Non-invasive prediction of overall survival time for glioblastoma multiforme patients based on multimodal MRI radiomics.

Glioblastoma multiforme (GBM) is the most common and deadly primary malignant brain tumor. As GBM tumor is aggressive and shows high biological heterogeneity, the overall survival (OS) time is extremely low even with the most aggressive treatment. If the OS time can be predicted before surgery, developing personalized treatment plans for GBM patients will be beneficial. Magnetic resonance imaging (MRI) is a commonly used diagnostic tool for brain tumors with high-resolution and sound imaging effects. However, in clinical practice, doctors mainly rely on manually segmenting the tumor regions in MRI and predicting the OS time of GBM patients, which is time-consuming, subjective and repetitive, limiting the effectiveness of clinical diagnosis and treatment. Therefore, it is crucial to segment the brain tumor regions in MRI, and an accurate pre-operative prediction of OS time for personalized treatment is highly desired. In this study, we present a multimodal MRI radiomics-based automatic framework for non-invasive prediction of the OS time for GBM patients. A modified 3D-UNet model is built to segment tumor subregions in MRI of GBM patients; then, the radiomic features in the tumor subregions are extracted and combined with the clinical features input into the Support Vector Regression (SVR) model to predict the OS time. In the experiments, the BraTS2020, BraTS2019 and BraTS2018 datasets are used to evaluate our framework. Our model achieves competitive OS time prediction accuracy compared to most typical approaches.

Combined Channel Attention and Spatial Attention Module Network for Chinese Herbal Slices Automated Recognition.

Chinese Herbal Slices (CHS) are critical components of Traditional Chinese Medicine (TCM); the accurate recognition of CHS is crucial for applying to medicine, production, and education. However, existing methods to recognize the CHS are mainly performed by experienced professionals, which may not meet vast CHS market demand due to time-consuming and the limited number of professionals. Although some automated CHS recognition approaches have been proposed, the performance still needs further improvement because they are primarily based on the traditional machine learning with hand-crafted features, resulting in relatively low accuracy. Additionally, few CHS datasets are available for research aimed at practical application. To comprehensively address these problems, we propose a combined channel attention and spatial attention module network (CCSM-Net) for efficiently recognizing CHS with 2-D images. The CCSM-Net integrates channel and spatial attentions, focusing on the most important information as well as the position of the information of CHS image. Especially, pairs of max-pooling and average pooling operations are used in the CA and SA module to aggregate the channel information of the feature map. Then, a dataset of 14,196 images with 182 categories of commonly used CHS is constructed. We evaluated our framework on the constructed dataset. Experimental results show that the proposed CCSM-Net indicates promising performance and outperforms other typical deep learning algorithms, achieving a recognition rate of 99.27%, a precision of 99.33%, a recall of 99.27%, and an F1-score of 99.26% with different numbers of CHS categories.

scSE-NL V-Net: A Brain Tumor Automatic Segmentation Method Based on Spatial and Channel "Squeeze-and-Excitation" Network With Non-local Block.

Intracranial tumors are commonly known as brain tumors, which can be life-threatening in severe cases. Magnetic resonance imaging (MRI) is widely used in diagnosing brain tumors because of its harmless to the human body and high image resolution. Due to the heterogeneity of brain tumor height, MRI imaging is exceptionally irregular. How to accurately and quickly segment brain tumor MRI images is still one of the hottest topics in the medical image analysis community. However, according to the brain tumor segmentation algorithms, we could find now, most segmentation algorithms still stay in two-dimensional (2D) image segmentation, which could not obtain the spatial dependence between features effectively. In this study, we propose a brain tumor automatic segmentation method called scSE-NL V-Net. We try to use three-dimensional (3D) data as the model input and process the data by 3D convolution to get some relevance between dimensions. Meanwhile, we adopt non-local block as the self-attention block, which can reduce inherent image noise interference and make up for the lack of spatial dependence due to convolution. To improve the accuracy of convolutional neural network (CNN) image recognition, we add the "Spatial and Channel Squeeze-and-Excitation" Network (scSE-Net) to V-Net. The dataset used in this paper is from the brain tumor segmentation challenge 2020 database. In the test of the official BraTS2020 verification set, the Dice similarity coefficient is 0.65, 0.82, and 0.76 for the enhanced tumor (ET), whole tumor (WT), and tumor core (TC), respectively. Thereby, our model can make an auxiliary effect on the diagnosis of brain tumors established.

3D FRN-ResNet: An Automated Major Depressive Disorder Structural Magnetic Resonance Imaging Data Identification Framework.

Major Depressive Disorder (MDD) is the most prevalent psychiatric disorder, seriously affecting people's quality of life. Manually identifying MDD from structural magnetic resonance imaging (sMRI) images is laborious and time-consuming due to the lack of clear physiological indicators. With the development of deep learning, many automated identification methods have been developed, but most of them stay in 2D images, resulting in poor performance. In addition, the heterogeneity of MDD also results in slightly different changes reflected in patients' brain imaging, which constitutes a barrier to the study of MDD identification based on brain sMRI images. We propose an automated MDD identification framework in sMRI data (3D FRN-ResNet) to comprehensively address these challenges, which uses 3D-ResNet to extract features and reconstruct them based on feature maps. Notably, the 3D FRN-ResNet fully exploits the interlayer structure information in 3D sMRI data and preserves most of the spatial details as well as the location information when converting the extracted features into vectors. Furthermore, our model solves the feature map reconstruction problem in closed form to produce a straightforward and efficient classifier and dramatically improves model performance. We evaluate our framework on a private brain sMRI dataset of MDD patients. Experimental results show that the proposed model exhibits promising performance and outperforms the typical other methods, achieving the accuracy, recall, precision, and F1 values of 0.86776, 0.84237, 0.85333, and 0.84781, respectively.

Retinal Vessel Automatic Segmentation Using SegNet.

Extracting retinal vessels accurately is very important for diagnosing some diseases such as diabetes retinopathy, hypertension, and cardiovascular. Clinically, experienced ophthalmologists diagnose these diseases through segmenting retinal vessels manually and analysing its structural feature, such as tortuosity and diameter. However, manual segmentation of retinal vessels is a time-consuming and laborious task with strong subjectivity. The automatic segmentation technology of retinal vessels can not only reduce the burden of ophthalmologists but also effectively solve the problem that is a lack of experienced ophthalmologists in remote areas. Therefore, the automatic segmentation technology of retinal vessels is of great significance for clinical auxiliary diagnosis and treatment of ophthalmic diseases. A method using SegNet is proposed in this paper to improve the accuracy of the retinal vessel segmentation. The performance of the retinal vessel segmentation model with SegNet is evaluated on the three public datasets (DRIVE, STARE, and HRF) and achieved accuracy of 0.9518, 0.9683, and 0.9653, sensitivity of 0.7580, 0.7747, and 0.7070, specificity of 0.9804, 0.9910, and 0.9885, F 1 score of 0.7992, 0.8369, and 0.7918, MCC of 0.7749, 0.8227, and 0.7643, and AUC of 0.9750, 0.9893, and 0.9740, respectively. The experimental results showed that the method proposed in this research presented better results than many classical methods studied and may be expected to have clinical application prospects.

OTO-Net: An Automated MRA Image Segmentation Network for Intracranial Aneurysms.

Intracranial aneurysms are local dilations of the cerebral blood vessels; people with intracranial aneurysms have a high risk to cause bleeding in the brain, which is related to high mortality and morbidity rates. Accurate detection and segmentation of intracranial aneurysms from Magnetic Resonance Angiography (MRA) images are essential in the clinical routine. Manual annotations used to assess the intracranial aneurysms on MRA images are substantial interobserver variability for both aneurysm detection and assessment of aneurysm size and growth. Many prior automated segmentation works have focused their efforts on tackling the problem, but there is still room for performance improvement due to the significant variability of lesions in the location, size, structure, and morphological appearance. To address these challenges, we propose a novel One-Two-One Fully Convolutional Networks (OTO-Net) for intracranial aneurysms automated segmentation in MRA images. The OTO-Net uses full convolution to achieve intracranial aneurysms automated segmentation through the combination of downsampling, upsampling, and skip connection. In addition, loss ensemble is used as the objective function to steadily improve the backpropagation efficiency of the network structure during the training process. We evaluated the proposed OTO-Net on one public benchmark dataset and one private dataset. Our proposed model can achieve the automated segmentation accuracy with 98.37% and 97.86%, average surface distances with 1.081 and 0.753, dice similarity coefficients with 0.9721 and 0.9813, and Hausdorff distance with 0.578 and 0.642 on these two datasets, respectively.

Automated Segmentation of Mass Regions in DBT Images Using a Dilated DCNN Approach.

To overcome the limitations of conventional breast screening methods based on digital mammography, a quasi-3D imaging technique, digital breast tomosynthesis (DBT) has been developed in the field of breast cancer screening in recent years. In this work, a computer-aided architecture for mass regions segmentation in DBT images using a dilated deep convolutional neural network (DCNN) is developed. First, to improve the low contrast of breast tumour candidate regions and depress the background tissue noise in the DBT image effectively, the constraint matrix is established after top-hat transformation and multiplied with the DBT image. Second, input image patches are generated, and the data augmentation technique is performed to create the training data set for training a dilated DCNN architecture. Then the mass regions in DBT images are preliminarily segmented; each pixel is divided into two different kinds of labels. Finally, the postprocessing procedure removes all false-positives regions with less than 50 voxels. The final segmentation results are obtained by smoothing the boundaries of the mass regions with a median filter. The testing accuracy (ACC), sensitivity (SEN), and the area under the receiver operating curve (AUC) are adopted as the evaluation metrics, and the ACC, SEN, as well as AUC are 86.3%, 85.6%, and 0.852 for segmenting the mass regions in DBT images on the entire data set, respectively. The experimental results indicate that our proposed approach achieves promising results compared with other classical CAD-based frameworks.

3D AGSE-VNet: an automatic brain tumor MRI data segmentation framework.

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.

ATF4/TXNIP/REDD1/mTOR signaling mediates the antitumor activities of liver X receptor in pancreatic cancers.

Background: Limited by difficulties in early detection and availabilities of effective treatments, pancreatic cancer is a highly malignant disease with poor prognosis. Nuclear receptors are a family of ligand-dependent transcription factors that are highly druggable therapeutic targets playing critical roles in human physiological and pathological development, including cancer. In this study, we explored the therapeutic potential as well as the molecular mechanisms of liver X receptor (LXR) agonist GW3965 in pancreatic cancer. Methods: Soft-agar colony formation assay, xenograft tumors, Oligonucleotide microarray, Reverse transcription real-time polymerase chain reaction, Western immunoblotting and Immunohistochemistry were used in this study. Results: We demonstrated pleotropic in vitro activities of GW3965 in pancreatic cell lines MIA PaCa-2 and BXPC3 including reduction of cell viability, inhibition of cell proliferation, stimulation of cell death, and suppression of colony formation, which translated to significant inhibition of xenograft tumor growth in vitro. By mapping the gene expression profiles, we identified the up-regulations of 188 and the down-regulations of 92 genes common to both cell lines following GW3965 treatment. Genes responsive to GW3965 represent a variety of biological pathways vital for multiple cellular functions. Specifically, we identified that the activating transcription factor 4/thioredoxin-interacting protein/regulated in development and DNA damage responses 1/mechanistic target of rapamycin (ATF4/TXNIP/REDD1/mTOR) signaling critically controls GW3965-mediated regulation of cell proliferation/death. The significance of the ATF4/TXNIP/REDD1/mTOR pathway was further supported by associated expressions in xenograft tumors as well as human pancreatic cancer samples. Conclusions: This study provides the pre-clinical evidence that LXR agonist is a promising therapy for pancreatic cancer.

Overall Survival Prediction for Gliomas Using a Novel Compound Approach.

As a highly malignant tumor, the incidence and mortality of glioma are not optimistic. Predicting the survival time of patients with glioma by extracting the feature information from gliomas is beneficial for doctors to develop more targeted treatments. Magnetic resonance imaging (MRI) is a way to quickly and clearly capture the details of brain tissue. However, manually segmenting brain tumors from MRI will cost doctors a lot of energy, and doctors can only vaguely estimate the survival time of glioma patients, which are not conducive to the formulation of treatment plans. Therefore, automatically segmenting brain tumors and accurately predicting survival time has important significance. In this article, we first propose the NLSE-VNet model, which integrates the Non-Local module and the Squeeze-and-Excitation module into V-Net to segment three brain tumor sub-regions in multimodal MRI. Then extract the intensity, texture, wavelet, shape and other radiological features from the tumor area, and use the CNN network to extract the deep features. The factor analysis method is used to reduce the dimensionality of features, and finally the dimensionality-reduced features and clinical features such as age and tumor grade are combined into the random forest regression model to predict survival. We evaluate the effect on the BraTS 2019 and BraTS 2020 datasets. The average Dice of brain tumor segmentation tasks up to 79% and the average RMSE of the survival predictive task is as low as 311.5. The results indicate that the method in this paper has great advantages in segmentation and survival prediction of gliomas.

A Deep Multi-Task Learning Framework for Brain Tumor Segmentation.

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.

3D PBV-Net: An automated prostate MRI data segmentation method.

Prostate cancer is one of the most common deadly diseases in men worldwide, which is seriously affecting people's life and health. Reliable and automated segmentation of the prostate gland in MRI data is exceptionally critical for diagnosis and treatment planning of prostate cancer. Although many automated segmentation methods have emerged, including deep learning based approaches, segmentation performance is still poor due to the large variability of image appearance, anisotropic spatial resolution, and imaging interference. This study proposes an automated prostate MRI data segmentation approach using bicubic interpolation with improved 3D V-Net (dubbed 3D PBV-Net). Considering the low-frequency components in the prostate gland, the bicubic interpolation is applied to preprocess the MRI data. On this basis, a 3D PBV-Net is developed to perform prostate MRI data segmentation. To illustrate the effectiveness of our approach, we evaluate the proposed 3D PBV-Net on two clinical prostate MRI data datasets, i.e., PROMISE 12 and TPHOH, with the manual delineations available as the ground truth. Our approach generates promising segmentation results, which have achieved 97.65% and 98.29% of average accuracy, 0.9613 and 0.9765 of Dice metric, 3.120 mm and 0.9382 mm of Hausdorff distance, and average boundary distance of 1.708, 0.7950 on PROMISE 12 and TPHOH datasets, respectively. Our method has effectively improved the accuracy of automated segmentation of the prostate MRI data and is promising to meet the accuracy requirements for telehealth applications.

DBT Masses Automatic Segmentation Using U-Net Neural Networks.

To improve the automatic segmentation accuracy of breast masses in digital breast tomosynthesis (DBT) images, we propose a DBT mass automatic segmentation algorithm by using a U-Net architecture. Firstly, to suppress the background tissue noise and enhance the contrast of the mass candidate regions, after the top-hat transform of DBT images, a constraint matrix is constructed and multiplied with the DBT image. Secondly, an efficient U-Net neural network is built and image patches are extracted before data augmentation to establish the training dataset to train the U-Net model. And then the presegmentation of the DBT tumors is implemented, which initially classifies per pixel into two different types of labels. Finally, all regions smaller than 50 voxels considered as false positives are removed, and the median filter smoothes the mass boundaries to obtain the final segmentation results. The proposed method can effectively improve the performance in the automatic segmentation of the masses in DBT images. Using the detection Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), and area under the curve (AUC) as evaluation indexes, the Acc, Sen, Spe, and AUC for DBT mass segmentation in the entire experimental dataset is 0.871, 0.869, 0.882, and 0.859, respectively. Our proposed U-Net-based DBT mass automatic segmentation system obtains promising results, which is superior to some classical architectures, and may be expected to have clinical application prospects.

Left ventricle automatic segmentation in cardiac MRI using a combined CNN and U-net approach.

Cardiovascular diseases can be effectively prevented from worsening through early diagnosis. To this end, various methods have been proposed to detect the disease source by analyzing cardiac magnetic resonance images (MRI), wherein left ventricular segmentation plays an indispensable role. However, since the left ventricle (LV) is easily confused with other regions in cardiac MRI, segmentation of the LV is a challenging problem. To address this issue, we propose a composite model combining CNN and U-net to accurately segment the LV. In our model, CNN is used to locate the region of interest (ROI) and the U-net network achieve segmentation of LV. We used the cardiac MRI datasets of the MICCAI 2009 left ventricular segmentation challenge to train and test our model and demonstrated the accuracy and robustness of the proposed model. The proposed model achieved state-of-the-art results. The metrics are Dice metric (DM), volumetric overlap error (VOE) and Hausdorff distance (HD), in which DM reaches 0.951, VOE reaches 0.053 and HD reaches 3.641.

Characterizing the composition of intestinal microflora by 16S rRNA gene sequencing.

BACKGROUND: This study determined the composition and diversity of intestinal microflora in patients with colorectal adenoma (CRA), which may provide precedence for investigating the role of intestinal microflora in the pathogenesis of colorectal tumors, the composition of intestinal microflora closely related to CRA, and further validating the possibility of intestinal flora as a biomarker of CRA. AIM: To study the relationship between intestinal microflora and CRA. METHODS: This is a prospective control case study from October 2014 to June 2015 involving healthy volunteers and patients with advanced CRA. High-throughput sequencing and bioinformatics analysis were used to investigate the composition and diversity of intestinal microflora in 36 healthy subjects and 49 patients with advanced CRA. Endpoints measured were operational taxonomic units of intestinal flora, as well as their abundance and diversity (α and ß types). RESULTS: In this study, the age, gender, body mass index, as well as location between controls and patients had no significant differences. The mucosa-associated gut microbiota diversity and bacterial distribution in healthy controls and colorectal adenomas were similar. The operational taxonomic unit, abundance, and α and ß diversity were all reduced in patients with CRA compared to controls. At the phylum level, the composition of intestinal microflora was comparable between patients and controls, but the abundance of Proteobacteria was increased, and Firmicutes and Bacteroides were significantly decreased (P < 0.05). The increase in Halomonadaceae and Shewanella algae, and reduction in Coprococcus and Bacteroides ovatus, could serve as biomarkers of CRA. High-throughput sequencing confirms the special characteristics and diversity of intestinal microflora in healthy controls and patients with CRA. CONCLUSION: The diversity of intestinal microflora was decreased in patients with CRA. An increase in Halomonadaceae and Shewanella algae are markers of CRA.

Predicting the response to neoadjuvant chemotherapy for breast cancer: wavelet transforming radiomics in MRI.

BACKGROUND: The purpose of this study was to investigate the value of wavelet-transformed radiomic MRI in predicting the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) for patients with locally advanced breast cancer (LABC). METHODS: Fifty-five female patients with LABC who underwent contrast-enhanced MRI (CE-MRI) examination prior to NAC were collected for the retrospective study. According to the pathological assessment after NAC, patient responses to NAC were categorized into pCR and non-pCR. Three groups of radiomic textures were calculated in the segmented lesions, including (1) volumetric textures, (2) peripheral textures, and (3) wavelet-transformed textures. Six models for the prediction of pCR were Model I: group (1), Model II: group (1) + (2), Model III: group (3), Model IV: group (1) + (3), Model V: group (2) + (3), and Model VI: group (1) + (2) + (3). The performance of predicting models was compared using the area under the receiver operating characteristic (ROC) curves (AUC). RESULTS: The AUCs of the six models for the prediction of pCR were 0.816 ± 0.033 (Model I), 0.823 ± 0.020 (Model II), 0.888 ± 0.025 (Model III), 0.876 ± 0.015 (Model IV), 0.885 ± 0.030 (Model V), and 0.874 ± 0.019 (Model VI). The performance of four models with wavelet-transformed textures (Models III, IV, V, and VI) was significantly better than those without wavelet-transformed textures (Model I and II). In addition, the inclusion of volumetric textures or peripheral textures or both did not result in any improvements in performance. CONCLUSIONS: Wavelet-transformed textures outperformed volumetric and/or peripheral textures in the radiomic MRI prediction of pCR to NAC for patients with LABC, which can potentially serve as a surrogate biomarker for the prediction of the response of LABC to NAC.

NIMA-related kinase 2 regulates hepatocellular carcinoma cell growth and proliferation.

NIMA-related kinase 2 (Nek2) is often upregulated in human cancer and is important in regulating the cell cycle and gene expression, and maintaining centrosomal structure and function. The present study aimed to investigate the expression pattern, clinical significance, and biological function of Nek2 in hepatocellular carcinoma (HCC). mRNA and protein levels of Nek2 were examined in HCC and corresponding normal liver tissues. The MTT and soft agar colony formation assays, and flow cytometry were employed to assess the roles of Nek2 in cell proliferation and growth. In addition, western blot analysis was performed to assess the expression of cell cycle- and proliferation-related proteins. The results revealed that Nek2 was upregulated in HCC tissues and cell lines. The clinical significance of Nek2 expression was also analyzed. Inhibiting Nek2 expression by siRNA suppressed cell proliferation, growth, and colony formation in hepatocellular carcinoma cell line HepG2 cells, induced cell cycle arrest in the G2/M phase by retarding the S-phase, and promoted apoptosis. Furthermore, Nek2 depletion downregulated ß-catenin expression in HepG2 cells and diminished expression of Myc proto-oncogene protein (c-Myc), cyclins D1, B1, and E and cyclin-dependent kinase 1, whilst increasing protein levels of p27. This demonstrates that overexpression of Nek2 is associated with the malignant evolution of HCC. Targeting Nek2 may inhibit HCC cell growth and proliferation through the regulation of ß-catenin by the Wnt/ß-catenin pathway and therefore may be developed as a novel therapeutic strategy to treat HCC.

Induction of the liver cancer-down-regulated long noncoding RNA uc002mbe.2 mediates trichostatin-induced apoptosis of liver cancer cells.

Differential expression of long non-coding RNAs (lncRNAs) plays critical roles in hepatocarcinogenesis. Considerable attention has focused on the antitumor effect of histone deacetylase inhibitor (Trichostatin A, TSA) as well as the coding gene expression-induced apoptosis of cancer cells. However, it is not known whether lncRNA has a role in TSA-induced apoptosis of human hepatocellular carcinoma (HCC) cells. The global expression of lncRNAs and coding genes was analyzed with the Human LncRNA Array V2.0 after 24 h treatment. Expression was verified in cell lines and tissues by quantitative real-time PCR. The data showed that 4.8% (959) of lncRNA and 6.1% (1849) of protein coding gene were significantly differentially expressed. The differential expressions of lncRNA and protein coding genes had distinguishable hierarchical clustering expression profiling pattern. Among these differentially expressed lncRNAs, the greatest change was noted for uc002mbe.2, which had more than 300 folds induction upon TSA treatment. TSA selectively induced uc002mbe.2 in four studied HCC cell lines. Compared with normal human hepatocytes and adjacent noncancerous tissues, uc002mbe.2 expression level was significantly lower in the HCC cell lines and liver cancer tissues. The TSA-induced uc002mbe.2 expression was positively correlated with the apoptotic effect of TSA in HCC cells. In addition, knockdown the expression of uc002mbe.2 significantly reduced TSA-induced apoptosis of Huh7cells. Therefore, TSA-induced apoptosis of HCC cells is uc002mbe.2 dependent and reduced expression of uc002mbe.2 may be associated with liver carcinogenesis.

Influence of polygenetic polymorphisms on the susceptibility to non-alcoholic fatty liver disease of Chinese people.

BACKGROUND AND AIM: The aim of this study was to investigate the influence of polygenetic polymorphisms, which play a role in the pathogenesis of metabolic syndrome, on the susceptibility to non-alcoholic fatty liver disease (NAFLD) of Chinese people. METHODS: The subjects were selected from an epidemiological survey in the Guangdong province of southern China. In each polymorphism study, 50-117 subjects who met the diagnostic criteria of NAFLD and had typical clinical and ultrasonographic findings were placed into the case group. Using a nested case-control design, the same numbers of matched people without NAFLD were included as controls. Single nucleotide polymorphisms (SNP) at nine positions in seven candidate genes were tested. These SNP were found to be associated with the pathogenesis of metabolic syndrome. Genetic analyses were performed using genomic DNA extracted from peripheral blood leukocytes. Polymerase chain reaction-restriction fragment length polymorphism was applied to detect SNP. RESULTS: Most candidate genes' SNP were associated with susceptibility to NAFLD. Some showed positive relationships (increased risk): tumor necrosis factor-alpha-238, adiponectin-45, leptin-2548, peroxisome proliferator-activated receptors-161 and phosphatidyletha-nolamine N-methyltransferase-175. Other SNP demonstrated a negative association (decreased risk): adiponectin-276 and hepatic lipase-514. Only two were not associated: tumor necrosis factor-alpha-380 and peroxisome proliferator-activated receptors-gamma co-activator-1alpha-482. CONCLUSION: Most candidate genes' SNP examined in metabolic syndrome patients were associated with susceptibility to NAFLD.