Metformin plays an antitumor role by downregulating inhibitory cells and immune checkpoint molecules while activating protective immune responses in breast cancer.

This study seeks to test the effect of metformin treatment on the outcomes of breast cancer in BALB/c mice bearing 4 T1 breast cancer cells. The survival rate and tumor size of mice were compared, as well as evaluation of the changes of immune cells in spleens and the microenvironment of tumors using flow cytometry and ELISA. Our results demonstrate that metformin prolongs mouse survival. A significant decrease in M2-like macrophages (F4/80+CD206+) was found in mice spleen treated with metformin. The treatment also inhibited monocytic myeloid-derived suppressor cells (M-MDSCs, CD11b+Gr-1+) and regulatory T cells (Tregs, CD4+CD25+Foxp3+). Metformin treatment resulted in an increase in the level of IFN-γ and a decrease in IL-10. Expression of the immune checkpoint molecule PD-1 on T cells was inhibited following treatment. Metformin enhances local antitumor activity in the tumor microenvironment, and our data supports the drug as a candidate for evaluation in the treatment of breast cancer.

Potential roles of synaptotagmin family members in cancers: Recent advances and prospects.

With the continuous development of bioinformatics and public database, more and more genes that play a role in cancers have been discovered. Synaptotagmins (SYTs) are abundant, evolutionarily conserved integral membrane proteins composed of a short N-terminus, a variable linker domain, a single transmembrane domain, and two C2 domains, and they constitute a family of 17 isoforms. The synaptotagmin family members are known to regulate calcium-dependent membrane fusion events. Some SYTs play roles in hormone secretion or neurotransmitter release or both, and much evidence supports SYTs as Ca2+ sensors of exocytosis. Since 5 years ago, an increasing number of studies have found that SYTs also played important roles in the occurrence and development of lung cancer, gastric cancer, colon cancer, and other cancers. Down-regulation of SYTs inhibited cell proliferation, migration, and invasion of cancer cells, but promoted cell apoptosis. Growth of peritoneal nodules is inhibited and survival is prolonged in mice administrated with siSYTs intraperitoneally. Therefore, most studies have found SYTs serve as an oncogene after overexpression and may become potential prognostic biomarkers for multiple cancers. This article provides an overview of recent studies that focus on SYT family members' roles in cancers and highlights the advances that have been achieved.

MRPL13 is a Prognostic Cancer Biomarker and Correlates with Immune Infiltrates in Breast Cancer.

OBJECTIVE: To study the expression of MRPL13 in breast cancer tissues using TCGA database, analyze the correlation between the expression and clinicopathological characteristics of patients, and explore the role of MRPL13 in the development of breast cancer (BC). METHODS: The BC mRNA data and clinical information were downloaded from TCGA database. The correlation between MRPL13 expression and clinicopathological parameters was analyzed. Cox regression multivariate analysis was used to explore the factors affecting the prognosis of BC patients. The UALCAN database was used to analyze the expression level of MRPL13 in BC and its relationship with clinical pathological factors. The GSEA method was used to predict the possible regulatory pathways of MRPL13. Immune responses of MRPL13 expression were analyzed using TISIDB and CIBERSORT. Additionally, GEPIA, K-M survival analysis and data from the HPA were used to validate the outcomes. RESULTS: The expression of MRPL13 in BC tissues was significantly higher than normal counterparts, patients with low MRPL13 expression had a better survival prognosis, also indicated an independent prognostic factor. GSEA analysis showed that the regulation of cell migration, positive regulation of endothelial cell migration, and Notch signaling pathway were enriched in tissues with low expression of MRPL13. Additionally, depleting MRPL13 expression inhibited invasion in MCF-10A and MCF-7 cells. Furthermore, PCR showed that MRPL13 affected VEGFA and MMP gene expression. CIBERSORT analysis revealed that the amount of NK cells decreased when MRPL13 expression was high. CONCLUSION: The expression of MRPL13 mRNA is upregulated in BC tissues, and the expression level of MRPL13 is significantly related to the clinicopathological factors of patients. High MRPL13 expression is a poor prognostic factor for BC, and it can be used as a molecular marker for prognosis judgment and as a potential therapeutic target.

Immune-related lncRNAs as predictors of survival in breast cancer: a prognostic signature.

BACKGROUND: Breast cancer is a highly heterogeneous disease, this poses challenges for classification and management. Long non-coding RNAs play acrucial role in the breast cancersdevelopment and progression, especially in tumor-related immune processes which have become the most rapidly investigated area. Therefore, we aimed at developing an immune-related lncRNA signature to improve the prognosis prediction of breast cancer. METHODS: We obtained breast cancer patient samples and corresponding clinical data from The Cancer Genome Atlas (TCGA) database. Immune-related lncRNAs were screened by co-expression analysis of immune-related genes which were downloaded from the Immunology Database and Analysis Portal (ImmPort). Clinical patient samples were randomly separated into training and testing sets. In the training set, univariate Cox regression analysis and LASSO regression were utilized to build a prognostic immune-related lncRNA signature. The signature was validated in the training set, testing set, and whole cohorts by the Kaplan-Meier log-rank test, time-dependent ROC curve analysis, principal component analysis, univariate andmultivariate Cox regression analyses. RESULTS: A total of 937 immune- related lncRNAs were identified, 15 candidate immune-related lncRNAs were significantly associated with overall survival (OS). Eight of these lncRNAs (OTUD6B-AS1, AL122010.1, AC136475.2, AL161646.1, AC245297.3, LINC00578, LINC01871, AP000442.2) were selected for establishment of the risk prediction model. The OS of patients in the low-risk group was higher than that of patients in the high-risk group (p = 1.215e - 06 in the training set; p = 0.0069 in the validation set; p = 1.233e - 07 in whole cohort). The time-dependent ROC curve analysis revealed that the AUCs for OS in the first, eighth, and tenth year were 0.812, 0.81, and 0.857, respectively, in the training set, 0.615, 0.68, 0.655 in the validation set, and 0.725, 0.742, 0.741 in the total cohort. Multivariate Cox regression analysis indicated the model was a reliable and independent indicator for the prognosis of breast cancer in the training set (HR = 1.432; 95% CI 1.204-1.702, p < 0.001), validation set (HR = 1.162; 95% CI 1.004-1.345, p = 0.044), and whole set (HR = 1.240; 95% CI 1.128-1.362, p < 0.001). GSEA analysis revealed a strong connection between the signature and immune-related biological processes and pathways. CONCLUSIONS: We constructed and verified a robust signature of 8 immune-related lncRNAs for the prediction of breast cancer patient survival.

Coexpression Network Analysis of Genes Related to the Characteristics of Tumor Stemness in Triple-Negative Breast Cancer.

Cancer stem cells (CSCs) are subsets of cells with the ability of self-renewal and differentiation in neoplasm, which are considered to be related to tumor heterogeneity. It has been reported that CSCs act on tumorigenesis and tumor biology of triple-negative breast cancer (TNBC). However, the key genes that cause TNBC showing stem cell characteristics are still unclear. We combined the RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) database and mRNA expression-based stemness index (mRNAsi) to further analyze mRNAsi with regard to molecular subtypes, tumor depth, and pathological staging characteristics of breast cancer (BC). Secondly, we extract the differential gene expression of tumor vs. normal group and TNBC vs. other subtypes of BC group, respectively, and intersect them to achieve precise results. We used a weighted gene coexpression network analysis (WGCNA) to screen significant gene modules and the functions of selected genes including BIRC5, CDC25A, KIF18B, KIF2C, ORC1, RAD54L, and TPX2 were carried out through gene ontology (GO) functional annotation. The Oncomine, bc-GenExMiner v4.4, GeneMANIA, Kaplan-Meier Plotter (KM-plotter), and GEPIA were used to verify the expression level and functions of key genes. In this study, we found that TNBC had the highest stem cell characteristics in BC compared with other subtypes. The lower the mRNAsi score, the better the overall survival and treatment outcome. Seven key genes of TNBC were screened and functional annotation indicated that there were strong correlations between them, relating to nuclear division, organelle fission, mitotic nuclear division, and other events that determine cell fate. Among these genes, we found four genes that were highly associated with adverse survival events. Seven key genes identified in this study were found to be closely related to the maintenance of TNBC stemness, and the overexpression of four showed earlier recurrence. The overall survival (OS) curves of all key genes between differential expression level crossed at around nine-year follow-up, which was consistent with the trend of the OS curve related to mRNAsi. These findings may provide new ideas for screening therapeutic targets in order to depress TNBC stemness.

Nomogram for predicting the risk of bone metastasis in breast cancer: a SEER population-based study.

BACKGROUND: Bone is the most common metastasis site of breast cancer. The prognosis of bone metastasis is better than other distant metastases, but patients with skeletal related events (SREs) have a poor quality of life, high healthcare costs and low survival rates. This study aimed to establish an effective nomogram for predicting risk of bone metastasis of breast cancer. METHODS: The nomogram was built on 4,895 adult/female/primary invasive breast cancer patients with complete clinicopathologic information, captured by the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. Five biological factors (age, grade, histologic type, surgery of breast lesions and subtypes) were assessed with logistic regression to predict the risk of bone metastases. The predictive accuracy and discriminative ability of the nomogram were determined by the Receiver Operating Characteristic (ROC) curves and the calibration plot. Results were validated on a separate 2,093 cohort using bootstrap resampling from 2010 to 2015 as an internal group and a retrospective study on 120 patients in the First Affiliated Hospital of China Medical University from 2010 to 2014 at the same situation as an external group. RESULTS: On multivariate logistic regression of the primary cohort, independent factors for bone metastases were age, grade, histologic type, surgery of breast lesions and subtypes, which were all selected into the nomogram. The calibration plot for probability of incidence showed good agreement between prediction by nomogram and two observations. The ROC curves presented a good statistical model for risk of bone metastasis, and the corresponding AUC value of the development group, internal validation group and external validation group were 0.678, 0.689 and 0.704 respectively. CONCLUSIONS: The proposed nomogram resulted in more-accurate prognostic prediction for breast cancer patients with bone metastases.