BackgroundA kinase-interacting protein 1 (AKIP1) has been reported to play an important role in the development and progression of cancer. However, the clinicopathological and biological roles of AKIP1 in colorectal cancer (CRC) remain largely unknown. The aim of this study was to investigate AKIP1 protein expression in CRC and determine the correlation between AKIP1 protein expression and clinicopathological features, as well as prognosis in CRC patients.Materials and methodsAKIP1 protein expression was determined by immunohistochemical analysis using tissue microarrays of CRC. We also used an siRNA approach to knock down AKIP1 expression and determine the effect of AKIP1 on CRC cell migration by transwell analysis.ResultsAKIP1 expression in CRC tissue was significantly higher compared with that of noncancerous colorectal mucosa (P<0.001). Further analysis showed that AKIP1 expression was significantly associated with tumor diameter, TNM stage, and lymph node metastasis (P<0.05). Kaplan–Meier survival analysis demonstrated that patients with a positive AKIP1 expression had significantly poorer overall survival rates when compared with those with negative AKIP1 expression (P=0.031). Multivariate analysis using the Cox proportional hazard model, however, revealed that AKIP1 expression was not a significant independent prognostic factor for CRC. Transwell assay showed that the migration potential of si-AKIP1-transfected cells was significantly reduced when compared with control cells.ConclusionElevated AKIP1 expression may contribute to metastasis and progression of CRC. Moreover, high AKIP1 expression in CRC significantly correlated with a patient’s shorter survival time. Therefore, AKIP1 may be a useful prognostic marker for CRC and a promising novel target for the treatment of CRC.

Background Glioma is the most common brain malignant tumor, with a high morbidity rate and a mortality rate of more than three percent, which seriously endangers human health. The main method of acquiring brain tumors in the clinic is MRI. Segmentation of brain tumor regions from multi-modal MRI scan images is helpful for treatment inspection, post-diagnosis monitoring, and effect evaluation of patients. However, the common operation in clinical brain tumor segmentation is still manual segmentation, lead to its time-consuming and large performance difference between different operators, a consistent and accurate automatic segmentation method is urgently needed. With the continuous development of deep learning, researchers have designed many automatic segmentation algorithms; however, there are still some problems: (1) The research of segmentation algorithm mostly stays on the 2D plane, this will reduce the accuracy of 3D image feature extraction to a certain extent. (2) MRI images have gray-scale offset fields that make it difficult to divide the contours accurately. Methods To meet the above challenges, we propose an automatic brain tumor MRI data segmentation framework which is called AGSE-VNet. In our study, the Squeeze and Excite (SE) module is added to each encoder, the Attention Guide Filter (AG) module is added to each decoder, using the channel relationship to automatically enhance the useful information in the channel to suppress the useless information, and use the attention mechanism to guide the edge information and remove the influence of irrelevant information such as noise. Results We used the BraTS2020 challenge online verification tool to evaluate our approach. The focus of verification is that the Dice scores of the whole tumor, tumor core and enhanced tumor are 0.68, 0.85 and 0.70, respectively. Conclusion Although MRI images have different intensities, AGSE-VNet is not affected by the size of the tumor, and can more accurately extract the features of the three regions, it has achieved impressive results and made outstanding contributions to the clinical diagnosis and treatment of brain tumor patients.

Green tea, the fresh leaves of Camellia sinensis, is not only a health-promoting beverage but also a traditional Chinese medicine used for prevention or treatment of cancer, such as lung cancer. Theabrownin (TB) is the main fraction responsible for the medicinal effects of green tea, but whether it possesses anti-cancer effect is unknown yet. This study aimed to determine the in vitro and in vivo anti-lung cancer effect of TB and explore the underlying molecular mechanism, by using A549 cell line and Lewis lung carcinoma-bearing mice. In cellular experiment, MTT assay was performed to evaluate the inhibitory effect and IC50 values of TB, and flow cytometry was conducted to analyze the cell cycle progression affected by TB. In animal experiment, mice body mass, tumor incidence, tumor size and tumor weight were measured, and histopathological analysis on tumor was performed with Transferase dUTP nick-end labeling staining. Real time PCR and western blot assays were adopted to detect the expression of C-MYC associated genes and proteins for mechanism clarification. TB was found to inhibit A549 cell viability in a dose- and time-dependent manner and block A549 cell cycle at G0/G1 phase. Down-regulation of c-myc, cyclin A, cyclin D, cdk2, cdk4, proliferation of cell nuclear antigen and up-regulation of p21, p27, and phosphate and tension homolog in both gene and protein levels were observed with TB treatment. A c-myc-related mechanism was thereby proposed, since c-myc could transcriptionally regulate all other genes in its downstream region for G1/S transitions of cell cycle and proliferation of cancer cells. This is the first report regarding the anti-NSCLC effect and the underlying mechanism of TB on cell cycle progression and proliferation of A549 cells. The in vivo data verified the in vitro result that TB could significantly inhibit the lung cancer growth in mice and induce apoptosis on tumors in a dose-dependent manner. It provides a promising candidate of natural products for lung cancer therapy and new development of anti-cancer agent.

With the highest cancer incidence rate, lung cancer, especially non-small cell lung cancer (NSCLC), is the leading cause of cancer death in the world. Tea (leaves of Camellia sinensis) has been widely used as a traditional beverage beneficial to human health, including anti-NSCLC activity. Theabrownin (TB) is one major kind of tea pigment responsible for the beneficial effects of tea liquor. However, its effect on NSCLC is unknown. The aim of the present study was to evaluate anti-proliferative and apoptosis-inducing effect of TB on NSCLC (A549) cells, using MTT assay, morphological observation (DAPI staining), in situ terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay, and annexin-V/PI flow cytometry. Subsequently, the expression of several genes associated with cell proliferation and apoptosis were detected by real time PCR assay to explore its potential underlying mechanism. TB was revealed to inhibit cell proliferation of A549 cells in a concentration-dependent and time-dependent manner. Morphological observation, TUNEL assay and flow cytometric analysis evidenced an apoptosis-inducing effect of TB on A549 cells in a concentration-dependent manner. The real time PCR assay demonstrated that TB down-regulated the expression of TOPO I, TOPO II, and BCL-2, and up-regulated the expression of E2F1, P53, GADD45, BAX, BIM, and CASP 3,7,8,9, which suggests an activation of P53-mediated apoptotic (caspase-dependent) pathway in response to TB treatment. The western blot analysis showed a similar trend for the corresponding protein expression (P53, Bax, Bcl-2, caspase 3,9, and PARP) and further revealed DNA damage as a trigger of the apoptosis (phosphorylation of histone H2A.X). Accordingly, TB can be speculated as a DNA damage inducer and topoisomerase (Topo I and Topo II) inhibitor that can up-regulate P53 expression and subsequently modulate the expression of the downstream genes to induce cell proliferation inhibition and apoptosis of A549 cells. Our results indicate that TB exhibits its anti-NSCLC activity via a P53-dependent mechanism, which may be a promising candidate of natural product for anti-cancer drug development in the treatment of NSCLC.

Glioma is the most common primary central nervous system tumor, accounting for about half of all intracranial primary tumors. As a non-invasive examination method, MRI has an extremely important guiding role in the clinical intervention of tumors. However, manually segmenting brain tumors from MRI requires a lot of time and energy for doctors, which affects the implementation of follow-up diagnosis and treatment plans. With the development of deep learning, medical image segmentation is gradually automated. However, brain tumors are easily confused with strokes and serious imbalances between classes make brain tumor segmentation one of the most difficult tasks in MRI segmentation. In order to solve these problems, we propose a deep multi-task learning framework and integrate a multi-depth fusion module in the framework to accurately segment brain tumors. In this framework, we have added a distance transform decoder based on the V-Net, which can make the segmentation contour generated by the mask decoder more accurate and reduce the generation of rough boundaries. In order to combine the different tasks of the two decoders, we weighted and added their corresponding loss functions, where the distance map prediction regularized the mask prediction. At the same time, the multi-depth fusion module in the encoder can enhance the ability of the network to extract features. The accuracy of the model will be evaluated online using the multispectral MRI records of the BraTS 2018, BraTS 2019, and BraTS 2020 datasets. This method obtains high-quality segmentation results, and the average Dice is as high as 78%. The experimental results show that this model has great potential in segmenting brain tumors automatically and accurately.