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Therapeutic antibody discovery using synthetic diversity has been proved productive, especially for target proteins not suitable for traditional animal immunization-based antibody discovery approaches. Recently, many lines of evidences suggest that the quality of synthetic diversity design limits the development success of synthetic antibody hits. The aim of our study is to understand the quality limitation and to properly address the challenges with a better design. Using VH3–23 as a model framework, we observed and quantitatively mapped CDR-H3 loop length-dependent usage of human IGHJ4 and IGHJ6 germline genes in the natural human immune repertoire. Skewed usage of DH2-JH6 and DH3-JH6 rearrangements was quantitatively determined in a CDR-H3 length-dependent manner in natural human antibodies with long CDR-H3 loops. Structural modeling suggests choices of JH help to stabilize antibody CDR-H3 loop and JH only partially contributes to the paratope. Our observations shed light on the design of next-generation synthetic diversity with improved probability of success.

Hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) remains a global health concern, and HBV proteins may be ideal targets for T cell-based immunotherapy for HCC. There is a need for fast and efficient identification of HBV-specific T cell receptors (TCRs) for the development of TCR-transduced T (TCR-T) cell-based immunotherapy. Two widely employed TCR identification approaches, T cell clonal expansion and single-cell sequencing, involve a TCR singularization process for the direct identification of Vα and Vβ pairs of TCR chains. Clonal expansion of T cells is well known to have tedious time and effort requirements due to the use of T cell cultures, whereas single-cell sequencing is limited by the requirements of cell sorting and the preparation of a single-cell immune-transcriptome library as well as the massive cost of the whole procedure. Here, we present a next-generation sequencing (NGS)-based HBV-specific TCR identification that does not require the TCR singularization process. Conclusion: Two pairing strategies, ranking-based strategy and α-β chain mixture-based strategy, have proved to be useful for NGS-based TCR identification, particularly for polyclonal T cells purified by a peptide-major histocompatibility complex (pMHC) multimer-based approach. Functional evaluation confirmed the specificity and avidity of two identified HBV-specific TCRs, which may potentially be used to produce TCR-T cells to treat patients with HBV-related HCC.

Background: Osteoarthritis (OA), one of the most important causes leading to joint disability, was considered as an untreatable disease. A series of genes were reported to regulate the pathogenesis of OA, including microRNAs, Long non-coding RNAs and Circular RNA. So far, the expression profiles and functions of lncRNAs, mRNAs, and circRNAs in OA are not fully understood. Objective: The present study aimed to identify differentially expressed genes in OA. Methods: The present study conducted RNA-seq to identify differentially expressed genes in OA. Ontology (GO) analysis was used to analyze the Molecular Function and Biological Process. KEGG pathway analysis was used to perform the differentially expressed lncRNAs in biological pathways. Results: Hierarchical clustering revealed a total of 943 mRNAs, 518 lncRNAs, and 300 circRNAs, which were dysregulated in OA compared to normal samples. Furthermore, we constructed differentially expressed mRNAs mediated protein-protein interaction network, differentially expressed lncRNAs mediated trans-regulatory networks, and competitive endogenous RNA (ceRNA) to reveal the interaction among these genes in OA. Bioinformatics analysis revealed that these dysregulated genes were involved in regulating multiple biological processes, such as wound healing, negative regulation of ossification, sister chromatid cohesion, positive regulation of interleukin-1 alpha production, sodium ion transmembrane transport, positive regulation of cell migration, and negative regulation of inflammatory response. To the best of our knowledge, this study for the first time, revealed the expression pattern of mRNAs, lncRNAs and circRNAs in OA. Conclusion: This study provided novel information to validate these differentially expressed RNAs may be as possible biomarkers and targets in OA.

The cell is the unit of life for all organisms, and all cells are certainly not the same. So the technology to generate transcription expression or genomic DNA profiles from single cells is crucial. Since its establishment in 2009, single-cell RNA sequencing (scRNA-seq) has emerged as a major driver of progress in biomedical research. During the last three years, several new single-cell sequencing platforms have emerged. Yet there are only a few systematic comparisons of the advantages and limitations of these commonly used platforms. Here we compare two single-cell sequencing platforms: BD Rhapsody and 10x Genomics Chromium, including their different mechanisms and some scRNA-seq results obtained with them.

Long noncoding RNAs have key roles in glioma progression. However, the function and mechanisms of action of the long noncoding RNA, LINC00346, in glioma remain unclear. In our study, we observed that LINC00346 levels were increased in glioma tissue samples, and according to Gene Expression Profiling Interactive Analysis, its levels were related to disease‐free survival and overall survival rates, suggesting that a high level of LINC00346 expression corresponds to a poor prognosis. We next confirmed the high levels of LINC00346 expression in glioma tissues and cell lines and showed that LINC00346 knockdown suppressed glioma cell proliferation, migration and invasion; promoted apoptosis; and delayed tumour growth. Moreover, the oncogenic function of LINC00346 may be explained, in part, by the down‐regulation of miR‐340‐5p and the de‐repression of ROCK1. We showed that LINC00346 may function as a competing endogenous RNA of miR‐340‐5p, thereby de‐repressing ROCK1. This study revealed a new regulatory network in glioma and identified potential therapeutic targets for this cancer.

Background Shanzhuyu (the dried mature sarcocarp of Cornus officinalis Sieb. et Zucc., DMSCO) is a Chinese herb that can be used for the treatment of Alzheimer’s disease (AD), but its mechanism remains unknown. The present study aimed to investigate the active ingredients and effective mechanisms of DMSCO for the treatment of AD based on a network pharmacology approach. Methods The active components of DMSCO were collected from the TCMSP and ETCM databases and the target proteins of these compounds were predicted using TCMSP, SwissTargetPrediction and the STITCH database. The AD-related target proteins were identified from the OMIM, DisGeNet, GEO and GeneCards databases. The network interaction model of the compound-target-disease was established and was used to obtain the key targets of DMSCO on AD through network topology analysis. Subsequently, gene enrichment in Gene Ontology (GO) and KEGG pathways were conducted using the David 6.8 online tool. Results A total of 30 DMSCO effective compounds and 209 effective drug targets were obtained. A total of 172 AD-related genes and 37 shared targets of DMSCO and AD were identified. A total of 43 key targets for the treatment of AD were obtained from the topological analysis of the DMSCO-AD target network. These key targets were involved in a variety of biological processes, including amyloid deposition, apoptosis, autophagy, inflammatory response and oxidative stress and pathways, such as the PI3K-AKT, MAPK and TNF pathways. Three key compounds, namely ursolic acid, anethole and β-sitosterol were obtained from the analysis of the key targets. Conclusions Ursolic acid, anethole and β-sitosterol may be the main active components of DMSCO in the treatment of AD. DMSCO can treat AD by regulating amyloid deposition, apoptosis, autophagy, inflammatory response and oxidative stress via the PI3K-AKT, MAPK and other signaling pathways.

Graphical abstract, Single-cell genomics has advanced rapidly as trace-DNA amplification technologies evolved. However, current technologies are subject to a variety of pitfalls such as contamination, uneven genomic coverage, and amplification errors. Even for the “golden” strategy of single stem cell-derived clonal formation, high-fidelity amplification is applicable merely to single stem cells. It’s still challenging to accurately define somatic mutations of a single cell in various cell types. Herein, we provided evidence, for the first time, to prove that induced pluripotent stem cells (iPS cells or iPSC), being a single somatic cell-derived clone, are recording almost identical (>90%) mutational profile of the initial cell progenitor. This finding demonstrates iPS technique, applicable to any cell type, can be utilized as a cell cloning strategy favorable for single-cell genomic amplification. This novel strategy is not limited by cell-type constraints or amplification artifacts, and thus enables our detailed investigation on the characteristics of somatic mutations in heterogeneous normal cells.

Description, Spatial transcriptome technique was applied to decipher the spatial architecture and TME characteristics of liver cancers., Heterogeneity is the major challenge for cancer prevention and therapy. Here, we first constructed high-resolution spatial transcriptomes of primary liver cancers (PLCs) containing 84,823 spots within 21 tissues from seven patients. The progressive comparison of spatial tumor microenvironment (TME) characteristics from nontumor to leading-edge to tumor regions revealed that the tumor capsule potentially affects intratumor spatial cluster continuity, transcriptome diversity, and immune cell infiltration. Locally, we found that the bidirectional ligand-receptor interactions at the 100-μm-wide cluster-cluster boundary contribute to maintaining intratumor architecture and the PROM1+ and CD47+ cancer stem cell niches are related to TME remodeling and tumor metastasis. Last, we proposed a TLS-50 signature to accurately locate tertiary lymphoid structures (TLSs) spatially and unveiled that the distinct composition of TLSs is shaped by their distance to tumor cells. Our study provides previous unknown insights into the diverse tumor ecosystem of PLCs and has potential benefits for cancer intervention.

There are many types of cancers. Although they share some hallmarks, such as proliferation and metastasis, they are still very different from many perspectives. They grow on different organ or tissues. Does each cancer have a unique gene expression pattern that makes it different from other cancer types? After the Cancer Genome Atlas (TCGA) project, there are more and more pan-cancer studies. Researchers want to get robust gene expression signature from pan-cancer patients. But there is large variance in cancer patients due to heterogeneity. To get robust results, the sample size will be too large to recruit. In this study, we tried another approach to get robust pan-cancer biomarkers by using the cell line data to reduce the variance. We applied several advanced computational methods to analyze the Cancer Cell Line Encyclopedia (CCLE) gene expression profiles which included 988 cell lines from 20 cancer types. Two feature selection methods, including Boruta, and max-relevance and min-redundancy methods, were applied to the cell line gene expression data one by one, generating a feature list. Such list was fed into incremental feature selection method, incorporating one classification algorithm, to extract biomarkers, construct optimal classifiers and decision rules. The optimal classifiers provided good performance, which can be useful tools to identify cell lines from different cancer types, whereas the biomarkers (e.g. NCKAP1, TNFRSF12A, LAMB2, FKBP9, PFN2, TOM1L1) and rules identified in this work may provide a meaningful and precise reference for differentiating multiple types of cancer and contribute to the personalized treatment of tumors.

Small open reading frames (sORFs) are an important class of genes with less than 100 codons. They were historically annotated as noncoding or even junk sequences. In recent years, accumulating evidence suggests that sORFs could encode a considerable number of polypeptides, many of which play important roles in both physiology and disease pathology. However, it has been technically challenging to directly detect sORF-encoded peptides (SEPs). Here, we discuss the latest advances in methodologies for identifying SEPs with mass spectrometry, as well as the progress on functional studies of SEPs.

Cancer has been generally defined as a cluster of systematic malignant pathogenesis involving abnormal cell growth. Genetic mutations derived from environmental factors and inherited genetics trigger the initiation and progression of cancers. Although several well-known factors affect cancer, mutation features and rules that affect cancers are relatively unknown due to limited related studies. In this study, a computational investigation on mutation profiles of cancer samples in 27 types was given. These profiles were first analyzed by the Monte Carlo Feature Selection (MCFS) method. A feature list was thus obtained. Then, the incremental feature selection (IFS) method adopted such list to extract essential mutation features related to 27 cancer types, find out 207 mutation rules and construct efficient classifiers. The top 37 mutation features corresponding to different cancer types were discussed. All the qualitatively analyzed gene mutation features contribute to the distinction of different types of cancers, and most of such mutation rules are supported by recent literature. Therefore, our computational investigation could identify potential biomarkers and prediction rules for cancers in the mutation signature level.

MHC-I peptides are a group of important immunopeptides presented by major histocompatibility complex (MHC) on the cell surface for immune recognition. The majority of reported MHC-I peptides are derived from protein coding sequences, and noncanonical peptides translated from small open reading frames (sORF) are largely unknown due to the lack of accurate and sensitive detection methods. Herein we report an efficient approach that implements complementary bioinformatic strategies to improve the identification of noncanonical MHC-I peptides. In a database search strategy, noncanonical immunopeptides mapping was optimized by combining three complementary pipelines to construct predicted sORF databases from Ribo-seq data. In a de novo peptide sequencing strategy, MS data search results were filtered against sORF databases to pin down additional noncanonical immunopeptides. In total, 308 noncanonical immunopeptides were identified from two tumor cell lines with selected ones vigorously validated. Our approach is a handy solution to identify noncanonical MHC peptides with Ribo-seq and MS data. Meanwhile, the novel noncanonical immunopeptides identified with this method could shed insights on fundamental immunology as well as cancer immunotherapies.

Non-small cell lung cancer is a major lethal subtype of epithelial lung cancer, with high morbidity and mortality. The single-cell sequencing technique plays a key role in exploring the pathogenesis of non-small cell lung cancer. We proposed a computational method for distinguishing cell subtypes from the different pathological regions of non-small cell lung cancer on the basis of transcriptomic profiles, including a group of qualitative classification criteria (biomarkers) and various rules. The random forest classifier reached a Matthew’s correlation coefficient (MCC) of 0.922 by using 720 features, and the decision tree reached an MCC of 0.786 by using 1880 features. The obtained biomarkers and rules were analyzed in the end of this study.

Type 2 diabetes (T2D) is a systematic chronic metabolic condition with abnormal sugar metabolism dysfunction, and its complications are the most harmful to human beings and may be life-threatening after long-term durations. Considering the high incidence and severity at late stage, researchers have been focusing on the identification of specific biomarkers and potential drug targets for T2D at the genomic, epigenomic, and transcriptomic levels. Microbes participate in the pathogenesis of multiple metabolic diseases including diabetes. However, the related studies are still non-systematic and lack the functional exploration on identified microbes. To fill this gap between gut microbiome and diabetes study, we first introduced eggNOG database and KEGG ORTHOLOGY (KO) database for orthologous (protein/gene) annotation of microbiota. Two datasets with these annotations were employed, which were analyzed by multiple machine-learning models for identifying significant microbiota biomarkers of T2D. The powerful feature selection method, Max-Relevance and Min-Redundancy (mRMR), was first applied to the datasets, resulting in a feature list for each dataset. Then, the list was fed into the incremental feature selection (IFS), incorporating support vector machine (SVM) as the classification algorithm, to extract essential annotations and build efficient classifiers. This study not only revealed potential pathological factors for diabetes at the microbiome level but also provided us new candidates for drug development against diabetes.

Adoptive transfer of T cells genetically engineered with a T cell receptor (TCR) is a promising cancer treatment modality that requires the identification of TCRs with good characteristics. Most T cell cloning methods involve a stringent singularization process, which necessitates either tedious hands-on operations or high cost. We present an efficient and nonstringent cloning approach based on existing techniques. We hypothesize that after elimination of most nonspecific T cells, a clonotype with high quality could outcompete other clonotypes and finally form a predominant population. This TCR identification method can be used to clone virus-specific TCRs efficiently from cancer patients and is easily adoptable by any laboratory. Supplementary Information The online version contains supplementary material available at 10.1186/s12865-021-00455-3.

Exosomes are involved in the pathophysiology of neuropsychiatric diseases, but the role of exosomes in schizophrenia (SCZ) is unclear. Here, we demonstrate that transplantation of serum exosomes from SCZ patients into mice caused behavioral abnormalities such as deficits in prepulse inhibition and sociability, hyperactivity, and anxiogenesis. A comparative bioinformatics analysis suggested shared and distinct differentially expressed genes (DEGs) and enriched molecular pathways in the brains of SCZ exosome-recipient mice, methylazoxymethanol acetate-treated rats, and SCZ patients, which correlates evidence of altered prefrontal–hippocampal functional coherence in SCZ. A large proportion of SCZ-relevant DEGs in the exosome-recipient mice were targets of DE exosomal miRNAs in SCZ patients. Furthermore, we identified 20 hub genes for SCZ risk genes, including BDNF and NRG1, which were DE miRNA targets in SCZ. Collectively, our study suggests that SCZ exosome transplantation caused SCZ-relevant behaviors in mice, and epigenetic regulation may contribute to the phenotypes in the SCZ exosome-recipient mice. Our results may provide a potential animal model and novel therapeutic targets for SCZ.

Acute myeloid leukemia (AML) is a type of blood cancer characterized by the rapid growth of immature white blood cells from the bone marrow. Therapy resistance resulting from the persistence of leukemia stem cells (LSCs) are found in numerous patients. Comparative transcriptome studies have been previously conducted to analyze differentially expressed genes between LSC+ and LSC- cells. However, these studies mainly focused on a limited number of genes with the most obvious expression differences between the two cell types. We developed a computational approach incorporating several machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), support vector machine (SVM), Repeated Incremental Pruning to Produce Error Reduction (RIPPER), to identify gene expression features specific to LSCs. One thousand 0ne hudred fifty-nine features (genes) were first identified, which can be used to build the optimal SVM classifier for distinguishing LSC+ and LSC- cells. Among these 1159 genes, the top 17 genes were identified as LSC-specific biomarkers. In addition, six classification rules were produced by RIPPER algorithm. The subsequent literature review on these features/genes and the classification rules and functional enrichment analyses of the 1159 features/genes confirmed the relevance of extracted genes and rules to the characteristics of LSCs.

Oral cancer (OC) is one of the most common cancers threatening human lives. However, OC pathogenesis has yet to be fully uncovered, and thus designing effective treatments remains difficult. Identifying genes related to OC is an important way for achieving this purpose. In this study, we proposed three computational models for inferring novel OC-related genes. In contrast to previously proposed computational methods, which lacked the learning procedures, each proposed model adopted a one-class learning algorithm, which can provide a deep insight into features of validated OC-related genes. A network embedding algorithm (i.e., node2vec) was applied to the protein-protein interaction network to produce the representation of genes. The features of the OC-related genes were used in the training of the one-class algorithm, and the performance of the final inferring model was improved through a feature selection procedure. Then, candidate genes were produced by applying the trained inferring model to other genes. Three tests were performed to screen out the important candidate genes. Accordingly, we obtained three inferred gene sets, any two of which were different. The inferred genes were also different from previous reported genes and some of them have been included in the public Oral Cancer Gene Database. Finally, we analyzed several inferred genes to confirm whether they are novel OC-related genes.

An embryo develops from a single-celled zygote, which produces a multi-cellular organism by mitosis. Due to the complication of processes and mechanisms, research on embryo cell clusters in different early embryo developmental stages with significant phenotypic differences is still lacking. In this work, we identified some gene characters and expression rules to classify these individual cells using several advanced computational methods. The single cell expression profiles of embryo cells were analyzed by the Monte Carlo feature selection (MCFS) method, resulting in a feature list. Then, the incremental feature selection (IFS) method, incorporating support vector machine (SVM), applied on such list to extract key gene characters. These gene characters include KHDC1, HMGN1, DCP, GDF9, RNF11, DNMT3L, and CDX1. Furthermore, a rule learning algorithm, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), was applied to the informative features yielded by MCFS method, producing a group of classification rules. These rules can clearly uncover different expression patterns on cells in different stages. This study provided a group of effective gene signatures and rules for embryo cell subtyping and presented an applicable computational tool to further dig into the regulatory mechanisms of embryo development.

Objective The relationship between the gut microbiota and the central nervous system has been gradually recognized while whether microbiome plays a role in the pathogenesis of drug-resistant epilepsy is still unknown. The aim of our work was to explore whether dysbiosis is involved in the mechanism of drug-resistant epilepsy. Methods Patients with epilepsy attending West China Hospital of Sichuan University were enrolled from March to May 2017. Patients were grouped into drug-resistant (n = 42) and drug-sensitive (n = 49) groups, another 65 healthy controls were from the same families of the patients. The fecal samples were collected and the microbiome composition was analyzed by high-throughout sequencing of the 16s ribosomal DNA. Results We found that the gut microbial community of drug-resistant epilepsy was significantly altered with an abnormal increased abundance of rare flora. While the gut microbiome composition of drug-sensitive epilepsy was similar with that of healthy controls. Specifically, patients with four seizures per year or fewer showed an increase of Bifidobacteria and Lactobacillus than those with more than four seizures per year. Conclusions Dysbiosis may be involved in the mechanism of drug-resistant epilepsy and restoring the gut microbial community may be a novel therapeutic method for drug-resistant epilepsy.

ATF3 has been reported to be dysregulated in various cancers and involved in various steps of tumorigenesis. However, the mechanisms underlying the abnormal expression of ATF3 and its biological function in gastric cancer (GC) have not been well investigated. Here, we report ATF3 as one of the key regulators of GC development and progression. Patients with low ATF3 expression had shorter survival and a poorer prognosis. In vitro and in vivo assays investigating ATF3 alterations revealed a complex integrated phenotype that affects cell growth and migration. Strikingly, high-throughput sequencing and microarray analysis of cells with ATF3 silencing or of ATF3-low GC tissues indicated alterations in the Wnt signaling pathway, focal adhesions and adherens junctions. Mechanistically, the expression of β-catenin and cell migration inducing hyaluronidase 1 (CEMIP) was significantly upregulated in GC cells with downregulated ATF3, which was synergistically repressed by the β-catenin/TCF3 signaling axis and noncoding RNA miR-17-5p and HOXA11-AS. In addition, we found that WDR5 expression was promoted by TCF3 and is involved in miR-17-5p and HOXA11-AS activation in GC cells. Taken together, our findings revealed the mechanism of ATF3 downregulation and its biological role in regulating the expression of Wnt signaling-related genes during GC progression, suggesting new informative biomarkers of malignancy and therapeutic directions for GC patients., Gastric cancer: Possible treatment targets identified New treatments for gastric cancer could involve controlling the activity of a regulatory gene and associated signaling pathway. Over-activation of the Wnt signaling pathway, which regulates many cellular functions, occurs in around half of gastric cancers. Further, the activating transcription factor 3 gene (ATF3) is thought to influence tumorigenesis, although its role in gastric cancer is unclear. Guohua Xie and co-workers at Shanghai Jiao Tong University, China, explored the function of ATF3 in human gastric cancer tissues. Patients with low ATF3 expression had poorer prognosis and shorter life expectancy. The team discovered that reduced expression of ATF3 triggered the increased expression of two of its target genes, which then altered Wnt signaling. Reduced ATF3 expression also boosted the invasiveness of gastric cancer cells. Initial results suggest that overexpression of ATF3 could suppress gastric cancer progression.

Cancer is one of the most threatening diseases to humans. It can invade multiple significant organs, including lung, liver, stomach, pancreas, and even brain. The identification of cancer biomarkers is one of the most significant components of cancer studies as the foundation of clinical cancer diagnosis and related drug development. During the large-scale screening for cancer prevention and early diagnosis, obtaining cancer-related tissues is impossible. Thus, the identification of cancer-associated circulating biomarkers from liquid biopsy targeting has been proposed and has become the most important direction for research on clinical cancer diagnosis. Here, we analyzed pan-cancer extracellular microRNA profiles by using multiple machine-learning models. The extracellular microRNA profiles on 11 cancer types and non-cancer were first analyzed by Boruta to extract important microRNAs. Selected microRNAs were then evaluated by the Max-Relevance and Min-Redundancy feature selection method, resulting in a feature list, which were fed into the incremental feature selection method to identify candidate circulating extracellular microRNA for cancer recognition and classification. A series of quantitative classification rules was also established for such cancer classification, thereby providing a solid research foundation for further biomarker exploration and functional analyses of tumorigenesis at the level of circulating extracellular microRNA.

Phenotype is one of the most significant concepts in genetics, which is used to describe all the characteristics of a research object that can be observed. Considering that phenotype reflects the integrated features of genotype and environment factors, it is hard to define phenotype characteristics, even difficult to predict unknown phenotypes. Restricted by current biological techniques, it is still quite expensive and time-consuming to obtain sufficient structural information of large-scale phenotype-associated genes/proteins. Various bioinformatics methods have been presented to solve such problem, and researchers have confirmed the efficacy and prediction accuracy of functional network-based prediction. But general functional descriptions have highly complicated inner structures for phenotype prediction. To further address this issue and improve the efficacy of phenotype prediction on more than ten kinds of phenotypes, we first extract functional enrichment features from GO and KEGG, and then use node2vec to learn functional embedding features of genes from a gene-gene network. All these features are analyzed by some feature selection methods (Boruta, minimum redundancy maximum relevance) to generate a feature list. Such list is fed into the incremental feature selection, incorporating some multi-label classifiers built by RAkEL and some classic base classifiers, to build an optimum multi-label multi-class classification model for phenotype prediction. According to recent researches, our method has indeed identified many literature-supported genes/proteins and their associated phenotypes, and even some candidate genes with re-assigned new phenotypes, which provide a new computational tool for the accurate and effective phenotypic prediction.

The world-wide Coronavirus Disease 2019 (COVID-19) pandemic was triggered by the widespread of a new strain of coronavirus named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Multiple studies on the pathogenesis of SARS-CoV-2 have been conducted immediately after the spread of the disease. However, the molecular pathogenesis of the virus and related diseases has still not been fully revealed. In this study, we attempted to identify new transcriptomic signatures as candidate diagnostic models for clinical testing or as therapeutic targets for vaccine design. Using the recently reported transcriptomics data of upper airway tissue with acute respiratory illnesses, we integrated multiple machine learning methods to identify effective qualitative biomarkers and quantitative rules for the distinction of SARS-CoV-2 infection from other infectious diseases. The transcriptomics data was first analyzed by Boruta so that important features were selected, which were further evaluated by the minimum redundancy maximum relevance method. A feature list was produced. This list was fed into the incremental feature selection, incorporating some classification algorithms, to extract qualitative biomarker genes and construct quantitative rules. Also, an efficient classifier was built to identify patients infected with SARS-COV-2. The findings reported in this study may help in revealing the potential pathogenic mechanisms of COVID-19 and finding new targets for vaccine design.

Glioblastoma, also called glioblastoma multiform (GBM), is the most aggressive cancer that initiates within the brain. GBM is produced in the central nervous system. Cancer cells in GBM are similar to stem cells. Several different schemes for GBM stratification exist. These schemes are based on intertumoral molecular heterogeneity, preoperative images, and integrated tumor characteristics. Although the formation of glioblastoma is remarkably related to gene methylation, GBM has been poorly classified by epigenetics. To classify glioblastoma subtypes on the basis of different degrees of genes’ methylation, we adopted several powerful machine learning algorithms to identify numerous methylation features (sites) associated with the classification of GBM. The features were first analyzed by an excellent feature selection method, Monte Carlo feature selection (MCFS), resulting in a feature list. Then, such list was fed into the incremental feature selection (IFS), incorporating one classification algorithm, to extract essential sites. These sites can be annotated onto coding genes, such as CXCR4, TBX18, SP5, and TMEM22, and enriched in relevant biological functions related to GBM classification (e.g., subtype-specific functions). Representative functions, such as nervous system development, intrinsic plasma membrane component, calcium ion binding, systemic lupus erythematosus, and alcoholism, are potential pathogenic functions that participate in the initiation and progression of glioblastoma and its subtypes. With these sites, an efficient model can be built to classify the subtypes of glioblastoma.

Cancer can be generally defined as a cluster of systematic diseases triggered by abnormal cell proliferation and growth. With the development of biological sciences and biotechnologies, the etiology of cancer is partially revealed, including some of the most substantial pathogenic factors [either endogenous (genetics) or exogenous (environmental)]. However, some remaining factors that contribute to the tumorigenesis but have not been analyzed and discussed in detail remain. For instance, some typical correlations between microorganisms and tumorigenesis have been reported already, but previous studies are just sporadic studies on single microorganism–cancer subtype pairs and do not explain and validate the specific contribution of microbiome on tumorigenesis. On the basis of the systematic microbiome analyses of blood and cancer-associated tissues in cancer patients/controls in public domain, we performed interpretable analyses. We identified several core regulatory microorganisms that contribute to the classification of multiple tumor subtypes and established quantitative predictive models for interpretable prediction by using multiple machine learning methods. We also compared the optimal features (microorganisms) and rules identified from microbiome profiles processed using the Kraken and the SHOGUN. Collectively, our study identified new microbiome signatures and their interpretable classification rules for cancer discrimination and carried out reliable methodological comparison for robust cancer microbiome analyses, thereby promoting the development of tumor etiology at the microbiome level.

Cancer, which refers to abnormal cell proliferative diseases with systematic pathogenic potential, is one of the leading threats to human health. The final causes for patients’ deaths are usually cancer recurrence, metastasis, and drug resistance against continuing therapy. Epithelial-to-mesenchymal transition (EMT), which is the transformation of tumor cells (TCs), is a prerequisite for pathogenic cancer recurrence, metastasis, and drug resistance. Conventional biomarkers can only define and recognize large tissues with obvious EMT markers but cannot accurately monitor detailed EMT processes. In this study, a systematic workflow was established integrating effective feature selection, multiple machine learning models [Random forest (RF), Support vector machine (SVM)], rule learning, and functional enrichment analyses to find new biomarkers and their functional implications for distinguishing single-cell isolated TCs with unique epithelial or mesenchymal markers using public single-cell expression profiling. Our discovered signatures may provide an effective and precise transcriptomic reference to monitor EMT progression at the single-cell level and contribute to the exploration of detailed tumorigenesis mechanisms during EMT.

Members of the ATP-binding cassette (ABC) transporter superfamily translocate a broad spectrum of chemically diverse substrates. While their eponymous ATP-binding cassette in the nucleotide-binding domains (NBDs) is highly conserved, their transmembrane domains (TMDs) forming the translocation pathway exhibit distinct folds and topologies, suggesting that during evolution, the ancient motor domains were combined with different transmembrane mechanical systems to orchestrate a variety of cellular processes. In recent years, it has become increasingly evident that the distinct TMD folds are best suited to categorize the multitude of ABC transporters. We therefore propose a new ABC transporter classification that currently comprises seven different types based on structural homology in the TMDs.

Rationale: Long extracellular RNAs (exRNAs) in plasma can be profiled by new sequencing technologies, even with low abundance. However, cancer-related exRNAs and their variations remain understudied. Methods: We investigated different variations (i.e. differential expression, alternative splicing, alternative polyadenylation, and differential editing) in diverse long exRNA species (e.g. long noncoding RNAs and circular RNAs) using 79 plasma exosomal RNA-seq (exoRNA-seq) datasets of multiple cancer types. We then integrated 53 exoRNA-seq datasets and 65 self-profiled cell-free RNA-seq (cfRNA-seq) datasets to identify recurrent variations in liver cancer patients. We further combined TCGA tissue RNA-seq datasets and validated biomarker candidates by RT-qPCR in an individual cohort of more than 100 plasma samples. Finally, we used machine learning models to identify a signature of 3 noncoding RNAs for the detection of liver cancer. Results: We found that different types of RNA variations identified from exoRNA-seq data were enriched in pathways related to tumorigenesis and metastasis, immune, and metabolism, suggesting that cancer signals can be detected from long exRNAs. Subsequently, we identified more than 100 recurrent variations in plasma from liver cancer patients by integrating exoRNA-seq and cfRNA-seq datasets. From these datasets, 5 significantly up-regulated long exRNAs were confirmed by TCGA data and validated by RT-qPCR in an independent cohort. When using machine learning models to combine two of these validated circular and structured RNAs (SNORD3B-1, circ-0080695) with a miRNA (miR-122) as a panel to classify liver cancer patients from healthy donors, the average AUROC of the cross-validation was 89.4%. The selected 3-RNA panel successfully detected 79.2% AFP-negative samples and 77.1% early-stage liver cancer samples in the testing and validation sets. Conclusions: Our study revealed that different types of RNA variations related to cancer can be detected in plasma and identified a 3-RNA detection panel for liver cancer, especially for AFP-negative and early-stage patients.

Coronary artery disease (CAD) is the most common cardiovascular disease. CAD research has greatly progressed during the past decade. mRNA is a traditional and popular pipeline to investigate various disease, including CAD. Compared with mRNA, lncRNA has better stability and thus may serve as a better disease indicator in blood. Investigating potential CAD-related lncRNAs and mRNAs will greatly contribute to the diagnosis and treatment of CAD. In this study, a computational analysis was conducted on patients with CAD by using a comprehensive transcription dataset with combined mRNA and lncRNA expression data. Several machine learning algorithms, including feature selection methods and classification algorithms, were applied to screen for the most CAD-related RNA molecules. Decision rules were also reported to provide a quantitative description about the effect of these RNA molecules on CAD progression. These new findings (CAD-related RNA molecules and rules) can help understand mRNA and lncRNA expression levels in CAD.

BackgroundFundamental to viral biology is identification and annotation of viral genes and their function. Determining the level of coronavirus gene expression is inherently difficult due to the positive stranded RNA genome and the identification of sub-genomic RNAs (sgRNAs) that are required for expression of most viral genes. In the COVID-19 epidemic so far, few genomic studies have looked at viral sgRNAs and none have systematically examined the sgRNA profiles of large numbers of SARS-CoV2 datasets in conjuction with data for other coronaviruses.ResultsWe developed a bioinformatic pipeline to analyze the sgRNA profiles of coronaviruses and applied it to 588 individual samples from 20 independent studies, covering more than 10 coronavirus species. Our result showed that SARS-CoV, SARS-CoV-2 and MERS-CoV each had a core sgRNA repertoire generated via a canonical mechanism. Novel sgRNAs that encode peptides with evolutionarily conserved structures were identified in several coronaviruses and were expressed in vitro and in vivo. Two novel peptides may have direct functional relevance to disease, by alluding interferon responses and disrupting IL17E (IL25) signaling. Relevant to coronavirus infectivity and transmission, we also observed that the level of Spike sgRNAs were significantly higher in-vivo than in-vitro, while the opposite held true for the Nucleocapside protein.ConclusionsOur results greatly expanded the predicted number of coronaviruses proteins and identified potential viral peptide suggested to be involved in viral virulence. These methods and findings shed new light on coronavirus biology and provides a valuable resource for future genomic studies of coronaviruses.

Two-component signal transduction systems are still poorly functionally characterized in the model cyanobacterium Synechocystis sp. PCC 6803. To address the issue, a GC-MS based comparative metabolomic analysis was conducted on a library of 44 knockout mutants for the response regulators (RRs) in Synechocystis. The metabolomic profiling analysis showed that 7 RRs mutants, namely Δslr1909, Δsll1291, Δslr6040, Δsll1330, Δslr2024, Δslr1584, and Δslr1693, were significantly different at metabolomic level, although their growth patterns are similar to the wild type under the normal autotrophic growth condition, suggesting regulatory diversity of RRs at metabolite level in Synechocystis. Additionally, a detailed metabolomic analysis coupled with RT-PCR verification led to useful clues for possible function of these 7 RRs, which were found involved in regulation of multiple aspects of cellular metabolisms in Synechocystis. Moreover, an integrative metabolomic and evolutionary analysis of all RR showed that four groups of RR genes clustered together in both metabolomic and evolutionary trees, suggesting of possible functional conservation of these RRs during the evolutionary process. Meanwhile, six groups of RRs with close evolutionary origin were found with different metabolomic profiles, suggesting possible functional changes during evolution. In contrast, more than 10 groups of RR genes with different clustering patterns in the evolutionary tree were found clustered together in metabolomics-based tree, suggesting possible functional convergences during the evolution. This study provided a metabolomic view of RR function, and the most needed functional clues for further characterization of these regulatory proteins in Synechocystis.

Single-cell sequencing technologies have emerged to address new and longstanding biological and biomedical questions. Previous studies focused on the analysis of bulk tissue samples composed of millions of cells. However, the genomes within the cells of an individual multicellular organism are not always the same. In this study, we aimed to identify the crucial and characteristically expressed genes that may play functional roles in tissue development and organogenesis, by analyzing a single-cell transcriptomic atlas of mice. We identified the most relevant gene features and decision rules classifying 18 cell categories, providing a list of genes that may perform important functions in the process of tissue development because of their tissue-specific expression patterns. These genes may serve as biomarkers to identify the origin of unknown cell subgroups so as to recognize specific cell stages/states during the dynamic process, and also be applied as potential therapy targets for developmental disorders.

Circular RNAs (circRNAs) have displayed dysregulated expression in several types of cancer. However, the functions of the majority of circRNAs in pancreatic ductal adenocarcinoma (PDAC) remain unknown. The present study aimed to investigate the expression, functions and molecular mechanisms of circRNAs in PDAC. The circRNAs, mRNAs and the microRNA (miRNAs) expression profiles were obtained from three Gene Expression Omnibus microarray datasets, and a circRNA-miRNA-mRNA and circRNA-miRNA-hubgene network was established. The interactions between proteins were analyzed using the Search Tool for the Retrieval of Interacting Genes/Proteins database, and hubgenes were identified using the MCODE plugin. A total of eight differentially expressed circRNAs (DEcircRNAs), 44 differentially expressed miRNAs (DEmiRNAs), and 2,052 differentially expressed mRNAs (DEmRNAs) were identified. The present study successfully constructed a circRNA-miRNA-mRNA competing endogenous RNA (ceRNA) network based on four circRNAs, six miRNAs and 111 mRNAs in PDAC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathways analyses indicated that DEmRNAs may participate in the pathogenesis and progression of PDAC. The protein-protein interaction network and module analysis identified six hubgenes (THBS1, FN1, TIMP3, TGFB2, ITGA1 and ITGA3). Furthermore, the circRNA-miRNA-hubgene regulatory modules were constructed based on the three DEcircRNAs, one DEmiRNAs and five DEmRNAs. In conclusion, the results of the present study improve the current understanding of the pathogenesis of PDAC.

DNA methylation is an essential epigenetic modification for multiple biological processes. DNA methylation in mammals acts as an epigenetic mark of transcriptional repression. Aberrant levels of DNA methylation can be observed in various types of tumor cells. Thus, DNA methylation has attracted considerable attention among researchers to provide new and feasible tumor therapies. Conventional studies considered single-gene methylation or specific loci as biomarkers for tumorigenesis. However, genome-scale methylated modification has not been completely investigated. Thus, we proposed and compared two novel computational approaches based on multiple machine learning algorithms for the qualitative and quantitative analyses of methylation-associated genes and their dys-methylated patterns. This study contributes to the identification of novel effective genes and the establishment of optimal quantitative rules for aberrant methylation distinguishing tumor cells with different origin tissues.

Among various risk factors for the initiation and progression of cancer, alternative polyadenylation (APA) is a remarkable endogenous contributor that directly triggers the malignant phenotype of cancer cells. APA affects biological processes at a transcriptional level in various ways. As such, APA can be involved in tumorigenesis through gene expression, protein subcellular localization, or transcription splicing pattern. The APA sites and status of different cancer types may have diverse modification patterns and regulatory mechanisms on transcripts. Potential APA sites were screened by applying several machine learning algorithms on a TCGA-APA dataset. First, a powerful feature selection method, minimum redundancy maximum relevancy, was applied on the dataset, resulting in a feature list. Then, the feature list was fed into the incremental feature selection, which incorporated the support vector machine as the classification algorithm, to extract key APA features and build a classifier. The classifier can classify cancer patients into cancer types with perfect performance. The key APA-modified genes had a potential prognosis ability because of their significant power in the survival analysis of TCGA pan-cancer data.

Intelectin is a recently characterized soluble galactofuranose-binding lectin that exists in species ranging from amphioxus to human. Interestingly, intelectin does not contain a canonical carbohydrate-recognition domain (CRD). Therefore, we designed serial deletions of intelectin in the Chinese amphioxus (Branchiostoma belcheri tsingtauense, AmphiITLN71469) in order to identify functional regions required for carbohydrate binding. Our results revealed that Domain 5 (aa 203–302) was able to bind lipopolysaccarides (LPS) or peptidoglycan (PGN) and agglutinate bacteria as efficiently as the full-length protein. Three dimensional (3D) atomic models of Domain 5 were generated by ab initio based program QUARK and by Iterative Threading Assembly Refinement (I-TASSER) programs, in which four amino acids mediating calcium-binding (G54-G55-G56-E91) were identified by hemagglutination assay. Furthermore, a striking functional conservation of Domain 5 was detected in zebrafish intelectin 1. Taken together, our findings identified for the first time a new CRD domain in intelectin, thereby providing new knowledge leading to a better understanding of pathogen-host interactions.

Isocitrate dehydrogenase (IDH) is an oncogene, and the expression of a mutated IDH promotes cell proliferation and inhibits cell differentiation. IDH exists in three different isoforms, whose mutation can cause many solid tumors, especially gliomas in adults. No effective method for classifying gliomas on genetic signatures is currently available. DNA methylation may be applied to distinguish cancer cells from normal tissues. In this study, we focused on three subtypes of IDH-mutation gliomas by examining methylation data. Several advanced computational methods were used, such as Monte Carlo feature selection (MCFS), incremental feature selection (IFS), support machine vector (SVM), etc. The MCFS method was adopted to analyze methylation features, resulting in a feature list. Then, the IFS method incorporating SVM was applied to the list to extract important methylation features and construct an optimal SVM classifier. As a result, several methylation features (sites) were found to relate to glioma subclasses, which are annotated onto multiple genes, such as FLJ37543, LCE3D, FAM89A, ADCY5, ESR1, C2orf67, REST, EPHA7, etc. These genes are enriched in biological functions, including cellular developmental process, neuron differentiation, cellular component morphogenesis, and G-protein-coupled receptor signaling pathway. Our results, which are supported by literature reports and independent dataset validation, showed that our identified genes and functions contributed to the detailed glioma subtypes. This study provided a basic research on IDH-mutation gliomas.

The 16S rRNA gene has been a mainstay of sequence-based bacterial analysis for decades. However, high-throughput sequencing of the full gene has only recently become a realistic prospect. Here, we use in silico and sequence-based experiments to critically re-evaluate the potential of the 16S gene to provide taxonomic resolution at species and strain level. We demonstrate that targeting of 16S variable regions with short-read sequencing platforms cannot achieve the taxonomic resolution afforded by sequencing the entire (~1500 bp) gene. We further demonstrate that full-length sequencing platforms are sufficiently accurate to resolve subtle nucleotide substitutions (but not insertions/deletions) that exist between intragenomic copies of the 16S gene. In consequence, we argue that modern analysis approaches must necessarily account for intragenomic variation between 16S gene copies. In particular, we demonstrate that appropriate treatment of full-length 16S intragenomic copy variants has the potential to provide taxonomic resolution of bacterial communities at species and strain level., Here, the authors explore the potential of the 16S gene for discriminating bacterial taxa and show that full-length sequencing combined with appropriate clustering of intragenomic sequence variation can provide accurate representation of bacterial species in microbiome datasets.

The fast-growing cyanobacterium Synechococcus elongatus UTEX 2973 (hereafter Synechococcus 2973) has been considered a good chassis candidate for “microbial cell factory” as it can perform oxygenic photosynthesis and its doubling time can be as short as 1.9 h. However, the limited genetic tools currently restrict its further research and application efforts using synthetic biology approaches. In this study, a series of genetic tools were systematically developed and optimized for Synechococcus 2973. First, the introduction of Tfp pilus assembly protein encoding gene pilN into Synechococcus 2973 successfully recovered its natural transformability, which greatly simplified the DNA transformation process. Second, a series of promoters with different strengths were evaluated and the super-strong promoters including Pcpc560 from Synechocystis sp. PCC 6803, native PpsbA2 and PpsbA3 of Synechococcus 2973 were found with the highest activity of β-galactosidase among those evaluated by miller values. Some promoters related to photosystems (i.e., PpsbA2, PpsbA3, P6803psbA2 and Pcpc560) were also demonstrated to be induced by high intensity of light. Third, three lactose induction systems were evaluated, among which Plac combined with lacIq showed the best application prospect with great induction capacity, low leakage and middle induced expression. Fourth, the translational on riboswitch theoE* , the transcriptional off riboswitches theo/yitJ and xpt(C74U)/metE and an artificial inducing system combining theoE* with T7 RNA polymerase were successfully developed and characterized in Synechococcus 2973. Finally, by using T7 induction system to control the expression of both small RNA and chaperone Hfq, a small RNA regulatory tool was developed and optimized to be a strictly inducible off system for gene regulation in Synechococcus 2973. The work here presented valuable genetic toolboxes necessary for metabolic engineering and synthetic biology research in Synechococcus 2973, which will facilitate the future application of the fast growing cyanobacterial chassis.

Background The post-translational modifications (PTMs) on the side chains of conserved lysine (Lys) residues play important roles in myriad cellular processes, such as histone modification, protein degradation, and regulation of DNA damage responses. To date, several computational methods have been developed to identify different PTMs on Lys residues. However, most of these methods focused on identifying one particular PTM regardless of other types of PTMs. Objective In this study, we first conducted a computational investigation of three types of PTMs (acetylation, sumoylation, and ubiquitination) at the same time by analyzing the protein structure and sequence factors surrounding the substrate Lys residues in these types of PTMs. Method To extract fully the structural and sequence information around the Lys residues, six types of features were used to encode the peptide segments containing the substrates. Next, through a feature selection method, i.e., maximum relevance minimum redundancy (mRMR), two feature lists, i.e., MaxRel feature list and mRMR feature list, were obtained. For the mRMR feature list, it was applied to extract optimal features of the random forest algorithm for distinguishing three types of PTMs. Results An optimal classification model with overall accuracy of 0.989 was built. For the MaxRel feature list, we investigated the top-ranked features to uncover the site-preference and residue-preference of Lys residues. Conclusion The results suggested that the disorder structure and the preference of flanking residues were the most important attributes to distinguish the three types of PTMs, which was consistent with the results reported in previous studies.

As non-coding RNAs, circular RNAs (cirRNAs) and long non-coding RNAs (lncRNAs) have attracted an increasing amount of attention. They have been confirmed to participate in many biological processes, including playing roles in transcriptional regulation, regulating protein-coding genes, and binding to RNA-associated proteins. Until now, the differences between these two types of non-coding RNAs have not been fully uncovered. It is still quite difficult to detect cirRNAs from other lncRNAs using simple techniques. In this study, we investigated these two types of non-coding RNAs using several computational methods. The purpose was to extract important factors that could distinguish cirRNAs from other lncRNAs and build an effective classification model to distinguish them. First, we collected cirRNAs, lncRNAs and their representations from a previous study, in which each cirRNA or lncRNA was represented by 188 features derived from its graph representation, sequence and conservation properties. Second, these features were analyzed by the minimum redundancy maximum relevance (mRMR) method. The obtained mRMR feature list, incremental feature selection method and hierarchical extreme learning machine algorithm were employed to build an optimal classification model with sensitivity of 0.703, specificity of 0.850, accuracy of 0.789 and a Matthews correlation coefficient of 0.561. Finally, we analyzed the 16 most important features. Of them, the sequences and structures of the RNA molecule were top ranking, implying they can be potential indicators of differences between cirRNAs and other lncRNAs. Meanwhile, other features of evolutionary conversation, sequence consecution were also important.

The microRNAs are a group of 20 nucleotides-long non-coding RNAs. By binding to the 3'UTR region of target mRNA, microRNAs can perform extensive actions mediating gene expression at post-trancriptional stages. It makes microRNAs serve as very crucial regulators in various biological progress including carcinogenesis. Long non-coding RNAs, however, are a subgroup of RNA with the length of 200 nucleotides. Unlike microRNAs, long non-coding RNAs can form secondary of tertiary domain based on their length. With the ability of directly interacting with DNA, RNA, protein, long non-coding RNAs have promoting or inhibitive functions in gene expression regulation. Furthermore, the abnormal expression of certain long non-coding RNAs has roused people's interest in the role of long non-coding RNAs in tumorigenesis. Although the connection between microRNA/long non-coding RNA and CCA has been a hot field to researchers, the link between molecular mechanism and clinical outcome has been barely built. This review takes a retrospect at the latest researches on the link between microRNA/long non-coding RNA and cholangiocarcinoma and the potential of microRNA/long non-coding RNA serving as distinctive biomarkers for CCA in clinical practice.

Clear and accurate understanding of diversity in antibody complementarity-determining regions (CDRs) is critical for antibody discovery and engineering. Previous observations of antibody CDR-H3 diversity were based on analyzing available antibody sequences in the public databases. The results may not accurately reflect that of natural antibody repertoire due to erroneous species annotation and the presence of man-made CDR loop diversity in public antibody sequence databases. In this study, in a precisely controlled germline context, we explored the relationship between amino acid composition and CDR-H3 length using 5518 unique productively rearranged human VH3-23*01 gene sequences. CDR-H3 length-dependent usage of the Cys-Xn-Cys motif is reported here.

Background Oncogenes are a type of genes that have the potential to cause cancer. Most normal cells undergo programmed cell death, namely apoptosis, but activated oncogenes can help cells avoid apoptosis and survive. Thus, studying oncogenes is helpful for obtaining a good understanding of the formation and development of various types of cancers. Methods In this study, we proposed a computational method, called OPM, for investigating oncogenes from the view of Gene Ontology (GO) and biological pathways. All investigated genes, including validated oncogenes retrieved from some public databases and other genes that have not been reported to be oncogenes thus far, were encoded into numeric vectors according to the enrichment theory of GO terms and KEGG pathways. Some popular feature selection methods, minimum redundancy maximum relevance and incremental feature selection, and an advanced machine learning algorithm, random forest, were adopted to analyze the numeric vectors to extract key GO terms and KEGG pathways. Results Along with the oncogenes, GO terms and KEGG pathways were discussed in terms of their relevance in this study. Some important GO terms and KEGG pathways were extracted using feature selection methods and were confirmed to be highly related to oncogenes. Additionally, the importance of these terms and pathways in predicting oncogenes was further demonstrated by finding new putative oncogenes based on them. Conclusions This study investigated oncogenes based on GO terms and KEGG pathways. Some important GO terms and KEGG pathways were confirmed to be highly related to oncogenes. We hope that these GO terms and KEGG pathways can provide new insight for the study of oncogenes, particularly for building more effective prediction models to identify novel oncogenes. The program is available upon request. General significance We hope that the new findings listed in this study may provide a new insight for the investigation of oncogenes. This article is part of a Special Issue entitled “System Genetics” Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

BACKGROUND Juvenile idiopathic arthritis (JIA) is one of the most common inflammatory disorders of unknown etiology. We introduced a novel method to identify dysregulated pathways associated with polyarticular JIA (pJIA). MATERIAL AND METHODS Gene expression profiling of 61 children with pJIA and 59 healthy controls were collected from E-GEOD-13849; 300 pathways were obtained from Kyoto Encyclopedia of Genes and Genomes (KEGG) database and 787,896 protein-protein interaction sets were gathered from the Retrieval of Interacting Genes. Attractor and crosstalk were designed to complement each other to increase the integrity of pathways assessment. Then, impact factor was used to assess the interactions inter-pathways, and RP-value was used to evaluate the comprehensive influential ability of attractors. RESULTS There were seven attractors with p