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

"VSports最新版本" Mendelian randomization and transcriptomic data analysis reveals the causal association between gout and breast cancer

Hengheng Zhang  1   2 , Xingfa Huo  3 , Jinming Li  1   2 , Na Li  1   2 , Wenjun Xiao  1   2 , Miaozhou Wang  1   2 , Fuxing Zhao  1   2 , Yi Zhao  1   2
Affiliations

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

Hengheng Zhang et al. Medicine (Baltimore). .

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 VSports手机版. 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: Mendelian randomization; breast cancer; gout; prognosis; transcriptomic data. V体育安卓版.

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Conflict of interest statement

The authors have no conflicts of interest to disclose.

Figures (VSports手机版)

Figure 1.
Figure 1.
Flow diagram for study.
Figure 2.
Figure 2.
Forest plot of OR values between gout and overall breast cancer.
Figure 3.
Figure 3.
Scatter plot and leave-one-out plot illustrating the causal relationship between gout and breast cancer.
Figure 4.
Figure 4.
Identification of gout-associated genes in breast cancer patients.
Figure 5.
Figure 5.
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.
Figure 6.
Figure 6.
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.
(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.
Figure 8.
Figure 8.
(A) Gene ontology enrichment analysis. (B) Kyoto Encyclopedia of Genes and Genomes enrichment analysis.
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