ZNF320 is a hypomethylated prognostic biomarker involved in immune infiltration of hepatocellular carcinoma and associated with cell cycle.

Hepatocellular carcinoma (HCC) is one of the most deadly and common malignant cancers around the world, and the prognosis of HCC patients is not optimistic. ZNF320 belongs to Krüppel like zinc finger gene family. However, no studies have focused on the influence of ZNF320 in HCC. We first analyzed ZNF320 expression in HCC by using data from TCGA and ICGC, then conducted a joint analysis with TIMER and UALCAN, and validated by immunohistochemistry in clinical HCC samples. Then we applied UALCAN to explore the correlation between ZNF320 expression and clinicopathological characteristics. Consequently, using Kaplan-Meier Plotter analysis and the Cox regression, we can predict the prognostic value of ZNF320 for HCC patients. Next, the analysis by GO, KEGG, and GSEA revealed that ZNF320 was significantly correlated to cell cycle and immunity. Finally, TIMER and GEPIA analysis verified that ZNF320 expression is closely related to tumor infiltrating immune cells (TIIC), including B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells. The analysis of the TCGA and ICGC data sets revealed that ZNF320 expression was significantly correlated with m6A related genes (RBMX, YTHDF1, and METTL3). In conclusion, ZNF320 may be a prognostic biomarker related to immunity as a candidate for liver cancer.

Deubiquitinase ZRANB1 drives hepatocellular carcinoma progression through SP1-LOXL2 axis.

Deubiquitinase (DUB) zinc finger RANBP2-type containing 1 (ZRANB1) has been reported to have a close relationship with cancers. However, its underlying role and molecular mechanisms in hepatocellular carcinoma (HCC) remain elusive. In this study, we demonstrated that ZRANB1 was highly expressed in HCC tissues. Additionally, ZRANB1 overexpression was correlated with poorer survival and ZRANB1 could be an independent predictor of poor prognosis for HCC patients. Through gain- and loss-of-function assays, we examined the oncogenic role of ZRANB1 in regulating HCC cell growth and metastasis in vitro and in vivo. To identify the downstream targets of ZRANB1 in regulating HCC tumorigenesis, we performed RNA-seq and demonstrated that Lysyl oxidase-like 2 (LOXL2) was the most significantly downregulated gene after ZRANB1 knockdown. Furthermore, the scatter plots indicated a significant positive correlation between ZRANB1 and LOXL2 expression in clinical HCC specimens. We also demonstrated that ZRANB1 knockdown downregulated the expression of LOXL2 and suppressed HCC growth and metastasis in vitro and in vivo. The effects of ZRANB1 knockdown were reversed by LOXL2 overexpression. More importantly, ZRANB1 regulated LOXL2 through specificity protein 1 (SP1) and SP1 overexpression rescued the suppression of HCC growth and metastasis induced by ZRANB1 knockdown. Mechanistically, ZRANB1 bound with SP1 directly and stabilized the SP1 protein by deubiquitinating it. The expression patterns of ZRANB1, SP1 and LOXL2 were evaluated in HCC patients. In summary, our research highlights a novel role of ZRANB1 in the tumorigenesis of HCC and suggests a new candidate prognostic biomarker for HCC treatment.

AI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes.

The current challenge in cancer research is to increase the resolution of driver prediction from gene-level to mutation-level, which is more closely aligned with the goal of precision cancer medicine. Improved methods to distinguish drivers from passengers are urgently needed to dig out driver mutations from increasing exome sequencing studies. Here, we developed an ensemble method, AI-Driver (AI-based driver classifier, https://github.com/hatchetProject/AI-Driver), to predict the driver status of somatic missense mutations based on 23 pathogenicity features. AI-Driver has the best overall performance compared with any individual tool and two cancer-specific driver predicting methods. We demonstrate the superior and stable performance of our model using four independent benchmarks. We provide pre-computed AI-Driver scores for all possible human missense variants (http://aidriver.maolab.org/) to identify driver mutations in the sea of somatic mutations discovered by personal cancer sequencing. We believe that AI-Driver together with pre-computed database will play vital important roles in the human cancer studies, such as identification of driver mutation in personal cancer genomes, discovery of targeting sites for cancer therapeutic treatments and prediction of tumor biomarkers for early diagnosis by liquid biopsy.

Extreme Gradient Boosting Model Has a Better Performance in Predicting the Risk of 90-Day Readmissions in Patients with Ischaemic Stroke.

OBJECT: Ischemic stroke readmission within 90 days of hospital discharge is an important quality of care metric. The readmission rates of ischemic stroke patients are usually higher than those of patients with other chronic diseases. Our aim was to identify the ischemic stroke readmission risk factors and establish a 90-day readmission prediction model for first-time ischemic stroke patients. METHODS: The readmission prediction model was developed using the extreme gradient boosting (XGboost) model, which can generate an ensemble of classification trees and assign a predictive risk score to each feature. The patient data were split into a training set (5159) and a validation set (911). The prediction results were evaluated with the receiver operating characteristic (ROC) curve and time-dependent ROC curve, which were compared with the outputs from the logistic regression (LR) model. RESULTS: A total of 6070 adult patients (39.6% female, median age 67 years) without any ischemic attack (IS) history were included, and 520 (8.6%) were readmitted within 90 days. The XGboost-based prediction model achieved a standard area under the curve (AUC) value of .782 (.729-.834), and the best time-dependent AUC value was .808 in 54 days for the validation set. In contrast, the LR model yielded a standard AUC value of .771 (.714-.828) and best time-dependent AUC value of .797. CONCLUSIONS: The XGboost model obtained a better risk prediction for 90-day readmission for first-time ischemic stroke patients than the LR model. This model can also reveal the high risk factors for stroke readmission in first-time ischemic stroke patients.

Predicting post-stroke pneumonia using deep neural network approaches.

BACKGROUND AND PURPOSE: Pneumonia is a common complication after stroke, causing an increased length of hospital stay and death. Therefore, the timely and accurate prediction of post-stroke pneumonia would be highly valuable in clinical practice. Previous pneumonia risk score models were often built on simple statistical methods such as logistic regression. This study aims to investigate post-stroke pneumonia prediction models using more advanced machine learning algorithms, specifically deep learning approaches. METHODS: Using a hospital's electronic health record(EHR) data from 2007-2017, 13,930 eligible patients with acute ischaemic stroke (AIS) were identified to build and evaluate the models (85% of the patients were used for training, and 15% were used for testing). In total, 1012 patients (7.23%) contracted pneumonia during hospitalization. A number of machine learning methods were developed and compared to predict pneumonia in the stroke population in China. In addition to the classic methods (i.e., logistic regression (LR), support vector machines (SVMs), extreme gradient boosting (XGBoost)), methods based on multiple layer perceptron (MLP) neural networks and recurrent neural network (RNNs) (i.e., attention-augmented gated recurrent unit (GRU)) are also implemented to make use of the temporal sequence information in electronic health record (EHR) systems. Prediction models for pneumonia were built for two time windows, i.e., within 7 days and within 14 days after stroke onset. In particular, pneumonia occurring within the 7-day window is considered highly associated with stroke (stroke-associated pneumonia, SAP). MAIN FINDINGS: The attention-augmented GRU model achieved the best performance based on an area under the receiver operating characteristic curve (AUC) of 0.928 for pneumonia prediction within 7 days and an AUC of 0.905 for pneumonia prediction within 14 days. This method outperformed the other machine learning-based methods and previously published pneumonia risk score models. Considering that pneumonia prediction after stroke requires a high sensitivity to facilitate its prevention at a relatively low cost (i.e., increasing the nursing level), we also compared the prediction performance using other evaluation criteria by setting the sensitivity to 0.90. The attention-augmented GRU achieved the optimal performance, with a specificity of 0.85, a positive predictive value (PPV) of 0.32 and a negative predictive value (NPV) of 0.99 for pneumonia within 7 days and a specificity of 0.82, a PPV of 0.29 and an NPV of 0.99 for pneumonia within 14 days. CONCLUSIONS: The deep learning-based predictive model is feasible for stroke patient management and achieves the optimal performance compared to many classic machine learning methods.

A pilot study imaging integrin αvß3 with RGD PET/CT in suspected lung cancer patients.

PURPOSE: Angiogenesis is an essential step in tumour development and metastasis. Integrin αvß3 plays a major role in angiogenesis, tumour growth and progression. A new tracer, (18)F-AL-NOTA-PRGD2, denoted as (18)F-alfatide, has been developed for positron emission tomography (PET) imaging of integrin αvß3. This is a pilot study to test the safety and diagnostic value of (18)F- arginine-glycine-aspartic acid (RGD) PET/computed tomography (CT) in suspected lung cancer patients. METHODS: Twenty-six patients with suspected lung cancer on enhanced CT underwent (18)F-alfatide RGD PET/CT examination before surgery and puncture biopsy. Standard uptake values (SUVs) and the tumour-to-blood ratios were measured, and diagnoses were pathologically confirmed. RESULTS: RGD PET/CT with (18)F-alfatide was performed successfully in all patients and no clinically significant adverse events were observed. The (18)F-alfatide RGD PET/CT analysis correctly recognized 17 patients with lung cancer, 4 patients (hamartoma) as true negative, and 5 patients (4 chronic inflammation and 1 inflammatory pseudotumour) as false positive. The sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of (18)F-alfatide RGD PET/CT for the diagnosis of suspected lung cancer patients was 100, 44.44, 80.77, 77.27, and 100%, respectively. The area under a receiver operating characteristic (ROC) curve was 0.75 (P = 0.038), and ROC analysis suggested an SUVmax cut-off value of 2.65 to differentiate between malignant lesions and benign lesions. The SUV for malignant lesions was 5.37 ± 2.17, significantly higher than that for hamartomas (1.60 ± 0.11; P < 0.001). The difference between the tumour-to-blood ratio for malignant lesions (4.13 ± 0.91) and tissue of interest-to-blood ratio for hamartomas (1.56 ± 0.24) was also statistically significant (P < 0.001). Neither the SUVmax nor the tumour-to-blood ratio was significantly different between malignant lesions and inflammatory lesions or inflammatory pseudotumours (P > 0.05). Sixteen of 26 patients later underwent successful surgery, and pathologic examination confirmed nodes positive for metastasis in 14 of 152 lymph nodes. The sensitivity, specificity, accuracy, PPV, and NPV of PET/CT for lymph nodes was 92.86, 95.65, 95.40, 61.90, and 99.25%, respectively. CONCLUSION: Our results suggest that RGD PET/CT with the new tracer (18)F-alfatide is safe and potentially effective in the diagnosis of non-small cell lung cancer. It may be used in the diagnosis of lung cancer, successfully distinguishing malignant lesions from hamartoma. However, it is difficult to clearly differentiate inflammatory or inflammatory pseudotumours from malignant lesions. Additional studies with a larger number of patients are needed to validate our findings.

Q-RRBS: a quantitative reduced representation bisulfite sequencing method for single-cell methylome analyses.

Reduced representation bisulfite sequencing (RRBS) is a powerful method of DNA methylome profiling that can be applied to single cells. However, no previous report has described how PCR-based duplication-induced artifacts affect the accuracy of this method when measuring DNA methylation levels. For quantifying the effects of duplication-induced artifacts on methylome profiling when using ultra-trace amounts of starting material, we developed a novel method, namely quantitative RRBS (Q-RRBS), in which PCR-induced duplication is excluded through the use of unique molecular identifiers (UMIs). By performing Q-RRBS on varying amounts of starting material, we determined that duplication-induced artifacts were more severe when small quantities of the starting material were used. However, through using the UMIs, we successfully eliminated these artifacts. In addition, Q-RRBS could accurately detect allele-specific methylation in absence of allele-specific genetic variants. Our results demonstrate that Q-RRBS is an optimal strategy for DNA methylation profiling of single cells or samples containing ultra-trace amounts of cells.

Lysosomal storage disease in the brain: mutations of the ß-mannosidase gene identified in autosomal dominant nystagmus.

PURPOSE: Genetic etiology of congenital/infantile nystagmus remains largely unknown. This study aimed to identify genomic mutations in patients with infantile nystagmus and an associated disease network. METHODS: Patients with inherited and sporadic infantile nystagmus were recruited for whole-exome and Sanger sequencing. ß-Mannosidase activities were measured. Gene expression, protein-protein interaction, and nystagmus-associated lysosomal storage disease (LSD) genes were analyzed. RESULTS: A novel heterozygous mutation (c.2013G>A; p.R638H) of MANBA, which encodes lysosomal ß-mannosidase, was identified in patients with autosomal-dominant nystagmus. An additional mutation (c.2346T>A; p.L749H) in MANBA was found by screening patients with sporadic nystagmus. MANBA was expressed in the pretectal nucleus of the developing midbrain, known to be involved in oculomotor and optokinetic nystagmus. Functional validation of these mutations demonstrated a significant decrease of ß-mannosidase activities in the patients as well as in mutant-transfected HEK293T cells. Further analysis revealed that nystagmus is present in at least 24 different LSDs involving the brain. CONCLUSION: This is the first identification of MANBA mutations in patients with autosomal-dominant nystagmus, suggesting a new clinical entity. Because ß-mannosidase activities are required for development of the oculomotor nervous system, our findings shed new light on the role of LSD-associated genes in the pathogenesis of infantile nystagmus.

A Bayesian framework to identify methylcytosines from high-throughput bisulfite sequencing data.

High-throughput bisulfite sequencing technologies have provided a comprehensive and well-fitted way to investigate DNA methylation at single-base resolution. However, there are substantial bioinformatic challenges to distinguish precisely methylcytosines from unconverted cytosines based on bisulfite sequencing data. The challenges arise, at least in part, from cell heterozygosis caused by multicellular sequencing and the still limited number of statistical methods that are available for methylcytosine calling based on bisulfite sequencing data. Here, we present an algorithm, termed Bycom, a new Bayesian model that can perform methylcytosine calling with high accuracy. Bycom considers cell heterozygosis along with sequencing errors and bisulfite conversion efficiency to improve calling accuracy. Bycom performance was compared with the performance of Lister, the method most widely used to identify methylcytosines from bisulfite sequencing data. The results showed that the performance of Bycom was better than that of Lister for data with high methylation levels. Bycom also showed higher sensitivity and specificity for low methylation level samples (<1%) than Lister. A validation experiment based on reduced representation bisulfite sequencing data suggested that Bycom had a false positive rate of about 4% while maintaining an accuracy of close to 94%. This study demonstrated that Bycom had a low false calling rate at any methylation level and accurate methylcytosine calling at high methylation levels. Bycom will contribute significantly to studies aimed at recalibrating the methylation level of genomic regions based on the presence of methylcytosines.

Coexistence of peripheral myelin protein 22 and dystrophin mutations in a chinese boy.

INTRODUCTION: We describe a 10-year-old Chinese boy with features of Charcot-Marie-Tooth disease (CMT) and Duchenne muscular dystrophy (DMD). METHODS: Case report. RESULTS: Weakness and mild sensory loss in the distal extremities, pes cavus, and nerve conduction findings suggested demyelinating neuropathy, while moderate calf pseudohypertrophy, proximal muscle weakness, a myopathic pattern on electromyography, and deficiency of dystrophin immunohistochemical staining on muscle biopsy indicated DMD. Genetic testing revealed a large deletion spanning exon 50 in the gene coding for dystrophin and duplications in the gene coding for peripheral myelin protein 22. CONCLUSIONS: This is an interesting and very rare case of CMT type 1A comorbid with DMD. This results in an unusual phenotype and rapid deterioration of motor function. Usage of both target region capture and next generation sequencing is a powerful tool for predicting precisely the range of the large DNA fragment deletion in DMD.