Background: Beclin 1, an important autophagy-related protein in human cells, is involved in cell death and cell survival. Beclin 1 mapped to human chromosome 17q21. It is widely expressed in normal mammary epithelial cells. Although down-regulated expression with mono-allelic deletions of beclin 1 gene was frequently observed in breast tumors, whether there was other regulatory mechanism of beclin 1 was to be investigated. We studied the expression of beclin 1 and explored the possible regulatory mechanisms on its expression in breast tumors. Methods: 20 pairs of tumors and adjacent normal tissues from patients with sporadic breast invasive ductal cancer (IDCs) were collected. The mRNA expression of beclin 1 was detected by real-time quantitative RT-PCR. Loss of heterozygosity (LOH) was determined by real-time quantitative PCR and microsatellite methods. The protein expression of beclin 1, p53, BRCA1 and BRCA2 was assessed by immunohistochemistry. CpG islands in 5' genomic region of beclin 1 gene were identified using MethylPrimer Program. Sodium bisulfite sequencing was used in examining the methylation status of each CpG island. Results: Decreased beclin 1 mRNA expression was detected in 70% of the breast tumors, and the protein levels were co-related to the mRNA levels. Expression of beclin 1 mRNA was demonstrated to be much higher in the BRCA1 positive tumors than that in the BRCA1 negative ones. Loss of heterozygosity was detected in more than 45% of the breast tumors, and a dense cluster of CpG islands was found from the 5' end to the intron 2 of the beclin 1 gene. Methylation analysis showed that the promoter and the intron 2 of beclin 1 were aberrantly methylated in the tumors with decreased expression. Conclusions: These data indicated that LOH and aberrant DNA methylation might be the possible reasons of the decreased expression of beclin 1 in the breast tumors. The findings here shed some new light on the regulatory mechanisms of beclin 1 in breast cancer.

The mechanism underlying breast cancer stem cell (BCSCs) characteristics remains to be fully elucidated. Accumulating evidence implies that long noncoding RNAs (lncRNAs) play a pivotal role in regulating BCSCs stemness. LncRNA LUCAT1 expression was assessed in breast cancer tissues (n = 151 cases) by in situ hybridization. Sphere-formation assay and colony formation assay were used to detect cell self-renewal and proliferation, respectively. RNA immunoprecipitation, RNA pull down and luciferase reporter assays were used to identify LUCAT1 and TCF7L2 as the direct target of miR-5582-3p. The activity of the Wnt/β-catenin pathway was analyzed by TOP/FOP-Flash reporter assays, western blot and immunohistochemistry (IHC). This study found LUCAT1 expression was related to tumor size (p = 0.015), lymph node metastasis (p = 0.002) and TNM staging (p < 0.001). High LUCAT1 expression indicated a shorter overall survival (p = 0.006) and disease-free survival (p = 0.011). Furthermore, LUCAT1 was more expressed in BCSCs than in breast cancer cells (BCCs) by lncRNA microarray chips. LUCAT1 up-regulation promoted proliferation of BCCs, while LUCAT1 down-regulation inhibited self-renewal of BCSCs. MiR-5582-3p was directly bound to LUCAT1 and TCF7L2 and negatively regulated their expression. LUCAT1 affected Wnt/β-catenin pathway. LUCAT1 might be a significant biomarker to evaluate prognosis in breast cancer. LUCAT1 increased stem-like properties of BCCs and stemness of BCSCs by competitively binding miR-5582-3p with TCF7L2 and enhancing the Wnt/β-catenin pathway. The LUCAT1/miR-5582-3p/TCF7L2 axis provides insights for regulatory mechanism of stemness, and new strategies for clinical practice.

CXCR4 and its ligand CXCL12 can promote the proliferation, survival, and invasion of cancer cells. They have been shown to play an important role in regulating metastasis of breast cancer to specific organs. High CXCR4 expression was also correlated to poor clinical outcome. Previous study also showed that tumor cells express a high level of CXCR4 and that tumor metastasis target tissues (lung, liver, and bone) express high levels of the ligand CXCL12, allowing tumor cells to directionally migrate to target organs via a CXCL12-CXCR4 chemotactic gradient. However, the exact mechanisms of how CXCR4 and CXCL12 enhance metastasis and/or tumor growth and their full implications on breast cancer progression are unknown. Yet it is likely that chemokine receptor signaling may provide more than just a migrational advantage by also helping the metastasized cells establish and survive in secondary environments. In this study, we investigated CXCR4 and CXCL12 expression in breast cancer and analyzed its association with clinicopathological factors by immunohistochemistry first. Then, we detected the mRNA and protein expression of CXCR4 and CXCL12 in breast cancer cell lines by Western blot and RT-PCR. The MDA-MB-231 has CXCR4 expression and very weak CXCL12 expression. So, we constructed the functional CXCL12 expression in MDA-MB-231 using a gene transfection technique. Further experiments were conducted to evaluate the effect of CXCL12 transfection on the biological behaviors of MDA-MB-231. The cell proliferation of MDA-MB-231–CXCL12 was accessed by MTT assay; the apoptosis was analyzed by an AnnexinV-FITC/propidium iodide double staining of flow cytometry method; and the cell invasive ability was examined by Matrigel invasion assay. Immunohistochemical analysis showed the co-expression of CXCR4 and CXCL12 correlated with lymph node metastasis and TNM stage (p < 0.01). It suggested that the chemokine CXCL12 and its sole ligand CXCR4 play important role in the malignance of breast cancer. To gain a deeper insight into it, we picked CXCR4-expressing cells MDA-MB-231 to be transfected with CXCL12 stably. The decreased cellular proliferation, increased apoptosis, and invasive ability were found in MDA-MB-231 with successful CXCL12 transfection (p < 0.05). Our findings underlined the CXCL12-CXCR4 axis correlated tightly with breast cancer metastasis. CXCL12-CXCR4 axis can increase the invasion and apoptosis of MDA-MB-231 simultaneously. These data strongly support the hypothesis that CXCL12-CXCR4 axis promotes the natural selection of breast cancer cell metastasis. Our findings could have significant implications in terms of breast cancer aggressiveness and the effectiveness of targeting the receptors and downstream signaling pathways for the treatment of breast cancer.

L-Arg is involved in many biological activities, including the activation of T cells. In breast cancer patients, L-Arg is depleted by nitric oxide synthase 2 (NOS2) and arginase 1 (ARG-1) produced by myeloid-derived suppressor cells (MDSCs). Our aim was to test whether L-Arg supplementation could enhance antitumor immune response and improve survivorship in a rodent model of mammary tumor. Tumor volumes in control and L-Arg treated 4 T1 tumor bearing (TB) BALB/c mice were measured and survival rates were recorded. The percentages of MDSCs, dendritic cells (DCs), regulatory T cells (Tregs), macrophages, CD4+ T cells, and CD8+ T cells were examined by flow cytometry. Additionally, levels of IL-10, TNF-α, and IFN-γ were measured by enzyme-linked immunosorbent assay (ELISA) and nitric oxide (NO) levels were measured by the Griess reaction. IFN-γ, T-bet, Granzyme B, ARG-1 and iNOS mRNA levels were examined by real-time RT-PCR. L-Arg treatment inhibited tumor growth and prolonged the survival time of 4 T1 TB mice. The frequency of MDSCs was significantly suppressed in L-Arg treated TB mice. In contrast, the numbers and function of macrophages, CD4+ T cells, and CD8+ T cells were significantly enhanced. The IFN-γ, TNF-α, NO levels in splenocytes supernatant, as well as iNOS, IFN-γ, Granzyme B mRNA levels in splenocytes and tumor blocks were significantly increased. The ARG-1 mRNA level in tumor blocks, the frequency of Tregs, and IL-10 level were not affected. L-Arg supplementation significantly inhibited tumor growth and prolonged the survival time of 4 T1 TB mice, which was associated with the reduction of MDSCs, and enhanced innate and adaptive immune responses.

Long non‐coding RNAs (lncRNAs) are well known as crucial regulators to breast cancer development and are implicated in controlling autophagy. LncRNAs are also emerging as valuable prognostic factors for breast cancer patients. It is critical to identify autophagy‐related lncRNAs with prognostic value in breast cancer. In this study, we identified autophagy‐related lncRNAs in breast cancer by constructing a co‐expression network of autophagy‐related mRNAs‐lncRNAs from The Cancer Genome Atlas (TCGA). We evaluated the prognostic value of these autophagy‐related lncRNAs by univariate and multivariate Cox proportional hazards analyses and eventually obtained a prognostic risk model consisting of 11 autophagy‐related lncRNAs (U62317.4, LINC01016, LINC02166, C6orf99, LINC00992, BAIAP2‐DT, AC245297.3, AC090912.1, Z68871.1, LINC00578 and LINC01871). The risk model was further validated as a novel independent prognostic factor for breast cancer patients based on the calculated risk score by Kaplan‐Meier analysis, univariate and multivariate Cox regression analyses and time‐dependent receiver operating characteristic (ROC) curve analysis. Moreover, based on the risk model, the low‐risk and high‐risk groups displayed different autophagy and oncogenic statues by principal component analysis (PCA) and Gene Set Enrichment Analysis (GSEA) functional annotation. Taken together, these findings suggested that the risk model of the 11 autophagy‐related lncRNAs has significant prognostic value for breast cancer and might be autophagy‐related therapeutic targets in clinical practice.

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.