The State of Systematic Therapies in Clinic for Hepatobiliary Cancers.

Hepatobiliary cancer (HBC) includes hepatocellular carcinoma and biliary tract carcinoma (cholangiocarcinoma and gallbladder carcinoma), and its morbidity and mortality are significantly correlated with disease stage. Surgery is the cornerstone of curative therapy for early stage of HBC. However, a large proportion of patients with HBC are diagnosed with advanced stage and can only receive systemic treatment. According to the results of clinical trials, the first-line and second-line treatment programs are constantly updated with the improvement of therapeutic effectiveness. In order to improve the therapeutic effect, reduce the occurrence of drug resistance, and reduce the adverse reactions of patients, the treatment of HBC has gradually developed from single-agent therapy to combination. The traditional therapeutic philosophy proposed that patients with advanced HBC are only amenable to systematic therapies. With some encouraging clinical trial results, the treatment concept has been revolutionized, and patients with advanced HBC who receive novel systemic combination therapies with multi-modality treatment (including surgery, transplant, TACE, HAIC, RT) have significantly improved survival time. This review summarizes the treatment options and the latest clinical advances of HBC in each stage and discusses future direction, in order to inform the development of more effective treatments for HBC.

TIF1γ and SMAD4 regulation in colorectal cancer: impact on cell proliferation and liver metastasis.

We investigated the effects of transcriptional intermediary factor 1γ (TIF1γ) and SMAD4 on the proliferation and liver metastasis of colorectal cancer (CRC) cells through knockdown of TIF1γ and/or SMAD4 and knockdown of TIF1γ and/or restoration of SMAD4 expression. Furthermore, we examined TIF1γ and SMAD4 expression in human primary CRC and corresponding liver metastatic CRC specimens. TIF1γ promoted but SMAD4 inhibited the proliferation of CRC cells by competitively binding to activated SMAD2/SMAD3 complexes and then reversely regulating c-Myc, p21, p27, and cyclinA2 levels. Surprisingly, both TIF1γ and SMAD4 reduced the liver metastasis of all studied CRC cell lines via inhibition of MEK/ERK pathway-mediated COX-2, Nm23, uPA, and MMP9 expression. In patients with advanced CRC, reduced TIF1γ or SMAD4 expression was correlated with increased invasion and liver metastasis and was a significant, independent risk factor for recurrence and survival after radical resection. Patients with advanced CRC with reduced TIF1γ or SAMD4 expression had higher recurrence rates and shorter overall survival. TIF1γ and SMAD4 competitively exert contrasting effects on cell proliferation but act complementarily to suppress the liver metastasis of CRC via MEK/ERK pathway inhibition. Thus, reduced TIF1γ or SMAD4 expression in advanced CRC predicts earlier liver metastasis and poor prognosis.

Deep learning in mesoscale brain image analysis: A review.

Mesoscale microscopy images of the brain contain a wealth of information which can help us understand the working mechanisms of the brain. However, it is a challenging task to process and analyze these data because of the large size of the images, their high noise levels, the complex morphology of the brain from the cellular to the regional and anatomical levels, the inhomogeneous distribution of fluorescent labels in the cells and tissues, and imaging artifacts. Due to their impressive ability to extract relevant information from images, deep learning algorithms are widely applied to microscopy images of the brain to address these challenges and they perform superiorly in a wide range of microscopy image processing and analysis tasks. This article reviews the applications of deep learning algorithms in brain mesoscale microscopy image processing and analysis, including image synthesis, image segmentation, object detection, and neuron reconstruction and analysis. We also discuss the difficulties of each task and possible directions for further research.

MicroRNA-188-5p inhibits hepatocellular carcinoma proliferation and migration by targeting forkhead box N2.

BACKGROUND: This study aimed to identify the biological functions, expression modes, and possible mechanisms underlying the relationship between metastatic human hepatocellular carcinoma (HCC) and MicroRNA-188-5p (miR-188) dysregulation using cell lines. METHODS: A decrease in miR-188 was detected in low and high metastatic HCC cells compared to that in normal hepatic cells and non-invasive cell lines. Gain- and loss-of-function experiments were performed in vitro to investigate the role of miR-188 in cancer cell (Hep3B, HepG2, HLF, and LM3) proliferation and migration. RESULTS: miR-188 mimic transfection inhibited the proliferation of metastatic HLF and LM3 cells but not non-invasive HepG2 and Hep3B cells; nonetheless, miR-188 suppression promoted the growth of HLF and LM3 cells. miR-188 upregulation inhibited the migratory rate and invasive capacity of HLF and LM3, rather than HepG2 and Hep3B cells, whereas transfection of a miR-188 inhibitor in HLF and LM3 cells had the opposite effects. Dual-luciferase reporter assays and bioinformatics prediction confirmed that miR-188 could directly target forkhead box N2 (FOXN2) in HLF and LM3 cells. Transfection of miR-188 mimics reduced FOXN2 levels, whereas miR-188 inhibition resulted in the opposite result, in HLF and LM3 cells. Overexpression of FOXN2 in HLF and LM3 cells abrogated miR-188 mimic-induced downregulation of proliferation, migration, and invasion. In addition, we found that miR-188 upregulation impaired tumor growth in vivo. CONCLUSIONS: In summary, this study showed thatmiR-188 inhibits the proliferation and migration of metastatic HCC cells by targeting FOXN2.

Exploring a four-gene risk model based on doxorubicin resistance-associated lncRNAs in hepatocellular carcinoma.

Background: Liver cancer is a lethal cancer type among which hepatocellular carcinoma (HCC) is the most common manifestation globally. Drug resistance is a central problem impeding the efficiency of HCC treatment. Long non-coding RNAs reportedly result in drug resistance. This study aimed to identify key lncRNAs associated with doxorubicin resistance and HCC prognosis. Materials and Methods: HCC samples with gene expression profiles and clinical data were accessed from public databases. We applied differential analysis to identify key lncRNAs that differed between HCC and normal samples and between drug-fast and control samples. We also used univariate Cox regression analysis to screen lncRNAs or genes associated with HCC prognosis. The least absolute shrinkage and selection operator (LASSO) was used to identify the key prognostic genes. Finally, we used receiver operating characteristic analysis to validate the effectiveness of the risk model. Results: The results of this study revealed RNF157-AS1 as a key lncRNA associated with both doxorubicin resistance and HCC prognosis. Metabolic pathways such as fatty acid metabolism and oxidative phosphorylation were enriched in RNF157-AS1-related genes. LASSO identified four protein-coding genes-CENPP, TSGA10, MRPL53, and BFSP1-to construct a risk model. The four-gene risk model effectively classified HCC samples into two risk groups with different overall survival. Finally, we established a nomogram, which showed superior performance in predicting the long-term prognosis of HCC. Conclusion: RNF157-AS1 may be involved in doxorubicin resistance and may serve as a potential therapeutic target. The four-gene risk model showed potential for the prediction of HCC prognosis.

Foreign Object Detection in Railway Images Based on an Efficient Two-Stage Convolutional Neural Network.

Foreign object intrusion is one of the main causes of train accidents that threaten human life and public property. Thus, the real-time detection of foreign objects intruding on the railway is important to prevent the train from colliding with foreign objects. Currently, the detection of railway foreign objects is mainly performed manually, which is prone to negligence and inefficient. In this study, an efficient two-stage framework is proposed for foreign object detection in railway images. In the first stage, a lightweight railway image classification network is established to classify any input railway images into one of two classes: normal or intruded. To enable real-time and accurate classification, we propose an improved inverted residual unit by introducing two improvements to the original inverted residual unit. First, the selective kernel convolution is used to dynamically select kernel size and learn multiscale features from railway images. Second, we employ a lightweight attention mechanism, called the convolutional block attention module, to exploit both spatial and channel-wise relationships between feature maps. In the second stage of our framework, the intruded image is fed to the foreign object detection network to further detect the location and class of the objects in the image. Experimental results confirm that the performance of our classification network is comparable to the widely used baselines, and it obtains outperforming efficiency. Moreover, the performances of the second-stage object detection are satisfying.

The state of therapy modalities in clinic for biliary tract cancer.

Biliary tract cancers (BTCs) include intrahepatic cholangiocarcinoma (iCCA), perihilar and distal cholangiocarcinoma (pCCA and dCCA), and gallbladder carcinoma based on the epithelial site of origin. BTCs are highly aggressive tumors associated with poor prognosis due to widespread metastasis and high recurrence. Surgery is the typical curative-intent treatment, yet the cornerstone of cure depends on the anatomical site of the primary tumor, and only a minority of patients (approximately 30%) has an indication necessitating surgery. Similarly, only a small subset of carefully selected patients with early iCCA who are not candidates for liver resection can opt for liver transplantation. Chemotherapy, target therapy, and immunotherapy are the main treatment options for patients who have advanced stage or unresectable disease. The genetic background of each cholangiocarcinoma subtype has been accurately described based on whole gene exome and transcriptome sequencing. Accordingly, precision medicine in targeted therapies has been identified to be aimed at distinct patient subgroups harboring unique molecular alterations. Immunotherapy such as immune checkpoint inhibitors (ICIs) was identified as antitumor responses in a minority of select patients. Current studies indicate that immunotherapy of adoptive cell therapy represents a promising approach in hematological and solid tumor malignancies, yet clinical trials are needed to validate its effectiveness in BTC. Herein, we review the progress of BTC treatment, stratified patients according to the anatomic subtypes of cholangiocarcinoma and the gene drivers of cholangiocarcinoma progression, and compare the efficacy and safety of chemotherapy, targeted therapy, and immunotherapy, which will be conducive to the design of individualized therapies.

Primary spindle cell sarcoma of gallbladder: An unusual case report and a literature review.

INTRODUCTION: Primary spindle cell sarcoma of the gallbladder is a rare condition. PATIENT CONCERNS: A 67-year-old woman was admitted to a local hospital with a chief complaint of abdominal pain in the right upper quadrant for the past 2 months. DIAGNOSIS AND INTERVENTION: Surgical resection was performed following the diagnosis of primary gallbladder sarcoma with local hepatic metastasis. Histological examination confirmed a diagnosis of primary spindle cell sarcoma and hepatic metastasis with simultaneous cholecystolithiasis. OUTCOMES: Adjuvant chemoradiation therapy was not performed because the patient refused treatment. Three months after the surgery, a relapsed lesion was diagnosed. The patient underwent transcatheter arterial chemoembolization. CONCLUSIONS: The disease should be differentially diagnosed from gallbladder carcinoma or carcinosarcoma with hepatic metastasis. An aggressive surgical approach should be based on a balance between the risk of surgery and the outcome.

DeepRayburst for Automatic Shape Analysis of Tree-Like Structures in Biomedical Images.

Precise quantification of tree-like structures from biomedical images, such as neuronal shape reconstruction and retinal blood vessel caliber estimation, is increasingly important in understanding normal function and pathologic processes in biology. Some handcrafted methods have been proposed for this purpose in recent years. However, they are designed only for a specific application. In this paper, we propose a shape analysis algorithm, DeepRayburst, that can be applied to many different applications based on a Multi-Feature Rayburst Sampling (MFRS) and a Dual Channel Temporal Convolutional Network (DC-TCN). Specifically, we first generate a Rayburst Sampling (RS) core containing a set of multidirectional rays. Then the MFRS is designed by extending each ray of the RS to multiple parallel rays which extract a set of feature sequences. A Gaussian kernel is then used to fuse these feature sequences and outputs one feature sequence. Furthermore, we design a DC-TCN to make the rays terminate on the surface of tree-like structures according to the fused feature sequence. Finally, by analyzing the distribution patterns of the terminated rays, the algorithm can serve multiple shape analysis applications of tree-like structures. Experiments on three different applications, including soma shape reconstruction, neuronal shape reconstruction, and vessel caliber estimation, confirm that the proposed method outperforms other state-of-the-art shape analysis methods, which demonstrate its flexibility and robustness.

Deep-Learning-Based Automated Neuron Reconstruction From 3D Microscopy Images Using Synthetic Training Images.

Digital reconstruction of neuronal structures from 3D microscopy images is critical for the quantitative investigation of brain circuits and functions. It is a challenging task that would greatly benefit from automatic neuron reconstruction methods. In this paper, we propose a novel method called SPE-DNR that combines spherical-patches extraction (SPE) and deep-learning for neuron reconstruction (DNR). Based on 2D Convolutional Neural Networks (CNNs) and the intensity distribution features extracted by SPE, it determines the tracing directions and classifies voxels into foreground or background. This way, starting from a set of seed points, it automatically traces the neurite centerlines and determines when to stop tracing. To avoid errors caused by imperfect manual reconstructions, we develop an image synthesizing scheme to generate synthetic training images with exact reconstructions. This scheme simulates 3D microscopy imaging conditions as well as structural defects, such as gaps and abrupt radii changes, to improve the visual realism of the synthetic images. To demonstrate the applicability and generalizability of SPE-DNR, we test it on 67 real 3D neuron microscopy images from three datasets. The experimental results show that the proposed SPE-DNR method is robust and competitive compared with other state-of-the-art neuron reconstruction methods.

Downregulation of lncRNA SBF2-AS1 inhibits hepatocellular carcinoma proliferation and migration by regulating the miR-361-5p/TGF-ß1 signaling pathway.

SBF2-AS1 is an oncogenic long non-coding RNA (lncRNA). However, its role and mechanism in hepatocellular carcinoma (HCC) is still not completely clear. The HepG2, Hep3B, Bel-7402 and HL-7702 cell lines were used in our experiments. The CCK-8 kit and EdU staining were applied to detect cell viability and multiplication. The wound healing and Boyden chamber cell migration assays were employed to test the migration ability of cells. The levels of TGF-ß1 mRNA, lncRNA SBF2-AS1, and miR-361-5p were assessed by real-time PCR. TGF-ß1 protein levels were evaluated by western blotting. The direct interaction between miR-361-5p and TGF-ß1 was determined by luciferase reporter assays. A xenograft mouse model (XMM) was established to comprehensively study the effect and mechanisms of lncRNA SBF2-AS1. lncRNA SBF2-AS1 concentration in HCC cells exceeded that in a normal hepatocyte cell line. The downregulation of lncRNA SBF2-AS1 upregulated miR-361-5p levels in HCC cells. And, miR-361-5p negatively regulate TGF-ß1 expression in HCC cells. The suppression of miR-361-5p attenuated the influence of lncRNA SBF2-AS1 downregulation on the viability, proliferation, and migration capability of HCC cells. Further, the downregulation of lncRNA SBF2-AS1 inhibited neoplasm growth in an XMM of HCC. Simultaneously, miR-361-5p was upregulated and TGF-ß1 was downregulated after lncRNA SBF2-AS1 knocked down. In conclusion, downregulation of lncRNA SBF2-AS1 inhibits HCC proliferation and migration through the regulation of the miR-361-5p/TGF-ß1 signaling pathway.

An extremely atypical presentation of esophageal squamous cell carcinoma with pancreatic and hepatic metastases: A case report and overview of the literature.

RATIONALE: Esophageal carcinoma is an aggressive cancer with extremely poor therapeutic outcomes due to its high metastatic potential and a significant risk of recurrence after radical resection. Liver is the most common metastatic target organ of esophageal carcinoma, followed by the lungs, bones, and brain. Few cases of solitary pancreatic and hepatic metastases of esophageal carcinoma have been reported. PATIENT CONCERNS: We report the case of a 67-year-old male presenting with pancreatic and hepatic lesions. In addition, a friable lesion with an irregular nodular surface in the distal esophagus was detected by esophagogastroduodenoscopy. DIAGNOSIS: Pathohistological examination confirmed esophageal squamous cell carcinoma. The pancreatic lesion was also biopsied via ultrasound-guided fine needle aspiration, which also revealed squamous cell carcinoma. The hepatic lesion was also identified as metastatic carcinoma by magnetic resonance imaging, most likely of the same origin. INTERVENTIONS: Due to comorbidities that precluded surgery, the patient was administered adjuvant therapy and a multidisciplinary decision was made for palliative care. OUTCOMES: The patient died 1 month later due to multiorgan failure caused by hemorrhage from a peptic ulcer. CONCLUSION: To our knowledge, this is only the sixth case of pancreatic metastasis of esophageal squamous cell carcinoma. This case report suggests to clinicians the importance of considering potential comorbidities in every patient with advanced cancer, such as gastric ulcer and cachexia.

Spherical-Patches Extraction for Deep-Learning-Based Critical Points Detection in 3D Neuron Microscopy Images.

Digital reconstruction of neuronal structures is very important to neuroscience research. Many existing reconstruction algorithms require a set of good seed points. 3D neuron critical points, including terminations, branch points and cross-over points, are good candidates for such seed points. However, a method that can simultaneously detect all types of critical points has barely been explored. In this work, we present a method to simultaneously detect all 3 types of 3D critical points in neuron microscopy images, based on a spherical-patches extraction (SPE) method and a 2D multi-stream convolutional neural network (CNN). SPE uses a set of concentric spherical surfaces centered at a given critical point candidate to extract intensity distribution features around the point. Then, a group of 2D spherical patches is generated by projecting the surfaces into 2D rectangular image patches according to the orders of the azimuth and the polar angles. Finally, a 2D multi-stream CNN, in which each stream receives one spherical patch as input, is designed to learn the intensity distribution features from those spherical patches and classify the given critical point candidate into one of four classes: termination, branch point, cross-over point or non-critical point. Experimental results confirm that the proposed method outperforms other state-of-the-art critical points detection methods. The critical points based neuron reconstruction results demonstrate the potential of the detected neuron critical points to be good seed points for neuron reconstruction. Additionally, we have established a public dataset dedicated for neuron critical points detection, which has been released along with this article.

Neuron Image Segmentation via Learning Deep Features and Enhancing Weak Neuronal Structures.

Neuron morphology reconstruction (tracing) in 3D volumetric images is critical for neuronal research. However, most existing neuron tracing methods are not applicable in challenging datasets where the neuron images are contaminated by noises or containing weak filament signals. In this paper, we present a two-stage 3D neuron segmentation approach via learning deep features and enhancing weak neuronal structures, to reduce the impact of image noise in the data and enhance the weak-signal neuronal structures. In the first stage, we train a voxel-wise multi-level fully convolutional network (FCN), which specializes in learning deep features, to obtain the 3D neuron image segmentation maps in an end-to-end manner. In the second stage, a ray-shooting model is employed to detect the discontinued segments in segmentation results of the first-stage, and the local neuron diameter of the broken point is estimated and direction of the filamentary fragment is detected by rayburst sampling algorithm. Then, a Hessian-repair model is built to repair the broken structures, by enhancing weak neuronal structures in a fibrous structure determined by the estimated local neuron diameter and the filamentary fragment direction. Experimental results demonstrate that our proposed segmentation approach achieves better segmentation performance than other state-of-the-art methods for 3D neuron segmentation. Compared with the neuron reconstruction results on the segmented images produced by other segmentation methods, the proposed approach gains 47.83% and 34.83% improvement in the average distance scores. The average Precision and Recall rates of the branch point detection with our proposed method are 38.74% and 22.53% higher than the detection results without segmentation.

3D Neuron Microscopy Image Segmentation via the Ray-Shooting Model and a DC-BLSTM Network.

The morphology reconstruction (tracing) of neurons in 3D microscopy images is important to neuroscience research. However, this task remains very challenging because of the low signal-to-noise ratio (SNR) and the discontinued segments of neurite patterns in the images. In this paper, we present a neuronal structure segmentation method based on the ray-shooting model and the Long Short-Term Memory (LSTM)-based network to enhance the weak-signal neuronal structures and remove background noise in 3D neuron microscopy images. Specifically, the ray-shooting model is used to extract the intensity distribution features within a local region of the image. And we design a neural network based on the dual channel bidirectional LSTM (DC-BLSTM) to detect the foreground voxels according to the voxel-intensity features and boundary-response features extracted by multiple ray-shooting models that are generated in the whole image. This way, we transform the 3D image segmentation task into multiple 1D ray/sequence segmentation tasks, which makes it much easier to label the training samples than many existing Convolutional Neural Network (CNN) based 3D neuron image segmentation methods. In the experiments, we evaluate the performance of our method on the challenging 3D neuron images from two datasets, the BigNeuron dataset and the Whole Mouse Brain Sub-image (WMBS) dataset. Compared with the neuron tracing results on the segmented images produced by other state-of-the-art neuron segmentation methods, our method improves the distance scores by about 32% and 27% in the BigNeuron dataset, and about 38% and 27% in the WMBS dataset.

DeepBranch: Deep Neural Networks for Branch Point Detection in Biomedical Images.

Morphology reconstruction of tree-like structures in volumetric images, such as neurons, retinal blood vessels, and bronchi, is of fundamental interest for biomedical research. 3D branch points play an important role in many reconstruction applications, especially for graph-based or seed-based reconstruction methods and can help to visualize the morphology structures. There are a few hand-crafted models proposed to detect the branch points. However, they are highly dependent on the empirical setting of the parameters for different images. In this paper, we propose a DeepBranch model for branch point detection with two-level designed convolutional networks, a candidate region segmenter and a false positive reducer. On the first level, an improved 3D U-Net model with anisotropic convolution kernels is employed to detect initial candidates. Compared with the traditional sliding window strategy, the improved 3D U-Net can avoid massive redundant computations and dramatically speed up the detection process by employing dense-inference with fully convolutional neural networks (FCN). On the second level, a method based on multi-scale multi-view convolutional neural networks (MSMV-Net) is proposed for false positive reduction by feeding multi-scale views of 3D volumes into multiple streams of 2D convolution neural networks (CNNs), which can take full advantage of spatial contextual information as well as fit different sizes. Experiments on multiple 3D biomedical images of neurons, retinal blood vessels and bronchi confirm that the proposed 3D branch point detection method outperforms other state-of-the-art detection methods, and is helpful for graph-based or seed-based reconstruction methods.

Significant response to anti-PD-1 based immunotherapy plus lenvatinib for recurrent intrahepatic cholangiocarcinoma with bone metastasis: A case report and literature review.

INTRODUCTION: The prognosis for recurrent intrahepatic cholangiocarcinoma with bone metastasis remains dismal and its treatment poses a challenge for oncologists. To date, only 2 cases were reported in which pembrolizumab, an agent against programmed cell death protein-1 (PD-1), combined with chemotherapy led to a complete response. The safety and efficacy of nivolumab-based immunotherapy combined with lenvatinibin intrahepatic cholangiocarcinoma is unknown. PATIENT CONCERNS: A 40-year-old female was identified as having a lesion of 7.0 cm in diameter in the right lobe of the liver. In addition, calculi in the main and left hepatic bile ducts as well as the gallbladder were found. DIAGNOSIS: Based on the results of imaging studies and tumor biomarker level, the patient was initially diagnosed as having intrahepatic cholangiocellular carcinoma and cholelithiasis, after which surgery was performed. The pathological examination confirmed that the tumor was cholangiocarcinoma. Adjuvant chemotherapy was administered after surgery. However, the patient developed recurrent lesions at the 5th month after surgery, and the cholangiocarcinoma expanded to the right thoracic vertebral pedicle (T7-8) at the 6th month. INTERVENTIONS: The patient underwent percutaneous microwave ablation after recurrence in the liver was identified. After that, the patient received nivolumab plus lenvatinib. OUTCOMES: The lesions in the liver decreased in size and disappeared after treatment with nivolumab plus lenvatinib. Additionally, the metastases in the right thoracic vertebral pedicle were stable after 9 months of therapy. LESSONS: Immunotherapy has revolutionized the treatment of non-small-cell lung cancer, melanoma, and advanced renal cell carcinoma. In this case, the patient achieved an excellent radiological and symptomatic response after receiving nivolumab plus lenvatinib combination therapy. Patients suffering from cholangiocarcinoma with dMMR status and a high tumor mutation burden (TMB) may have a consistent eutherapeutic effect with anti-PD-1-directed treatment.

Development and validation of a CIMP-associated prognostic model for hepatocellular carcinoma.

BACKGROUND: CpG island methylator phenotype (CIMP), a common biological phenomenon characterized by a subset of concurrently methylated genes, can have an influence on the progression of multiple cancers. However, the potential mechanism of CIMP in hepatocarcinogenesis and its clinical relevance remains only partially understood. METHODS: We used a methylation array from the cancer genome atlas (TCGA) to stratify HCC patients into different CIMP subtypes, and evaluated their correlation with clinical characteristics. In addition, mutation, CNV, and transcriptome profiles were also utilized to evaluate the distinctive genomic patterns correlated with CIMP. Finally, a CIMP-associated prognostic model (CPM) was trained and validated using four independent datasets. FINDINGS: A subgroup of patients was identified as having CIMP-H, which was associated with worse OS and DFS. Gene enrichment analysis indicated that the terms "liver cancer with EPCAM up", "tumor invasiveness up", "methyltransferase complex", and "translational initiation" were enriched in CIMP-H subgroup. Notably, somatic mutation analysis indicated that CIMP-H patients presented with a higher mutation burden of BRD4, DDIAS and NOX1. Moreover, four CPM associated genes could significantly categorize patients into low- and high-risk groups in the training dataset and another 3 independent validation datasets. Finally, a nomogram incorporating a classifier based on four mRNAs, pathological M stage and CIMP status was established, which showed a favorable discriminating ability and might contribute to clinical decision-making for HCC. INTERPRETATION: Our work highlights the potential clinical application value of CPM in predicting the overall survival of HCC patients and the mechanisms underlying the role of CIMP in hepatocarcinogenesis. FUND: This work was supported by the State Key Project on Infectious Diseases of China (2018ZX10723204-003), the National Nature Science Foundation of China (Nos. 81874065, 81500565, 81874149, 81572427, and 81401997), the Hepato-Biliary-Pancreatic Malignant Tumor Investigation Fund of Chen Xiao-ping Foundation for the Development of Science and Technology of Hubei Province (CXPJJH11800001-2018356).

Genome-wide identification of a competing endogenous RNA network in cholangiocarcinoma.

Cholangiocarcinoma (CCA) is the second widespread liver tumor with relatively poor survival. Increasing evidence in recent studies showed long noncoding RNAs (lncRNAs) exert a crucial impact on the development and progression of CCA based on the mechanism of competing endogenous RNAs (ceRNAs). However, functional roles and regulatory mechanisms of lncRNA-regulated ceRNA in CCA, are only partially understood. The expression profile of messenger RNAs (mRNAs), lncRNAs, and microRNAs (miRNAs) downloaded from The Cancer Genome Atlas were comprehensively investigated. Differential expression of these three types of RNA between CCA and corresponding precancerous tissues were screened out for further analysis. On the basis of interactive information generated from miRDB, miRTarBase, TargetScan, and miRcode public databases, we then constructed an mRNA-miRNA-lncRNA regulatory network. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses were conducted to identify the biological function of the ceRNA network involved in CCA. As a result, 2883 mRNAs, 136 miRNAs, and 993 lncRNAs were screened out as differentially expressed RNAs in CCA. In addition, a ceRNA network in CCA was constructed, composing of 50 up and 27 downregulated lncRNAs, 14 up and 7 downregulated miRNAs, 29 up and 25 downregulated mRNAs. Finally, gene set enrichment and pathway analysis indicated our CCA-specific ceRNA network was related with cancer-related pathway and molecular function. In conclusion, our research identified a novel lncRNA-related ceRNA network in CCA, which might act as a potential therapeutic target for patients with CCA.

A Multiscale Ray-Shooting Model for Termination Detection of Tree-Like Structures in Biomedical Images.

Digital reconstruction (tracing) of tree-like structures, such as neurons, retinal blood vessels, and bronchi, from volumetric images and 2D images is very important to biomedical research. Many existing reconstruction algorithms rely on a set of good seed points. The 2D or 3D terminations are good candidates for such seed points. In this paper, we propose an automatic method to detect terminations for tree-like structures based on a multiscale ray-shooting model and a termination visual prior. The multiscale ray-shooting model detects 2D terminations by extracting and analyzing the multiscale intensity distribution features around a termination candidate. The range of scale is adaptively determined according to the local neurite diameter estimated by the Rayburst sampling algorithm in combination with the gray-weighted distance transform. The termination visual prior is based on a key observation-when observing a 3D termination from three orthogonal directions without occlusion, we can recognize it in at least two views. Using this prior with the multiscale ray-shooting model, we can detect 3D terminations with high accuracies. Experiments on 3D neuron image stacks, 2D neuron images, 3D bronchus image stacks, and 2D retinal blood vessel images exhibit average precision and recall rates of 87.50% and 90.54%. The experimental results confirm that the proposed method outperforms other the state-of-the-art termination detection methods.