, including genetics, environment, and psychology. Therefore, when exploring the relationship between neurotransmitters and mental disorders, we need to maintain a cautious and comprehensive approach.\u003c/p\u003e \u003cp\u003eTo date, the genetic association between pan-cancer and multiple MDs remain largely unclear. Previous research has verified that the SCZ factor would not produce a predominant influence on several cancers risk such as breast cancer[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Some cohort studies have identified a notable correlation between the risk of SCZ and certain malignancies, which could be linked to specific confounding variables. For instance, women with severe mental illness had a greater likelihood of developing breast cancer compared to the overall population. The use of antipsychotic medications that increase prolactin levels is believed to be a potential cause. According to Solmi et al, there is a connection between long-term use of antipsychotic medications that increase prolactin levels and an increased risk of developing breast cancer in women with severe major depression. However, the exact cause of this relationship is yet uncertain[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The link between schizophrenia and cancer is still a subject of debate, possibly because of differences in research methods and the influence of factors that can distort the results, such as smoking, diet, antipsychotic drugs, and inconsistencies in cancer detection and treatment[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In addition, cancer primarily affects older individuals, while those with mental illness generally have a life expectancy that is shortened by over 10 years[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Cancer and mental illnesses significantly affect people of different age groups, but the manifestations and concerns may vary. However, in recent years, an increasing number of young people are also facing the risk of cancer, which may be related to bad living habits, environmental factors, genetics, and various other factors. There are also differences in the types of cancer that are more prevalent among different age groups. For example, among adolescents and young adults, blood system tumors such as lymphoma and leukemia are more common; while in middle-aged and older adults, solid tumors such as lung cancer, breast cancer, and colorectal cancer are more prevalent[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe strength of MR resides in its capacity to mitigate the influence of confounding variables, therefore yielding more dependable and accurate observations regarding the genetic-level association between exposure and outcome. This study examined the causal relationship between genetically predicted 11 mental disorders, such as schizophrenia, drug dependence, alcohol dependence, bipolar disorder, generalized epilepsy, depression, anxiety disorder, bipolar disorder, ADHD, and the risk of developing 14 types of cancer, including lung cancer, gastric cancer, breast cancer, and others. We have found compelling evidence suggesting a genetic link between SCZ (OR\u0026thinsp;=\u0026thinsp;1.03, 95% CI: 1.010\u0026ndash;1.050, P\u0026thinsp;=\u0026thinsp;0.00031), AZD (OR\u0026thinsp;=\u0026thinsp;1.04, 95% CI: 1.020\u0026ndash;1.050, P\u0026thinsp;=\u0026thinsp;3.51E-7), and the likelihood of developing breast cancer. This study found that patients with SCZ and AD have a higher likelihood of developing breast cancer. A new study has uncovered that the association between AD and cancer may encompass various processes. Chen et colleagues have confirmed that circular RNAs (circRNAs) associated with AD diagnosis and clinical severity exhibit a negative correlation in several types of cancer[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Notably, the circRNAs that were shown to be expressed differently in temporal lobe epilepsy showed no connections with malignancy. The circRNAs have a key role in regulating genes that are involved in interleukin-12-mediated signaling and viral response. The genes regulated by circRNAs are significantly involved in interleukin-12-mediated signaling and viral response. The inflammation signaling response induced by circ-PICALM, circRTN4, and circMAN2A1 may serve as the shared pathogenesis in both cancer and AD. In the aging female population with breast cancer, estrogen-modulating treatments (EMTs) have been found to have estrogenic agonist activity in neural tissues. This activity is linked to a decreased risk of Alzheimer's disease. Additionally, EMTs operate as potent estrogen receptor antagonists in breast tissue[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The role of estrogen-regulating therapy in breast tissue and its potential impact on the risk of breast cancer is a complex and variable issue. Different EMT protocols, treatment durations, patient individual differences, and other factors can all affect the outcomes. Therefore, when applying EMT, it is necessary to consider the specific conditions and treatment goals of each patient to develop a personalized treatment plan. Additional studies have shown that individuals with a genetic predisposition to schizophrenia have a higher likelihood of developing breast cancer, ovarian cancer, and thyroid cancer[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].These data confirmed the casual association of MDs and breast cancer, implying the significant influence of mental factor in breast cancer development.\u003c/p\u003e \u003cp\u003eConsidering the critical role of neurotransmitters in orchestrating neural cell homeostasis, we furthermore chose the overlapping genes between neurotransmitters-associated-genes (NAGs) and DEGs in BC to explore the genetic influence of MD on BC. A total of 2 genes (PADI4 and PRDX1) derived from tissue-specific eQTL and 7 genes (PER3, PPT1, NEGR1, KRTCAP2, DPYD, TOR1AIP1, and HCN3) derived from blood-specific eQTL were identified to have a causal relationship with BC. These potential genes identified by eQTL provide a rationale for them as the therapeutic targets. For example, neuronal peptidyl arginine deiminase 4 (PADI4) has been reported to have the potential to induce the brain-autonomous neural repair by activating the transcription of genes involved in recovery processes through histone citrullination[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The new quinone-fused oxazepine, CM728, can effectively inhibit the growth of triple-negative breast cancer (TNBC) cells by boosting the oxidation of Peroxiredoxin-1 (Prdx1). Targeting drug inhibitors that reduce the function of Prdx1 could be a promising approach for treating refractory TNBC[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA total of 129 methylation sites were identified by mQTL analysis, showing a causal link with BC. These specific CpG sites were shown to be considerably enriched in the enhancer marker H3K4me1 alteration. H3K4me1 is a particular alteration of histones that is controlled by LSD1, an important regulator of chromatin structure. In breast cancer, LSD1 functions as an oncogene by removing methyl groups from H3K4me1/2. The stability of LSD1 is precisely controlled by the mechanisms of ubiquitination and deubiquitylation. FBXO24 is classified as an E3 ligase that has the role of ubiquitinating and degrading LSD1. FBXO24 hinders the development of tumors caused by LSD1, making it a tumor suppressor in breast cancer. This is associated with a negative correlation between FBXO24 and LSD1, and is linked to a more favorable prognosis[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This study highlights the predominant role of DNA CpG methylation in BC progress.\u003c/p\u003e \u003cp\u003eFurthermore, KRTCAP2 were found have causal relationships with matching CpG sites (KRTCAP2 vs cg24674445 sites). Colocalization analysis of KRTCAP2 (more methylation sites) indicated that the higher methylation of cg24674445 site of KRTCAP2 CDS region upregulated KRTCAP2 expression, thus increasing BC risk. The protein encoded by the KRTCAP2 gene has specific functions in the organism and may be related to certain functions or characteristics of keratinocytes. Keratinocytes are the main cell type of the skin and are responsible for forming the protective barrier of the skin. Studies have shown that the skin microbiome maintains barrier integrity in keratinocytes through the AHR signaling pathway, which is significant for the management of skin diseases[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. There is a synergistic impact shown between antimalarial medications (mefloquine, artesunate, chloroquine) and antineoplastic agents in BC cells. The antimalarial medications mentioned here block KRTCAP2, which in turn disrupts lysosomal metabolism. This disruption leads to a decrease in lysosomal drug accumulation and an increase in the bioavailability of antineoplastic therapies[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. KRTCAP2 is associated with autoimmune plasma cell dysplasia, Alzheimer's disease, and various tumor types[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. However, little is known about the role of KRTCAP2 in BC. Here, we identified KRTCAP2 as a novel prompting-cancer gene by genetic layer, provide a rationale for the definition of KRTCAP2 in BC progression. However, little is known about the role of KRTCAP2 in BC and more comprehensive study would be urged to clarify the specific role of KRTCAP2 in BC.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations that should be considered when interpreting the findings. First, the sample size of certain GWAS and eQTL datasets may limit the power to detect weaker causal associations, particularly for less common genetic variants. Second, the SMR approach relies heavily on the quality and comprehensiveness of the eQTL and mQTL datasets, which may not fully capture tissue-specific regulatory mechanisms, potentially leading to missed causal signals. Third, while Mendelian randomization is a robust method for inferring causality, it is not immune to horizontal pleiotropy, where genetic instruments may influence the outcome through pathways other than the exposure of interest. Fourth, the study focuses primarily on blood tissue for eQTL and mQTL analyses, which may not fully represent the regulatory landscape of breast cancer, potentially overlooking critical tissue-specific mechanisms. Additionally, the genetic instruments used in this study may explain only a small proportion of the variance in neurotransmitter-related gene expression, limiting the ability to detect subtler effects. Lastly, the clinical relevance of the identified genes and methylation sites in terms of therapeutic targeting remains to be validated in prospective studies. These limitations highlight the need for further research to address these gaps and to explore the biological mechanisms underlying the observed associations in more detail.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset(s) supporting the conclusions of this article is(are) included within the article (and its additional file(s)).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by grants from Beijing Heart to Heart Foundation (HXXT2021ktyj002/ HXXT2021ktyj001) and Haiyan science Foundation (JJMS2022-08).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed with writing\u0026ndash;original draft and writing\u0026ndash;review and editing. QY, YLX and HY generated the figures, completed the writing and literature search. NW and RJY performed data curation and validation. MN and GY contributed equally to project administration and funding acquisition. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFitzmaurice C, Akinyemiju TF, Al Lami FH, Alam T, Alizadeh-Navaei R, Allen C, Alsharif U, Alvis-Guzman N, Amini E, Anderson BO\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGlobal, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2016: A Systematic Analysis for the Global Burden of Disease Study\u003c/strong\u003e. \u003cem\u003eJAMA Oncol \u003c/em\u003e2018, \u003cstrong\u003e4\u003c/strong\u003e(11):1553-1568.\u003c/li\u003e\n\u003cli\u003eSiegel RL, Miller KD, Fuchs HE, Jemal A: \u003cstrong\u003eCancer statistics, 2022\u003c/strong\u003e. \u003cem\u003eCA Cancer J Clin 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\u003cstrong\u003e21\u003c/strong\u003e(12):1303-1316.\u003c/li\u003e\n\u003cli\u003eAlwan W, Di Meglio P: \u003cstrong\u003eGuardians of the barrier: Microbiota engage AHR in keratinocytes to mantain skin homeostasis\u003c/strong\u003e. \u003cem\u003eCell Host Microbe \u003c/em\u003e2021, \u003cstrong\u003e29\u003c/strong\u003e(8):1213-1216.\u003c/li\u003e\n\u003cli\u003eDuarte D, Vale N: \u003cstrong\u003eHow Antimalarials and Antineoplastic Drugs can Interact in Combination Therapies: A Perspective on the Role of PPT1 Enzyme\u003c/strong\u003e. \u003cem\u003eCurr Drug Metab \u003c/em\u003e2021, \u003cstrong\u003e22\u003c/strong\u003e(13):1009-1016.\u003c/li\u003e\n\u003cli\u003eKannan K, Kordestani GK, Galagoda A, Coarfa C, Yen L: \u003cstrong\u003eAberrant MUC1-TRIM46-KRTCAP2 Chimeric RNAs in High-Grade Serous Ovarian Carcinoma\u003c/strong\u003e. \u003cem\u003eCancers \u003c/em\u003e2015, \u003cstrong\u003e7\u003c/strong\u003e(4):2083-2093.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Hanlon TP, Rider LG, Gan L, Fannin R, Paules RS, Umbach DM, Weinberg CR, Shah RR, Mav D, Gourley MF\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGene expression profiles from discordant monozygotic twins suggest that molecular pathways are shared among multiple systemic autoimmune diseases\u003c/strong\u003e. \u003cem\u003eArthritis research \u0026amp; therapy \u003c/em\u003e2011, \u003cstrong\u003e13\u003c/strong\u003e(2):R69.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 8 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":"Pan-cancer, mental disorders, Mendelian randomization, multi-omics, neurotransmitter","lastPublishedDoi":"10.21203/rs.3.rs-6036757/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6036757/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eEpidemiological evidence indicates a potential association between mental disorders and cancer. However, the specific role of neurotransmitter-related genes (NRG) in cancer development remains unclear. This study employs Mendelian randomization with multi-omics summary data to explore the causal effects and underlying mechanisms of NRG in cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods: \u003c/strong\u003eThe causal relationships between 10 mental disorders and 14 cancer types were investigated. NRG was retrieved from the GeneCards database. Transcriptome datasets from breast cancer were gathered from the Gene Expression Omnibus (GEO). The Summary-data-based Mendelian Randomization (SMR) method was utilized for genome-wide association study (GWAS) analysis, incorporating expression quantitative trait loci (eQTLs), DNA methylation QTLs (mQTLs), genetic variants that influence gene expression in the intestines (intestinal eQTLs) and influence the composition of the fecal microbiota (mbQTLs). Colocation analysis was performed to identify potential links between host gene expression and gut microbiota. Sensitivity analyses were conducted using two additional Mendelian randomization techniques.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eMendelian randomization analysis established a causal association between mental disorders and breast cancer. A meta-analysis of five breast cancer datasets identified 821 differentially expressed genes (DEGs) among 829 non-redundant genes. KRTCAP2 was identified as a potential causal gene in blood tissues, while SMR analysis highlighted cg24674445 as a significant methylation site. KRTCAP2 expression was inversely correlated with breast cancer, whereas cg24674445 methylation negatively affected KRTCAP2 expression, suggesting a positive influence of cg24674445 on breast cancer progression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis study using multi-omics Mendelian randomization found that DNA methylation regulates the association between NRG and breast cancer.\u003c/p\u003e","manuscriptTitle":"Mendelian Randomization Analysis of the Relationship between Neurotransmitter-Related Genes and Cancer: Insights from Multi-omics Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-24 10:00:55","doi":"10.21203/rs.3.rs-6036757/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":"3e17b83c-4514-45ec-a30e-34ea8780f184","owner":[],"postedDate":"April 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-06T11:38:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-24 10:00:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6036757","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6036757","identity":"rs-6036757","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}