Background: Lactate, an intermediate product of glycolysis, has become an essential regulator of tumor maintenance, development, and metastasis. Lactate can drive tumors by changing the microenvironment of tumor cells. Because of lactate’s important role in cancer, we aim to find a novel prognostic signature based on lactate metabolism-related genes (LMRGs) of breast cancer (BC).Methods: RNA-sequencing data and clinical information of BC were enrolled from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We obtained LMRGs from the Molecular Signature Database v7.4 and articles, and then we compared candidate genes with TCGA data to get differential genes. Univariate analysis and most minor absolute shrinkage and selector operator (LASSO) Cox regression were employed to filter prognostic genes. A novel lactate metabolism-related risk signature was constructed using a multivariate Cox regression analysis. The signature was validated by time-dependent ROC curve analyses and Kaplan–Meier analyses in TCGA and GEO cohorts. Then, we further investigated in depth the function of the model’s immune microenvironment.Results: We constructed a 3-LMRG-based risk signature. Kaplan–Meier curves confirmed that high-risk score subgroups had a worse prognosis in TCGA and GEO cohorts. Then a nomogram to predict the probability of survival for BC was constructed. We also performed Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway function analysis. The function analysis showed that the lactate metabolism-related signature was significantly related to immune response. A significant correlation was observed between prognostic LMRGs and tumor mutation burden, checkpoints, and immune cell infiltration. An mRNA–miRNA network was built to identify an miR-203a-3p/LDHD/LYRM7 regulatory axis in BC.Conclusion: In conclusion, we constructed a novel 3-LMRG signature and nomogram that can be used to predict the prognosis of BC patients. In addition, the signature is closely related to the immune microenvironment, which may provide new insight into future anticancer therapies.
Background Evidence of the association between psychiatric disorders, and breast cancer have been inconsistent, and their interpretation often encounters confounding factors. In this study, we used bidirectional Mendelian randomization (MR) analysis to explore the genetic relationships between them. Methods We performed an MR analysis using publicly available genome-wide association studies of European ancestry to simultaneously examine the relationship between psychiatric disorders, and breast cancer risk. The inverse-variance weighted method for the assessment of the risk psychiatric disorders bring to breast cancer. Weighted media, and MR-Egger regression were used as sensitivity analyses. Results The MR analysis revealed that major depressive disorder (MDD) may increase the risk of overall breast cancer (odds ratio [OR] = 1.095, 95% confidence interval [CI] = 1.019-1.277, P = 0.014), but not the subtype of breast cancer. Schizophrenia (SCZ) was a risk factor for overall breast cancer (OR = 1.031, 95%CI = 1.012-1.050, P = 0.002), ER+ breast cancer (OR = 1.034, 95%CI = 1.012-1.057, P = 0.002) and ER- breast cancer (OR = 1.050, 95%CI = 1.017-1.084, P = 0.003). However, patients with breast cancer are less likely to have the risk of MDD and SCZ. Conclusion: Our results found that MDD may be a risk factor for overall breast cancer but is not related to its subtypes. SCZ may increase the risk of breast cancer and its subtypes.
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