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Copy number variations (CNVs) play an important role in many types of cancer. With the rapid development of next generation sequencing (NGS) techniques, many methods for detecting CNVs of a single sample have emerged: (ⅰ) require genome-wide data of both case and control samples, (ⅱ) depend on sequencing depth and GC content correction algorithm, (ⅲ) rely on statistical models built on CNV positive and negative sample datasets. These make them costly in the data analysis and ineffective in the targeted sequencing data. In this study, we developed a novel alignment-free method called DL-CNV to call CNV from the target sequencing data of a single sample. Specifically, we collected two sets of samples. The first set consists of 1301 samples, in which 272 have CNVs in ERBB2 and the second set is composed of 1148 samples with 63 samples containing CNVs in MET. Finally, we found that a testing AUC of 0.9454 for ERBB2 and 0.9220 for MET. Furthermore, we hope to make the CNV detection could be more accurate with clinical pgold standardq (e.g. FISH) information and provide a new research direction, which can be used as the supplement to the existing NGS methods.

Luminescent metal–organic frameworks (LMOFs) have been widely developed in the field of chemical sensing owing to their outstanding photoluminescence performance, high selectivity, anti-interference, high sensitivity, and fast response, and have become one of the research hotspots of emerging functional materials. However, in practical applications, many tests are carried out in the water environment, and fragile water stability greatly limits the application of MOFs in the field. Therefore, it is important to develop a method to enhance the water stability of MOFs. Herein, a new complex {[Zn(L)]·CH3CN}n (Zn-MOF, H2L = 5-(benzimidazol-1-yl) isophthalic acid) with a superior photophysical property has been synthesized first. Its water stability was highly enhanced by the doping of CuII ions by the one-pot method. In addition, the detection performances of doping material Cu0.1/Zn-MOF for sixteen metal ions and thirteen antibiotics were well studied. It was found that Cu0.1/Zn-MOF displays high sensitivity, fast response, lower detection limit, and long-term stability for the detection of Fe3+, NFT, NFZ, FZD, and TC in the aqueous medium.

Immunoglobulins have a pivotal role in disease regulation. Therefore, it is vital to accurately identify immunoglobulins to develop new drugs and research related diseases. Compared with utilizing high-dimension features to identify immunoglobulins, this research aimed to examine a method to classify immunoglobulins and non-immunoglobulins using two features, FC* and GC*. Classification of 228 samples (109 immunoglobulin samples and 119 non-immunoglobulin samples) revealed that the overall accuracy was 80.7% in 10-fold cross-validation using the J48 classifier implemented in Weka software. The FC* feature identified in this study was found in the immunoglobulin subtype domain, which demonstrated that this extracted feature could represent functional and structural properties of immunoglobulins for forecasting.

Many diseases are accompanied by changes in certain biochemical indicators called biomarkers in cells or tissues. A variety of biomarkers, including proteins, nucleic acids, antibodies, and peptides, have been identified. Tumor biomarkers have been widely used in cancer risk assessment, early screening, diagnosis, prognosis, treatment, and progression monitoring. For example, the number of circulating tumor cell (CTC) is a prognostic indicator of breast cancer overall survival, and tumor mutation burden (TMB) can be used to predict the efficacy of immune checkpoint inhibitors. Currently, clinical methods such as polymerase chain reaction (PCR) and next generation sequencing (NGS) are mainly adopted to evaluate these biomarkers, which are time-consuming and expansive. Pathological image analysis is an essential tool in medical research, disease diagnosis and treatment, functioning by extracting important physiological and pathological information or knowledge from medical images. Recently, deep learning-based analysis on pathological images and morphology to predict tumor biomarkers has attracted great attention from both medical image and machine learning communities, as this combination not only reduces the burden on pathologists but also saves high costs and time. Therefore, it is necessary to summarize the current process of processing pathological images and key steps and methods used in each process, including: (1) pre-processing of pathological images, (2) image segmentation, (3) feature extraction, and (4) feature model construction. This will help people choose better and more appropriate medical image processing methods when predicting tumor biomarkers.

Age-related hearing loss (ARHL) is associated with cognitive dysfunction; however, the detailed underlying mechanisms remain unclear. The aim of this study is to investigate the potential underlying mechanism with a system genetics approach. A transcriptome-wide association study was performed on aged (12–32 months old) BXD mice strains. The hippocampus gene expression was obtained from 56 BXD strains, and the hearing acuity was assessed from 54 BXD strains. Further correlation analysis identified a total of 1,435 hearing-related genes in the hippocampus (p < 0.05). Pathway analysis of these genes indicated that the impaired glutamatergic synapse pathway is involved in ARHL (p = 0.0038). Further gene co-expression analysis showed that the expression level of glutamine synthetase (Gls), which is significantly correlated with ARHL (n = 26, r = −0.46, p = 0.0193), is a crucial regulator in glutamatergic synapse pathway and associated with learning and memory behavior. In this study, we present the first systematic evaluation of hippocampus gene expression pattern associated with ARHL, learning, and memory behavior. Our results provide novel potential molecular mechanisms involved in ARHL and cognitive dysfunction association.

Tamoxifen (TAM) is the most commonly used adjuvant endocrine drug for hormone receptor-positive (HR+) breast cancer patients. However, how to accurately evaluate the risk of breast cancer recurrence and metastasis after adjuvant TAM therapy is still a major concern. In recent years, many studies have shown that the clinical outcomes of TAM-treated breast cancer patients are influenced by the activity of some cytochrome P450 (CYP) enzymes that catalyze the formation of active TAM metabolites like endoxifen and 4-hydroxytamoxifen. In this study, we aimed to first develop and validate an algorithm combining polymorphisms in CYP genes and clinicopathological signatures to identify a subpopulation of breast cancer patients who might benefit most from TAM adjuvant therapy and meanwhile evaluate major risk factors related to TAM resistance. Specifically, a total of 256 patients with invasive breast cancer who received adjuvant endocrine therapy were selected. The genotypes at 10 loci from three TAM metabolism-related CYP genes were detected by time-of-flight mass spectrometry and multiplex long PCR. Combining the 10 loci with nine clinicopathological characteristics, we obtained 19 important features whose association with cancer recurrence was assessed by importance score via random forests. After that, a logistic regression model was trained to calculate TAM risk-of-recurrence score (TAM RORs), which is adopted to assess a patient’s risk of recurrence after TAM treatment. The sensitivity and specificity of the model in an independent test cohort were 86.67% and 64.56%, respectively. This study showed that breast cancer patients with high TAM RORs were less sensitive to TAM treatment and manifested more invasive characteristics, whereas those with low TAM RORs were highly sensitive to TAM treatment, and their conditions were stable during the follow-up period. There were some risk factors that had a significant effect on the efficacy of TAM. They were tissue classification (tumor Grade < 2 vs. Grade ≥ 2, p = 2.2e−16), the number of lymph node metastases (Node-Negative vs. Node < 4, p = 5.3e−07; Node < 4 vs. Node ≥ 4, p = 0.003; Node-Negative vs. Node ≥ 4, p = 7.2e−15), and the expression levels of estrogen receptor (ER) and progesterone receptor (PR) (ER < 50% vs. ER ≥ 50%, p = 1.3e−12; PR < 50% vs. PR ≥ 50%, p = 2.6e−08). The really remarkable thing is that different genotypes of CYP2D6*10(C188T) show significant differences in prediction function (CYP2D6*10 CC vs. TT, p < 0.019; CYP2D6*10 CT vs. TT, p < 0.037). There are more than 50% Chinese who have CYP2D6*10 mutation. So the genotype of CYP2D6*10(C188T) should be tested before TAM therapy.

Critical in revealing cell heterogeneity and identifying new cell subtypes, cell clustering based on single-cell RNA sequencing (scRNA-seq) is challenging. Due to the high noise, sparsity, and poor annotation of scRNA-seq data, existing state-of-the-art cell clustering methods usually ignore gene functions and gene interactions. In this study, we propose a feature extraction method, named FEGFS, to analyze scRNA-seq data, taking advantage of known gene functions. Specifically, we first derive the functional gene sets based on Gene Ontology (GO) terms and reduce their redundancy by semantic similarity analysis and gene repetitive rate reduction. Then, we apply the kernel principal component analysis to select features on each non-redundant functional gene set, and we combine the selected features (for each functional gene set) together for subsequent clustering analysis. To test the performance of FEGFS, we apply agglomerative hierarchical clustering based on FEGFS and compared it with seven state-of-the-art clustering methods on six real scRNA-seq datasets. For small datasets like Pollen and Goolam, FEGFS outperforms all methods on all four evaluation metrics including adjusted Rand index (ARI), normalized mutual information (NMI), homogeneity score (HOM), and completeness score (COM). For example, the ARIs of FEGFS are 0.955 and 0.910, respectively, on Pollen and Goolam; and those of the second-best method are only 0.938 and 0.910, respectively. For large datasets, FEGFS also outperforms most methods. For example, the ARIs of FEGFS are 0.781 on both Klein and Zeisel, which are higher than those of all other methods but slight lower than those of SC3 (0.798 and 0.807, respectively). Moreover, we demonstrate that CMF-Impute is powerful in reconstructing cell-to-cell and gene-to-gene correlation and in inferring cell lineage trajectories. As for application, take glioma as an example; we demonstrated that our clustering methods could identify important cell clusters related to glioma and also inferred key marker genes related to these cell clusters.

Illumina is the leading sequencing platform in the next-generation sequencing (NGS) market globally. In recent years, MGI Tech has presented a series of new sequencers, including DNBSEQ-T7, MGISEQ-2000 and MGISEQ-200. As a complex application of NGS, cancer-detecting panels pose increasing demands for the high accuracy and sensitivity of sequencing and data analysis. In this study, we used the same capture DNA libraries constructed based on the Illumina protocol to evaluate the performance of the Illumina Nextseq500 and MGISEQ-2000 sequencing platforms. We found that the two platforms had high consistency in the results of hotspot mutation analysis; more importantly, we found that there was a significant loss of fragments in the 101–133 bp size range on the MGISEQ-2000 sequencing platform for Illumina libraries, but not for the capture DNA libraries prepared based on the MGISEQ protocol. This phenomenon may indicate fragment selection or low fragment ligation efficiency during the DNA circularization step, which is a unique step of the MGISEQ-2000 sequence platform. In conclusion, these different sequencing libraries and corresponding sequencing platforms are compatible with each other, but protocol and platform selection need to be carefully evaluated in combination with research purpose.

Ketamine is a popular recreational substance of abuse that induces persistent behavioral deficits. Although disrupted oxytocinergic systems have been considered to modulate vulnerability to developing drugs of abuse, the involvement of central oxytocin in behavioral abnormalities caused by chronic ketamine has remained largely unknown. Herein, we aimed to investigate the potential role of oxytocin in the medial prefrontal cortex (mPFC) in social avoidance and cognitive impairment resulting from repeated ketamine administration in mice. We found that ketamine injection (5 mg/kg, i.p.) for 10 days followed by a 6-day withdrawal period induced behavioral disturbances in social interaction and cognitive performance, as well as reduced oxytocin levels both at the periphery and in the mPFC. Repeated ketamine exposure also inhibited mPFC neuronal activity as measured by a decrease in c-fos-positive cells. Furthermore, direct microinjection of oxytocin into the mPFC reversed the social avoidance and cognitive impairment following chronic ketamine exposure. In addition, oxytocin administration normalized ketamine-induced inflammatory cytokines including TNF-α, IL-6, and IL-1β levels. Moreover, the activation of immune markers such as neutrophils and monocytes, by ketamine was restored in oxytocin-treated mice. Finally, the reversal effects of oxytocin on behavioral performance were blocked by pre-infusion of the oxytocin receptor antagonist atosiban into the mPFC. These results demonstrate that enhancing oxytocin signaling in the mPFC is a potential pathway to reverse social avoidance and cognitive impairment caused by ketamine, partly through inhibition of inflammatory stimulation.

Objectives: COVID-19 is highly infectious and has been widely spread worldwide, with more than 159 million confirmed cases and more than 3 million deaths as of May 11, 2021. It has become a serious public health event threatening people’s lives and safety. Due to the rapid transmission and long incubation period, shortage of medical resources would easily occur in the short term of discovering disease cases. Therefore, we aimed to construct an artificial intelligent framework to rapidly distinguish patients with COVID-19 from common pneumonia and non-pneumonia populations based on computed tomography (CT) images. Furthermore, we explored artificial intelligence (AI) algorithms to integrate CT features and laboratory findings on admission to predict the clinical classification of COVID-19. This will ease the burden of doctors in this emergency period and aid them to perform timely and appropriate treatment on patients.Methods: We collected all CT images and clinical data of novel coronavirus pneumonia cases in Inner Mongolia, including domestic cases and those imported from abroad; then, three models based on transfer learning to distinguish COVID-19 from other pneumonia and non-pneumonia population were developed. In addition, CT features and laboratory findings on admission were combined to predict clinical types of COVID-19 using AI algorithms. Lastly, Spearman’s correlation test was applied to study correlations of CT characteristics and laboratory findings.Results: Among three models to distinguish COVID-19 based on CT, vgg19 showed excellent diagnostic performance, with area under the curve (AUC) of the receiver operating characteristic (ROC) curve at 95%. Together with laboratory findings, we were able to predict clinical types of COVID-19 with AUC of the ROC curve at 90%. Furthermore, biochemical markers, such as C-reactive protein (CRP), LYM, and lactic dehydrogenase (LDH) were identified and correlated with CT features.Conclusion: We developed an AI model to identify patients who were positive for COVID-19 according to the results of the first CT examination after admission and predict the progression combined with laboratory findings. In addition, we obtained important clinical characteristics that correlated with the CT image features. Together, our AI system could rapidly diagnose COVID-19 and predict clinical types to assist clinicians perform appropriate clinical management.

BackgroundNon-small cell lung cancer (NSCLC) is one of the most prevalent causes of cancer-related death worldwide. Recently, there are many important medical advancements on NSCLC, such as therapies based on tyrosine kinase inhibitors and immune checkpoint inhibitors. Most of these therapies require tumor molecular testing for selecting patients who would benefit most from them. As invasive biopsy is highly risky, NSCLC molecular testing based on liquid biopsy has received more and more attention recently.ObjectiveWe aimed to introduce liquid biopsy and its potential clinical applications in NSCLC patients, including cancer diagnosis, treatment plan prioritization, minimal residual disease detection, and dynamic monitoring on the response to cancer treatment.MethodWe reviewed recent studies on circulating tumor DNA (ctDNA) testing, which is a minimally invasive approach to identify the presence of tumor-related mutations. In addition, we evaluated potential clinical applications of ctDNA as blood biomarkers for advanced NSCLC patients.ResultsMost studies have indicated that ctDNA testing is critical in diagnosing NSCLC, predicting clinical outcomes, monitoring response to targeted therapies and immunotherapies, and detecting cancer recurrence. Moreover, the changes of ctDNA levels are associated with tumor mutation burden and cancer progression.ConclusionThe ctDNA testing is promising in guiding the therapies on NSCLC patients.

Complex diseases, such as breast cancer, are often caused by mutations of multiple functional genes. Identifying disease-related genes is a critical and challenging task for unveiling the biological mechanisms behind these diseases. In this study, we develop a novel computational framework to analyze the network properties of the known breast cancer–associated genes, based on which we develop a random-walk-with-restart (RCRWR) algorithm to predict novel disease genes. Specifically, we first curated a set of breast cancer–associated genes from the Genome-Wide Association Studies catalog and Online Mendelian Inheritance in Man database and then studied the distribution of these genes on an integrated protein–protein interaction (PPI) network. We found that the breast cancer–associated genes are significantly closer to each other than random, which confirms the modularity property of disease genes in a PPI network as revealed by previous studies. We then retrieved PPI subnetworks spanning top breast cancer–associated KEGG pathways and found that the distribution of these genes on the subnetworks are non-random, suggesting that these KEGG pathways are activated non-uniformly. Taking advantage of the non-random distribution of breast cancer–associated genes, we developed an improved RCRWR algorithm to predict novel cancer genes, which integrates network reconstruction based on local random walk dynamics and subnetworks spanning KEGG pathways. Compared with the disease gene prediction without using the information from the KEGG pathways, this method has a better prediction performance on inferring breast cancer–associated genes, and the top predicted genes are better enriched on known breast cancer–associated gene ontologies. Finally, we performed a literature search on top predicted novel genes and found that most of them are supported by at least wet-lab experiments on cell lines. In summary, we propose a robust computational framework to prioritize novel breast cancer–associated genes, which could be used for further in vitro and in vivo experimental validation.

There′re numbers of welding spots and types in vehicle seat framework (VSF) and high welding quality requirement, while commercial CAD weld design has low efficiency and cannot satisfy customers′ requirement, so it′s urgent to realize automated processing design through self-defined development. Aiming at that problems, based on analysis of processing parameters and restrains of resistance welding in VSF, a framework for welding spot modeling via the technical route of CAA user interface design, self-defined object, parameter validation and entity creation, is advanced. Furthermore, problems of parameter automatic computing and validation are studied through examples of spot diameter computation of nut side weld and distance computation between spot center and sideline. Finally, parametric modeling of plate projection weld and nut weld of resistance welding of resistance welding for VSF is realized via CAA redevelopment. This case manifests that advanced development functions of CATIA can realize creation of self-defined objects and management of processing parameters to satisfy advanced customers′ requirement. CATIA CAA has integrated functions of GUI design, object extension and geometric and topological object edition, which grants its powerful ability in object self-definition, so it can be effectively applied to mature customers.

Ni-based alloy cladding layers with different graphene sheet additions were prepared by laser cladding on the 40CrNi2Si2MoV steel substrate. The influence of the graphene sheet on the microstructure and properties of the cladding layers was investigated. The results show that owing to the diffusion-reaction dissolution, the graphene sheet addition does not bring a corresponding change in the phase constitutions of the cladding layers, i. e., the cladding layers are still composed of γ-Ni, Ni3B, and M7C3 phases. But what has changed is that the solidified structure is refined, and the volume fractions of the eutectic and the carbide are increased with the increase of graphene sheet addition. As a result, the hardness and the wear resistance of the cladding layers gradually increase, whereas the friction coefficient firstly decreases and then increases, with the lowest friction coefficient obtained at 0.5 vol.% graphene sheet addition. Compared to the Ni-based alloy cladding layers with micro-size graphite additions, the studied cladding layers exhibit improved hardness and wear resistance, good forming quality, and increased friction coefficient. DOI: http://dx.doi.org/10.5755/j01.ms.25.3.19173

Abstract Background Esophageal cancer (EC) is one of the malignant tumors with a poor prognosis. The early stage of EC is asymptomatic, so identification of cancer biomarkers is important for early detection and clinical practice. Methods In this study, we compared the protein expression profiles in esophageal squamous cell carcinoma (ESCC) tissues and adjacent normal esophageal tissues from five patients through high-resolution label-free mass spectrometry. Through bioinformatics analysis, we found the differentially expressed proteins of ESCC. To perform the rapid identification of biomarkers, we adopted a high-throughput protein identification technique of Quantitative Dot Blot (QDB). Meanwhile, the QDB results were verified by classical immunohistochemistry. Results In total 2297 proteins were identified, out of which 308 proteins were differentially expressed between ESCC tissues and normal tissues. By bioinformatics analysis, the four up-regulated proteins (PTMA, PAK2, PPP1CA, HMGB2) and the five down-regulated proteins (Caveolin, Integrin beta-1, Collagen alpha-2(VI), Leiomodin-1 and Vinculin) were selected and validated in ESCC by Western Blot. Furthermore, we performed the QDB and IHC analysis in 64 patients and 117 patients, respectively. The PTMA expression was up-regulated gradually along the progression of ESCC, and the PTMA expression ratio between tumor and adjacent normal tissue was significantly increased along with the progression. Therefore, we suggest that PTMA might be a potential candidate biomarker for ESCC. Conclusion In this study, label-free quantitative proteomics combined with QDB revealed that PTMA expression was up-regulated in ESCC tissues, and PTMA might be a potential candidate for ESCC. Since Western Blot cannot achieve rapid and high-throughput screening of mass spectrometry results, the emergence of QDB meets this demand and provides an effective method for the identification of biomarkers.

Context: In vitro and in vivo research has shown that curcumin can alleviate diabetes and the relevant complications. Objective: To investigate the effect of curcumin on gestational diabetes (GD). Materials and methods: C57 BL/KsJdb/+(db/+) mice and C57 BL/KsJ+/+ mice (10–12 weeks old) were divided into four groups (n = 15): normal pregnancy (C57 BL/KsJ+/+), GD (C57 BL/KsJdb/+), GD plus low dose curcumin (50 mg/kg, orally gavage every day) and GD plus high dose curcumin (100 mg/kg, orally gavage every day). The tolerance of glucose and insulin were measured on gestation day 10. Body weight at birth and litter size of offspring were investigated, and the expression of oxidative stress factors [thiobarbituric acid reactive substance (TBARS), glutathione (GSH), superoxide dismutase (SOD) and catalase (CAT)] and AMP-activated protein kinase (AMPK), phospho-AMPK, histone deacetylases 4 (HDAC4), pHDAC4 and glucose-6-phosphatase (G6Pase) in the livers were analyzed by ELISA and Western blot on gestation day 20. Results: High dose curcumin could partly ameliorate the intolerance of glucose and insulin, and completely restore the litter size and the body weight of GD mice through decreased TBARS expression (p

Carcinoma of unknown primary (CUP) is a type of metastatic cancer, the primary tumor site of which cannot be identified. CUP occupies approximately 5% of cancer incidences in the United States with usually unfavorable prognosis, making it a big threat to public health. Traditional methods to identify the tissue-of-origin (TOO) of CUP like immunohistochemistry can only deal with around 20% CUP patients. In recent years, more and more studies suggest that it is promising to solve the problem by integrating machine learning techniques with big biomedical data involving multiple types of biomarkers including epigenetic, genetic, and gene expression profiles, such as DNA methylation. Different biomarkers play different roles in cancer research; for example, genomic mutations in a patient’s tumor could lead to specific anticancer drugs for treatment; DNA methylation and copy number variation could reveal tumor tissue of origin and molecular classification. However, there is no systematic comparison on which biomarker is better at identifying the cancer type and site of origin. In addition, it might also be possible to further improve the inference accuracy by integrating multiple types of biomarkers. In this study, we used primary tumor data rather than metastatic tumor data. Although the use of primary tumors may lead to some biases in our classification model, their tumor-of-origins are known. In addition, previous studies have suggested that the CUP prediction model built from primary tumors could efficiently predict TOO of metastatic cancers (Lal et al., 2013; Brachtel et al., 2016). We systematically compared the performances of three types of biomarkers including DNA methylation, gene expression profile, and somatic mutation as well as their combinations in inferring the TOO of CUP patients. First, we downloaded the gene expression profile, somatic mutation and DNA methylation data of 7,224 tumor samples across 21 common cancer types from the cancer genome atlas (TCGA) and generated seven different feature matrices through various combinations. Second, we performed feature selection by the Pearson correlation method. The selected features for each matrix were used to build up an XGBoost multi-label classification model to infer cancer TOO, an algorithm proven to be effective in a few previous studies. The performance of each biomarker and combination was compared by the 10-fold cross-validation process. Our results showed that the TOO tracing accuracy using gene expression profile was the highest, followed by DNA methylation, while somatic mutation performed the worst. Meanwhile, we found that simply combining multiple biomarkers does not have much effect in improving prediction accuracy.

Summary: COVID-19 has spread globally to over 200 countries with more than 40 million confirmed cases and one million deaths as of November 1, 2020. The SARS-CoV-2 virus, leading to COVID-19, shows extremely high rates of infectivity and replication, and can result in pneumonia, acute respiratory distress, or even mortality. SARS-CoV-2 has been found to continue to rapidly evolve, with several genomic variants emerging in different regions throughout the world. In addition, despite intensive study of the spike protein, its origin, and molecular mechanisms in mediating host invasion are still only partially resolved. Finally, the repertoire of drugs for COVID-19 treatment is still limited, with several candidates still under clinical trial and no effective therapeutic yet reported. Although vaccines based on either DNA/mRNA or protein have been deployed, their efficacy against emerging variants requires ongoing study, with multivalent vaccines supplanting the first-generation vaccines due to their low efficacy against new strains. Here, we provide a systematic review of studies on the epidemiology, immunological pathogenesis, molecular mechanisms, and structural biology, as well as approaches for drug or vaccine development for SARS-CoV-2. Public summary: • Clinical manifestations and epidemiology of COVID-19 • The efficacy of the developed vaccine against SARS-CoV-2 • Phylogenetic tree of evolutionary relationships in current SARS-CoV-2 strains • Structural analysis and origin tracking of SARS-CoV-2 • Mechanism involved in infection and immunological pathogenesis of SARS-CoV-2

Circular RNAs (circRNAs) play important roles in the self-renewal of stem cells. However, their significance and regulatory mechanisms in female germline stem cells (FGSCs) are largely unknown. Here, we identified an N6-methyladenosine (m6A)-modified circRNA, circGFRα1, which is highly abundant in mouse ovary and stage-specifically expressed in mouse FGSC development. Knockdown of circGFRα1 in FGSCs significantly reduced their self-renewal. In contrast, overexpression of circGFRα1 enhanced FGSC self-renewal. Mechanistically, circGFRα1 promotes FGSC self-renewal by acting as a competing endogenous RNA (ceRNA) that sponges miR-449, leading to enhanced GFRα1 expression and activation of the glial cell derived neurotrophic factor (GDNF) signaling pathway. Furthermore, circGFRα1 acts as a ceRNA based on METTL14-mediated cytoplasmic export through the GGACU motif. Our study should help to understand the mechanisms regulating germ cell development, add new evidence on the mechanism of action of circRNA, and deepen our understanding of the development of FGSCs.

PurposeEstablish a suitable machine learning model to identify its primary lesions for primary metastatic tumors in an integrated learning approach, making it more accurate to improve primary lesions’ diagnostic efficiency.MethodsAfter deleting the features whose expression level is lower than the threshold, we use two methods to perform feature selection and use XGBoost for classification. After the optimal model is selected through 10-fold cross-validation, it is verified on an independent test set.ResultsSelecting features with around 800 genes for training, the R2-score of a 10-fold CV of training data can reach 96.38%, and the R2-score of test data can reach 83.3%.ConclusionThese findings suggest that by combining tumor data with machine learning methods, each cancer has its corresponding classification accuracy, which can be used to predict primary metastatic tumors’ location. The machine-learning-based method can be used as an orthogonal diagnostic method to judge the machine learning model processing and clinical actual pathological conditions.

Diabetes-related diseases (DRDs), especially cancers pose a big threat to public health. Although people have explored pathological pathways of a few common DRDs, there is a lack of systematic studies on important biological processes (BPs) connecting diabetes and its related diseases/cancers. We have proposed and compared 10 protein–protein interaction (PPI)-based computational methods to study the connections between diabetes and 254 diseases, among which a method called DIconnectivity_eDMN performs the best in the sense that it infers a disease rank (according to its relation with diabetes) most consistent with that by literature mining. DIconnectivity_eDMN takes diabetes-related genes, other disease-related genes, a PPI network, and genes in BPs as input. It first maps genes in a BP into the PPI network to construct a BP-related subnetwork, which is expanded (in the whole PPI network) by a random walk with restart (RWR) process to generate a so-called expanded modularized network (eMN). Since the numbers of known disease genes are not high, an RWR process is also performed to generate an expanded disease-related gene list. For each eMN and disease, the expanded diabetes-related genes and disease-related genes are mapped onto the eMN. The association between diabetes and the disease is measured by the reachability of their genes on all eMNs, in which the reachability is estimated by a method similar to the Kolmogorov–Smirnov (KS) test. DIconnectivity_eDMN achieves an area under receiver operating characteristic curve (AUC) of 0.71 for predicting both Type 1 DRDs and Type 2 DRDs. In addition, DIconnectivity_eDMN reveals important BPs connecting diabetes and DRDs. For example, “respiratory system development” and “regulation of mRNA metabolic process” are critical in associating Type 1 diabetes (T1D) and many Type 1 DRDs. It is also found that the average proportion of diabetes-related genes interacting with DRDs is higher than that of non-DRDs.

A new coronavirus called SARS-CoV-2 is rapidly spreading around the world. Over 16,558,289 infected cases with 656,093 deaths have been reported by July 29th, 2020, and it is urgent to identify effective antiviral treatment. In this study, potential antiviral drugs against SARS-CoV-2 were identified by drug repositioning through Virus-Drug Association (VDA) prediction. 96 VDAs between 11 types of viruses similar to SARS-CoV-2 and 78 small molecular drugs were extracted and a novel VDA identification model (VDA-RLSBN) was developed to find potential VDAs related to SARS-CoV-2. The model integrated the complete genome sequences of the viruses, the chemical structures of drugs, a regularized least squared classifier (RLS), a bipartite local model, and the neighbor association information. Compared with five state-of-the-art association prediction methods, VDA-RLSBN obtained the best AUC of 0.9085 and AUPR of 0.6630. Ribavirin was predicted to be the best small molecular drug, with a higher molecular binding energy of −6.39 kcal/mol with human angiotensin-converting enzyme 2 (ACE2), followed by remdesivir (−7.4 kcal/mol), mycophenolic acid (−5.35 kcal/mol), and chloroquine (−6.29 kcal/mol). Ribavirin, remdesivir, and chloroquine have been under clinical trials or supported by recent works. In addition, for the first time, our results suggested several antiviral drugs, such as FK506, with molecular binding energies of −11.06 and −10.1 kcal/mol with ACE2 and the spike protein, respectively, could be potentially used to prevent SARS-CoV-2 and remains to further validation. Drug repositioning through virus–drug association prediction can effectively find potential antiviral drugs against SARS-CoV-2.

Studying transcriptome chronological change from tissues across the whole body can provide valuable information for understanding aging and longevity. Although there has been research on the effect of single-tissue transcriptomes on human aging or aging in mice across multiple tissues, the study of human body-wide multi-tissue transcriptomes on aging is not yet available. In this study, we propose a quantitative model to predict human age by using gene expression data from 46 tissues generated by the Genotype-Tissue Expression (GTEx) project. Specifically, the biological age of a person is first predicted via the gene expression profile of a single tissue. Then, we combine the gene expression profiles from two tissues and compare the predictive accuracy between single and two tissues. The best performance as measured by the root-mean-square error is 3.92 years for single tissue (pituitary), which deceased to 3.6 years when we combined two tissues (pituitary and muscle) together. Different tissues have different potential in predicting chronological age. The prediction accuracy is improved by combining multiple tissues, supporting that aging is a systemic process involving multiple tissues across the human body.

Cancer of unknown primary site (CUPS) is a type of metastatic tumor for which the sites of tumor origin cannot be determined. Precise diagnosis of the tissue origin for metastatic CUPS is crucial for developing treatment schemes to improve patient prognosis. Recently, there have been many studies using various cancer biomarkers to predict the tissue-of-origin (TOO) of CUPS. However, only a very few of them use copy number alteration (CNA) to trance TOO. In this paper, a two-step computational framework called CNA_origin is introduced to predict the tissue-of-origin of a tumor from its gene CNA levels. CNA_origin set up an intellectual deep-learning network mainly composed of an autoencoder and a convolution neural network (CNN). Based on real datasets released from the public database, CNA_origin had an overall accuracy of 83.81% on 10-fold cross-validation and 79% on independent datasets for predicting tumor origin, which improved the accuracy by 7.75 and 9.72% compared with the method published in a previous paper. Our results suggested that the autoencoder model can extract key characteristics of CNA and that the CNN classifier model developed in this study can predict the origin of tumors robustly and effectively. CNA_origin was written in Python and can be downloaded from https://github.com/YingLianghnu/CNA_origin.

Flow past a circular cylinder at the subcritical Reynolds number of 3900 is numerically investigated based on large-eddy simulations (LESs) via the open-source toolbox OpenFOAM. Good grid convergence property is achieved for the present LESs with different subgrid-scale models as far as the near-wake statistics are concerned. On the intermediate-resolution grid, current LES data in terms of mean flow and turbulence quantities are in good agreement with the benchmark experimental results. A comparison study on the Smagorinsky model manifests that increasing the model coefficient results in a longer recirculation length and smaller drag and lift coefficients. The budget analysis of the turbulent kinetic energy (TKE) equation suggests that the main source in the wake area is the TKE production, which is transported downstream mainly through the advection by mean flow and further moved upward and downward through the transport terms. The flow patterns indicate that the weak production rates induced by the shear layers delay the downward movement of the mean flow, resulting in a longer recirculation region. This dynamic picture provides a reliable explanation for the differences in the recirculation length and mean flow profiles observed in the near-wake area from different experimental and numerical studies.

Sequencing-based identification of tumor tissue-of-origin (TOO) is critical for patients with cancer of unknown primary lesions. Even if the TOO of a tumor can be diagnosed by clinicopathological observation, reevaluations by computational methods can help avoid misdiagnosis. In this study, we developed a neural network (NN) framework using the expression of a 150-gene panel to infer the tumor TOO for 15 common solid tumor cancer types, including lung, breast, liver, colorectal, gastroesophageal, ovarian, cervical, endometrial, pancreatic, bladder, head and neck, thyroid, prostate, kidney, and brain cancers. To begin with, we downloaded the RNA-Seq data of 7,460 primary tumor samples across the above mentioned 15 cancer types, with each type of cancer having between 142 and 1,052 samples, from the cancer genome atlas. Then, we performed feature selection by the Pearson correlation method and performed a 150-gene panel analysis; the genes were significantly enriched in the GO:2001242 Regulation of intrinsic apoptotic signaling pathway and the GO:0009755 Hormone-mediated signaling pathway and other similar functions. Next, we developed a novel NN model using the 150 genes to predict tumor TOO for the 15 cancer types. The average prediction sensitivity and precision of the framework are 93.36 and 94.07%, respectively, for the 7,460 tumor samples based on the 10-fold cross-validation; however, the prediction sensitivity and precision for a few specific cancers, like prostate cancer, reached 100%. We also tested the trained model on a 20-sample independent dataset with metastatic tumor, and achieved an 80% accuracy. In summary, we present here a highly accurate method to infer tumor TOO, which has potential clinical implementation.

Circulating tumor cells (CTCs) derived from primary tumors and/or metastatic tumors are markers for tumor prognosis, and can also be used to monitor therapeutic efficacy and tumor recurrence. Circulating tumor cells enrichment and screening can be automated, but the final counting of CTCs currently requires manual intervention. This not only requires the participation of experienced pathologists, but also easily causes artificial misjudgment. Medical image recognition based on machine learning can effectively reduce the workload and improve the level of automation. So, we use machine learning to identify CTCs. First, we collected the CTC test results of 600 patients. After immunofluorescence staining, each picture presented a positive CTC cell nucleus and several negative controls. The images of CTCs were then segmented by image denoising, image filtering, edge detection, image expansion and contraction techniques using python’s openCV scheme. Subsequently, traditional image recognition methods and machine learning were used to identify CTCs. Machine learning algorithms are implemented using convolutional neural network deep learning networks for training. We took 2300 cells from 600 patients for training and testing. About 1300 cells were used for training and the others were used for testing. The sensitivity and specificity of recognition reached 90.3 and 91.3%, respectively. We will further revise our models, hoping to achieve a higher sensitivity and specificity.

Patients with carcinoma of unknown primary (CUP) account for 3–5% of all cancer cases. A large number of metastatic cancers require further diagnosis to determine their tissue of origin. However, diagnosis of CUP and identification of its primary site are challenging. Previous studies have suggested that molecular profiling of tissue-specific genes could be useful in inferring the primary tissue of a tumor. The purpose of this study was to evaluate the performance somatic mutations detected in a tumor to identify the cancer tissue of origin. We downloaded the somatic mutation datasets from the International Cancer Genome Consortium project. The random forest algorithm was used to extract features, and a classifier was established based on the logistic regression. Specifically, the somatic mutations of 300 genes were extracted, which are significantly enriched in functions, such as cell-to-cell adhesion. In addition, the prediction accuracy on tissue-of-origin inference for 3,374 cancer samples across 13 cancer types reached 81% in a 10-fold cross-validation. Our method could be useful in the identification of cancer tissue of origin, as well as the diagnosis and treatment of cancers.

Data quality control and preprocessing are often the first step in processing next-generation sequencing (NGS) data of tumors. Not only can it help us evaluate the quality of sequencing data, but it can also help us obtain high-quality data for downstream data analysis. However, by comparing data analysis results of preprocessing with Cutadapt, FastP, Trimmomatic, and raw sequencing data, we found that the frequency of mutation detection had some fluctuations and differences, and human leukocyte antigen (HLA) typing directly resulted in erroneous results. We think that our research had demonstrated the impact of data preprocessing steps on downstream data analysis results. We hope that it can promote the development or optimization of better data preprocessing methods, so that downstream information analysis can be more accurate.

With the development of high throughput technologies, there are more and more protein–protein interaction (PPI) networks available, which provide a need for efficient computational tools for network alignment. Network alignment is widely used to predict functions of certain proteins, identify conserved network modules, and study the evolutionary relationship across species or biological entities. However, network alignment is an NP-complete problem, and previous algorithms are usually slow or less accurate in aligning big networks like human vs. yeast. In this study, we proposed a fast yet accurate algorithm called Network Alignment by Integrating Biological Process (NAIGO). Specifically, we first divided the networks into subnets taking the advantage of known prior knowledge, such as gene ontology. For each subnet pair, we then developed a novel method to align them by considering both protein orthologous information and their local structural information. After that, we expanded the obtained local network alignments in a greedy manner. Taking the aligned pairs as seeds, we formulated the global network alignment problem as an assignment problem based on similarity matrix, which was solved by the Hungarian method. We applied NAIGO to align human and Saccharomyces cerevisiae S288c PPI network and compared the results with other popular methods like IsoRank, GRAAL, SANA, and NABEECO. As a result, our method outperformed the competitors by aligning more orthologous proteins or matched interactions. In addition, we found a few potential functional orthologous proteins such as RRM2B in human and DNA2 in S. cerevisiae S288c, which are related to DNA repair. We also identified a conserved subnet with six orthologous proteins EXO1, MSH3, MSH2, MLH1, MLH3, and MSH6, and six aligned interactions. All these proteins are associated with mismatch repair. Finally, we predicted a few proteins of S. cerevisiae S288c potentially involving in certain biological processes like autophagosome assembly.

During the carcinogenesis of cervical cancer, the DNA of human papillomavirus (HPV) is frequently integrated into the human genome, which might be a biomarker for the early diagnosis of cervical cancer. Although the detection sensitivity of virus infection status increased significantly through the Illumina sequencing platform, there were still disadvantages remain for further improvement, including the detection accuracy and the complex integrated genome structure identification, etc. Nanopore sequencing has been proven to be a fast yet accurate technique of detecting pathogens in clinical samples with significant longer sequencing length. However, the identification of virus integration sites, especially HPV integration sites was seldom carried out by using nanopore platform. In this study, we evaluated the feasibility of identifying HPV integration sites by nanopore sequencer. Specifically, we re-sequenced the integration sites of a previously published sample by both nanopore and Illumina sequencing. After analyzing the results, three points of conclusions were drawn: first, 13 out of 19 previously published integration sites were found from all three datasets (i.e., nanopore, Illumina, and the published data), indicating a high overlap rate and comparability among the three platforms; second, our pipeline of nanopore and Illumina data identified 66 unique integration sites compared with previous published paper with 13 of them being verified by Sanger sequencing, indicating the higher integration sites detection sensitivity of our results compared with published data; third, we established a pipeline which could be used in HPV integration site detection by nanopore sequencing data without doing error correction analysis. In summary, a new nanopore data analysis method was tested and proved to be reliable in integration sites detection compared with methods of existing Illumina data analysis pipeline with less sequencing data required. It provides a solid evidence and tool to support the potential application of nanopore in virus status identification.

Metastatic cancers require further diagnosis to determine their primary tumor sites. However, the tissue-of-origin for around 5% tumors could not be identified by routine medical diagnosis according to a statistics in the United States. With the development of machine learning techniques and the accumulation of big cancer data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), it is now feasible to predict cancer tissue-of-origin by computational tools. Metastatic tumor inherits characteristics from its tissue-of-origin, and both gene expression profile and somatic mutation have tissue specificity. Thus, we developed a computational framework to infer tumor tissue-of-origin by integrating both gene mutation and expression (TOOme). Specifically, we first perform feature selection on both gene expressions and mutations by a random forest method. The selected features are then used to build up a multi-label classification model to infer cancer tissue-of-origin. We adopt a few popular multiple-label classification methods, which are compared by the 10-fold cross validation process. We applied TOOme to the TCGA data containing 7,008 non-metastatic samples across 20 solid tumors. Seventy four genes by gene expression profile and six genes by gene mutation are selected by the random forest process, which can be divided into two categories: (1) cancer type specific genes and (2) those expressed or mutated in several cancers with different levels of expression or mutation rates. Function analysis indicates that the selected genes are significantly enriched in gland development, urogenital system development, hormone metabolic process, thyroid hormone generation prostate hormone generation and so on. According to the multiple-label classification method, random forest performs the best with a 10-fold cross-validation prediction accuracy of 96%. We also use the 19 metastatic samples from TCGA and 256 cancer samples downloaded from GEO as independent testing data, for which TOOme achieves a prediction accuracy of 89%. The cross-validation validation accuracy is better than those using gene expression (i.e., 95%) and gene mutation (53%) alone. In conclusion, TOOme provides a quick yet accurate alternative to traditional medical methods in inferring cancer tissue-of-origin. In addition, the methods combining somatic mutation and gene expressions outperform those using gene expression or mutation alone.

Recently, many studies have demonstrated that microRNAs (miRNAs) are new small molecule drug targets. Identifying small molecule-miRNA associations (SMiRs) plays an important role in finding new clues for various human disease therapy. Wet experiments can discover credible SMiR associations; however, this is a costly and time-consuming process. Computational models have therefore been developed to uncover possible SMiR associations. In this study, we designed a new SMiR association prediction model, RWNS. RWNS integrates various biological information, credible negative sample selections, and random walk on a triple-layer heterogeneous network into a unified framework. It includes three procedures: similarity computation, negative sample selection, and SMiR association prediction based on random walk on the constructed small molecule-disease-miRNA association network. To evaluate the performance of RWNS, we used leave-one-out cross-validation (LOOCV) and 5-fold cross validation to compare RWNS with two state-of-the-art SMiR association methods, namely, TLHNSMMA and SMiR-NBI. Experimental results showed that RWNS obtained an AUC value of 0.9829 under LOOCV and 0.9916 under 5-fold cross validation on the SM2miR1 dataset, and it obtained an AUC value of 0.8938 under LOOCV and 0.9899 under 5-fold cross validation on the SM2miR2 dataset. More importantly, RWNS successfully captured 9, 17, and 37 SMiR associations validated by experiments among the predicted top 10, 20, and 50 SMiR candidates with the highest scores, respectively. We inferred that enoxacin and decitabine are associated with mir-21 and mir-155, respectively. Therefore, RWNS can be a powerful tool for SMiR association prediction.

Human blood contains cell-free DNA (cfDNA), with circulating tumor-derived DNAs (ctDNAs) widely used in cancer diagnosis and treatment. However, it is still difficult to efficiently and accurately identify and distinguish specific ctDNAs from normal cfDNA in cancer patient blood samples. In this study, ctDNA fragment length distribution analysis showed that ctDNA fragments are frequently shorter than the normal cfDNAs, which is consistent with previous findings. Interestingly, the ctDNA fragment length was found to be partially associated with the mutant allele frequency, with a low mutant allele frequency (< ~0.6%) associated with a longer ctDNA fragment length when compared to normal cfDNAs. The findings of this study contribute to improving the detection of low-frequency tumor mutations.