Identifying survival-associated modules from the dysregulated triplet network in glioblastoma multiforme

Association of a novel antisense lncRNA TP73-AS1 polymorphisms and expression with colorectal cancer susceptibility and prognosis

BACKGROUND

TP73-AS1 is a novel antisense long noncoding RNA and plays an important role in cell proliferation and cancer development. However, the link between TP73-AS1 and colorectal cancer (CRC) has not yet been reported.

OBJECTIVE

To explore the association of genetic variants in TP73-AS1 and its expression with CRC susceptibility and prognosis.

METHODS

A case-control study (including 507 CRC cases and 503 controls) and bioinformatics analysis were conducted.

RESULTS

rs9800 polymorphism was significantly related to higher risk in CRC [adjusted odds ratio (AOR) = 1.33, 95% confidence interval (CI) = 1.02-1.75, P = 0.034 in heterozygote codominant model]. There was no difference between TP73-AS1 polymorphisms and different tumor node metastasis (TNM) stages in the adjusted model. Moreover, TP73-AS1 expression level was positively related to different TNM stages. After adjusted for age, gender and TNM, higher TP73-AS1 expression levels were related to shorter recurrence-free survival time [hazard ratio (HR) = 1.66, 95% CI = 1.02-2.71, P = 0.043].

CONCLUSION

TP73-AS1 polymorphisms and expression may be associated with susceptibility and prognosis of CRC.

Joint application of biochemical markers and imaging techniques in the accurate and early detection of glioblastoma

Glioblastoma is a primary brain tumor with the most metastatic effect in adults. Despite the wide range of multidimensional treatments, tumor heterogeneity is one of the main causes of tumor spread and gives great complexity to diagnostic and therapeutic methods. Therefore, featuring noble noninvasive prognostic methods that are focused on glioblastoma heterogeneity is perceived as an urgent need. Imaging neuro-oncological biomarkers including MGMT (O⁶-methylguanine-DNA methyltransferase) promoter methylation status, tumor grade along with other tumor characteristics and demographic features (e.g., age) are commonly referred to during diagnostic, therapeutic and prognostic processes. Therefore, the use of new noninvasive prognostic methods focused on glioblastoma heterogeneity is considered an urgent need. Some neuronal biomarkers, including the promoter methylation status of the promoter MGMT, the characteristics and grade of the tumor, along with the patient's demographics (such as age and sex) are involved in diagnosis, treatment, and prognosis. Among the wide array of imaging techniques, magnetic resonance imaging combined with the more physiologically detailed technique of H-magnetic resonance spectroscopy can be useful in diagnosing neurological cancer patients. In addition, intracranial tumor qualitative analysis and sometimes tumor biopsies help in accurate diagnosis. This review summarizes the evidence for biochemical biomarkers being a reliable biomarker in the early detection and disease management in GBM. Moreover, we highlight the correlation between Imaging techniques and biochemical biomarkers and ask whether they can be combined.

RFX1: a promising therapeutic arsenal against cancer

Regulatory factor X1 (RFX1) is an evolutionary conserved transcriptional factor that influences a wide range of cellular processes such as cell cycle, cell proliferation, differentiation, and apoptosis, by regulating a number of target genes that are involved in such processes. On a closer look, these target genes also play a key role in tumorigenesis and associated events. Such observations paved the way for further studies evaluating the role of RFX1 in cancer. These studies were indispensable due to the failure of conventional chemotherapeutic drugs to target key cellular hallmarks such as cancer stemness, cellular plasticity, enhanced drug efflux, de-regulated DNA repair machinery, and altered pathways evading apoptosis. In this review, we compile significant evidence for the tumor-suppressive activities of RFX1 while also analyzing its oncogenic potential in some cancers. RFX1 induction decreased cellular proliferation, modulated the immune system, induced apoptosis, reduced chemoresistance, and sensitized cancer stem cells for chemotherapy. Thus, our review discusses the pleiotropic function of RFX1 in multitudinous gene regulations, decisive protein–protein interactions, and also its role in regulating key cell signaling events in cancer. Elucidation of these regulatory mechanisms can be further utilized for RFX1 targeted therapy.

Determination of the key ccRCC-related molecules from monolayer network to three-layer network

Clear cell renal cell carcinoma (ccRCC), with an increasing incidence rate, is one of the ubiquitous cancers. Its pathogenic factors are complicated and the molecular mechanism is not clear. It is essential to analyze the potential key genes related to ccRCC carcinogenesis. In this study, the differentially expressed mRNAs, miRNAs and lncRNAs (DEmRNAs, DEmiRNAs and DElncRNAs) of ccRCC were screened from TCGA database. Then the miRNA-mRNA network, lncRNA-miRNA network and lncRNA-mRNA network were constructed by online database or WGCNA algorithm. Topology attributes of these monolayer networks showed that hsa-mir-155, hsa-mir-200c, hsa-mir-122, hsa-mir-506, hsa-mir-216b, hsa-mir-141, lncRNA AC137723.1 and AC021074.3 are the crucial genes related with the regulatory effects on the proliferation, metastasis and invasion of ccRCC cells. Subsequently, these three monolayer networks were integrated into a lncRNA-miRNA-mRNA multilayer network. Considering node degree, closeness centrality and betweenness centrality, we found hsa-mir-122 is screened out as the only crucial gene in three-layer network. In order to better illustrate the effect of hsa-mir-122 on ccRCC, the lncRNA-hsa-mir-122-mRNA network was constructed with hsa-mir-122 as the center. Pathway analysis of the unique target gene GALNT3 linked to hsa-mir-122 showed that GALNT3 influenced the metabolic process of mucin type O-Glycan biosynthesis. LncRNA AC090377.1 is the unique gene that has target genes among lncRNAs with clinical significance that linked to hsa-mir-122 in the lncRNA-hsa-mir-122-mRNA network. Pathway analysis of AC090377.1 suggested that GUCY2F enriched in phototransduction pathway associated with retina. From monolayer network to three-layer network, hsa-mir-122 is identified as an important molecule in the oncogenesis and progression of ccRCC, offering new strategies to further study of the carcinogenic mechanism of ccRCC.

Exploring the secrets of brain transcriptional regulation: developing methodologies, recent significant findings, and perspectives

Exploring and revealing the secret of the function of the human brain has been the dream of mankind and science. Delineating brain transcriptional regulation has been extremely challenging, but recent technological advances have facilitated a deeper investigation of molecular processes in the brain. Tracing the molecular regulatory mechanisms of different gene expression profiles in the brain is divergent and has made it possible to connect spatial and temporal variations in gene expression to distributed properties of brain structure and function. Here, we review the molecular diversity of the brain among rodents, non-human primates and humans. We also discuss the molecular mechanism of non-coding DNA/RNA at the transcriptional/post-transcriptional level based on recent technical advances to highlight an improved understanding of the complex transcriptional network in the brain. Spatiotemporal and single-cell transcriptomics have attempted to gain novel insight into the development and evolution of the brain as well as the progression of human diseases. Although it is clear that the field is developing and challenges remain to be resolved, the impressive recent progress provides a solid foundation to better understand the brain and evidence-based recommendations for the diagnosis and treatment of brain diseases.

An Overview of the Computational Models Dealing with the Regulatory ceRNA Mechanism and ceRNA Deregulation in Cancer

Pools of RNA molecules can act as competing endogenous RNAs (ceRNAs) and indirectly alter their expression levels by competitively binding shared microRNAs. This ceRNA cross talk yields an additional posttranscriptional regulatory layer, which plays key roles in both physiological and pathological processes. MicroRNAs can act as decoys by binding multiple RNAs, as well as RNAs can act as ceRNAs by competing for binding multiple microRNAs, leading to many cross talk interactions that could favor significant large-scale effects in spite of the weakness of single interactions. Identifying and studying these extended ceRNA interaction networks could provide a global view of the fine-tuning gene regulation in a wide range of biological processes and tumor progressions. In this chapter, we review current progress of predicting ceRNA cross talk, by summarizing the most up-to-date databases, which collect computationally predicted and/or experimentally validated miRNA-target and ceRNA-ceRNA interactions, as well as the widespread computational methods for discovering and modeling possible evidences of ceRNA-ceRNA interaction networks. These methods can be grouped in two categories: statistics-based methods exploit multivariate analysis to build ceRNA networks, by considering the miRNA expression levels when evaluating miRNA sponging relationships; mathematical methods build deterministic or stochastic models to analyze and predict the behavior of ceRNA cross talk.

CeRNASeek: an R package for identification and analysis of ceRNA regulation

Competitive endogenous RNA (ceRNA) represents a novel layer of gene regulation that controls both physiological and pathological processes. However, there is still lack of computational tools for quickly identifying ceRNA regulation. To address this problem, we presented an R-package, CeRNASeek, which allows identifying and analyzing ceRNA-ceRNA interactions by integration of multiple-omics data. CeRNASeek integrates six widely used computational methods to identify ceRNA-ceRNA interactions, including two global and four context-specific ceRNA regulation prediction methods. In addition, it provides several downstream analyses for predicted ceRNA-ceRNA pairs, including regulatory network analysis, functional annotation and survival analysis. With examples of cancer-related ceRNA prioritization and cancer subtyping, we demonstrate that CeRNASeek is a valuable tool for investigating the function of ceRNAs in complex diseases. In summary, CeRNASeek provides a comprehensive and efficient tool for identifying and analysis of ceRNA regulation. The package is available on the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/package=CeRNASeek.

Identification of a long noncoding RNA signature to predict outcomes of glioblastoma

Long noncoding RNAs (lncRNAs) are a novel class of gene regulators involved in tumor biogenesis. Glioblastoma is the most common and malignant type of brain tumor. The function and prognostic significance of lncRNAs in glioblastoma remain unclear. In the present study, updated gene annotations were adopted to investigate lncRNA expression profiles in publicly available glioma microarray datasets from the Gene Expression Omnibus and the Repository for Molecular Brain Neoplasia Data. In a training set of 108 samples of glioblastoma, using univariate Cox regression analysis with a permutation P<0.005, four lncRNAs, including insulin‑like growth factor binding protein 7‑antisense 1 (IGFBP7‑AS1), were significantly associated with patient overall survival. These four lncRNAs were integrated as an expression‑based molecular signature to divide patients in the training set into high‑risk and low‑risk subgroups, with distinct survival rates (hazard ratio, 2.72; 95% CI, 1.71‑4.31; P<0.001). The prognostic value of the lncRNA signature was confirmed in two additional datasets comprising a total of 147 samples from patients with glioblastoma. The prognostic value of this signature was independent of age and Karnofsky performance status. This signature was also able to predict different outcomes in cases of glioblastoma associated with an isocitrate dehydrogenase 1 mutation. Further bioinformatics analyses revealed that 'epithelial‑mesenchymal transition' and 'p53 pathway' gene sets were enriched in glioblastoma samples with higher IGFBP7‑AS1 expression. Furthermore, in vitro experiments demonstrated that knockdown of IGFBP7‑AS1 inhibited the viability, migration and invasion of U87 and U251 glioma cells. In conclusion, the present study identified a lncRNA signature able to predict glioblastoma outcomes, and provided novel information regarding the role of IGFBP7‑AS1 in glioma development.

LncRNA TP73-AS1 sponges miR-141-3p to promote the migration and invasion of pancreatic cancer cells through the up-regulation of BDH2

LncRNA TP73 antisense RNA 1T (TP73-AS1) plays an important role in human malignancies. However, the levels of TP73-AS1 and its functional mechanisms in pancreatic cancer metastasis remain unknown, and the clinical significance of TP73-AS1 in human pancreatic cancer is also unclear. In the present study, the levels of TP73-AS1 and its candidate target miR-141 in pancreatic cancer and adjacent normal tissue were detected using qRT-PCR. The association between TP73-AS1 levels and the clinicopathologic characteristics of pancreatic cancer patients were analyzed. The relationship between TP73-AS1 and miR-141, and miR-141 and its candidate target 3-hydroxybutyrate dehydrogenase type 2 (BDH2) was confirmed using dual-luciferase reporter assays. TP73-AS1 and/or miR-141 were knocked down using siRNA or an inhibitor in pancreatic cancer cells and cell migration and invasion then examined. The results showed that TP73-AS1 was up-regulated in pancreatic cancer tissue and cell lines. High levels of TP73-AS1 were correlated with poor clinicopathological characteristics and shorter overall survival. MiR-141 was a direct target for TP73-AS1, while BDH2 was a direct target for miR-141. The knockdown of TP73-AS1 significantly inhibited the migration and invasion of pancreatic cancer cells, while the miR-141 inhibitor significantly restored the migration and invasion. Therefore, TP73-AS1 positively regulated BDH2 expression by sponging miR-141. These findings suggest that TP73-AS1 serves as an oncogene and promotes the metastasis of pancreatic cancer. Moreover, TP73-AS1 could serve as a predictor and a potential drug biotarget for pancreatic cancer.

LncRNA TP73-AS1 predicts poor prognosis and functions as oncogenic lncRNA in osteosarcoma

TP73 antisense RNA 1 (TP73-AS1), a novel long noncoding RNA (lncRNA), has been suggested to be deregulated in various human cancers and serve as a tumor suppressor or promoter, depending on tumor types. The role of TP73-AS1 in osteosarcoma is still unknown. In our results, TP73-AS1 was highly expressed in osteosarcoma tissue samples and cell lines compared with matching adjacent nontumor tissue specimens and a normal human osteoblast cell line, respectively. Moreover, high expression of TP73-AS1 was statistically associated with advanced Enneking stage, large tumor size, present distant metastasis, and poor histological grade, while exhibiting no statistical association with age, sex, and tumor site. The survival analyses showed that patients with osteosarcoma with high expression of TP73-AS1 obviously had lower overall survival than osteosarcoma patients with low expression of TP73-AS1, and high expression of TP73-AS1 was an independent poor prognostic factor for osteosarcoma patients. The experiments in vitro indicated that inhibition of TP73-AS1 expression depressed osteosarcoma cell viability, migration, and invasion, and arrested cell cycle. In conclusion, TP73-AS1 serves as oncogenic lncRNA participated in osteosarcoma progression.

Competing endogenous RNA (ceRNA) cross talk and language in ceRNA regulatory networks: A new look at hallmarks of breast cancer

Breast cancer (BC) is the most frequently occurring malignancy in women worldwide. Despite the substantial advancement in understanding the molecular mechanisms and management of BC, it remains the leading cause of cancer death in women. One of the main reasons for this obstacle is that we have not been able to find the Achilles heel for the BC as a highly heterogeneous disease. Accumulating evidence has revealed that noncoding RNAs (ncRNAs), play key roles in the development of BC; however, the involving of complex regulatory interactions between the different varieties of ncRNAs in the development of this cancer has been poorly understood. In the recent years, the newly discovered mechanism in the RNA world is "competing endogenous RNA (ceRNA)" which proposes regulatory dialogues between different RNAs, including long ncRNAs (lncRNAs), microRNAs (miRNAs), transcribed pseudogenes, and circular RNAs (circRNAs). In the latest BC research, various studies have revealed that dysregulation of several ceRNA networks (ceRNETs) between these ncRNAs has fundamental roles in establishing the hallmarks of BC development. And it is thought that such a discovery could open a new window for a better understanding of the hidden aspects of breast tumors. Besides, it probably can provide new biomarkers and potential efficient therapeutic targets for BC. This review will discuss the existing body of knowledge regarding the key functions of ceRNETs and then highlights the emerging roles of some recently discovered ceRNETs in several hallmarks of BC. Moreover, we propose for the first time the "ceRnome" as a new term in the present article for RNA research.

Construction and analysis of mRNA, miRNA, lncRNA, and TF regulatory networks reveal the key genes associated with prostate cancer

Purpose

Prostate cancer (PCa) causes a common male urinary system malignant tumour, and the molecular mechanisms of PCa are related to the abnormal regulation of various signalling pathways. An increasing number of studies have suggested that mRNAs, miRNAs, lncRNAs, and TFs could play important roles in various biological processes that are associated with cancer pathogenesis. This study aims to reveal functional genes and investigate the underlying molecular mechanisms of PCa with bioinformatics.

Methods

Original gene expression profiles were obtained from the GSE64318 and GSE46602 datasets in the Gene Expression Omnibus (GEO). We conducted differential screens of the expression of genes (DEGs) between two groups using the online tool GEO2R based on the R software limma package. Interactions between differentially expressed miRNAs, mRNAs and lncRNAs were predicted and merged with the target genes. Co-expression of miRNAs, lncRNAs and mRNAs was selected to construct mRNA-miRNA-lncRNA interaction networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the DEGs. Protein-protein interaction (PPI) networks were constructed, and transcription factors were annotated. Expression of hub genes in the TCGA datasets was verified to improve the reliability of our analysis.

Results

The results demonstrate that 60 miRNAs, 1578 mRNAs and 61 lncRNAs were differentially expressed in PCa. The mRNA-miRNA-lncRNA networks were composed of 5 miRNA nodes, 13 lncRNA nodes, and 45 mRNA nodes. The DEGs were mainly enriched in the nuclei and cytoplasm and were involved in the regulation of transcription, related to sequence-specific DNA binding, and participated in the regulation of the PI3K-Akt signalling pathway. These pathways are related to cancer and focal adhesion signalling pathways. Furthermore, we found that 5 miRNAs, 6 lncRNAs, 6 mRNAs and 2 TFs play important regulatory roles in the interaction network. The expression levels of EGFR, VEGFA, PIK3R1, DLG4, TGFBR1 and KIT were significantly different between PCa and normal prostate tissue.

Conclusion

Based on the current study, large-scale effects of interrelated mRNAs, miRNAs, lncRNAs, and TFs established a new prostate cancer network. In addition, we conducted functional module analysis within the network. In conclusion, this study provides new insight for exploration of the molecular mechanisms of PCa and valuable clues for further research into the process of tumourigenesis and its development in PCa.

Construction of implantation failure related lncRNA-mRNA network and identification of lncRNA biomarkers for predicting endometrial receptivity

Insufficient endometrial receptivity is a major factor leading to implantation failure (IF), and the traditional way of morphological observation of endometrium cannot determine the condition of receptivity sufficiently. Considering that long-noncoding RNAs (lncRNAs) regulate endometrial receptivity and competing endogenous RNA (ceRNA) mechanism works in plenty of biological processes, ceRNA is likely to function in the pathology of IF. In the present study, we aim to construct an implantation failure related lncRNA-mRNA network (IFLMN), and to identify the key lncRNAs as the candidates for predicting endometrial receptivity. The global background network was constructed based on the presumed lncRNA-miRNA and miRNA-mRNA pairs obtained from lncRNASNP and miRTarBase. Differentially expressed genes (DEGs) of IF were calculated using the data of GSE26787, and then re-annotated as differentially expressed mRNAs (DEMs) and lncRNAs (DELs). IFLMN was constructed by hypergeometric test, including 255 lncRNA-mRNA pairs, 10 lncRNAs, and 212 mRNAs. Topological analysis determined the key lncRNAs with the highest centroid. Functional enrichment analyses were performed by unsupervised clustering, GO classification, KEGG pathway, and co-expression module analyses, achieving six key lncRNAs and their ceRNA sub-networks, which were involved in immunological activity, growth factor binding, vascular proliferation, apoptosis, and steroid biosynthesis in uterus and prepared endometrium for embryo implantation. Sixteen endometrial samples were collected during mid-luteal phase, including 8 recurrent implantation failure (RIF) or recurrent miscarriage (RM) women and 8 controls who conceived successfully. Quantitative real-time PCR was performed to compare the expression of the above six lncRNAs, which validated that the expression of all these lncRNAs was significantly elevated in endometrium of RIF/RM patients. Further studies are needed to investigate the underlying mechanism, and the lncRNAs may be developed into predictive biomarkers for endometrial receptivity.

Inferences of individual drug responses across diverse cancer types using a novel competing endogenous RNA network

Differences in individual drug responses are an obstacle to progression in cancer treatment, and predicting responses would help to plan treatment. The accumulation of cancer molecular profiling and drug response data provides opportunities and challenges to identify novel molecular signatures and mechanisms of tumor responsiveness to drugs. This study evaluated drug responses with a competing endogenous RNA (ceRNA) system that depended on competition between diverse RNA species. We identified drug response‐related ceRNA (DRCEs) by combining the sequence and expression data of long noncoding RNA (lncRNA), microRNA (miRNA), and messenger RNA (mRNA), and the survival data of cancer patients treated with drugs. We constructed a patient–drug two‐layer integrated network and used a linear weighting method to predict individual drug responses. DRCEs were found to be significantly enriched in known cancer and drug‐associated data resources, involved in biological processes known to mediate drug responses, and correlated to drug activity in cancer cell lines. The dysregulation of DRCE expression influenced drug response‐associated functions and pathways, suggesting DRCEs as potential therapeutic targets affecting drug responses. A further case study in breast invasive carcinoma (BRCA) found that DRCE expression was consistent with the drug response pattern and the aberrant expression of the two NEAT1‐related DRCEs may lead to poor response to tamoxifen therapy for patients with TP53 mutations. In summary, this study provides a framework for ceRNA‐based evaluation of clinical drug responses across multiple cancer types. Understanding the underlying molecular mechanisms of drug responses will allow improved response to chemotherapy and outcomes of cancer treatment.

Knockdown of lncRNA TP73-AS1 inhibits gastric cancer cell proliferation and invasion via the WNT/β-catenin signaling pathway

Long non-coding RNAs (lncRNAs) function as tumor suppressors or oncogenes in tumor development and progression. The purpose of the present study was to investigate the clinical significance and functional role of lncRNA TP73-AS1 in gastric cancer (GC). Reverse transcription-quantitative polymerase chain reaction analysis demonstrated that TP73-AS1 was significantly upregulated in GC tissues compared with adjacent normal tissues. The higher expression of TP73-AS1 was closely associated with lymph node metastasis and tumor-node-metastasis stage in patients with GC. Those patients with GC showing a higher expression of TP73-AS1 were predicted to have shorter disease-free survival and overall survival rates. The knockdown of TP73-AS1 was shown to markedly inhibit cell proliferation, cell colony formation and cell invasion. In addition, the downregulation of TP73-AS1 suppressed the expression of transcription factor 4 and β-catenin, which suggested that a decrease in TP73-AS1 suppressed the WNT/β-catenin signaling pathway in GC. Therefore, these results indicated that TP73-AS1 may be a target for GC treatment.

The Long Noncoding RNA TP73-AS1 Interacted with miR-124 to Modulate Glioma Growth by Targeting Inhibitor of Apoptosis-Stimulating Protein of p53

P73 antisense RNA 1T (non-protein coding), known as TP73-AS1 or PDAM, is a long noncoding RNA (lncRNA), which may regulate apoptosis by regulation of p53-dependent antiapoptotic genes. An abnormal change of TP73-AS1 expression was noticed in cancers. The effects of TP73-AS1 in brain glioma growth and the underlying mechanism remain unclear so far. In this study, the effect of TP73-AS1 in human brain glioma cell lines and clinical tumor samples was detected so as to reveal its role and function. In this study, TP73-AS1 was specifically upregulated in brain glioma cell lines and promoted glioma cell growth through targeting miR-124. TP73-AS1 knocking down suppressed human brain glioma cell proliferation, invasion, and metastasis in vitro. The inhibitory effect of TP73-AS1 knocking down on glioma cell proliferation and invasion could partly be restored by miR-124 inhibition. In addition, miR-124-dependent inhibitor of apoptosis-stimulating protein of p53 (iASPP) regulation was required in TP73-AS1-induced brain glioma cell growth. Data from this study revealed that TP73-AS1 inhibited the brain glioma growth and metastasis as a competing endogenous RNA (ceRNA) through miR-124-dependent iASPP regulation. In conclusion, we regarded TP73-AS1 as an oncogenic lncRNA promoting brain glioma proliferation and metastasis and a potential target for human brain glioma treatment.

The Long Non-Coding RNA TP73-AS1 Interacted With miR-142 to Modulate Brain Glioma Growth Through HMGB1/RAGE Pathway

P73 antisense RNA 1T (non-protein coding), also known as TP73-AS1 or PDAM, is a long non-coding RNA which may regulate apoptosis via regulation of p53-dependent anti-apoptotic genes. An abnormal change of TP73-AS1 expression was noticed in cancers. The effects of TP73-AS1 in brain glioma growth and the underlying mechanism remain unclear so far. In the present study, TP73-AS1 was specifically upregulated in brain glioma tissues and cell lines, and was associated with poorer prognosis in patients with glioma. TP73-AS1 knocking down suppressed human brain glioma cell proliferation and invasion in vitro, as well as HMGB1 protein. MiR-142 has been reported to play a pivotal role in cancers; here we observed that TP73-AS1 and miR-142 could negatively regulate each other. Results from luciferase assays suggested that TP73-AS1 might compete with HMGB1 for miR-142 binding. Further, HMGB1/RAGE was involved in TP73-AS1/miR-142 regulation of glioma cell proliferation and invasion. In glioma tissues, TP73-AS1 and HMGB1 expression was up-regulated, whereas miR-142 expression was down-regulated. Data from the present study revealed that TP73-AS1 promoted the brain glioma growth and invasion through acting as a competing endogenous RNA (ceRNA) to promote HMGB1 expression by sponging miR-142. In conclusion, we regarded TP73-AS1 as an oncogenic lncRNA promoting brain glioma proliferation and invasion, and a potential target for human brain glioma treatment. J. Cell. Biochem. 119: 3007-3016, 2018. © 2017 Wiley Periodicals, Inc.

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.