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The tumor microenvironment (TME) is crucial in cancer development and therapeutic response. Immunotherapy is increasingly recognized as a critical component of cancer treatment. While immunotherapies have shown efficacy in various cancers, including breast cancer, patient responses vary widely. Some patients receive significant benefits, while others experience minimal or no improvement. This disparity underscores the complexity and diversity of the immune system. In this study, we investigated the immune landscape and cell-cell communication within the TME of breast cancer through integrated analysis of bulk and single-cell RNA sequencing data. We established profiles of tumor immune infiltration that span across a broad spectrum of adaptive and innate immune cells. Our clustering analysis of immune infiltration identified three distinct patient groups: high T cell abundance, moderate infiltration, and low infiltration. Patients with low immune infiltration exhibited the poorest survival rates, while those in the moderate infiltration group showed better outcomes than those with high T cell abundance. Moreover, the high cell abundance group was associated with a greater tumor burden and higher rates of TP53 mutations, whereas the moderate infiltration group was characterized by a lower tumor burden and elevated PIK3CA mutations. Analysis of an independent single-cell RNA-seq breast cancer dataset confirmed the presence of similar infiltration patterns. Further investigation into ligand-receptor interactions within the TME unveiled significant variations in cell-cell communication patterns among these groups. Notably, we found that the signaling pathways SPP1 and EGF were exclusively active in the low immune infiltration group, suggesting their involvement in immune suppression. This work comprehensively characterizes the composition and dynamic interplay in the breast cancer TME. Our findings reveal associations between the extent of immune infiltration and clinical outcomes, providing valuable prognostic information for patient stratification. The unique mutations and signaling pathways associated with different patient groups offer insights into the mechanisms underlying diverse tumor immune infiltration and the formation of an immunosuppressive tumor microenvironment.

Apoptosis induction with taxanes or anthracyclines is the primary therapy for TNBC. Cancer cells can develop resistance to anticancer drugs, causing them to recur and metastasize. Therefore, non-apoptotic cell death inducers could be a potential treatment to circumvent apoptotic drug resistance. In this study, we discovered two novel compounds, TPH104c and TPH104m, which induced non-apoptotic cell death in TNBC cells. These lead compounds were 15- to 30-fold more selective in TNBC cell lines and significantly decreased the proliferation of TNBC cells compared to that of normal mammary epithelial cell lines. TPH104c and TPH104m induced a unique type of non-apoptotic cell death, characterized by the absence of cellular shrinkage and the absence of nuclear fragmentation and apoptotic blebs. Although TPH104c and TPH104m induced the loss of the mitochondrial membrane potential, TPH104c- and TPH104m-induced cell death did not increase the levels of cytochrome c and intracellular reactive oxygen species (ROS) and caspase activation, and cell death was not rescued by incubating cells with the pan-caspase inhibitor, carbobenzoxy-valyl-alanyl-aspartyl-[O-methyl]-fluoromethylketone (Z-VAD-FMK). Furthermore, TPH104c and TPH104m significantly downregulated the expression of the mitochondrial fission protein, DRP1, and their levels determined their cytotoxic efficacy. Overall, TPH104c and TPH104m induced non-apoptotic cell death, and further determination of their cell death mechanisms will aid in the development of new potent and efficacious anticancer drugs to treat TNBC.

The advent of single-cell RNA sequencing (scRNA-seq) technology has revolutionized our ability to explore cellular diversity and unravel the complexities of intricate diseases. However, due to the inherently low signal-to-noise ratio and the presence of an excessive number of missing values, scRNA-seq data analysis encounters unique challenges. Here, we present cnnImpute, a novel convolutional neural network (CNN) based method designed to address the issue of missing data in scRNA-seq. Our approach starts by estimating missing probabilities, followed by constructing a CNN-based model to recover expression values with a high likelihood of being missing. Through comprehensive evaluations, cnnImpute demonstrates its effectiveness in accurately imputing missing values while preserving the integrity of cell clusters in scRNA-seq data analysis. It achieved superior performance in various benchmarking experiments. cnnImpute offers an accurate and scalable method for recovering missing values, providing a useful resource for scRNA-seq data analysis., (© 2024. The Author(s).)

Fluvoxamine is a selective serotonin reuptake inhibitor commonly used for various types of depression. The purpose of this study was to evaluate the pharmacokinetics and bioequivalence of fluvoxamine maleate tablets orally on an empty stomach and after a meal in healthy adult Chinese subjects and to preliminarily evaluate their safety. A single-center, randomized, open-label, two-drug, two-period, crossover, single-dose trial protocol was designed. Sixty healthy Chinese participants were enrolled and randomly classified into fasting (n = 30) and fed groups (n = 30). Each week, subjects took fluvoxamine maleate tablets 50 mg orally once as a test preparation or as a reference preparation on an empty stomach/after meals. To evaluate the bioequivalence of test and reference tables, the concentration of fluvoxamine maleate in the plasma of the subjects at different time points after administration was detected by liquid chromatography-tandem mass spectrometry, and pharmacokinetic parameters including the maximum plasma drug concentration (C max ), the time to reach maximum concentration (T max ), the area under the plasma concentration-time curve from time 0 to the last measurable concentration (AUC 0-t ) and the area under the plasma concentration-time curve from time 0 to infinity (AUC 0-∞ ) were calculated. Our data revealed that the 90% confidence intervals of the geometric mean ratio of the test or reference drugs for the C max , AUC 0-t and AUC 0-∞ fell within the acceptance range for bioequivalence (92.30-102.77%). The absorption, measured by AUC, did not show a significant difference between the two groups. There were no suspected serious adverse reactions or serious adverse events over the entire trial. Our results demonstrated that the test and reference tablets were bioequivalent under fasting and fed conditions., (© 2023 John Wiley & Sons Ltd.)

Background: Glioblastoma is the most common and most aggressive type of primary brain tumor., Objective: The aim of this study was to investigate the efficacy and safety of intranasal granulocyte-macrophage colony stimulating factor (GM-CSF) administration combined with chemoradiotherapy in patients with glioblastoma who underwent surgery., Methods: Ninety-two patients were randomly divided into two groups: a control group (n= 46), who received radiotherapy with adjuvant local delivery of nimustine hydrochloride (ACNU) and systemic administration of temozolomide, and an intervention group (n= 46), who received intranasal GM-CSF prior to each cycle of adjuvant chemotherapy in addition to the treatment of the control group. Karnofsky performance status (KPS) scores, progression-free survival (PFS), overall survival (OS), and adverse effects were calculated and compared between the two groups., Results: Compared with the control group, the intervention group had longer PFS (7.8 vs. 6.9 months, P= 0.016) and OS (19.2 vs. 17.1 months, P= 0.045, without adjustment for interim analyses). The KPS scores were also higher in the intervention group than in the control group after 6 months (84.35 ± 8.86 vs. 80.65 ± 7.72; t= 4.552, P= 0.036). Furthermore, the patients in the intervention group had lower incidence of neutropenia and thrombocytopenia (8.7% vs. 29.5%, P= 0.012; 8.7% vs. 18.2%, P= 0.186). Other adverse events were similar in both groups, and most adverse events were grade I/II and resolved spontaneously., Conclusion: Intranasal GM-CSF enhances the efficacy of the local ACNU administration combined with oral temozolomide chemotherapy. The survival and performance status were significantly improved in patients with glioblastoma after surgery. Additionally, the GM-CSF therapy was able to reduce the occurrence of chemotherapy-related neutropenia and thrombocytopenia.

Chronic myeloid leukemia (CML) is a myeloproliferative disease characterized by a unique BCR-ABL fusion gene. Tyrosine kinase inhibitors (TKIs) were developed to target the BCR-ABL oncoprotein, inhibiting its abnormal kinase activity. TKI treatments have significantly improved CML patient outcomes. However, the patients can develop drug resistance and relapse after therapy discontinues largely due to intratumor heterogeneity. It is critical to understand the differences in therapeutic responses among subpopulations of cells. Single-cell RNA sequencing measures the transcriptome of individual cells, allowing us to differentiate and analyze individual cell populations. Here, we integrated a single-cell RNA sequencing profile of CML stem cells and network analysis to decipher the mechanisms of distinct TKI responses. Compared to normal hematopoietic stem cells, a set of genes that were concordantly differentially expressed in various types of stem cells of CML patients was revealed. Further transcription regulatory network analysis found that most of these genes were directly controlled by one or more transcript factors and the genes have more regulators in the cells of the patients who responded to the treatment. The molecular markers including a known drug-resistance gene and novel gene signatures for treatment response were also identified. Moreover, we combined protein-protein interaction network construction with a cancer drug database and uncovered the drugs that target the marker genes directly or indirectly via the protein interactions. The gene signatures and their interacted proteins identified by this work can be used for treatment response prediction and lead to new strategies for drug resistance monitoring and prevention. Our single-cell-based findings offered novel insights into the mechanisms underlying the therapeutic response of CML.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that impedes patients' cognition, social, speech and communication skills. ASD is highly heterogeneous with a variety of etiologies and clinical manifestations. The prevalence rate of ASD increased steadily in recent years. Presently, molecular mechanisms underlying ASD occurrence and development remain to be elucidated. Here, we integrated multi-layer genomics data to investigate the transcriptome and pathway dysregulations in ASD development. The RNA sequencing (RNA-seq) expression profiles of induced pluripotent stem cells (iPSCs), neural progenitor cells (NPCs) and neuron cells from ASD and normal samples were compared in our study. We found that substantially more genes were differentially expressed in the NPCs than the iPSCs. Consistently, gene set variation analysis revealed that the activity of the known ASD pathways in NPCs and neural cells were significantly different from the iPSCs, suggesting that ASD occurred at the early stage of neural system development. We further constructed comprehensive brain- and neural-specific regulatory networks by incorporating transcription factor (TF) and gene interactions with long 5 non-coding RNA(lncRNA) and protein interactions. We then overlaid the transcriptomes of different cell types on the regulatory networks to infer the regulatory cascades. The variations of the regulatory cascades between ASD and normal samples uncovered a set of novel disease-associated genes and gene interactions, particularly highlighting the functional roles of ELF3 and the interaction between STAT1 and lncRNA ELF3-AS 1 in the disease development. These new findings extend our understanding of ASD and offer putative new therapeutic targets for further studies.

Salivary secretions play critical roles in interactions among insects, insect-vectored pathogens, and host plants. The Asian citrus psyllid Diaphorina citri is a sap-sucking Hemipteran that serves as a vector for Candidatus Liberibacter asiaticus, the causal agent of citrus greening disease ("Huanglongbing" or HLB). D. citri continuously injects saliva into host plants using specialized stylets so as to feed and transmit the HLB pathogen. Knowledge on the composition and function of salivary proteins of this pest is very limited. In this study, proteomic and transcriptomic approaches were adopted to characterize the protein composition of the saliva and salivary glands in D. citri. A total of 246 and 483 proteins were identified in saliva and dissected salivary glands, respectively, via LC-MS/MS analyses. Comparative analyses of the identified proteins were performed between D. citri and other reported Hemipteran insect species. Transcription levels of the genes coding for the identified proteins were determined via RNA-sequencing among different tissues including salivary glands and other digestive tissues. Identification of putative effectors that are expressed exclusively or abundantly in salivary glands provides the foundation for future functional studies towards the understanding of their roles in interactions among D. citri, HLB pathogen, and their citrus host. BIOLOGICAL SIGNIFICANCE: This is a systematic analysis on proteins in saliva and dissected salivary glands. A high percentage of novel proteins have been identified due to the large amounts of samples collected. This report gives a more comprehensive repertoire of potential effector proteins that may be possibly involved in modulating host defense, altering nutrient metabolism, and facilitating Ca. L. asiaticus transmission., (Copyright © 2021. Published by Elsevier B.V.)

Alzheimer's disease (AD) is a progressive neurodegenerative disorder, accounting for nearly 60% of all dementia cases. The occurrence of the disease has been increasing rapidly in recent years. Presently about 46.8 million individuals suffer from AD worldwide. The current absence of effective treatment to reverse or stop AD progression highlights the importance of disease prevention and early diagnosis. Brain structural Magnetic Resonance Imaging (MRI) has been widely used for AD detection as it can display morphometric differences and cerebral structural changes. In this study, we built three machine learning-based MRI data classifiers to predict AD and infer the brain regions that contribute to disease development and progression. We then systematically compared the three distinct classifiers, which were constructed based on Support Vector Machine (SVM), 3D Very Deep Convolutional Network (VGGNet) and 3D Deep Residual Network (ResNet), respectively. To improve the performance of the deep learning classifiers, we applied a transfer learning strategy. The weights of a pre-trained model were transferred and adopted as the initial weights of our models. Transferring the learned features significantly reduced training time and increased network efficiency. The classification accuracy for AD subjects from elderly control subjects was 90%, 95%, and 95% for the SVM, VGGNet and ResNet classifiers, respectively. Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to show discriminative regions that contributed most to the AD classification by utilizing the learned spatial information of the 3D-VGGNet and 3D-ResNet models. The resulted maps consistently highlighted several disease-associated brain regions, particularly the cerebellum which is a relatively neglected brain region in the present AD study. Overall, our comparisons suggested that the ResNet model provided the best classification performance as well as more accurate localization of disease-associated regions in the brain compared to the other two approaches., Competing Interests: The authors declare there are no competing interests., (©2021 Li and Yang.)

The rice bug, Leptocorisa acuta (Tunberg) (Hemiptera: Alydidae), is a notorious pest in Asia, and it is significantly attracted by the volatiles derived from host plants. However, it remains unknown how L. acuta recognizes host volatile compounds at the molecular level. Odorant-binding proteins (OBPs) and chemosensory proteins (CSPs) are thought to be responsible for the initial biochemical recognition during olfactory perception. Here, we followed the RNA Sequencing (RNA-Seq) approach to identify candidate genes encoding OBPs and CSPs from dissected antennae and mouthparts of L. acuta. In total, 26 unigenes were identified coding for OBPs (22 Classic OBPs and four Plus-C OBPs), and 17 unigenes coding for CSPs. Real-time quantitative PCR (RT-qPCR) revealed that 11 OBPs (LacuOBP1, 5, 6, 7, 9, 11, 13, 14, 17, 20 and 23) and nine CSPs (LacuCSP2, 3, 4, 5, 6, 8, 9, 10 and 12) were predominantly expressed in antennae, indicating that they might be essential for detection of general odorants and pheromones. Among these antennae-predominantly expressed genes, LacuOBP11 and LacuOBP13 showed male-biased expression and therefore may play crucial roles in the detection of sex pheromones. Seven LacuOBPs (LacuOBP4, 8, 10, 12, 21, 25 and 26) and two CSPs (LacuCSP7 and LacuOBP11) were predominantly expressed in mouthparts, suggesting that these genes might be involved in taste perception. Our work provides a starting point to facilitate functional study of these OBPs and CSPs in L. acuta at the molecular level in the future., Competing Interests: Declaration of competing interest The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted., (Copyright © 2019 Elsevier Inc. All rights reserved.)

The southern green stink bug Nezara viridula (Hemiptera: Pentatomidae) is a highly polyphagous pest that can significantly impact many major crops worldwide. Insect odorant binding proteins (OBPs) and chemosensory proteins (CSPs) transport chemicals and play critical roles in chemoreception. Studies on N. viridula OBPs and CSPs should increase our overall understandings on chemosensory systems and chemical ecology of stink bugs, which may lead to improved pest control. In this study, we identified candidate genes encoding putative OBPs and CSPs in N. viridula by generating transcriptomes from dissected antennae and mouthparts. In total, the 42 unigenes were identified coding for OBPs (34 Classic OBPs and eight Plus-C OBPs) and 13 unigenes coding for CSPs. Expression profiles of OBP- and CSP -encoding genes were compared between antennae and mouthparts based on FKPM values. Candidates for antenna-predominant OBPs and CSPs were selected for real-time quantitative PCR analyses. Analyses of tissue expression profiles revealed that 17 OBP-encoding genes, and four CSP genes were primarily expressed in antennae, suggesting their putative roles in perception of volatiles. The sex-biased expression patterns of these antenna-predominant genes suggested that they may have important functions in reproduction of the insect. This is a systematic analysis on OBPs and CSPs in a stink bug, providing a comprehensive resource for future functional studies not only for N. viridula, but also for other stink bugs as well., (Copyright © 2019. Published by Elsevier Inc.)

Spiroacetals are widespread in nature as components of volatile semiochemical secretions from many insect species. The general pathway for spiroacetal biosynthesis in Bactrocera sp. is preliminarily established, but many genes involved in this pathway remain to be characterized. By analyzing transcriptomes of the rectal glands (RGs) from immature and mature females of the oriental fruit fly, Bactrocera dorsalis, a set of genes encoding two acetyl-CoA carboxylases (ACCs), two fatty acid synthases (FASs), eight desaturases (DESs), twelve fatty acyl-CoA reductases (FARs), seventy-two cytochrome P450 enzymes (CYPs), and twenty-three odorant binding proteins (OBPs) were identified. We investigated the expression of candidate genes in immature and mature stages based on the RNA-seq data and Real-time quantitative PCR. Expression profiling revealed that some of these genes were primarily expressed in female rectal glands among different tissues, and were up-regulated in mature females. Semi-quantitative RT-PCR assays were also adapted to examine tissue-specific expression of selected candidate genes. Additionally, their putative functions in spiroacetal synthesis and transportation are proposed. Our study provided large-scale sequence information for further functional studies on spiroacetal biosynthetic pathways., (Copyright © 2019. Published by Elsevier Inc.)

Automatically describing contents of an image using natural language has drawn much attention because it not only integrates computer vision and natural language processing but also has practical applications. Using an end-to-end approach, we propose a bidirectional semantic attention-based guiding of long short-term memory (Bag-LSTM) model for image captioning. The proposed model consciously refines image features from previously generated text. By fine-tuning the parameters of convolution neural networks, Bag-LSTM obtains more text-related image features via feedback propagation than other models. As opposed to existing guidance-LSTM methods which directly add image features into each unit of an LSTM block, our fine-tuned model dynamically leverages more text-conditional image features, acquired by the semantic attention mechanism, as guidance information. Moreover, we exploit bidirectional gLSTM as the caption generator, which is capable of learning long term relations between visual features and semantic information by making use of both historical and future contextual information. In addition, variations of the Bag-LSTM model are proposed in an effort to sufficiently describe high-level visual-language interactions. Experiments on the Flickr8k and MSCOCO benchmark datasets demonstrate the effectiveness of the model, as compared with the baseline algorithms, such as it is 51.2% higher than BRNN on CIDEr metric.

With the massive volume and rapid increasing of data, feature space study is of great importance. To avoid the complex training processes in deep learning models which project original feature space into low-dimensional ones, we propose a novel feature space learning (FSL) model. The main contributions in our approach are: (1) FSL can not only select useful features but also adaptively update feature values and span new feature spaces; (2) four FSL algorithms are proposed with the feature space updating procedure; (3) FSL can provide a better data understanding and learn descriptive and compact feature spaces without the tough training for deep architectures. Experimental results on benchmark data sets demonstrate that FSL-based algorithms performed better than the classical unsupervised, semi-supervised learning and even incremental semi-supervised algorithms. In addition, we show a visualization of the learned feature space results. With the carefully designed learning strategy, FSL dynamically disentangles explanatory factors, depresses the noise accumulation and semantic shift, and constructs easy-to-understand feature spaces.

Malignant gliomas are aggressive primary neoplasms that originate in the glial cells of the brain or the spine with notable resistance to standard treatment options. We carried out the study with the aim to shed light on the sensitization of resveratrol to temozolomide (TMZ) against glioma through the Wnt signaling pathway. Initially, glioma cell lines with strong resistance to TMZ were selected by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Then, the glioma cells were subjected to resveratrol, TMZ, Wnt signaling pathway inhibitors, and activators. Cell survival rate and inhibitory concentration at half maximum value were detected by MTT, apoptosis by flow cytometry, and terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling staining, in vitro proliferation by hanging drop method and β-catenin translocation into nuclei by TOP/FOP-FLASH assay. The expressions of the Wnt signaling pathway-related and apoptosis-related factors were determined by western blot analysis. Nude mice with glioma xenograft were established to detect tumorigenic ability. Glioma cell lines T98G and U138 which were highly resistant to TMZ were selected for subsequent experiments. Resveratrol increased the efficacy of TMZ by restraining cell proliferation, tumor growth, and promoting cell apoptosis in glioma cells. Resveratrol inhibited Wnt2 and β-catenin expressions yet elevated GSK-3β expression. Moreover, the Wnt signaling pathway participates in the sensitivity enhancing of resveratrol to TMZ via regulating O 6 -methylguanine-DNA methyltransferase (MGMT) expression. Resveratrol sensitized TMZ-induced glioma cell apoptosis by repressing the activation of the Wnt signaling pathway and downregulating MGMT expression, which may confer new thoughts to the chemotherapy of glioma., (© 2018 Wiley Periodicals, Inc.)

Background: Adenocarcinoma in situ (AIS) is a pre-invasive lesion in the lung and a subtype of lung adenocarcinoma. The patients with AIS can be cured by resecting the lesion completely. In contrast, the patients with invasive lung adenocarcinoma have very poor 5-year survival rate. AIS can develop into invasive lung adenocarcinoma. The investigation and comparison of AIS and invasive lung adenocarcinoma at the genomic level can deepen our understanding of the mechanisms underlying lung cancer development., Results: In this study, we identified 61 lung adenocarcinoma (LUAD) invasive-specific differentially expressed genes, including nine long non-coding RNAs (lncRNAs) based on RNA sequencing techniques (RNA-seq) data from normal, AIS, and invasive tissue samples. These genes displayed concordant differential expression (DE) patterns in the independent stage III LUAD tissues obtained from The Cancer Genome Atlas (TCGA) RNA-seq dataset. For individual invasive-specific genes, we constructed subnetworks using the Genetic Algorithm (GA) based on protein-protein interactions, protein-DNA interactions and lncRNA regulations. A total of 19 core subnetworks that consisted of invasive-specific genes and at least one putative lung cancer driver gene were identified by our study. Functional analysis of the core subnetworks revealed their enrichment in known pathways and biological progresses responsible for tumor growth and invasion, including the VEGF signaling pathway and the negative regulation of cell growth., Conclusions: Our comparison analysis of invasive cases, normal and AIS uncovered critical genes that involved in the LUAD invasion progression. Furthermore, the GA-based network method revealed gene clusters that may function in the pathways contributing to tumor invasion. The interactions between differentially expressed genes and putative driver genes identified through the network analysis can offer new targets for preventing the cancer invasion and potentially increase the survival rate for cancer patients.

Background: Nowadays, because of the huge economic burden on society causing by obesity and diabetes, they turn into the most serious public health challenges in the world. To reveal the close and complex relationships between diabetes, obesity and other diseases, search the effective treatment for them, a novel model named as representative latent Dirichlet allocation (RLDA) topic model is presented., Results: RLDA was applied to a corpus of more than 337,000 literatures of diabetes and obesity which were published from 2007 to 2016. To unveil those meaningful relationships between diabetes mellitus, obesity and other diseases, we performed an explicit analysis on the output of our model with a series of visualization tools. Then, with the clinical reports which were not used in the training data to show the credibility of our discoveries, we find that a sufficient number of these records are matched directly. Our results illustrate that in the last 10 years, for obesity accompanying diseases, scientists and researchers mainly focus on 17 of them, such as asthma, gastric disease, heart disease and so on; for the study of diabetes mellitus, it features a more broad scope of 26 diseases, such as Alzheimer's disease, heart disease and so forth; for both of them, there are 15 accompanying diseases, listed as following: adrenal disease, anxiety, cardiovascular disease, depression, heart disease, hepatitis, hypertension, hypothalamic disease, respiratory disease, myocardial infarction, OSAS, liver disease, lung disease, schizophrenia, tuberculosis. In addition, tumor necrosis factor, tumor, adolescent obesity or diabetes, inflammation, hypertension and cell are going be the hot topics related to diabetes mellitus and obesity in the next few years., Conclusions: With the help of RLDA, the hotspots analysis-relation discovery results on diabetes and obesity were achieved. We extracted the significant relationships between them and other diseases such as Alzheimer's disease, heart disease and tumor. It is believed that the new proposed representation learning algorithm can help biomedical researchers better focus their attention and optimize their research direction.

Background: Single-cell RNA sequencing (scRNA-seq) technology provides an effective way to study cell heterogeneity. However, due to the low capture efficiency and stochastic gene expression, scRNA-seq data often contains a high percentage of missing values. It has been showed that the missing rate can reach approximately 30% even after noise reduction. To accurately recover missing values in scRNA-seq data, we need to know where the missing data is; how much data is missing; and what are the values of these data., Methods: To solve these three problems, we propose a novel model with a hybrid machine learning method, namely, missing imputation for single-cell RNA-seq (MISC). To solve the first problem, we transformed it to a binary classification problem on the RNA-seq expression matrix. Then, for the second problem, we searched for the intersection of the classification results, zero-inflated model and false negative model results. Finally, we used the regression model to recover the data in the missing elements., Results: We compared the raw data without imputation, the mean-smooth neighbor cell trajectory, MISC on chronic myeloid leukemia data (CML), the primary somatosensory cortex and the hippocampal CA1 region of mouse brain cells. On the CML data, MISC discovered a trajectory branch from the CP-CML to the BC-CML, which provides direct evidence of evolution from CP to BC stem cells. On the mouse brain data, MISC clearly divides the pyramidal CA1 into different branches, and it is direct evidence of pyramidal CA1 in the subpopulations. In the meantime, with MISC, the oligodendrocyte cells became an independent group with an apparent boundary., Conclusions: Our results showed that the MISC model improved the cell type classification and could be instrumental to study cellular heterogeneity. Overall, MISC is a robust missing data imputation model for single-cell RNA-seq data.

Background: Autism Spectrum Disorder (ASD) is the umbrella term for a group of neurodevelopmental disorders convergent on behavioral phenotypes. While many genes have been implicated in the disorder, the predominant focus of previous research has been on protein coding genes. This leaves a vast number of long non-coding RNAs (lncRNAs) not characterized for their role in the disorder although lncRNAs have been shown to play important roles in development and are highly represented in the brain. Studies have also shown lncRNAs to be differentially expressed in ASD affected brains. However, there has yet to be an enrichment analysis of the shared ontologies and pathways of known ASD genes and lncRNAs in normal brain development., Results: In this study, we performed co-expression network analysis on the developing brain transcriptome to identify potential lncRNAs associated with ASD and possible annotations for functional role of lncRNAs in brain development. We found co-enrichment of lncRNA genes and ASD risk genes in two distinct groups of modules showing elevated prenatal and postnatal expression patterns, respectively. Further enrichment analysis of the module groups indicated that the early expression modules were comprised mainly of transcriptional regulators while the later expression modules were associated with synapse formation. Finally, lncRNAs were prioritized for their connectivity with the known ASD risk genes through analysis of an adjacency matrix. Collectively, the results imply early developmental repression of synaptic genes through lncRNAs and ASD transcriptional regulators., Conclusion: Here we demonstrate the utility of mining the publically available brain gene expression data to further functionally annotate the role of lncRNAs in ASD. Our analysis indicates that lncRNAs potentially have a key role in ASD due to their convergence on shared pathways, and we identify lncRNAs of interest that may lead to further avenues of study.

Background: Breast cancer is the most common type of invasive cancer in woman. It accounts for approximately 18% of all cancer deaths worldwide. It is well known that somatic mutation plays an essential role in cancer development. Hence, we propose that a prognostic prediction model that integrates somatic mutations with gene expression can improve survival prediction for cancer patients and also be able to reveal the genetic mutations associated with survival., Method: Differential expression analysis was used to identify breast cancer related genes. Genetic algorithm (GA) and univariate Cox regression analysis were applied to filter out survival related genes. DAVID was used for enrichment analysis on somatic mutated gene set. The performance of survival predictors were assessed by Cox regression model and concordance index(C-index)., Results: We investigated the genome-wide gene expression profile and somatic mutations of 1091 breast invasive carcinoma cases from The Cancer Genome Atlas (TCGA). We identified 118 genes with high hazard ratios as breast cancer survival risk gene candidates (log rank p <  0.0001 and c-index = 0.636). Multiple breast cancer survival related genes were found in this gene set, including FOXR2, FOXD1, MTNR1B and SDC1. Further genetic algorithm (GA) revealed an optimal gene set consisted of 88 genes with higher c-index (log rank p <  0.0001 and c-index = 0.656). We validated this gene set on an independent breast cancer data set and achieved a similar performance (log rank p <  0.0001 and c-index = 0.614). Moreover, we revealed 25 functional annotations, 15 gene ontology terms and 14 pathways that were significantly enriched in the genes that showed distinct mutation patterns in the different survival risk groups. These functional gene sets were used as new features for the survival prediction model. In particular, our results suggested that the Fanconi anemia pathway had an important role in breast cancer prognosis., Conclusions: Our study indicated that the expression levels of the gene signatures remain the effective indicators for breast cancer survival prediction. Combining the gene expression information with other types of features derived from somatic mutations can further improve the performance of survival prediction. The pathways that were associated with survival risk suggested by our study can be further investigated for improving cancer patient survival.

Background: Non-small cell lung cancer (NSCLC) represents more than about 80% of the lung cancer. The early stages of NSCLC can be treated with complete resection with a good prognosis. However, most cases are detected at late stage of the disease. The average survival rate of the patients with invasive lung cancer is only about 4%. Adenocarcinoma in situ (AIS) is an intermediate subtype of lung adenocarcinoma that exhibits early stage growth patterns but can develop into invasion., Methods: In this study, we used RNA-seq data from normal, AIS, and invasive lung cancer tissues to identify a gene module that represents the distinguishing characteristics of AIS as AIS-specific genes. Two differential expression analysis algorithms were employed to identify the AIS-specific genes. Then, the subset of the best performed AIS-specific genes for the early lung cancer prediction were selected by random forest. Finally, the performances of the early lung cancer prediction were assessed using random forest, support vector machine (SVM) and artificial neural networks (ANNs) on four independent early lung cancer datasets including one tumor-educated blood platelets (TEPs) dataset., Results: Based on the differential expression analysis, 107 AIS-specific genes that consisted of 93 protein-coding genes and 14 long non-coding RNAs (lncRNAs) were identified. The significant functions associated with these genes include angiogenesis and ECM-receptor interaction, which are highly related to cancer development and contribute to the smoking-free lung cancers. Moreover, 12 of the AIS-specific lncRNAs are involved in lung cancer progression by potentially regulating the ECM-receptor interaction pathway. The feature selection by random forest identified 20 of the AIS-specific genes as early stage lung cancer signatures using the dataset obtained from The Cancer Genome Atlas (TCGA) lung adenocarcinoma samples. Of the 20 signatures, two were lncRNAs, BLACAT1 and CTD-2527I21.15 which have been reported to be associated with bladder cancer, colorectal cancer and breast cancer. In blind classification for three independent tissue sample datasets, these signature genes consistently yielded about 98% accuracy for distinguishing early stage lung cancer from normal cases. However, the prediction accuracy for the blood platelets samples was only 64.35% (sensitivity 78.1%, specificity 50.59%, and AUROC 0.747)., Conclusions: The comparison of AIS with normal and invasive tumor revealed diseases-specific genes and offered new insights into the mechanism underlying AIS progression into an invasive tumor. These genes can also serve as the signatures for early diagnosis of lung cancer with high accuracy. The expression profile of gene signatures identified from tissue cancer samples yielded remarkable early cancer prediction for tissues samples, however, relatively lower accuracy for boold platelets samples.

After publication of the original article [1], it was noticed that the Acknowledgement statement was incorrect. The original statement reads.

The authors wish to make the following change to their paper [...].

The war on cancer is progressing globally but slowly as researchers around the world continue to seek and discover more innovative and effective ways of curing this catastrophic disease. Organizing biological information, representing it, and making it accessible, or biocuration, is an important aspect of biomedical research and discovery. However, because maintaining sophisticated biocuration is highly resource dependent, it continues to lag behind the continually being generated biomedical data. Another critical aspect of cancer research, pathway analysis, has proven to be an efficient method for gaining insight into the underlying biology associated with cancer. We propose a deep-learning-based model, Stacked Denoising Autoencoder Multi-Label Learning (SdaMLL), for facilitating gene multi-function discovery and pathway completion. SdaMLL can capture intermediate representations robust to partial corruption of the input pattern and generate low-dimensional codes superior to conditional dimension reduction tools. Experimental results indicate that SdaMLL outperforms existing classical multi-label algorithms. Moreover, we found some gene functions, such as Fused in Sarcoma (FUS, which may be part of transcriptional misregulation in cancer) and p27 (which we expect will become a member viral carcinogenesis), that can be used to complete the related pathways. We provide a visual tool ( https://www.keaml.cn/gpvisual ) to view the new gene functions in cancer pathways.

Lung cancer is the second most commonly diagnosed carcinoma and is the leading cause of cancer death. Although significant progress has been made towards its understanding and treatment, unraveling the complexities of lung cancer is still hampered by a lack of comprehensive knowledge on the mechanisms underlying the disease. High-throughput and multidimensional genomic data have shed new light on cancer biology. In this study, we developed a network-based approach integrating somatic mutations, the transcriptome, DNA methylation, and protein-DNA interactions to reveal the key regulators in lung adenocarcinoma (LUAD). By combining Bayesian network analysis with tissue-specific transcription factor (TF) and targeted gene interactions, we inferred 15 disease-related core regulatory networks in co-expression gene modules associated with LUAD. Through target gene set enrichment analysis, we identified a set of key TFs, including known cancer genes that potentially regulate the disease networks. These TFs were significantly enriched in multiple cancer-related pathways. Specifically, our results suggest that hepatitis viruses may contribute to lung carcinogenesis, highlighting the need for further investigations into the roles that viruses play in treating lung cancer. Additionally, 13 putative regulatory long non-coding RNAs (lncRNAs), including three that are known to be associated with lung cancer, and nine novel lncRNAs were revealed by our study. These lncRNAs and their target genes exhibited high interaction potentials and demonstrated significant expression correlations between normal lung and LUAD tissues. We further extended our study to include 16 solid-tissue tumor types and determined that the majority of these lncRNAs have putative regulatory roles in multiple cancers, with a few showing lung-cancer specific regulations. Our study provides a comprehensive investigation of transcription factor and lncRNA regulation in the context of LUAD regulatory networks and yields new insights into the regulatory mechanisms underlying LUAD. The novel key regulatory elements discovered by our research offer new targets for rational drug design and accompanying therapeutic strategies., Competing Interests: The authors declare that they have no competing interests.

Background: Glioblastoma (GBM) is one of the worst cancers with bad prognosis despite systemic chemotherapy and radiotherapy after surgery., Methods: In this study, 71 patients with GBM were enrolled and randomly assigned to two groups: Receiving radiotherapy with concomitant and adjuvant temozolomide (TMZ) (TMZ, standard therapy) after surgery, or receiving radiotherapy with concomitant and adjuvant local delivery of nimustine (ACNU) rendezvousing with oral TMZ (rendezvous therapy). In the follow-up of all patients and the progression-free survival (PFS), overall survival (OS), Karnofsky performance score (KPS) and toxicities were recorded., Results: For the whole cohort, the median OS was 18.0 months, and the median PFS was 7.8 months. A significantly longer OS was observed in patients received rendezvous therapy than those who receiving standard therapy (18.5 months vs. 16.0 months; P = 0.014), as well as PFS (8.8 months vs. 7.0 months; P = 0.008). The KPS ≥70 rates were 81.8%, 40.9%, 20.5% in 1, 2, and 3 years for the rendezvous therapy group, significantly superior to standard therapy group. The most common toxicities were tolerable gastrointestinal reaction, liver dysfunction, and hematological toxicities, which were relieved with symptomatic treatment. Grade 3 or 4 toxicity was documented in 8 (18.3%) patients in rendezvous therapy group, while it was observed in 6 (22.2%) patients in standard therapy group during whole treatment process., Conclusions: Compared to standard therapy, the antitumor effects of rendezvous therapy were more effective in GBM patients without increasing the toxicities., Competing Interests: There are no conflicts of interest.

Background: Long noncoding RNAs (lncRNAs) are involved in diverse biological processes and play an essential role in various human diseases. The number of lncRNAs identified has increased rapidly in recent years owing to RNA sequencing (RNA-Seq) technology. However, presently, most lncRNAs are not well characterized, and their regulatory mechanisms remain elusive. Many lncRNAs show poor evolutionary conservation. Thus, the lncRNAs that are conserved across species can provide insight into their critical functional roles., Results: Here, we performed an orthologous analysis of lncRNAs in human and rat brain tissues. Over two billion RNA-Seq reads generated from 80 human and 66 rat brain tissue samples were analyzed. Our analysis revealed a total of 351 conserved human lncRNAs corresponding to 646 rat lncRNAs. Among these human lncRNAs, 140 were newly identified by our study, and 246 were present in known lncRNA databases; however, the majority of the lncRNAs that have been identified are not yet functionally annotated. We constructed co-expression networks based on the expression profiles of conserved human lncRNAs and protein-coding genes, and produced 79 co-expression modules. Gene ontology (GO) analysis of the co-expression modules suggested that the conserved lncRNAs were involved in various functions such as brain development (P-value = 1.12E-2), nervous system development (P-value = 1.26E-3), and cerebral cortex development (P-value = 1.31E-2). We further predicted the interactions between lncRNAs and protein-coding genes to better understand the regulatory mechanisms of lncRNAs. Moreover, we investigated the expression patterns of the conserved lncRNAs at different time points during rat brain growth. We found that the expression levels of three out of four such lncRNA genes continuously increased from week 2 to week 104, which is consistent with our functional annotation., Conclusion: Our orthologous analysis of lncRNAs in human and rat brain tissues revealed a set of conserved lncRNAs. Further expression analysis provided the functional annotation of these lncRNAs in humans and rats. Our results offer new targets for developing better experimental designs to investigate regulatory molecular mechanisms of lncRNAs and the roles lncRNAs play in brain development. Additionally, our method could be generalized to study and characterize lncRNAs conserved in other species and tissue types.

Clear cell renal cell carcinoma (ccRCC) is the most common and most aggressive form of renal cell cancer (RCC). The incidence of RCC has increased steadily in recent years. The pathogenesis of renal cell cancer remains poorly understood. Many of the tumor suppressor genes, oncogenes, and dysregulated pathways in ccRCC need to be revealed for improvement of the overall clinical outlook of the disease. Here, we developed a systems biology approach to prioritize the somatic mutated genes that lead to dysregulation of pathways in ccRCC. The method integrated multi-layer information to infer causative mutations and disease genes. First, we identified differential gene modules in ccRCC by coupling transcriptome and protein-protein interactions. Each of these modules consisted of interacting genes that were involved in similar biological processes and their combined expression alterations were significantly associated with disease type. Then, subsequent gene module-based eQTL analysis revealed somatic mutated genes that had driven the expression alterations of differential gene modules. Our study yielded a list of candidate disease genes, including several known ccRCC causative genes such as BAP1 and PBRM1 , as well as novel genes such as NOD2, RRM1, CSRNP1, SLC4A2, TTLL1 and CNTN1. The differential gene modules and their driver genes revealed by our study provided a new perspective for understanding the molecular mechanisms underlying the disease. Moreover, we validated the results in independent ccRCC patient datasets. Our study provided a new method for prioritizing disease genes and pathways.

The sweetpotato weevil, Cylas formicarius (Fabricius), is a serious pest of sweetpotato. Olfaction-based approaches, such as use of synthetic sex pheromones to monitor populations and the bait-and-kill method to eliminate males, have been applied successfully for population management of C. formicarius. However, the molecular basis of olfaction in C. formicarius remains unknown. In this study, we produced antennal transcriptomes from males and females of C. formicarius using high-throughput sequencing to identify gene families associated with odorant detection. A total of 54 odorant receptors (ORs), 11 gustatory receptors (GRs), 15 ionotropic receptors (IRs), 3 sensory neuron membrane proteins (SNMPs), 33 odorant binding proteins (OBPs), and 12 chemosensory proteins (CSPs) were identified. Tissue-specific expression patterns revealed that all 54 ORs and 11 antennal IRs, one SNMP, and three OBPs were primarily expressed in antennae, suggesting their putative roles in olfaction. Sex-specific expression patterns of these antenna-predominant genes suggest that they have potential functions in sexual behaviors. This study provides a framework for understanding olfaction in coleopterans as well as future strategies for controlling the sweetpotato weevil pest.

Coding rules for Chinese medicines and their codes (GB/T 31774-2015) was issued by General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China (AQSIQ) and Standardization Administration of the People's Republic of China (SAC) in 2015. Coding system for Chinese medicines (ISO 18668-1, 2, 3 and 4) series were issued one after another by International Organization for Standardization in 2016 and 2017. In this paper, the comparative study on the GB/T 31774 and ISO 18668-1, 2, 3 to 4 would be conducted to expound the similarities and differences among them. This essay aims at promoting the application of national and international standards of coding system in production and operation enterprises and the medical institutions of traditional Chinese medicine (TCM), reducing their repetitive investment to meet the Chinese medicine import and export trade requirement in future. Moreover, it provides the cornerstone and support for TCM standardization, and makes Chinese medicines standard gain dominance in field of international TCM standards, occupying the high ground of international market in the traditional medicine field of the world. It may promote the "Internet + TCM service" in our country, and let the Chinese medicine industry go out of the country and into the world to contribute to human health., Competing Interests: The authors of this article and the planning committee members and staff have no relevant financial relationships with commercial interests to disclose., (Copyright© by the Chinese Pharmaceutical Association.)

Studies on insect olfaction have increased our understanding of insect's chemosensory system and chemical ecology, and have improved pest control strategies based on insect behavior. In this study, we assembled the antennal transcriptomes of the lychee giant stink bug, Tessaratoma papillosa, by using next generation sequencing to identify the major olfaction gene families in this species. In total, 59 odorant receptors, 14 ionotropic receptors (8 antennal IRs), and 33 odorant binding proteins (28 classic OBPs and 5 plus-C OBPs) were identified from the male and female antennal transcriptomes. Analyses of tissue expression profiles revealed that all 59 OR transcripts, 2 of the 8 antennal IRs, and 6 of the 33 OBPs were primarily expressed in the antennae, suggesting their putative role in olfaction. The sex-biased expression patterns of these antenna-predominant genes suggested that they may have important functions in the reproductive behavior of these insects. This is the first report that provides a comprehensive resource to future studies on olfaction in the lychee giant stink bug.

Antennal and abdominal transcriptomes of males and females of the coconut hispine beetle Brontispa longissima were sequenced to identify and compare the expression patterns of genes involved in odorant reception and detection. Representative proteins from the chemosensory gene families likely essential for insect olfaction were identified. These include 48 odorant receptors (ORs), 19 ionotropic receptors (IRs), 4 sensory neuron membrane proteins (SNMPs), 34 odorant binding proteins (OBPs) and 16 chemosensory proteins (CSPs). Phylogenetic analysis revealed the evolutionary relationship of these proteins with homologs from Coleopterans or other insects, and led to the identification of putative aggregation pheromone receptors in B. longissima. Comparative expression analysis performed by calculating FPKM values were also validated using quantitative real time-PCR (qPCR). The results revealed that all ORs and antennal IRs, two IR co-receptors (BlonIR8a and BlonIR25a) and one SNMP (BlonSNMP1a) were predominantly expressed in antennae when compared to abdomens, and approximately half of the OBPs (19) and CSPs (7) were enriched in antennae. These findings for the first time reveal the identification of key molecular components in B. longissima olfaction and provide a valuable resource for future functional analyses of olfaction, and identification of potential targets to control this quarantine pest.

Objective To identify serum proteome of ejaculation praecox(EP) with Shen-yang de- ficiency, and to explore its pathogenesis of EP in the protein-protein interaction ( PPI) network. Methods The serum samples were respectively collected from 4 EP with Shen-yang deficiency patients and 4 healthy controls. After the serum proteome of EP with Shen-yang deficiency was obtained, the technology of isobaric tags for relative and absolute quantitation (iTRAQ) was adopted for identification. The STRING data- base was applied to construct the PPI network whose function was analyzed through bioinformatics meth- ods. Results A group of 238 serum proteins were identified in total, of which, 162 proteins reached the strict quantitative standard. Nine proteins were differently expressed, including 1 up-regulated and 8 down-regulated. The constructed PPI network was constituted by 72 protein nodes and 283 protein couples, and could be clustered to 16 clusters, in which 10 clusters were composed of 3 or more proteins. Each cluster could be found with a core protein correspondingly. The core protein of C3,C5,C1S and MASP2 were all main constituents of complement system, whose function involves in biological process of complement ac- tivation. Conclusions The protein models in PPI network of differently expressed serum proteome about EP with Shen-yang deficiency were functional enriched in the biological process of complement activa-, tion; which indicate that a immune dysfuction dominated by abnormal process of complent activation may' be one of the main mechanisms of EP with Shen-yang deficiency.

As there are multiple factors causing hydrocephalus subsequent to intraventricular hemorrhage (IVH), it is difficult to achieve the best treatment effect using a single drug alone. In the present study, the protective effect of combination treatment with granulocyte-colony stimulating factor (G-CSF) and lithium chloride against hydrocephalus after IVH was investigated. A total of 130 adult male Sprague-Dawley rats were divided into five groups, including the IVH control, G-CSF treatment, lithium chloride treatment, combination treatment and sham surgery groups. An IVH rat model was established in order to examine the effect of combination treatment on hydrocephalus incidence. A TUNEL assay was performed to detect neuronal apoptosis in the five groups. In addition, the protein expression levels of B-cell lymphoma 2 (Bcl-2) and Bcl-2-associated X protein (Bax) were detected by western blot analysis. The differentiation of nerve cells in the brain tissue obtained from the five rat groups was also determined with double immunofluorescence staining. The results demonstrated that administration of G-CSF or lithium chloride alone was able to only partly relieve the incidence of hydrocephalus after IVH. By contrast, combination treatment with G-CSF and lithium chloride significantly attenuated the development of hydrocephalus following IVH. TUNEL assay showed that neuronal apoptosis was significantly reduced by the combination treatment with G-CSF and lithium chloride. Furthermore, the expression of Bcl-2 was upregulated, whereas Bax expression was downregulated in the combination treatment group. The results also detected the highest expression of BrdU/GFAP, BrdU/NeuN and BrdU/PSA-NCAM in the combination treatment group. In conclusion, the combination of endogenous neural stem cell mobilization (using G-CSF) and lithium chloride treatment resulted in highly reduced incidence of hydrocephalus after IVH by inhibiting neuronal apoptosis.

Ovarian carcinomas can be aggressive with a high mortality rate (e.g., high-grade serous ovarian carcinomas, or HGSOCs), or indolent with much better long-term outcomes (e.g., low-malignant-potential, or LMP, serous ovarian carcinomas). By comparing LMP and HGSOC tumors, we can gain insight into the mechanisms underlying malignant progression in ovarian cancer. However, previous studies of the two subtypes have been focused on gene expression analysis. Here, we applied a systems biology approach, integrating gene expression profiles derived from two independent data sets containing both LMP and HGSOC tumors with protein-protein interaction data. Genes and related networks implicated by both data sets involved both known and novel disease mechanisms and highlighted the different roles of BRCA1 and CREBBP in the two tumor types. In addition, the incorporation of somatic mutation data revealed that amplification of PAK4 is associated with poor survival in patients with HGSOC. Thus, perturbations in protein interaction networks demonstrate differential trafficking of network information between malignant and benign ovarian cancers. The novel network-based molecular signatures identified here may be used to identify new targets for intervention and to improve the treatment of invasive ovarian cancer as well as early diagnosis., Competing Interests: The authors have declared that no competing interests exist.

Background: Snail is a typical transcription factor that could induce epithelial-mesenchymal transition (EMT) and cancer progression. There are some related reports about the clinical significance of snail protein expression in gastric cancer. However, the published results were not completely consistent. This study was aimed to investigate snail expression and clinical significance in gastric cancer., Results: A systematic review of PubMed, CNKI, Weipu, and Wanfang database before March 2015 was conducted. We established an inclusion criterion according to subjects, method of detection, and results evaluation of snail protein. Meta-analysis was conducted using RevMan4.2 software. And merged odds ratio (OR) and 95 % CI (95 % confidence interval) were calculated. Also, forest plots and funnel plot were used to assess the potential of publication bias. A total of 10 studies were recruited. The meta-analysis was conducted to evaluate the positive rate of snail protein expression. OR and 95 % CI for different groups were listed below: (1) gastric cancer and para-carcinoma tissue [OR = 6.15, 95 % CI (4.70, 8.05)]; (2) gastric cancer and normal gastric tissue [OR = 17.00, 95 % CI (10.08, 28.67)]; (3) non-lymph node metastasis and lymph node metastasis [OR = 0.40, 95 % CI (0.18, 0.93)]; (4) poor differentiated cancer, highly differentiated cancer, and moderate cancer [OR = 3.34, 95 % CI (2.22, 5.03)]; (5) clinical stage TI + TII and stage TIII + TIV [OR = 0.38, 95 % CI (0.23, 0.60)]; (6) superficial muscularis and deep muscularis [OR = 0.18, 95 % CI (0.11, 0.31)]., Conclusions: Our results indicated that the increase of snail protein expression may play an important role in the carcinogenesis, progression, and metastasis of gastric cancer. And this result might provide instruction for the diagnosis, therapy, and prognosis of gastric cancer.

Background: Green tea polyphenol epigallocatechin-3-gallate (EGCG) has been demonstrated to inhibit cancer in experimental studies through its antioxidant activity and modulations on cellular functions by binding specific proteins. By means of computational analysis and functional genomic approaches, we previously identified a set of protein coding genes and microRNAs whose expressions were significantly modulated in response to the EGCG treatment in tobacco carcinogen-induced lung adenocarcinoma in A/J mice. However, to what degree these genes are involved in the cancer inhibition of EGCG remains unclear., Results: In this study, we further employed statistical methods and literature research to analyze these data in combination with The Cancer Genome Atlas (TCGA) lung adenocarcinoma datasets for additional data mining. Under the assumption that, if a gene mediates EGCG's cancer inhibition, its expression level change caused by EGCG should be opposite to what occurred in the carcinogenesis, we identified Myb and Peg3 as the primary putative genes involved in the cancer inhibitory activity. Further analysis suggested that the regulation of Myb could be mediated through an EGCG-upregulated microRNA, miR-449c-5p., Conclusions: Although the actions of EGCG involve multiple targets/pathways, further analysis by mining the existing genomic datasets revealed that the upregulations of Myb and Peg3 are likely the key anti-cancer events of EGCG in vivo.

Background: Chronic inflammation has been widely considered to be the major risk factor of coronary heart disease (CHD). The goal of our study was to explore the possible association with CHD for inflammation-related single nucleotide polymorphisms (SNPs) involved in cytosine-phosphate-guanine (CpG) dinucleotides. A total of 784 CHD patients and 739 non-CHD controls were recruited from Zhejiang Province, China. Using the Sequenom MassARRAY platform, we measured the genotypes of six inflammation-related CpG-SNPs, including IL1B rs16944, IL1R2 rs2071008, PLA2G7 rs9395208, FAM5C rs12732361, CD40 rs1800686, and CD36 rs2065666). Allele and genotype frequencies were compared between CHD and non-CHD individuals using the CLUMP22 software with 10,000 Monte Carlo simulations., Results: Allelic tests showed that PLA2G7 rs9395208 and CD40 rs1800686 were significantly associated with CHD. Moreover, IL1B rs16944, PLA2G7 rs9395208, and CD40 rs1800686 were shown to be associated with CHD under the dominant model. Further gender-based subgroup tests showed that one SNP (CD40 rs1800686) and two SNPs (FAM5C rs12732361 and CD36 rs2065666) were associated with CHD in females and males, respectively. And the age-based subgroup tests indicated that PLA2G7 rs9395208, IL1B rs16944, and CD40 rs1800686 were associated with CHD among individuals younger than 55, younger than 65, and over 65, respectively., Conclusions: In conclusion, all the six inflammation-related CpG-SNPs (rs16944, rs2071008, rs12732361, rs2065666, rs9395208, and rs1800686) were associated with CHD in the combined or subgroup tests, suggesting an important role of inflammation in the risk of CHD.

Objective: To explore an approach to rapidly and accurately identify the compounds as biomarkers of Chinese medicine (CM) syndromes., Method: The Fourier transform infrared (FT-IR) spectrometry was applied to investigate the characteristic components of a mice model of Kidney (Shen)-yang deficiency syndrome (KDS), and the remedial effect of a typical CM formula Shenqi Pill (). Thirty-six females and 18 males of Balb/c mice were randomly divided into KDS, Shenqi or control group. The females and males of the same group freely were mated for 96 h, and the males were taken out and only the female mice were raised. Females of the KDS group were threatened by a ferocious cat every other day for 14 d. After delivery, the KDS, or gestational threatened, offspring were raised at standard condition for 11 weeks. Then 10 male offspring were randomly selected, anaesthetized and their representative organs, i.e. testes, kidneys, lungs and feet were collected, for the FT-IR scan. Mice of the Shenqi group were intragastric administered Shenqi Pill; while mice in the KDS and control groups were given the same volume of saline., Results: The attenuated birth outcomes of the KDS group were displayed. The remarkable FT-IR differences of all organs between KDS mice and healthy control were mainly at 1,735-1,745 cm(-1) (indicating the increased levels of lipids) and at 1,640-1,647 cm(-1) and 1,539-1,544 cm(-1) (displaying the decreased proteins). No statistic FT-IR difference between Shenqi and control mice was observed., Conclusion: In accordance with major traits of KDS, prenatal stress extensively impaired the building up of proteins and resulting in the excessive lipid storage, and FT-IR could effectively identify the biomarkers of KDS.

Background: Prenatal environmental enrichment (EE) has been proven to positively affect but prenatal stress negatively influence the physiological and psychological processes in animals, whose trans-generational genetic mechanism remains unclearly defined. We aimed to investigate and find out key genes underlying the positive-negative effects derived from prenatal interventions., Materials and Methods: Pregnant rats were randomized into EE group (EEG), earthquake simulation group (ESG), herbal group (HG) received herbal supplements in feed after earthquake simulation, and control group (CG)., Results: Light Box Defecation Test (LBDT) showed EEG offspring presented less fecal pellets than CG offspring, ESG's more than CG's, and HG's less than ESG (p's<0.05). Open-field Test (OFT) score of EEG was higher than CG offspring, of ESG's was lower than CG's, and HG's higher than ESG's. Irf7 and Ninj were screened, which were up-regulated in EEG, down-regulated in ESG (FC<0.5), and were neutralized in HG. Prenatal EE could positively promote the nervous system development, prenatal earthquake simulation could retard the nervous system development and Chinese herbal remedy (JKSQW) which could correct the retardation., Conclusion: The negative-positive prenatal effect could contribute to altered gene expression of Irf7 and Ninj2 which also could play a key role in the improving function of JKSQW for the kidneys.

Background: Epigallocatechin-3-gallate (EGCG) has been demonstrated to inhibit cancer in experimental studies through its antioxidant activity and modulations on cellular functions by binding specific proteins. We demonstrated previously that EGCG upregulates the expression of microRNA (i.e. miR-210) by binding HIF-1α, resulting in reduced cell proliferation and anchorage-independent growth. However, the binding affinities of EGCG to HIF-1α and many other targets are higher than the EGCG plasma peak level in experimental animals administered with high dose of EGCG, raising a concern whether the microRNA regulation by HIF-1α is involved in the anti-cancer activity of EGCG in vivo., Results: We employed functional genomic approaches to elucidate the role of microRNA in the EGCG inhibition of tobacco carcinogen-induced lung tumors in A/J mice. By analysing the microRNA profiles, we found modest changes in the expression levels of 21 microRNAs. By correlating these 21 microRNAs with the mRNA expression profiles using the computation methods, we identified 26 potential targeted genes of the 21 microRNAs. Further exploration using pathway analysis revealed that the most impacted pathways of EGCG treatment are the regulatory networks associated to AKT, NF-κB, MAP kinases, and cell cycle, and the identified miRNA targets are involved in the networks of AKT, MAP kinases and cell cycle regulation, Conclusions: These results demonstrate that the miRNA-mediated regulation is actively involved in the major aspects of the anti-cancer activity of EGCG in vivo.

Synergistically integrating multi-layer genomic data at systems level not only can lead to deeper insights into the molecular mechanisms related to disease initiation and progression, but also can guide pathway-based biomarker and drug target identification. With the advent of high-throughput next-generation sequencing technologies, sequencing both DNA and RNA has generated multi-layer genomic data that can provide DNA polymorphism, non-coding RNA, messenger RNA, gene expression, isoform and alternative splicing information. Systems biology on the other hand studies complex biological systems, particularly systematic study of complex molecular interactions within specific cells or organisms. Genomics and molecular systems biology can be merged into the study of genomic profiles and implicated biological functions at cellular or organism level. The prospectively emerging field can be referred to as systems genomics or genomic systems biology. The Mid-South Bioinformatics Centre (MBC) and Joint Bioinformatics Ph.D. Program of University of Arkansas at Little Rock and University of Arkansas for Medical Sciences are particularly interested in promoting education and research advancement in this prospectively emerging field. Based on past investigations and research outcomes, MBC is further utilizing differential gene and isoform/exon expression from RNA-seq and co-regulation from the ChiP-seq specific for different phenotypes in combination with protein-protein interactions, and protein-DNA interactions to construct high-level gene networks for an integrative genome-phoneme investigation at systems biology level.

Background: RDX is a well-known pollutant to induce neurotoxicity. MicroRNAs (miRNA) and messenger RNA (mRNA) profiles are useful tools for toxicogenomics studies. It is worthy to integrate MiRNA and mRNA expression data to understand RDX-induced neurotoxicity., Results: Rats were treated with or without RDX for 48 h. Both miRNA and mRNA profiles were conducted using brain tissues. Nine miRNAs were significantly regulated by RDX. Of these, 6 and 3 miRNAs were up- and down-regulated respectively. The putative target genes of RDX-regulated miRNAs were highly nervous system function genes and pathways enriched. Fifteen differentially genes altered by RDX from mRNA profiles were the putative targets of regulated miRNAs. The induction of miR-71, miR-27ab, miR-98, and miR-135a expression by RDX, could reduce the expression of the genes POLE4, C5ORF13, SULF1 and ROCK2, and eventually induce neurotoxicity. Over-expression of miR-27ab, or reduction of the expression of unknown miRNAs by RDX, could up-regulate HMGCR expression and contribute to neurotoxicity. RDX regulated immune and inflammation response miRNAs and genes could contribute to RDX- induced neurotoxicity and other toxicities as well as animal defending reaction response to RDX exposure., Conclusions: Our results demonstrate that integrating miRNA and mRNA profiles is valuable to indentify novel biomarkers and molecular mechanisms for RDX-induced neurological disorder and neurotoxicity.

Studying the literature of blood stasis syndrome (BSS), we reviewed the research course and the perspective of molecular regulation network and BSS. The essence study of BSS was firstly proposed by Chen Ke-ji and Wang Jie, and developed for more than thirty years. The course for BSS study mainly included the formulation of BSS diagnostic standard, the establishment of BSS animal model, pedigree methods, twins combined clinical epidemiological survey of BSS research, the four "zu" subjects combined molecular regulation network of BSS, signal transduction system network and BSS research, and so on. Along with a new sequencing approach in basic research, clinical diagnostics, and drug development, we are promising to see the whole gene network research of human diseases, such as metabolic disease, cancer, and etc. These achievements could provide a new way of thinking for further studying the essence of BSS.

Objective. To explore the genetic traits of Kidney-yang deficiency syndrome (KDS). Design. Twelve KDS subjects and three spouses from a typical KDS family were recruited. Their genomic DNA samples were genotyped by Affymetrix 100K single-nucleotide polymorphism (SNP) arrays. The linkage disequilibrium (LD) SNPs were generated using GeneChip DNA analysis software (GDAS, Affymetrix). Genes located within 100 bp of the flanks of LD SNPs were mined via GeneView. 29 exons of the doublecortin domain containing 5 (DCDC5), a representative gene within the flank of an LD SNP, were resequenced. Results. Five LD SNPs display midrange linkage with KDS. Two genes with established functions, DCDC5 and Leucyl-tRNA synthetase, were mined in the flanks of LD SNPs. Resequencing of DCDC5 revealed a nonsynonymous variation, in which 3764T/A was replaced by C/G. Accordingly, the Ser(1172) was substituted by Pro(1172). The S1172P substitution effect was evaluated as "possibly damaging" by PolyPhen. Conclusion. We have identified a genomic variation of DCDC5 based on the LD SNPs derived from a KDS family. DCDC5 and other genes surrounding these SNPs display some relationships with key symptoms of KDS.

We have performed an analysis of a family with kidney-yang deficiency syndrome (KDS) in order to determine the structural genomic variations through a novel approach designated as "copy number variants" (CNVs). Twelve KDS subjects and three healthy spouses from this family were included in this study. Genomic DNA samples were genotyped utilizing an Affymetrix 100 K single nucleotide polymorphism array, and CNVs were identified by Copy Number Algorithm (CNAT4.0, Affymetrix). Our results demonstrate that 447 deleted and 476 duplicated CNVs are shared among KDS subjects within the family. The homologus ratio of deleted CNVs was as high as 99.78%. One-copy-duplicated CNVs display mid-range homology. For two copies of duplicated CNVs (CNV(4)), a markedly heterologous ratio was observed. Therefore, with the important exception of CNV(4), our data shows that CNVs shared among KDS subjects display typical Mendelian inheritance. A total of 113 genes with established functions were identified from the CNV flanks; significantly enriched genes surrounding CNVs may contribute to certain adaptive benefit. These genes could be classified into categories including: binding and transporter, cell cycle, signal transduction, biogenesis, nerve development, metabolism regulation and immune response. They can also be included into three pathways, that is, signal transduction, metabolic processes and immunological networks. Particularly, the results reported here are consistent with the extensive impairments observed in KDS patients, involving the mass-energy-information-carrying network. In conclusion, this article provides the first set of CNVs from KDS patients that will facilitate our further understanding of the genetic basis of KDS and will allow novel strategies for a rational therapy of this disease.

Background: Understanding how biomolecules interact is a major task of systems biology. To model protein-nucleic acid interactions, it is important to identify the DNA or RNA-binding residues in proteins. Protein sequence features, including the biochemical property of amino acids and evolutionary information in terms of position-specific scoring matrix (PSSM), have been used for DNA or RNA-binding site prediction. However, PSSM is rather designed for PSI-BLAST searches, and it may not contain all the evolutionary information for modelling DNA or RNA-binding sites in protein sequences., Results: In the present study, several new descriptors of evolutionary information have been developed and evaluated for sequence-based prediction of DNA and RNA-binding residues using support vector machines (SVMs). The new descriptors were shown to improve classifier performance. Interestingly, the best classifiers were obtained by combining the new descriptors and PSSM, suggesting that they captured different aspects of evolutionary information for DNA and RNA-binding site prediction. The SVM classifiers achieved 77.3% sensitivity and 79.3% specificity for prediction of DNA-binding residues, and 71.6% sensitivity and 78.7% specificity for RNA-binding site prediction., Conclusions: Predictions at this level of accuracy may provide useful information for modelling protein-nucleic acid interactions in systems biology studies. We have thus developed a web-based tool called BindN+ (http://bioinfo.ggc.org/bindn+/) to make the SVM classifiers accessible to the research community.

Background: Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task., Results: In order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods., Conclusions: The hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively.