Unraveling the Causal Links and Novel Molecular Classification of Crohn’s Disease in Breast Cancer: A Two-Sample Mendelian Randomization and Transcriptome Analysis with Prognostic Modeling

Unraveling the Causal Links and Novel Molecular Classification of Crohn’s Disease in Breast Cancer: A Two-Sample Mendelian Randomization and Transcriptome Analysis with Prognostic Modeling | 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 Unraveling the Causal Links and Novel Molecular Classification of Crohn’s Disease in Breast Cancer: A Two-Sample Mendelian Randomization and Transcriptome Analysis with Prognostic Modeling Xin Yu, Yushuai Yu, Xiewei Huang, ZiRong Jiang, Qing Wang, Xiaoqin Yu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3642218/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Sep, 2024 Read the published version in BMC Cancer → Version 1 posted 12 You are reading this latest preprint version Abstract Background Crohn’s disease (CD), a prominent manifestation of chronic gastrointestinal inflammation, and breast cancer (BC), seemingly disparate in the medical domain, exhibit a shared characteristic. This convergence arises from their involvement in chronic inflammation and immune responses, an aspect that has progressively captivated the attention of investigators but remain controversy. Methods We used two-sample Mendelian Randomization (MR) and transcriptomics to explore the relationship between CD and BC. MR assessed causality of CD on different BC subtypes and reverse causality of BC on CD. We identified CD-related differentially expressed genes and their prognostic impact on BC, and developed a new molecular BC classification based on these key genes. Results MR revealed a causal link between CD and increased BC risk, especially in estrogen receptor-positive (ER+) patients, but not in ER-negative (ER-) cases. BC showed no causal effect on CD. Transcriptomics pinpointed genes like B4GALNT2 and FGF19 that affected BC prognosis in CD patients. A nomogram based on these genes predicted BC outcomes with high accuracy. BC patients were reclassified into three prognostically distinct subtypes using these genes. Conclusions CD is a risk factor for ER + BC but not for ER- BC. BC does not causally affect CD. Our prognostic model and new BC molecular classifications offer insights for personalized treatment strategies. Crohn’s Disease Breast Cancer Mendelian Randomization Prognostic Model Molecular Classification Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Breast cancer (BC) and Crohn’s disease (CD) are distinct medical conditions, with BC being a prevalent malignancy among women and CD being a chronic inflammatory disease of the gastrointestinal tract 1,2 . Currently, there's no consensus on the relationship between them. This study aims to explore any underlying association between these diseases. While CD mainly affects the gastrointestinal tract, leading to inflammation, ulceration, and stenosis, its exact cause remains uncertain, though genetics, environmental factors, and changes in gut microbiota are believed to play roles 3 . In contrast, BC is a widespread malignant tumor that originates in breast tissue and is also possible in men, only at a lower incidence rate 4 . BC is currently the most commonly diagnosed cancer in the world, posing a significant threat to both human health and the economy 5 . Although CD and BC arise in distinct physiological systems, there is a burgeoning interest in investigating the potential connection between these two diseases. For the recognition that both diseases share a fundamental characteristic of chronic inflammation and an associated immune response. Chronic inflammation has been identified as a potential catalyst for tumorigenesis in a variety of contexts 6,7 . Some studies have confirmed that first-degree relatives of individuals with CD disease exhibit a higher incidence of BC 8,9 . Therefore, this has also prompted us to investigate the potential interactions between CD and BC. However, studies related to the effect of the chronic inflammatory disease CD on BC are scarce and have yielded mixed conclusions 10–12 . Moreover, to our knowledge, there have been no further studies on the subtypes, mechanisms and immunologic effects of CD on BC. Moreover, the investigation of the intricate relationship between CD and BC is expected to reveal potential common molecular pathways and common risk factors that influence the development of these diseases. These findings hold the potential to open avenues for novel strategies in risk stratification, early detection, and personalized therapeutic interventions. Our research focuses on these interactions, exploring shared mechanisms and clinical implications. We believe that understanding their connection is vital for better medical care. To minimize interference from other factors, we used a two-sample MR approach to analyze the link between CD and BC risk, including its different forms 13 . We also conducted transcriptomic, molecular typing, and immunocorrelation analyses to deepen our understanding of their association. Through this comprehensive exploration, we aim to provide insights beneficial for future medical practices and expand knowledge on CD and BC. Methods The schematic representation of our research design is illustrated (S1 Fig.). The sources of MR and transcriptome data are publicly accessible online. Mendelian randomization analysis We utilized genetic markers as instrumental variables (IVs) drawn from the GWAS repository ( https://gwas.mrcieu.ac.uk ). We employed a two-sample MR technique, supplemented with bidirectional MR for robustness. To better understand links between CD and BC variants, we separately assessed associations with ER + and ER- breast cancers. Further details on these groupings are delineated in S2 Table. To ensure analytical rigor and mitigate confounders, the following single nucleotide polymorphisms (SNPs) criteria as IVs were set: ( 1 ) Independence among SNPs; ( 2 ) Specific thresholds: P < 5e-08, r 2 < 0.001, and clumping distance exceeding 10,000 kb, ensuring minimized linkage effects; ( 3 ) A demonstrable strength in the SNPs, as evidenced by F-statistics above 20; ( 4 ) Using the PhenoScanner tool ( http://www.phenoscanner.medschl.cam.ac.uk/ ). Analyses were executed using the ‘Two Sample MR’ R package and the IVW method was our primary tool. Transcriptomic Data Analysis Data Acquisition and Differentially Expressed Genes (DEGs) Analysis DEGs analysis for the CD expression profile dataset GSE69762 obtained from the GEO database ( https://www.ncbi.nlm.nih.gov/ ) used the ‘limma’ R package. DEGs were identified with a significance threshold of P < 0.05 and a minimum fold change of 1.2. For The Cancer Genome Atlas (TCGA) ( https://www.cancer.gov/ ) BC dataset, we conducted Principal Component Analysis (PCA) after z-score normalization and the removal of outlier samples. We performed Limma analysis with a significance threshold of P < 0.05 and a minimum fold change of 1.5 determine the overlap of DEGs. Weighted Gene Co-expression Network Analysis (WGCNA) of Differential Genes Utilizing WGCNA, we analyzed a group of 549 consistently detected DEGs. For analytical robustness, the Median Absolute Deviation (MAD) for every gene was computed initially. Using the ‘goodSamplesGenes’ function within the WGCNA package in R, we pinpointed and excluded outlier genes and samples. Enrichment Analysis An enrichment assessment was carried out on 549 DEGs, with 8 genes being omitted due to ineffective clustering. For this analysis, we sourced Gene Ontology (GO) annotations and utilized the Kyoto Encyclopedia of Genes and Genomes (KEGG) for pathway annotations. Regression Analysis and Prognostic Prediction Model Construction In this study, we selected a cohort of 1034 patients excluding those with incomplete clinical information and patients with a follow-up time of zero. In our research, we employed the ‘glmnet’ package in R to execute regression through the Least Absolute Shrinkage and Selection Operator combined with the Cox Proportional-Hazards approach (Lasso-Cox). We also implemented a 10-fold cross-validation for optimal model determination. By setting the Lambda parameter to 0.0243 and merging the regression coefficient results (coef values) with gene expression profiles, we derived a ‘RiskScore’ based on 5 genes. The determination of the optimal cutoff value for the riskscore was executed using the R package ‘maxstat’. Subsequently, we harnessed the ‘survival’ package to scrutinize the differences in overall survival (OS). We undertook an in-depth analysis that combined several clinical indicators, such as survival duration, survival outcome, RiskScore, age, T stage, N stage, M stage, and other pertinent data. This was achieved using the ‘rms’ package in R. For evaluating the precision of our models, we initiated Receiver Operating Characteristic (ROC) curve analysis using the ‘pROC’ package. Novel Molecular classification of Crohn’s Disease in Breast Cancer The key genes for molecular classification were determined through Lasso-Cox analysis. The R package ‘ConsensusClusterPlus’ was utilized for executing consensus clustering on the TCGA BRCA dataset. The optimal cluster was determined using Euclidean distances with k-means algorithm. Prognostic comparisons between distinct subgroups were conducted utilizing the R package ‘clusterSur’. We subsequently scrutinized the associations between subgroups and gene expressions. Following this, the Gene Set Variation Analysis (GSVA) was employed to compare the variations in gene sets between different groups, based on the R package ‘GSVA’. Additionally, we performed single-sample Gene Set Enrichment Analysis (ssGSEA) between different subgroups using the R package ‘GSEABase’. Results Results of studies related to Mendelian randomization Details regarding the inclusion of SNPs in each exposure and outcome group can be found in S3 Table. The results from the IVW analysis indicate a causal relationship between CD and BC. CD is identified as a risk factor for BC (OR = 1.0431, 95%CI:1.0149–1.0721, P = 0.0025). Additionaly, MR methods all show β > 0, indicating consistent directional predictions (Fig. 1 C). Moreover, tests for genetic pleiotropy and heterogeneity did not find statistically significant differences (P > 0.05), suggesting the reliability and stability of our results. Furthermore, we created SNPs forest plots and conducted leave-one-out validation. These results confirm that CD is a risk factor for BC, and the causal relationship is not driven by a single SNP (Fig. 1 A, 1 B). The funnel plot exhibits excellent bilateral symmetry (Fig. 1 D). In contrast, the reverse validation for the relationship between BC and CD shows that the causal relationship is not established (P > 0.05) (Fig. 1 E, 1 F). To further investigate the impact of CD on BC subtypes, our results indicate that CD has a causal relationship with ER + BC (OR = 1.0213, 95%CI:1.0026–1.0404, P = 0.0258). Consistently, all methods show β > 0, and scatter plots confirm the consistent direction of predictions (Fig. 2 C). There is some horizontal heterogeneity in the results(P 0.05). Furthermore, SNPs forest plots and leave-one-out validation provide additional evidence that CD is a risk factor for ER + BC, with the causal relationship not being influenced by a single SNP (Fig. 2 A-B). The funnel plot also demonstrates excellent bilateral symmetry (Fig. 2 D). However, it’s worth noting that the causal relationship between CD and ER- BC was not established (P > 0.05) according to the validation results (Fig. 2 E-F). Transcriptome analysis results Regarding the differential expression analysis in the GSE69762 CD dataset using limma, we observed that the CD group exhibits significant differential expression compared to the control group. There were 880 upregulated genes and 842 downregulated genes. (Fig. 3 A-B). Performing PCA analysis on the BC dataset (Fig. 3 C). After removing outlier samples, we observed 3,134 significantly upregulated genes and 4,496 downregulated genes in BC relative to normal samples (Fig. 3 D-E). Interestingly, there is an overlap of 557 differentially expressed genes between CD and BC (Fig. 3 F). We conducted WGCNA based on the data of 557 shared differentially expressed genes. Firstly, we determined the optimal soft threshold value as 4 through analysis of scale independence and average connectivity (Figs. 4 A-B). The selection of this soft threshold value improved the quality of sample clustering (Fig. 4 C) and gene clustering (Fig. 4 D). The results of the WGCNA analysis revealed a total of 6 co-expression modules (Fig. 4 E). Notably, the ‘grey’ module comprised 8 genes that could not be assigned to any other module. Additionally, the ‘turquoise’ and ‘brown’ module encompassed 274 differentially expressed genes highly correlated with BC, a correlation consistent with clinical information (Fig. 4 F). The correlation was further confirmed by the scatterplot of the Gene Significance (GS) and Module Membership (MM) of these genes (Fig. 4 G-H). After omitting 8 genes from the ‘grey’ module, we proceeded with both GO and KEGG enrichment analyses to gain insights into the biological roles and pathways of the remaining 549 differentially expressed genes. They illustrate the foremost ten GO enrichment categories, indicating a profound enrichment of the genes in fundamental biological undertakings, notably immune regulation and cell signaling (Figs. 4 I-K). These genes also prominently feature in cellular structures like the cytoplasm and cell membrane and partake in molecular functions encompassing receptor binding, growth factor activation, and signal transduction. Complementing these insights, the KEGG analysis pinpoints the major involvement of these genes in several tumor immunity-related pathways (Fig. 4 L). Noteworthy are the TNF signaling pathway and the pathways associated with PD-L1 expression and the PD-1 checkpoint. The centrality of these pathways in governing immune reactions, cellular signaling, and inflammatory responses underscores the genes’ pivotal roles in influencing tumor immunity. Nomogram Model Construction Based on the 5 prognostically relevant genes expression data and coef values, we constructed a RiskScore = 0.0135*B4GALNT2 + 0.0184*C10orf10-0.0266*FGF19 + 0.0354*HLF + 0.0748*SAP30 (Fig. 5 A-B). In our survival analysis, we set the optimal threshold at 1.0173, categorizing patients into high-risk and low-risk cohorts. This distinction yielded a pronounced prognosis difference between the groups (P < 0.05) (Fig. 5 C). Analyzing the correlation between RiskScores and clinical data, we observed a direct trend: a surge in the risk score corresponded to a decline in patient survival and a rise in mortality, which was anticipated. Remarkably, FGF19 exhibited a protective influence, whereas B4GALNT2, C10orf10, HLF, and SAP30 emerged as risk determinants (Fig. 5 D). In order to delve deeper into the potential of the RiskScore predictive model for prognosis in BC patients, we concurrently considered clinical factors such as age and TNM staging, and conducted Cox regression analysis. Our results unequivocally reveal a significant and pivotal correlation between age and RiskScore with the prognosis of BC patients. Furthermore, we vividly illustrate the impact of different T, N and M subgroups on prognosis (Fig. 5 E). This visual representation assists us in gaining a more profound understanding of the distinct roles played by various staging categories in the survival prognosis of BC patients. Using the Cox method, we constructed a nomogram to assess the prognostic significance of the aforementioned features in the cohort of 1034 samples. The overall C-index of the model was 0.78, 95%CI:(0.74–0.82), P < 0.001 (Fig. 5 F). The calibration curve demonstrated good performance (Fig. 5 G). By calculating, the optimal cutoff value for the predictive model score was determined to be 0.3282. Based on this value, patients were stratified into high-risk (H) and low-risk (L) groups. Further analysis and testing revealed a significant difference in OS between the two groups (HR = 4.52, 95%CI:3.20–6.38, P < 0.001) (Fig. 5 H). ROC analysis results indicated the following AUC values: AUC (OS:1 year) = 0.86, 95% CI:0.78–0.94; AUC (3 years) = 0.79, 95% CI:0.73–0.85; AUC (5 years) = 0.79, 95% CI:0.74–0.84 (Fig. 5 I). Novel Molecular classification of Crohn’s Disease in Breast Cance In our endeavor to uncover a new molecular categorization of BC informed by prognostic gene expression, we undertook unsupervised consensus clustering for 1034 BC patients from the TCGA database. We ascertained the ideal cluster count through alterations in the cumulative distribution function (CDF) curve area and through consensus heatmaps (S4 Fig.), was identified as 3 clusters (k = 3) (Fig. 6 A). The heatmap reveals strong consensus within these groups, as indicated by the blue blocks along the diagonal. Subsequently, we categorized all 1034 patients into three subgroups: CD-BC1 (367 cases, 35.5% of the total), CD-BC2 (239 cases, 23.1% of the total), and CD-BC3 (428 cases, 41.4% of the total). The Kaplan-Meier survival evaluation revealed that patients classified under CD-BC3 presented with notably reduced overall survival. In contrast, those under CD-BC1 displayed a relatively better survival outcome (P = 0.041) (Fig. 6 B). This suggests that our novel molecular classification has a significant impact on the survival of BC patients. We also observed substantial differences in gene expression within the new BC clustering modules, in the genes B4GALNT2, C10orf10, FGF19, HLF, and SAP30 (Fig. 6 C). In the analyzed data, the gene B4GALNT2 stood out for its significant high expression in the CD-BC2 group. This marked upregulation suggests the potential of B4GALNT2 as a marker gene for diagnostic subgroups, especially for distinguishing the CD-BC2 group from others. Concurrently, both HLF and C10orf10 exhibited marked down-regulation in the CD-BC1 subgroup, while showing notable up-regulation in the CD-BC3 cohort. The trend for SAP30 was observed to be the inverse. Furthermore, immunoinfiltration analysis indicated marked differences in tumor immune infiltrating cells between different modules (Fig. 6 D). The infiltration level of several immune cells, like activated B cells, activated CD4 + T cells, MDSC (Myeloid-Derived Suppressor Cells), and macrophages, displays significant variations among the clusters. Lastly, through ssGSEA, we identified significant differences in the enrichment of signaling pathways among these three novel molecular subgroups (Fig. 6 E-G). The color gradients, from blue (low enrichment) to red (high enrichment), illustrate the pathway enrichment intensities. Compared to the other two groups, the CD-BC2 group distinctly exhibited upregulation in the pathways ‘Basal Cell Carcinoma’ and ‘Hedgehog Signaling Pathway’. This marked increase in pathway activity may offer insights into the pathogenic mechanism and biological behavior of breast cancer in the CD-BC3 group, providing a more detailed explanation for its underlying processes. These findings underscore the clinical significance of our new molecular classification in terms of prognosis and immune characteristics, offering valuable insights for the treatment and management of BC patients. Discussion Inflammation and immune-related aspects have long been pivotal in cancer research. CD is an autoimmune disorder characterized by an exaggerated immune response against the normal intestinal flora, causing damage to the intestinal mucosal tissues, intestinal wall thinning, and the formation of ulcers and fistulas 1 . BC is a grave and hereditary disease marked by the excessive proliferation of breast epithelial cells, leading to high morbidity 14 . Despite their significance, both CD and BC lack comprehensive investigations into their pathogenesis. To address potential confounding and reverse causality, we conducted a MR analysis to explore the causal link between CD and BC. For a deeper investigation into the underlying mechanisms and impacts, we developed and validated a genetic prognostic model through gene transcriptome analysis. Furthermore, we introduced an innovative molecular classification specific to BC. We also examined its influence on the tumor immune microenvironment in BC. The results demonstrated the establishment of a causal relationship between CD and the development of BC, validating CD as a risk factor for BC development. And this study further explored to validate the effect of CD on BC subtypes, and surprisingly found that the causal relationship of CD was established for ER + BC but not for ER- BC, which suggests that CD has a greater impact on the prevalence of ER + BC. A previous meta-analysis of cohort studies, however, failed to confirm a significant correlation between the risk of BC in patients with CD 11 . Another study published also failed to confirm the causal relationship 12 . In addition, the results of the latest study also confirmed that CD is a risk factor for the development of BC, which is in line with the overall trend of the findings of the present study, and the nuances may be due to the differences in parameter settings such as the setting of KB > 5000 by this author 10 . Moreover, studies have demonstrated that patients with CD and their relatives have a high prevalence of BC, which is more advanced when detected 8,9 . It has been shown that BC patients with CD tend to have later stage and worse prognosis than BC patients without CD 15 . This suggests that patients with CD, especially premenopausal women with CD, and their first-degree family members may need to be actively monitored for gastrointestinal and breast health, as well as actively treated for CD. It is important to emphasize that causality analysis is a complex task that requires the integration of several factors and requires in-depth research at different levels 13 . While our study has uncovered a potential causal relationship between CD and BC, further experimental and clinical investigations are imperative to validate this finding and delve into the underlying biological mechanisms. In this context, our research delves into the transcriptome gene correlation analysis between CD and BC. We employed a comprehensive approach, including WGCNA and GO and KEGG, to scrutinize the 557 DEGs common to CD and BC. Our results affirm a high correlation between 274 genes within the ‘turquoise’ and ‘brown’ module and BC incidence. Furthermore, some genes within the ‘turquoise’ and ‘brown’ module exhibit a correlation with patients’ OS. A LASSO regression analysis were instrumental in determining prognostically relevant hub genes, including B4GALNT2, C10orf10, FGF19, HLF, and SAP30. Based on the coef values and gene expression data, we devised a RiskScore and a nomogram to predict prognosis for patients. Notably, FGF19 is a protective factor for prognosis, demonstrating a downregulation trend with an increase in the risk score, while B4GALNT2, C10orf10, HLF and SAP30 genes are prognostic risk factors. This study indicates that the RiskScore derived from DEGs associated with CD can effectively stratify BC patients into high-risk(H) and low-risk(L) groups. Kaplan-Meier survival curves reveal a significant difference in OS between these two groups. Thus, RiskScore serves as a robust predictor of clinical endpoints. Overall, our findings signify the potential utility of these molecular risk assessment in optimizing patient care and prognosis for those grappling with both CD and BC. Nonetheless, further studies and clinical validations are necessary to cement the clinical applicability of these findings. B4GALNT2 is crucial for glycoprotein synthesis and it's significant in the SID blood group system and melanoma prognosis 16,17 . Research has looked into using Hsp1-Hsp2Cas9-Y to knock out B4GALNT2 in porcine fetal fibroblasts (PFF) 18 . This could potentially benefit CD-related BC prognosis. C10orf10 plays a role in cell cycle regulation and DNA repair 19 . Lower expression levels correlate with worse BC outcomes 20 and higher levels are linked to glioma proliferation 21 . These findings suggest C10orf10’s relevance not only to gliomas but also to the prognosis of CD-related BC. FGF19 is part of the fibroblast growth factor family, vital for cellular growth and tissue repair 22 . Increased FGF19 levels were found in nasopharyngeal carcinoma (NPC) patients, suggesting its role in disease progression 23 . FGF19 is also associated with gallbladder cancer 24 . This is different from the prognostic protective factor in this study, therefore, further validation of the role of FGF19 in BC is warranted. HLF is a transcription factor, a type of protein that plays a crucial role in regulating gene expression. Some findings indicated that HLF regulates apoptosis and autophagy 25 . SAP30 proteins typically regulate gene expression. They interact with Sin3A proteins to form the Sin3 complex, which impacts gene silencing and expression 26 . Our study revealed significant differential expression of SAP30 in a novel BC molecular classification and was associated with BC prognosis. Although the exact mechanism of SAP30 is not fully understood, SAP30 has been found to be involved in the regulation of gene expression and chromatin structure in other studies. Studies have demonstrated that SAP30 is associated with BC poor prognosis, consistent with the findings in this paper 26 . This study conducted a novel molecular classification of 1034 BC patients based on prognostic genes associated with CD. The patients were categorized into three subgroups, namely CD-BC1, CD-BC2, and CD-BC3. Significant differences in survival outcomes, tumor immune infiltration, and enrichment of signaling pathways were observed among these subgroups. These findings provide a plausible explanation for the differing prognostic outcomes in these three subgroups. As seen in the previous section, the immune microenvironment and inflammatory response closely link the two diseases, CD and BC, which are seemingly unrelated. It has been shown that the systemic inflammatory response in patients with ulcerative colitis (UC) leads to the down-regulation of BC resistance protein (BCRP/ABCG2), which in turn induces breast carcinogenesis 27,28 .Since CD and UC belong to the same group of infammatory bowel disease 29 . Therefore, this may also provide theoretical support for the mechanism in this study, but this needs to be further verified by relevant basic research. After reading a large amount of literature, a researcher believes that there may be a common molecular mechanism between CD and BC, which may be related to immune and inflammatory signaling pathways such as IL-17, NF-κB, and TNF-α. The researcher believes that anxiety and depression, as well as intestinal dysbiosis, may also be part of the reason why CD triggers the development of BC 30 . Differential gene GO, KEGG enrichment analysis in this study similarly enriched numerous immune-inflammation-related pathways. In summary, while this study suggests the presence of shared biological factors between CD and BC, the inherent limitations of MR and transcriptomic analysis are fundamental constraints that are inescapable. To delve deeper into this association, we strongly advocate for more multicenter research. Such multicenter studies will contribute to a more comprehensive understanding of the potential relationship between CD and BC, unveiling variations and commonalities among different populations. Simultaneously, these studies will enable a better assessment of patient risks and prognosis, providing a stronger foundation for personalized healthcare and interventions based on novel molecular classification. Ultimately, this interdisciplinary collaboration and data sharing will enhance disease prevention and treatment strategies, positively impacting public health. Conclusions In our study, we established that CD is a significant risk factor for BC development, especially in hormone receptor-positive (ER+) BC cases. However, the association between CD and BC was not significant in hormone receptor-negative (ER-) BC cases. Our reverse validation confirmed that BC does not directly affect CD. Regarding prognosis, we identified five genes, B4GALNT2, C10orf10, FGF19, HLF, and SAP30. We developed a prognostic model presented as a nomogram, with a strong predictive value, offering valuable insights into patient prognosis and individualized treatment. Furthermore, based on the aforementioned genes, we classified BC patients into three new groups characterized by differences in prognosis and significant variations in immune profiles. To conclude, our study sheds light on the complex relationship between CD and BC, with specific relevance to certain BC subtypes. These findings have the potential to guide future disease prevention, treatment, and personalized medicine efforts. List Of Abbreviations CD Crohn’s Disease BC Breast Cancer MR Mendelian Randomization GWAS Genome Wide Association Studies IVW Inverse-variance Weighted ER+ Estrogen Receptor-Positive ER- Estrogen Receptor-Negative IVs Instrumental Variables SNPs Single Nucleotide Polymorphisms TCGA The Cancer Genome Atlas BRCA Breast Cancer LASSO Least Absolute Shrinkage and Selection Operator DEGs Differentially Expressed Genes OS Overall Survival CDF Cumulative Distribution Function ssGSEA single-sample Gene Set Enrichment Analysis GSVA Gene Set Variation Analysis UC Ulcerative Colitis Significance levels **** (P < 0.0001), *** (P < 0.001), ** (P < 0.01), * (P < 0.05). Declarations Funding This paper was not funded. Author contributions YX and YYS contributed equally to this work. SCG and YX conceived the study. YYS and YX had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. YX and YXQ performed data analyses. YYS and HXW wrote the first draft of this manuscript. JZR, WQ and SCG critically revised the manuscript. All authors approved the final version of the manuscript. Availability of data and materials All data generated or analyzed in this study can be found in the IEU Open GWAS(https://gwas.mrcieu.ac.uk/), TCGA(https://www.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/). Ethics approval and consent to participate Given that this study utilized data sourced from publicly available datasets, neither ethics approval nor informed consent were deemed necessary. Consent for publication Not applicable. Conflict of Interest Statement The authors declare that they have no financial or personal relationships with third parties that might inappropriately influence the content of this paper. 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The four epidemiological stages in the global evolution of inflammatory bowel disease. Nat Rev Gastroenterol Hepatol. 2021;18(1):56–66. Zhou S, Yu J. Crohn's disease and breast cancer: a literature review of the mechanisms and treatment. Intern Emerg Med. 2023;18(5):1303–16. Additional Declarations No competing interests reported. Supplementary Files S4FIgure.tif Supplementary 1 Figure: Study Design Overview S2S3table.docx Supplementary 2 Table : Key Information on GWAS Studies Included in Current MR Analysis Supplementary 3 Table : Causal Effects of CD on BC and different Subtypes S1GraphicalAbstract.tif Supplement 4 Figure: We employed an unsupervised consensus clustering algorithm to investigate molecular subtyping of CD-BC patients. (A) Calculated consensus a range of k values (k = 2-9) and generated CDF curve. (B) Analyzed the area under the CDF curve, including k values ranging from 2 to 9. CD: Crohn’s Disease; BC: Breast Cancer; CDF: cumulative distribution function Cite Share Download PDF Status: Published Journal Publication published 11 Sep, 2024 Read the published version in BMC Cancer → Version 1 posted Editorial decision: Revision requested 17 Jun, 2024 Reviews received at journal 13 Jun, 2024 Reviewers agreed at journal 30 May, 2024 Reviews received at journal 14 May, 2024 Reviewers agreed at journal 29 Apr, 2024 Reviewers agreed at journal 25 Apr, 2024 Reviewers agreed at journal 26 Jan, 2024 Reviewers invited by journal 24 Jan, 2024 Editor assigned by journal 24 Jan, 2024 Editor invited by journal 30 Nov, 2023 Submission checks completed at journal 30 Nov, 2023 First submitted to journal 21 Nov, 2023 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. 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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-3642218","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":254889796,"identity":"328506dd-fef2-446a-b931-2806e27de39b","order_by":0,"name":"Xin Yu","email":"","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Yu","suffix":""},{"id":254889797,"identity":"4d2689d8-a32e-4806-85c9-118e6e90b992","order_by":1,"name":"Yushuai Yu","email":"","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yushuai","middleName":"","lastName":"Yu","suffix":""},{"id":254889798,"identity":"f72f47e9-344f-4b02-894a-af952128373a","order_by":2,"name":"Xiewei Huang","email":"","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiewei","middleName":"","lastName":"Huang","suffix":""},{"id":254889799,"identity":"d1d61824-ac1d-49a5-87ff-3494f232ed77","order_by":3,"name":"ZiRong Jiang","email":"","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"ZiRong","middleName":"","lastName":"Jiang","suffix":""},{"id":254889800,"identity":"29dc2dd4-7c5d-4213-89b3-f29f6242163d","order_by":4,"name":"Qing Wang","email":"","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Wang","suffix":""},{"id":254889801,"identity":"329c7fc6-1afb-4279-91bc-9586152cb63e","order_by":5,"name":"Xiaoqin Yu","email":"","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoqin","middleName":"","lastName":"Yu","suffix":""},{"id":254889802,"identity":"93370f0d-90de-4575-ace8-6d1f4ce3442c","order_by":6,"name":"Chuangui Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYPACCTn7w8wHgAwLBgYe4rRYGDMcZ0tgYEiQIFpLRWLDeR4D4rTIt/cefvHjl0RiYzPPx8e8PyTk+HkOMH74mINbi8GZc2mWvX0Sxs3MvJuNeRIkjCV7G5glZ27Do0Uix8yYsUdCto2Zd5s0UEvihvMMbEA2HofNgGhh7GHmeUacFoYbOcaPGX5IKM5g5mGDaDnbgF+LwZkzZoy9DRLGBsxsxoZz0oB+6TnYjNcv8u09xh9+/KmTM+A//PDBGxsbYIglH/zwEZ/DGBjYJBjbUAQYG/CqBwLmDwx/CKkZBaNgFIyCEQ0A8phKgRHUefsAAAAASUVORK5CYII=","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":true,"prefix":"","firstName":"Chuangui","middleName":"","lastName":"Song","suffix":""}],"badges":[],"createdAt":"2023-11-21 05:29:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3642218/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3642218/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12885-024-12838-x","type":"published","date":"2024-09-11T15:56:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":47571139,"identity":"751d143b-172e-4d86-9b3d-d1d1a0fbf86e","added_by":"auto","created_at":"2023-12-04 17:17:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":291362,"visible":true,"origin":"","legend":"\u003cp\u003eCausal Relationship between CD and BC: (A) The forest plot of all SNPs represents the causal effects using IVW and MR-Egger methods. (B) Leave-one-out analysis was employed to evaluate the individual impact of each SNP on the causal effect. (C) A scatter plot illustrates the results from all MR methods. (D) The funnel plot indicates the absence of significant horizontal pleiotropy.\u003c/p\u003e\n\u003cp\u003eImpact of a BC Diagnosis on CD Infection Risk: (E) Forest Plot: Similar to (A). (F) Leave-one-out analysis: Similar to (B).\u003c/p\u003e\n\u003cp\u003eCD: Crohn’s Disease; BC: Breast Cancer; MR: Mendelian Randomization; IVW: Inverse-variance Weighted; SNP: Single Nucleotide Polymorphisms\u003c/p\u003e","description":"","filename":"FIGURE1.png","url":"https://assets-eu.researchsquare.com/files/rs-3642218/v1/6dd389e16870a68480e1a6f4.png"},{"id":47571807,"identity":"e884e86e-3b90-43c0-aecc-26a57edbf4bb","added_by":"auto","created_at":"2023-12-04 17:25:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":424310,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of CD Infection on ER+ BC Diagnosis. (A) Forest Plot: Depicts the causal effects using IVW and MR-Egger methods for ER+ BC subtype. (B) Leave-One-Out Analysis: Demonstrates the sensitivity analysis by systematically excluding individual variants from the analysis for ER+ BC diagnosis. (C) Scatter Plot: Shows the relationship between different MR methods and their estimated effects on ER+ BC. (D) Funnel Plot: Illustrates the symmetry of estimates for ER+ BC, suggesting the absence of potential horizontal pleiotropy.\u003c/p\u003e\n\u003cp\u003eImpact of CD Infection on ER- BC Diagnosis. (E) Forest Plot: Similar to (A), but focuses on ER- BC subtype. (F) Leave-One-Out Analysis: Similar to (B), but for ER- BC diagnosis.\u003c/p\u003e\n\u003cp\u003eCD: Crohn’s Disease; BC: Breast Cancer; MR: Mendelian Randomization; IVW: Inverse-variance Weighted; ER+: Estrogen receptor-positive; ER-: Estrogen receptor-negative.\u003c/p\u003e","description":"","filename":"FIGUR2.png","url":"https://assets-eu.researchsquare.com/files/rs-3642218/v1/410c8b0567d697f7640d4e43.png"},{"id":47571131,"identity":"d9ed1974-feac-4790-933f-b8ce97ac58fe","added_by":"auto","created_at":"2023-12-04 17:17:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":479380,"visible":true,"origin":"","legend":"\u003cp\u003eExpression Patterns of CD DEGs in the GSE69762 Database: (A) Volcano plot. (B) Heatmap. Expression Patterns of BC DEGs in the TCGA-BRCA Database. (C) Principal Component Analysis. (D) Volcano plot. (E) Heatmap. (F) Venn diagram illustrating the common and unique DEGs between CD and BC.\u003c/p\u003e\n\u003cp\u003eCD: Crohn’s Disease; BC: Breast Cancer; DEGs:Differentially Expressed Genes; TCGA: The Cancer Genome Atlas; BRCA: Breast Cancer\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3642218/v1/f36a5dd64a12fd3346504340.png"},{"id":47571128,"identity":"19207768-aaa8-4934-8570-ac2175a020e4","added_by":"auto","created_at":"2023-12-04 17:17:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":546770,"visible":true,"origin":"","legend":"\u003cp\u003eWGCNA analysis of 557 CD and BC co-differentially expressed genes. (A) Scale Independence: Depicts the scale independence test of the network under different soft threshold values, showcasing its sensitivity to thresholds. (B) Optimal Soft Threshold: Analysis of average connectivity across varying soft threshold values highlights the appropriateness of a soft threshold of 4. (C) Sample Clustering: Hierarchical sample clustering based on heterogeneity measurements of 557 genes results in a dendrogram, delineating genes into six distinct modules. (D) Gene Clustering: Hierarchical clustering of genes, segregating them into distinct modules. (E) Module Eigengene Clustering: Clustering based on the similarity of module eigengenes. (F) Module-Phenotype Correlation Heatmap: Illustrates the correlation between different modules and phenotypic characteristics of breast tumors. (G) Gene Significance and Module Membership Heatmap of Turquoise Module and (H) Brown Module. Gene enrichment analysis of 549DEGs with grey module genes removed. GO enrichment analysis (I) Biological Processes. (J) Cellular Components. (K) Molecular Functions. (L) KEGG enrichment analysis.\u003c/p\u003e\n\u003cp\u003eCD: Crohn’s Disease; BC: Breast Cancer; WGCNA: Weighted Gene Co-expression Network Analysis; DEGs: Differentially Expressed Genes; GO:Gene Ontology; KEGG: Encyclopedia of Genes and Genomes\u003c/p\u003e","description":"","filename":"FIGURE4.png","url":"https://assets-eu.researchsquare.com/files/rs-3642218/v1/aca1b996b3e69c7e416c3b8a.png"},{"id":47571138,"identity":"343a6034-a661-4362-b19e-68891e7fba6d","added_by":"auto","created_at":"2023-12-04 17:17:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":475341,"visible":true,"origin":"","legend":"\u003cp\u003eSelection of prognostic genes and construction of risk score in CD associated BC patients. (A-B) A regression model with LASSO algorithm was used to avoid overfitting and identify key prognostic genes in CD-associated BC patients. (C) Kaplan-Meier survival analysis showed the survival curves of high-risk group (red, H) and low-risk group (blue, L). (D) The distribution of risk score, the scatter plot of survival status, and the heatmap of gene expression in the prognostic model were presented. Construction of prognostic prediction models. (E) Clinical information forest plot. (F) Development of a nomogram for predicting overall survival in Breast cancer patients. (G) Calibration plot displaying actual risk probability at 1, 3, and 5 Years. (H) Survival disparity curves of the nomogram prognostic model. Blue represents the low-risk group named L, and red represents the high-risk named H group. (I) Time-dependent ROC curves demonstrate nomogram accuracy. AUC values: 1-year,3-year and 5-year.\u003c/p\u003e\n\u003cp\u003eCD: Crohn’s Disease; BC: Breast Cancer; LASSO: least absolute shrinkage and selection operator; ROC: Receiver Operating Characteristic; AUC: Area Under the Curve; ****:P\u0026lt;0.0001\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3642218/v1/98e411a2c0da68149145a340.png"},{"id":47571130,"identity":"086f9156-c98d-4c01-b0d9-2e7d07a79b1d","added_by":"auto","created_at":"2023-12-04 17:17:54","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":7547819,"visible":true,"origin":"","legend":"\u003cp\u003eWe employed an unsupervised consensus clustering algorithm to investigate the molecular subtypes of CD-BC patients. (A) The consensus clustering matrix for k = 3, determined as the optimal number of clusters, is displayed. (B) Showed Kaplan-Meier survival analysis for the newly defined CD-BC1, CD-BC2, and CD-BC3 molecular subgroups. (C) Box plots depicting the differential expression of prognostic-related genes and (D) box plots displaying tumor immune infiltration levels. Furthermore. (E-G) present heatmaps representing the results of ssGSEA signal pathway enrichment analysis.\u003c/p\u003e\n\u003cp\u003eCD: Crohn’s Disease; BC: Breast Cancer; *:P\u0026lt;0.05, **: P\u0026lt;0.01, ***:P\u0026lt;0.001\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3642218/v1/432150439d2dc86aa9a569cf.jpg"},{"id":64618882,"identity":"a9e7fa46-b256-483a-afca-e1d50ea11c64","added_by":"auto","created_at":"2024-09-16 16:04:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10141338,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3642218/v1/ac4c6018-2701-4288-8102-b76e45e09085.pdf"},{"id":47571136,"identity":"674f9301-caf8-4cb4-81d7-9ad31027cf8f","added_by":"auto","created_at":"2023-12-04 17:17:55","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":131992,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary 1 Figure: \u003c/strong\u003eStudy Design Overview\u003c/p\u003e","description":"","filename":"S4FIgure.tif","url":"https://assets-eu.researchsquare.com/files/rs-3642218/v1/64a2445c96a8f6e03338d214.tif"},{"id":47571133,"identity":"851dd4cc-c7a7-4022-ab05-c79c55b3f95d","added_by":"auto","created_at":"2023-12-04 17:17:54","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19193,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary 2 Table : \u003c/strong\u003eKey Information on GWAS Studies Included in Current MR Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary 3 Table : \u003c/strong\u003eCausal Effects of CD on BC and different Subtypes\u003c/p\u003e","description":"","filename":"S2S3table.docx","url":"https://assets-eu.researchsquare.com/files/rs-3642218/v1/a7bbeb6415feff8704a3c85d.docx"},{"id":47571135,"identity":"032144f9-5c4d-4ff8-9c23-c73acd48676a","added_by":"auto","created_at":"2023-12-04 17:17:54","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":333852,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplement 4 Figure: \u003c/strong\u003eWe employed an unsupervised consensus clustering algorithm to investigate molecular subtyping of CD-BC patients. (A) Calculated consensus a range of k values (k = 2-9) and generated CDF curve. (B) Analyzed the area under the CDF curve, including k values ranging from 2 to 9.\u003c/p\u003e\n\u003cp\u003eCD: Crohn’s Disease; BC: Breast Cancer; CDF: cumulative distribution function\u003c/p\u003e","description":"","filename":"S1GraphicalAbstract.tif","url":"https://assets-eu.researchsquare.com/files/rs-3642218/v1/1b72393c6da517733cc55af0.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unraveling the Causal Links and Novel Molecular Classification of Crohn’s Disease in Breast Cancer: A Two-Sample Mendelian Randomization and Transcriptome Analysis with Prognostic Modeling","fulltext":[{"header":"Background","content":"\u003cp\u003eBreast cancer (BC) and Crohn\u0026rsquo;s disease (CD) are distinct medical conditions, with BC being a prevalent malignancy among women and CD being a chronic inflammatory disease of the gastrointestinal tract \u003csup\u003e1,2\u003c/sup\u003e. Currently, there's no consensus on the relationship between them. This study aims to explore any underlying association between these diseases. While CD mainly affects the gastrointestinal tract, leading to inflammation, ulceration, and stenosis, its exact cause remains uncertain, though genetics, environmental factors, and changes in gut microbiota are believed to play roles \u003csup\u003e3\u003c/sup\u003e. In contrast, BC is a widespread malignant tumor that originates in breast tissue and is also possible in men, only at a lower incidence rate \u003csup\u003e4\u003c/sup\u003e. BC is currently the most commonly diagnosed cancer in the world, posing a significant threat to both human health and the economy \u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough CD and BC arise in distinct physiological systems, there is a burgeoning interest in investigating the potential connection between these two diseases. For the recognition that both diseases share a fundamental characteristic of chronic inflammation and an associated immune response. Chronic inflammation has been identified as a potential catalyst for tumorigenesis in a variety of contexts \u003csup\u003e6,7\u003c/sup\u003e. Some studies have confirmed that first-degree relatives of individuals with CD disease exhibit a higher incidence of BC \u003csup\u003e8,9\u003c/sup\u003e. Therefore, this has also prompted us to investigate the potential interactions between CD and BC. However, studies related to the effect of the chronic inflammatory disease CD on BC are scarce and have yielded mixed conclusions \u003csup\u003e10\u0026ndash;12\u003c/sup\u003e. Moreover, to our knowledge, there have been no further studies on the subtypes, mechanisms and immunologic effects of CD on BC. Moreover, the investigation of the intricate relationship between CD and BC is expected to reveal potential common molecular pathways and common risk factors that influence the development of these diseases. These findings hold the potential to open avenues for novel strategies in risk stratification, early detection, and personalized therapeutic interventions.\u003c/p\u003e \u003cp\u003eOur research focuses on these interactions, exploring shared mechanisms and clinical implications. We believe that understanding their connection is vital for better medical care. To minimize interference from other factors, we used a two-sample MR approach to analyze the link between CD and BC risk, including its different forms \u003csup\u003e13\u003c/sup\u003e. We also conducted transcriptomic, molecular typing, and immunocorrelation analyses to deepen our understanding of their association. Through this comprehensive exploration, we aim to provide insights beneficial for future medical practices and expand knowledge on CD and BC.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe schematic representation of our research design is illustrated (S1 Fig.). The sources of MR and transcriptome data are publicly accessible online.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMendelian randomization analysis\u003c/h2\u003e \u003cp\u003eWe utilized genetic markers as instrumental variables (IVs) drawn from the GWAS repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We employed a two-sample MR technique, supplemented with bidirectional MR for robustness. To better understand links between CD and BC variants, we separately assessed associations with ER\u0026thinsp;+\u0026thinsp;and ER- breast cancers. Further details on these groupings are delineated in S2 Table.\u003c/p\u003e \u003cp\u003eTo ensure analytical rigor and mitigate confounders, the following single nucleotide polymorphisms (SNPs) criteria as IVs were set: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Independence among SNPs; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Specific thresholds: P\u0026thinsp;\u0026lt;\u0026thinsp;5e-08, r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and clumping distance exceeding 10,000 kb, ensuring minimized linkage effects; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) A demonstrable strength in the SNPs, as evidenced by F-statistics above 20; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Using the PhenoScanner tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.phenoscanner.medschl.cam.ac.uk/\u003c/span\u003e\u003cspan address=\"http://www.phenoscanner.medschl.cam.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Analyses were executed using the \u0026lsquo;Two Sample MR\u0026rsquo; R package and the IVW method was our primary tool.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptomic Data Analysis\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eData Acquisition and Differentially Expressed Genes (DEGs) Analysis\u003c/h2\u003e \u003cp\u003eDEGs analysis for the CD expression profile dataset GSE69762 obtained from the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) used the \u0026lsquo;limma\u0026rsquo; R package. DEGs were identified with a significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a minimum fold change of 1.2. For The Cancer Genome Atlas (TCGA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://www.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) BC dataset, we conducted Principal Component Analysis (PCA) after z-score normalization and the removal of outlier samples. We performed Limma analysis with a significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a minimum fold change of 1.5 determine the overlap of DEGs.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eWeighted Gene Co-expression Network Analysis (WGCNA) of Differential Genes\u003c/h2\u003e \u003cp\u003eUtilizing WGCNA, we analyzed a group of 549 consistently detected DEGs. For analytical robustness, the Median Absolute Deviation (MAD) for every gene was computed initially. Using the \u0026lsquo;goodSamplesGenes\u0026rsquo; function within the WGCNA package in R, we pinpointed and excluded outlier genes and samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment Analysis\u003c/h2\u003e \u003cp\u003eAn enrichment assessment was carried out on 549 DEGs, with 8 genes being omitted due to ineffective clustering. For this analysis, we sourced Gene Ontology (GO) annotations and utilized the Kyoto Encyclopedia of Genes and Genomes (KEGG) for pathway annotations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRegression Analysis and Prognostic Prediction Model Construction\u003c/h2\u003e \u003cp\u003eIn this study, we selected a cohort of 1034 patients excluding those with incomplete clinical information and patients with a follow-up time of zero. In our research, we employed the \u0026lsquo;glmnet\u0026rsquo; package in R to execute regression through the Least Absolute Shrinkage and Selection Operator combined with the Cox Proportional-Hazards approach (Lasso-Cox). We also implemented a 10-fold cross-validation for optimal model determination. By setting the Lambda parameter to 0.0243 and merging the regression coefficient results (coef values) with gene expression profiles, we derived a \u0026lsquo;RiskScore\u0026rsquo; based on 5 genes. The determination of the optimal cutoff value for the riskscore was executed using the R package \u0026lsquo;maxstat\u0026rsquo;. Subsequently, we harnessed the \u0026lsquo;survival\u0026rsquo; package to scrutinize the differences in overall survival (OS). We undertook an in-depth analysis that combined several clinical indicators, such as survival duration, survival outcome, RiskScore, age, T stage, N stage, M stage, and other pertinent data. This was achieved using the \u0026lsquo;rms\u0026rsquo; package in R. For evaluating the precision of our models, we initiated Receiver Operating Characteristic (ROC) curve analysis using the \u0026lsquo;pROC\u0026rsquo; package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eNovel Molecular classification of Crohn\u0026rsquo;s Disease in Breast Cancer\u003c/h2\u003e \u003cp\u003eThe key genes for molecular classification were determined through Lasso-Cox analysis. The R package \u0026lsquo;ConsensusClusterPlus\u0026rsquo; was utilized for executing consensus clustering on the TCGA BRCA dataset. The optimal cluster was determined using Euclidean distances with k-means algorithm. Prognostic comparisons between distinct subgroups were conducted utilizing the R package \u0026lsquo;clusterSur\u0026rsquo;. We subsequently scrutinized the associations between subgroups and gene expressions. Following this, the Gene Set Variation Analysis (GSVA) was employed to compare the variations in gene sets between different groups, based on the R package \u0026lsquo;GSVA\u0026rsquo;. Additionally, we performed single-sample Gene Set Enrichment Analysis (ssGSEA) between different subgroups using the R package \u0026lsquo;GSEABase\u0026rsquo;.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eResults of studies related to Mendelian randomization\u003c/h2\u003e \u003cp\u003eDetails regarding the inclusion of SNPs in each exposure and outcome group can be found in S3 Table. The results from the IVW analysis indicate a causal relationship between CD and BC. CD is identified as a risk factor for BC (OR\u0026thinsp;=\u0026thinsp;1.0431, 95%CI:1.0149\u0026ndash;1.0721, P\u0026thinsp;=\u0026thinsp;0.0025). Additionaly, MR methods all show β\u0026thinsp;\u0026gt;\u0026thinsp;0, indicating consistent directional predictions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Moreover, tests for genetic pleiotropy and heterogeneity did not find statistically significant differences (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting the reliability and stability of our results. Furthermore, we created SNPs forest plots and conducted leave-one-out validation. These results confirm that CD is a risk factor for BC, and the causal relationship is not driven by a single SNP (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The funnel plot exhibits excellent bilateral symmetry (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). In contrast, the reverse validation for the relationship between BC and CD shows that the causal relationship is not established (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eTo further investigate the impact of CD on BC subtypes, our results indicate that CD has a causal relationship with ER\u0026thinsp;+\u0026thinsp;BC (OR\u0026thinsp;=\u0026thinsp;1.0213, 95%CI:1.0026\u0026ndash;1.0404, P\u0026thinsp;=\u0026thinsp;0.0258). Consistently, all methods show β\u0026thinsp;\u0026gt;\u0026thinsp;0, and scatter plots confirm the consistent direction of predictions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). There is some horizontal heterogeneity in the results(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), therefore a random effects IVW analysis was used. Importantly, these results were verified to be free from genetic pleiotropy (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Furthermore, SNPs forest plots and leave-one-out validation provide additional evidence that CD is a risk factor for ER\u0026thinsp;+\u0026thinsp;BC, with the causal relationship not being influenced by a single SNP (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B). The funnel plot also demonstrates excellent bilateral symmetry (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). However, it\u0026rsquo;s worth noting that the causal relationship between CD and ER- BC was not established (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) according to the validation results (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-F).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptome analysis results\u003c/h2\u003e \u003cp\u003eRegarding the differential expression analysis in the GSE69762 CD dataset using limma, we observed that the CD group exhibits significant differential expression compared to the control group. There were 880 upregulated genes and 842 downregulated genes. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B). Performing PCA analysis on the BC dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). After removing outlier samples, we observed 3,134 significantly upregulated genes and 4,496 downregulated genes in BC relative to normal samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-E). Interestingly, there is an overlap of 557 differentially expressed genes between CD and BC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe conducted WGCNA based on the data of 557 shared differentially expressed genes. Firstly, we determined the optimal soft threshold value as 4 through analysis of scale independence and average connectivity (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B). The selection of this soft threshold value improved the quality of sample clustering (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) and gene clustering (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). The results of the WGCNA analysis revealed a total of 6 co-expression modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Notably, the \u0026lsquo;grey\u0026rsquo; module comprised 8 genes that could not be assigned to any other module. Additionally, the \u0026lsquo;turquoise\u0026rsquo; and \u0026lsquo;brown\u0026rsquo; module encompassed 274 differentially expressed genes highly correlated with BC, a correlation consistent with clinical information (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). The correlation was further confirmed by the scatterplot of the Gene Significance (GS) and Module Membership (MM) of these genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG-H). After omitting 8 genes from the \u0026lsquo;grey\u0026rsquo; module, we proceeded with both GO and KEGG enrichment analyses to gain insights into the biological roles and pathways of the remaining 549 differentially expressed genes. They illustrate the foremost ten GO enrichment categories, indicating a profound enrichment of the genes in fundamental biological undertakings, notably immune regulation and cell signaling (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI-K). These genes also prominently feature in cellular structures like the cytoplasm and cell membrane and partake in molecular functions encompassing receptor binding, growth factor activation, and signal transduction. Complementing these insights, the KEGG analysis pinpoints the major involvement of these genes in several tumor immunity-related pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eL). Noteworthy are the TNF signaling pathway and the pathways associated with PD-L1 expression and the PD-1 checkpoint. The centrality of these pathways in governing immune reactions, cellular signaling, and inflammatory responses underscores the genes\u0026rsquo; pivotal roles in influencing tumor immunity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eNomogram Model Construction\u003c/h2\u003e \u003cp\u003eBased on the 5 prognostically relevant genes expression data and coef values, we constructed a RiskScore\u0026thinsp;=\u0026thinsp;0.0135*B4GALNT2\u0026thinsp;+\u0026thinsp;0.0184*C10orf10-0.0266*FGF19\u0026thinsp;+\u0026thinsp;0.0354*HLF\u0026thinsp;+\u0026thinsp;0.0748*SAP30 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B). In our survival analysis, we set the optimal threshold at 1.0173, categorizing patients into high-risk and low-risk cohorts. This distinction yielded a pronounced prognosis difference between the groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Analyzing the correlation between RiskScores and clinical data, we observed a direct trend: a surge in the risk score corresponded to a decline in patient survival and a rise in mortality, which was anticipated. Remarkably, FGF19 exhibited a protective influence, whereas B4GALNT2, C10orf10, HLF, and SAP30 emerged as risk determinants (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). In order to delve deeper into the potential of the RiskScore predictive model for prognosis in BC patients, we concurrently considered clinical factors such as age and TNM staging, and conducted Cox regression analysis. Our results unequivocally reveal a significant and pivotal correlation between age and RiskScore with the prognosis of BC patients. Furthermore, we vividly illustrate the impact of different T, N and M subgroups on prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). This visual representation assists us in gaining a more profound understanding of the distinct roles played by various staging categories in the survival prognosis of BC patients. Using the Cox method, we constructed a nomogram to assess the prognostic significance of the aforementioned features in the cohort of 1034 samples. The overall C-index of the model was 0.78, 95%CI:(0.74\u0026ndash;0.82), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). The calibration curve demonstrated good performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). By calculating, the optimal cutoff value for the predictive model score was determined to be 0.3282. Based on this value, patients were stratified into high-risk (H) and low-risk (L) groups. Further analysis and testing revealed a significant difference in OS between the two groups (HR\u0026thinsp;=\u0026thinsp;4.52, 95%CI:3.20\u0026ndash;6.38, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH). ROC analysis results indicated the following AUC values: AUC (OS:1 year)\u0026thinsp;=\u0026thinsp;0.86, 95% CI:0.78\u0026ndash;0.94; AUC (3 years)\u0026thinsp;=\u0026thinsp;0.79, 95% CI:0.73\u0026ndash;0.85; AUC (5 years)\u0026thinsp;=\u0026thinsp;0.79, 95% CI:0.74\u0026ndash;0.84 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eNovel Molecular classification of Crohn\u0026rsquo;s Disease in Breast Cance\u003c/h2\u003e \u003cp\u003eIn our endeavor to uncover a new molecular categorization of BC informed by prognostic gene expression, we undertook unsupervised consensus clustering for 1034 BC patients from the TCGA database. We ascertained the ideal cluster count through alterations in the cumulative distribution function (CDF) curve area and through consensus heatmaps (S4 Fig.), was identified as 3 clusters (k\u0026thinsp;=\u0026thinsp;3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The heatmap reveals strong consensus within these groups, as indicated by the blue blocks along the diagonal. Subsequently, we categorized all 1034 patients into three subgroups: CD-BC1 (367 cases, 35.5% of the total), CD-BC2 (239 cases, 23.1% of the total), and CD-BC3 (428 cases, 41.4% of the total). The Kaplan-Meier survival evaluation revealed that patients classified under CD-BC3 presented with notably reduced overall survival. In contrast, those under CD-BC1 displayed a relatively better survival outcome (P\u0026thinsp;=\u0026thinsp;0.041) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). This suggests that our novel molecular classification has a significant impact on the survival of BC patients. We also observed substantial differences in gene expression within the new BC clustering modules, in the genes B4GALNT2, C10orf10, FGF19, HLF, and SAP30 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). In the analyzed data, the gene B4GALNT2 stood out for its significant high expression in the CD-BC2 group. This marked upregulation suggests the potential of B4GALNT2 as a marker gene for diagnostic subgroups, especially for distinguishing the CD-BC2 group from others. Concurrently, both HLF and C10orf10 exhibited marked down-regulation in the CD-BC1 subgroup, while showing notable up-regulation in the CD-BC3 cohort. The trend for SAP30 was observed to be the inverse. Furthermore, immunoinfiltration analysis indicated marked differences in tumor immune infiltrating cells between different modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). The infiltration level of several immune cells, like activated B cells, activated CD4\u003csup\u003e+\u003c/sup\u003e T cells, MDSC (Myeloid-Derived Suppressor Cells), and macrophages, displays significant variations among the clusters. Lastly, through ssGSEA, we identified significant differences in the enrichment of signaling pathways among these three novel molecular subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE-G). The color gradients, from blue (low enrichment) to red (high enrichment), illustrate the pathway enrichment intensities. Compared to the other two groups, the CD-BC2 group distinctly exhibited upregulation in the pathways \u0026lsquo;Basal Cell Carcinoma\u0026rsquo; and \u0026lsquo;Hedgehog Signaling Pathway\u0026rsquo;. This marked increase in pathway activity may offer insights into the pathogenic mechanism and biological behavior of breast cancer in the CD-BC3 group, providing a more detailed explanation for its underlying processes. These findings underscore the clinical significance of our new molecular classification in terms of prognosis and immune characteristics, offering valuable insights for the treatment and management of BC patients.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eInflammation and immune-related aspects have long been pivotal in cancer research. CD is an autoimmune disorder characterized by an exaggerated immune response against the normal intestinal flora, causing damage to the intestinal mucosal tissues, intestinal wall thinning, and the formation of ulcers and fistulas \u003csup\u003e1\u003c/sup\u003e. BC is a grave and hereditary disease marked by the excessive proliferation of breast epithelial cells, leading to high morbidity \u003csup\u003e14\u003c/sup\u003e. Despite their significance, both CD and BC lack comprehensive investigations into their pathogenesis. To address potential confounding and reverse causality, we conducted a MR analysis to explore the causal link between CD and BC. For a deeper investigation into the underlying mechanisms and impacts, we developed and validated a genetic prognostic model through gene transcriptome analysis. Furthermore, we introduced an innovative molecular classification specific to BC. We also examined its influence on the tumor immune microenvironment in BC.\u003c/p\u003e \u003cp\u003eThe results demonstrated the establishment of a causal relationship between CD and the development of BC, validating CD as a risk factor for BC development. And this study further explored to validate the effect of CD on BC subtypes, and surprisingly found that the causal relationship of CD was established for ER\u0026thinsp;+\u0026thinsp;BC but not for ER- BC, which suggests that CD has a greater impact on the prevalence of ER\u0026thinsp;+\u0026thinsp;BC. A previous meta-analysis of cohort studies, however, failed to confirm a significant correlation between the risk of BC in patients with CD \u003csup\u003e11\u003c/sup\u003e. Another study published also failed to confirm the causal relationship \u003csup\u003e12\u003c/sup\u003e. In addition, the results of the latest study also confirmed that CD is a risk factor for the development of BC, which is in line with the overall trend of the findings of the present study, and the nuances may be due to the differences in parameter settings such as the setting of KB\u0026thinsp;\u0026gt;\u0026thinsp;5000 by this author \u003csup\u003e10\u003c/sup\u003e. Moreover, studies have demonstrated that patients with CD and their relatives have a high prevalence of BC, which is more advanced when detected \u003csup\u003e8,9\u003c/sup\u003e. It has been shown that BC patients with CD tend to have later stage and worse prognosis than BC patients without CD \u003csup\u003e15\u003c/sup\u003e. This suggests that patients with CD, especially premenopausal women with CD, and their first-degree family members may need to be actively monitored for gastrointestinal and breast health, as well as actively treated for CD.\u003c/p\u003e \u003cp\u003eIt is important to emphasize that causality analysis is a complex task that requires the integration of several factors and requires in-depth research at different levels \u003csup\u003e13\u003c/sup\u003e. While our study has uncovered a potential causal relationship between CD and BC, further experimental and clinical investigations are imperative to validate this finding and delve into the underlying biological mechanisms. In this context, our research delves into the transcriptome gene correlation analysis between CD and BC. We employed a comprehensive approach, including WGCNA and GO and KEGG, to scrutinize the 557 DEGs common to CD and BC. Our results affirm a high correlation between 274 genes within the \u0026lsquo;turquoise\u0026rsquo; and \u0026lsquo;brown\u0026rsquo; module and BC incidence. Furthermore, some genes within the \u0026lsquo;turquoise\u0026rsquo; and \u0026lsquo;brown\u0026rsquo; module exhibit a correlation with patients\u0026rsquo; OS. A LASSO regression analysis were instrumental in determining prognostically relevant hub genes, including B4GALNT2, C10orf10, FGF19, HLF, and SAP30. Based on the coef values and gene expression data, we devised a RiskScore and a nomogram to predict prognosis for patients. Notably, FGF19 is a protective factor for prognosis, demonstrating a downregulation trend with an increase in the risk score, while B4GALNT2, C10orf10, HLF and SAP30 genes are prognostic risk factors. This study indicates that the RiskScore derived from DEGs associated with CD can effectively stratify BC patients into high-risk(H) and low-risk(L) groups. Kaplan-Meier survival curves reveal a significant difference in OS between these two groups. Thus, RiskScore serves as a robust predictor of clinical endpoints. Overall, our findings signify the potential utility of these molecular risk assessment in optimizing patient care and prognosis for those grappling with both CD and BC. Nonetheless, further studies and clinical validations are necessary to cement the clinical applicability of these findings.\u003c/p\u003e \u003cp\u003eB4GALNT2 is crucial for glycoprotein synthesis and it's significant in the SID blood group system and melanoma prognosis \u003csup\u003e16,17\u003c/sup\u003e. Research has looked into using Hsp1-Hsp2Cas9-Y to knock out B4GALNT2 in porcine fetal fibroblasts (PFF) \u003csup\u003e18\u003c/sup\u003e. This could potentially benefit CD-related BC prognosis. C10orf10 plays a role in cell cycle regulation and DNA repair \u003csup\u003e19\u003c/sup\u003e. Lower expression levels correlate with worse BC outcomes \u003csup\u003e20\u003c/sup\u003e and higher levels are linked to glioma proliferation \u003csup\u003e21\u003c/sup\u003e. These findings suggest C10orf10\u0026rsquo;s relevance not only to gliomas but also to the prognosis of CD-related BC. FGF19 is part of the fibroblast growth factor family, vital for cellular growth and tissue repair \u003csup\u003e22\u003c/sup\u003e. Increased FGF19 levels were found in nasopharyngeal carcinoma (NPC) patients, suggesting its role in disease progression \u003csup\u003e23\u003c/sup\u003e. FGF19 is also associated with gallbladder cancer \u003csup\u003e24\u003c/sup\u003e. This is different from the prognostic protective factor in this study, therefore, further validation of the role of FGF19 in BC is warranted. HLF is a transcription factor, a type of protein that plays a crucial role in regulating gene expression. Some findings indicated that HLF regulates apoptosis and autophagy \u003csup\u003e25\u003c/sup\u003e. SAP30 proteins typically regulate gene expression. They interact with Sin3A proteins to form the Sin3 complex, which impacts gene silencing and expression \u003csup\u003e26\u003c/sup\u003e. Our study revealed significant differential expression of SAP30 in a novel BC molecular classification and was associated with BC prognosis. Although the exact mechanism of SAP30 is not fully understood, SAP30 has been found to be involved in the regulation of gene expression and chromatin structure in other studies. Studies have demonstrated that SAP30 is associated with BC poor prognosis, consistent with the findings in this paper \u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study conducted a novel molecular classification of 1034 BC patients based on prognostic genes associated with CD. The patients were categorized into three subgroups, namely CD-BC1, CD-BC2, and CD-BC3. Significant differences in survival outcomes, tumor immune infiltration, and enrichment of signaling pathways were observed among these subgroups. These findings provide a plausible explanation for the differing prognostic outcomes in these three subgroups. As seen in the previous section, the immune microenvironment and inflammatory response closely link the two diseases, CD and BC, which are seemingly unrelated. It has been shown that the systemic inflammatory response in patients with ulcerative colitis (UC) leads to the down-regulation of BC resistance protein (BCRP/ABCG2), which in turn induces breast carcinogenesis \u003csup\u003e27,28\u003c/sup\u003e.Since CD and UC belong to the same group of infammatory bowel disease \u003csup\u003e29\u003c/sup\u003e. Therefore, this may also provide theoretical support for the mechanism in this study, but this needs to be further verified by relevant basic research. After reading a large amount of literature, a researcher believes that there may be a common molecular mechanism between CD and BC, which may be related to immune and inflammatory signaling pathways such as IL-17, NF-κB, and TNF-α. The researcher believes that anxiety and depression, as well as intestinal dysbiosis, may also be part of the reason why CD triggers the development of BC \u003csup\u003e30\u003c/sup\u003e. Differential gene GO, KEGG enrichment analysis in this study similarly enriched numerous immune-inflammation-related pathways.\u003c/p\u003e \u003cp\u003eIn summary, while this study suggests the presence of shared biological factors between CD and BC, the inherent limitations of MR and transcriptomic analysis are fundamental constraints that are inescapable. To delve deeper into this association, we strongly advocate for more multicenter research. Such multicenter studies will contribute to a more comprehensive understanding of the potential relationship between CD and BC, unveiling variations and commonalities among different populations. Simultaneously, these studies will enable a better assessment of patient risks and prognosis, providing a stronger foundation for personalized healthcare and interventions based on novel molecular classification. Ultimately, this interdisciplinary collaboration and data sharing will enhance disease prevention and treatment strategies, positively impacting public health.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn our study, we established that CD is a significant risk factor for BC development, especially in hormone receptor-positive (ER+) BC cases. However, the association between CD and BC was not significant in hormone receptor-negative (ER-) BC cases. Our reverse validation confirmed that BC does not directly affect CD. Regarding prognosis, we identified five genes, B4GALNT2, C10orf10, FGF19, HLF, and SAP30. We developed a prognostic model presented as a nomogram, with a strong predictive value, offering valuable insights into patient prognosis and individualized treatment. Furthermore, based on the aforementioned genes, we classified BC patients into three new groups characterized by differences in prognosis and significant variations in immune profiles. To conclude, our study sheds light on the complex relationship between CD and BC, with specific relevance to certain BC subtypes. These findings have the potential to guide future disease prevention, treatment, and personalized medicine efforts.\u003c/p\u003e"},{"header":"List Of Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCrohn\u0026rsquo;s Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBreast Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMendelian Randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenome Wide Association Studies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInverse-variance Weighted\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eER+\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEstrogen Receptor-Positive\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eER-\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEstrogen Receptor-Negative\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInstrumental Variables\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNPs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle Nucleotide Polymorphisms\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTCGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBRCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBreast Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLASSO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeast Absolute Shrinkage and Selection Operator\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferentially Expressed Genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOverall Survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCDF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCumulative Distribution Function\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003essGSEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esingle-sample Gene Set Enrichment Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGSVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Set Variation Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUlcerative Colitis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSignificance levels\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e**** (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), *** (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ** (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), * (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper was not funded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYX and YYS contributed equally to this work. SCG and YX conceived the study. YYS and YX had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. YX and YXQ performed data analyses. YYS and HXW wrote the first draft of this manuscript. JZR, WQ and SCG critically revised the manuscript. All authors approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed in this study can be found in the IEU Open GWAS(https://gwas.mrcieu.ac.uk/), TCGA(https://www.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven that this study utilized data sourced from publicly available datasets, neither ethics approval nor informed consent were deemed necessary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no financial or personal relationships with third parties that might inappropriately influence the content of this paper. No financial support was received for the research and authorship of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRoda G, Chien Ng S, Kotze PG, et al. Crohn's disease. Nat Rev Dis Primers. 2020;6(1):22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe Berre C, Ananthakrishnan AN, Danese S, et al. Ulcerative Colitis and Crohn's Disease Have Similar Burden and Goals for Treatment. Clin Gastroenterol Hepatol. 2020;18(1):14\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDonegan WL. Cancer of the breast in men. CA Cancer J Clin. 1991;41(6):339\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilkinson L, Gathani T. Understanding breast cancer as a global health concern. Br J Radiol. 2022;95(1130):20211033.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurocziova M, Grusanovic S, Vanickova K et al. Chronic Inflammation Promotes Cancer Progression as a Second hit. Exp Hematol 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim ES, Kim SY, Moon A. C-Reactive Protein Signaling Pathways in Tumor Progression. Biomol Ther (Seoul). 2023;31(5):473\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePellino G, Sciaudone G, Patturelli M, et al. Relatives of Crohn's disease patients and breast cancer: an overlooked condition. Int J Surg. 2014;12(Suppl 1):156\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiegler G, Caserta L, Castiglione F, et al. Increased risk of breast cancer in first-degree relatives of Crohn's disease patients. An IG-IBD study. Dig Liver Dis. 2006;38(1):18\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao H, Zheng S, Yuan X, et al. Causal association between inflammatory bowel disease and 32 site-specific extracolonic cancers: a Mendelian randomization study. BMC Med. 2023;21(1):389.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong C, Xu R, Zou P, et al. Inflammatory bowel disease and risk of breast cancer: a meta-analysis of cohort studies. Eur J Cancer Prev. 2022;31(1):54\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu Y, Ma L. Investigation of the causal relationship between breast cancer and autoimmune diseases: A bidirectional mendelian randomization study. Med (Baltim). 2023;102(34):e34612.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37(7):658\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBravi F, Decarli A, Russo AG. Risk factors for breast cancer in a cohort of mammographic screening program: a nested case-control study within the FRiCaM study. Cancer Med. 2018;7(5):2145\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026oslash;gaard KK, Cronin-Fenton DP, Pedersen L, et al. Survival in Danish patients with breast cancer and inflammatory bowel disease: a nationwide cohort study. Inflamm Bowel Dis. 2008;14(4):519\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStenfelt L, Nilsson J, Hellberg \u0026Aring; et al. Glycoproteomic and Phenotypic Elucidation of B4GALNT2 Expression Variants in the SID Histo-Blood Group System. Int J Mol Sci 2022;23(7).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKe G, Cheng N, Sun H, Meng X, et al. Explore the impact of hypoxia-related genes (HRGs) in Cutaneous melanoma. BMC Med Genomics. 2023;16(1):160.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamada M, Watanabe Y, Gootenberg JS, et al. Crystal Structure of the Minimal Cas9 from Campylobacter jejuni Reveals the Molecular Diversity in the CRISPR-Cas9 Systems. Mol Cell. 2017;65(6):1109\u0026ndash;1121e1103.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalcher S, Hermann M, Kiechl-Kohlendorfer U, et al. C10ORF10/DEPP-mediated ROS accumulation is a critical modulator of FOXO3-induced autophagy. Mol Cancer. 2017;16(1):95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng J, Dong Y, Li C, et al. Decreased expression of C10orf10 and its prognostic significance in human breast cancer. PLoS ONE. 2014;9(6):e99730.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Y, Tang M, Li H, et al. Effects of C10orf10 on growth and prognosis of glioma under hypoxia. Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2023;48(4):499\u0026ndash;507.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi L, Zhao T, Huang L, et al. Engineered FGF19(∆KLB) protects against intrahepatic cholestatic liver injury in ANIT-induced and Mdr2-/- mice model. BMC Biotechnol. 2023;23(1):43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi S, Zhang Q, Zhang K et al. FGF19 promotes nasopharyngeal carcinoma progression by inducing angiogenesis via inhibiting TRIM21-mediated ANXA2 ubiquitination. Cell Oncol (Dordr). 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen T, Liu H, Liu Z, et al. FGF19 and FGFR4 promotes the progression of gallbladder carcinoma in an autocrine pathway dependent on GPBAR1-cAMP-EGR1 axis. Oncogene. 2021;40(30):4941\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXue P, Liu Y, Wang H, et al. miRNA-103-3p-Hlf regulates apoptosis and autophagy by targeting hepatic leukaemia factor in heart failure. ESC Heart Fail. 2023;10(5):3038\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBao L, Kumar A, Zhu M et al. SAP30 promotes breast tumor progression by bridging the transcriptional corepressor SIN3 complex and MLL1. J Clin Invest 2023;133(17).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGutmann H, Hruz P, Zimmermann C, et al. Breast cancer resistance protein and P-glycoprotein expression in patients with newly diagnosed and therapy-refractory ulcerative colitis compared with healthy controls. Digestion. 2008;78(2\u0026ndash;3):154\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEnglund G, Jacobson A, Rorsman F, et al. Efflux transporters in ulcerative colitis: decreased expression of BCRP (ABCG2) and Pgp (ABCB1). Inflamm Bowel Dis. 2007;13(3):291\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaplan GG, Windsor JW. The four epidemiological stages in the global evolution of inflammatory bowel disease. Nat Rev Gastroenterol Hepatol. 2021;18(1):56\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou S, Yu J. Crohn's disease and breast cancer: a literature review of the mechanisms and treatment. Intern Emerg Med. 2023;18(5):1303\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Crohn’s Disease, Breast Cancer, Mendelian Randomization, Prognostic Model, Molecular Classification","lastPublishedDoi":"10.21203/rs.3.rs-3642218/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3642218/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCrohn\u0026rsquo;s disease (CD), a prominent manifestation of chronic gastrointestinal inflammation, and breast cancer (BC), seemingly disparate in the medical domain, exhibit a shared characteristic. This convergence arises from their involvement in chronic inflammation and immune responses, an aspect that has progressively captivated the attention of investigators but remain controversy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used two-sample Mendelian Randomization (MR) and transcriptomics to explore the relationship between CD and BC. MR assessed causality of CD on different BC subtypes and reverse causality of BC on CD. We identified CD-related differentially expressed genes and their prognostic impact on BC, and developed a new molecular BC classification based on these key genes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMR revealed a causal link between CD and increased BC risk, especially in estrogen receptor-positive (ER+) patients, but not in ER-negative (ER-) cases. BC showed no causal effect on CD. Transcriptomics pinpointed genes like B4GALNT2 and FGF19 that affected BC prognosis in CD patients. A nomogram based on these genes predicted BC outcomes with high accuracy. BC patients were reclassified into three prognostically distinct subtypes using these genes.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eCD is a risk factor for ER\u0026thinsp;+\u0026thinsp;BC but not for ER- BC. BC does not causally affect CD. Our prognostic model and new BC molecular classifications offer insights for personalized treatment strategies.\u003c/p\u003e","manuscriptTitle":"Unraveling the Causal Links and Novel Molecular Classification of Crohn’s Disease in Breast Cancer: A Two-Sample Mendelian Randomization and Transcriptome Analysis with Prognostic Modeling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-12-04 17:17:19","doi":"10.21203/rs.3.rs-3642218/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-17T07:24:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-14T02:05:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"171419079715063164484797642223432645725","date":"2024-05-31T03:00:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-14T14:22:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"195972276368597090400502670530104802065","date":"2024-04-29T10:26:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103109669754586458189356332579683856760","date":"2024-04-25T13:12:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"c5090e35-5743-4672-90b4-c8dd07db167b","date":"2024-01-26T12:31:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-24T11:38:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-24T08:42:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-11-30T13:01:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-11-30T07:52:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2023-11-21T05:25:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"92ebd86f-63c1-4c27-b558-7eb13076cd1b","owner":[],"postedDate":"December 4th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-16T15:58:50+00:00","versionOfRecord":{"articleIdentity":"rs-3642218","link":"https://doi.org/10.1186/s12885-024-12838-x","journal":{"identity":"bmc-cancer","isVorOnly":false,"title":"BMC Cancer"},"publishedOn":"2024-09-11 15:56:52","publishedOnDateReadable":"September 11th, 2024"},"versionCreatedAt":"2023-12-04 17:17:19","video":"","vorDoi":"10.1186/s12885-024-12838-x","vorDoiUrl":"https://doi.org/10.1186/s12885-024-12838-x","workflowStages":[]},"version":"v1","identity":"rs-3642218","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3642218","identity":"rs-3642218","version":["v1"]},"buildId":"2u56kwukJI3zHK-uzyFNs","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}