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Although computer-aided diagnosis (CAD) have shown excellent performance in Breast cancer (BC) histopathological image, it commonly requires a high-level network, and the recognition efficiency is frequently unsatisfied due to the complex structure of histopathological image. In this study, a ten-layers convolutional neural network (CNN) model called "ColorDeep" is used to extract color features corresponding to the different tissue parts in cell, pure color image slices obtained by a three-channel separation and reconstruction method are used as model input. Two models are tested on the BreaKHis dataset show that images under four magnifications achieved the recognition accuracy of 96.89%–99.67% at the image level, which is better than many state-of-the-art methods. The characteristics contained by the B channel have the largest effect on BC recognition, and compared to other research results, the proposed model improves the recognition speed on a single image by about 0.1s. More importantly, instead of using large histopathological images to input into the model for BC diagnosis, and instead of segmenting the nuclei, only the reconstructed B-channel features containing the nuclei region in the stained BC image need to be input into the model to enable accurate diagnosis of BC. • Three-channel reconstructed slices contain better color features of breast cancer. • Reconstructed B-channel features contain more information about the cell nucleus. • Using color features to diagnose breast cancer can avoid segmentation of nuclei. • Reconstructed color slices enable low-complexity model to identify breast cancer. • Considering recognition accuracy and speed, feature fusion model is more suitable for breast cancer diagnosis. [ABSTRACT FROM AUTHOR]

It was reported that within the head and neck cancer (HNC) cell line CAL21 the epithelial-mesenchymal transition (EMT) and cell proliferation were promoted by Urokinase-Type Plasminogen Activator (PLAU) proteinase through TNFRSF12A. Additionally, in this paper HNC cell lines refer to Fadu and Tu686. A novel PLAU-STAT3 axis was found to be involved in HNC cell line proliferation and metastasis. PLAU expression in HNC samples was upregulated, besides, the elevated expression of PLAU was linked to the lower overall survival (OS) and disease-free survival (DFS). Ectopic PLAU expression promoted cell proliferation and migration, while PLAU knockdown exhibited opposite results. RNA-seq data identified the JAK-STAT signaling pathway, confirmed by western blotting. A recovery assay using S3I-201, a selective inhibitor of signal transducer and activator of transcription 3 (STAT3), indicated that PLAU promoted HNC cell line progression via STAT3 signaling in vitro. The oncogenic role of PLAU in HNC tumor growth in vivo was confirmed using xenograft models. In summary, we identified the tumorigenic PLAU function in the HNC progress. PLAU may represent a potential prognostic biomarker of HNC and the PLAU-STAT3 pathway might be considered a therapeutic target of HNC. • High PLAU expression indicates poor overall and disease-free survival of HNC. • PLAU promotes HNC cell proliferation and migration via JAK-STAT signaling pathway. • PLAU suppresses HNC tumor growth in vivo. [ABSTRACT FROM AUTHOR]