BackgroundEmerging evidence have illustrated the vital role of long noncoding RNAs (lncRNAs) long intergenic non-protein coding RNA 00511 (LINC00511) on the human cancer progression and tumorigenesis. However, the role of LINC00511 in breast cancer tumourigenesis is still unknown. This research puts emphasis on the function of LINC00511 on the breast cancer tumourigenesis and stemness, and investigates the in-depth mechanism.MethodsThe lncRNA and RNA expression were measured using RT-PCR. Protein levels were measured using western blotting analysis. CCK-8, colony formation assays and transwell assay were performed to evaluate the cell proliferation ability and invasion. Sphere-formation assay was also performed for the stemness. Bioinformatic analysis, chromatin immunoprecipitation (ChIP) and luciferase reporter assays were carried to confirm the molecular binding.ResultsLINC00511 was measured to be highly expressed in the breast cancer specimens and the high-expression was correlated with the poor prognosis. Functionally, the gain and loss-of-functional experiments revealed that LINC00511 promoted the proliferation, sphere-formation ability, stem factors (Oct4, Nanog, SOX2) expression and tumor growth in breast cancer cells. Mechanically, LINC00511 functioned as competing endogenous RNA (ceRNA) for miR-185-3p to positively recover E2F1 protein. Furthermore, transcription factor E2F1 bind with the promoter region of Nanog gene to promote it transcription.ConclusionIn conclusion, our data concludes that LINC00511/miR-185-3p/E2F1/Nanog axis facilitates the breast cancer stemness and tumorigenesis, providing a vital insight for them.Electronic supplementary materialThe online version of this article (10.1186/s13046-018-0945-6) contains supplementary material, which is available to authorized users.

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Background: An increasing number of studies have demonstrated a role for the tumor microenvironment in tumorigenesis, disease progression, and therapeutic response. This present study aimed to screen the significant immune-related genes and their possible role in the prognosis of breast cancer (BRCA). Methods:The transcriptome data and clinical data of breast cancer were collected from The Cancer Genome Atlas (TCGA), and the immune scores and stromal scores were calculated by ESTIMATE algorithm. The differentially expressed genes were screened base on immune and stromal scores (high score vs. low score), than the intersected genes were used for subsequent functional enrichment analysis and protein-protein interaction (PPI) analysis. Furthermore, the key gene was identified by the intersection of the hub genes of PPI network and the prognostic genes of breast cancer. Finally, we explored the infiltration of immune cells of BRCA base on the CIBERSORT algorithm, and analysis the relationship between key gene and immune cells.Results: High levels of CD52 expression were detected in the early stages of breast cancer and were associated with favorable prognosis. Overexpression of CD52 led to higher infiltrations of M1 macrophages, monocytes, T follicular helper cells, and resting memory CD4 T cells. Downregulation of CD52 resulted in high infiltrations of M2 macrophages. Therefore, high expression of CD52 may negatively regulate the infiltration of M2 macrophages but accelerate the infiltration of anti-cancer immune cells, and thus, high expression of CD52 may have a protective effect in breast cancer patients.Conclusions: CD52 can increase the infiltration of anti-cancer immune cells but inhibit the infiltration of M2 macrophages, thereby improving the prognosis of breast cancer patients.

Background: The role of autophagy-related long-stranded non-coding RNA (lncRNA) in breast cancer (BRCA) is unclear. We proposed to screen autophagy-related lncRNAs in BRCA and construct a prognostic risk assessment model to explore prognostic correlates. Methods:We extracted BRCA lncRNAs from The Cancer Genome Atlas (TCGA) database and autophagy-related genes from the Human Autophagy Database (HADb), to screen for autophagy-related lncRNA pairs (ARLP) in BRCA. Single-factor Cox regression analysis and multi-factor Cox regression analysis were used to screen lncRNAs associated with BRCA prognosis, and risk models were established. We divided BRCA patients into high-risk and low-risk groups based on median risk scores. The single-sample gene set enrichment analysis (ssGSEA) algorithm was used to calculate the abundance of 28 immune cells in the TCGA-BRCA cohort and to analyze the relationship between the risk score and the level of immune cell infiltration by ARLP characteristics.Results: Univariate Cox regression results showed that 42 ARLPs were significantly associated with overall survival (OS) in BRCA patients. Further multifactorial analysis showed that a total of 11 lncRNAs, including

Background: Neoadjuvant chemotherapy (NAC) is an important treatment for breast cancer (BC) patients.However, due to the lack of specific therapeutic targets, only 1/3 of human epidermal growth factor receptor 2 (HER2)-negative patients reach pathological complete response (pCR). Therefore, there is an urgent need to identify novel biomarkers to distinguish and predict NAC sensitive in BC patients.Methods: The GSE163882 dataset, containing 159 BC patients treated with NAC, was downloaded from the Gene Expression Omnibus (GEO) database. Patients with pathological complete response (pCR) and those with residual disease (RD) were compared to obtain the differentially expressed genes (DEGs).Functional enrichment analyses were conducted on these DEGs. Then, we intersect the DEGs and immunerelated genes to obtain the hub immune biomarkers, and then use the linear fitting model ("glm" package) to construct a prediction model composed of 9 immune biomarkers. Finally, the single sample gene set enrichment analysis (ssGSEA) algorithm was used to analyze immune cell invasion in BC patients, and the correlation between immune cell content and immune gene expression levels was analyzed.Results: Nine immune-related biomarkers were obtained in the intersection of DEGs and immune-related genes. Compared with RD patients, CXCL9, CXCL10, CXCL11, CXCL13, GZMB, IDO1, and LYZ were highly expressed in pCR patients, while CXCL14 and ESR1 were lowly expressed in pCR patients. After linear fitting of the multi-gene expression model, the area under the curve (AUC) value of the ROC curve diagnosis of pCR patients was 0.844. Immunoinfiltration analysis showed that compared with RD patients, 15 of the 28 immune cell types examined showed high-infiltration in pCR patients, including activated CD8 T cells, effector memory CD8 T cells, and activated CD4 T cells.Conclusions: This investigation ultimately identified 9 immune-related biomarkers as potential tools for assessing the sensitivity of NAC in HER2-negative BC patients. These biomarkers have great potential for predicting pCR BC patients.