Exosomes are a novel class of intercellular signal modulators that contain a wide range of molecules and deliver information between cells and tissues. MicroRNAs (miRNAs), a type of regulatory non‐coding RNA, are often incorporated into exosomes as signaling molecules. In this review, we discuss the expression of exosomal miRNAs from diverse origins such as tumor cells, solid tumor tissue, and biological fluids in various cancers (lung, breast, colorectal, liver, stomach, and pancreatic). We address the biological functions of exosome‐derived miRNAs in processes such as tumor‐cell proliferation, angiogenesis, metastasis, and chemoresistance in the tumor microenvironment. In particular, we discuss three oncogenic miRNAs, miR‐21, miR‐141, and miR‐451, which occur within exosomes, in terms of gene regulation and intercellular communication. We consider therapeutic miRNA‐based nanoparticles, which are widely expressed in tumors and show promise in drug therapy. The review assesses the wide‐ranging evidence for using exosomal miRNAs as tumor markers in molecular diagnosis. Further, we consider the use of nanoparticle platforms to transport miRNAs, in the targeted treatment of disease and tumors.

Breast cancer is a prominent cause of death among women worldwide. Recent studies have demonstrated that artemisinin (ART) displays anti-tumor activity. Using a mouse breast cancer model, we investigated the effects of ART in vitro and in vivo to determine how it influences the anti-tumor immune response. We measured the proliferation and apoptosis of 4T1 cells in vitro after ART treatment by MTT assay and FACS. To examine the effects of ART in vivo, tumor volumes and survival rates were measured in 4T1 tumor-bearing mice. FACS was used to determine the frequencies of Tregs, MDSCs, CD4+ IFN-γ+ T cells, and CTLs in the tumors and spleens of the mice. mRNA levels of the transcription factors T-bet and FOXP3 and cytokines IFN-γ, TNF-α, TGF-β, and IL-10 were also determined by real-time RT-PCR. ELISA was used to measure TGF-β protein levels in the cell culture supernatants. ART supplementation significantly increased 4T1 cell apoptosis and decreased TGF-β levels in vitro. ART also impeded tumor growth in 4T1 TB mice and extended their survival. MDSC and Treg frequencies significantly decreased in the 4T1 TB mice after ART treatment while CD4+ IFN-γ+ T cells and CTLs significantly increased. ART significantly increased T-bet, IFN-γ, and TNF-α mRNA levels within the tumor and significantly decreased TGF-β mRNA levels. ART supplementation hindered 4T1 tumor growth in vivo by promoting T cell activation and quelling immunosuppression from Tregs and MDSCs in the tumor.

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.

Objectives. This meta‐analysis was conducted to compare the complication rates between arm and chest ports in patients with breast cancer. Design and Data Sources. PubMed, Embase, Cochrane library, Chinese National Knowledge Infrastructure (CNKI), and Wanfang database were used to perform a systematic review and meta‐analysis of publications published from the inception of the database to 11, October 2019. Our search generated a total of 22 articles published from 2011 to 2019, including 6 comparative studies and 16 single‐arm articles, involving 4131 cases and 5272 controls. Single‐arm studies combined with comparative studies were also pooled and analyzed. Finally, subgroup analysis was performed to compare the rates of infection and thrombosis between these two ports. Eligibility Criteria. Included articles were research studies comparing complication rates of arm ports with chest ports in patients with breast cancer. Any review or meta‐analysis article would be removed. Data Extraction and Synthesis. Demographic data and information for the following analysis were extracted. DerSimonian and Laird random effect meta‐analysis was conducted to analyze comparative studies while Begg’s and Egger’s tests were used for assessment of publication bias. Meta‐regression analysis was performed to explain the sources of heterogeneity. Results. There was no difference in the risk of overall complications between arm and chest ports for comparative studies (P = 0.083). While results of pooled comparative and single‐arm studies indicated that arm port would increase the overall complication risks with RR of 2.64, results of the subgroup analysis showed that there was no difference in the risk of catheter‐related infection between these two ports. However, arm port might be associated with the higher thrombosis rates compared with chest port according to the results of the analysis for only comparative studies (RR = 2.23, P = 0.041) as well as pooled comparative and single‐arm studies (RR = 1.21, P = 0.029). Conclusions. This study indicated that the arm port might increase the risk of overall complication risks as well as the risk of catheter‐related thrombosis compared with the chest port. However, these reported findings still need to be verified by large randomized clinical trials.

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.