Class similarity network for coding and long non-coding RNA classification.
Ontology highlight
ABSTRACT: Background
Long non-coding RNAs (lncRNAs) play significant roles in varieties of physiological and pathological processes.The premise of the lncRNA functional study is that the lncRNAs are identified correctly. Recently, deep learning method like convolutional neural network (CNN) has been successfully applied to identify the lncRNAs. However, the traditional CNN considers little relationships among samples via an indirect way.Results
Inspired by the Siamese Neural Network (SNN), here we propose a novel network named Class Similarity Network in coding RNA and lncRNA classification. Class Similarity Network considers more relationships among input samples in a direct way. It focuses on exploring the potential relationships between input samples and samples from both the same class and the different classes. To achieve this, Class Similarity Network trains the parameters specific to each class to obtain the high-level features and represents the general similarity to each class in a node. The comparison results on the validation dataset under the same conditions illustrate the superiority of our Class Similarity Network to the baseline CNN. Besides, our method performs effectively and achieves state-of-the-art performances on two test datasets.Conclusions
We construct Class Similarity Network in coding RNA and lncRNA classification, which is shown to work effectively on two different datasets by achieving accuracy, precision, and F1-score as 98.43%, 0.9247, 0.9374, and 97.54%, 0.9990, 0.9860, respectively.
SUBMITTER: Zhang Y
PROVIDER: S-EPMC8691036 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:In our study, we aimed to reveal potential long non-coding RNAs (lncRNA) biomarkers in lung adenocarcinoma (LAD) using lncRNA-mediated competing endogenous RNAs (ceRNAs) network (LMCN). Competing lncRNA-mRNA interactions were identified using the hypergeometric test. Co-expression analysis for the competing lncRNA-mRNA interactions was implemented, and relying on the weight value >0.8, a highly competitive LMCN was further constructed. Degree distribution, betweenness and closeness for LMCN were carried out to analyze the network structure. Functional analyses of mRNAs in LMCN were carried out to further explore the biological functions of lncRNAs. Biclique algorithm was utilized to extract competing modules from the LMCN. Finally, we verified our findings in an independent sample set using qRT-PCR. Based on degrees >60, we identified 4 hubs, including DLEU2, SNHG12, HCP5, and LINC00472. Furthermore, 2 competing modules were identified, and LINC00472 in module 1 functioned as a hub in both LMCN and module. Functional implications of lncRNAs demonstrated that lncRNAs were related to histone modification, negative regulation of cell cycle, neuroactive ligand-receptor interaction, and regulation of actin cytoskeleton. qRT-PCR results demonstrated that lncRNAs LINC00472, and HCP5 were down-regulated in LAD tissues, while the expression level of SNHG12 was up-regulated in LAD tissues. Our study sheds novel light on the roles of lncRNA-related ceRNA network in LAD and facilitates the detection of potential lncRNA biomarkers for LAD diagnosis and treatment. Remarkably, in our study, LINC00472, HCP5, and SNHG12 might be potential biomarkers for LAD management.
| S-EPMC5572850 | biostudies-literature
Project description:BackgroundEven though only 1.5% of the human genome is translated into proteins, recent reports indicate that most of it is transcribed into non-coding RNAs (ncRNAs), which are becoming the subject of increased scientific interest. We hypothesized that examining how different classes of ncRNAs co-localized with annotated epigenomic elements could help understand the functions, regulatory mechanisms, and relationships among ncRNA families.ResultsWe examined 15 different ncRNA classes for statistically significant genomic co-localizations with cell type-specific chromatin segmentation states, transcription factor binding sites (TFBSs), and histone modification marks using GenomeRunner (http://www.genomerunner.org). P-values were obtained using a Chi-square test and corrected for multiple testing using the Benjamini-Hochberg procedure. We clustered and visualized the ncRNA classes by the strength of their statistical enrichments and depletions.ConclusionsSearching for statistically significant associations between ncRNA classes and epigenomic elements permits detection of potential functional and/or regulatory relationships among ncRNA classes, and suggests cell type-specific biological roles of ncRNAs.
| S-EPMC3851203 | biostudies-literature
Project description:Although accumulating evidence has provided insight into the various functions of long-non-coding RNAs (lncRNAs), the exact functions of the majority of such transcripts are still unknown. Here, we report the first computational annotation of lncRNA functions based on public microarray expression profiles. A coding-non-coding gene co-expression (CNC) network was constructed from re-annotated Affymetrix Mouse Genome Array data. Probable functions for altogether 340 lncRNAs were predicted based on topological or other network characteristics, such as module sharing, association with network hubs and combinations of co-expression and genomic adjacency. The functions annotated to the lncRNAs mainly involve organ or tissue development (e.g. neuron, eye and muscle development), cellular transport (e.g. neuronal transport and sodium ion, acid or lipid transport) or metabolic processes (e.g. involving macromolecules, phosphocreatine and tyrosine).
| S-EPMC3089475 | biostudies-other
Project description:MOTIVATION:Over the past two decades, a circular form of RNA (circular RNA), produced through alternative splicing, has become the focus of scientific studies due to its major role as a microRNA (miRNA) activity modulator and its association with various diseases including cancer. Therefore, the detection of circular RNAs is vital to understanding their biogenesis and purpose. Prediction of circular RNA can be achieved in three steps: distinguishing non-coding RNAs from protein coding gene transcripts, separating short and long non-coding RNAs and predicting circular RNAs from other long non-coding RNAs (lncRNAs). However, the available tools are less than 80 percent accurate for distinguishing circular RNAs from other lncRNAs due to difficulty of classification. Therefore, the availability of a more accurate and fast machine learning method for the identification of circular RNAs, which considers the specific features of circular RNA, is essential to the development of systematic annotation. RESULTS:Here we present an End-to-End deep learning framework, circDeep, to classify circular RNA from other lncRNA. circDeep fuses an RCM descriptor, ACNN-BLSTM sequence descriptor and a conservation descriptor into high level abstraction descriptors, where the shared representations across different modalities are integrated. The experiments show that circDeep is not only faster than existing tools but also performs at an unprecedented level of accuracy by achieving a 12 percent increase in accuracy over the other tools. AVAILABILITY AND IMPLEMENTATION:https://github.com/UofLBioinformatics/circDeep. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
| S-EPMC6956777 | biostudies-literature
Project description:Schizophrenia (SCZ) is a serious psychiatric condition with a 1% lifetime risk. SCZ is one of the top ten global causes of disabilities. Despite numerous attempts to understand the function of genetic factors in SCZ development, genetic components in SCZ pathophysiology remain unknown. The competing endogenous RNA (ceRNA) network has been demonstrated to be involved in the development of many kinds of diseases. The ceRNA hypothesis states that cross-talks between coding and non-coding RNAs, including long non-coding RNAs (lncRNAs), via miRNA complementary sequences known as miRNA response elements, creates a large regulatory network across the transcriptome. In the present study, we developed a lncRNA-related ceRNA network to elucidate molecular regulatory mechanisms involved in SCZ. Microarray datasets associated with brain regions (GSE53987) and lymphoblasts (LBs) derived from peripheral blood (sample set B from GSE73129) of SCZ patients and control subjects containing information about both mRNAs and lncRNAs were downloaded from the Gene Expression Omnibus database. The GSE53987 comprised 48 brain samples taken from SCZ patients (15 HPC: hippocampus, 15 BA46: Brodmann area 46, 18 STR: striatum) and 55 brain samples taken from control subjects (18 HPC, 19 BA46, 18 STR). The sample set B of GSE73129 comprised 30 LB samples (15 patients with SCZ and 15 controls). Differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) were identified using the limma package of the R software. Using DIANA-LncBase, Human MicroRNA Disease Database (HMDD), and miRTarBase, the lncRNA- associated ceRNA network was generated. Pathway enrichment of DEmRNAs was performed using the Enrichr tool. We developed a protein-protein interaction network of DEmRNAs and identified the top five hub genes by the use of STRING and Cytoscape, respectively. Eventually, the hub genes, DElncRNAs, and predictive miRNAs were chosen to reconstruct the subceRNA networks. Our bioinformatics analysis showed that twelve key DEmRNAs, including BDNF, VEGFA, FGF2, FOS, CD44, SOX2, NRAS, SPARC, ZFP36, FGG, ELAVL1, and STARD13, participate in the ceRNA network in SCZ. We also identified DLX6-AS1, NEAT1, MINCR, LINC01094, DLGAP1-AS1, BABAM2-AS1, PAX8-AS1, ZFHX4-AS1, XIST, and MALAT1 as key DElncRNAs regulating the genes mentioned above. Furthermore, expression of 15 DEmRNAs (e.g., ADM and HLA-DRB1) and one DElncRNA (XIST) were changed in both the brain and LB, suggesting that they could be regarded as candidates for future biomarker studies. The study indicated that ceRNAs could be research candidates for investigating SCZ molecular pathways.
| S-EPMC8709859 | biostudies-literature
Project description:Over the past few decades, researchers have become aware of the importance of non-coding RNA, which makes up the vast majority of the transcriptome. Long non-coding RNAs (lncRNAs) in turn constitute the largest fraction of non-coding transcripts. Increasing evidence has been found for the crucial roles of lncRNAs in both tissue homeostasis and development, and for their functional contributions to and regulation of the development and progression of various human diseases such as cancers. However, so far, only few findings with regards to functional lncRNAs in cancers have been translated into clinical applications. Based on multiple factors such as binding affinity of miRNAs to their lncRNA sponges, we analyzed the competitive endogenous RNA (ceRNA) network for the colorectal cancer RNA-seq datasets from The Cancer Genome Atlas (TCGA). After performing the ceRNA network construction and survival analysis, the lncRNA KCNQ1OT1 was found to be significantly upregulated in colorectal cancer tissues and associated with the survival of patients. A KCNQ1OT1-related lncRNA-miRNA-mRNA ceRNA network was constructed. A gene set variation analysis (GSVA) indicated that the expression of the KCNQ1OT1 ceRNA network in colorectal cancer tissues and normal tissues were significantly different, not only in the TCGA-COAD dataset but also in three other GEO datasets used as validation. By predicting comprehensive immune cell subsets from gene expression data, in samples grouped by differential expression levels of the KCNQ1OT1 ceRNA network in a cohort of patients, we found that CD4+, CD8+, and cytotoxic T cells and 14 other immune cell subsets were at different levels in the high- and low-KCNQ1OT1 ceRNA network score groups. These results indicated that the KCNQ1OT1 ceRNA network could be involved in the regulation of the tumor microenvironment, which would provide the rationale to further exploit KCNQ1OT1 as a possible functional contributor to and therapeutic target for colorectal cancer.
| S-EPMC8493092 | biostudies-literature
Project description:BackgroundLong non-coding RNAs (lncRNAs) have been the subject of considerable attention in recent years due to their role in gene regulation. However, the function of lncRNAs remains poorly understood, especially in the context of infection with low-risk human papillomaviruses (HPVs). To further understanding on this issue, we investigated lncRNA expression in HPV-induced common warts.MethodsA publicly available high-throughput sequencing dataset for common warts was downloaded from the Gene Expression Omnibus (GEO). lncRNA profiles were generated using the NetworkAnalyst 3.0 workflow, and a list of differentially expressed (DE) lncRNAs in common warts was identified and inputted into the ENCODE, RegNetwork, DisGeNet, and miRNet platforms.ResultsA total of 54 lncRNAs were revealed to be significantly dysregulated in common warts. Of these 54 lncRNAs, 24 and 30 were upregulated and downregulated, respectively. The most significantly differentially expressed lncRNAs in common warts included the CERNA2, LINC02159, SH3PXD2A-AS1, and UNC5B-AS1 genes.ConclusionThe current findings suggest that HPV-induced warts impact the host lncRNA transcriptome. To the best of our knowledge, the present study is the first to explore the impact of low-risk HPV infection on lncRNA expression profiles.
| S-EPMC9706709 | biostudies-literature
Project description:Background Adrenocortical carcinoma (ACC) is a malignant tumor with poor prognosis and unclear pathogenesis. This study aimed to explore the role of long non-coding RNAs (lncRNAs) in ACC. Methods We obtained the lncRNA expression profiles of 10 ACC samples and 6 normal control samples from the GEO database and identified differentially expressed RNAs using the limma package in R. Results We obtained a total of 391 differentially expressed lncRNAs (DElncRNAs) and 1,313 differentially expressed mRNAs (DEmRNAs) between ACC samples and normal control samples. Using Cytoscape v3.7.0, we then constructed a lncRNA-miRNA-mRNA (competing endogenous RNA, or ceRNA) network consisting of 87 lncRNAs, 31 miRNAs, and 78 mRNAs. Applying GO and KEGG enrichment analysis for 78 mRNAs in the ceRNA network, we identified 9 GO terms and 21 significantly enriched pathways. A PPI network was constructed using STRING online tools and Cytoscape v3.7.0, identifying 10 key genes. Finally, through Kaplan-Meier survival analysis, we identified five lncRNAs (LINC00887, MEIS1-AS2, MIR29B2CHG, MIR503HG, and SREBF2-AS1) associated with prognosis in patients with ACC. Conclusions In summary, we constructed a ceRNA network and propose a new method for lncRNA research in ACC. Our results provide new clues for further exploration of lncRNAs in the pathogenesis of ACC.
| S-EPMC8798464 | biostudies-literature
Project description:Gastric cancer (GC) is a common type of cancer, and identification of novel diagnostic biomarkers associated with this disease is important. The present study aimed to identify novel diagnostic biomarkers associated with the prognosis of GC, using an integrated bioinformatics approach. Differentially expressed long non-coding RNAs (lncRNAs) associated with GC were identified using Gene Expression Omnibus datasets (GSE58828, GSE72305 and GSE99416) and The Cancer Genome Atlas database. A competing endogenous RNA network that incorporated five lncRNAs [long intergenic non-protein coding RNA 501 (LINC00501), LINC00365, SOX21 antisense divergent transcript 1 (SOX21-AS1), GK intronic transcript 1 (GK-IT1) and DLEU7 antisense RNA 1 (DLEU7-AS1)], 29 microRNAs and 114 mRNAs was constructed. Gene Ontology and protein-protein interaction network analyses revealed that these lncRNAs may be involved in 'biological regulation', 'metabolic process', 'cell communication', 'developmental process', 'cell proliferation', 'reproduction' and the 'cell cycle'. The results of receiver operating characteristic curve analysis demonstrated that LINC00501 (AUC=0.819), LINC00365 (AUC=0.580), SOX21-AS1 (AUC=0.736), GK-IT1 (AUC=0.823) and DLEU7-AS1 (AUC=0.932) had the potential to become valuable diagnostic biomarkers for GC. Associations with clinicopathological characteristics demonstrated that LINC00501 expression was significantly associated with sex (P=0.015) and tumor grade (P=0.022). Furthermore, LINC00365 expression was significantly associated with lymph node metastasis (P=0.025). Gene set enrichment analysis revealed that LINC00501, LINC00365 and SOX21-AS1 were enriched in signaling pathways associated with GC. Reverse transcription-quantitative PCR analysis demonstrated that LINC00501 expression (P=0.043) was significantly upregulated in GC tissues, whereas the expression levels of LINC00365 (P=0.033) and SOX21-AS1 (P=0.037) were significantly downregulated in GC tissues. Taken together, the results of the present study suggest that LINC00501, LINC00365, SOX21-AS1, GK-IT1 and DLEU7-AS1 may be used as novel diagnostic biomarkers for GC, and may be functionally associated with GC development and progression.
| S-EPMC8063256 | biostudies-literature
Project description:Long non-coding RNAs (lncRNAs) can act as competing endogenous RNAs (ceRNAs), interacting with microRNAs (miRNAs) and playing an important role in tumor progression. However, the role of lncRNA-mediated ceRNAs in glioma remains largely unknown. The present study aimed to identify novel lncRNAs and their associated function in glioma. RNA sequencing and corresponding clinical data from patients with glioma were obtained from The Cancer Genome Atlas. A total of 598 glioma tissues and 5 normal brain tissues were analyzed in the present study. The differentially expressed (DE) lncRNAs, mRNAs and miRNAs were identified using R packages and were used to construct a ceRNA network. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to investigate the biological functions of the DEmRNAs. Kaplan-Meier curve analysis was performed to investigate the association between DElncRNA expression and patient outcome. A total of 752 DElncRNAs, 2,079 DEmRNAs and 113 DEmiRNAs were identified between glioma and normal tissues. A lncRNA-miRNA-mRNA ceRNA network consisting of 61 lncRNAs, 12 miRNAs and 92 mRNAs was constructed. Survival analysis indicated that 36 DElncRNAs, 72 DEmRNAs and 3 DEmiRNAs were associated with overall survival in patients with glioma. The present study identified novel lncRNAs associated with survival prognosis and may facilitate further investigation of lncRNA-mediated ceRNA regulatory mechanisms in glioma.
| S-EPMC7436175 | biostudies-literature