Androgens enhance the ability of intratumoral macrophages to promote breast cancer progression

Toll-like receptor (TLR) 4 is a potent prognostic factor in prostate cancer associated with proliferation and invasion

Prostate cancer is one of the most common malignancies in men, and there is a need to explore novel biomarkers or therapeutic targets. Toll-like receptor 4 (TLR4) is expressed not only in antigen-presenting cells but also types of human malignancies, contributing to disease progression, although its clinical significance or functional role in prostate cancer remains unclear. Therefore, we immunolocalized TLR4 in 117 prostate cancer tissues to address its clinicopathological significance. Additionally, we performed in vitro assays to examine the effects of TLR4 on proliferation and migration of prostate cancer cell lines (LNCaP, DU-145 and PC-3). TLR4 immunoreactivity was predominantly detected in the cytoplasm of prostate cancer cells, and it was positively associated with proliferation and invasion abilities, as well as Gleason score. Subsequent in vitro experiments revealed that the inhibition of TLR4 by Sparstolonin B (SsnB) significantly suppressed the proliferation and migration of LNCaP, DU-145 and PC-3 cells. Therefore, we concluded that TLR4 was a potent prognostic factor associated with proliferation and invasion, and it might serve as a therapeutic target in prostate cancer.

The Pro-Tumorigenic Role of Chemotherapy-Induced Extracellular HSP70 from Breast Cancer Cells via Intratumoral Macrophages

Tumor-associated macrophages (TAMs) contribute to tumor progression and chemoresistance; it is therefore important to clarify the altered functions of macrophages following chemotherapy. While extracellular heat shock protein (HSP) 70 is associated with therapeutic resistance, the effects of HSP70 on TAMs remain largely unknown. Here, we conducted in vitro experiments and immunohistochemistry in 116 breast carcinoma specimens to determine whether the secretion of HSP70 from breast cancer cells following chemotherapy affects macrophage function. It was revealed that the interaction of epirubicin (EPI)-exposed breast cancer cells with macrophages enhanced tumor progression, and EPI promoted the secretion of extracellular HSP70 from breast cancer cells. The expression of pro-tumorigenic macrophage marker CD163 was decreased in macrophages treated with a conditioned medium (CM) from HSP70-silenced breast cancer cells. Breast cancer cells treated with CM from HSP70-silenced breast cancer cells showed decreased expression of transforming growth factor (TGF)-β, and the pro-tumorigenic effects of macrophages were impaired when TGF-β signaling was inhibited. Immunohistochemistry demonstrated that HSP70 served as a poor prognostic factor in conjunction with macrophage infiltration. It was therefore concluded that extracellular HSP70 levels increased following chemotherapy and enhanced the pro-tumorigenic effects of TAMs, either directly or indirectly, by regulating TGF-β expression in breast cancer cells.

Role of androgen receptor signaling pathway-related lncRNAs in the prognosis and immune infiltration of breast cancer

Androgen receptor (AR) is strong association with breast cancer (BRCA). We aimed to investigate the effect of the androgen receptor signaling pathway-related long non-coding RNAs (ARSP-related lncRNAs) on the process of subtype classification and the tumor microenvironment (TME) of breast cancer (BRCA). Our study screen ARSP-related lncRNAs for the construction of a risk model. The single-sample gene set enrichment analysis (ssGSEA) method was used to detect the differences between the immune responses generated by the patients belonging to the low- and high-risk groups. The relationship between the ARSP-related lncRNAs and TME was explored following the process of cluster analysis. The univariate Cox analysis and the Lasso regression analysis method was used to screen nine of these lncRNAs to develop a risk model. It was observed that risk score could function as an independent prognostic factor, affecting the prognoses of patients suffering from BRCA. The validity of the model was assessed by analyzing the generated calibration curves and a nomogram. Additionally, the effect of the risk score on the extent of immune cell infiltration realized in TME was explored. M2 macrophages correlated positively, whereas NK cells, CD4+ T cells, and naive B cells correlated negatively with the risk score. Results obtained using the cluster analysis indicated that immune scores correlated with clustered subtypes. Finally, the risk score and cluster subtypes were analyzed to study the sensitivity of the patients toward different drugs to identify the appropriate therapeutic agents. The prognoses of patients suffering from BRCA can be accurately predicted by ARSP-related lncRNAs.

Automatic breast carcinoma detection in histopathological micrographs based on Single Shot Multibox Detector

Background

A diagnosis with histological classification by pathologists is very important for appropriate treatments to improve the prognosis of patients with breast cancer. However, the number of pathologists is limited, and assisting the pathological diagnosis by artificial intelligence becomes very important. Here, we presented an automatic breast lesions detection model using microscopic histopathological images based on a Single Shot Multibox Detector (SSD) for the first time and evaluated its significance in assisting the diagnosis.

Methods

We built the data set and trained the SSD model with 1361 microscopic images and evaluated using 315 images. Pathologists and medical students diagnosed the images with or without the assistance of the model to investigate the significance of our model in assisting the diagnosis.

Results

The model achieved 88.3% and 90.5% diagnostic accuracies in 3-class (benign, non-invasive carcinoma, or invasive carcinoma) or 2-class (benign or malignant) classification tasks, respectively, and the mean intersection over union was 0.59. Medical students achieved a remarkably higher diagnostic accuracy score (average 84.7%) with the assistance of the model compared to those without assistance (average 67.4%). Some people diagnosed images in a short time using the assistance of the model (shorten by average 6.4 min) while others required a longer time (extended by 7.2 min).

Conclusion

We presented the automatic breast lesions detection method at high speed using histopathological micrographs. The present system may conveniently support the histological diagnosis by pathologists in laboratories.