RNA exosome-regulated long non-coding RNA transcription controls super-enhancer activity.

We have ablated the cellular RNA degradation machinery in differentiated B cells and pluripotent embryonic stem cells (ESCs) by conditional mutagenesis of core (Exosc3) and nuclear RNase (Exosc10) components of RNA exosome and identified a vast number of long non-coding RNAs (lncRNAs) and enhancer RNAs (eRNAs) with emergent functionality. Unexpectedly, eRNA-expressing regions accumulate R-loop structures upon RNA exosome ablation, thus demonstrating the role of RNA exosome in resolving deleterious DNA/RNA hybrids arising from active enhancers. We have uncovered a distal divergent eRNA-expressing element (lncRNA-CSR) engaged in long-range DNA interactions and regulating IgH 3' regulatory region super-enhancer function. CRISPR-Cas9-mediated ablation of lncRNA-CSR transcription decreases its chromosomal looping-mediated association with the IgH 3' regulatory region super-enhancer and leads to decreased class switch recombination efficiency. We propose that the RNA exosome protects divergently transcribed lncRNA expressing enhancers by resolving deleterious transcription-coupled secondary DNA structures, while also regulating long-range super-enhancer chromosomal interactions important for cellular function.

Mechanism of noncoding RNA-associated N6-methyladenosine recognition by an RNA processing complex during IgH DNA recombination.

Immunoglobulin heavy chain (IgH) locus-associated G-rich long noncoding RNA (SµGLT) is important for physiological and pathological B cell DNA recombination. We demonstrate that the METTL3 enzyme-catalyzed N6-methyladenosine (m6A) RNA modification drives recognition and 3' end processing of SµGLT by the RNA exosome, promoting class switch recombination (CSR) and suppressing chromosomal translocations. The recognition is driven by interaction of the MPP6 adaptor protein with nuclear m6A reader YTHDC1. MPP6 and YTHDC1 promote CSR by recruiting AID and the RNA exosome to actively transcribe SµGLT. Direct suppression of m6A modification of SµGLT or of m6A reader YTHDC1 reduces CSR. Moreover, METTL3, an essential gene for B cell development in the bone marrow and germinal center, suppresses IgH-associated aberrant DNA breaks and prevents genomic instability. Taken together, we propose coordinated and central roles for MPP6, m6A modification, and m6A reader proteins in controlling long noncoding RNA processing, DNA recombination, and development in B cells.

LogicGep: Boolean networks inference using symbolic regression from time-series transcriptomic profiling data.

Reconstructing the topology of gene regulatory network from gene expression data has been extensively studied. With the abundance functional transcriptomic data available, it is now feasible to systematically decipher regulatory interaction dynamics in a logic form such as a Boolean network (BN) framework, which qualitatively indicates how multiple regulators aggregated to affect a common target gene. However, inferring both the network topology and gene interaction dynamics simultaneously is still a challenging problem since gene expression data are typically noisy and data discretization is prone to information loss. We propose a new method for BN inference from time-series transcriptional profiles, called LogicGep. LogicGep formulates the identification of Boolean functions as a symbolic regression problem that learns the Boolean function expression and solve it efficiently through multi-objective optimization using an improved gene expression programming algorithm. To avoid overly emphasizing dynamic characteristics at the expense of topology structure ones, as traditional methods often do, a set of promising Boolean formulas for each target gene is evolved firstly, and a feed-forward neural network trained with continuous expression data is subsequently employed to pick out the final solution. We validated the efficacy of LogicGep using multiple datasets including both synthetic and real-world experimental data. The results elucidate that LogicGep adeptly infers accurate BN models, outperforming other representative BN inference algorithms in both network topology reconstruction and the identification of Boolean functions. Moreover, the execution of LogicGep is hundreds of times faster than other methods, especially in the case of large network inference.

GeoBind: segmentation of nucleic acid binding interface on protein surface with geometric deep learning.

Unveiling the nucleic acid binding sites of a protein helps reveal its regulatory functions in vivo. Current methods encode protein sites from the handcrafted features of their local neighbors and recognize them via a classification, which are limited in expressive ability. Here, we present GeoBind, a geometric deep learning method for predicting nucleic binding sites on protein surface in a segmentation manner. GeoBind takes the whole point clouds of protein surface as input and learns the high-level representation based on the aggregation of their neighbors in local reference frames. Testing GeoBind on benchmark datasets, we demonstrate GeoBind is superior to state-of-the-art predictors. Specific case studies are performed to show the powerful ability of GeoBind to explore molecular surfaces when deciphering proteins with multimer formation. To show the versatility of GeoBind, we further extend GeoBind to five other types of ligand binding sites prediction tasks and achieve competitive performances.

ActivePPI: quantifying protein-protein interaction network activity with Markov random fields.

MOTIVATION: Protein-protein interactions (PPI) are crucial components of the biomolecular networks that enable cells to function. Biological experiments have identified a large number of PPI, and these interactions are stored in knowledge bases. However, these interactions are often restricted to specific cellular environments and conditions. Network activity can be characterized as the extent of agreement between a PPI network (PPIN) and a distinct cellular environment measured by protein mass spectrometry, and it can also be quantified as a statistical significance score. Without knowing the activity of these PPI in the cellular environments or specific phenotypes, it is impossible to reveal how these PPI perform and affect cellular functioning. RESULTS: To calculate the activity of PPIN in different cellular conditions, we proposed a PPIN activity evaluation framework named ActivePPI to measure the consistency between network architecture and protein measurement data. ActivePPI estimates the probability density of protein mass spectrometry abundance and models PPIN using a Markov-random-field-based method. Furthermore, empirical P-value is derived based on a nonparametric permutation test to quantify the likelihood significance of the match between PPIN structure and protein abundance data. Extensive numerical experiments demonstrate the superior performance of ActivePPI and result in network activity evaluation, pathway activity assessment, and optimal network architecture tuning tasks. To summarize it succinctly, ActivePPI is a versatile tool for evaluating PPI network that can uncover the functional significance of protein interactions in crucial cellular biological processes and offer further insights into physiological phenomena. AVAILABILITY AND IMPLEMENTATION: All source code and data are freely available at https://github.com/zpliulab/ActivePPI.

LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data.

MOTIVATION: From a systematic perspective, it is crucial to infer and analyze gene regulatory network (GRN) from high-throughput single-cell RNA sequencing data. However, most existing GRN inference methods mainly focus on the network topology, only few of them consider how to explicitly describe the updated logic rules of regulation in GRNs to obtain their dynamics. Moreover, some inference methods also fail to deal with the over-fitting problem caused by the noise in time series data. RESULTS: In this article, we propose a novel embedded Boolean threshold network method called LogBTF, which effectively infers GRN by integrating regularized logistic regression and Boolean threshold function. First, the continuous gene expression values are converted into Boolean values and the elastic net regression model is adopted to fit the binarized time series data. Then, the estimated regression coefficients are applied to represent the unknown Boolean threshold function of the candidate Boolean threshold network as the dynamical equations. To overcome the multi-collinearity and over-fitting problems, a new and effective approach is designed to optimize the network topology by adding a perturbation design matrix to the input data and thereafter setting sufficiently small elements of the output coefficient vector to zeros. In addition, the cross-validation procedure is implemented into the Boolean threshold network model framework to strengthen the inference capability. Finally, extensive experiments on one simulated Boolean value dataset, dozens of simulation datasets, and three real single-cell RNA sequencing datasets demonstrate that the LogBTF method can infer GRNs from time series data more accurately than some other alternative methods for GRN inference. AVAILABILITY AND IMPLEMENTATION: The source data and code are available at https://github.com/zpliulab/LogBTF.

Cytosolic DNA initiates a vicious circle of aging-related endothelial inflammation and mitochondrial dysfunction via STING: the inhibitory effect of Cilostazol.

Endothelial senescence, aging-related inflammation, and mitochondrial dysfunction are prominent features of vascular aging and contribute to the development of aging-associated vascular disease. Accumulating evidence indicates that DNA damage occurs in aging vascular cells, especially in endothelial cells (ECs). However, the mechanism of EC senescence has not been completely elucidated, and so far, there is no specific drug in the clinic to treat EC senescence and vascular aging. Here we show that various aging stimuli induce nuclear DNA and mitochondrial damage in ECs, thus facilitating the release of cytoplasmic free DNA (cfDNA), which activates the DNA-sensing adapter protein STING. STING activation led to a senescence-associated secretory phenotype (SASP), thereby releasing pro-aging cytokines and cfDNA to further exacerbate mitochondrial damage and EC senescence, thus forming a vicious circle, all of which can be suppressed by STING knockdown or inhibition. Using next-generation RNA sequencing, we demonstrate that STING activation stimulates, whereas STING inhibition disrupts pathways associated with cell senescence and SASP. In vivo studies unravel that endothelial-specific Sting deficiency alleviates aging-related endothelial inflammation and mitochondrial dysfunction and prevents the development of atherosclerosis in mice. By screening FDA-approved vasoprotective drugs, we identified Cilostazol as a new STING inhibitor that attenuates aging-related endothelial inflammation both in vitro and in vivo. We demonstrated that Cilostazol significantly inhibited STING translocation from the ER to the Golgi apparatus during STING activation by targeting S162 and S243 residues of STING. These results disclose the deleterious effects of a cfDNA-STING-SASP-cfDNA vicious circle on EC senescence and atherogenesis and suggest that the STING pathway is a promising therapeutic target for vascular aging-related diseases. A proposed model illustrates the central role of STING in mediating a vicious circle of cfDNA-STING-SASP-cfDNA to aggravate age-related endothelial inflammation and mitochondrial damage.

Critically ill children with SARS-COV-2 Omicron infection at a national children medical center, Guangdong, China.

BACKGROUND: SARS-CoV-2 infection is described as asymptomatic, mild, or moderate disease in most children. SARS-CoV-2 infection related death in children and adolescents is rare according to the current reports. COVID-19 cases increased significantly in China during the omicron surge, clinical data regarding pediatric critical patients infected with the omicron variant is limited. In this study, we aim to provide an overview of the clinical characteristics and outcomes of critically ill children admitted to a national children's medical center in Guangdong Province, China, during the outbreak of the omicron variant infection. METHODS: We conducted a retrospective study from November 25, 2022, to February 8, 2023, which included 63 critically ill children, under the age of 18, diagnosed with SARS-CoV-2 infection. The patients were referred from medical institutions of Guangdong province. The medical records of these patients were analyzed and summarized. RESULTS: The median age of patients was 2 years (Interquartile Range, IQR: 1.0-8.0), sex-ratio (male/female) was 1.52. 12 (19%) patients (age ≥ 3 years) were vaccinated. The median length of hospital stay was 14 days (IQR: 6.5-23) in 63 cases, and duration of fever was 5 days (IQR: 3-8.5), pediatric intensive care unit (PICU) stay was 8 days (IQR 4.0-14.0) in 57 cases. 30 (48%) cases had clear contact history with family members who were infected with SARS-CoV-2. Three children who tested positive for SARS-CoV-2 infection did not show any abnormalities on chest imaging examination. Out of the total patients, 33 (52%) had a bacterial co-infection, with Staphylococcus aureus being the most commonly detected bacterial pathogen. Our cohort exhibited respiratory and nervous system involvement as the primary features. Furthermore, fifty (79%) patients required mechanical ventilation, with a median duration of 7 days (IQR 3.75-13.0). Among these patients, 35 (56%) developed respiratory failure, 16 (25%) patients experienced a deteriorating progression of symptoms and ultimately succumbed to the illness, septic shock was the most common condition among these patients (15 cases), followed by multiple organ failure in 12 cases, and encephalopathy identified in 7 cases. CONCLUSION: We present a case series of critically ill children infected with the SARS-CoV-2 omicron variant. While there is evidence suggesting that Omicron may cause less severe symptoms, it is important to continue striving for measures that can minimize the pathogenic impact of SARS-CoV-2 infection in children.

Inhibition of Jumonji demethylases reprograms severe dilated cardiomyopathy and prolongs survival.

Hypertrophic/dilated cardiomyopathy, often a prequel to heart failure, is accompanied by maladaptive transcriptional changes that contribute to arrythmias and contractile misfunction. Transgenic mice constitutively expressing high levels of calcineurin are known to develop extreme heart hypertrophy, which progresses to dilated cardiomyopathy, and to die several weeks after birth. Here, we characterized aberrant transcriptional and epigenetic pathways in this mouse model and established a pharmacological approach to treat established cardiomyopathy. We found that H3K4me3 (trimethyl histone 3 lysine 4) and H3K9me3 (trimethyl histone 3 lysine 9) Jumonji histone demethylases are markedly increased at the protein level and show enhanced enzymatic activity in diseased hearts. These epigenetic regulators continued to increase with time, further affecting cardiac gene expression. Our findings parallel the lower H3K4me3 and H3K9me3 levels seen in human patients. Inhibition of Jumonji demethylase activities in vivo results in lower histone demethylase enzymatic function in the heart and higher histone methylation levels and leads to partial reduction of heart size, reversal of maladaptive transcriptional programs, improved heart function, and prolonged survival. At the molecular level, target genes of transcription factor myocyte enhancer factor 2 are specifically regulated in response to pharmacological or genetic inhibition of Jumonji demethylases. Similar transcriptional reversal of disease-associated genes is seen in a second disease model based on cardiac mechanical overload. Our findings validate pharmacological inhibitors of Jumonji demethylases as potential therapeutics for the treatment of cardiomyopathies across disease models and provide evidence of the reversal of maladaptive transcriptional reprogramming leading to partial restoration of cardiac function. In addition, this study defines pathways of therapeutic resistance upregulated with disease progression.

PST-PRNA: prediction of RNA-binding sites using protein surface topography and deep learning.

MOTIVATION: Protein-RNA interactions play essential roles in many biological processes, including pre-mRNA processing, post-transcriptional gene regulation and RNA degradation. Accurate identification of binding sites on RNA-binding proteins (RBPs) is important for functional annotation and site-directed mutagenesis. Experimental assays to sparse RBPs are precise and convincing but also costly and time consuming. Therefore, flexible and reliable computational methods are required to recognize RNA-binding residues. RESULTS: In this work, we propose PST-PRNA, a novel model for predicting RNA-binding sites (PRNA) based on protein surface topography (PST). Taking full advantage of the 3D structural information of protein, PST-PRNA creates representative topography images of the entire protein surface by mapping it onto a unit spherical surface. Four kinds of descriptors are encoded to represent residues on the surface. Then, the potential features are integrated and optimized by using deep learning models. We compile a comprehensive non-redundant RBP dataset to train and test PST-PRNA using 10-fold cross-validation. Numerous experiments demonstrate PST-PRNA learns successfully the latent structural information of protein surface. On the non-redundant dataset with sequence identity of 0.3, PST-PRNA achieves area under the receiver operating characteristic curves (AUC) value of 0.860 and Matthew's correlation coefficient value of 0.420. Furthermore, we construct a completely independent test dataset for justification and comparison. PST-PRNA achieves AUC value of 0.913 on the independent dataset, which is superior to the other state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: The code and data are available at https://www.github.com/zpliulab/PST-PRNA. A web server is freely available at http://www.zpliulab.cn/PSTPRNA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Graph attention network for link prediction of gene regulations from single-cell RNA-sequencing data.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) data provides unprecedented opportunities to reconstruct gene regulatory networks (GRNs) at fine-grained resolution. Numerous unsupervised or self-supervised models have been proposed to infer GRN from bulk RNA-seq data, but few of them are appropriate for scRNA-seq data under the circumstance of low signal-to-noise ratio and dropout. Fortunately, the surging of TF-DNA binding data (e.g. ChIP-seq) makes supervised GRN inference possible. We regard supervised GRN inference as a graph-based link prediction problem that expects to learn gene low-dimensional vectorized representations to predict potential regulatory interactions. RESULTS: In this paper, we present GENELink to infer latent interactions between transcription factors (TFs) and target genes in GRN using graph attention network. GENELink projects the single-cell gene expression with observed TF-gene pairs to a low-dimensional space. Then, the specific gene representations are learned to serve for downstream similarity measurement or causal inference of pairwise genes by optimizing the embedding space. Compared to eight existing GRN reconstruction methods, GENELink achieves comparable or better performance on seven scRNA-seq datasets with four types of ground-truth networks. We further apply GENELink on scRNA-seq of human breast cancer metastasis and reveal regulatory heterogeneity of Notch and Wnt signalling pathways between primary tumour and lung metastasis. Moreover, the ontology enrichment results of unique lung metastasis GRN indicate that mitochondrial oxidative phosphorylation (OXPHOS) is functionally important during the seeding step of the cancer metastatic cascade, which is validated by pharmacological assays. AVAILABILITY AND IMPLEMENTATION: The code and data are available at https://github.com/zpliulab/GENELink. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Matricellular Protein Cilp1 Promotes Myocardial Fibrosis in Response to Myocardial Infarction.

[Figure: see text].

Regulation of toll-like receptor (TLR) signaling pathways in atherosclerosis: from mechanisms to targeted therapeutics.

Atherosclerosis, one of the life-threatening cardiovascular diseases (CVDs), has been demonstrated to be a chronic inflammatory disease, and inflammatory and immune processes are involved in the origin and development of the disease. Toll-like receptors (TLRs), a class of pattern recognition receptors that trigger innate immune responses by identifying pathogen-associated molecular patterns (PAMPs) and danger-associated molecular patterns (DAMPs), regulate numerous acute and chronic inflammatory diseases. Recent studies reveal that TLRs have a vital role in the occurrence and development of atherosclerosis, including the initiation of endothelial dysfunction, interaction of various immune cells, and activation of a number of other inflammatory pathways. We herein summarize some other inflammatory signaling pathways, protein molecules, and cellular responses associated with TLRs, such as NLRP3, Nrf2, PCSK9, autophagy, pyroptosis and necroptosis, which are also involved in the development of AS. Targeting TLRs and their regulated inflammatory events could be a promising new strategy for the treatment of atherosclerotic CVDs. Novel drugs that exert therapeutic effects on AS through TLRs and their related pathways are increasingly being developed. In this article, we comprehensively review the current knowledge of TLR signaling pathways in atherosclerosis and actively seek potential therapeutic strategies using TLRs as a breakthrough point in the prevention and therapy of atherosclerosis.

Block-based teaching method based on cybernetics: a trial with 115 Chinese undergraduate medical students.

BACKGROUND: Class attendance is important for academic performance. Personal interactions between teachers and students are difficult in large classes; the number of medical undergraduate students in China ranges from dozens to over 100. It is important for teachers to control the teaching process to improve student attendance and participation. METHODS: Two classes of fourth-year undergraduate medical students, with each class comprising 115 students, participated in the study. One class, the trial group, was taught by the block-based teaching method based on cybernetics. This study was conducted with three of the courses in the Introduction to Oncology subject, and the trial group's courses included several blocks. Each block had a test paper that the students responded to immediately in class using the Internet. The teacher obtained feedback from the students when the rate of correct responses to block-test questions was less than 90%. The teacher adjusted the teaching in the following blocks according to the feedback information. The other class, the control group, was taught using the traditional lecture-based teaching method. RESULTS: The average attendance in the trial group was 104/115 (90.43%), and that in the control group was 83/115 (72.17%) (p = 0.0003). The teacher adjusted the teaching three times in the radiotherapy course owing to the complex ideas. After feedback, information on chemotherapy for the upper body was adjusted once, as was that on chemotherapy for the lower body, owing to students' attitudes. The average total score of the trial group was 86.06 ± 17.46 and that of the control group was 80.38 ± 6.97 (p = 0.041). Questionnaire I showed that the trial group students' attendance and participation were better than in the control group. Questionnaire II showed that the block-based teaching method based on cybernetics was approved by the students. CONCLUSIONS: The block-based teaching method based on cybernetics used in medical classes with large numbers of Chinese undergraduate students had positive effects.

Identifying biomarkers for breast cancer by gene regulatory network rewiring.

BACKGROUND: Mining gene regulatory network (GRN) is an important avenue for addressing cancer mechanism. Mutations in cancer genome perturb GRN and cause a rewiring in an orchestrated network. Hence, the exploration of gene regulatory network rewiring is significant to discover potential biomarkers and indicators for discriminating cancer phenotypes. RESULTS: Here, we propose a new bioinformatics method of identifying biomarkers based on network rewiring in different states. It firstly reconstructs GRN in different phenotypic conditions from gene expression data with a priori background network. We employ the algorithm based on path consistency algorithm and conditional mutual information to delete false-positive regulatory interactions between independent nodes/genes or not closely related gene pairs. And then a differential gene regulatory network (D-GRN) is constructed from the rewiring parts in the two phenotype-specific GRNs. Community detection technique is then applied for D-GRN to detect functional modules. Finally, we apply logistic regression classifier with recursive feature elimination to select biomarker genes in each module individually. The extracted feature genes result in a gene set of biomarkers with impressing ability to distinguish normal samples from controls. We verify the identified biomarkers in external independent validation datasets. For a proof-of-concept study, we apply the framework to identify diagnostic biomarkers of breast cancer. The identified biomarkers obtain a maximum AUC of 0.985 in the internal sample classification experiments. And these biomarkers achieve a maximum AUC of 0.989 in the external validations. CONCLUSION: In conclusion, network rewiring reveals significant differences between different phenotypes, which indicating cancer dysfunctional mechanisms. With the development of sequencing technology, the amount and quality of gene expression data become available. Condition-specific gene regulatory networks that are close to the real regulations in different states will be established. Revealing the network rewiring will greatly benefit the discovery of biomarkers or signatures for phenotypes. D-GRN is a general method to meet this demand of deciphering the high-throughput data for biomarker discovery. It is also easy to be extended for identifying biomarkers of other complex diseases beyond breast cancer.

Automated machine learning for secure key rate in discrete-modulated continuous-variable quantum key distribution.

Continuous-variable quantum key distribution (CV QKD) with discrete modulation has attracted increasing attention due to its experimental simplicity, lower-cost implementation and compatibility with classical optical communication. Correspondingly, some novel numerical methods have been proposed to analyze the security of these protocols against collective attacks, which promotes key rates over one hundred kilometers of fiber distance. However, numerical methods are limited by their calculation time and resource consumption, for which they cannot play more roles on mobile platforms in quantum networks. To improve this issue, a neural network model predicting key rates in nearly real time has been proposed previously. Here, we go further and show a neural network model combined with Bayesian optimization. This model automatically designs the best architecture of neural network computing key rates in real time. We demonstrate our model with two variants of CV QKD protocols with quaternary modulation. The results show high reliability with secure probability as high as 99.15% - 99.59%, considerable tightness and high efficiency with speedup of approximately 107 in both cases. This inspiring model enables the real-time computation of unstructured quantum key distribution protocols' key rate more automatically and efficiently, which has met the growing needs of implementing QKD protocols on moving platforms.

A Case Report of Arteriovenous Fistula Penetrating into the Ascending Aorta Due to Right Internal Jugular Vein Placement.

Internal jugular vein placement is frequently utilized in clinical practice for rapid infusion, intraoperative monitoring, peritoneal dialysis, and access for interventions. Additionally, the process may lead to complications like hematoma, infection, misdirection of the artery, pneumothorax, and arteriovenous fistula. In the case described in this report, all vascular ruptures effectively were repaired because when internal jugular vein placement was adopted, a dialysis catheter would go through the right internal jugular vein into the subclavian artery, then the ascending aorta via the cephalic trunk, and finally the ectopic catheter would be surgically removed. The patient was released from the hospital on the seventh postoperative day after maintaining stable vital signs throughout the procedure.

Complete Thoracoscopic Resection of Left Ventricular Myxoma - A Case Report.

Cardiac myxoma is a common cardiac tumor. Most are found in the left cardiac system, of which 75% of myxomas are located in the left atrium [Pinede 2001], and the origin of the left ventricle is relatively rare. Surgical resection is the most effective method for the treatment of myxoma, but because of the complex anatomy of the left ventricle, most of the reported cases are performed through the traditional median thoracotomy through the ascending aorta and vena cava to establish cardiopulmonary bypass. It is rare to establish cardiopulmonary bypass through the femoral artery and femoral vein to remove left ventricular myxoma under complete video-assisted thoracoscopy. This paper reports the surgical process and perioperative echocardiographic, magnetic resonance, radiological and pathological features of a completely thoracoscopic resection of left ventricular myxoma.

Predicting Box-Office Markets with Machine Learning Methods.

The accurate prediction of gross box-office markets is of great benefit for investment and management in the movie industry. In this work, we propose a machine learning-based method for predicting the movie box-office revenue of a country based on the empirical comparisons of eight methods with diverse combinations of economic factors. Specifically, we achieved a prediction performance of the relative root mean squared error of 0.056 in the US and of 0.183 in China for the two case studies of movie markets in time-series forecasting experiments from 2013 to 2016. We concluded that the support-vector-machine-based method using gross domestic product reached the best prediction performance and satisfies the easily available information of economic factors. The computational experiments and comparison studies provided evidence for the effectiveness and advantages of our proposed prediction strategy. In the validation process of the predicted total box-office markets in 2017, the error rates were 0.044 in the US and 0.066 in China. In the consecutive predictions of nationwide box-office markets in 2018 and 2019, the mean relative absolute percentage errors achieved were 0.041 and 0.035 in the US and China, respectively. The precise predictions, both in the training and validation data, demonstrate the efficiency and versatility of our proposed method.

Detecting prognostic biomarkers of breast cancer by regularized Cox proportional hazards models.

BACKGROUND: The successful identification of breast cancer (BRCA) prognostic biomarkers is essential for the strategic interference of BRCA patients. Recently, various methods have been proposed for exploring a small prognostic gene set that can distinguish the high-risk group from the low-risk group. METHODS: Regularized Cox proportional hazards (RCPH) models were proposed to discover prognostic biomarkers of BRCA from gene expression data. Firstly, the maximum connected network with 1142 genes by mapping 956 differentially expressed genes (DEGs) and 677 previously BRCA-related genes into the gene regulatory network (GRN) was constructed. Then, the 72 union genes of the four feature gene sets identified by Lasso-RCPH, Enet-RCPH, [Formula: see text]-RCPH and SCAD-RCPH models were recognized as the robust prognostic biomarkers. These biomarkers were validated by literature checks, BRCA-specific GRN and functional enrichment analysis. Finally, an index of prognostic risk score (PRS) for BRCA was established based on univariate and multivariate Cox regression analysis. Survival analysis was performed to investigate the PRS on 1080 BRCA patients from the internal validation. Particularly, the nomogram was constructed to express the relationship between PRS and other clinical information on the discovery dataset. The PRS was also verified on 1848 BRCA patients of ten external validation datasets or collected cohorts. RESULTS: The nomogram highlighted that the importance of PRS in guiding significance for the prognosis of BRCA patients. In addition, the PRS of 301 normal samples and 306 tumor samples from five independent datasets showed that it is significantly higher in tumors than in normal tissues ([Formula: see text]). The protein expression profiles of the three genes, i.e., ADRB1, SAV1 and TSPAN14, involved in the PRS model demonstrated that the latter two genes are more strongly stained in tumor specimens. More importantly, external validation illustrated that the high-risk group has worse survival than the low-risk group ([Formula: see text]) in both internal and external validations. CONCLUSIONS: The proposed pipelines of detecting and validating prognostic biomarker genes for BRCA are effective and efficient. Moreover, the proposed PRS is very promising as an important indicator for judging the prognosis of BRCA patients.