STIL facilitates the development and malignant progression of triple-negative breast cancer through activation of Fanconi anemia pathway via interacting with KLF16.

BACKGROUND: STIL is an important cell cycle-regulating protein specifically recruited to the mitotic centrosome to promote the replication of centrioles in dividing cells. However, the potential role of STIL in the regulation of the biological functions of triple-negative breast cancer remains still unclear. METHODS: We screened for differentially expressed STIL in the Cancer Genome Atlas database. The expression of STIL protein in 10 pairs of breast cancer tissues and adjacent normal tissues was further assessed by western blotting. Functionally, the knockdown and overexpression of STIL have been used to explore the effects of STIL on breast cancer cell proliferation, migration, and invasion. Mechanistically, RNA-seq, dual-luciferase reporter assay, chromatin immunoprecipitation assay, mass spectrometry, immunoprecipitation assay, and DNA pull-down assay were performed. RESULTS: Breast cancer tissues and cells have higher STIL expression than normal tissues and cells. STIL knockdown impairs breast cancer cell growth, migration, and invasion, whereas STIL overexpression accelerates these processes. STIL promotes breast cancer progression by regulating FANCD2 expression, and exploration of its molecular mechanism demonstrated that STIL interacts with KLF16 to regulate the expression of FANCD2. CONCLUSIONS: Collectively, our findings identified STIL as a critical promoter of breast cancer progression that interacts with KLF16 to regulate Fanconi anemia pathway protein FANCD2. In summary, STIL is a potential novel biomarker and therapeutic target for breast cancer.

Quantitative detection of Aflatoxin B1 by subpixel CNN regression.

Aflatoxin is a highly toxic substance dispersed in peanuts, which seriously harms the health of humans and animals. In this paper, we propose a new method for aflatoxin B1(AFB1) detection inspired by quantitative remote sensing. Firstly, we obtained the relative content of AFB1 at the sub-pixel level by subpixel decomposition (endmember extraction, nonnegative matrix decomposition). Then we modified the transfer learning models (LeNet5, AlexNet, VGG16, and ResNet18) to construct a deep learning regression network for quantitative detection of AFB1. There are 67,178 pixels used for training and 67,164 pixels used for testing. After subpixel decomposition, each aflatoxin pixel was determined to contain content, and each pixel had 400 hyperspectral values (415-799 nm). The experimental results showed that, among the four models, the modified ResNet18 model achieved the best effect, with R2 of 0.8898, RMSE of 0.0138, and RPD of 2.8851. Here, we implemented a sub-pixel model for quantitative AFB1 detection and proposed a regression method based on deep learning. Meanwhile, the modified convolution classification model has high predictive ability and robustness. This method provides a new scheme in designing the sorting machine and has practical value.

A potential role for metastasis-associated in colon cancer 1 (MACC1) as a pan-cancer prognostic and immunological biomarker.

BACKGROUND: Metastasis-Associated in Colon Cancer 1(MACC1) is a validated biomarker for metastasis and is linked to survival. Although extensive experimental evidence indicates an association between MACC1 and diverse cancers, no pan-cancer analyses have yet been performed for this marker, and the role of MACC1 in immunology remains unknown. MATERIAL AND METHODS: In our study, we performed the analysis of MACC1 expression and its influence on prognosis using multiple databases, including TIMER2, GEPIA2, Kaplan-Meier plotter. MACC1 promoter methylation levels were evaluated using the UALCAN database. Based on the TCGA database, we explored the relationship between MACC1 and tumor mutational burden (TMB), microsatellite instability (MSI), immune checkpoints using the R programming language. We evaluated the association between MACC1 and immune infiltration via TIMER and UALCAN. RESULTS: Our results revealed that abnormal DNA methylation may be an important cause for the different expression of MACC1 across cancer types. Meanwhile, we explored the potential oncogenic roles of MACC1 and found significant prognostic value. MACC1 may be related to T-cell function and the polarization of tumor-associated macrophages, especially in STAD and LGG. Its expression was associated with immune infiltration and was found to be closely related to immune checkpoint-associated genes, especially CD274 and SIGLEC15, indicating that MACC1 may be a potential immune therapeutic target for several malignancies. Our paper reveals for the first time the relationship between MACC1 and cancer immunology. CONCLUSIONS: MACC1 might act as a predictor for the immune response in cancer patients, and could also represent a new potential immunotherapeutic target.

A novel method for peanut variety identification and classification by Improved VGG16.

Crop variety identification is an essential link in seed detection, phenotype collection and scientific breeding. This paper takes peanut as an example to explore a new method for crop variety identification. Peanut is a crucial oil crop and cash crop. The yield and quality of different peanut varieties are different, so it is necessary to identify and classify different peanut varieties. The traditional image processing method of peanut variety identification needs to extract many features, which has defects such as intense subjectivity and insufficient generalization ability. Based on the deep learning technology, this paper improved the deep convolutional neural network VGG16 and applied the improved VGG16 to the identification and classification task of 12 varieties of peanuts. Firstly, the peanut pod images of 12 varieties obtained by the scanner were preprocessed with gray-scale, binarization, and ROI extraction to form a peanut pod data set with a total of 3365 images of 12 varieties. A series of improvements have been made to VGG16. Remove the F6 and F7 fully connected layers of VGG16. Add Conv6 and Global Average Pooling Layer. The three convolutional layers of conv5 have changed into Depth Concatenation and add the Batch Normalization(BN) layers to the model. Besides, fine-tuning is carried out based on the improved VGG16. We adjusted the location of the BN layers. Adjust the number of filters for Conv6. Finally, the improved VGG16 model's training test results were compared with the other classic models, AlexNet, VGG16, GoogLeNet, ResNet18, ResNet50, SqueezeNet, DenseNet201 and MobileNetv2 verify its superiority. The average accuracy of the improved VGG16 model on the peanut pods test set was 96.7%, which was 8.9% higher than that of VGG16, and 1.6-12.3% higher than that of other classical models. Besides, supplementary experiments were carried out to prove the robustness and generality of the improved VGG16. The improved VGG16 was applied to the identification and classification of seven corn grain varieties with the same method and an average accuracy of 90.1% was achieved. The experimental results show that the improved VGG16 proposed in this paper can identify and classify peanut pods of different varieties, proving the feasibility of a convolutional neural network in variety identification and classification. The model proposed in this experiment has a positive significance for exploring other Crop variety identification and classification.

Potential Prospect of CDK4/6 Inhibitors in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is an aggressive, difficult-to-treat subtype of cancer with a poor prognosis; there is an urgent need for effective, targeted molecular therapies. The cyclin D/cyclin-dependent kinase (CDK)4/6-retinoblastoma protein (Rb) pathway plays a critical role in regulating cell cycle checkpoints, a process which is often disrupted in cancer cells. Selective CDK4/6 inhibitors can prevent retinoblastoma protein phosphorylation by invoking cell cycle arrest in the first growth phase (G1), and may therefore represent an effective treatment option. In this article, we review the molecular mechanisms and therapeutic efficacy of CDK4/6 inhibitors in combination with other targeted therapies for the treatment of triple-negative breast cancer. Three selective CDK4/6 inhibitors have so far received the approval of the Food and Drug Administration (FDA) for patients with estrogen receptor (ER)+/human epidermal growth factor receptor 2 (HER2) breast cancer. Trilaciclib, a small molecule short-acting inhibitor of CDK4/6, has also been approved recently for people with small cell lung cancer, and is also expected to be clinically effective against breast cancer. Although the efficacy of CDK4/6 inhibitors in patients with triple-negative breast cancer remains uncertain, their use in conjunction with other targeted therapies may improve outcomes and is therefore currently being explored. Identifying biomarkers for response or resistance to CDK4/6 inhibitor treatment may optimize the personalization of treatment strategies for this disease. Ongoing and future clinical trials and biomarker studies will shed further light on these topics, and help to realize the full potential of CDK4/6 inhibitor treatment in triple-negative breast cancer.

Aflatoxin rapid detection based on hyperspectral with 1D-convolution neural network in the pixel level.

Aflatoxin is commonly exists in moldy foods, it is classified as a class one carcinogen by the World Health Organization. In this paper, we used one dimensional convolution neural network (1D-CNN) to classify whether a pixel contains aflatoxin. Firstly we found the best combination of 1D-CNN parameters were epoch = 30, learning rate = 0.00005 and 'relu' for active function, the highest test accuracy reached 96.35% for peanut, 92.11% for maize and 94.64% for mix data. Then we compared 1D-CNN with feature selection and methods in other papers, result shows that neural network has greatly improved the detection efficiency than feature selection. Finally we visualized the classification result of different training 1D-CNN networks. This research provides the core algorithm for the intelligent sorter with aflatoxin detection function, which is of positive significance for grain processing and the prenatal detoxification of foreign trade enterprises.

A huge malignant phyllodes tumor of the breast with osteoclast-like giant cells: a case report.

The malignant phyllodes tumor (PT) of the breasts is a rare type of fibroepithelial neoplasm. Osteoclast-like giant cells (OLGCs) exist in many types of tumors. But malignant PTs with OLGCs were rarely reported. Here, we presented a case of a 49-year-old woman who had a 23 cm ×21 cm ×6 cm mass which was growing for 2 years in her left breast. The patient had moderate anemia due to the hemorrhage and exudation on the surface of the tumor. The imaging examinations such as PET-CT found no lymphatic involvement and distant metastasis. We performed mastectomy with a 2 cm surgical margin and free skin flap transplantation to restore the big wound. The vacuum assisted closure (VAC) system was used to promote wound healing. Histological examination of the surgical specimen showed atypical spindle-like stroma cells, marked nuclear pleomorphism, focal necrosis, and mitotic activity. Typical leaf-like architectures of PTs were observed in some regions. OLGCs were found in many sections of the tumor with a number of vascular proliferations. The final diagnosis was malignant PT with OLGCs. After a three-month follow-up, no local recurrence or metastasis was found. Autogenous skin grafts with VAC are available for large area skin defect after excising a huge breast tumor. The presence of OLGCs in malignant tumors may be related to necrosis and hemorrhage of the tumor. These findings also provide opportunities for understanding the mechanisms of tumor formation and development.

Correction: Oct-4 and Nanog promote the epithelial-mesenchymal transition of breast cancer stem cells and are associated with poor prognosis in breast cancer patients.

[This corrects the article DOI: 10.18632/oncotarget.2506.].

Clinical characteristics and clinicopathological correlations of bilateral breast cancer in China: A multicenter study from Chinese Society of Breast Surgery (CSBrS-006).

OBJECTIVE: To investigate the clinical characteristics and clinicopathological correlations of bilateral breast cancer (BBC) in China. METHODS: Data of 440 patients diagnosed with BBC in 2018 were collected from 33 centers of the Chinese Society of Breast Surgery. Demographic characteristics, bilateral tumor characteristics, and comprehensive treatment data were obtained. Correlations between the clinicopathological characteristics of bilateral tumors were analyzed. RESULTS: The proportion of BBC was 0.22%-3.08%. A total of 33 (7.5%) patients had a family history of malignant tumors, 304 (69.1%) patients had synchronous BBC. Only 1 (0.2%) patient was male. More than half of all patients received concurrent or asynchronous endocrine/chemotherapy, 32.5% of all human epidermal growth factor receptor 2 (HER2)-positive patients received HER2-targeted therapy, and approximately 21.6% of all patients received radiotherapy. The most common pathological cancer type was invasive ductal cancer (>60%). Approximately 70% of all patients had bilateral hormone receptor (HR)-positive tumors and presented with a single breast mass. Significant correlations were found with pathological type, histological grade, locations of tumor, molecular subtype, Ki-67 index, tumor site and size of bilateral tumors. Results of the subgroup analysis showed more clinicopathological characteristics when synchronous BBC was compared with metachronous BBC. CONCLUSIONS: In China, the clinicopathological characteristics of bilateral tumors showed significant correlations, and more significant clinicopathological correlations were observed when synchronous BBC was compared with metachronous BBC.

A comprehensive hierarchical classification based on multi-features of breast DCE-MRI for cancer diagnosis.

Computer-aided diagnosis (CAD) is widely used for early diagnosis of breast cancer. The commonly used morphological feature (MF), dynamic feature (DF), and texture feature (TF) from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) have been proved very valuable and are studied in this paper. However, previous studies ignored the prior knowledge that most of the benign lesions have clearer and smoother edges than malignant ones. Therefore, two new TFs are proposed. To obtain an optimal feature subset and an accurate classification result, feature selection is applied in this paper. Moreover, most existing CAD models with simple structure only focus on common lesions and ignore hard-to-spot lesions so that a satisfied performance can be obtained for common lesions but there are some contradictions for those hard-to-spot lesions. Therefore, in this paper, a comprehensive hierarchical model is proposed to deal with contradictions and predict all kinds of lesions. The experimental result shows that the new features obviously increase ACC of TF from 0.7788 to 0.8584 and feature selection increases ACC of DF form 0.6991 to 0.7345. More importantly, compared with the existing CAD models and deep learning method, the proposed model which provides a higher performance for both common and hard-to-spot lesions significantly increases the classification performance with sensitivity of 0.9452 and specificity of 0.9000. Graphical abstract.

Identifying novel oncogenic RET mutations and characterising their sensitivity to RET-specific inhibitors.

BACKGROUND: Rearranged during transfection (RET) is a well-known proto-oncogene. Multiple RET oncogenic alterations have been identified, including fusions and mutations. Although RET fusions have been reported in multiple cancers, RET mutations were mainly found in multiple endocrine neoplasia type 2 and medullary thyroid carcinoma. RET mutations in other cancers were underinvestigated and their functional annotation was less well studied. METHODS: We retrospectively reviewed next-generation sequencing data from 37 056 patients with cancer to search for RET mutations. We excluded patients with other co-occurring known driver mutations to enrich potential activating RET mutations for further analysis. Moreover, we performed in vitro functional validation of the oncogenic property of several high frequent and novel RET mutants and their sensitivity to RET-specific inhibitors LOXO-292 and BLU-667. RESULTS: Within 560 (1.5%) patients with cancer who harbour RET mutations, we identified 380 distinct RET mutation sites, including 252 sites without co-occurring driver mutations. RET mutations were more frequently found in thyroid cancer, mediastinal tumour and several other cancers. The mutation sites spread out through the whole protein with a few hotspots within the kinase domain. In addition, we functionally validated that 898-901del, T930P and T930K were novel RET-activating mutations and they were all sensitive to RET inhibitors. CONCLUSION: Our results demonstrated the frequency of RET mutations across different cancers. We reported and/or validated several previously uncharacterised RET oncogenic mutations and demonstrated their sensitivity to RET-specific inhibitors. Our results help to stratify patients with cancer based on their RET mutation status and potentially provide more targeted treatment options.

Pixel-level aflatoxin detecting in maize based on feature selection and hyperspectral imaging.

Aflatoxin is highly toxic and is easily found in maize, a little aflatoxin can induce liver cancer. In this paper, we used hyperspectral data in the pixel-level to build the aflatoxin classifying model, each of the pixel have 600 hyperspectral bands and labeled 'clean' or 'contaminated'. We use 3 method to extracted feature bands, one method is to select 4 hyperspectral bands from other articles: 390 nm, 440 nm, 540 nm and 710 nm, another method is to use feature extraction PCA to obtain first 5 pcs to shrink the hyperspectral volume, the third method is to use Fscnca, Fscmrmr, Relieff and Fishier algorithm to select top 10 feature bands. After feature band selection or extraction, we put the feature bands into Random Forest (RF) and K-nearest neighbor (KNN) to classify whether a pixel is polluted by aflatoxin. The highest accurate for feature selection is Relieff, it reached the accuracy of 99.38% with RF classifier and 98.77% in KNN classifier. PCA feature extraction with RF classifier also reached a high accuracy 93.83%. And the 600 bands without feature extraction reached the accuracy of 100%. Feature bands selected from other papers could reach an accuracy of 89.51%. The result shows that the feature extraction performs well on its own data set. And if the computing time is not taken into account, we could use full band to classify the aflatoxin due to its high accuracy.

TIGAR Promotes Tumorigenesis and Protects Tumor Cells From Oxidative and Metabolic Stresses in Gastric Cancer.

Cancer cells adopt glycolysis to facilitate the generation of biosynthetic substrates demanded by cell proliferation and growth, and to adapt to stress conditions such as excessive reactive oxygen species (ROS) accumulation. TIGAR (TP53-induced glycolysis and apoptosis regulator) is a fructose-2,6-bisphosphatase that is regulated by p53. TIGAR functions to inhibit glycolysis and promote antioxidative activities, which assists the generation of NADPH to maintain the levels of GSH and thus reduces intracellular ROS. However, the functions of TIGAR in gastric cancer (GC) remain unclear. TIGAR expression levels were detected by immunoblotting and immunohistochemistry in gastric cancer samples, along with four established cell lines of GC. The functions of TIGAR were determined by utilizing shRNA-mediated knockdown experiments. The NADPH/NADP+ ratio, ROS, mitochondrial ATP production, and phosphorus oxygen ratios were determined in TIGAR-depleted cells. Xenograft experiment was conducted with BALB/c nude mice. TIGAR was up-regulated compared with corresponding non-cancerous tissues in primary GCs. TIGAR knockdown significantly reduced cell proliferation and increased apoptosis. TIGAR protected cancer cells from oxidative stress-caused damages, but also glycolysis defects. TIGAR also increased the production of NADPH in gastric cancer cells. TIGAR knockdown led to increased ROS production, elevated mitochondrial ATP production, and phosphorus oxygen ratios. The prognosis of high TIGAR expression patients was significantly poorer than those with low TIGAR expression. Taken together, TIGAR exhibits oncogenic features in GC, which can be evaluated as a target for intervention in the treatment of GC.

High Expression of CCR7 Predicts Lymph Node Metastasis and Good Prognosis in Triple Negative Breast Cancer.

BACKGROUND/AIMS: Previous preclinical and clinical studies have reported a positive correlation between the expression of the C-C chemokine receptor 7 (CCR7) and the incidence of lymph node metastasis in breast cancer. However, the prognostic relevance of CCR7 expression in breast cancer remains contradictory till now. The aim of this study is to assess the correlation of the CCR7 expression with other clinicopathological features and prognosis in breast cancer. METHODS: The CCR7 gene amplification and mRNA expression levels from approximately 3,000 patients were retrieved from human breast cancer databases and analyzed. Furthermore, a total of 188 primary triple negative breast cancer patients were enrolled in this study (diagnosed since January 2009 to January 2013 from the Second Hospital of Dalian Medical University). The protein levels of CCR7 were examined by immunohistochemistry using paraffin-embedded tumor tissues. RESULTS: The analysis of gene amplification and mRNA levels showed the expression of CCR7 in breast cancer correlated with better prognosis. When we compared the CCR7 expressions in different subtypes, the basal-like group showed the highest expression of CCR7 and exhibited a better prognosis. Consistently, Kaplan-Meier analysis of 188 triple negative breast cancer patients showed that the prognosis of patients with positive CCR7 expression was significantly better than those with negative expression (HR=0.642, p=0.0275). Additionally, we also observed a positive correlation between lymph node metastasis and the CCR7 expression (p=0.0096). CONCLUSIONS: Our results indicated that elevated CCR7 expression as a marker for increased lymph node metastasis, in addition to serve as an independent prognostic indicator for better overall survival in triple negative breast cancer patients.

Oct-4 and Nanog promote the epithelial-mesenchymal transition of breast cancer stem cells and are associated with poor prognosis in breast cancer patients.

Oct-4 and Nanog in regulating the epithelial-mesenchymal transition (EMT) and metastasis of breast cancer has not been clarified. We found that both Oct-4 and Nanog expression were significantly associated with tumor pathology and poor prognosis in 126 breast cancer patients. Characterization of CD44+CD24-Cancer stem cell(CSC) derived from breast cancer cells indicated that CSC rapidly formed mammospheres and had potent tumorigenicity in vivo. Furthermore, TGF-ß up-regulated the expression of Oct-4, Nanog, N-cadherin, vimentin, Slug, and Snail, but down-regulated E-cadherin and cytokeratin 18 expression, demonstrating that CSC underwent EMT. Knockdown of both Oct-4 and Nanog expression inhibited spontaneous changes in the expression of EMT-related genes, while induction of both Oct-4 and Nanog over-expression enhanced spontaneous changes in the expression of EMT-related genes in CSC. However, perturbing alternation of Oct-4 and Nanog expression also modulated TGF-ß-induced EMT-related gene expression in CSC. Induction of Oct-4 and Nanog over-expression enhanced the invasiveness of CSC, but knockdown of both Oct-4 and Nanog inhibited the migration of CSC in vitro. Our data suggest that both Oct-4 and Nanog may serve as biomarkers for evaluating breast cancer prognosis. Our findings indicate that Oct-4 and Nanog positively regulate the EMT process, contributing to breast cancer metastasis.

Clinical implications of SPRR1A expression in diffuse large B-cell lymphomas: a prospective, observational study.

BACKGROUND: Certain markers have been identified over the last 10 years that facilitate the prediction of a patient's prognosis; these markers have been proposed to be useful for risk stratification of lymphoma patients and for the development of specific therapeutic strategies. In the present study, we assessed the potential prognostic value of SPRR1A expression in 967 patients with diffuse large B-cell lymphomas. METHODS: All patients were enrolled between 2001 and 2007 (median follow-up, 53.3 months) in the Second Hospital of Dalian Medical University, First Hospital of China Medical University, and Liaoning Cancer Hospital. Immunohistochemical analysis was used to evaluate the expression of SPRR1A. Survival was analyzed using the Kaplan-Meier method. Multivariate analysis was conducted to adjust the effect of SPRR1A expression for potential, well-known, independent prognostic factors. RESULTS: Of the 967 patients examined, SPRR1A expression was detected in 305 (31.54%) patients on immunohistochemical analysis. The 5-year survival rate was significantly lower in patients with SPRR1A expression than in those without (26.9% vs. 53.2%, P < 0.001). Multivariate analysis identified SPRR1A expression as an independent predictor of survival in addition to lactate dehydrogenase level, clinical stage, and histologic subtype. CONCLUSIONS: SPRR1A expression may be useful as a prognostic factor for diffuse large B-cell lymphoma.

[Atmospheric adjacency effect correction of ETM images].

It is an important precondition to retrieve the ground surface reflectance exactly for improving the subsequent product of remote sensing images and the quantitative application of remote sensing. However, because the electromagnetic wave is scattered by the atmosphere during its transmission from the ground surface to the sensor, the electromagnetic wave signal of the target received by the sensor contained the signal of the background. The adjacency effect emerges. Because of the adjacency effect, the remote sensing images become blurry, and their contrast reduces. So the ground surface reflectance retrieved from the remote sensing images is also inaccurate. Finally, the quality of subsequent product of remote sensing images and the accuracy of quantitative application of remote sensing might decrease. In the present paper, according to the radiative transfer equation, the atmospheric adjacency effect correction experiment of ETM images was carried out by using the point spread function method. The result of the experiment indicated that the contrast of the corrected ETM images increased, and the ground surface reflectance retrieved from those images was more accurate.