MicroRNAs (miRNAs) are endogenous non-coding RNAs, which play an essential role in the regulation of gene expression during carcinogenesis. The role of miRNAs in breast cancer has been thoroughly investigated, and although many miRNAs are identified as cancer related, little is known about their involvement in benign tumors. In this study, we investigated miRNA expression profiles in the two most common types of human benign tumors (fibroadenoma/fibroadenomatosis) and in malignant breast tumors and explored their role as oncomirs and tumor suppressor miRNAs. Here, we identified 33 miRNAs with similar deregulated expression in both benign and malignant tumors compared with the expression levels of those in normal tissue, including breast cancer-related miRNAs such as let-7, miR-21 and miR-155. Additionally, messenger RNA (mRNA) expression profiles were obtained for some of the same samples. Using integrated mRNA/miRNA expression analysis, we observed that overexpression of certain miRNAs co-occurred with a significant downregulation of their candidate target mRNAs in both benign and malignant tumors. In support of these findings, in vitro functional screening of the downregulated miRNAs in non-malignant and breast cancer cell lines identified several possible tumor suppressor miRNAs, including miR-193b, miR-193a-3p, miR-126, miR-134, miR-132, miR-486-5p, miR-886-3p, miR-195 and miR-497, showing reduced growth when re-expressed in cancer cells. The finding of deregulated expression of oncomirs and tumor suppressor miRNAs in benign breast tumors is intriguing, indicating that they may play a role in proliferation. A role of cancer-related miRNAs in the early phases of carcinogenesis and malignant transformation can, therefore, not be ruled out.

Highlights d miR551b-3p translocates to the nucleus and activates STAT3 transcription d Importin-8 (IPO8) is required for the nuclear translocation of miR551b-3p d miR551b-3p activates the expression of OSM family genes for autocrine signaling loop d Inhibition of miR551b-3p disrupts OSM-mediated signaling addiction

The tumor microenvironment is now widely recognized for its role in tumor progression, treatment response, and clinical outcome. The intratumoral immunological landscape, in particular, has been shown to exert both pro-tumorigenic and anti-tumorigenic effects. Identifying immunologically active or silent tumors may be an important indication for administration of therapy, and detecting early infiltration patterns may uncover factors that contribute to early risk. Thus far, direct detailed studies of the cell composition of tumor infiltration have been limited; with some studies giving approximate quantifications using immunohistochemistry and other small studies obtaining detailed measurements by isolating cells from excised tumors and sorting them using flow cytometry. Herein we utilize a machine learning based approach to identify lymphocyte markers with which we can quantify the presence of B cells, cytotoxic T-lymphocytes, T-helper 1, and T-helper 2 cells in any gene expression data set and apply it to studies of breast tissue. By leveraging over 2,100 samples from existing large scale studies, we are able to find an inherent cell heterogeneity in clinically characterized immune infiltrates, a strong link between estrogen receptor activity and infiltration in normal and tumor tissues, changes with genomic complexity, and identify characteristic differences in lymphocyte expression among molecular groupings. With our extendable methodology for capturing cell type specific signal we systematically studied immune infiltration in breast cancer, finding an inverse correlation between beneficial lymphocyte infiltration and estrogen receptor activity in normal breast tissue and reduced infiltration in estrogen receptor negative tumors with high genomic complexity.