Exploring the causal relationship between autoimmune diseases and gastrointestinal tumors: A two-sample Mendelian randomization study

doi:10.21203/rs.3.rs-4898009/v1

Download PDF

Research Article

Exploring the causal relationship between autoimmune diseases and gastrointestinal tumors: A two-sample Mendelian randomization study

https://doi.org/10.21203/rs.3.rs-4898009/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Purpose: Autoimmune diseases (AID) may be associated with gastrointestinal cancer. This study used a two-sample Mendelian randomization method to examine the potential correlation between AID and gastrointestinal cancer. AD, such as sarcoidosis, Graves’ disease (GD), multiple sclerosis (MS), psoriasis, rheumatoid arthritis (RA), Sjögren’s syndrome (SS), systemic lupus erythematosus (SLE), type 1 diabetes (T1D), and celiac disease (CD), were selected. Gastrointestinal tumors include colorectal cancer (CRC), colonic pseudopolyposis, colorectal neuroendocrine tumors and carcinomas, and gastrointestinal stromal tumors and sarcomas.

Methods: We used genome-wide association study data from the Finngen R10 database and the IEU study data. We employed the inverse variance-weighted method to explore the causal relationship between the exposure and outcomes.

Results: Sarcoidosis and psoriasis were associated with a reduced risk of CRC, whereas GD was linked to an increased risk. SLE, RA, T1D, and GD are associated with a decreased risk of colonic pseudopolyps, whereas CD, sarcoidosis, psoriasis, and MS are associated with an increased risk.Sarcoidosis, SS, and T1D were associated with a reduced risk of colorectal neuroendocrine tumors and carcinomas, whereas CD and MS were associated with an increased risk. Sarcoidosis, SS, and MS are linked to a reduced risk of developing gastrointestinal stromal tumors and sarcomas, while RA is associated with an increased risk. Additionally, CRC is associated with an increased risk of sarcoidosis.

Conclusion: Autoimmune diseases may be associated with the incidence and development of gastrointestinal tumors, whereas cancer may promote sarcoidosis.

Autoimmune diseases

digestive tract tumors

Mendelian randomization

Autoimmune diseases (AIDs) have an unknown etiology but are hypothesized to result from interactions between undetermined genes and the environment [1]. Meanwhile, as the incidence of AID has increased over the years [2], they not only pose a threat to our lives but also significantly impact our quality of life. Owing to the technological advancements brought about by the Human Genome Project and related research, many risk factors associated with AIDs have been identified [3]. A large-scale, long-term follow-up of patients with AIDs found that these diseases are more common in females and young patients. While the location and timing of cancer onset vary by type of AIDs, the overall cancer incidence rate is higher in these groups [4]. Many researchers have also reported that patients with AIDs have a higher susceptibility to certain types of cancer [5, 6]. Understanding the relationship between AIDs and gastrointestinal cancer may enable us to provide better clinical treatment for AIDs and prevent gastrointestinal cancer in the future.

The incidence and mortality rates of gastrointestinal cancer have increased over the years, significantly affecting human health and posing a major challenge and economic burden to clinical practice and healthcare systems [7, 8]. Colorectal cancer (CRC) has become the third most common cancer globally and the second leading cause of cancer-related deaths [9]. Therefore, it is crucial to explore the various risk factors and causes of gastrointestinal cancer, including genetic factors, sleep duration, obesity, smoking, dietary habits, sedentary lifestyles, and changes in the gut microbiome [10–12]. Autoimmune diseases may disrupt the body’s natural homeostatic balance, leading to complex interactions in the development of digestive tract tumors.

Mendelian randomization (MR) is an analytical approach used to investigate causal relationships between diseases. It leverages single-nucleotide polymorphisms (SNPs) from non-experimental data as instrumental variables (IVs) to infer the impact of exposure on an outcome. This method allows researchers to gain insight into the potential causal effects of genetic variants on disease outcomes, even without experimental manipulation [13]. Furthermore, MR leverages random genetic variation and relatively stable germline DNA sequences across disease states. Compared to traditional observational studies, this approach is less susceptible to confounding factors and reverse causality. Bidirectional MR can quantify the causal influence of each variable, reducing the likelihood of errors [14–16].

Therefore, in this study, we used summary data from genome-wide association studies (GWAS) from both the Finngen R10 and IEU (2011) databases to perform MR analysis. We aimed to determine the causal relationships between nine autoimmune diseases [sarcoidosis, Graves' disease (GD), multiple sclerosis (MS), psoriasis, rheumatoid arthritis (RA), Sjögren's syndrome (SS), systemic lupus erythematosus (SLE), type 1 diabetes (T1D), and celiac disease (CD)] and four gastrointestinal tumors [colorectal cancer (CRC), colonic pseudopolyposis, colorectal neuroendocrine tumors (CR-NETs) and carcinomas, and gastrointestinal stromal tumors (GISTs) and sarcomas].

2.1. Research design

The MR analysis in this study was based on three core assumptions: (1) the selected genetic IVs are strongly related to the exposure, (2) are unrelated to potential confounding factors, and (3) influence the outcome only through the exposure. This two-way MR analysis was conducted in two steps: an AID was studied as the exposure, with gastrointestinal tumors as the outcome in the first step and vice versa in the second step. Figure 1 illustrates the three assumptions and the roadmap of the research design.

2.2. Sources of GWAS data for AIDs and gastrointestinal tumors

GWAS summary statistics for AIDs and gastrointestinal tumors were sourced from the Finnigen and IEU Open databases (for CD). All data were obtained from European populations. The AIDs included sarcoidosis (N = 410,019), GD (N = 412,181), MS (N = 410,970), psoriasis (N = 407,876), RA (N = 276,465), SjS (N = 402,090), SLE (N = 411,343), T1D (N = 331,094), and CD (N = 24,269). Gastrointestinal tumors included CRC (N = 321,040), colonic pseudopolyposis (N = 393,056), CR-NETs and carcinomas (N = 314,544), and GISTs and sarcomas (N = 314,405).

2.3. Genetic IV selection

(1) The IVs selected for analysis were highly related to the corresponding exposures, and significant SNPs were selected based on a loose cutoff of P < 5× 10^− 8 to ensure sufficient IVs for screening.

(2) The IVs were mutually independent and avoided the offset caused by linkage disequilibrium between SNPs (r² < 0.1, linkage disequilibrium distance > 500 kb).

(3) IVs with an F-statistic < 10 were excluded to minimize potential weak instrument bias [F = R²(n - k − 1) / (k(1 – R²)), where n is the sample size, k is the number of included IVs, and R² is the exposure variance explained by the selected SNPs]. To eliminate the impact of confounding factors, IVs strongly associated with body mass index, smoking, drinking, sedentary behavior, and inflammatory bowel disease (P < 5×10^− 8) were excluded. MR analysis was then performed with the remaining IVs.

2.4. MR analysis

The bidirectional relationship between AIDs and gastrointestinal tumors was evaluated using inverse variance-weighted (IVW), weighted median, weighted mode, simple mode, and MR-Egger methods as primary statistical approaches (https://mrcieu.github.io/TwoSampleMR/). If there is no clear evidence of directional pleiotropy (MR-Egger intercept, P > 0.05), the IVW method is considered the most accurate approach for estimating causality [17, 18]. When there was insufficient evidence of heterogeneity (P for MR heterogeneity > 0.05) in the selected genetic IVs, a random-effects model was used; otherwise, a fixed-effects model was used. A weighted median method was also used, which can generate effective causal estimates when at least 50% of valid IVs are present in all the selected genetic IVs [18]. MR-PRESSO was used to test for pleiotropy and detect outliers. Considering multiple tests, we applied a moderate approach by adjusting the p-values using the false discovery rate (FDR).

2.5. Statistical analysis

To assess the causal relationship between AIDs and gastrointestinal tumors, the “Mendelian Randomization” software package (version 0.5.8) [19] was used to conduct IVW [20], weighted median [18], and weighted model-based methods [21]. Cochran’s Q statistic and related P-values were used to test for heterogeneity among the IVs. Random-effects IVW, as opposed to fixed-effects IVW, was employed if the null hypothesis was rejected [20]. MR-Egger, a widely used approach that assumes the presence of horizontal multiplicity if its intercept term is large, was employed to eliminate the horizontal pleiotropic effect [22]. The MR pleiotropy residual sum and outlier (MR-PRESSO) method was applied to exclude any potential horizontal pleiotropic outliers that might significantly impact the estimation outcomes within the MR-PRESSO package [23]. In addition, scatter and forest plots were used to visualize the impact of exposure on the outcome.

2.6. Ethical approval

This study utilized publicly available Finnigen and IEU Open GWAS data; therefore, ethics approval was not required.

3.1. IVs

Using the above analytical methods, to determine the relationship between AIDs as the exposure and CRC as the outcome, AIDs were filtered to yield 113 SNPs for sarcoidosis, 112 for GD, 100 for MS, 201 for psoriasis, 243 for RA, 90 for SjS, 6 for SLE, 12 for T1D, and 177 for CD. To determine the relationship between AIDs as the exposure and CR-NETs and carcinomas as the outcome, AIDs were filtered to yield 113 SNPs for sarcoidosis, 112 for GD, 101 for MS, 202 for psoriasis, 244 for RA, 89 for SjS, 6 for SLE, 13 for T1D, and 175 for CD. To determine the relationship between AIDs as exposure and colonic pseudopolyps, AIDs were filtered to yield 114 SNPs for sarcoidosis, 110 for GD, 100 for MS, 202 for psoriasis, 245 for RA, 92 for SjS, 6 for SLE, 13 for T1D, and 177 for CD. Regarding the relationship between AID and GISTs and sarcomas, AIDs were filtered to yield 115 SNPs for sarcoidosis, 112 for GD, 100 for MS, 201 for psoriasis, 245 for RA, 91 for SjS, 6 for SLE, 13 for T1D, and 177 for CD. Regarding the relationship between gastrointestinal tumors as the exposure and AIDs as the outcome, only CRC showed a mutual causal relationship with sarcoidosis, with 16 SNPs remaining after filtering. For a detailed examination of the relationship between AIDs and gastrointestinal tumors, please refer to Attachment 2.

3.2. AIDs and CRC

MR analysis indicated a causal relationship between AIDs (sarcoidosis, GD, and psoriasis) and CRC (IVW P-values = 0.008, 0.001, and 2.34E-06, respectively; odds ratio (OR) = 0.97, 95% confidence interval (CI) = 0.95–0.99; OR = 1.04, 95% CI = 1.02–1.06; and OR = 0.95, 95% CI = 0.94–0.97, respectively; Scatter Plots 2A-2C; Figure 2).

3.3. AIDs and colonic pseudopolyps

MR analysis showed a significant causal relationship between AIDs (sarcoidosis, GD, MS, psoriasis, RA, SLE, T1D, and CD) and colonic pseudopolyps (IVW P-values = 0.018, 5.6×10^-6, 0.001, 2.15×10^-8, 0.008, 0.010, 0.018, and 8.1×10^-8, respectively; OR = 1.18, 95% CI = 1.03–1.36; OR = 0.73, 95% CI = 0.64–0.84; OR = 1.27, 95% CI = 1.12–1.44; OR = 1.54, 95% CI = 1.32–1.78; OR = 0.83, 95% CI = 0.72–0.95; OR = 0.68, 95% CI = 0.51–0.91; OR = 0.87, 95% CI = 0.78–0.98; and OR = 1.16, 95% CI= 1.10–1.22, respectively; Scatter Plots 3A-3H; Figure 3).

3.4. AIDs and CR-NETs and carcinomas

MR analysis showed a significant causal relationship between AIDs (sarcoidosis, MS, SjS, T1D, and CD) and CR-NETs and carcinomas (IVW P-values = 0.032, 0.031, 0.011, 0.002, and 0.012, respectively; OR = 0.93, 95% CI = 0.86–0.99; OR = 1.07, 95% CI = 1.01–1.14; OR = 0.92, 95% CI = 0.86–0.98; OR = 0.92, 95% CI = 0.87–0.97; OR = 1.03, 95% CI = 1.01–1.06, respectively; Scatter Plots 4A-E; Figure 4).

3.5. AIDs and GISTs and sarcomas MR analysis indicated a significant causal relationship between AIDs (sarcoidosis, RA, MS, and SjS) and GISTs and sarcomas (IVW P-values = 4.92E-07, 0.001646, 0.001936, and 0.001, respectively; OR = 0.82, 95% CI = 0.76–0.89; OR = 1.13, 95% CI = 1.05–1.22; OR = 0.89, 95% CI = 0.83–0.96; OR = 0.87, 95% CI = 0.81–0.94, respectively; Scatter Plots 5A-D; Figure 5).

3.6. Reverse MR analysis

Reverse MR analysis indicated a causal relationship between CRC and sarcoidosis among gastrointestinal tumors (IVW P-value = 0.024; OR = 1.11 and 95% CI=1.01–1.21; Scatter Plot 6A). A summary of the analysis is presented in Figure 6. For the remaining gastrointestinal tumor and AID pairs, the IVW P-values were >0.05, and MR analysis could not be performed. For more details, please refer to attachment 3.

3.7. Co-localization analysis

Bayesian co-localization analysis can evaluate the shared local genetic architecture between two traits and is valuable for further identifying MR associations caused by LD confounding [24]. In this study, the “COLOC” package was used for Bayesian co-localization analysis [25]. This package incorporates sophisticated algorithms and models to estimate the probability of co-localization between genes associated with the two traits under investigation. To determine if co-localization had occurred, a threshold was established based on previous studies. Specifically, PPH4 represents the probability that both traits share the same causal variant; if the posterior probability of hypothesis 4 (PPH4) exceeded 80%, it was considered significant evidence supporting the co-localization of genes [26]. Co-localization analysis between AIDs and gastrointestinal tumors in this study did not reveal any significant findings.

To the best of our knowledge, this is the first study to use two-sample MR to analyze the causal relationship between autoimmune diseases (sarcoidosis, GD, MS, psoriasis, RA, SjS, SLE, T1D, and CD) and gastrointestinal cancers (CRC, colonic pseudopolyposis, CR-NETs, carcinomas, and GISTs) in European populations using the FinnGen and IEU Open databases.

Currently, the overall prevalence of AIDs is approximately 3–5%, and there is a significant familial tendency [27, 28]. Moreover, AIDs can lead to alterations in the gut microbiota [29], which in turn can have complex effects on the development of gastrointestinal cancer, particularly because of their impact on the intestinal mucosa [30]. Current research on the etiology of CRC is extensive, and inflammatory bowel diseases related to AIDs are recognized as high-risk factors. Our study found that GD also contributes to the incidence of CRC (OR = 1.04, 95% CI = 1.02–1.06, P = 0.001), consistent with Munoz et al.'s report that GD can promote the occurrence of malignancies [31]. One possible reason is that GD promotes the expression of cathepsin B (CB), which is frequently found in CRC. Previous studies have shown that CB can enhance the invasiveness of malignant cells into tissues [32, 33]. However, our study suggests that sarcoidosis and psoriasis may have a protective effect against CRC (IVW OR = 0.97, 95% CI = 0.95–0.99; OR = 0.95, 95% CI = 0.94–0.97, respectively; P = 0.008 and 2.34E-06, respectively). Sarcoidosis is a chronic inflammatory disease of unknown etiology, characterized by widespread and localized inflammatory responses and cytokine secretion. This protective effect may be attributed to the impaired function of myeloid dendritic cells and their imbalance with regulatory T cells, leading to decreased cellular immune function [34]. However, Mathew et al. found that T cell function in patients with sarcoidosis remains relatively unchanged [35], which may help combat the development of CRC. Additionally, Zielonka et al. observed a significant increase in the production of monocytes in the peripheral blood of patients with sarcoidosis (P < 0.05), which was most prominent in stage II disease [36].Our findings suggest that patients with psoriasis have a reduced risk of CRC, which is inconsistent with a meta-analysis of a previous observational study [37]. Several complex factors may have contributed to this inconsistency: 1). P53 expression is increased in patients with psoriasis [38]; 2). Psoriasis is more prevalent in younger individuals, while colon cancer typically affects older populations; and 3).CRC has been associated with alcohol consumption, smoking, and obesity [39]. In the absence of adjustments for these risk factors, several studies have not found a significant association between psoriasis and CRC [40-42].

The precise mechanism of colonic pseudopolyp formation currently remains unknown; however, it is believed to involve non-tumorous lesions formed by the colonic mucosa after repeated inflammation and ulceration during the healing process. Pseudopolyps are also considered at risk for developing CRC [43], and large pseudopolyps can cause severe intestinal obstruction. This study demonstrated that SLE, RA, T1D, and GD were associated with a reduced risk of colonic pseudopolyps (IVW OR = 0.68, 95% CI = 0.51–0.91; OR = 0.83, 95% CI = 0.72–0.95; OR = 0.87, 95% CI = 0.78–0.98; OR = 0.73, 95% CI = 0.64–0.84, respectively; P = 0.010, 0.008, 8.10E-08, and 5.60E-06, respectively). Possible reasons for this finding may be as follows: 1). Macrophages in patients with SLE exhibit a significant proinflammatory role during the onset of the disease and tend to shift toward an anti-inflammatory phenotype during treatment or recovery [44]. 2). Before the onset of these diseases, low levels of inflammatory factors in the blood can promote immune system activation and autoantibody production, creating an "anti-inflammatory" state in parts of the body that are not typically affected. 3). Some studies have shown that patients with T1D have elevated levels of mast cells [45], which play a crucial role in anti-inflammatory responses in the gastrointestinal tract [46]. Additionally, the abundance of bifidobacteria is positively correlated with the incidence of T1D, and an increase in bifidobacteria can further enhance immunity [47, 48]. 4). Immunoglobulins in patients with GD may activate the insulin-like growth factor-1 receptor, and autoantibodies against this receptor contribute to fibroblast activation [49]. This study also indicated that CD, sarcoidosis, psoriasis, and MS pose a risk for the development of colonic pseudopolyps, with the following odds ratios: CD (IVW OR = 1.16, 95% CI = 1.10–1.22; P = 8.10E-08), sarcoidosis (OR = 1.18, 95% CI = 1.03–1.36; P = 0.019), psoriasis (OR = 1.54, 95% CI = 1.32–1.78; P = 2.15E-08), and MS (OR = 1.27, 95% CI = 1.12–1.44; P = 0.001). The possible reasons for this are as follows. 1). CD causes dysbiosis, which further exacerbates intestinal inflammation [50, 51]. 2). In sarcoidosis, T cells are suppressed by transforming them into regulatory T cells [52, 53]. 3). Psoriasis, a representative skin inflammatory disease, involves the circulation of immune cells and inflammatory cytokines from the skin throughout the body, which can promote the development of systemic inflammatory diseases [54-56]. 4). Patients with MS have numerous proinflammatory cells, including Th1 and Th17 cells, that act throughout the body [57, 58].

Although CR-NETs are rare, poorly differentiated neuroendocrine carcinomas have a poor prognosis. CR-NETs are the fastest-growing among all NETs and are the second most common NETs in China [59]. The development of CR-NETs is very slow, and most cases are asymptomatic, often discovered incidentally during colonoscopy screening for CRC. Some individuals may experience anal discomfort, rectal bleeding, or changes in bowel habits. Since up to 90% of NETs are <10 mm, they are typically seen during colonoscopy as small, round, polypoid-like lesions with a smooth appearance and normal or slightly discolored mucosa [60, 61]. This often leads to endoscopic misdiagnosis of intestinal polyps during colonoscopy, causing delays in treatment [62]. When the tumor size is >20 mm, the patient’s prognosis is significantly poor [63]. An increased risk of rectal NETs development is associated with metabolic syndrome. In addition, NETs are associated with hereditary neuroendocrine syndromes [64]. This study demonstrates that sarcoidosis, SjS, and T1D are associated with a reduced risk of developing CR-NETs and carcinomas, with odds ratios as follows: sarcoidosis (IVW OR = 0.93, 95% CI = 0.86–0.99; P = 0.032), SjS (OR = 0.92, 95% CI = 0.86–0.98; P = 0.011), and T1D (OR = 0.92, 95% CI = 0.87–0.97; P = 0.002). Possible reasons for this are as follows. First, sarcoidosis is characterized by the expression of several somatostatin receptors [65]. Somatostatin has inhibitory (growth arrest) and cytotoxic (apoptosis) effects on cells and can suppress the release of growth factors, inhibit angiogenesis, and regulate immune cells [66]. Second, SjS is characterized by high expression of absent in melanoma 2 (AIM2), which enhances certain immune functions, such as antitumor activity and the initiation of immune responses. This may have an inhibitory effect on CR-NETs and carcinomas [67]. Third, T1D often affects young patients and can lead to severe complications in multiple systems, potentially reducing the lifespan. In contrast, CR-NETS and carcinomas typically occur in older adults. Therefore, early mortality associated with T1D may contribute to a reduced incidence of CR-NETs and carcinomas. However, this study indicates that CD and MS may promote the occurrence of CR-NETs and carcinomas (IVW OR = 1.03, 95% CI = 1.01–1.06; OR = 1.07, 95% CI = 1.01–1.14; P = 0.012 and 0.031, respectively). The possible reasons for this are as follows. First, in patients with CD, long-term malabsorption and malnutrition, increased intestinal permeability, chronic intestinal inflammation, endocrine disorders, and dysbiosis may contribute to the development of metabolic syndrome and the occurrence of CR-NETs and carcinomas [68]. Second, MS is often associated with hypertension and hyperlipidemia [69], and the interrelationship between these conditions remains unclear. The presence of vascular disease can increase the risk of CR-NETs and worsen the progression of MS [70].

GISTs are malignant tumors of the digestive tract. These are the most common mesenchymal tumors and sarcomas [71]. GISTs and sarcomas often lead to gastrointestinal bleeding, abdominal pain, weight loss, and intestinal obstruction. This study indicates that sarcoidosis, SjS, and MS are associated with a reduced risk of developing GISTs and sarcomas (IVW OR = 0.82, 95% CI = 0.76–0.89; OR = 0.87, 95% CI = 0.81–0.94; OR = 0.89, 95% CI = 0.83–0.96; P = 4.92E-07, 0.001, and 0.002, respectively). The possible reasons for this are as follows. First, as mentioned previously, patients with sarcoidosis often have intact T-cell function, significantly enhanced generation of peripheral blood mononuclear cells, and abundant expression of somatostatin receptors. These characteristics contribute to tumor growth suppression. Second, in some patients with SjS, elevated interferon levels have been observed, which significantly enhance immune function [72]. Third, natural killer cells play a protective role in patients with MS [73]. Similarly, natural killer cells in patients exhibit antitumor effects. This study also showed that RA is associated with an increased risk of developing GISTs and sarcomas (IVW OR = 1.13, 95% CI = 1.05–1.22; P = 0.002). The possible reasons for this are as follows. First, elevated levels of gastrointestinal tumor markers (CEA, CA199, CA15-3, and CA125) are often found in patients with RA [74], although the exact relationship between these markers and tumor occurrence is unclear. Second, immunosuppressive anti-RA treatments may suppress the ability of the immune system to monitor the body’s internal environment and destroy malignant cells, thereby creating an internal environment conducive to tumor development.

Persistent inflammation associated with RA can promote WNT5A signaling [75], which is involved in the development of multiple malignancies [76]. For example, the correlation between RA and lymphoma has been widely recognized. Interestingly, our research indicates that CRC may promote the occurrence of sarcoidosis (IVW OR = 1.11, 95% CI = 1.01–1.21, P = 0.024). Sarcoidosis is primarily caused by abnormal immune responses that trigger immune reactions in the affected organs, leading to cell aggregation, proliferation, differentiation, and the formation of granulomas. In early CRC, activation of the WNT pathway through intestinal epithelial cells can result in the loss of intestinal barrier function, allowing for the interaction of gut microbes, immune cells, and cytokines. Simultaneously, an increase in inflammatory factors can accelerate tumor growth [77]. Furthermore, tumors can trigger an inflammatory tumor microenvironment (TME) and rely primarily on intercellular interactions within the TME to promote local tumor growth and the formation of distant metastases [78]. In the later stages of tumor development, the loss of P53 function in colon cells can compromise the integrity of the epithelium, facilitating microbial activation of the NF-κB and STAT3 pathways and further triggering inflammation [79]. CRC shapes the internal inflammatory environment, potentially leading to abnormal immune responses and triggering reactions in the affected organs, which may promote the occurrence of sarcoidosis. Additionally, the tumor itself creates an immunosuppressive phenotype, protecting it from .

Co-localization analysis did not reveal any definitive significance, leading us to believe that there was no direct correlation between AIDs and gastrointestinal tumors at the genetic level. Nevertheless, statistically significant associations between these diseases were observed in our study, indicating that their potential relationship requires further research.

Our study offers several key advantages. First, by employing an MR design, we avoided the confounding factors and reverse causality issues that often affect observational studies. Second, our sensitivity analyses considered potential influencing factors, which enhances the robustness of our results. Third, Bayesian co-localization analysis indicated no direct genetic correlation between AIDs and gastrointestinal tumors, although other effects of AIDs on the body might influence gastrointestinal tumor risk, offering valuable insights for future clinical treatment. Finally, using the Finngen database, which includes over 200,000 participants, ensured the reliability of our findings.

However, the study also had some limitations. First, we primarily utilized data from the Finngen R10 database and IEU (2011) open GWAS data. The IVs used in our study were derived from European samples, which may have resulted in data overlap. Second, because our sample originated in Europe, the generalizability of our conclusions to other ethnic groups remains to be validated. Third, the absence of a causal relationship between certain diseases in our study may have been due to insufficient sample sizes. Further research is required to explore these relationships further.

MR analysis indicated that sarcoidosis and psoriasis were associated with a reduced risk of CRC, whereas GD was associated with an increased risk. SLE, RA, T1D, and GD are associated with a decreased risk of colonic pseudopolyps, whereas CD, sarcoidosis, psoriasis, and MS are associated with an increased risk. Sarcoidosis, SjS, and T1D were associated with a reduced risk of CR-NETs and carcinomas, while CD and MS were linked to an increased risk. Sarcoidosis, SjS, and MS are associated with a reduced risk of GISTs and sarcomas, whereas RA is associated with an increased risk. CRC is associated with an increased risk of sarcoidosis. Although the mechanisms underlying these interactions remain unclear, they provide important directions for future research.

In summary, AIDs are closely associated with gastrointestinal tumors. The results of this study suggest that clinicians should prioritize cancer screening and early tumor intervention for patients with AIDs. Further investigation into the mechanisms by which AIDs influence tumor development could provide crucial insights for researchers working on new drug development.

Funding: This work was supported by the National Natural Science Foundation of China (No. 82271943,81971672), the Fundamental Research Funds for the Central Universities (No. 21623314), the Guangzhou Science and Technology Plan Project (No. 2023A03J1037), and the Clinical Frontier Technology Program of the First Affiliated Hospital of Jinan University, China (No. JNU1AF-CFTP-2022-a01233).

Competing Interests: The authors declare that they have no competing interests.

Data Availability Statement: The availability of data can be found in the Finngen R10 database and IEU open GWAS data.

Author Contributions: X.C and J.W designed the study and wrote the manuscript. X.X has enhanced the manuscript. D.Z, Q.D, and W.L provided technical and financial support for the research proposal. C.S and L.L provided financial support. All authors read and approved the final manuscript.

Institutional Review Board Statement: The study utilized publicly available Finnigen and IEU open GWAS data; therefore, ethical approval was not required.

Informed Consent Statement: Not applicable.

Acknowledgments: Not applicable.

Ellis JA, Kemp AS, Ponsonby AL. Gene-environment interaction in autoimmune disease. Expert Rev Mol Med. 2014;16:e4. 10.1017/erm.2014.5.
Dinse GE, Parks CG, Weinberg CR, Co CA, Wilkerson J, Zeldin DC, Chan EKL, Miller FW. Increasing Prevalence of Antinuclear Antibodies in the United States. Arthritis Rheumatol. 2022;74:2032–41. 10.1002/art.42330.
Caliskan M, Brown CD, Maranville JC. A catalog of GWAS fine-mapping efforts in autoimmune disease. Am J Hum Genet. 2021;108:549–63. 10.1016/j.ajhg.2021.03.009.
Zhou Z, Liu H, Yang Y, Zhou J, Zhao L, Chen H, Fei Y, Zhang W, Li M, Zhao Y, Zeng X, Zhang F, Yang H, Zhang X. The five major autoimmune diseases increase the risk of cancer: epidemiological data from a large-scale cohort study in China. Cancer Commun (Lond). 2022;42:435–46. 10.1002/cac2.12283.
Vannella L, Sbrozzi-Vanni A, Lahner E, Bordi C, Pilozzi E, Corleto VD, Osborn JF, Delle Fave G, Annibale B. Development of type I gastric carcinoid in patients with chronic atrophic gastritis. Aliment Pharmacol Ther. 2011;33:1361–9. 10.1111/j.1365-2036.2011.04659.x.
Chang SH, Park JK, Lee YJ, Yang JA, Lee EY, Song YW, Lee EB. Comparison of cancer incidence among patients with rheumatic disease: a retrospective cohort study. Arthritis Res Ther. 2014;16:428. 10.1186/s13075-014-0428-x.
Rocken C, Neumann U, Ebert MP. [New approaches to early detection, estimation of prognosis and therapy for malignant tumours of the gastrointestinal tract]. Z Gastroenterol. 2008;46:216–22. 10.1055/s-2007-963448.
Kloppel G, Anlauf M. Epidemiology, tumour biology and histopathological classification of neuroendocrine tumours of the gastrointestinal tract. Best Pract Res Clin Gastroenterol. 2005;19:507–17. 10.1016/j.bpg.2005.02.010.
Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73:17–48. 10.3322/caac.21763.
Yuan S, Mason AM, Titova OE, Vithayathil M, Kar S, Chen J, Li X, Burgess S, Larsson SC. Morning chronotype and digestive tract cancers: Mendelian randomization study. Int J Cancer. 2023;152:697–704. 10.1002/ijc.34284.
Song M, Garrett WS, Chan AT. Nutrients, foods, and colorectal cancer prevention. Gastroenterology. 2015;148:1244-60 e16. 10.1053/j.gastro.2014.12.035
Jensen RT, Berna MJ, Bingham DB, Norton JA. Inherited pancreatic endocrine tumor syndromes: advances in molecular pathogenesis, diagnosis, management, and controversies. Cancer. 2008;113:1807–43. 10.1002/cncr.23648.
Bowden J, Holmes MV. Meta-analysis and Mendelian randomization: A review. Res Synth Methods. 2019;10:486–96. 10.1002/jrsm.1346.
Burgess S, Scott RA, Timpson NJ, Davey Smith G, Thompson SG, Consortium E-I. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol. 2015;30:543–52. 10.1007/s10654-015-0011-z.
Nattel S. Canadian Journal of Cardiology January 2013: genetics and more. Can J Cardiol. 2013;29:1–2. 10.1016/j.cjca.2012.11.015.
Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23:R89–98. 10.1093/hmg/ddu328.
Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–25. 10.1093/ije/dyv080.
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;40:304–14. 10.1002/gepi.21965.
Yavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol. 2017;46:1734–9. 10.1093/ije/dyx034.
Burgess S, Small DS, Thompson SG. A review of instrumental variable estimators for Mendelian randomization. Stat Methods Med Res. 2017;26:2333–55. 10.1177/0962280215597579.
Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol. 2017;46:1985–98. 10.1093/ije/dyx102.
Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32:377–89. 10.1007/s10654-017-0255-x.
Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693–8. 10.1038/s41588-018-0099-7.
Kanduri C, Bock C, Gundersen S, Hovig E, Sandve GK. Colocalization analyses of genomic elements: approaches, recommendations and challenges. Bioinformatics. 2019;35:1615–24. 10.1093/bioinformatics/bty835.
Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, Plagnol V. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014;10:e1004383. 10.1371/journal.pgen.1004383.
Lin J, Zhou J, Xu Y. Potential drug targets for multiple sclerosis identified through Mendelian randomization analysis. Brain. 2023;146:3364–72. 10.1093/brain/awad070.
Eaton WW, Rose NR, Kalaydjian A, Pedersen MG, Mortensen PB. Epidemiology of autoimmune diseases in Denmark. J Autoimmun. 2007;29:1–9. 10.1016/j.jaut.2007.05.002.
Jacobson DL, Gange SJ, Rose NR, Graham NM. Epidemiology and estimated population burden of selected autoimmune diseases in the United States. Clin Immunol Immunopathol. 1997;84:223–43. 10.1006/clin.1997.4412.
Scher JU, Sczesnak A, Longman RS, Segata N, Ubeda C, Bielski C, Rostron T, Cerundolo V, Pamer EG, Abramson SB, Huttenhower C, Littman DR. Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis. Elife. 2013;2:e01202. 10.7554/eLife.01202.
Wong-Rolle A, Wei HK, Zhao C, Jin C. Unexpected guests in the tumor microenvironment: microbiome in cancer. Protein Cell. 2021;12:426–35. 10.1007/s13238-020-00813-8.
Munoz JM. Incidence of Malignant Neoplasms of All Types in Patients With Graves' Disease. Arch Intern Med. 1978;138:944–7. 10.1001/archinte.1978.03630310034015.
Shuja S, Cai J, Iacobuzio-Donahue C, Zacks J, Beazley RM, Kasznica JM, O'Hara CJ, Heimann R, Murnane MJ. Cathepsin B activity and protein levels in thyroid carcinoma, Graves' disease, and multinodular goiters. Thyroid. 1999;9:569–77. 10.1089/thy.1999.9.569.
Duffy MJ. The role of proteolytic enzymes in cancer invasion and metastasis. Clin Exp Metastasis. 1992;10:145–55. 10.1007/BF00132746.
Miyara M, Amoura Z, Parizot C, Badoual C, Dorgham K, Trad S, Kambouchner M, Valeyre D, Chapelon-Abric C, Debre P, Piette JC, Gorochov G. The immune paradox of sarcoidosis and regulatory T cells. J Exp Med. 2006;203:359–70. 10.1084/jem.20050648.
Mathew S, Bauer KL, Fischoeder A, Bhardwaj N, Oliver SJ. The anergic state in sarcoidosis is associated with diminished dendritic cell function. J Immunol. 2008;181:746–55. 10.4049/jimmunol.181.1.746.
Zielonka TM, Demkow U, Puscinska E, Golian-Geremek A, Filewska M, Zycinska K, Bialas-Chromiec B, Wardyn KA, Skopinska-Rozewska E. TNFalpha and INFgamma inducing capacity of sera from patients with interstitial lung disease in relation to its angiogenesis activity. J Physiol Pharmacol. 2007;58(Suppl 5):767–80. 10.1007/s00408-011-9291-6.
Fu Y, Lee CH, Chi CC. Association of psoriasis with colorectal cancer. J Am Acad Dermatol. 2021;85:1429–36. 10.1016/j.jaad.2020.09.050.
Kim SA, Ryu YW, Kwon JI, Choe MS, Jung JW, Cho JW. Differential expression of cyclin D1, Ki–67, pRb, and p53 in psoriatic skin lesions and normal skin. Mol Med Rep. 2018;17:735–42. 10.3892/mmr.2017.8015.
Rossi M, Jahanzaib Anwar M, Usman A, Keshavarzian A, Bishehsari F. Colorectal Cancer and Alcohol Consumption-Populations to Molecules. Cancers (Basel). 2018;10. 10.3390/cancers10020038.
Ji J, Shu X, Sundquist K, Sundquist J, Hemminki K. Cancer risk in hospitalised psoriasis patients: a follow-up study in Sweden. Br J Cancer. 2009;100:1499–502. 10.1038/sj.bjc.6605027.
Boffetta P, Gridley G, Lindelof B. Cancer risk in a population-based cohort of patients hospitalized for psoriasis in Sweden. J Invest Dermatol. 2001;117:1531–7. 10.1046/j.0022-202x.2001.01520.x.
Paul CF, Ho VC, McGeown C, Christophers E, Schmidtmann B, Guillaume JC, Lamarque V, Dubertret L. Risk of malignancies in psoriasis patients treated with cyclosporine: a 5 y cohort study. J Invest Dermatol. 2003;120:211–6. 10.1046/j.1523-1747.2003.12040.x.
Politis DS, Katsanos KH, Tsianos EV, Christodoulou DK. Pseudopolyps in inflammatory bowel diseases: Have we learned enough? World J Gastroenterol. 2017;23:1541–51. 10.3748/wjg.v23.i9.1541.
Wang J, Xie L, Wang S, Lin J, Liang J, Xu J. Azithromycin promotes alternatively activated macrophage phenotype in systematic lupus erythematosus via PI3K/Akt signaling pathway. Cell Death Dis. 2018;9:1080. 10.1038/s41419-018-1097-5.
Martino L, Masini M, Bugliani M, Marselli L, Suleiman M, Boggi U, Nogueira TC, Filipponi F, Occhipinti M, Campani D, Dotta F, Syed F, Eizirik DL, Marchetti P, De Tata V. Mast cells infiltrate pancreatic islets in human type 1 diabetes. Diabetologia. 2015;58:2554–62. 10.1007/s00125-015-3734-1.
Bischoff SC. Mast cells in gastrointestinal disorders. Eur J Pharmacol. 2016;778:139–45. 10.1016/j.ejphar.2016.02.018.
Xu Q, Ni JJ, Han BX, Yan SS, Wei XT, Feng GJ, Zhang H, Zhang L, Li B, Pei YF. Causal Relationship Between Gut Microbiota and Autoimmune Diseases: A Two-Sample Mendelian Randomization Study. Front Immunol. 2021;12:746998. 10.3389/fimmu.2021.746998.
Yang X, Cao Q, Ma B, Xia Y, Liu M, Tian J, Chen J, Su C, Duan X. Probiotic powder ameliorates colorectal cancer by regulating Bifidobacterium animalis, Clostridium cocleatum, and immune cell composition. PLoS ONE. 2023;18:e0277155. 10.1371/journal.pone.0277155.
Place RF, Krieger CC, Neumann S, Gershengorn MC. Inhibiting thyrotropin/insulin-like growth factor 1 receptor crosstalk to treat Graves' ophthalmopathy: studies in orbital fibroblasts in vitro. Br J Pharmacol. 2017;174:328–40. 10.1111/bph.13693.
Collado MC, Donat E, Ribes-Koninckx C, Calabuig M, Sanz Y. Specific duodenal and faecal bacterial groups associated with paediatric coeliac disease. J Clin Pathol. 2009;62:264–9. 10.1136/jcp.2008.061366.
Heyman M, Abed J, Lebreton C, Cerf-Bensussan N. Intestinal permeability in coeliac disease: insight into mechanisms and relevance to pathogenesis. Gut. 2012;61:1355–64. 10.1136/gutjnl-2011-300327.
Lu LF, Thai TH, Calado DP, Chaudhry A, Kubo M, Tanaka K, Loeb GB, Lee H, Yoshimura A, Rajewsky K, Rudensky AY. Foxp3-dependent microRNA155 confers competitive fitness to regulatory T cells by targeting SOCS1 protein. Immunity. 2009;30:80–91. 10.1016/j.immuni.2008.11.010.
Allantaz F, Cheng DT, Bergauer T, Ravindran P, Rossier MF, Ebeling M, Badi L, Reis B, Bitter H, D'Asaro M, Chiappe A, Sridhar S, Pacheco GD, Burczynski ME, Hochstrasser D, Vonderscher J, Matthes T. Expression profiling of human immune cell subsets identifies miRNA-mRNA regulatory relationships correlated with cell type specific expression. PLoS ONE. 2012;7:e29979. 10.1371/journal.pone.0029979.
Dainichi T, Kitoh A, Otsuka A, Nakajima S, Nomura T, Kaplan DH, Kabashima K. The epithelial immune microenvironment (EIME) in atopic dermatitis and psoriasis. Nat Immunol. 2018;19:1286–98. 10.1038/s41590-018-0256-2.
Sawada Y, Gallo RL. Role of Epigenetics in the Regulation of Immune Functions of the Skin. J Invest Dermatol. 2021;141:1157–66. 10.1016/j.jid.2020.10.012.
Tomura M, Honda T, Tanizaki H, Otsuka A, Egawa G, Tokura Y, Waldmann H, Hori S, Cyster JG, Watanabe T, Miyachi Y, Kanagawa O, Kabashima K. Activated regulatory T cells are the major T cell type emigrating from the skin during a cutaneous immune response in mice. J Clin Invest. 2010;120:883–93. 10.1172/JCI40926.
Wang J, Jelcic I, Muhlenbruch L, Haunerdinger V, Toussaint NC, Zhao Y, Cruciani C, Faigle W, Naghavian R, Foege M, Binder TMC, Eiermann T, Opitz L, Fuentes-Font L, Reynolds R, Kwok WW, Nguyen JT, Lee JH, Lutterotti A, Munz C, Rammensee HG, Hauri-Hohl M, Sospedra M, Stevanovic S, Martin R. HLA-DR15 Molecules Jointly Shape an Autoreactive T Cell Repertoire in Multiple Sclerosis. Cell. 2020;183:1264-81 e20. 10.1016/j.cell.2020.09.054
Hu D, Notarbartolo S, Croonenborghs T, Patel B, Cialic R, Yang TH, Aschenbrenner D, Andersson KM, Gattorno M, Pham M, Kivisakk P, Pierre IV, Lee Y, Kiani K, Bokarewa M, Tjon E, Pochet N, Sallusto F, Kuchroo VK, Weiner HL. Transcriptional signature of human pro-inflammatory T(H)17 cells identifies reduced IL10 gene expression in multiple sclerosis. Nat Commun. 2017;8:1600. 10.1038/s41467-017-01571-8.
Fan JH, Zhang YQ, Shi SS, Chen YJ, Yuan XH, Jiang LM, Wang SM, Ma L, He YT, Feng CY, Sun XB, Liu Q, Deloso K, Chi Y, Qiao YL. A nation-wide retrospective epidemiological study of gastroenteropancreatic neuroendocrine neoplasms in china. Oncotarget. 2017;8:71699–708. 10.18632/oncotarget.17599.
Ahmed M. Gastrointestinal neuroendocrine tumors in 2020. World J Gastrointest Oncol. 2020;12:791–807. 10.4251/wjgo.v12.i8.791.
Maione F, Chini A, Milone M, Gennarelli N, Manigrasso M, Maione R, Cassese G, Pagano G, Tropeano FP, Luglio G, De Palma GD. Diagnosis and Management of Rectal Neuroendocrine Tumors (NETs). Diagnostics (Basel). 2021;11. 10.3390/diagnostics11050771
Dabkowski K, Szczepkowski M, Kos-Kudla B, Starzynska T. Endoscopic management of rectal neuroendocrine tumours. How to avoid a mistake and what to do when one is made? Endokrynol Pol. 2020;71:343–9. 10.5603/EP.a2020.0045.
Basuroy R, Haji A, Ramage JK, Quaglia A, Srirajaskanthan R. Review article: the investigation and management of rectal neuroendocrine tumours. Aliment Pharmacol Ther. 2016;44:332–45. 10.1111/apt.13697.
Ko SH, Baeg MK, Ko SY, Jung HS. Clinical characteristics, risk factors and outcomes of asymptomatic rectal neuroendocrine tumors. Surg Endosc. 2017;31:3864–71. 10.1007/s00464-016-5413-9.
Ferone D, Lombardi G, Colao A. [Somatostatin receptors in immune system cells]. Minerva Endocrinol. 2001;26:165–73.
Patel YC. Somatostatin and its receptor family. Front Neuroendocrinol. 1999;20:157–98. 10.1006/frne.1999.0183.
Zhu H, Zhao M, Chang C, Chan V, Lu Q, Wu H. The complex role of AIM2 in autoimmune diseases and cancers. Immun Inflamm Dis. 2021;9:649–65. 10.1002/iid3.443.
Sahin Y. Celiac disease in children: A review of the literature. World J Clin Pediatr. 2021;10:53–71. 10.5409/wjcp.v10.i4.53.
Khurana SR, Bamer AM, Turner AP, Wadhwani RV, Bowen JD, Leipertz SL, Haselkorn JK. The prevalence of overweight and obesity in veterans with multiple sclerosis. Am J Phys Med Rehabil. 2009;88:83–91. 10.1097/PHM.0b013e318194f8b5.
Marrie RA, Rudick R, Horwitz R, Cutter G, Tyry T, Campagnolo D, Vollmer T. Vascular comorbidity is associated with more rapid disability progression in multiple sclerosis. Neurology. 2010;74:1041–7. 10.1212/WNL.0b013e3181d6b125.
Caturegli I, Raut CP. Gastrointestinal Stromal Tumors and the General Surgeon. Surg Clin North Am. 2022;102:625–36. 10.1016/j.suc.2022.04.005.
Apostolou E, Tzioufas AG. Type-III interferons in Sjogren's syndrome. Clin Exp Rheumatol. 2020;38 Suppl 126:245 – 52.
Takahashi K, Aranami T, Endoh M, Miyake S, Yamamura T. The regulatory role of natural killer cells in multiple sclerosis. Brain. 2004;127:1917–27. 10.1093/brain/awh219.
Bergamaschi S, Morato E, Bazzo M, Neves F, Fialho S, Castro G, Zimmermann A, Pereira I. Tumor markers are elevated in patients with rheumatoid arthritis and do not indicate presence of cancer. Int J Rheum Dis. 2012;15:179–82. 10.1111/j.1756-185X.2011.01671.x.
Katoh M, Katoh M. STAT3-induced WNT5A signaling loop in embryonic stem cells, adult normal tissues, chronic persistent inflammation, rheumatoid arthritis and cancer (Review). Int J Mol Med. 2007;19:273–8. 10.3892/ijmm.19.2.273.
Iozzo RV, Eichstetter I, Danielson KG. Aberrant expression of the growth factor Wnt-5A in human malignancy. Cancer Res. 1995;55:3495–9.
Grivennikov SI, Wang K, Mucida D, Stewart CA, Schnabl B, Jauch D, Taniguchi K, Yu GY, Osterreicher CH, Hung KE, Datz C, Feng Y, Fearon ER, Oukka M, Tessarollo L, Coppola V, Yarovinsky F, Cheroutre H, Eckmann L, Trinchieri G, Karin M. Adenoma-linked barrier defects and microbial products drive IL-23/IL-17-mediated tumour growth. Nature. 2012;491:254–8. 10.1038/nature11465.
Greten FR, Grivennikov SI. Inflammation and Cancer: Triggers, Mechanisms, and Consequences. Immunity. 2019;51:27–41. 10.1016/j.immuni.2019.06.025.
Schwitalla S, Ziegler PK, Horst D, Becker V, Kerle I, Begus-Nahrmann Y, Lechel A, Rudolph KL, Langer R, Slotta-Huspenina J, Bader FG, Prazeres, da Costa O, Neurath MF, Meining A, Kirchner T, Greten FR. Loss of p53 in enterocytes generates an inflammatory microenvironment enabling invasion and lymph node metastasis of carcinogen-induced colorectal tumors. Cancer Cell. 2013;23:93–106. 10.1016/j.ccr.2012.11.014

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Exploring the causal relationship between autoimmune diseases and gastrointestinal tumors: A two-sample Mendelian randomization study

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials and Methods

2.1. Research design

2.2. Sources of GWAS data for AIDs and gastrointestinal tumors

2.3. Genetic IV selection

2.4. MR analysis

2.5. Statistical analysis

2.6. Ethical approval

3. Results

4. Discussion

5. Conclusion

Declarations

References

Additional Declarations

Status:

Version 1