Construction of a New Immune-Related Competing Endogenous RNA Network with Prognostic Value in Lung Adenocarcinoma

Tumor microenvironment has significant influence on the gene expression of tumor tissues and on the clinical outcomes in lung adenocarcinoma. Infiltrating immune and stromal cells not only perturb the tumor signal in molecular studies, but also play crucial roles in cancer biology. The competing endogenous RNAs (ceRNAs) are useful to explain the post-transcriptional layer regulated by gene translation and play an important role in the occurrence and progression of lung adenocarcinoma. Therefore, identifying novel molecular markers by constructing ceRNA associated with immune infiltration is of great significance to guide the treatment of lung adenocarcinoma in the future. According to the immune and stromal scores of lung adenocarcinoma samples in The Cancer Genome Atlas (TCGA) database calculated by the ESTIMATE algorithm, we identified differentially expressed lncRNAs, miRNAs and mRNAs associated with immune infiltration, including 60 dysregulated lncRNAs, 38 dysregulated mRNAs, and 29 dysregulated miRNAs. Based on the PPI network and Cox regression analysis, 5 mRNAs including CNR2, P2RY12, ZNF831, RSPO1, and F2 were identified to be related to immune infiltration and prognosis in lung adenocarcinoma, and their differential expression, prognosis and correlation with immune cells were verified. Next, through target binding prediction, pearson correlation analysis and expression analysis, a novel immune-related ceRNA network containing 6 lncRNAs, 4 miRNAs, and 3 mRNAs was finally constructed. The present study constructed a novel immune-associated lncRNA-miRNA-mRNA ceRNA network, which deepens our understanding on the molecular network mechanism of lung adenocarcinoma and provides potential prognostic markers and novel therapeutic targets for the patients with lung adenocarcinoma.

RNA-RNA competitive interactions: a molecular civil war ruling cell physiology and diseases

The idea that proteins are the main determining factors in the functioning of cells and organisms, and their dysfunctions are the first cause of pathologies, has been predominant in biology and biomedicine until recently. This protein-centered view was too simplistic and failed to explain the physiological and pathological complexity of the cell. About 80% of the human genome is dynamically and pervasively transcribed, mostly as non-protein-coding RNAs (ncRNAs), which competitively interact with each other and with coding RNAs generating a complex RNA network regulating RNA processing, stability, and translation and, accordingly, fine-tuning the gene expression of the cells. Qualitative and quantitative dysregulations of RNA-RNA interaction networks are strongly involved in the onset and progression of many pathologies, including cancers and degenerative diseases. This review will summarize the RNA species involved in the competitive endogenous RNA network, their mechanisms of action, and involvement in pathological phenotypes. Moreover, it will give an overview of the most advanced experimental and computational methods to dissect and rebuild RNA networks.

CTRR-ncRNA: A Knowledgebase for Cancer Therapy Resistance and Recurrence Associated Non-coding RNAs

Cancer therapy resistance and recurrence (CTRR) are the dominant causes of death in cancer patients. Recent studies have indicated that non-coding RNAs (ncRNAs) can not only reverse the resistance to cancer therapy but also are crucial biomarkers for the evaluation and prediction of CTRR. Herein, we developed CTRR-ncRNA, a knowledgebase of CTRR-associated ncRNAs, aiming to provide an accurate and comprehensive resource for research involving the association between CTRR and ncRNAs. Compared to most of the existing cancer databases, CTRR-ncRNA is focused on the clinical characterization of cancers, including cancer subtypes, as well as survival outcomes and responses to personalized therapy of cancer patients. Information pertaining to biomarker ncRNAs has also been documented for the development of personalized CTRR prediction. A user-friendly interface and several functional modules have been incorporated into the database. Based on the preliminary analysis of genotype-phenotype relationships, universal ncRNAs have been found to be potential biomarkers for CTRR. The CTRR-ncRNA is a translation-oriented knowledgebase and it provides a valuable resource for mechanistic investigations and explainable artificial intelligence-based modeling. CTRR-ncRNA is freely available to the public at http://ctrr.bioinf.org.cn/.

Pan-cancer transcriptomic analysis identified six classes of immunosenescence genes revealed molecular links between aging, immune system and cancer

Aging is a complex process that significantly impacts the immune system. The aging-related decline of the immune system, termed immunosenescence, can lead to disease development, including cancer. The perturbation of immunosenescence genes may characterize the associations between cancer and aging. However, the systematical characterization of immunosenescence genes in pan-cancer remains largely unexplored. In this study, we comprehensively investigated the expression of immunosenescence genes and their roles in 26 types of cancer. We developed an integrated computational pipeline to identify and characterize immunosenescence genes in cancer based on the expression profiles of immune genes and clinical information of patients. We identified 2218 immunosenescence genes that were significantly dysregulated in a wide variety of cancers. These immunosenescence genes were divided into six categories based on their relationships with aging. Besides, we assessed the importance of immunosenescence genes in clinical prognosis and identified 1327 genes serving as prognostic markers in cancers. BTN3A1, BTN3A2, CTSD, CYTIP, HIF1AN, and RASGRP1 were associated with ICB immunotherapy response and served as prognostic factors after ICB immunotherapy in melanoma. Collectively, our results furthered the understanding of the relationship between immunosenescence and cancer and provided insights into immunotherapy for patients.

Identification of non-coding RNA related prognosis biomarkers based on ceRNA network in thyroid cancer

Introduction: Thyroid cancer (THCA) has become a serious malignant tumor worldwide. Identification of non-coding RNA related regulators is very necessary to improve the knowledge of THCA treatment. The aim of this study was to identify novel therapeutic targets and prognosis biomarkers for predicting pathological characteristics and subsequently treating THCA. Methods: We investigated the alterations of miRNAs, mRNAs and lncRNAs in THCA. Functional enrichment and clustering analysis were conducted for these aberrantly expressed RNAs. Multiple interaction networks among miRNAs, mRNAs and lncRNAs were constructed and the functional modules associated with THCA patients' prognosis were identified. Furthermore, we evaluated the prognostic roles of the important miRNAs, mRNAs and lncRNAs in THCA and investigated the regulatory potential of non-coding RNAs on immune cell infiltration. Results: We firstly identified that miR-4709-3p and miR-146b-3p could significantly classify patients into high/low risk groups, which may be potential prognosis biomarkers of THCA. Secondly, we constructed a THCA-related miRNA-mRNA network, which displayed small world network topological characters. Two THCA-related functional modules were identified from the miRNA-mRNA network by MCODE. Results showed that two modules could implicate in known cancer pathways, such as apoptosis and focal adhesion. Thirdly, a THCA-related miRNA-lncRNA network was constructed. A subnetwork of miRNA-lncRNA network showed strong prognosis effect in THCA. Fourthly, we constructed a THCA-related mRNA-lncRNA network and detected several typical lncRNA-miRNA-mRNA crosstalk, such as AC068138, BCL2, miR-21 and miR-146b, which had good prognosis effect in THCA. Immune infiltration results showed that lncRNAs LA16c-329F2, RP11-395N3, RP11-423H2, RP11-399B17 and RP11-1036E20 were high related to neutrophil and dendritic cell infiltration. Discussion: Non-coding RNA-mediated gene regulatory network has the strong regulatory potential in pathological processes of THCA. All these results could help us uncover the non-coding RNA-mediated regulatory mechanism in THCA.

Decoding competing endogenous RNA networks for cancer biomarker discovery

Crosstalk between competing endogenous RNAs (ceRNAs) is mediated by shared microRNAs (miRNAs) and plays important roles both in normal physiology and tumorigenesis; thus, it is attractive for systems-level decoding of gene regulation. As ceRNA networks link the function of miRNAs with that of transcripts sharing the same miRNA response elements (MREs), e.g. pseudogenes, competing mRNAs, long non-coding RNAs, and circular RNAs, the perturbation of crucial interactions in ceRNA networks may contribute to carcinogenesis by affecting the balance of cellular regulatory system. Therefore, discovering biomarkers that indicate cancer initiation, development, and/or therapeutic responses via reconstructing and analyzing ceRNA networks is of clinical significance. In this review, the regulatory function of ceRNAs in cancer and crucial determinants of ceRNA crosstalk are firstly discussed to gain a global understanding of ceRNA-mediated carcinogenesis. Then, computational and experimental approaches for ceRNA network reconstruction and ceRNA validation, respectively, are described from a systems biology perspective. We focus on strategies for biomarker identification based on analyzing ceRNA networks and highlight the translational applications of ceRNA biomarkers for cancer management. This article will shed light on the significance of miRNA-mediated ceRNA interactions and provide important clues for discovering ceRNA network-based biomarker in cancer biology, thereby accelerating the pace of precision medicine and healthcare for cancer patients.

Time to infer miRNA sponge modules

Inferring competing endogenous RNA (ceRNA) or microRNA (miRNA) sponge modules is a challenging and meaningful task for revealing ceRNA regulation mechanism at the module level. Modules in this context refer to groups of miRNA sponges which have mutual competitions and act as functional units for achieving biological processes. The recent development of computational methods based on heterogeneous data provides a novel way to discern the competitive effects of miRNA sponges on human complex diseases. This article aims to provide a comprehensive perspective of miRNA sponge module discovery methods. We first review the publicly available databases of cancer-related miRNA sponges, as the miRNA sponges involved in human cancers contribute to the discovery of cancer-associated modules. Then we review the existing computational methods for inferring miRNA sponge modules. Furthermore, we conduct an assessment on the performance of the module discovery methods with the pan-cancer dataset, and the comparison study indicates that it is useful to infer biologically meaningful miRNA sponge modules by directly mapping heterogeneous data to the competitive modules. Finally, we discuss the future directions and associated challenges in developing in silico methods to infer miRNA sponge modules. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Small Molecule-RNA Interactions Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs.

Differential metabolic network construction for personalized medicine: Study of type 2 diabetes mellitus patients' response to gliclazide-modified-release-treated

Individual variation in genetic and environmental factors can cause the differences in metabolic phenotypes, which may have an effect on drug responses of patients. Deep exploration of patients' responses to therapeutic agents is a crucial and urgent event in the personalized treatment study. Using machine learning methods for the discovery of suitability evaluation biomarkers can provide deep insight into the mechanism of disease therapy and facilitate the development of personalized medicine. To find important metabolic network signals for the prediction of patients' drug responses, a novel method referred to as differential metabolic network construction (DMNC) was proposed. In DMNC, concentration changes in metabolite ratios between different pathological states are measured to construct differential metabolic networks, which can be used to advance clinical decision-making. In this study, DMNC was applied to characterize type 2 diabetes mellitus (T2DM) patients' responses against gliclazide modified-release (MR) therapy. Two T2DM metabolomics datasets from different batches of subjects treated by gliclazide MR were analyzed in depth. A network biomarker was defined to assess the patients' suitability for gliclazide MR. It can be effective in the prediction of significant responders from nonsignificant responders, achieving area under the curve values of 0.893 and 1.000 for the discovery and validation sets, respectively. Compared with the metabolites selected by the other methods, the network biomarker selected by DMNC was more stable and precise to reflect the metabolic responses in patients to gliclazide MR therapy, thereby contributing for the personalized medicine of T2DM patients. The better performance of DMNC validated its potential for the identification of network biomarkers to characterize the responses against therapeutic treatments and provide valuable information for personalized medicine.

miRSM: an R package to infer and analyse miRNA sponge modules in heterogeneous data

In molecular biology, microRNA (miRNA) sponges are RNA transcripts which compete with other RNA transcripts for binding with miRNAs. Research has shown that miRNA sponges have a fundamental impact on tissue development and disease progression. Generally, to achieve a specific biological function, miRNA sponges tend to form modules or communities in a biological system. Until now, however, there is still a lack of tools to aid researchers to infer and analyse miRNA sponge modules from heterogeneous data. To fill this gap, we develop an R/Bioconductor package, miRSM, for facilitating the procedure of inferring and analysing miRNA sponge modules. miRSM provides a collection of 50 co-expression analysis methods to identify gene co-expression modules (which are candidate miRNA sponge modules), four module discovery methods to infer miRNA sponge modules and seven modular analysis methods for investigating miRNA sponge modules. miRSM will enable researchers to quickly apply new datasets to infer and analyse miRNA sponge modules, and will consequently accelerate the research on miRNA sponges.

A Signature of Five Long Non-Coding RNAs for Predicting the Prognosis of Alzheimer's Disease Based on Competing Endogenous RNA Networks

Long non-coding RNAs (lncRNAs) play important roles in the pathogenesis of Alzheimer's disease (AD). However, the functions and regulatory mechanisms of lncRNA are largely unclear. Herein, we obtained 3,158 lncRNAs by microarray re-annotation. A global network of competing endogenous RNAs (ceRNAs) was developed for AD and normal samples were based on the gene expressions profiles. A total of 255 AD-deficient messenger RNA (mRNA)-lncRNAs were identified by the expression correlation analysis. Genes in the dysregulated ceRNAs were found to be mainly enriched in transcription factors and micro RNAs (miRNAs). Analysis of the disordered miRNA in the lncRNA-mRNA network revealed that 40 pairs of lncRNA shared more than one disordered miRNA. Among them, nine lncRNAs were closely associated with AD, Parkinson's disease, and other neurodegenerative diseases. Of note, five lncRNAs were found to be potential biomarkers for AD. Real-time quantitative reverse transcription PCR (qRT-PCR) assay revealed that PART1 was downregulated, while SNHG14 was upregulated in AD serum samples when compared to normal samples. This study elucidates the role of lncRNAs in the pathogenesis of AD and presents new lncRNAs that can be exploited to design diagnostic and therapeutic agents for AD.

A Seven-Long Non-coding RNA Signature Improves Prognosis Prediction of Lung Adenocarcinoma: An Integrated Competing Endogenous RNA Network Analysis

Early and precise prediction is an important way to reduce the poor prognosis of lung adenocarcinoma (LUAD) patients. Nevertheless, the widely used tumor, node, and metastasis (TNM) staging system based on anatomical information only often could not achieve adequate performance on foreseeing the prognosis of LUAD patients. This study thus aimed to examine whether the long non-coding RNAs (lncRNAs), known highly involved in the tumorigenesis of LUAD through the competing endogenous RNAs (ceRNAs) mechanism, could provide additional information to improve prognosis prediction of LUAD patients. To prove the hypothesis, a dataset consisting of both RNA sequencing data and clinical pathological data, obtained from The Cancer Genome Atlas (TCGA) database, was analyzed. Then, differentially expressed RNAs (DElncRNAs, DEmiRNAs, and DEmRNAs) were identified and a lncRNA-miRNA-mRNA ceRNA network was constructed based on those differentially expressed RNAs. Functional enrichment analysis revealed that this ceRNA network was highly enriched in some cancer-associated signaling pathways. Next, lasso-Cox model was run 1,000 times to recognize the potential survival-related combinations of the candidate lncRNAs in the ceRNA network, followed by the "best subset selection" to further optimize these lncRNA-based combinations, and a seven-lncRNA prognostic signature with the best performance was determined. Based on the median risk score, LUAD patients could be well distinguished into high-/low-risk subgroups. The Kaplan-Meier survival curve showed that LUAD patients in the high-risk group had significantly shorter overall survival than those in the low-risk group (log-rank test P = 4.52 × 10^-9). The ROC curve indicated that the clinical genomic model including both the TNM staging system and the signature had a superior performance in predicting the patients' overall survival compared to the clinical model with the TNM staging system only. Further stratification analysis suggested that the signature could work well in the different strata of the stage, gender, or age, rendering it to be a wide application. Finally, a ceRNA subnetwork related to the signature was extracted, demonstrating its high involvement in the tumorigenesis mechanism of LUAD. In conclusion, the present study established a lncRNA-based molecular signature, which can significantly improve prognosis prediction for LUAD patients.

Long Noncoding RNAs in the Progression of Atherosclerosis: An Integrated Analysis Based on Competing Endogenous RNA Theory

Long noncoding RNAs (lncRNAs) have been increasingly accepted to function importantly in human diseases by serving as competing endogenous RNAs (ceRNAs). To date, the ceRNA mechanisms of lncRNAs in the progression of atherosclerosis (AS) remain largely unclear. On the basis of ceRNA theory, we implemented a multistep computational analysis to construct an lncRNA-mRNA network for AS progression (ASpLMN). The probe reannotation method and microRNA-target interactions from databases were systematically integrated. Three lncRNAs (GS1-358P8.4, OIP5-AS1, and TUG1) with central topological features in the ASpLMN were firstly identified. By using subnetwork analysis, we then obtained two highly clustered modules and one dysregulated module from the ASpLMN network. These modules, sharing three lncRNAs (GS1-358P8.4, OIP5-AS1, and RP11-690D19.3), were significantly enriched in biological pathways such as regulation of actin cytoskeleton, tryptophan metabolism, lysosome, and arginine and proline metabolism. In addition, random walking in the ASpLMN network indicated that lncRNA RP1-39G22.7 and MBNL1-AS1 may also play an essential role in the pathology of AS progression. The identified six lncRNAs from the aforementioned steps could distinguish advanced- from early-staged AS, with a strong diagnostic power for AS occurrence. In conclusion, the results of this study will improve our understanding about the ceRNA-mediated regulatory mechanisms in AS progression, and provide novel lncRNAs as biomarkers or therapeutic targets for acute cardiovascular events.

Liquid Chromatography-Mass Spectrometry-Based Nontargeted Metabolomics Predicts Prognosis of Hepatocellular Carcinoma after Curative Resection

Assessment and prediction of prognostic risk in patients with hepatocellular carcinoma (HCC) would greatly benefit the optimal treatment selection. Here, we aimed to identify the critical metabolites associated with the outcomes and develop a risk score to assess the prognosis of HCC patients after curative resection. A total of 78 serum samples of HCC patients were analyzed by liquid chromatography-mass spectrometry to characterize the metabolic profiling. A novel network-based feature selection method (NFSM) was developed to define the critical metabolites with the most discriminant capacity to outcomes. The metabolites defined by NFSM was further reduced by Cox regression analysis to generate a prognostic metabolite panel-phenylalanine and choline. Furthermore, univariate and multivariate Cox regression analyses were applied to combine the metabolite panel with the presence of satellite nodes to generate a global prognostic index (GPI) score for overall survival assessment. Compared with the current clinical classification systems, including the Barcelona-clinic liver cancer stage, tumor-node-metastasis stage, and albumin-bilirubin grade, the GPI score presented comparable performance, according to the time-dependent receiver operating characteristic curves and was validated in an independent cohort, which suggested that metabolomics could serve as a helpful tool to stratify the HCC prognostic risk after operation.

Prediction of Specific Subtypes and Common Markers of Non-Small Cell Lung Cancer Based on Competing Endogenous RNA Network

Background

There are various pathological types of lung cancer, including squamous cell carcinoma and adenocarcinoma. Although both of them are lung cancers, there are significant differences in diagnosis, pathogenesis, location, imaging, metastasis, and treatment. According to the competing endogenous RNA (ceRNA) theory, long non-coding RNAs (lncRNAs) compete with encoding protein genes (mRNAs) to connect with miRNAs, thus affecting the level of mRNA.

Material/Methods

First, using the t test, we identified mRNAs and lncRNAs that have different expressions (fold change >2, P<0.01) in normal samples and in tumor samples. We calculated the significance of the shared miRNAs for mRNAs and lncRNAs by hypergeometric test (P<0.01). Further, mRNA and lncRNA pairs with co-expression relationships in cancer samples were used to establish ceRNA networks. Then, the random walk algorithm was used to optimize the specific ceRNA networks and identify potential prognostic markers of survival. Finally, we built a common ceRNA network to find markers of non-small-cell lung cancer.

Results

We identified some potential key markers, such as PVT1, LINC00472, CDKN2A, and FAM83B, in LUSC and HOXA11-AS, HNF1A-AS1, LINC00511, and HOTAIR in LUAD by analyzing the ceRNA networks. Moreover, a number of common ceRNA pairs, such as CDC25C/CDK1/RRM2-LINC00355, have been found, and they are also significant markers for tumor survival and prognosis.

Conclusions

In summary, the present study provides a comparative analysis in 2 kinds of lung cancer ceRNA networks. Some specific and common markers we predicted that may be of great importance for clinical diagnosis and treatment.

Dynamical network analysis reveals key microRNAs in progressive stages of lung cancer

Non-coding RNAs are fundamental to the competing endogenous RNA (CeRNA) hypothesis in oncology. Previous work focused on static CeRNA networks. We construct and analyze CeRNA networks for four sequential stages of lung adenocarcinoma (LUAD) based on multi-omics data of long non-coding RNAs (lncRNAs), microRNAs and mRNAs. We find that the networks possess a two-level bipartite structure: common competing endogenous network (CCEN) composed of an invariant set of microRNAs over all the stages and stage-dependent, unique competing endogenous networks (UCENs). A systematic enrichment analysis of the pathways of the mRNAs in CCEN reveals that they are strongly associated with cancer development. We also find that the microRNA-linked mRNAs from UCENs have a higher enrichment efficiency. A key finding is six microRNAs from CCEN that impact patient survival at all stages, and four microRNAs that affect the survival from a specific stage. The ten microRNAs can then serve as potential biomarkers and prognostic tools for LUAD.

Identification of long non-coding RNA HERC2P2 as a tumor suppressor in glioma

Long non-coding RNAs (lncRNAs) have been reported to play important roles in glioma; however, most of them promote glioma progression. We constructed a competing endogenous (ceRNA) network based on the Chinese Glioma Genome Atlas dataset, and lncRNA hect domain and RLD 2 pseudogene 2 (HERC2P2) is the core of this network. Highly connected genes in the ceRNA network classified the glioma patients into three clusters with significantly different survival rates. The expression of HERC2P2 is positively correlated with survival and negatively correlated with clinical grade. Cell colony formation, Transwell and cell scratch tests were performed to evaluate the role of HERC2P2 in glioblastoma growth. Furthermore, we overexpressed HERC2P2 in U87 cells and established a mouse intracranial glioma model to examine the function of HERC2P2 in vivo. In conclusion, we identified a lncRNA with tumor suppressor functions in glioma that could be a potential biomarker for glioma patients.

LMSM: A modular approach for identifying lncRNA related miRNA sponge modules in breast cancer

Until now, existing methods for identifying lncRNA related miRNA sponge modules mainly rely on lncRNA related miRNA sponge interaction networks, which may not provide a full picture of miRNA sponging activities in biological conditions. Hence there is a strong need of new computational methods to identify lncRNA related miRNA sponge modules. In this work, we propose a framework, LMSM, to identify LncRNA related MiRNA Sponge Modules from heterogeneous data. To understand the miRNA sponging activities in biological conditions, LMSM uses gene expression data to evaluate the influence of the shared miRNAs on the clustered sponge lncRNAs and mRNAs. We have applied LMSM to the human breast cancer (BRCA) dataset from The Cancer Genome Atlas (TCGA). As a result, we have found that the majority of LMSM modules are significantly implicated in BRCA and most of them are BRCA subtype-specific. Most of the mediating miRNAs act as crosslinks across different LMSM modules, and all of LMSM modules are statistically significant. Multi-label classification analysis shows that the performance of LMSM modules is significantly higher than baseline’s performance, indicating the biological meanings of LMSM modules in classifying BRCA subtypes. The consistent results suggest that LMSM is robust in identifying lncRNA related miRNA sponge modules. Moreover, LMSM can be used to predict miRNA targets. Finally, LMSM outperforms a graph clustering-based strategy in identifying BRCA-related modules. Altogether, our study shows that LMSM is a promising method to investigate modular regulatory mechanism of sponge lncRNAs from heterogeneous data.

Previous studies have revealed that long non-coding RNAs (lncRNAs), as microRNA (miRNA) sponges or competing endogenous RNAs (ceRNAs), can regulate the expression levels of messenger RNAs (mRNAs) by decreasing the amount of miRNAs interacting with mRNAs. In this work, we hypothesize that the “tug-of-war” between RNA transcripts for attracting miRNAs is across groups or modules. Based on the hypothesis, we propose a framework called LMSM, to identify LncRNA related MiRNA Sponge Modules. Based on the two miRNA sponge modular competition principles, significant sharing of miRNAs and high canonical correlation between the sponge lncRNAs and mRNAs, LMSM is also capable of predicting miRNA targets. LMSM not only extends the ceRNA hypothesis, but also provides a novel way to investigate the biological functions and modular mechanism of lncRNAs in breast cancer.

Computer-aided biomarker discovery for precision medicine: data resources, models and applications

Biomarkers are a class of measurable and evaluable indicators with the potential to predict disease initiation and progression. In contrast to disease-associated factors, biomarkers hold the promise to capture the changeable signatures of biological states. With methodological advances, computer-aided biomarker discovery has now become a burgeoning paradigm in the field of biomedical science. In recent years, the 'big data' term has accumulated for the systematical investigation of complex biological phenomena and promoted the flourishing of computational methods for systems-level biomarker screening. Compared with routine wet-lab experiments, bioinformatics approaches are more efficient to decode disease pathogenesis under a holistic framework, which is propitious to identify biomarkers ranging from single molecules to molecular networks for disease diagnosis, prognosis and therapy. In this review, the concept and characteristics of typical biomarker types, e.g. single molecular biomarkers, module/network biomarkers, cross-level biomarkers, etc., are explicated on the guidance of systems biology. Then, publicly available data resources together with some well-constructed biomarker databases and knowledge bases are introduced. Biomarker identification models using mathematical, network and machine learning theories are sequentially discussed. Based on network substructural and functional evidences, a novel bioinformatics model is particularly highlighted for microRNA biomarker discovery. This article aims to give deep insights into the advantages and challenges of current computational approaches for biomarker detection, and to light up the future wisdom toward precision medicine and nation-wide healthcare.

A susceptibility biomarker identification strategy based on significantly differentially expressed ceRNA triplets for ischemic cardiomyopathy

Ischemic cardiomyopathy (ICM) is a common human heart disease that causes death. No effective biomarkers for ICM could be found in existing databases, which is detrimental to the in-depth study of this disease. In the present study, ICM susceptibility biomarkers were identified using a proposed strategy based on RNA-Seq and miRNA-Seq data of ICM and normal samples. Significantly differentially expressed competing endogenous RNA (ceRNA) triplets were constructed using permutation tests and differentially expressed mRNAs, miRNAs and lncRNAs. Candidate ICM susceptible genes were screened out as differentially expressed genes in significantly differentially expressed ceRNA triplets enriched in ICM-related functional classes. Finally, eight ICM susceptibility genes and their significantly correlated lncRNAs with high classification accuracy were identified as ICM susceptibility biomarkers. These biomarkers would contribute to the diagnosis and treatment of ICM. The proposed strategy could be extended to other complex diseases without disease biomarkers in public databases.

Computational Identification of Cross-Talking ceRNAs

Competing endogenous RNAs (ceRNAs) are kinds of RNAs that regulate each other at post-transcription level through competing for miRNA regulators. CeRNA-ceRNA networks provide another type of function for protein-coding mRNAs, which link non-coding RNAs such as miRNA, long non-coding RNA, pseudogenes and circular RNAs. In this chapter, we will introduce the definition of ceRNAs, mainly provide the computational method to predict ceRNA interactions in general condition and complex diseases. In addition, we also illustrated several computational methods that are commonly used to identify the perturbed ceRNA networks in human diseases compared to normal conditions. Finally, we also summarized the principles of methods that integrated ceRNA theory to identify human disease biomarkers. Understanding of RNA-RNA crosstalk will provide significant insights into gene regulatory network that has been implicated in human development and/or diseases.

A Long Noncoding RNA (lncRNA)-Associated Competing Endogenous RNA (ceRNA) Network Identifies Eight lncRNA Biomarkers in Patients with Osteoarthritis of the Knee

Background

Osteoarthritis (OA) of the knee is a common disease that is associated with chronic pain. This study aimed to identify and investigate the functional role of biomarkers associated with long noncoding RNA (lncRNA) in the progression of OA of the knee by lncRNA-associated competing endogenous RNA (ceRNA) integrated network analysis.

Material/Methods

High-quality microRNA (miRNA)-lncRNA and miRNA-mRNA interactions and lncRNA and mRNA expression profiles for patients with OA of the knee with mild and severe pain were obtained from the Gene Expression Omnibus (GEO) database (GSE99662). A three-step computational method was used to construct the lncRNA-associated ceRNA interaction network in OA by integrating miRNA-lncRNA/mRNA interactions and lncRNA/mRNA expression profiles in patients with OA with mild and severe pain.

Results

A total of 1,870 dysregulated lncRNA-mRNA interactions were obtained in the lncRNA-associated ceRNA network in OA, including 476 gain and 1,394 loss interactions, covering 131 lncRNAs and 1,251 mRNAs. Characterization of the lncRNA-associated ceRNA network in OA indicated that lncRNAs had roles in the network. Further differential expression analysis identified eight lncRNA biomarkers, which could distinguish between patients with OA with mild pain and severe pain. These lncRNA-associated interactions showed significantly different co-expression patterns in samples from patients with OA of the knee associated with mild pain.

Conclusions

Integrated network analysis of lncRNA-associated ceRNA identified eight lncRNA molecular biomarkers associated with the progression of OA of the knee.

Interplay Between Long Noncoding RNAs and MicroRNAs in Cancer

In the last decade noncoding RNAs (ncRNAs) have been extensively studied in several biological processes and human diseases including cancer. microRNAs (miRNAs) are the best-known class of ncRNAs. miRNAs are small ncRNAs of around 20-22 nucleotides (nt) and are crucial posttranscriptional regulators of protein coding genes. Recently, new classes of ncRNAs, longer than miRNAs have been discovered. Those include intergenic noncoding RNAs (lincRNAs) and circular RNAs (circRNAs). These novel types of ncRNAs opened a very exciting field in biology, leading researchers to discover new relationships between miRNAs and long noncoding RNAs (lncRNAs), which act together to control protein coding gene expression. One of these new discoveries led to the formulation of the "competing endogenous RNA (ceRNA) hypothesis." This hypothesis suggests that an lncRNA acts as a sponge for miRNAs reducing their expression and causing the upregulation of miRNA targets. In this chapter we first discuss some recent discoveries in this field showing the mutual regulation of miRNAs, lncRNAs, and protein-coding genes in cancer. We then discuss the general approaches for the study of ceRNAs and present in more detail a recent computational approach to explore the ability of lncRNAs to act as ceRNAs in human breast cancer that has been shown to be, among the others, the most precise and promising.

Differential expression patterns of specific long noncoding RNAs and competing endogenous RNA network in alopecia areata

BACKGROUND

Long noncoding RNAs (lncRNAs) regulate gene expression by acting with microRNAs (miRNAs) and indirectly interact with messenger RNA (mRNAs). However, the roles of specific lncRNA and its related competing endogenous RNAs (ceRNA) network in alopecia areata (AA) are not fully understood.

METHODS

The blood lncRNA profiles were obtained by microarray from 10 samples, including five alopecia areata samples and five normal samples. Based on bioinformatics generated from miRcode, starBase, and miRTarBase, we constructed an lncRNA-miRNA-mRNA network (ceRNA network) in alopecia areata.

RESULTS

We found 154 differentially expressed lncRNAs and 46 differentially expressed genes (DEGs). The functional enrichment indicated that the DEGs mainly regulated the pathways of focal adhesion, Mucin type O-glycan biosynthesis, and so on. The differentially expressed lncRNA (DElncRNA) involved in the pathway of thyronamine and iodothyronamine metabolism and so on. Through integrated lncRNA-mRNA and miRNA-mRNA pairs, the ceRNA network was constructed, thereafter, six ceRNA subnetworks were identified and subnetwork 1 were found to be significantly associated with the occurrence of alopecia areata.

CONCLUSION

Our results showed blood lncRNA expression patterns and a complex ceRNA network in alopecia areata. However, futher studies on blood and tissue verification of these lncRNAs and relative pathways are needed.

Potential ceRNA networks involved in autophagy suppression of pancreatic cancer caused by chloroquine diphosphate: A study based on differentially‑expressed circRNAs, lncRNAs, miRNAs and mRNAs

Autophagy has been reported to be involved in the occurrence and development of pancreatic cancer. However, the mechanism of autophagy‑associated non‑coding RNAs (ncRNAs) in pancreatic cancer remains largely unknown. In the present study, microarrays were used to detect differential expression of mRNAs, microRNAs (miRNAs), long ncRNAs (lncRNAs) and circular RNAs (circRNAs) post autophagy suppression by chloroquine diphosphate in PANC‑1 cells. Collectively, 3,966 mRNAs, 3,184 lncRNAs and 9,420 circRNAs were differentially expressed. Additionally, only two miRNAs (hsa‑miR‑663a‑5p and hsa‑miR‑154‑3p) were underexpressed in the PANC‑1 cells in the autophagy‑suppression group. Furthermore, miR‑663a‑5p with 9 circRNAs, 8 lncRNAs and 46 genes could form a prospective ceRNA network associated with autophagy in pancreatic cancer cells. In addition, another ceRNA network containing miR‑154‑3p, 5 circRNAs, 2 lncRNAs and 11 genes was also constructed. The potential multiple ceRNA, miRNA and mRNA associations may serve pivotal roles in the autophagy of pancreatic cancer cells, which lays the theoretical foundation for subsequent investigations on pancreatic cancer.

Identification of functional lncRNAs in atrial fibrillation by integrative analysis of the lncRNA-mRNA network based on competing endogenous RNAs hypothesis

A mounting body of evidence has suggested that long noncoding RNAs (lncRNAs) play critical roles in human diseases by acting as competing endogenous RNAs (ceRNAs). However, the functions and ceRNA mechanisms of lncRNAs in atrial fibrillation (AF) remain to date unclear. In this study, we constructed an AF-related lncRNA-mRNA network (AFLMN) based on ceRNA theory, by integrating probe reannotation pipeline and microRNA (miRNA)-target regulatory interactions. Two lncRNAs with central topological properties in the AFLMN were first obtained. By using bidirectional hierarchical clustering, we identified two modules containing four lncRNAs, which were significantly enriched in many known pathways of AF. To elucidate the ceRNA interactions in certain disease or normal condition, the dysregulated lncRNA-mRNA crosstalks in AF were further analyzed, and six hub lncRNAs were obtained from the network. Furthermore, random walk analysis of the AFLMN suggested that lncRNA RP11-296O14.3 may function importantly in the pathological process of AF. All these eight lncRNAs that were identified from previous steps (RP11-363E7.4, GAS5, RP11-410L14.2, HAGLR, RP11-421L21.3, RP11-111K18.2, HOTAIRM1, and RP11-296O14.3) exhibited a strong diagnostic power for AF. The results of our study provide new insights into the functional roles and regulatory mechanisms of lncRNAs in AF, and facilitate the discovery of novel diagnostic biomarkers or therapeutic targets.

Integrative analysis of competing endogenous RNA networks reveals the functional lncRNAs in heart failure

Heart failure has become one of the top causes of death worldwide. It is increasing evidence that lncRNAs play important roles in the pathology processes of multiple cardiovascular diseases. Additionally, lncRNAs can function as ceRNAs by sponging miRNAs to affect the expression level of mRNAs, implicating in numerous biological processes. However, the functional roles and regulatory mechanisms of lncRNAs in heart failure are still unclear. In our study, we constructed a heart failure-related lncRNA-mRNA network by integrating probe re-annotation pipeline and miRNA-target interactions. Firstly, some lncRNAs that had the central topological features were found in the heart failure-related lncRNA-mRNA network. Then, the lncRNA-associated functional modules were identified from the network, using bidirectional hierarchical clustering. Some lncRNAs that involved in modules were demonstrated to be enriched in many heart failure-related pathways. To investigate the role of lncRNA-associated ceRNA crosstalks in certain disease or physiological status, we further identified the lncRNA-associated dysregulated ceRNA interactions. And we also performed a random walk algorithm to identify more heart failure-related lncRNAs. All these lncRNAs were verified to show a strong diagnosis power for heart failure. These results will help us to understand the mechanism of lncRNAs in heart failure and provide novel lncRNAs as candidate diagnostic biomarkers or potential therapeutic targets.

Cancerin: A computational pipeline to infer cancer-associated ceRNA interaction networks

MicroRNAs (miRNAs) inhibit expression of target genes by binding to their RNA transcripts. It has been recently shown that RNA transcripts targeted by the same miRNA could “compete” for the miRNA molecules and thereby indirectly regulate each other. Experimental evidence has suggested that the aberration of such miRNA-mediated interaction between RNAs—called competing endogenous RNA (ceRNA) interaction—can play important roles in tumorigenesis. Given the difficulty of deciphering context-specific miRNA binding, and the existence of various gene regulatory factors such as DNA methylation and copy number alteration, inferring context-specific ceRNA interactions accurately is a computationally challenging task. Here we propose a computational method called Cancerin to identify cancer-associated ceRNA interactions. Cancerin incorporates DNA methylation, copy number alteration, gene and miRNA expression datasets to construct cancer-specific ceRNA networks. We applied Cancerin to three cancer datasets from the Cancer Genome Atlas (TCGA) project. Our results indicated that ceRNAs were enriched with cancer-related genes, and ceRNA modules in the inferred ceRNA networks were involved in cancer-associated biological processes. Using LINCS-L1000 shRNA-mediated gene knockdown experiment in breast cancer cell line to assess accuracy, Cancerin was able to predict expression outcome of ceRNA genes with high accuracy.

CeRNA interaction is a post-transcriptional gene regulation that involves interactions between RNAs competing for common miRNA regulators. Dysregulation of ceRNA interactions have been implicated in multiple diseases including cancer. Here we propose a computational pipeline called Cancerin that infers genome-wide ceRNA interactions in cancer. Unlike existing ceRNA inference tools that consider miRNAs as the only factor that regulate gene expression, Cancerin considers other types of gene regulators besides miRNAs, namely transcription factors, copy number alteration, and DNA methylation. To identify miRNA regulators for each gene, Cancerin incorporates a LASSO-based variable selection procedure that leverages both sequence-based and gene expression information. Then multiple expression-based filtering conditions are employed to select ceRNA interactions. Cancerin was applied to three cancer datasets from TCGA. Functional analysis indicated that the inferred ceRNAs were enriched with cancer-related genes, and ceRNAs within ceRNA modules (densely-connected ceRNAs) were involved in cancer-associated biological processes. Survival analysis showed that compared to non-ceRNAs, ceRNAs hold better prognostic power to predict survival outcomes. Our results showed that Cancerin can be used to identify genome-wide and functionally important ceRNA interactions, which makes it a valuable tool to better understand this recently discovered gene regulation mechanism and its role in cancer biology.

Comprehensive characterization of lncRNA-mRNA related ceRNA network across 12 major cancers

Recent studies indicate that long noncoding RNAs (lncRNAs) can act as competing endogenous RNAs (ceRNAs) to indirectly regulate mRNAs through shared microRNAs, which represents a novel layer of RNA crosstalk and plays critical roles in the development of tumor. However, the global regulation landscape and characterization of these lncRNA related ceRNA crosstalk in cancers is still largely unknown. Here, we systematically characterized the lncRNA related ceRNA interactions across 12 major cancers and the normal physiological states by integrating multidimensional molecule profiles of more than 5000 samples. Our study suggest the large difference of ceRNA regulation between normal and tumor states and the higher similarity across similar tissue origin of tumors. The ceRNA related molecules have more conserved features in tumor networks and they play critical roles in both the normal and tumorigenesis processes. Besides, lncRNAs in the pan-cancer ceRNA network may be potential biomarkers of tumor. By exploring hub lncRNAs, we found that these conserved key lncRNAs dominate variable tumor hallmark processes across pan-cancers. Network dynamic analysis highlights the critical roles of ceRNA regulation in tumorigenesis. By analyzing conserved ceRNA interactions, we found that miRNA mediate ceRNA regulation showed different patterns across pan-cancer; while analyzing the cancer specific ceRNA interactions reveal that lncRNAs synergistically regulated tumor driver genes of cancer hallmarks. Finally, we found that ceRNA modules have the potential to predict patient survival. Overall, our study systematically dissected the lncRNA related ceRNA networks in pan-cancer that shed new light on understanding the molecular mechanism of tumorigenesis.

Analyzing the interactions of mRNAs, miRNAs, lncRNAs and circRNAs to predict competing endogenous RNA networks in glioblastoma

Cross-talk between competitive endogenous RNAs (ceRNAs) may play a critical role in revealing potential mechanisms of tumor development and physiology. Glioblastoma is the most common type of malignant primary brain tumor, and the mechanisms of tumor genesis and development in glioblastoma are unclear. Here, to investigate the role of non-coding RNAs and the ceRNA network in glioblastoma, we performed paired-end RNA sequencing and microarray analyses to obtain the expression profiles of mRNAs, lncRNAs, circRNAs and miRNAs. We identified that the expression of 501 lncRNAs, 1999 mRNAs, 2038 circRNAs and 143 miRNAs were often altered between glioblastoma and matched normal brain tissue. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed on these differentially expressed mRNAs and miRNA-mediated target genes of lncRNAs and circRNAs. Furthermore, we used a multi-step computational framework and several bioinformatics methods to construct a ceRNA network combining mRNAs, miRNAs, lncRNAs and circRNA, based on co-expression analysis between the differentially expressed RNAs. We identified that plenty of lncRNAs, CircRNAs and their downstream target genes in the ceRNA network are related to glutamatergic synapse, suggesting that glutamate metabolism is involved in glioma biological functions. Our results will accelerate the understanding of tumorigenesis, cancer progression and even therapeutic targeting in glioblastoma.

A Computational Method of Defining Potential Biomarkers based on Differential Sub-Networks

Analyzing omics data from a network-based perspective can facilitate biomarker discovery. To improve disease diagnosis and identify prospective information indicating the onset of complex disease, a computational method for identifying potential biomarkers based on differential sub-networks (PB-DSN) is developed. In PB-DSN, Pearson correlation coefficient (PCC) is used to measure the relationship between feature ratios and to infer potential networks. A differential sub-network is extracted to identify crucial information for discriminating different groups and indicating the emergence of complex diseases. Subsequently, PB-DSN defines potential biomarkers based on the topological analysis of these differential sub-networks. In this study, PB-DSN is applied to handle a static genomics dataset of small, round blue cell tumors and a time-series metabolomics dataset of hepatocellular carcinoma. PB-DSN is compared with support vector machine-recursive feature elimination, multivariate empirical Bayes statistics, analyzing time-series data based on dynamic networks, molecular networks based on PCC, PinnacleZ, graph-based iterative group analysis, KeyPathwayMiner and BioNet. The better performance of PB-DSN not only demonstrates its effectiveness for the identification of discriminative features that facilitate disease classification, but also shows its potential for the identification of warning signals.

Systematic review of computational methods for identifying miRNA-mediated RNA-RNA crosstalk

Posttranscriptional crosstalk and communication between RNAs yield large regulatory competing endogenous RNA (ceRNA) networks via shared microRNAs (miRNAs), as well as miRNA synergistic networks. The ceRNA crosstalk represents a novel layer of gene regulation that controls both physiological and pathological processes such as development and complex diseases. The rapidly expanding catalogue of ceRNA regulation has provided evidence for exploitation as a general model to predict the ceRNAs in silico. In this article, we first reviewed the current progress of RNA-RNA crosstalk in human complex diseases. Then, the widely used computational methods for modeling ceRNA-ceRNA interaction networks are further summarized into five types: two types of global ceRNA regulation prediction methods and three types of context-specific prediction methods, which are based on miRNA-messenger RNA regulation alone, or by integrating heterogeneous data, respectively. To provide guidance in the computational prediction of ceRNA-ceRNA interactions, we finally performed a comparative study of different combinations of miRNA–target methods as well as five types of ceRNA identification methods by using literature-curated ceRNA regulation and gene perturbation. The results revealed that integration of different miRNA–target prediction methods and context-specific miRNA/gene expression profiles increased the performance for identifying ceRNA regulation. Moreover, different computational methods were complementary in identifying ceRNA regulation and captured different functional parts of similar pathways. We believe that the application of these computational techniques provides valuable functional insights into ceRNA regulation and is a crucial step for informing subsequent functional validation studies.

The global view of mRNA-related ceRNA cross-talks across cardiovascular diseases

Competing endogenous RNA (ceRNA) have received wide attention because they are a novel way to regulate genes through sharing microRNAs (miRNAs) that are crucial for complex processes in many diseases. However, no systematic analysis of ceRNA mechanism in cardiovascular disease (CVD) is known. To gain insights into the global properties of ceRNAs in multi-CVDs, we constructed the global view of mRNA-related ceRNA cross-talk in eight major CVDs from ~2,800 samples. We found common features that could be used to uncover similarities among different CVDs and highlighted a common core ceRNA network across CVDs. Comparative analysis of hub ceRNAs in each network revealed three types of hubs, which might play key roles in diverse biological processes. Importantly, by combining CVD-related pathway genes with ceRNA-ceRNA interactions, common modules that might exert functions in specific mechanisms were identified. In addition, our study investigated a potential mechanistic linkage between pathway cross-talk and ceRNA cross-talk. In summary, this study uncovered and systematically characterized global properties of mRNA-related ceRNA cross-talks across CVDs, which may provide a new layer for exploring biological mechanisms and shed new light on cardiology.

Inferring miRNA sponge co-regulation of protein-protein interactions in human breast cancer

Background

Recent studies have shown that the crosstalk between microRNA (miRNA) sponges plays an important role in human cancers. However, the co-regulation roles of miRNA sponges in protein-protein interactions (PPIs) are still unknown.

Results

In this study, we propose a multi-step method called miRSCoPPI to infer miRNA sponge co-regulation of PPIs. We focus on investigating breast cancer (BRCA) related miRNA sponge co-regulation, by integrating heterogeneous data, including miRNA, long non-coding RNA (lncRNA) and messenger RNA (mRNA) expression data, experimentally validated miRNA-target interactions, PPIs and lncRNA-target interactions, and the list of breast cancer genes. We find that the inferred BRCA-related miRSCoPPI network is highly connected and scale free. The top 10% hub genes in the BRCA-related miRSCoPPI network have potential biological implications in breast cancer. By utilizing a graph clustering method, we discover 17 BRCA-related miRSCoPPI modules. Through pathway enrichment analysis of the modules, we find that several modules are significantly enriched in pathways associated with breast cancer. Moreover, 10 modules have good performance in classifying breast tumor and normal samples, and can act as module signatures for prognostication. By using putative computationally predicted miRNA-target interactions, we have consistent results with those obtained using experimentally validated miRNA-target interactions, indicating that miRSCoPPI is robust in inferring miRNA sponge co-regulation of PPIs in human breast cancer.

Conclusions

Taken together, the results demonstrate that miRSCoPPI is a promising tool for inferring BRCA-related miRNA sponge co-regulation of PPIs and it can help with the understanding of the co-regulation roles of miRNA sponges on the PPIs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1672-2) contains supplementary material, which is available to authorized users.

Identifying miRNA sponge modules using biclustering and regulatory scores

Background

MicroRNA (miRNA) sponges with multiple tandem miRNA binding sequences can sequester miRNAs from their endogenous target mRNAs. Therefore, miRNA sponge acting as a decoy is extremely important for long-term loss-of-function studies both in vivo and in silico. Recently, a growing number of in silico methods have been used as an effective technique to generate hypotheses for in vivo methods for studying the biological functions and regulatory mechanisms of miRNA sponges. However, most existing in silico methods only focus on studying miRNA sponge interactions or networks in cancer, the module-level properties of miRNA sponges in cancer is still largely unknown.

Results

We propose a novel in silico method, called miRSM (miRNA Sponge Module) to infer miRNA sponge modules in breast cancer. We apply miRSM to the breast invasive carcinoma (BRCA) dataset provided by The Cancer Genome Altas (TCGA), and make functional validation of the computational results. We discover that most miRNA sponge interactions are module-conserved across two modules, and a minority of miRNA sponge interactions are module-specific, existing only in a single module. Through functional annotation and differential expression analysis, we also find that the modules discovered using miRSM are functional miRNA sponge modules associated with BRCA. Moreover, the module-specific miRNA sponge interactions among miRNA sponge modules may be involved in the progression and development of BRCA. Our experimental results show that miRSM is comparable to the benchmark methods in recovering experimentally confirmed miRNA sponge interactions, and miRSM outperforms the benchmark methods in identifying interactions that are related to breast cancer.

Conclusions

Altogether, the functional validation results demonstrate that miRSM is a promising method to identify miRNA sponge modules and interactions, and may provide new insights for understanding the roles of miRNA sponges in cancer progression and development.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1467-5) contains supplementary material, which is available to authorized users.

Pseudogenes regulate parental gene expression via ceRNA network

The concept of competitive endogenous RNA (ceRNA) was first proposed by Salmena and colleagues. Evidence suggests that pseudogene RNAs can act as a 'sponge' through competitive binding of common miRNA, releasing or attenuating repression through sequestering miRNAs away from parental mRNA. In theory, ceRNAs refer to all transcripts such as mRNA, tRNA, rRNA, long non-coding RNA, pseudogene RNA and circular RNA, because all of them may become the targets of miRNA depending on spatiotemporal situation. As binding of miRNA to the target RNA is not 100% complementary, it is possible that one miRNA can bind to multiple target RNAs and vice versa. All RNAs crosstalk through competitively binding to miRNAvia miRNA response elements (MREs) contained within the RNA sequences, thus forming a complex regulatory network. The ratio of a subset of miRNAs to the corresponding number of MREs determines repression strength on a given mRNA translation or stability. An increase in pseudogene RNA level can sequester miRNA and release repression on the parental gene, leading to an increase in parental gene expression. A massive number of transcripts constitute a complicated network that regulates each other through this proposed mechanism, though some regulatory significance may be mild or even undetectable. It is possible that the regulation of gene and pseudogene expression occurring in this manor involves all RNAs bearing common MREs. In this review, we will primarily discuss how pseudogene transcripts regulate expression of parental genes via ceRNA network and biological significance of regulation.

RNA Function Prediction

Recent studies have shown that a considerable proportion of eukaryotic genomes are transcribed as noncoding RNA (ncRNA), and regulatory ncRNAs have attracted much attention from researchers in many fields, especially of microRNA (miRNA) and long noncoding RNA (lncRNA). However, most ncRNAs are functionally uncharacterized due to the difficulty to accurately identify their targets. In this chapter, we first summarize the most recent advances in ncRNA research and their primary function. We then discuss the current state-of-the-art computational methods for predicting RNA functions, which comprise three different categories: miRNA function prediction approaches using target genes, lncRNA function prediction based on the guilt-by-association principle, and RNA function prediction approaches based on competing endogenous RNA partners. We consider that the application of these techniques can provide valuable functional and mechanistic insights into ncRNAs, and that they are crucial steps in future functional studies.

Interactions Between Genetics, Lifestyle, and Environmental Factors for Healthcare

The occurrence and progression of diseases are strongly associated with a combination of genetic, lifestyle, and environmental factors. Understanding the interplay between genetic and nongenetic components provides deep insights into disease pathogenesis and promotes personalized strategies for people healthcare. Recently, the paradigm of systems medicine, which integrates biomedical data and knowledge at multidimensional levels, is considered to be an optimal way for disease management and clinical decision-making in the era of precision medicine. In this chapter, epigenetic-mediated genetics-lifestyle-environment interactions within specific diseases and different ethnic groups are systematically discussed, and data sources, computational models, and translational platforms for systems medicine research are sequentially presented. Moreover, feasible suggestions on precision healthcare and healthy longevity are kindly proposed based on the comprehensive review of current studies.