In vitro cytotoxicity of curcuminoids against head and neck cancer HNO97 cell line

Oral squamous cell carcinoma (OSCC) is a malignant tumour of Head and Neck Cancer (HNC). The recent therapeutic approaches used to treat cancer have adverse side effects. The natural agents exhibiting anticancer activities are generally considered to have a robust therapeutic potential. Curcuminoids, one of the major active compounds of the turmeric herb, are used as a therapeutic agent for several diseases including cancer. In this study, the cytotoxicity of curcuminoids was investigated against OSCC cell line HNO97. Our data showed that curcuminoids significantly inhibits the proliferation of HNO97 in a time and dose-dependent manner (IC50=35 μM). Cell cycle analysis demonstrated that curcuminoids increased the percentage of G2/M phase cell populations in the treated groups. Treating HNO97 cells with curcuminoids led to cell shrinking and increased detached cells, which are the typical appearance of apoptotic cells. Moreover, flow cytometry analysis revealed that curcuminoids significantly induced apoptosis in a time-dependent manner. Furthermore, as a response to curcuminoids treatment, comet tails were formed in cell nuclei due to the induction of DNA damage. Curcuminoids treatment reduced the colony formation capacity of HNO97 cells and induced morphological changes. Overall, these findings demonstrate that curcuminoids can in vitro inhibit HNC proliferation and metastasis and induce apoptosis.

Stoichioproteomics study of differentially expressed proteins and pathways in head and neck cancer

Hypoxia is a prominent feature of head and neck cancer. However, the oxygen element characteristics of proteins and how they adapt to hypoxia microenvironments of head and neck cancer are still unknown. Human genome sequences and proteins expressed data of head and neck cancer were retrieved from pathology atlas of Human Protein Atlas project. Then compared the oxygen and carbon element contents between proteomes of head and neck cancer and normal oral mucosa-squamous epithelial cells, genome locations, pathways, and functional dissection associated with head and neck cancer were also studied. A total of 902 differentially expressed proteins were observed where the average oxygen content is higher than that of the lowly expressed proteins in head and neck cancer proteins. Further, the average oxygen content of the up regulated proteins was 2.54% higher than other. None of their coding genes were distributed on the Y chromosome. The up regulated proteins were enriched in endocytosis, apoptosis and regulation of actin cytoskeleton. The increased oxygen contents of the highly expressed and the up regulated proteins might be caused by frequent activity of cytoskeleton and adapted to the rapid growth and fast division of the head and neck cancer cells. The oxygen usage bias and key proteins may help us to understand the mechanisms behind head and neck cancer in targeted therapy, which lays a foundation for the application of stoichioproteomics in targeted therapy and provides promise for potential treatments for head and neck cancer.

Automatic Parotid Gland Segmentation in MVCT Using Deep Convolutional Neural Networks

Radiation-induced xerostomia, as a major problem in radiation treatment of the head and neck cancer, is mainly due to the overdose irradiation injury to the parotid glands. Helical Tomotherapy-based megavoltage computed tomography (MVCT) imaging during the Tomotherapy treatment can be applied to monitor the successive variations in the parotid glands. While manual segmentation is time consuming, laborious, and subjective, automatic segmentation is quite challenging due to the complicated anatomical environment of head and neck as well as noises in MVCT images. In this article, we propose a localization-refinement scheme to segment the parotid gland in MVCT. After data pre-processing we use mask region convolutional neural network (Mask R-CNN) in the localization stage after data pre-processing, and design a modified U-Net in the following fine segmentation stage. To the best of our knowledge, this study is a pioneering work of deep learning on MVCT segmentation. Comprehensive experiments based on different data distribution of head and neck MVCTs and different segmentation models have demonstrated the superiority of our approach in terms of accuracy, effectiveness, flexibility, and practicability. Our method can be adopted as a powerful tool for radiation-induced injury studies, where accurate organ segmentation is crucial.