Boosting Clear Cell Renal Carcinoma-Specific Drug Discovery Using a Deep Learning Algorithm and Single-Cell Analysis.

Clear cell renal carcinoma (ccRCC), the most common subtype of renal cell carcinoma, has the high heterogeneity of a highly complex tumor microenvironment. Existing clinical intervention strategies, such as target therapy and immunotherapy, have failed to achieve good therapeutic effects. In this article, single-cell transcriptome sequencing (scRNA-seq) data from six patients downloaded from the GEO database were adopted to describe the tumor microenvironment (TME) of ccRCC, including its T cells, tumor-associated macrophages (TAMs), endothelial cells (ECs), and cancer-associated fibroblasts (CAFs). Based on the differential typing of the TME, we identified tumor cell-specific regulatory programs that are mediated by three key transcription factors (TFs), whilst the TF EPAS1/HIF-2α was identified via drug virtual screening through our analysis of ccRCC's protein structure. Then, a combined deep graph neural network and machine learning algorithm were used to select anti-ccRCC compounds from bioactive compound libraries, including the FDA-approved drug library, natural product library, and human endogenous metabolite compound library. Finally, five compounds were obtained, including two FDA-approved drugs (flufenamic acid and fludarabine), one endogenous metabolite, one immunology/inflammation-related compound, and one inhibitor of DNA methyltransferase (N4-methylcytidine, a cytosine nucleoside analogue that, like zebularine, has the mechanism of inhibiting DNA methyltransferase). Based on the tumor microenvironment characteristics of ccRCC, five ccRCC-specific compounds were identified, which would give direction of the clinical treatment for ccRCC patients.

TIMELESS promotes the proliferation and migration of lung adenocarcinoma cells by activating EGFR through AMPK and SPHK1 regulation.

BACKGROUND: Lung adenocarcinoma (LUAD) has high morbidity and is prone to recurrence. TIMELESS (TIM), which regulates circadian rhythms in Drosophila, is highly expressed in various tumors. Its role in LUAD has gained attention, but the detailed function and mechanism have not been clarified completely at present. METHODS: Tumor samples from patients with LUAD patient data from public databases were used to confirm the relationship of TIM expression with lung cancer. LUAD cell lines were used and siRNA of TIM was adopted to knock down TIM expression in LUAD cells, and further cell proliferation, migration and colony formation were analyzed. By using Western blot and qPCR, we detected the influence of TIM on epidermal growth factor receptor (EGFR), sphingosine kinase 1 (SPHK1) and AMP-activated protein kinase (AMPK). With proteomics analysis, we comprehensively inspected the different changed proteins influenced by TIM and did global bioinformatic analysis. RESULTS: We found that TIM expression was elevated in LUAD and that this high expression was positively correlated with more advanced tumor pathological stages and shorter overall and disease-free survival. TIM knockdown inhibited EGFR activation and also AKT/mTOR phosphorylation. We also clarified that TIM regulated the activation of SPHK1 in LUAD cells. And with SPHK1 siRNA to knock down the expression level of SPHK1, we found that EGFR activation were inhibited greatly too. Quantitative proteomics techniques combined with bioinformatics analysis clarified the global molecular mechanisms regulated by TIM in LUAD. The results of proteomics suggested that mitochondrial translation elongation and termination were altered, which were closely related to the process of mitochondrial oxidative phosphorylation. We further confirmed that TIM knockdown reduced ATP content and promoted AMPK activation in LUAD cells. CONCLUSIONS: Our study revealed that siTIM could inhibit EGFR activation through activating AMPK and inhibiting SPHK1 expression, as well as influencing mitochondrial function and altering the ATP level; TIM's high expression in LUAD is an important factor and a potential key target in LUAD.

Regularized survival learning and cross-database analysis enabled identification of colorectal cancer prognosis-related immune genes.

Colon adenocarcinoma is the most common type of colorectal cancer. The prognosis of advanced colorectal cancer patients who received treatment is still very poor. Therefore, identifying new biomarkers for prognosis prediction has important significance for improving treatment strategies. However, the power of biomarker analyses was limited by the used sample size of individual database. In this study, we combined Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases to expand the number of healthy tissue samples. We screened differentially expressed genes between the GTEx healthy samples and TCGA tumor samples. Subsequently, we applied least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis to identify nine prognosis-related immune genes: ANGPTL4, IDO1, NOX1, CXCL3, LTB4R, IL1RL2, CD72, NOS2, and NUDT6. We computed the risk scores of samples based on the expression levels of these genes and divided patients into high- and low-risk groups according to this risk score. Survival analysis results showed a significant difference in survival rate between the two risk groups. The high-risk group had a significantly lower overall survival rate and poorer prognosis. We found the receiver operating characteristic based on the risk score was showed to accurately predict patients' prognosis. These prognosis-related immune genes may be potential biomarkers for colorectal cancer diagnosis and treatment. Our open-source code is freely available from GitHub at https://github.com/gutmicrobes/Prognosis-model.git.

Tumor tissue microorganisms are closely associated with tumor immune subtypes.

Studies have found that different immune subtypes are present in the same tumor. Different tumor subtypes have different tumor microenvironments (TME). This means that the efficacy of immunotherapy in actual applications will, therefore, have different results. Existing tumor immune subtype studies have mostly focused on immune cells, stromal cells, genes and molecules without considering the presence of microbes. Some studies have shown that microflora can strongly promote many gastrointestinal cancers. The microbiome has, therefore, become an important biomarker and regulatory factor of cancer progression and therapeutic responses. In addition, the presence of microflora can strongly regulate the host immune system, indirectly affecting tumor growth. Taken together, it is important to study the relationships that develop among tumor tissue microorganisms, tumor immune subtype, and the TME. In this study, correlations between microbial abundance, immune cell infiltration, immune gene expression and tumor immune subtype were studied. To accomplish this, tissue microorganisms and immune cell ratios with significant differences between the different cancers were obtained by comparing 203 gastric cancer and intestinal cancer samples. Two immune subtypes of intestinal samples were obtained by K-means clustering algorithm and tissue microorganisms, immune cell ratios and immune-related genes with significant differences between different immune subtypes were screened through Wilcoxon rank sum test. The results showed that Clostridioides difficile, Aspergillus fumigatus, Yarrowia lipolytica, and Fusarium pseudograminearum were all closely associated with the identified tumor immune subtypes. Our open-source software is freely available from GitHub at https://github.com/gutmicrobes/IMM-subtype.git.

DPADM: a novel algorithm for detecting drug-pathway associations based on high-throughput transcriptional response to compounds.

Pathway genes functionally participate in the same biological process. They typically act cooperatively, and none is considered dispensable. The dominant paradigm in drug discovery is the one-to-one strategy, which aims to find the most sensitive drug to act on an individual target. However, many complex diseases, such as cancer, are caused by dysfunction among multiple-gene pathways, not just one. Therefore, identifying pathway genes that are responsive to synthetic compounds in a global physiological environment may be more effective in drug discovery. The high redundancy of crosstalk between biological pathways, though, hints that the covariance matrix, which only connects genes with strong marginal correlations, may miss higher-level interactions, such as group interactions. We herein report the development of DPADM-a Drug-Pathway association Detection Model that infers pathways responsive to specific drugs. This model elucidates higher-level gene-gene interactions by evaluating the conditional dependencies between genes under different drug treatments. The advantage of the proposed method is demonstrated using simulation studies by comparing with another two methods. We applied this model to the Connectivity Map data set (CMap), and demonstrated that DPADM is able to identify many drug-pathway associations, such as mitoxantrone (MTX)- PI3K/AKT association, which targets the topological conditions of DNA transcription. Surprisingly, apart from identifying pathways corresponding to specific drugs, our methodology also revealed new drug-related pathways with functions similarly to those of seed genes.

CTGF Promotes the Osteoblast Differentiation of Human Periodontal Ligament Stem Cells by Positively Regulating BMP2/Smad Signal Transduction.

Objective: This work is aimed at revealing the role and the molecular mechanism of connective tissue growth factor 2 (CTGF) in the osteoblast differentiation of periodontal ligament stem cells (PDLSCs). Methods: The osteogenic differentiation of PDLSCs was induced by osteogenic induction medium (OM), and the expression level of osteogenic related proteins ALP, RUNX2, OCN, and CTGF was estimated using quantitative real-time polymerase chain reaction (qRT-PCR) and Western blotting analysis. We constructed cell lines with CTGF overexpression or knockdown to verify the role of CTGF in the osteoblast differentiation of PDLSCs. Alkaline phosphatase (ALP) staining was introduced to measure the osteoblasts activity, and alizarin red S (ARS) staining was employed to test matrix mineralization. The interaction between CTGF and bone morphogenetic protein-2 (BMP-2) was determined by endogenous coimmunoprecipitation (Co-IP). Results: The expression level of CTGF was increased during the osteogenic induction of PDLSCs. Additionally, CTGF overexpression effectively maintained the stemness and facilitated the osteoblast differentiation in PDLSCs, and CTGF knockdown exerted opposite effects. Moreover, at molecular mechanism, CTGF increased the activity of BMP-2/Smad signaling pathway. Conclusion: This investigation verified that CTGF promotes the osteoblast differentiation in PDLSCs at least partly by activating BMP-2/Smad cascade signal.

Joint Analysis of Microbial and Immune Cell Abundance in Liver Cancer Tissue Using a Gene Expression Profile Deconvolution Algorithm Combined With Foreign Read Remapping.

Recent transcriptomics and metagenomics studies showed that tissue-infiltrating immune cells and bacteria interact with cancer cells to shape oncogenesis. This interaction and its effects remain to be elucidated. However, it is technically difficult to co-quantify immune cells and bacteria in their respective microenvironments. To address this challenge, we herein report the development of a complete a bioinformatics pipeline, which accurately estimates the number of infiltrating immune cells using a novel Particle Swarming Optimized Support Vector Regression (PSO-SVR) algorithm, and the number of infiltrating bacterial using foreign read remapping and the GRAMMy algorithm. It also performs systematic differential abundance analyses between tumor-normal pairs. We applied the pipeline to a collection of paired liver cancer tumor and normal samples, and we identified bacteria and immune cell species that were significantly different between tissues in terms of health status. Our analysis showed that this dual model of microbial and immune cell abundance had a better differentiation (84%) between healthy and diseased tissue. Caldatribacterium sp., Acidaminococcaceae sp., Planctopirus sp., Desulfobulbaceae sp.,Nocardia farcinica as well as regulatory T cells (Tregs), resting mast cells, monocytes, M2 macrophases, neutrophils were identified as significantly different (Mann Whitney Test, FDR< 0.05). Our open-source software is freely available from GitHub at https://github.com/gutmicrobes/PSO-SVR.git.

The changes of the calf-vein deformation and femoral vein peak velocity during ankle pump exercise with or without graduated compression stockings.

OBJECTIVES: To analyze the changes of lower limb hemodynamics parameters before and after wearing graduated compression stockings (GCS) during ankle pump exercise in patients preparing for arthroplastic surgery. METHOD: The leg veins of 16 patients awaiting arthroplasty were analyzed using a Sonosite M-Turbo ultrasound system during ankle pump exercise with or without GCS. The age of them was 70 ± 7 years (mean ± SD) (range 56-82 years) and body mass index was 25.8 ± 3.0 kg/m2 (range 18.0-30.5 kg/m2). Measured data including the cross-sectional area (CSA), anteroposterior (AP) diameter and lateromedial (LM) diameter of the soleus vein (SV), posterior tibial vein (PTV) and great saphenous vein (GSV). Additionally, the peak velocities of femoral vein (FV) were also measured. RESULTS: GCS could significantly decrease the cross-sectional area of SV, PTV and GSV in supine position at rest and maximum ankle plantar flexion. But the compression effect of GCS to SV and GSV was not observed during maximum ankle dorsiflexion. It was found that GCS application reduced the peak flow velocity of the femoral vein from 61.85 cm/s (95% CI = 50.94-72.75 cm/s) to 38.01 cm/s (95% CI = 28.42-47.59 cm/s) (P < 0.001) during ankle plantar flexion and decreased the femoral vein in these patients from 80.65 cm/s (95% CI = 70.37-90.92 cm/s) to 51.15 cm/s (95% CI = 42.58-59.73 cm/s) (P < 0.001) during ankle dorsiflexion. But this effect was not significant in supine position at rest. CONCLUSIONS: GCS could significantly reduce the peak flow velocity of the femoral vein during ankle pump exercise in the patients preparing for arthroplastic surgery.

A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis.

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

Cell Heterogeneity Analysis in Single-Cell RNA-seq Data Using Mixture Exponential Graph and Markov Random Field Model.

Advanced single-cell profiling technologies promote exploration of cell heterogeneity, and clustering of single-cell RNA (scRNA-seq) data enables discovery of coexpression genes and network relationships between genes. In particular, single-cell profiling of circulating tumor cells (CTCs) can provide unique insights into tumor heterogeneity (including in triple-negative breast cancer (TNBC)), while scRNA-seq leads to better understanding of subclonal architecture and biological function. Despite numerous reports suggesting a direct correlation between circulating tumor cells (CTCs) and poor clinical outcomes, few studies have provided a thorough heterogeneity characterization of CTCs. In addition, TNBC is a disease with not only intertumor but also intratumor heterogeneity and represents various biological distinct subgroups that may have relationships with immune functions that are not clearly established yet. In this article, we introduce a new scheme for detecting genotypic characterization of single-cell heterogeneities and apply it to CTC and TNBC single-cell RNA-seq data. First, we use an existing mixture exponential family graph model to partition the cell-cell network; then, with the Markov random field model, we obtain more flexible network rewiring. Finally, we find the cell heterogeneity and network relationships according to different high coexpression gene modules in different cell subsets. Our results demonstrate that this scheme provides a reasonable and effective way to model different cell clusters and different biological enrichment gene clusters. Thus, using different internal coexpression genes of different cell clusters, we can infer the differences in tumor composition and diversity.

Efficacy of perioperative intravenous dexmedetomidine administration for the prevention of postoperative sore throat: a meta-analysis.

OBJECTIVE: Postoperative sore throat (POST) is an undesirable intubation-related complication after surgery. Several studies have investigated the efficacy of perioperative intravenous dexmedetomidine administration for the prevention of POST, but the results have been inconsistent. We aimed to summarize all existing evidence and draw a more precise conclusion to guide future clinical work. METHODS: PubMed, Cochrane Library, EMBASE and China National Knowledge Infrastructure databases were comprehensively searched for all randomized controlled trials published before 1 February 2021 that investigated the efficacy of dexmedetomidine for the prevention of POST. RESULTS: Nine studies involving 400 patients were included in our meta-analysis. Compared with the control groups (i.e., saline and anesthetic drugs), perioperative intravenous use of dexmedetomidine significantly reduced the incidence of POST [risk ratio (RR): 0.56; 95% confidence interval (CI): 0.40-0.77; I2 = 0%) and coughing on the tube during extubation (RR: 0.58; 95% CI: 0.41-0.82; I2 = 0%). Additionally, patients in the dexmedetomidine group were more likely to develop bradycardia (RR: 2.46; 95% CI: 1.28-4.71; I2 = 0%) and hypotension (RR: 3.26; 95% CI: 1.14-9.33; I2 = 0%) during the administration of dexmedetomidine than those in the control group. CONCLUSION: Perioperative intravenous administration of dexmedetomidine has a positive effect on the prevention of POST.

Colorectal Cancer Prediction Based on Weighted Gene Co-Expression Network Analysis and Variational Auto-Encoder.

An effective feature extraction method is key to improving the accuracy of a prediction model. From the Gene Expression Omnibus (GEO) database, which includes 13,487 genes, we obtained microarray gene expression data for 238 samples from colorectal cancer (CRC) samples and normal samples. Twelve gene modules were obtained by weighted gene co-expression network analysis (WGCNA) on 173 samples. By calculating the Pearson correlation coefficient (PCC) between the characteristic genes of each module and colorectal cancer, we obtained a key module that was highly correlated with CRC. We screened hub genes from the key module by considering module membership, gene significance, and intramodular connectivity. We selected 10 hub genes as a type of feature for the classifier. We used the variational autoencoder (VAE) for 1159 genes with significantly different expressions and mapped the data into a 10-dimensional representation, as another type of feature for the cancer classifier. The two types of features were applied to the support vector machines (SVM) classifier for CRC. The accuracy was 0.9692 with an AUC of 0.9981. The result shows a high accuracy of the two-step feature extraction method, which includes obtaining hub genes by WGCNA and a 10-dimensional representation by variational autoencoder (VAE).

Calculation of immune cell proportion from batch tumor gene expression profile based on support vector regression.

In addition to tumor cells, a large number of immune cells are found in the tumor microenvironment (TME) of cancer patients. Tumor-infiltrating immune cells play an important role in tumor progression and patient outcome. We improved the relative proportion estimation algorithm of immune cells based on RNA-seq gene expression profiling and solved the multiple linear regression model by support vector regression ([Formula: see text]-SVR). These steps resulted in increased robustness of the algorithm and more accurate calculation of the relative proportion of different immune cells in cancer tissues. This method was applied to the analysis of infiltrating immune cells based on 41 pairs of colorectal cancer tissues and normal solid tissues. Specifically, we compared the relative fractions of six types of immune cells in colorectal cancer tissues to those found in normal solid tissue samples. We found that tumor tissues contained a higher proportion of CD8 T cells and neutrophils, while B cells and monocytes were relatively low. Our pipeline for calculating immune cell proportion using gene expression profile data can be freely accessed from GitHub at https://github.com/gutmicrobes/EICS.git.

Association network analysis identifies enzymatic components of gut microbiota that significantly differ between colorectal cancer patients and healthy controls.

The human gut microbiota plays a major role in maintaining human health and was recently recognized as a promising target for disease prevention and treatment. Many diseases are traceable to microbiota dysbiosis, implicating altered gut microbial ecosystems, or, in many cases, disrupted microbial enzymes carrying out essential physio-biochemical reactions. Thus, the changes of essential microbial enzyme levels may predict human disorders. With the rapid development of high-throughput sequencing technologies, metagenomics analysis has emerged as an important method to explore the microbial communities in the human body, as well as their functionalities. In this study, we analyzed 156 gut metagenomics samples from patients with colorectal cancer (CRC) and adenoma, as well as that from healthy controls. We estimated the abundance of microbial enzymes using the HMP Unified Metabolic Analysis Network method and identified the differentially abundant enzymes between CRCs and controls. We constructed enzymatic association networks using the extended local similarity analysis algorithm. We identified CRC-associated enzymic changes by analyzing the topological features of the enzymatic association networks, including the clustering coefficient, the betweenness centrality, and the closeness centrality of network nodes. The network topology of enzymatic association network exhibited a difference between the healthy and the CRC environments. The ABC (ATP binding cassette) transporter and small subunit ribosomal protein S19 enzymes, had the highest clustering coefficient in the healthy enzymatic networks. In contrast, the Adenosylhomocysteinase enzyme had the highest clustering coefficient in the CRC enzymatic networks. These enzymic and metabolic differences may serve as risk predictors for CRCs and are worthy of further research.

Identifying Gut Microbiota Associated With Colorectal Cancer Using a Zero-Inflated Lognormal Model.

Colorectal cancer (CRC) is the third most common cancer worldwide. Its incidence is still increasing, and the mortality rate is high. New therapeutic and prognostic strategies are urgently needed. It became increasingly recognized that the gut microbiota composition differs significantly between healthy people and CRC patients. Thus, identifying the difference between gut microbiota of the healthy people and CRC patients is fundamental to understand these microbes' functional roles in the development of CRC. We studied the microbial community structure of a CRC metagenomic dataset of 156 patients and healthy controls, and analyzed the diversity, differentially abundant bacteria, and co-occurrence networks. We applied a modified zero-inflated lognormal (ZIL) model for estimating the relative abundance. We found that the abundance of genera: Anaerostipes, Bilophila, Catenibacterium, Coprococcus, Desulfovibrio, Flavonifractor, Porphyromonas, Pseudoflavonifractor, and Weissella was significantly different between the healthy and CRC groups. We also found that bacteria such as Streptococcus, Parvimonas, Collinsella, and Citrobacter were uniquely co-occurring within the CRC patients. In addition, we found that the microbial diversity of healthy controls is significantly higher than that of the CRC patients, which indicated a significant negative correlation between gut microbiota diversity and the stage of CRC. Collectively, our results strengthened the view that individual microbes as well as the overall structure of gut microbiota were co-evolving with CRC.

Inferring Bacterial Infiltration in Primary Colorectal Tumors From Host Whole Genome Sequencing Data.

Colorectal cancer is the third most common cancer worldwide with abysmal survival, thus requiring novel therapy strategies. Numerous studies have frequently observed infiltrating bacteria within the primary tumor tissues derived from patients. These studies have implicated the relative abundance of these bacteria as a contributing factor in tumor progression. Infiltrating bacteria are believed to be among the major drivers of tumorigenesis, progression, and metastasis and, hence, promising targets for new treatments. However, measuring their abundance directly remains challenging. One potential approach is to use the unmapped reads of host whole genome sequencing (hWGS) data, which previous studies have considered as contaminants and discarded. Here, we developed rigorous bioinformatics and statistical procedures to identify tumor-infiltrating bacteria associated with colorectal cancer from such whole genome sequencing data. Our approach used the reads of whole genome sequencing data of colon adenocarcinoma tissues not mapped to the human reference genome, including unmapped paired-end read pairs and single-end reads, the mates of which were mapped. We assembled the unmapped read pairs, remapped all those reads to the collection of human microbiome reference, and then computed their relative abundance of microbes by maximum likelihood (ML) estimation. We analyzed and compared the relative abundance and diversity of infiltrating bacteria between primary tumor tissues and associated normal blood samples. Our results showed that primary tumor tissues contained far more diverse total infiltrating bacteria than normal blood samples. The relative abundance of Bacteroides fragilis, Bacteroides dorei, and Fusobacterium nucleatum was significantly higher in primary colorectal tumors. These three bacteria were among the top ten microbes in the primary tumor tissues, yet were rarely found in normal blood samples. As a validation step, most of these bacteria were also closely associated with colorectal cancer in previous studies with alternative approaches. In summary, our approach provides a new analytic technique for investigating the infiltrating bacterial community within tumor tissues. Our novel cloud-based bioinformatics and statistical pipelines to analyze the infiltrating bacteria in colorectal tumors using the unmapped reads of whole genome sequences can be freely accessed from GitHub at https://github.com/gutmicrobes/UMIB.git.

Using Decision Tree Aggregation with Random Forest Model to Identify Gut Microbes Associated with Colorectal Cancer.

The imbalance of human gut microbiota has been associated with colorectal cancer. In recent years, metagenomics research has provided a large amount of scientific data enabling us to study the dedicated roles of gut microbes in the onset and progression of cancer. We removed unrelated and redundant features during feature selection by mutual information. We then trained a random forest classifier on a large metagenomics dataset of colorectal cancer patients and healthy people assembled from published reports and extracted and analysed the information from the learned decision trees. We identified key microbial species associated with colorectal cancers. These microbes included Porphyromonasasaccharolytica, Peptostreptococcusstomatis, Fusobacterium,Parvimonas sp., Streptococcusvestibularis and Flavonifractorplautii. We obtained the optimal splitting abundance thresholds for these species to distinguish between healthy and colorectal cancer samples. This extracted consensus decision tree may be applied to the diagnosis of colorectal cancers.

SVEngine: an efficient and versatile simulator of genome structural variations with features of cancer clonal evolution.

Background: Simulating genome sequence data with variant features facilitates the development and benchmarking of structural variant analysis programs. However, there are only a few data simulators that provide structural variants in silico and even fewer that provide variants with different allelic fraction and haplotypes. Findings: We developed SVEngine, an open-source tool to address this need. SVEngine simulates next-generation sequencing data with embedded structural variations. As input, SVEngine takes template haploid sequences (FASTA) and an external variant file, a variant distribution file, and/or a clonal phylogeny tree file (NEWICK) as input. Subsequently, it simulates and outputs sequence contigs (FASTAs), sequence reads (FASTQs), and/or post-alignment files (BAMs). All of the files contain the desired variants, along with BED files containing the ground truth. SVEngine's flexible design process enables one to specify size, position, and allelic fraction for deletions, insertions, duplications, inversions, and translocations. Finally, SVEngine simulates sequence data that replicate the characteristics of a sequencing library with mixed sizes of DNA insert molecules. To improve the compute speed, SVEngine is highly parallelized to reduce the simulation time. Conclusions: We demonstrated the versatile features of SVEngine and its improved runtime comparisons with other available simulators. SVEngine's features include the simulation of locus-specific variant frequency designed to mimic the phylogeny of cancer clonal evolution. We validated SVEngine's accuracy by simulating genome-wide structural variants of NA12878 and a heterogeneous cancer genome. Our evaluation included checking various sequencing mapping features such as coverage change, read clipping, insert size shift, and neighboring hanging read pairs for representative variant types. Structural variant callers Lumpy and Manta and tumor heterogeneity estimator THetA2 were able to perform realistically on the simulated data. SVEngine is implemented as a standard Python package and is freely available for academic use .

Exercise Benefits Coronary Heart Disease.

Coronary heart disease (CHD) is a group of diseases that include: no symptoms, angina, myocardial infarction, ischemia cardiomyopathy and sudden cardiac death. And it results from multiple risks factors consisting of invariable factors (e.g. age, gender, etc.) and variable factors (e.g. dyslipidemia, hypertension, diabetes, smoking, etc.). Meanwhile, CHD could cause impact not only localized in the heart, but also on pulmonary function, whole-body skeletal muscle function, activity ability, psychological status, etc. Nowadays, CHD has been the leading cause of death in the world. However, many clinical researches showed that exercise training plays an important role in cardiac rehabilitation and can bring a lot of benefits for CHD patients.

Expression of the tumor suppressor gene p16, and lymph node metastasis in patients with ovarian cancer.

This study examined the relationship between the expression of p16, a tumor suppressor gene, and lymph node metastasis, as well as patient prognosis, in cases with ovarian cancer. SKOV-3, an ovarian cancer cell line, and IOSE80, a normal human ovarian cell line, were selected for testing. Western blot analysis was used to detect the p16 expression in ovarian cell culture samples. In the study, 20 cases with normal ovarian tissue and 64 cases with ovarian cancer tissue, including 38 cases with lymph node metastasis and 26 cases without lymph node metastasis, were also selected for testing. Immunohistochemical techniques were used to detect the expression of p16 protein in ovarian tissue samples. The influence of p16 protein on SKOV-3 cell invasion ability was studied using p16 gene high-expression vector transfection. Clinical and prognosis data were summarized and the influence of p16 on patient prognosis was analyzed through Kaplan-Meier single-factor survival analysis. The results showed that p16 expression in SKOV-3 was decreased significantly compared with that in IOSE80. The positive rate of p16 protein expression in ovarian cancer tissue was notably decreased compared with that in normal ovarian tissue. The positive rate of p16 protein expression in ovarian cancer tissue of patients with lymph node metastasis was significantly decreased compared with that of patients without lymph node metastasis. Therefore, transfection of the p16 gene significantly inhibited the protein expression and invasion ability of p16 in SKOV-3. Correlation analyses between p16 and survival prognosis demonstrated that lower expression of p16 was negatively correlated with the prognosis of patients with ovarian cancer. Overall, the abnormal expression of p16 in ovarian cancer is associated with an increased invasion ability of ovarian cancer and the lower expression of p16 in tissue samples indicates a poor prognosis in patients with ovarian cancer.