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. 2025 Sep 19;104(38):e44305. doi: 10.1097/MD.0000000000044305

Mendelian randomization and transcriptomic data analysis reveals the causal association between gout and breast cancer

Hengheng Zhang a,b, Xingfa Huo c, Jinming Li a,b, Na Li a,b, Wenjun Xiao a,b, Miaozhou Wang a,b, Fuxing Zhao a,b, Yi Zhao a,b,*
PMCID: PMC12459518  PMID: 40988277

Abstract

The association between gout and cancer risk has garnered significant interest, particularly in relation to breast cancer, which is the most prevalent cancer among women globally. Nevertheless, the coincidental link between gout and breast cancer, along with its underlying pathogenesis, remains inadequately elucidated. This study utilized publicly available genome-wide association study data from individuals of European ancestry, with sample sizes drawn from multiple genome-wide association study studies, covering genetic variations related to gout and breast cancer. Additionally, transcriptomic data analysis was conducted using datasets from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus databases, with the TCGA database comprising 199 adjacent normal tissue samples and 1085 breast cancer tissue samples. Following a rigorous sequence of quality control procedures, we incorporated suitable instrumental variables that exhibited significant associations with the exposure (gout). Five algorithms, namely Mendelian randomization (MR) Egger, weighted median, inverse variance weighting, simple mode, and weighted mode, were employed to deduce the causal link between gout and breast cancer. Moreover, we evaluated the reliability of the MR analysis through heterogeneity and pleiotropy assessments. Subsequently, transcriptomic data analysis was conducted utilizing TCGA and Gene Expression Omnibus databases to explore the possible correlation between gout and breast cancer. MR analysis revealed a stochastic relationship between genetic predisposition to gout and a decreased risk of breast cancer in individuals of European descent (odds ratio: 0.83, 95% CI: 0.71–0.98, P = .031). Additionally, the sensitivity analysis underscored the strength and reliability of the present MR findings. A key gene (MLX interacting protein-like) was identified using lasso regression methods. The gene showed a strong predictive performance in survival prediction (P < .05). Our MR study offers evidence indicating that genetic variations linked to gout are causally correlated with a decreased risk of breast cancer in the European population. Furthermore, transcriptomic data analysis indicates that the key gout-associated gene, MLX interacting protein-like, is implicated and holds predictive significance in the pathogenesis of breast cancer.

Keywords: breast cancer, gout, Mendelian randomization, prognosis, transcriptomic data

1. Introduction

Based on the most recent data published by the World Health Organization’s International Agency for Research on Cancer, it is projected that there will be over 2.3 million new cases of breast cancer in 2022, positioning it as the second most prevalent cancer type following lung cancer. Breast cancer ranks as the most frequently diagnosed cancer among women and is the leading cause of cancer-related mortality in the female population.[1] The etiology of breast cancer is multifaceted, with a significant portion believed to be linked to modifiable risk factors.[2] These factors encompass a high body mass index postmenopause, lack of physical activity, alcohol consumption, as well as the utilization of exogenous female hormones and reproductive elements.[3] Moreover, there is evidence indicating that chronic inflammation can impact cancer risk, exerting a pivotal role in the initiation and advancement of cancer pathology.[4,5]

Gout is a chronic inflammatory disorder affecting the joints, characterized by the accumulation of sodium urate crystals in both joint and non-joint tissues. Elevated serum urate levels represent a critical risk factor for gout development. The condition manifests as recurrent episodes of intense arthritic pain triggered by the innate immune reaction to deposited monosodium urate crystals.[6–8] The global prevalence and incidence of gout have shown a notable increase. Data obtained from various studies, while exhibiting variability due to differences in population demographics and research methodologies, indicate a prevalence range of < 1% to 6.8% and an annual incidence rate of 0.58 to 2.89 per 1000 individuals.[9]

A substantial association was identified between gout and the development of tumors including lung cancer, leukemia, non-Hodgkin lymphoma, endometrial cancer, gastrointestinal cancers, cervical cancer, and prostate cancer.[10–14] Additionally, existing studies provide evidence of the link between inflammation and breast cancer, suggesting a potential association between gout and breast cancer as well.[15–17] However, the relationship between gout and the incidence of breast cancer remains a topic of ongoing debate, and current research in this area is still insufficient. Mendelian randomization (MR) pertains to investigations utilizing genetic variability in observational epidemiology to draw causal inferences concerning modifiable (non-genetic) risk factors for diseases and health-related outcomes. This approach leverages the principles of Mendel second Law or the Law of Independent Assortment.[18] Genetic variations in the phenotype can serve as instrumental variables (IVs) to unveil a causal association between the exposure and outcome, effectively reducing the impact of potential confounders commonly encountered in observational studies.[19]

The aim of this study was to explore the relationship between breast cancer incidence and gout, including the specific pathogenesis and its implications on survival and prognosis, utilizing MR and bioinformatics analysis. In this study, we conducted a 2-sample MR analysis to investigate the possible causal association between gout and breast cancer, aiming to offer novel insights and evidence in this research domain.

2. Methods

2.1. Study design

The study design is illustrated in Figure 1. Our study consisted of 2 phases. In Phase I, we assessed the causality of the relationship between gout and breast cancer using 2-sample MR analysis. In Phase II, we identified single nucleotide polymorphisms (SNPs) indicative of a causal link between gout and breast cancer. Utilizing the National Center for Biotechnology Information (NCBI) database, we examined the gene information linked to the SNPs. Subsequently, we employed bioinformatics methodologies grounded in genetic data to explore the potential correlation between gout and breast cancer.

Figure 1.

Figure 1.

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Flow diagram for study.

The validity of the 2-sample MR relies on the following 3 assumptions: IVs must be linked to gout; IVs should be independent of confounding factors; IVs must influence breast cancer solely through gout.

The genome-wide association study (GWAS) summary statistics and transcriptomic data utilized in our investigation of gout and breast cancer were sourced from publicly accessible websites. As such, no further ethical committee approval was deemed necessary.

2.2. Two-sample MR analysis

2.2.1. GWAS data for breast cancer and gout

The GWAS data for breast cancer and gout included in this study were both sourced from the IEU OpenGWAS public database (https://gwas.mrcieu.ac.uk/). The cohorts consist exclusively of individuals of European ancestry. Specifically, the breast cancer GWAS data (ieu-b-4810) encompass 13,879 breast cancer patients and 198,523 non-breast cancer controls. The gout GWAS data (ukb-b-13251) include 6543 gout patients and 456,390 non-gout controls. The sources and sample size of the GWAS data for gout and breast cancer used in this study are summarized in Table 1.

Table 1.

Information on the dataset for breast cancer and gout.

GWAS ID Trait Year Consortium N. cases N. controls Sample size Number of SNPs GWAS ID
ukb-b-13251 Gout 2018 MRC-IEU 6543 456,390 462,933 9,851,867 ukb-b-13251
ieu-b-4810 Breast cancer 2021 UK Biobank 13,879 198,523 212,402 NA ieu-b-4810
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NA = not available.

2.2.2. Selection of genetic IVs

We implemented stringent criteria for the selection of SNPs as IVs, including the following steps: SNPs associated with gout and breast cancer were identified as IVs using a significance threshold of less than 5 × 10⁻⁸; to ensure independence, a linkage disequilibrium threshold of R² < 0.001 within a ± 1 MB window was set; subsequently, exposure and outcome datasets were harmonized to ensure allele consistency, and palindromic SNPs were excluded; the strength of exposure instruments was evaluated based on the variance explained by SNPs (R²) and the F statistic.[20–22] SNPs with an F statistic exceeding 10 were considered robust instruments for MR analysis, while those with an F statistic below 10 were eliminated to prevent bias resulting from weak instruments.[23]

2.2.3. Statistical analysis

This research employs the inverse variance weighting (IVW), weighted median, simple mode, weighted mode, and MR Egger methods for conducting 2-sample MR analysis. IVW serves as the primary MR analysis approach.[24] In addition to IVW, we utilized the weighted median, simple mode, weighted mode, and MR Egger models as supplementary methods, capable of yielding reliable causal estimates even in scenarios where IVW assumptions are not met.[25,26] Furthermore, we utilized the MR-PRESSO global test to address horizontal pleiotropy and employed Cochrane Q test and MR pleiotropy residual sum to evaluate heterogeneity, thereby bolstering the reliability of our findings.[27] In cases where outliers were identified through the MR-PRESSO outlier test, we conducted repeat MR and sensitivity analyses after excluding these outliers.[28,29] Subsequently, we assessed the stability of the MR analysis using the leave-one-out method, which systematically removes 1 genetic variant at a time to reassess the causal impact.

2.3. Transcriptomic analyses

2.3.1. Data sources

The Cancer Genome Atlas (TCGA) database, accessible at https://cancergenome.nih.gov/, offers publicly available cancer genomics datasets. We utilized the R 4.0 software (Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand) to retrieve the transcriptome Fragments Per Kilobase Million data package for breast cancer from the TCGA database (https://portal.gdc.cancer.gov), comprising 199 adjacent normal tissue samples and 1085 breast cancer tissue samples. Furthermore, we obtained the corresponding follow-up and clinical data for TCGA breast cancer from the UCSC Xena website (https://xenabrowser.net/datapages/). In cases of genes with duplicate names, we computed the average value, excluded genes with a mean expression level below 0.05, and preserved the expression levels of the remaining genes for subsequent analysis. The gene expression levels for each sample underwent preprocessing through log2(x + 0.01). Following the outlined procedures, the data were standardized accordingly. Subsequently, microarray expression data and clinical information for the validation set were retrieved from the Gene Expression Omnibus (GEO) database under accession number GSE42568.

2.3.2. Expressed genes and survival analysis

The raw data from the TCGA analysis were utilized to examine genes associated with gout. Survival analysis of the gout-related genes was performed using the “survival” R package with TCGA data. Statistical significance was defined as a P-value below .05.

2.3.3. LASSO dimension reduction analysis

Initially, the “glmnet” R package was employed to identify intersecting genes associated with overall survival (OS) using least absolute shrinkage and selection operator (LASSO) regression analysis. The LASSO algorithm was utilized to mitigate collinearity among gout-related genes within the TCGA-BRCA database, thereby addressing potential redundancies. By reducing the regression coefficients, MLX interacting protein-like (MLXIPL), was identified as the optimal differential gene linked to the prognosis of breast cancer.

2.4. Nomogram based on gout-related genes

A predictive nomogram was constructed utilizing the outcomes of multivariate Cox analysis with the “rms” R package to evaluate the 3-, 5-, and 8-year OS probabilities. Calibration curves were employed to assess the concordance between the predicted probabilities from the nomogram and the observed probabilities.

2.5. GO and KEGG enrichment analysis

In this study, we applied quantile normalization and ComBat batch effect correction to the GEO data to ensure the accuracy and comparability of the results. To determine the gene sets for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, we first mapped the SNPs that have a causal relationship with gout and breast cancer to their corresponding genes using NCBI database. Subsequently, we performed differential expression analysis of these mapped genes using data from TCGA Breast Cancer database and GEO database. Specifically, we utilized the GEO2R tool to identify differentially expressed genes (DEGs) with the criteria of |log2FC| ≥ 1 and P < .05. Genes that exhibited significant expression differences were selected for further transcriptomic analysis. Specifically, the “ClusterProfiler” R package was employed to conduct GO and KEGG enrichment analyses on genes related to gout, aiming to elucidate their biological functions and relevant metabolic pathways. Initially, utilizing the “ClusterProfiler” package, we conducted GO enrichment analysis to characterize the biological processes, cellular components, and molecular functions associated with these genes. A significance threshold of P-value <.05 was considered for determining enriched terms.[30,31] The data utilized in this study were obtained from the publicly available GEO database. As the data have been anonymized and de-identified, and the analysis involves secondary use of existing datasets, no additional ethics approval was required for this study.

2.6. Statistical analysis

All analyses were performed using R version 4.4.0, incorporating the “TwoSampleMR,” “MR-presso,” “Survival,” “Glmnet,” “rms,” and “Clusterprofiler” Packages.

3. Results

3.1. Causal relationship between gout and breast cancer

Through a 2-sample MR analysis, we established a causal association between gout and breast cancer, as depicted in Figure 2. Specifically, within the valid SNP range, the IVW method revealed a negative correlation between an increased risk of gout and the probability of developing breast cancer (β_IVW = -0.18, OR_ IVW = 0.83, 95% CI = 0.71–0.98, P = .031). Similar results were observed using the weighted median method (β_Wme = -0.31, OR_Wme = 0.73, 95% CI = 0.62–0.87, P = .0004) and weighted mode method (β_Wmo = -0.21, OR_Wmo = 0.81, 95% CI = 0.69–0.97, P = .030). These outcomes suggest that in the European population, gout may serve as a protective factor against breast cancer, potentially reducing the risk of its development. The MR Egger and simple mode methods produced consistent but non-significant results (β_ME = -0.19, OR_ME = 0.82, 95% CI = 0.62–1.08, P = .18; β_Sm = -0.07, OR_Sm = 0.93, 95% CI = 0.66–1.31, P = .67), as detailed in Table 2.

Figure 2.

Figure 2.

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Forest plot of OR values between gout and overall breast cancer.

Table 2.

Results of Mendelian randomized analysis of gout and breast cancer.

Exposure Outcome Method NSNP b SE P-value OR or_lci95 or_uci95
Gout Breast cancer MR Egger 21 -0.197393169 0.141995513 .180559867 0.82086783 0.62144717 1.084282022
Weighted median 21 -0.309559699 0.088282062 .000454068 0.733769965 0.617181248 0.87238289
Inverse variance weighted* 21 -0.18196793 0.08468974 .031662792 0.833628075 0.706127248 0.9841509
Simple mode 21 -0.075468746 0.176991354 .674372216 0.927308712 0.655490284 1.31184469
Weighted mode 21 -0.205415333 0.088029021 .030172229 0.814309037 0.685263246 0.967656169
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*

Indicates the primary inverse variance weighted result and significant findings visually.

In the course of the analysis, 2 SNPs, rs34555420 and rs34555420, were excluded due to the presence of palindromic sequences. Furthermore, rs10774624 and rs17298067 were eliminated following the MR Egger intercept test and MR-presso global test, which identified the presence of horizontal pleiotropy. Following the exclusion of these SNPs, no pleiotropy was observed (P > .05 for both MR Egger intercept and MR-presso global tests). Cochran Q test results indicated no evidence of heterogeneity (P_ME = .069 and P_IVW = .085, both > .05). Comprehensive results regarding heterogeneity and pleiotropy are provided in Table 3. The leave-one-out analysis validated the reliability of our results (Fig. 3A), while the scatter plot in Figure 3B illustrates the consistent directionality observed across the 5 analysis methods. The detailed results prior to outlier removal are available in Table S1, Supplemental Digital Content, https://links.lww.com/MD/P851.

Table 3.

The information on heterogeneity and MR-PRESSO test for gout and overall breast cancer.

Cochran Q derived P-value
Exposure Outcome Population MR Egger Inverse variance weighted MR-PRESSO global test derived P-value MR Egger intercept derived P-value
Gout Breast cancer European 0.1003197 0.1163573 .121 .6882802
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MR = Mendelian randomization.

Figure 3.

Figure 3.

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Scatter plot and leave-one-out plot illustrating the causal relationship between gout and breast cancer.

3.2. Identification and survival analysis of gout-associated genes in breast cancer patients

Based on the GEPIA2 analysis, it was determined that RFT1 exhibits high expression levels in breast cancer, whereas SLC17A1, SLC16A9, GCKR, MLXIPL, MLXIP, HFE, and SLC2A9 demonstrate low expression levels in breast cancer (Fig. 4). Subsequent analysis conducted using the Kaplan–Meier plotter database unveiled the influence of varying gene expression levels on the OS of breast cancer patients. Among the entire cohort of breast cancer patients (n = 1284), it was observed that the high expression group of RFT1 exhibited a notably superior OS compared to the low expression group (P = .023). Conversely, it was observed that the low expression cohorts of GCKR, MLXIPL, MLXIP, HFE, and SLC2A9 exhibited significantly improved OS compared to their corresponding high expression groups (P = .004, P < .001, P = .024, P = .002, and P = .014, respectively) (Fig. 5).

Figure 4.

Figure 4.

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Identification of gout-associated genes in breast cancer patients.

Figure 5.

Figure 5.

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Survival analysis of gout-associated genes in breast cancer patients. Kaplan–Meier survival curves showing the impact of gene expression levels of RFT1, GCKR, MLXIPL, MLXIP, HFE, and SLC2A9 on the overall survival of breast cancer patients. Patients were categorized into high and low expression groups based on gene expression levels. The survival probabilities for these genes over 25 years are presented, with statistical significance indicated by P-values.

3.3. Construction and validation of the prediction model

Utilizing the LASSO regression method, core genes were examined, leading to the identification of a pivotal gene (MLXIPL) through LASSO dimension reduction. Subsequently, univariate and multivariate Cox analyses were conducted on this key gene alongside various clinicopathological features, culminating in the development of a clinical predictive model. Following this, MLXIPL, age, and TNM stage were amalgamated to formulate a nomogram tailored for breast cancer patients, showcasing strong predictive performance for 3-, 5-, and 8-year OS in this cohort (Fig. 6). Validation was carried out in the GEO database, affirming the robust performance of the predictive model in accurately predicting 3-, 5-, and 8-year OS for breast cancer patients (Fig. 7).

Figure 6.

Figure 6.

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Analysis of gout-related key genes and prognostic model construction for breast cancer. (A) Protein–protein interaction network for the MLXIPL gene, illustrating its interactions with other key proteins related to both breast cancer and gout. (B, C) Application of the least absolute shrinkage and selection operator regression model to narrow the range of gout-related differentially expressed genes. (B and C) The use of the LASSO algorithm to reduce overfitting of recurrence features and select key genes. (D) Univariate and multivariate Cox regression analysis of the selected gout-related genes (including MLXIPL) to evaluate their association with breast cancer prognosis. (E) Construction of a nomogram to predict overall survival at 1, 3, and 5 years for breast cancer patients. This predictive model integrates MLXIPL expression levels with clinical factors, such as age and TNM stage, to estimate individual survival probabilities. The model was validated using clinical data, demonstrating strong predictive power (C-index: 0.716). (F) Calibration plot comparing the predicted 1-, 3-, and 5-year survival probabilities from the nomogram with the actual survival data.

Figure 7.

Figure 7.

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(A) Calibration plot of 3-, 5- and 8-years actual risk probability was exhibited, indicating moderate power for predicting survival for patients with breast cancer. (B) Forest plot of the validation set.

3.4. Analysis of the functional characteristics

In order to elucidate the potential functions of the 13 gout-related genes in breast cancer, we conducted GO and KEGG enrichment analyses. The GO analysis revealed that differentially expressed genes were enriched in processes related to urate metabolic pathways, organic anion transport, lipid homeostasis, and response to carbohydrate (Fig. 8A). The KEGG enrichment analysis predominantly highlighted involvement in the TGF-beta signaling pathway, insulin resistance, and nonalcoholic fatty liver disease (Fig. 8B).

Figure 8.

Figure 8.

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(A) Gene ontology enrichment analysis. (B) Kyoto Encyclopedia of Genes and Genomes enrichment analysis.

4. Discussion

There is limited existing literature on the association between gout and breast cancer. This study pioneered the identification of causally related SNPs through MR analysis. Subsequently, these SNPs were paired with their respective genetic information sourced from NCBI. The genes were then analyzed utilizing a bioinformatics approach to uncover the underlying biological mechanisms. By integrating Mendelian and bioinformatics analyses, novel insights into the connection between gout and breast cancer were revealed.

Gout is a prevalent inflammatory condition distinguished by acute arthritis and hyperuricemia. Numerous epidemiological studies have underscored the significant impact of gout on carcinogenesis.[32] Published studies have indicated that gout is linked to a heightened risk of various cancers, including prostate, liver, lung, and colon cancers in men, as well as breast cancer. The recent study by Gremke et al explored the association between gout and subsequent breast cancer development. Their retrospective cohort study, which included 67,598 patients from Germany, found a significant link between gout and the incidence of breast cancer, particularly in women under the age of 50 (HR = 1.58). This finding suggests that gout, as an inflammatory disease, may contribute to breast cancer progression through systemic inflammation and hyperuricemia, providing new insights into the mechanisms of breast cancer.[10–12,17] But there are studies that suggest otherwise.[33,34] In this study, we elucidated that the genetic predisposition to gout is correlated with a decreased risk of breast cancer in the European population. Our MR study may offer more robust evidence compared to findings from prior randomized controlled trials or cohort studies. Various potential confounding factors present in observational studies, such as study population, time frame, and inclusion criteria, could have varying degrees of influence on the results. MR has the capability to mitigate the impact of confounding variables and can serve as a surrogate for randomized controlled trials. Moreover, ethnic disparities may play a role in varying breast cancer risks among individuals with gout. Prior investigations did not address the heterogeneity within the study population. The methodology employed in this study addresses this gap, and the ethnic diversity observed in breast cancer risk among gout patients may imply the necessity for diverse breast cancer prevention strategies.

Given that gout exhibits a protective effect against breast cancer in MR analysis, we proceeded to identify gout-associated genes from the chosen SNPs. Notably, the genetic variations demonstrated substantial associations with pathways involving urate metabolic processes, organic anion transport, lipid homeostasis, response to carbohydrates, the TGF-beta signaling pathway, insulin resistance, and nonalcoholic fatty liver disease. These findings are consistent with previous studies where aberrant transcription or reprogramming of fatty acid metabolism was identified as a key driver of breast cancer progression.[35] Using the LASSO algorithm, we devised a novel prognostic score incorporating a key therapeutic target, MLXIPL, for managing breast cancer patients with gout. Experimental studies have substantiated the involvement of this gene in breast cancer.[36] MLXIPL, also known as carbohydrate-responsive element-binding protein (ChREBP), is a transcription factor that primarily participates in the regulation of glucose metabolism and fatty acid synthesis.[37,38] Evidence suggests that ChREBP may be involved in cancer pathology and mechanisms associated with tumorigenesis, potentially impacting breast cancer pathology. Firstly, breast cancer is epidemiologically linked to obesity,[39,40] suggesting a causal relationship with dysregulation of lipid metabolism. Secondly, glycolytic and lipogenic pathways may serve as integrated characteristics of tumor metabolism.[41] ChREBP plays a pivotal role in breast cancer by orchestrating metabolic processes and fostering cell proliferation and survival. Its significance renders it a promising therapeutic target for disrupting the metabolic reliance of breast cancer cells. This study also supports the above experimental findings.

Gout is characterized by inflammatory arthritis resulting from the accumulation of urate crystals in the joints, with uric acid being the final product of purine metabolism. ChREBP is involved in modulating the expression of genes associated with purine metabolism. For instance, research has indicated that the knockdown of ChREBP led to elevated plasma uric acid levels in mice.[42] It has been reported that ChREBP can modulate serum uric acid levels via the pentose-phosphate pathway. Decreased ChREBP expression has been shown to enhance the activity of the pentose-phosphate pathway, leading to elevated serum uric acid levels. In breast cancer, the activity of ChREBP frequently upregulated, resulting in heightened glycolysis and lipid synthesis, which supply the essential building blocks and energy required for tumor proliferation. The findings of this study align with the conclusions reported in previous literature. This critical molecule exhibits low expression levels in breast cancer samples, with decreased expression linked to extended OS. Therefore, both the MR analysis and the transcriptomic data analysis indicate that gout serves as a protective factor against breast cancer, reducing the risk of developing the disease.

Subsequently, the univariate and multivariate Cox models did not identify MLXIPL as an independent prognostic factor; however, age and TNM stage exhibited statistical significance. Additionally, the nomogram demonstrated favorable 1, 3, 5, and 8-year OS predictions for breast cancer patients. Although MLXIPL was identified as an important gene in LASSO regression, it showed no significance in the multiple model. However, MLXIPL still showed good predictive value in survival analysis. While the gene was recognized as a promising therapeutic target, further investigations involving both basic research and clinical studies are warranted to delve into the pathways and mechanisms associated with this tumor marker.

Several limitations of this study warrant consideration. Firstly, MR encompasses 5 methods, namely IVW, weighted median, simple mode, weighted mode, and MR Egger methods; however, complete elimination of potential horizontal pleiotropy may not be achievable. Secondly, the current study revealed a genetic association between gout and decreased breast cancer risk in European populations (OR = 0.83). Nevertheless, the small effect size and the fact that the results were mainly based on European populations may affect its universality. Further investigations involving diverse populations and regions are necessary to validate these outcomes. Moreover, the underlying mechanisms necessitate additional exploration and confirmation through in vivo and in vitro experiments.

5. Conclusions

Our MR study provides evidence that genetic variations associated with gout are causally linked to a decreased risk of breast cancer. Transcriptomic data analysis further suggests that the core gene associated with gout, MLXIPL, plays a significant role and has predictive value in breast cancer development. However, these findings still need to be validated through further experimental studies and prospective cohort research. Clinically, understanding the relationship between gout and breast cancer could offer new insights for personalized medicine and may influence early breast cancer screening and intervention strategies.

Author contributions

Conceptualization: Hengheng Zhang, Xingfa Huo, Jinming Li, Na Li.

Funding acquisition: Fuxing Zhao.

Methodology: Hengheng Zhang, Xingfa Huo, Jinming Li, Na Li, Wenjun Xiao, Miaozhou Wang, Fuxing Zhao, Yi Zhao.

Resources: Hengheng Zhang, Xingfa Huo, Jinming Li, Na Li.

Supervision: Yi Zhao.

Visualization: Miaozhou Wang, Fuxing Zhao.

Writing – original draft: Xingfa Huo.

Writing – review & editing: Jinming Li, Na Li, Wenjun Xiao.

Supplementary Material

medi-104-e44305-s001.xlsx (20.8KB, xlsx)

Abbreviations:

ChREBP
carbohydrate-responsive element-binding protein
GEO
Gene Expression Omnibus
GO
Gene Ontology
GWAS
genome-wide association study
IVs
instrumental variables
IVW
inverse variance weighting
KEGG
Kyoto Encyclopedia of Genes and Genomes
LASSO
Least Absolute Shrinkage and Selection Operator
MR
Mendelian randomization
MLXIPL
MLX interacting protein-like
NCBI
National Center for Biotechnology Information
OS
overall survival
SNPs
single nucleotide polymorphisms
TCGA
The Cancer Genome Atlas

This work has been supported by the “Kunlun Talent High end Innovation and Entrepreneurship Talent” project in Qinghai Province in 2023.

The authors have no conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Supplemental Digital Content is available for this article.

How to cite this article: Zhang H, Huo X, Li J, Li N, Xiao W, Wang M, Zhao F, Zhao Y. Mendelian randomization and transcriptomic data analysis reveals the causal association between gout and breast cancer. Medicine 2025;104:38(e44305).

HZ, XH, JL, and NL contributed equally to this work.

Contributor Information

Xingfa Huo, Email: xingfahuo@126.com.

Jinming Li, Email: 949647231@qq.com.

Na Li, Email: 949647231@qq.com.

Wenjun Xiao, Email: xiaowenjun02070@outlook.com.

Miaozhou Wang, Email: wangmiaozhou@163.com.

Fuxing Zhao, Email: zywm0001@163.com.

References

  • [1].Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74:229–63. [DOI] [PubMed] [Google Scholar]
  • [2].Loibl S, Poortmans P, Morrow M, Denkert C, Curigliano G. Breast cancer. Lancet. 2021;397:1750–69. [DOI] [PubMed] [Google Scholar]
  • [3].Britt KL, Cuzick J, Phillips KA. Key steps for effective breast cancer prevention. Nat Rev Cancer. 2020;20:417–36. [DOI] [PubMed] [Google Scholar]
  • [4].Turizo-Smith AD, Córdoba-Hernandez S, Mejía-Guarnizo LV, Monroy-Camacho PS, Rodríguez-García JA. Inflammation and cancer: friend or foe? Front Pharmacol. 2024;15:1385479. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [5].de Visser KE, Eichten A, Coussens LM. Paradoxical roles of the immune system during cancer development. Nat Rev Cancer. 2006;6:24–37. [DOI] [PubMed] [Google Scholar]
  • [6].Dalbeth N, Gosling AL, Gaffo A, Abhishek A. Gout. Lancet. 2021;397:1843–55. [DOI] [PubMed] [Google Scholar]
  • [7].Tausche AK. Gout. Internist (Berl). 2021;62:513–25. [DOI] [PubMed] [Google Scholar]
  • [8].Dalbeth N, Choi HK, Joosten LAB, et al. Gout. Nat Rev Dis Primers. 2019;5:69. [DOI] [PubMed] [Google Scholar]
  • [9].Dehlin M, Jacobsson L, Roddy E. Global epidemiology of gout: prevalence, incidence, treatment patterns and risk factors. Nat Rev Rheumatol. 2020;16:380–90. [DOI] [PubMed] [Google Scholar]
  • [10].Yang HC, Nguyen PAA, Islam M, et al. Gout drugs use and risk of cancer: a case-control study. Joint Bone Spine. 2018;85:747–53. [DOI] [PubMed] [Google Scholar]
  • [11].Kuo CF, Luo SF, See LC, Chou IJ, Fang YF, Yu KH. Increased risk of cancer among gout patients: a nationwide population study. Joint Bone Spine. 2012;79:375–8. [DOI] [PubMed] [Google Scholar]
  • [12].Wang W, Xu D, Wang B, et al. Increased risk of cancer in relation to gout: a review of three prospective cohort studies with 50,358 subjects. Mediators Inflamm. 2015;2015:680853. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [13].Xie Y, Xu P, Liu K, et al. Hyperuricemia and gout are associated with cancer incidence and mortality: a meta-analysis based on cohort studies. J Cell Physiol. 2019;234:14364–76. [DOI] [PubMed] [Google Scholar]
  • [14].Liu Y, Chen W, Yang R, Zeng X, Zhang J. Effect of serum uric acid and gout on the incidence of colorectal cancer: a meta-analysis. Am J Med Sci. 2024;367:119–27. [DOI] [PubMed] [Google Scholar]
  • [15].Pannu MK, Constantinou C. Inflammation, nutrition, and clinical outcomes in breast cancer survivors: a narrative review. Curr Nutr Rep. 2023;12:643–61. [DOI] [PubMed] [Google Scholar]
  • [16].Göbel A, Dell’Endice S, Jaschke N, et al. The role of inflammation in breast and prostate cancer metastasis to bone. Int J Mol Sci. 2021;22:5078. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [17].Gremke N, Griewing S, Kostev K, Wagner U, Kalder M. Association between gout and subsequent breast cancer: a retrospective cohort study including 67,598 primary care patients in Germany. Breast Cancer Res Treat. 2023;199:545–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [18].Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23:R89–98. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [19].Boehm FJ, Zhou X. Statistical methods for Mendelian randomization in genome-wide association studies: a review. Comput Struct Biotechnol J. 2022;20:2338–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [20].Boef AGC, Dekkers OM, le Cessie S. Mendelian randomization studies: a review of the approaches used and the quality of reporting. Int J Epidemiol. 2015;44:496–511. [DOI] [PubMed] [Google Scholar]
  • [21].Papadimitriou N, Dimou N, Gill D, et al. Genetically predicted circulating concentrations of micronutrients and risk of breast cancer: a mendelian randomization study. Int J Cancer. 2021;148:646–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [22].Brion MJA, Shakhbazov K, Visscher PM. Calculating statistical power in Mendelian randomization studies. Int J Epidemiol. 2013;42:1497–501. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [23].Pierce BL, Ahsan H, Vanderweele TJ. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int J Epidemiol. 2011;40:740–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [24].Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [25].Hemani G, Bowden J, Davey Smith G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet. 2018;27:R195–208. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [26].Bowden J, Spiller W, Del Greco M F, et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. Int J Epidemiol. 2018;47:2100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [27].Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40:304–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [28].Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32:377–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [29].Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [30].Wu T, Hu E, Xu S, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb). 2021;2:100141. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [31].Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [32].Robinson PC, Horsburgh S. Gout: joints and beyond, epidemiology, clinical features, treatment and co-morbidities. Maturitas. 2014;78:245–51. [DOI] [PubMed] [Google Scholar]
  • [33].Hiatt RA, Fireman BH. Serum uric acid unrelated to cancer incidence in humans. Cancer Res. 1988;48:2916–8. [PubMed] [Google Scholar]
  • [34].Wu CY, Hu HY, Chou YJ, et al. High serum uric acid levels are associated with all-cause and cardiovascular, but not cancer, mortality in elderly adults. J Am Geriatr Soc. 2015;63:1829–36. [DOI] [PubMed] [Google Scholar]
  • [35].Simeone P, Tacconi S, Longo S, et al. Expanding roles of de novo lipogenesis in breast cancer. Int J Environ Res Public Health. 2021;18:3575. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [36].Airley RE, McHugh P, Evans AR, et al. Role of carbohydrate response element-binding protein (ChREBP) in generating an aerobic metabolic phenotype and in breast cancer progression. Br J Cancer. 2014;110:715–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [37].Herman MA, Peroni OD, Villoria J, et al. A novel ChREBP isoform in adipose tissue regulates systemic glucose metabolism. Nature. 2012;484:333–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [38].Uyeda K, Repa JJ. Carbohydrate response element binding protein, ChREBP, a transcription factor coupling hepatic glucose utilization and lipid synthesis. Cell Metab. 2006;4:107–10. [DOI] [PubMed] [Google Scholar]
  • [39].Calle EE, Kaaks R. Overweight, obesity and cancer: epidemiological evidence and proposed mechanisms. Nat Rev Cancer. 2004;4:579–91. [DOI] [PubMed] [Google Scholar]
  • [40].Lorincz AM, Sukumar S. Molecular links between obesity and breast cancer. Endocr Relat Cancer. 2006;13:279–92. [DOI] [PubMed] [Google Scholar]
  • [41].Young CD, Anderson SM. Sugar and fat-that’s where it’s at: metabolic changes in tumors. Breast Cancer Res. 2008;10:202. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [42].Erion DM, Popov V, Hsiao JJ, et al. The role of the carbohydrate response element-binding protein in male fructose-fed rats. Endocrinology. 2013;154:36–44. [DOI] [PMC free article] [PubMed] [Google Scholar]

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