Elucidating the causal effects of plasma metabolites on breast cancer from multiple perspectives.
[BACKGROUND] Early studies found common metabolic reprogramming in breast cancer.
APA
Fang K, Zhou Y, et al. (2026). Elucidating the causal effects of plasma metabolites on breast cancer from multiple perspectives.. International journal of surgery (London, England), 112(1), 537-552. https://doi.org/10.1097/JS9.0000000000003544
MLA
Fang K, et al.. "Elucidating the causal effects of plasma metabolites on breast cancer from multiple perspectives.." International journal of surgery (London, England), vol. 112, no. 1, 2026, pp. 537-552.
PMID
41056019
Abstract
[BACKGROUND] Early studies found common metabolic reprogramming in breast cancer. However, the causal effects of plasma metabolites on breast cancer remain unclear.
[METHODS] Mendelian randomization (MR) and linkage disequilibrium score (LDSC) regression were performed based on previously published genome-wide association study (GWAS) summary statistics. The robustness of the causal inference was validated by sensitivity analyses. Gene mapping was performed by combining significant single-nucleotide polymorphisms (SNPs) and expression quantitative trait loci (eQTL) data to obtain the related gene set. Immune-related genes were identified by bulk and single-cell RNA analyses. Clinical samples were used for validation.
[RESULTS] After correction and sensitivity analysis, seven significant plasma metabolites were identified. LDSC did not detect significant genetic correlations. Mapping SNPs to genes showed that two genes (SPP1 and ADM2) were significantly upregulated in breast cancer. However, only SPP1 passed external cohort validation. Similar results were obtained in the single-cell RNA analysis. In addition, SPP1 was found to be more highly expressed in monocytes. In clinical samples of breast cancer, we verified the high expression of SPP1 in monocytes-macrophages. At the cellular level, the high expression of SPP1 in monocytes-macrophages can promote malignant phenotypes such as proliferation and migration of breast cancer cells, and its expression level is regulated by myristic acid in significantly differential plasma metabolites.
[CONCLUSION] The findings provided valuable insight for the development of personalized treatment strategies for breast cancer and indicate that SPP1 may be a therapeutic target.
[METHODS] Mendelian randomization (MR) and linkage disequilibrium score (LDSC) regression were performed based on previously published genome-wide association study (GWAS) summary statistics. The robustness of the causal inference was validated by sensitivity analyses. Gene mapping was performed by combining significant single-nucleotide polymorphisms (SNPs) and expression quantitative trait loci (eQTL) data to obtain the related gene set. Immune-related genes were identified by bulk and single-cell RNA analyses. Clinical samples were used for validation.
[RESULTS] After correction and sensitivity analysis, seven significant plasma metabolites were identified. LDSC did not detect significant genetic correlations. Mapping SNPs to genes showed that two genes (SPP1 and ADM2) were significantly upregulated in breast cancer. However, only SPP1 passed external cohort validation. Similar results were obtained in the single-cell RNA analysis. In addition, SPP1 was found to be more highly expressed in monocytes. In clinical samples of breast cancer, we verified the high expression of SPP1 in monocytes-macrophages. At the cellular level, the high expression of SPP1 in monocytes-macrophages can promote malignant phenotypes such as proliferation and migration of breast cancer cells, and its expression level is regulated by myristic acid in significantly differential plasma metabolites.
[CONCLUSION] The findings provided valuable insight for the development of personalized treatment strategies for breast cancer and indicate that SPP1 may be a therapeutic target.
MeSH Terms
Humans; Female; Breast Neoplasms; Polymorphism, Single Nucleotide; Genome-Wide Association Study; Mendelian Randomization Analysis; Quantitative Trait Loci; Linkage Disequilibrium
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