Expression of nonmuscle myosin IIC is regulated by non-canonical binding activity of miRNAs.

The expression of mechanoresponsive nonmuscle myosin II (NMII)C is found to be inducible during tumor progression, but its mechanism is yet to be explored. Here, we report a group of microRNAs (mmu-miR-200a-5p, mmu-miR-532-3p, mmu-miR-680, and mmu-miR-1901) can significantly repress the expression of nonmuscle myosin IIC (NMIIC). Interestingly, these microRNAs have both canonical and non-canonical binding sites at 3/UTR and coding sequence (CDS) of NMIIC's heavy chain (HC) mRNA. Each of the miRNA downregulates NMHC-IIC to a different degree as assessed by dual-luciferase and immunoblot analyses. When we abolish the complementary base pairing at canonical binding site, mmu-miR-532-3p can still bind at non-canonical binding site and form Argonaute2 (AGO2)-miRNA complex to downregulate the expression of NMIIC. Modulating the expression of NMIIC by miR-532-3p in mouse mammary tumor cells, 4T1, increases its tumorigenic potential both in vitro and in vivo. Together, these studies provide the functional role of miRNA's non-canonical binding mediated NMIIC regulation in tumor cells.

Glioma-BioDP: database for visualization of molecular profiles to improve prognosis of brain cancer.

Cancer researchers often seek user-friendly interactive tools for validation, exploration, analysis, and visualization of molecular profiles in cancer patient samples. To aid researchers working on the both low- and high-grade gliomas, we developed Glioma-BioDP, a web tool for exploration and visualization of RNA and protein expression profiles of interest in these tumor types. Glioma-BioDP is user friendly application that include expression data from both the low- and high-grade glioma patient samples from The Cancer Genome Atlas and enabled querying by mRNA, microRNA, and protein level expression data from Illumina HiSeq and RPPA platforms respectively. Glioma-BioDP provides advance query interface and enables users to explore the association of genes, proteins, and miRNA expression with molecular and/or histological subtypes of gliomas, surgical resection status and survival. The prognostic significance and visualization of the selected expression profiles can be explored using interactive utilities provided. This tool may also enable validation and generation of new hypotheses of novel therapies impacting gliomas that aid in personalization of treatment for optimum outcomes.

Immune signature of pheochromocytoma and paraganglioma in context of neuroendocrine neoplasms associated with prognosis.

PURPOSE: To understand prognostic immune cell infiltration signatures in neuroendocrine neoplasms (NENs), particularly pheochromocytoma and paraganglioma (PCPG), we analyzed tumor transcriptomic data from The Cancer Genome Atlas (TCGA) and other published tumor transcriptomic data of NENs. METHODS: We used CIBERSORT to infer immune cell infiltrations from bulk tumor transcriptomic data from PCPGs, in comparison to gastroenteropancreatic neuroendocrine tumors (GEPNETs) and small cell lung carcinomas (SCLCs). PCPG immune signature was validated with NanoString immune panel in an independent cohort. Unsupervised clustering of the immune infiltration scores from CIBERSORT was used to find immune clusters. A prognostic immune score model for PCPGs and the other NENs were calculated as a linear combination of the estimated infiltration of activated CD8+/CD4+ T cells, activated NK cells, and M0 and M2 macrophages. RESULTS: In PCPGs, we found five dominant immune clusters, associated with M2 macrophages, monocytes, activated NK cells, M0 macrophages and regulatory T cells, and CD8+/CD4+ T cells respectively. Non-metastatic tumors were associated with activated NK cells and metastatic tumors were associated with M0 macrophages and regulatory T cells. In GEPNETs and SCLCs, M0 macrophages and regulatory T cells were associated with unfavorable outcomes and features, such as metastasis and high-grade tumors. The prognostic immune score model for PCPGs and the NENs could predict non-aggressive and non-metastatic diseases. In PCPGs, the immune score was also an independent predictor of metastasis-free survival in a multivariate Cox regression analysis. CONCLUSION: The transcriptomic immune signature in PCPG correlates with clinical features like metastasis and prognosis.

Natural variation in macrophage polarization and function impact pneumocyte senescence and susceptibility to fibrosis.

Radiation-induced pulmonary fibrosis (RIPF), a late adverse event of radiation therapy, is characterized by infiltration of inflammatory cells, progressive loss of alveolar structure, secondary to the loss of pneumocytes and accumulation of collagenous extracellular matrix, and senescence of alveolar stem cells. Differential susceptibility to lung injury from radiation and other toxic insults across mouse strains is well described but poorly understood. The accumulation of alternatively activated macrophages (M2) has previously been implicated in the progression of lung fibrosis. Using fibrosis prone strain (C57L), a fibrosis-resistant strain (C3H/HeN), and a strain with intermediate susceptibility (C57BL6/J), we demonstrate that the accumulation of M2 macrophages correlates with the manifestation of fibrosis. A comparison of primary macrophages derived from each strain identified phenotypic and functional differences, including differential expression of NADPH Oxidase 2 and production of superoxide in response to M2 polarization and activation. Further, the sensitivity of primary AECII to senescence after coculture with M2 macrophages was strain dependent and correlated to observations of sensitivity to fibrosis and senescence in vivo. Taken together, these data support that the relative susceptibility of different strains to RIPF is closely related to distinct senescence responses induced through pulmonary M2 macrophages after thoracic irradiation.

Bowel and Bladder Reproducibility in Image Guided Radiation Therapy for Prostate Cancer: Results of a Patterns of Practice Survey.

Purpose: Optimal management of patients with prostate cancer (PCa) to achieve bowel and bladder reproducibility for radiation therapy (RT) and the appropriate planning target volume (PTV) expansions for use with modern image guidance is uncertain. We surveyed American Society of Radiation Oncology radiation oncologists to ascertain practice patterns for definitive PCa RT with respect to patient instructions and set up, daily image guidance, and subsequent PTV expansions. Methods and Materials: A pattern of practice survey was sent to American Society of Radiation Oncology radiation oncologists who self-identified as specializing in PCa. Respondents identified the fractionation regimens routinely used, and their practices regarding diet, bowel, and bladder instructions for patients with PCa before RT simulation and throughout treatment. Questions regarding PTV margins, daily set up practices, and use of image guidance were included. Results: Of 190 respondents, 158 reported using conventional fractionation (CFx), 49 moderate hypofractionation (MHFx), and 61 stereotactic body radiation therapy (SBRT). Diet modifications during RT were advised by 84% of respondents, treatment with full bladder by 96%, and bowel instructions by 78%. Prescription of bowel medication was higher for respondents using SBRT (95.1%) versus those using CFx/MHFx (55.1%; 34.7%). The most common implantable device reported was fiducial markers, with increased use in SBRT (86.0%; 68.9%) versus CFx/MHFx. Cone beam computed tomography was the most common daily imaging technique across fractionation regimens. SBRT showed correlation between PTV margin expansions, fiducial marker use, and image guidance. Conclusions: Survey results indicate heterogeneity in treatment modality, dose, patient instructions, and PTV expansions used by radiation oncologists in the treatment of patients with PCa. Further investigation to define appropriate patient instructions on bowel preparation to maximize target reproducibility in PCa is needed, as is continued guidance on evidence-based approaches for image guidance and PTV margin selection.

A long noncoding RNA-microRNA expression signature predicts metastatic signature in pheochromocytomas and paragangliomas.

PURPOSE: In hopes of discovering new markers for metastatic or aggressive phenotypes of pheochromocytomas and paragangliomas (PCPG), we analyzed the noncoding transcriptome from patient gene expression data in The Cancer Genome Atlas. METHODS: Differential expression of miRNAs was observed between PCPG molecular subtypes. We specifically characterized candidate miRNAs that are upregulated in pseudohypoxic PCPGs with mutations in succinate dehydrogenase complex subunits, B and/or D (SDHB and/or SDHD, respectively), which are mutations associated with unfavorable clinical outcomes. RESULTS: Our computational analysis identified four candidate miRNAs that showed elevated expression in metastatic compared to non-metastatic PCPGs: miR-182, miR-183, miR-96, and miR-383. We also found six candidate lncRNAs harboring opposite expression patterns from the miRNAs when we analyzed the expression profiles of their predicted target lncRNAs. Three of these lncRNA candidates, USP3-AS1, LINC00877, and AC009312.1, were validated to have reduced expression in metastatic compared to non-metastatic PCPGs. Finally, using univariate and multivariate analysis, we found miRNA miR-182 to be an independent predictor of metastasis-free survival in PCPGs. CONCLUSIONS: We identified candidate miRNA and lncRNAs associated with metastasis-free survival in PCPGs.

Synthetic Lethal Drug Combinations Targeting Proteasome and Histone Deacetylase Inhibitors in TP53-Mutated Cancers.

BACKGROUND: We have recently published SL-BioDP, a web resource for querying, exploration and visualization of potential synthetic lethal targets and possible synergistic drug combinations for 18 cancer types. METHODS: From our predictive synthetic lethality model used in SL-BioDP, we inferred TP53 mutation lead to potential synergistic drug combination of Bortezomib and Vorinostat. Here we show, how to extrapolate the drug combination results by combining drug screening data from cancer cell lines and showed the potential synergy of the drug targets, proteasome, and histone deacetylase (HDAC) pathways respectively, for patient survival advantage. RESULTS: We found that TP53 mutation is potentially synthetic lethal with multiple genes from the proteasome and HDAC pathways exclusively in many cancer types. Also, HDAC and proteasomes were found to have potential synthetic lethal relationship. Using drug screening data in cancer cell line, the sensitivity of the HDAC inhibitor drug Vorinostat was found to be increased in TP53 mutated cells where the proteasome pathway was downregulated. CONCLUSIONS: Our in-silico pharmacogenomic study indicates that the potential synergistic drug combination of proteasome and HDAC inhibitors may be considered as potential treatment for TP53-mutant cancers.

Cancer-Specific Immune Prognostic Signature in Solid Tumors and Its Relation to Immune Checkpoint Therapies.

To elucidate the role of immune cell infiltration as a prognostic signature in solid tumors, we analyzed immune-function-related genes from four publicly available single-cell RNA-Seq data sets and twenty bulk tumor RNA-Seq data sets from The Cancer Genome Atlas (TCGA). Unsupervised clustering of pan-cancer transcriptomic signature showed two major immune function types: one related to NK-, T-, and B-cell functions and the other related to monocyte, macrophage, dendritic cell, and Toll-like receptor functions. Kaplan-Meier analysis showed differential prognosis of these two groups, dependent on the cancer type. Our analysis of TCGA solid tumors with an elastic net model identified 155 genes associated with disease-free survival in different tumor types with varied influence across different cancer types. With this gene set, we computed cancer-specific prognostic immune score models for individual cancer types that predicted disease-free and overall survival. Validation of our model on available published data of immune checkpoint blockade therapies on melanoma, kidney renal cell carcinoma, non-small cell lung cancer, gastric cancer and bladder cancer confirmed that cancer-specific higher immune scores are associated with response to immunotherapy. Our analysis provides a comprehensive map of cancer-specific immune-related prognostic gene sets that are associated with immunotherapy response.

In silico Drug Repurposing to combat COVID-19 based on Pharmacogenomics of Patient Transcriptomic Data.

The ongoing global pandemic of coronavirus disease 2019 (COVID-19) continues to affect a growing number of populations in different parts of the world. In the current situation, drug repurposing is a viable strategy to combat COVID-19. The drugs targeting the host receptors that interact with SARS-CoV-2 are possible candidates. However, assessment of their effectiveness in COVID-19 patients is necessary before prioritizing them for further study. We attempted to shortlist the candidate drugs using an in-silico approach. First, we analysed two published transcriptomic data sets of COVID-19- and SARS-infected patients compared to healthy individuals to find the key pathways altered after infection. Then, using publicly available drug perturbational data sets in human cell lines from the Broad Institute Connectivity Map (CMAP), we assessed the effects of the approved drugs on the altered pathways. We also used the available pharmacogenomic data sets from the Genomics of Drug Sensitivity in Cancer (GDSC) portal to assess the effects of the altered pathways on resistance or sensitivity to the drugs in human cell lines. Our analysis identified many candidate drugs, some of which are already being investigated for treatment of COVID-19 and can serve as a basis for prioritizing additional viable candidate drugs for COVID-19.

Long intergenic noncoding RNA profiles of pheochromocytoma and paraganglioma: A novel prognostic biomarker.

Many long intergenic noncoding RNAs (lincRNAs) serve as cancer biomarkers for diagnosis or prognostication. To understand the role of lincRNAs in the rare neuroendocrine tumors pheochromocytoma and paraganglioma (PCPG), we performed first time in-depth characterization of lincRNA expression profiles and correlated findings to clinical outcomes of the disease. RNA-Seq data from patients with PCPGs and 17 other tumor types from The Cancer Genome Atlas and other published sources were obtained. Differential expression analysis and a machine-learning model were used to identify transcripts specific to PCPGs, as well as established PCPG molecular subtypes. Similarly, lincRNAs specific to aggressive PCPGs were identified, and univariate and multivariate analysis was performed for metastasis-free survival. The results were validated in independent samples using RT-PCR. From a pan-cancer context, PCPGs had a specific and unique lincRNA profile. Among PCPGs, five different molecular subtypes were identified corresponding to the established molecular classification. Upregulation of 13 lincRNAs was found to be associated with aggressive/metastatic PCPGs. RT-PCR validation confirmed the overexpression of four lincRNAs in metastatic compared to non-metastatic PCPGs. Kaplan-Meier analysis identified five lincRNAs as prognostic markers for metastasis-free survival of patients in three subtypes of PCPGs. Stratification of PCPG patients with a risk-score formulated using multivariate analysis of lincRNA expression profiles, presence of key driver mutations, tumor location, and hormone secretion profiles showed significant differences in metastasis-free survival. PCPGs thus exhibit a specific lincRNA expression profile that also corresponds to the established molecular subgroups and can be potential marker for the aggressive/metastatic PCPGs.

Pan-Cancer Analysis of Potential Synthetic Lethal Drug Targets Specific to Alterations in DNA Damage Response.

Alterations in DNA damage response (DDR) is one of the several hallmarks of cancer. Genomic instability resulting from a disrupted DDR mechanism is known to contribute to cancer progression, and are subjected to radiation, cytotoxic, or more recently targeted therapies with limited success. Synthetic lethality (SL), which is a condition where simultaneous loss-of-function of the genes from complementary pathways result in loss of viability of cancer cells have been exploited to treat malignancies resulting from defects in certain DDR pathways. Albeit being a promising therapeutic strategy, number of SL based drugs currently in clinical trial is limited. In this work we performed a comprehensive pan-cancer analysis of alterations in 10 DDR pathways with different components of DNA repair. Using unsupervised clustering of single sample enrichment of these pathways in 7,272 tumor samples from 17 tumor types from TCGA, we identified three prominent clusters, each associated with specific DDR mechanisms. Somatic mutations in key DDR genes were found to be dominant in each of these three clusters with distinct DDR component. Using a machine-learning based algorithm we predicted SL partners specific to somatic mutations in key genes representing each of the three DDR clusters and identified potential druggable targets. We explored the potential FDA-approved drugs for targeting the predicted SL genes and tested the sensitivity using the drug screening data in cell lines with mutation in the primary DDR genes. We have shown clinical relevance, for selected targetable SL interactions using Kaplan-Meier analysis in terms of improved disease-free survival. Thus, our computational framework provides a basis for clinically relevant and actionable SL based drug targets specific to alterations in DDR pathways.

SL-BioDP: Multi-Cancer Interactive Tool for Prediction of Synthetic Lethality and Response to Cancer Treatment.

Synthetic lethality exploits the phenomenon that a mutation in a cancer gene is often associated with new vulnerability which can be uniquely targeted therapeutically, leading to a significant increase in favorable outcome. DNA damage and survival pathways are among the most commonly mutated networks in human cancers. Recent data suggest that synthetic lethal interactions between a tumor defect and a DNA repair pathway can be used to preferentially kill tumor cells. We recently published a method, DiscoverSL, using multi-omic cancer data, that can predict synthetic lethal interactions of potential clinical relevance. Here, we apply the generality of our models in a comprehensive web tool called Synthetic Lethality Bio Discovery Portal (SL-BioDP) and extend the cancer types to 18 cancer genome atlas cohorts. SL-BioDP enables a data-driven computational approach to predict synthetic lethal interactions from hallmark cancer pathways by mining cancer's genomic and chemical interactions. Our tool provides queries and visualizations for exploring potentially targetable synthetic lethal interactions, shows Kaplan-Meier plots of clinical relevance, and provides in silico validation using short hairpin RNA (shRNA) and drug efficacy data. Our method would thus shed light on mechanisms of synthetic lethal interactions and lead to the discovery of novel anticancer drugs.

DiscoverSL: an R package for multi-omic data driven prediction of synthetic lethality in cancers.

SUMMARY: Synthetic lethality is a state when simultaneous loss of two genes is lethal to a cancer cell, while the loss of the individual genes is not. We developed an R package DiscoverSL to predict and visualize synthetic lethality in cancers using multi-omic cancer data. Mutation, copy number alteration and gene expression data from The Cancer Genome Atlas project were combined to develop a multi-parametric Random Forest classifier. The effects of selectively targeting the predicted synthetic lethal genes is tested in silico using shRNA and drug screening data from cancer cell line databases. The clinical outcome in patients with mutation in primary gene and over/under-expression in the synthetic lethal gene is evaluated using Kaplan-Meier analysis. The method helps to identify new therapeutic approaches by exploiting the concept of synthetic lethality. AVAILABILITY AND IMPLEMENTATION: DiscoverSL package with user manual and sample workflow is available for download from github url: https://github.com/shaoli86/DiscoverSL/releases/tag/V1.0 under GNU GPL-3. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Radiation-Induced Long Noncoding RNAs in a Mouse Model after Whole-Body Irradiation.

Long noncoding RNAs (lncRNAs) are emerging as key molecules in regulating many biological processes and have been implicated in development and disease pathogenesis. Biomarkers of cancer and normal tissue response to treatment are of great interest in precision medicine, as well as in public health and medical management, such as for assessment of radiation injury after an accidental or intentional exposure. Circulating and functional RNAs, including microRNAs (miRNAs) and lncRNAs, in whole blood and other body fluids are potential valuable candidates as biomarkers. Early prediction of possible acute, intermediate and delayed effects of radiation exposure enables timely therapeutic interventions. To address whether long noncoding RNAs (lncRNAs) could serve as biomarkers for radiation biodosimetry we performed whole genome transcriptome analysis in a mouse model after whole-body irradiation. Differential lncRNA expression patterns were evaluated at 16, 24 and 48 h postirradiation in total RNA isolated from whole blood of mice exposed to 1, 2, 4, 8 and 12 Gy of X rays. Sham-irradiated animals served as controls. Significant alterations in the expression patterns of lncRNAs were observed after different radiation doses at the various time points. We identified several radiation-induced lncRNAs known for DNA damage response as well as immune response. Long noncoding RNA targets of tumor protein 53 (P53), Trp53cor1, Dino, Pvt1 and Tug1 and an upstream regulator of p53, Meg3, were altered in response to radiation. Gm14005 ( Morrbid) and Tmevpg1 were regulated by radiation across all time points and doses. These two lncRNAs have important potential as blood-based radiation biomarkers; Gm14005 ( Morrbid) has recently been shown to play a key role in inflammatory response, while Tmevpg1 has been implicated in the regulation of interferon gamma. Precise molecular biomarkers, likely involving a diverse group of inducible molecules, will not only enable the development and effective use of medical countermeasures but may also be used to detect and circumvent or mitigate normal tissue injury in cancer radiotherapy.

Correlation of altered expression of a long non-coding RNA, NEAT1, in peripheral blood mononuclear cells with dengue disease progression.

The association of long non-coding RNAs (lncRNAs) with dengue disease progression is currently unknown. Therefore, the present study aimed to identify lncRNAs in different categories of dengue patients and evaluate their association with dengue disease progression. Herein, we examined the expression profiles of lncRNAs and protein-coding genes between other febrile illness (OFI) and different grade of dengue patients through high-throughput RNA sequencing. We identified Nuclear Enriched Abundant Transcript 1 (NEAT1) as one of the differentially expressed lncRNAs (adjusted P ≤ 0.05 and log-fold change ≥ 2) and subsequently validated the expression by qRT-PCR. The co-expression analysis further revealed that NEAT1 and the coding gene IFI27 were highly co-expressed and negatively correlated with dengue severity. Using regression analysis, we observed that NEAT1 expression was significantly dependent on disease progression (Coefficient = -0.27750, SE Coefficient = 0.07145, and t = -3.88).Further, receiver operating characteristic (ROC) curve revealed that NEAT1 expression could discriminate DI from DS (sensitivity and specificity of 100% (95%CI: 85.69 - 97.22) and area under the curve (AUC) = 0.97). Overall, the results of this study offer the first experimental evidence demonstrating the correlation between lncRNAs and severe dengue phenotype. Monitoring NEAT1and IFI27 expression in PBMC may be useful in understanding dengue virus-induced disease progression.

miRepress: modelling gene expression regulation by microRNA with non-conventional binding sites.

Some earlier studies have reported an alternative mode of microRNA-target interaction. We detected target regions within mRNA transcripts from AGO PAR-CLIP that did not contain any conventional microRNA seed pairing but only had non-conventional binding sites with microRNA 3' end. Our study from 7 set of data that measured global protein fold change after microRNA transfection pointed towards the association of target protein fold change with 6-mer and 7-mer target sites involving microRNA 3' end. We developed a model to predict the degree of microRNA target regulation in terms of protein fold changes from the number of different conventional and non-conventional target sites present in the target, and found significant correlation of its output with protein expression changes. We validated the effect of non-conventional interactions with target by modulating the abundance of microRNA in a human breast cancer cell line MCF-7. The validation was done using luciferase assay and immunoblot analysis for our predicted non-conventional microRNA-target pair WNT1 (3' UTR) and miR-367-5p and immunoblot analysis for another predicted non-conventional microRNA-target pair MYH10 (coding region) and miR-181a-5p. Both experiments showed inhibition of targets by transfection of microRNA mimics that were predicted to have only non-conventional sites.

Dynamic changes in global microRNAome and transcriptome reveal complex miRNA-mRNA regulated host response to Japanese Encephalitis Virus in microglial cells.

Microglia cells in the brain play essential role during Japanese Encephalitis Virus (JEV) infection and may lead to change in microRNA (miRNA) and mRNA profile. These changes may together control disease outcome. Using Affymetrix microarray platform, we profiled cellular miRNA and mRNA expression at multiple time points during viral infection in human microglial (CHME3) cells. In silico analysis of microarray data revealed a phased pattern of miRNAs expression, associated with JEV replication and provided unique signatures of infection. Target prediction and pathway enrichment analysis identified anti correlation between differentially expressed miRNA and the gene expression at multiple time point which ultimately affected diverse signaling pathways including Notch signaling pathways in microglia. Activation of Notch pathway during JEV infection was demonstrated in vitro and in vivo. The expression of a subset of miRNAs that target multiple genes in Notch signaling pathways were suppressed and their overexpression could affect JEV induced immune response. Further analysis provided evidence for the possible presence of cellular competing endogenous RNA (ceRNA) associated with innate immune response. Collectively, our data provide a uniquely comprehensive view of the changes in the host miRNAs induced by JEV during cellular infection and identify Notch pathway in modulating microglia mediated inflammation.

HumanViCe: host ceRNA network in virus infected cells in human.

Host-virus interaction via host cellular components has been an important field of research in recent times. RNA interference mediated by short interfering RNAs and microRNAs (miRNA), is a widespread anti-viral defense strategy. Importantly, viruses also encode their own miRNAs. In recent times miRNAs were identified as key players in host-virus interaction. Furthermore, viruses were shown to exploit the host miRNA networks to suite their own need. The complex cross-talk between host and viral miRNAs and their cellular and viral targets forms the environment for viral pathogenesis. Apart from protein-coding mRNAs, non-coding RNAs may also be targeted by host or viral miRNAs in virus infected cells, and viruses can exploit the host miRNA mediated gene regulatory network via the competing endogenous RNA effect. A recent report showed that viral U-rich non-coding RNAs called HSUR, expressed in primate virus herpesvirus saimiri (HVS) infected T cells, were able to bind to three host miRNAs, causing significant alteration in cellular level for one of the miRNAs. We have predicted protein coding and non protein-coding targets for viral and human miRNAs in virus infected cells. We identified viral miRNA targets within host non-coding RNA loci from AGO interacting regions in three different virus infected cells. Gene ontology (GO) and pathway enrichment analysis of the genes comprising the ceRNA networks in the virus infected cells revealed enrichment of key cellular signaling pathways related to cell fate decisions and gene transcription, like Notch and Wnt signaling pathways, as well as pathways related to viral entry, replication and virulence. We identified a vast number of non-coding transcripts playing as potential ceRNAs to the immune response associated genes; e.g., APOBEC family genes, in some virus infected cells. All these information are compiled in HumanViCe (http://gyanxet-beta.com/humanvice), a comprehensive database that provides the potential ceRNA networks in virus infected human cells.

lnCeDB: database of human long noncoding RNA acting as competing endogenous RNA.

UNLABELLED: Long noncoding RNA (lncRNA) influences post-transcriptional regulation by interfering with the microRNA (miRNA) pathways, acting as competing endogenous RNA (ceRNA). These lncRNAs have miRNA responsive elements (MRE) in them, and control endogenous miRNAs available for binding with their target mRNAs, thus reducing the repression of these mRNAs. lnCeDB provides a database of human lncRNAs (from GENCODE 19 version) that can potentially act as ceRNAs. The putative mRNA targets of human miRNAs and the targets mapped to AGO clipped regions are collected from TargetScan and StarBase respectively. The lncRNA targets of human miRNAs (up to GENCODE 11) are downloaded from miRCode database. miRNA targets on the rest of the GENCODE 19 lncRNAs are predicted by our algorithm for finding seed-matched target sites. These putative miRNA-lncRNA interactions are mapped to the Ago interacting regions within lncRNAs. To find out the likelihood of an lncRNA-mRNA pair for actually being ceRNA we take recourse to two methods. First, a ceRNA score is calculated from the ratio of the number of shared MREs between the pair with the total number of MREs of the individual candidate gene. Second, the P-value for each ceRNA pair is determined by hypergeometric test using the number of shared miRNAs between the ceRNA pair against the number of miRNAs interacting with the individual RNAs. Typically, in a pair of RNAs being targeted by common miRNA(s), there should be a correlation of expression so that the increase in level of one ceRNA results in the increased level of the other ceRNA. Near-equimolar concentration of the competing RNAs is associated with more profound ceRNA effect. In lnCeDB one can not only browse for lncRNA-mRNA pairs having common targeting miRNAs, but also compare the expression of the pair in 22 human tissues to estimate the chances of the pair for actually being ceRNAs. AVAILABILITY: Downloadable freely from http://gyanxet-beta.com/lncedb/.

Competing endogenous RNA: the key to posttranscriptional regulation.

Competing endogenous RNA, ceRNA, vie with messenger RNAs (mRNAs) for microRNAs (miRNAs) with shared miRNAs responses elements (MREs) and act as modulator of miRNA by influencing the available level of miRNA. It has recently been discovered that, apart from protein-coding ceRNAs, pseudogenes, long noncoding RNAs (lncRNAs), and circular RNAs act as miRNA "sponges" by sharing common MRE, inhibiting normal miRNA targeting activity on mRNA. These MRE sharing elements form the posttranscriptional ceRNA network to regulate mRNA expression. ceRNAs are widely implicated in many biological processes. Recent studies have identified ceRNAs associated with a number of diseases including cancer. This brief review focuses on the molecular mechanism of ceRNA as part of the complex post-transcriptional regulatory circuit in cell and the impact of ceRNAs in development and disease.