Identification of the Gene Expression Rules That Define the Subtypes in Glioma

DeepAutoGlioma: a deep learning autoencoder-based multi-omics data integration and classification tools for glioma subtyping

BACKGROUND AND OBJECTIVE

The classification of glioma subtypes is essential for precision therapy. Due to the heterogeneity of gliomas, the subtype-specific molecular pattern can be captured by integrating and analyzing high-throughput omics data from different genomic layers. The development of a deep-learning framework enables the integration of multi-omics data to classify the glioma subtypes to support the clinical diagnosis.

RESULTS

Transcriptome and methylome data of glioma patients were preprocessed, and differentially expressed features from both datasets were identified. Subsequently, a Cox regression analysis determined genes and CpGs associated with survival. Gene set enrichment analysis was carried out to examine the biological significance of the features. Further, we identified CpG and gene pairs by mapping them in the promoter region of corresponding genes. The methylation and gene expression levels of these CpGs and genes were embedded in a lower-dimensional space with an autoencoder. Next, ANN and CNN were used to classify subtypes using the latent features from embedding space. CNN performs better than ANN for subtyping lower-grade gliomas (LGG) and glioblastoma multiforme (GBM). The subtyping accuracy of CNN was 98.03% (± 0.06) and 94.07% (± 0.01) in LGG and GBM, respectively. The precision of the models was 97.67% in LGG and 90.40% in GBM. The model sensitivity was 96.96% in LGG and 91.18% in GBM. Additionally, we observed the superior performance of CNN with external datasets. The genes and CpGs pairs used to develop the model showed better performance than the random CpGs-gene pairs, preprocessed data, and single omics data.

CONCLUSIONS

The current study showed that a novel feature selection and data integration strategy led to the development of DeepAutoGlioma, an effective framework for diagnosing glioma subtypes.

Transcription Factor BCL11A Regulates Schwann Cell Behavior During Peripheral Nerve Regeneration

Nerve injury-induced Schwann cell dedifferentiation helps to construct a favorable microenvironment for axon growth. Transcription factors regulate cell reprogramming and thus may be critical for Schwann cell phenotype switch during peripheral nerve regeneration. Here, we show that transcription factor B-cell lymphoma/leukemia 11A (BCL11A) is up-regulated in Schwann cells of injured peripheral nerves. Bcl11a silencing suppresses Schwann cell viability, decreases Schwann cell proliferation and migration rates, and impairs the debris clearance ability of Schwann cells. Reduced Bcl11a in injured peripheral nerves results in restricted axon elongation and myelin wrapping, leading to recovery failure. Mechanistically, we demonstrate that BCL11A may mediate Schwann cell activity through binding to the promoter of nuclear receptor subfamily 2 group F member 2 (Nr2f2) and regulating Nr2f2 expression. Collectively, we conclude that BCL11A is essential for Schwann cell activation and peripheral nerve regeneration, providing a potential therapeutic target for the treatment of peripheral nerve injury.

Identification of Novel Key Genes and Pathways in Multiple Sclerosis Based on Weighted Gene Coexpression Network Analysis and Long Noncoding RNA-Associated Competing Endogenous RNA Network

Objective

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system characterized by chronic inflammation and demyelination. This study is aimed at identifying crucial genes and molecular pathways involved in MS pathogenesis.

Methods

Raw data in GSE52139 were collected from the Gene Expression Omnibus. The top 50% expression variants were subjected to weighted gene coexpression network analysis (WGCNA), and the key module associated with MS occurrence was identified. A long noncoding RNA- (lncRNA-) associated competing endogenous RNA (ceRNA) network was constructed in the key module. The hub gene candidates were subsequently verified in an individual database.

Results

Of the 18 modules obtained, the cyan module was designated as the key module. The established ceRNA network was composed of seven lncRNAs, 45 mRNAs, and 21 microRNAs (miRNAs), and the FAM13A-AS1 was the lncRNA with the highest centrality. Functional assessments indicated that the genes in the cyan module primarily gathered in ribosome-related functional terms. Interestingly, the targeted mRNAs of the ceRNA network enriched in diverse categories. Moreover, highly expressed CYBRD1, GNG12, and SMAD1, which were identified as hub genes, may be associated with “valine leucine and isoleucine degradation,” “base excision repair,” and “fatty acid metabolism,” respectively, according to the results of single gene-based genomes and gene set enrichment analysis (GSEA).

Conclusions

Combined with the WGCNA and ceRNA network, our findings provide novel insights into the pathogenesis of MS. The hub genes discovered herein might also serve as novel biomarkers that correlate with the development and management of MS.

Circular RNA hsa_circ_0000285 regulates the microRNA‐599/G‐protein subunit gamma 12 (miR‐599/GNG12) axis to promote glioma progression

Objective

Glioma is the most common, rapidly progressing, lethal brain tumor. However, underlying mechanisms behind its abnormal progression remain largely unknown. This study aimed to investigate mechanism of action and effects of the hsa_circ_0000285 on glioma progression.

Methods

RT‐qPCR was utilized to study RNA expression in glioma tissues and cell lines. The effects of hsa_circ_0000285 on glioma progression were studied by measuring cell proliferation and migration, apoptosis, tumor volume and weight in both glioma cells and xenograft glioma mice. The features of hsa_circ_0000285 were identified using chromatin fractionation and RNase digestion. Its mechanism of action was analyzed using bioinformatics, RNA‐binding protein immunoprecipitation, and luciferase reporter assay.

Results

We found glioma tissues and cell lines were overexpressing hsa_circ_0000285. While hsa_circ_0000285 promoted cell proliferation and migration, it inhibited apoptosis in vitro. It also increased tumor volume and weight in vivo. Using bioinformatic analysis and verification experiments for studying its mechanisms, we confirmed that hsa_circ_0000285 sponged miR‐599, which negatively regulated GNG12 by binding to its mRNA.

Conclusion

Hsa_circ_0000285 is overexpressed in the glioma and promotes its progression by directly regulating the miR‐599/GNG12 axis. This novel mechanism, therefore, shows that the hsa_circ_0000285/miR‐599/GNG12 axis may be a promising therapeutic target for glioma treatment.

Low GNG12 Expression Predicts Adverse Outcomes: A Potential Therapeutic Target for Osteosarcoma

Background

G protein subunit gamma 12 (GNG12) is observed in some types of cancer, but its role in osteosarcoma is unknown. This study hypothesized that GNG12 may be a potential biomarker and therapeutic target. We aimed to identify an association between GNG12 and osteosarcoma based on the Gene Expression Omnibus and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) databases.

Methods

Osteosarcoma samples in GSE42352 and TARGET database were selected as the test cohorts. As the external validation cohort, 78 osteosarcoma specimens from The Second Affiliated Hospital of Nanchang University were collected. Patients with osteosarcoma were divided into high and low GNG12 mRNA-expression groups; differentially expressed genes were identified as GNG12-related genes. The biological function of GNG12 was annotated using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, gene set enrichment analysis, and immune infiltration analysis. Gene expression correlation analysis and competing endogenous RNA regulatory network construction were used to determine potential biological regulatory relationships of GNG12. Overall survival, Kaplan–Meier analysis, and log-rank tests were calculated to determine GNG12 reliability in predicting survival prognosis.

Results

GNG12 expression decreased in osteosarcoma samples. GNG12 was a highly effective biomarker for osteosarcoma [area under the receiver operating characteristic (ROC) curve (AUC) = 0.920], and the results of our Kaplan–Meier analysis indicated that overall survival and progression-free survival differed significantly between low and high GNG-expression group (p < 0.05). Functional analyses indicated that GNG12 may promote osteosarcoma through regulating the endoplasmic reticulum. Expression correlation analysis and competing endogenous RNA network construction showed that HOTTIP/miR-27a-3p may regulate GNG12 expression. Furthermore, the subunit suppresses adaptive immunity via inhibiting M1 and M2 macrophage infiltration. GNG12 was inhibited in metastatic osteosarcoma compared with non-metastatic osteosarcoma, and its expression predicted survival of patients (1, 3, and 5-year AUCs were 0.961, 0.826, and 0.808, respectively).

Conclusion

This study identified GNG12 as a potential biomarker for osteosarcoma prognosis, highlighting its potential as an immunotherapy target.

BCL11A: a potential diagnostic biomarker and therapeutic target in human diseases

Transcription factor B-cell lymphoma/leukemia 11A (BCL11A) gene encodes a zinc-finger protein that is predominantly expressed in brain and hematopoietic tissue. BCL11A functions mainly as a transcriptional repressor that is crucial in brain, hematopoietic system development, as well as fetal-to-adult hemoglobin switching. The expression of this gene is regulated by microRNAs, transcription factors and genetic variations. A number of studies have recently shown that BCL11A is involved in β-hemoglobinopathies, hematological malignancies, malignant solid tumors, 2p15-p16.1 microdeletion syndrome, and Type II diabetes. It has been suggested that BCL11A may be a potential prognostic biomarker and therapeutic target for some diseases. In this review, we summarize the current research state of BCL11A, including its biochemistry, expression, regulation, function, and its possible clinical application in human diseases.

iATC-NRAKEL: an efficient multi-label classifier for recognizing anatomical therapeutic chemical classes of drugs

MOTIVATION

The anatomical therapeutic chemical (ATC) classification system plays an increasingly important role in drug repositioning and discovery. The correct identification of classes in each level of such system that a given drug may belong to is an essential problem. Several multi-label classifiers have been proposed in this regard. Although they provided satisfactory performance, the feature extraction procedures were still rough. More refined features may further improve the predicted quality.

RESULTS

In this article, we provide a novel multi-label classifier, called iATC-NRAKEL, to predict drug ATC classes in the first level. To obtain more informative drug features, we employed the drug association information in STITCH and KEGG, which was organized by seven drug networks. The powerful network embedding algorithm, Mashup, was adopted to extract informative drug features. The obtained features were fed into the RAndom k-labELsets (RAKEL) algorithm with support vector machine as the basic classification algorithm to construct the classifier. The 10-fold cross-validation of the benchmark dataset with 3883 drugs showed that the accuracy and absolute true were 76.56 and 74.51%, respectively. The comparison results indicated that iATC-NRAKEL was much superior to all previous reported classifiers. Finally, the contribution of each network was analyzed.

AVAILABILITY AND IMPLEMENTATION

The codes of iATC-NRAKEL are available at https://github.com/zhou256/iATC-NRAKEL.

Prognostic and Predictive Value of a Long Non-coding RNA Signature in Glioma: A lncRNA Expression Analysis

The current histologically based grading system for glioma does not accurately predict which patients will have better outcomes or benefit from adjuvant chemotherapy. We proposed that combining the expression profiles of multiple long non-coding RNAs (lncRNAs) into a single model could improve prediction accuracy. We included 1,094 glioma patients from three different datasets. Using the least absolute shrinkage and selection operator (LASSO) Cox regression model, we built a multiple-lncRNA-based classifier on the basis of a training set. The predictive and prognostic accuracy of the classifier was validated using an internal test set and two external independent sets. Using this classifier, we classified patients in the training set into high- or low-risk groups with significantly different overall survival (OS, HR = 8.42, 95% CI = 4.99–14.2, p < 0.0001). The prognostic power of the classifier was then assessed in the other sets. The classifier was an independent prognostic factor and had better prognostic value than clinicopathological risk factors. The patients in the high-risk group were found to have a favorable response to adjuvant chemotherapy (HR = 0.4, 95% CI = 0.25–0.64, p < 0.0001). We built a nomogram that integrated the 10-lncRNA-based classifier and four clinicopathological risk factors to predict 3 and 5 year OS. Gene set variation analysis (GSVA) showed that pathways related to tumorigenesis, undifferentiated cancer, and epithelial–mesenchymal transition were enriched in the high-risk groups. Our classifier built on 10-lncRNAs is a reliable prognostic and predictive tool for OS in glioma patients and could predict which patients would benefit from adjuvant chemotherapy.

Essential gene expression pattern of head and neck squamous cell carcinoma revealed by tumor-specific expression rule based on single-cell RNA sequencing

Head and neck squamous cell carcinoma (HNSCC) has been widely reported and considered as one of the most threatening diseases to human health. Derived from complicated tissue subtypes, HNSCC has diverse symptoms and pathogenesis. They make the identification of the core carcinogenic factors of such diseases at the multi-cell level difficult. With the development of single-cell sequencing technologies, the effects of non-malignant cells on traditional bulk sequencing data can be eliminated directly. On the basis of fresh single-cell RNA-seq data, we set up a computational filtering strategy for tumor cell identification in an expression rule manner. This strategy can reveal the accurate expression distinction between tumor cells and adjacent tumor microenvironment, which are all supported by literature reports. Validated by several independent datasets, these rule genes can further group HNSCC patients with significant difference on survival risks. Thus, the establishment of our computational approach may not only provide an efficient tool to identify malignant cells in the tumor ecosystem but also deepen our understanding of tumor heterogeneity and tumorigenesis.

Alternative Polyadenylation Modification Patterns Reveal Essential Posttranscription Regulatory Mechanisms of Tumorigenesis in Multiple Tumor Types

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.

Quantitative Analysis of Differential Expression of HOX Genes in Multiple Cancers

Transcription factors encoded by Homeobox (HOX) genes play numerous key functions during early embryonic development and differentiation. Multiple reports have shown that mis-regulation of HOX gene expression plays key roles in the development of cancers. Their expression levels in cancers tend to differ based on tissue and tumor type. Here, we performed a comprehensive analysis comparing HOX gene expression in different cancer types, obtained from The Cancer Genome Atlas (TCGA), with matched healthy tissues, obtained from Genotype-Tissue Expression (GTEx). We identified and quantified differential expression patterns that confirmed previously identified expression changes and highlighted new differential expression signatures. We discovered differential expression patterns that are in line with patient survival data. This comprehensive and quantitative analysis provides a global picture of HOX genes' differential expression patterns in different cancer types.

Identification and Analysis of Dysfunctional Genes and Pathways in CD8+ T Cells of Non-Small Cell Lung Cancer Based on RNA Sequencing

Lung cancer, the most common of malignant tumors, is typically of the non-small cell (NSCLC) type. T-cell-based immunotherapies are a promising and powerful approach to treating NSCLCs. To characterize the CD8⁺ T cells of non-small cell lung cancer, we re-analyzed the published RNA-Seq gene expression profiles of 36 CD8⁺ T cell isolated from tumor (TIL) samples and 32 adjacent uninvolved lung (NTIL) samples. With an advanced Monte Carlo method of feature selection, we identified the CD8⁺ TIL specific expression patterns. These patterns revealed the key dysfunctional genes and pathways in CD8⁺ TIL and shed light on the molecular mechanisms of immunity and use of immunotherapy.

Identifying Cell-Type Specific Genes and Expression Rules Based on Single-Cell Transcriptomic Atlas Data

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.

Analysis of gene expression profiles of lung cancer subtypes with machine learning algorithms

Lung cancer is one of the most common cancer types worldwide and causes more than one million deaths annually. Lung adenocarcinoma (AC) and lung squamous cell cancer (SCC) are two major lung cancer subtypes and have different characteristics in several aspects. Identifying their differentially expressed genes and different gene expression patterns can deepen our understanding of these two subtypes at the transcriptomic level. In this work, we used several machine learning algorithms to investigate the gene expression profiles of lung AC and lung SCC samples retrieved from Gene Expression Omnibus. First, the profiles were analyzed by using a powerful feature selection method, namely, Monte Carlo feature selection. A feature list, ranking all features according to their importance, and some informative features were obtained. Then, the feature list was used in the incremental feature selection method to extract optimal features, which can allow the support vector machine (SVM) to yield the best performance for classifying lung AC and lung SCC samples. Some top genes (CSTA, TP63, SERPINB13, CLCA2, BICD2, PERP, FAT2, BNC1, ATP11B, FAM83B, KRT5, PARD6G, PKP1) were extensively analyzed to prove that they can be differentially expressed genes between lung AC and lung SCC. Meanwhile, a rule learning procedure was applied on informative features to construct the classification rules. These rules provide a clear procedure of classification and show some different gene expression patterns between lung AC and lung SCC.

Investigating the gene expression profiles of cells in seven embryonic stages with machine learning algorithms

The development of embryonic cells involves several continuous stages, and some genes are related to embryogenesis. To date, few studies have systematically investigated changes in gene expression profiles during mammalian embryogenesis. In this study, a computational analysis using machine learning algorithms was performed on the gene expression profiles of mouse embryonic cells at seven stages. First, the profiles were analyzed through a powerful Monte Carlo feature selection method for the generation of a feature list. Second, increment feature selection was applied on the list by incorporating two classification algorithms: support vector machine (SVM) and repeated incremental pruning to produce error reduction (RIPPER). Through SVM, we extracted several latent gene biomarkers, indicating the stages of embryonic cells, and constructed an optimal SVM classifier that produced a nearly perfect classification of embryonic cells. Furthermore, some interesting rules were accessed by the RIPPER algorithm, suggesting different expression patterns for different stages.

Open Data for Differential Network Analysis in Glioma

The complexity of cancer diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer gene expression analysis, we apply refinement as well as enrichment analysis via gene ontology and conclude with graph-based visualization of involved protein interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-confidence interactions only. Several genes associated to glioma were isolated via a network analysis from top hub nodes as well as from an outlier analysis. The latter approach highlights a mitogen-activated protein kinase next to a member of histondeacetylases and a protein phosphatase as genes uncommonly associated with glioma. Cluster analysis from top hub nodes lists several identified glioma-associated gene products to function within protein complexes, including epidermal growth factors as well as cell cycle proteins or RAS proto-oncogenes. By using selected exemplary tools and open-access resources for cancer research and differential network analysis, we highlight disturbed signaling components in brain cancer subtypes of glioma.

Copy Number Variation Pattern for Discriminating MACROD2 States of Colorectal Cancer Subtypes

Copy number variation (CNV) is a common structural variation pattern of DNA, and it features a higher mutation rate than single-nucleotide polymorphisms (SNPs) and affects a larger fragment of genomes. CNV is related with the genesis of complex diseases and can thus be used as a strategy to identify novel cancer-predisposing markers or mechanisms. In particular, the frequent deletions of mono-ADP-ribosylhydrolase 2 (MACROD2) locus in human colorectal cancer (CRC) alters DNA repair and the sensitivity to DNA damage and results in chromosomal instability. The relationship between CNV and cancer has not been explained. In this study, on the basis of the genome variation profiling by the SNP array from 651 CRC primary tumors, we computationally analyzed the CNV data to select crucial SNP sites with the most relevance to three different states of MACROD2 (heterozygous deletion, homozygous deletion, and normal state), suggesting that these CNVs may play functional roles in CRC tumorigenesis. Our study can shed new insights into the genesis of cancer based on CNV, providing reference for clinical diagnosis, and treatment prognosis of CRC.

Predicting Drug Side Effects with Compact Integration of Heterogeneous Networks

Background:

The side effects of drugs are not only harmful to humans but also the major reasons for withdrawing approved drugs, bringing greater risks for pharmaceutical companies. However, detecting the side effects for a given drug via traditional experiments is time- consuming and expensive. In recent years, several computational methods have been proposed to predict the side effects of drugs. However, most of the methods cannot effectively integrate the heterogeneous properties of drugs.

Methods:

In this study, we adopted a network embedding method, Mashup, to extract essential and informative drug features from several drug heterogeneous networks, representing different properties of drugs. For side effects, a network was also built, from where side effect features were extracted. These features can capture essential information about drugs and side effects in a network level. Drug and side effect features were combined together to represent each pair of drug and side effect, which was deemed as a sample in this study. Furthermore, they were fed into a random forest (RF) algorithm to construct the prediction model, called the RF network model.

Results:

The RF network model was evaluated by several tests. The average of Matthews correlation coefficients on the balanced and unbalanced datasets was 0.640 and 0.641, respectively.

Conclusion:

The RF network model was superior to the models incorporating other machine learning algorithms and one previous model. Finally, we also investigated the influence of two feature dimension parameters on the RF network model and found that our model was not very sensitive to these parameters.

Immunosignature Screening for Multiple Cancer Subtypes Based on Expression Rule

Liquid biopsy (i.e., fluid biopsy) involves a series of clinical examination approaches. Monitoring of cancer immunological status by the “immunosignature” of patients presents a novel method for tumor-associated liquid biopsy. The major work content and the core technological difficulties for the monitoring of cancer immunosignature are the recognition of cancer-related immune-activating antigens by high-throughput screening approaches. Currently, one key task of immunosignature-based liquid biopsy is the qualitative and quantitative identification of typical tumor-specific antigens. In this study, we reused two sets of peptide microarray data that detected the expression level of potential antigenic peptides derived from tumor tissues to avoid the detection differences induced by chip platforms. Several machine learning algorithms were applied on these two sets. First, the Monte Carlo Feature Selection (MCFS) method was used to analyze features in two sets. A feature list was obtained according to the MCFS results on each set. Second, incremental feature selection method incorporating one classification algorithm (support vector machine or random forest) followed to extract optimal features and construct optimal classifiers. On the other hand, the repeated incremental pruning to produce error reduction, a rule learning algorithm, was applied on key features yielded by the MCFS method to extract quantitative rules for accurate cancer immune monitoring and pathologic diagnosis. Finally, obtained key features and quantitative rules were extensively analyzed.

Identifying Microbiota Signature and Functional Rules Associated With Bacterial Subtypes in Human Intestine

Gut microbiomes are integral microflora located in the human intestine with particular symbiosis. Among all microorganisms in the human intestine, bacteria are the most significant subgroup that contains many unique and functional species. The distribution patterns of bacteria in the human intestine not only reflect the different microenvironments in different sections of the intestine but also indicate that bacteria may have unique biological functions corresponding to their proper regions of the intestine. However, describing the functional differences between the bacterial subgroups and their distributions in different individuals is difficult using traditional computational approaches. Here, we first attempted to introduce four effective sets of bacterial features from independent databases. We then presented a novel computational approach to identify potential distinctive features among bacterial subgroups based on a systematic dataset on the gut microbiome from approximately 1,500 human gut bacterial strains. We also established a group of quantitative rules for explaining such distinctions. Results may reveal the microstructural characteristics of the intestinal flora and deepen our understanding on the regulatory role of bacterial subgroups in the human intestine.

Inferring novel genes related to oral cancer with a network embedding method and one-class learning algorithms

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.

Primary Tumor Site Specificity is Preserved in Patient-Derived Tumor Xenograft Models

Patient-derived tumor xenograft (PDX) mouse models are widely used for drug screening. The underlying assumption is that PDX tissue is very similar with the original patient tissue, and it has the same response to the drug treatment. To investigate whether the primary tumor site information is well preserved in PDX, we analyzed the gene expression profiles of PDX mouse models originated from different tissues, including breast, kidney, large intestine, lung, ovary, pancreas, skin, and soft tissues. The popular Monte Carlo feature selection method was employed to analyze the expression profile, yielding a feature list. From this list, incremental feature selection and support vector machine (SVM) were adopted to extract distinctively expressed genes in PDXs from different primary tumor sites and build an optimal SVM classifier. In addition, we also set up a group of quantitative rules to identify primary tumor sites. A total of 755 genes were extracted by the feature selection procedures, on which the SVM classifier can provide a high performance with MCC 0.986 on classifying primary tumor sites originated from different tissues. Furthermore, we obtained 16 classification rules, which gave a lower accuracy but clear classification procedures. Such results validated that the primary tumor site specificity was well preserved in PDX as the PDXs from different primary tumor sites were still very different and these PDX differences were similar with the differences observed in patients with tumor. For example, VIM and ABHD17C were highly expressed in the PDX from breast tissue and also highly expressed in breast cancer patients.

Genome-wide identification of lncRNAs as novel prognosis biomarkers of glioma

BACKGROUND

Glioma is the primary cancer of the central nervous system, and defining the prognosis of glioma is of great significance in the clinical. The long noncoding RNAs (lncRNAs) emerge as important regulators of pathological processes. This study aimed to identify lncRNAs which could function as potential prognosis biomarkers of glioma.

MATERIAL AND METHODS

Glioma RNA-seq data from TCGA and CGGA were analyzed to identify neoplasm grade associated lncRNAs by DEseq. 2R and weighted gene co-expression network analysis. Consensus module genes were analyzed in Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway to predict lncRNAs biological functions. Then neutrophil immune estimations were analyzed by Tumor Immune Estimation Resource. Transcrption factors of these lncRNAs were predicted by PROMO. Overall survival and receiver operating characteristic (ROC) analyses were applied to test the accuracy of predicted lncRNAs as the markers of prognosis.

RESULTS

We identified four lncRNAs most correlated with both higher neoplasm grade and worse prognosis, including AC064875.2, HOTAIRM1, LINC00908, and RP11-84A19.3. Neutrophil-mediated immunity and cell adhesion junction were considered as the main biological functions of these lncRNAs. In addition, the correlation of these four lncRNAs with glioma prognosis was validated.

CONCLUSION

Neutrophil immune infiltration is implicated in higher neoplasm grade and worse prognosis of glioma. AC064875.2, HOTAIRM1, LINC00908, and RP11-84A19.3 may serve as potential prognosis biomarkers of glioma.

Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms

Small nucleolar RNAs (snoRNAs) are a new type of functional small RNAs involved in the chemical modifications of rRNAs, tRNAs, and small nuclear RNAs. It is reported that they play important roles in tumorigenesis via various regulatory modes. snoRNAs can both participate in the regulation of methylation and pseudouridylation and regulate the expression pattern of their host genes. This research investigated the expression pattern of snoRNAs in eight major cancer types in TCGA via several machine learning algorithms. The expression levels of snoRNAs were first analyzed by a powerful feature selection method, Monte Carlo feature selection (MCFS). A feature list and some informative features were accessed. Then, the incremental feature selection (IFS) was applied to the feature list to extract optimal features/snoRNAs, which can make the support vector machine (SVM) yield best performance. The discriminative snoRNAs included HBII-52-14, HBII-336, SNORD123, HBII-85-29, HBII-420, U3, HBI-43, SNORD116, SNORA73B, SCARNA4, HBII-85-20, etc., on which the SVM can provide a Matthew’s correlation coefficient (MCC) of 0.881 for predicting these eight cancer types. On the other hand, the informative features were fed into the Johnson reducer and repeated incremental pruning to produce error reduction (RIPPER) algorithms to generate classification rules, which can clearly show different snoRNAs expression patterns in different cancer types. The analysis results indicated that extracted discriminative snoRNAs can be important for identifying cancer samples in different types and the expression pattern of snoRNAs in different cancer types can be partly uncovered by quantitative recognition rules.

Evaluation of Plasma Extracellular Vesicle MicroRNA Signatures for Lung Adenocarcinoma and Granuloma With Monte-Carlo Feature Selection Method

Extracellular Vesicle (EV) is a compilation of secreted vesicles, including micro vesicles, large oncosomes, and exosomes. It can be used in non-invasive diagnosis. MicroRNAs (miRNAs) processed by exosomes can be detected by liquid biopsy. To objectively evaluate the discriminative ability of miRNAs from whole plasma, EV and EV-free plasma, we analyzed the miRNA expression profiles in whole plasma, EV and EV-free plasma of 10 lung adenocarcinoma and 9 granuloma patients. With Monte-Carlo feature selection method, the top discriminative miRNAs in whole plasma, EV and EV-free plasma were identified, and they were quite different. Using the Repeated Incremental Pruning to Produce Error Reduction (RIPPER) method, we learned the classification rules: in whole plasma, granuloma patients did not express hsa-miR-223-3p while the lung adenocarcinoma patients expressed hsa-miR-223-3p; in EV, the hsa-miR-23b-3p was highly expressed in granuloma patients but not lung adenocarcinoma patients; in EV-free plasma, hsa-miR-376a-3p was expressed in granuloma patients but barely expressed in lung adenocarcinoma patients. For prediction performance, whole plasma had the highest weighted accuracy and EV outperformed EV-free plasma. Our results suggested that EV can be used as lung cancer biomarker. However, since it is less stable and not easy to detect, there are still technological difficulties to overcome.

Immune contexture defined by single cell technology for prognosis prediction and immunotherapy guidance in cancer

Tumor immune microenvironment is closely related to tumor initiation, prognosis, and response to immunotherapy. The immune landscapes, number of infiltrating immune cells, and the localization of lymphocytes in the tumor vary in across different types of tumors. The immune contexture in cancer, which is determined by the density, composition, functional state and organization of the leukocyte infiltrate of the tumor, can yield information relevant to the prediction of treatment response and patients’ prognosis. Better understanding of the immune atlas in human tumors have been achieved with the development and application of single-cell analysis technology, which has provided a reference for prognosis, and insights on new targets for immunotherapy. In this review, we summarized the different characteristics of immune contexture in cancer defined by a variety of single-cell techniques, which have enhanced our understanding on the pathophysiology of the tumor microenvironment. We believe that there are much more to be uncovered in this rapidly developing field of medicine, and they will predict the prognosis of cancer patients and guide the rational design of immunotherapies for success in cancer eradication.

Protein interactome of the deamidase phosphoribosylformylglycinamidine synthetase (PFAS) by LC-MS/MS

Phosphoribosylformylglycinamidine synthase (PFAS) is an essential enzyme in de novo synthesis of purine. Previously, PFAS has been reported to modulate RIG-I activation during viral infection via deamidation. In this study, we sought to identify potential substrates that PFAS can deamidate. Flag-PFAS was transfected into HEK-293T cells and PFAS associated proteins were purified with anti-Flag M2 magnetic beads. PFAS associated proteins were identified using mass spectrometry and were analyzed using bioinformatics tools including KEGG pathway analysis, gene ontology annotation, and protein interaction network analysis. A total of 441 proteins is suggested to potentially interact with PFAS. Of this number, 12 were previously identified and 429 are newly identified. The interactions of PFAS with CAD, CCT2, PRDX1, and PHGDH were confirmed by co-immunoprecipitation and western blotting. This study is first to report the interaction of PFAS with several proteins which play physiological roles in tumor development including CAD, CCT2, PRDX1, and PHGDH. Furthermore, we show here that PFAS is able to deamidate PHGDH, and induce other posttranslational modification into CAD, CCT2 and PRDX1. The present data provide insight on the biological function of PFAS. Further study to explore the role of these protein interactions in tumorigenesis and other diseases is recommended.

HIV infection alters the human epigenetic landscape

Many complex diseases or traits are the results of both genetic and environmental factors. The environmental factors affect the human body by modifying its epigenetics, which controls the activity of genomes without mutating it. Viral infection is one of the common environmental factors for complex diseases. For example, the human immunodeficiency virus (HIV) infection can cause acquired immune deficiency syndrome (AIDS), HBV, and HCV infections are associated with hepatocellular carcinoma, and human papillomavirus infection is a causal factor in cervical carcinoma. In this study, to investigate how HIV infection affects DNA methylation, we analyzed the blood DNA methylation data of 485 512 sites in 44 HIV- and 142 HIV + patients. Several advanced computational methods were applied to identify the core distinctive features that were different between the HIV patients and the healthy controls. These methods can be used for differentiating HIV-infected patients from uninfected ones. These core distinctive DNA methylation features were confirmed to be functionally connected to premature aging and abnormal immune regulation, two typical pathological symptoms of HIV infection, revealing the potential regulatory mechanisms of HIV infection on the DNA methylation status of the host cells and provided novel insights on the pathogenesis of HIV infection and AIDS.