miRNACancerMAP: an integrative web server inferring miRNA regulation network for cancer

BUB1, miR-495-3p, and E2F1/E2F8 axis is associated with poor prognosis of breast cancer patients and infiltration of Th2 cells in the tumor microenvironment

Breast cancer, the most common cancer in women, is characterized by cell cycle dysregulation and chromosome segregation errors, leading to mitotic catastrophe and genomic instability. Understanding these molecular mechanisms is crucial for better diagnosis and treatment. We used databases like TIMER 2.0, UALCAN, and Oncomine to determine the differential expression of Budding uninhibited by benzimidazole 1 (BUB1) in normal and pan-cancer tissues. we also used the Kaplan-Meier Plotter database to analyze gene expression associations with survival outcomes, bc-GenExMiner v5.0 to analyze BUB1 gene expression and histological subtypes, and ctcRbase and miR-TV to identify microRNAs associated with BUB1 expression in breast cancer. Our data show that BUB1 expression is overexpressed in breast cancer tumors, metastatic tissues, and circulating tumor cells, leading to shorter overall survival, disease-free survival, and relapse-free survival compared to low-expression patients. BUB1 expression is strongly correlated with E2F1/E2F8 expression, suggesting a potential regulatory relationship between these genes. The study revealed a negative correlation between target miRNA miR-495-3p and BUB1 expression in breast cancer tumors, indicating a potential regulatory relationship between these genes. The BUB1 expression was also strongly correlated with the infiltration of CD4+ T helper 2 (Th2) subtypes in the tumors, suggesting a need for further research.

Effective data visualization strategies in untargeted metabolomics

Covering: 2014 to 2023 for metabolomics, 2002 to 2023 for information visualizationLC-MS/MS-based untargeted metabolomics is a rapidly developing research field spawning increasing numbers of computational metabolomics tools assisting researchers with their complex data processing, analysis, and interpretation tasks. In this article, we review the entire untargeted metabolomics workflow from the perspective of information visualization, visual analytics and visual data integration. Data visualization is a crucial step at every stage of the metabolomics workflow, where it provides core components of data inspection, evaluation, and sharing capabilities. However, due to the large number of available data analysis tools and corresponding visualization components, it is hard for both users and developers to get an overview of what is already available and which tools are suitable for their analysis. In addition, there is little cross-pollination between the fields of data visualization and metabolomics, leaving visual tools to be designed in a secondary and mostly ad hoc fashion. With this review, we aim to bridge the gap between the fields of untargeted metabolomics and data visualization. First, we introduce data visualization to the untargeted metabolomics field as a topic worthy of its own dedicated research, and provide a primer on cutting-edge visualization research into data visualization for both researchers as well as developers active in metabolomics. We extend this primer with a discussion of best practices for data visualization as they have emerged from data visualization studies. Second, we provide a practical roadmap to the visual tool landscape and its use within the untargeted metabolomics field. Here, for several computational analysis stages within the untargeted metabolomics workflow, we provide an overview of commonly used visual strategies with practical examples. In this context, we will also outline promising areas for further research and development. We end the review with a set of recommendations for developers and users on how to make the best use of visualizations for more effective and transparent communication of results.

Deep self-reconstruction driven joint nonnegative matrix factorization model for identifying multiple genomic imaging associations in complex diseases

OBJECTIVE

Comprehensive analysis of histopathology images and transcriptomics data enables the identification of candidate biomarkers and multimodal association patterns. Most existing multimodal data association studies are derived from extensions of the joint nonnegative matrix factorization model for identifying complex data associations, which can make full use of clinical prior information. However, the raw data were usually taken as the input without considering the underlying complex multi-subspace structure, influencing the subsequent integration analysis results.

METHODS

This study proposed a deep-self reconstructed joint nonnegative matrix factorization (DSRJNMF) model to use self-expressive properties to reconstruct the raw data to characterize the similarity structure associated with clinical labels. Then, the sparsity, orthogonality, and regularization constraints constructed from prior information are added to the DSRJNMF model to determine the sparse set of biologically relevant features across modalities.

RESULTS

The algorithm has been applied to identify the imaging genetic association of triple negative breast cancer (TNBC). Multilevel experimental results demonstrate that the proposed algorithm better estimates potential associations between pathological image features and miRNA-gene and identifies consistent multimodal imaging genetic biomarkers to guide the interpretation of TNBC.

CONCLUSION

The propose method provides a novel idea of data association analysis oriented to complex diseases.

ExplORRNet: An interactive web tool to explore stage-wise miRNA expression profiles and their interactions with mRNA and lncRNA in human breast and gynecological cancers

Background

MicroRNAs (miRNAs) are key regulators of gene expression that have been implicated in gynecological and breast cancers. Understanding the cancer stage-wise expression patterns of miRNAs and their interactions with other RNA molecules in cancer is crucial to improve cancer diagnosis and treatment planning. Comprehensive web tools that integrate data on the transcriptome, circulating miRNAs, and their validated targets to derive beneficial conclusions in cancer research are lacking.

Methods

Using the Shiny R package, we developed a web tool called ExplORRNet that integrates transcriptomic profiles from The Cancer Genome Atlas and miRNA expression data derived from various sources, including tissues, cell lines, exosomes, serum, and plasma, available in the Gene Expression Omnibus database. Differential expression analyses between normal and tumor tissue samples as well as different stages of cancer, accompanied by gene enrichment and survival analyses, can be performed using specialized R packages. Additionally, a miRNA-messenger RNA (mRNA)-long non-coding RNA (lncRNA) networks are constructed to identify regulatory modules.

Results

Our tool identifies cancer stage-wise differentially regulated miRNAs, mRNAs, and lncRNAs in gynecological and breast cancers. Survival analysis identifies miRNAs associated with patient survival, and functional enrichment analysis provides insights into dysregulated miRNA-related biological processes and pathways. The miRNA-mRNA-lncRNA networks highlight interconnected regulatory molecular modules driving cancer progression. Case studies demonstrate the utility of the ExplORRNet for studying gynecological and breast cancers.

Conclusion

ExplORRNet is an intuitive and user-friendly web tool that provides a deeper understanding of dysregulated miRNAs and their functional implications in gynecological and breast cancers. We hope our ExplORRNet tool has potential utility among the clinical and basic researchers and will be beneficial to the entire cancer genomics community to encourage and facilitate mining the rapidly growing public databases to progress the field of precision oncology. The ExplORRNet is available at https://mirna.cs.ut.ee.

Bioinformatic Analysis of miR-200b/429 and Hub Gene Network in Cervical Cancer

The miR-200b/429 located at 1p36 is a highly conserved miRNA cluster emerging as a critical regulator of cervical cancer. Using publicly available miRNA expression data from TCGA and GEO followed by independent validation, we aimed to identify the association between miR-200b/429 expression and cervical cancer. miR-200b/429 cluster was significantly overexpressed in cancer samples compared to normal samples. miR-200b/429 expression did not correlate with patient survival; however, its overexpression correlated with histological type. Protein-protein interaction analysis of 90 target genes of miR-200b/429 identified EZH2, FLT1, IGF2, IRS1, JUN, KDR, SOX2, MYB, ZEB1, and TIMP2 as the top ten hub genes. PI3K-AKT and MAPK signaling pathways emerged as major target pathways of miR-200b/429 and their hub genes. Kaplan-Meier survival analysis showed the expression of seven miR-200b/429 target genes (EZH2, FLT1, IGF2, IRS1, JUN, SOX2, and TIMP2) to influence the overall survival of patients. The miR-200a-3p and miR-200b-5p could help predict cervical cancer with metastatic potential. The cancer hallmark enrichment analysis showed hub genes to promote growth, sustained proliferation, resistance to apoptosis, induction of angiogenesis, activation of invasion, and metastasis, enabling replicative immortality, evading immune destruction, and tumor-promoting inflammation. The drug-gene interaction analysis identified 182 potential drugs to interact with 27 target genes of miR-200b/429 with paclitaxel, doxorubicin, dabrafenib, bortezomib, docetaxel, ABT-199, eribulin, vorinostat, etoposide, and mitoxantrone emerging as the top ten best candidate drugs. Taken together, miR-200b/429 and associated hub genes can be helpful for prognostic application and clinical management of cervical cancer.

Integrated modeling framework reveals co-regulation of transcription factors, miRNAs and lncRNAs on cardiac developmental dynamics

AIMS

Dissecting complex interactions among transcription factors (TFs), microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) are central for understanding heart development and function. Although computational approaches and platforms have been described to infer relationships among regulatory factors and genes, current approaches do not adequately account for how highly diverse, interacting regulators that include noncoding RNAs (ncRNAs) control cardiac gene expression dynamics over time.

METHODS

To overcome this limitation, we devised an integrated framework, cardiac gene regulatory modeling (CGRM) that integrates LogicTRN and regulatory component analysis bioinformatics modeling platforms to infer complex regulatory mechanisms. We then used CGRM to identify and compare the TF-ncRNA gene regulatory networks that govern early- and late-stage cardiomyocytes (CMs) generated by in vitro differentiation of human pluripotent stem cells (hPSC) and ventricular and atrial CMs isolated during in vivo human cardiac development.

RESULTS

Comparisons of in vitro versus in vivo derived CMs revealed conserved regulatory networks among TFs and ncRNAs in early cells that significantly diverged in late staged cells. We report that cardiac genes ("heart targets") expressed in early-stage hPSC-CMs are primarily regulated by MESP1, miR-1, miR-23, lncRNAs NEAT1 and MALAT1, while GATA6, HAND2, miR-200c, NEAT1 and MALAT1 are critical for late hPSC-CMs. The inferred TF-miRNA-lncRNA networks regulating heart development and contraction were similar among early-stage CMs, among individual hPSC-CM datasets and between in vitro and in vivo samples. However, genes related to apoptosis, cell cycle and proliferation, and transmembrane transport showed a high degree of divergence between in vitro and in vivo derived late-stage CMs. Overall, late-, but not early-stage CMs diverged greatly in the expression of "heart target" transcripts and their regulatory mechanisms.

CONCLUSIONS

In conclusion, we find that hPSC-CMs are regulated in a cell autonomous manner during early development that diverges significantly as a function of time when compared to in vivo derived CMs. These findings demonstrate the feasibility of using CGRM to reveal dynamic and complex transcriptional and posttranscriptional regulatory interactions that underlie cell directed versus environment-dependent CM development. These results with in vitro versus in vivo derived CMs thus establish this approach for detailed analyses of heart disease and for the analysis of cell regulatory systems in other biomedical fields.

Identification of miR-30c-5p as a tumor suppressor by targeting the m6 A reader HNRNPA2B1 in ovarian cancer

BACKGROUND

microRNAs (miRNAs) and N6-methyladenosine (m⁶ A) play important roles in ovarian cancer (OvCa). However, the mechanisms by which miRNAs regulate m⁶ A in OvCa have not been elucidated so far.

METHODS

To screen m⁶ A-related miRNAs, Pearson's correlation analysis of miRNAs and m⁶ A regulators was implemented using The Cancer Genome Atlas database (TCGA). To determine the level of m⁶ A, RNA m⁶ A quantitative assays were used. Then, colony formation assays, EdU assays, wound healing assays, and Transwell assays were performed. The dual-luciferase reporter assay was used to confirm the miRNA target genes. Protein-protein interaction (PPI) analysis of the target genes was performed, and hub genes were discovered using the cytoHubba/Cytoscape software. The underlying molecular mechanisms were explored by bioinformatics and RNA stability assays.

RESULTS

A total of 126 miRNAs were identified as m⁶ A-related miRNAs by Pearson's correlation analysis. Among them, the high level of miR-30c-5p was associated with good prognosis in OvCa patients. In vitro, the miR-30c-5p agomir lowered the m⁶ A level and inhibited OvCa cell proliferation, migration, and invasion. The hub target genes of miR-30c-5p were identified as (i) XPO1, (ii) AGO1, (iii) HNRNPA2B1, of which m⁶ A reader HNRNPA2B1 was highly expressed in OvCa tissues and related with poor prognosis. In vitro, knockdown of HNRNPA2B1 significantly reduced m⁶ A level and hampered the proliferation and migration of OvCa cells. The inhibition of m⁶ A reader HNRNPA2B1 attenuated the suppression of proliferation and migration and the low m⁶ A level induced by the miR-30c-5p downregulation. Mechanistically, m⁶ A reader HNRNPA2B1 might regulate CDK19 mRNA stability to alter m⁶ A level.

CONCLUSIONS

miR-30c-5p inhibits OvCa progression and reduces the m⁶ A level by inhibiting m⁶ A reader HNRNPA2B1, thus providing new insights into the m⁶ A regulatory mechanism in OvCa.

Updated review of advances in microRNAs and complex diseases: experimental results, databases, webservers and data fusion

MicroRNAs (miRNAs) are gene regulators involved in the pathogenesis of complex diseases such as cancers, and thus serve as potential diagnostic markers and therapeutic targets. The prerequisite for designing effective miRNA therapies is accurate discovery of miRNA-disease associations (MDAs), which has attracted substantial research interests during the last 15 years, as reflected by more than 55 000 related entries available on PubMed. Abundant experimental data gathered from the wealth of literature could effectively support the development of computational models for predicting novel associations. In 2017, Chen et al. published the first-ever comprehensive review on MDA prediction, presenting various relevant databases, 20 representative computational models, and suggestions for building more powerful ones. In the current review, as the continuation of the previous study, we revisit miRNA biogenesis, detection techniques and functions; summarize recent experimental findings related to common miRNA-associated diseases; introduce recent updates of miRNA-relevant databases and novel database releases since 2017, present mainstream webservers and new webserver releases since 2017 and finally elaborate on how fusion of diverse data sources has contributed to accurate MDA prediction.

Illuminating lncRNA Function Through Target Prediction

Most of the transcribed human genome codes for noncoding RNAs (ncRNAs), and long noncoding RNAs (lncRNAs) make for the lion's share of the human ncRNA space. Despite growing interest in lncRNAs, because there are so many of them, and because of their tissue specialization and, often, lower abundance, their catalog remains incomplete and there are multiple ongoing efforts to improve it. Consequently, the number of human lncRNA genes may be lower than 10,000 or higher than 200,000. A key open challenge for lncRNA research, now that so many lncRNA species have been identified, is the characterization of lncRNA function and the interpretation of the roles of genetic and epigenetic alterations at their loci. After all, the most important human genes to catalog and study are those that contribute to important cellular functions-that affect development or cell differentiation and whose dysregulation may play a role in the genesis and progression of human diseases. Multiple efforts have used screens based on RNA-mediated interference (RNAi), antisense oligonucleotide (ASO), and CRISPR screens to identify the consequences of lncRNA dysregulation and predict lncRNA function in select contexts, but these approaches have unresolved scalability and accuracy challenges. Instead-as was the case for better-studied ncRNAs in the past-researchers often focus on characterizing lncRNA interactions and investigating their effects on genes and pathways with known functions. Here, we focus most of our review on computational methods to identify lncRNA interactions and to predict the effects of their alterations and dysregulation on human disease pathways.

MicroRNA Databases and Tools

In this era of big data, sets of methodologies and strategies are designed to extract knowledge from huge volumes of data. However, the cost of where and how to get this information accurately and quickly is extremely important, given the diversity of genomes and the different ways of representing that information. Among the huge set of information and relationships that the genome carries, there are sequences called miRNAs (microRNAs). These sequences were described in the 1990s and are mainly involved in mechanisms of regulation and gene expression. Having this in mind, this chapter focuses on exploring the available literature and providing useful and practical guidance on the miRNA database and tools topic. For that, we organized and present this text in two ways: (a) the update reviews and articles, which best summarize and discuss the theme; and (b) our update investigation on miRNA literature and portals about databases and tools. Finally, we present the main challenge and a possible solution to improve resources and tools.

NR2F1-AS1 Promotes Pancreatic Ductal Adenocarcinoma Progression Through Competing Endogenous RNA Regulatory Network Constructed by Sponging miRNA-146a-5p/miRNA-877-5p

The role of NR2F1-AS1 in pancreatic ductal adenocarcinoma (PDAC) remains unknown. Therefore, we aimed to investigate the biological mechanism of NR2F1-AS1 in PDAC. The expression of NR2F1-AS1 was measured by using microarray data and real-time PCR. The effects of NR2F1-AS1 knockdown on proliferation, cell cycle progression, invasion in vitro and tumorigenesis in vivo were investigated. The mechanism of competitive endogenous RNAs was determined from bioinformatics analyses and validated by a dual-luciferase reporter gene assay. Potential target mRNAs from TargetScan 7.2 were selected for subsequent bioinformatics analysis. Key target mRNAs were further identified by screening hub genes and coexpressed protein-coding genes (CEGs) of NR2F1-AS1. NR2F1-AS1 was highly expressed in PDAC, and the overexpression of NR2F1-AS1 was associated with overall survival and disease-free survival. The knockdown of NR2F1-AS1 impaired PDAC cell proliferation, migration, invasion and tumorigenesis. NR2F1-AS1 competitively sponged miR-146a-5p and miR-877-5p, and low expression of the two miRNAs was associated with a poor prognosis. An integrative expression and survival analysis of the hub genes and CEGs demonstrated that the NR2F1-AS1–miR-146a-5p/miR-877-5p–GALNT10/ZNF532/SLC39A1/PGK1/LCO3A1/NRP2/LPCAT2/PSMA4 and CLTC ceRNA networks were linked to the prognosis of PDAC. In conclusion, NR2F1-AS1 overexpression was significantly associated with poor prognosis. NR2F1-AS1 functions as an endogenous RNA to construct a novel ceRNA network by competitively binding to miR-146a-5p/miR-877-5p, which may contribute to PDAC pathogenesis and could represent a promising diagnostic biomarker or potential novel therapeutic target in PDAC.

TSCCA: A tensor sparse CCA method for detecting microRNA-gene patterns from multiple cancers

Existing studies have demonstrated that dysregulation of microRNAs (miRNAs or miRs) is involved in the initiation and progression of cancer. Many efforts have been devoted to identify microRNAs as potential biomarkers for cancer diagnosis, prognosis and therapeutic targets. With the rapid development of miRNA sequencing technology, a vast amount of miRNA expression data for multiple cancers has been collected. These invaluable data repositories provide new paradigms to explore the relationship between miRNAs and cancer. Thus, there is an urgent need to explore the complex cancer-related miRNA-gene patterns by integrating multi-omics data in a pan-cancer paradigm. In this study, we present a tensor sparse canonical correlation analysis (TSCCA) method for identifying cancer-related miRNA-gene modules across multiple cancers. TSCCA is able to overcome the drawbacks of existing solutions and capture both the cancer-shared and specific miRNA-gene co-expressed modules with better biological interpretations. We comprehensively evaluate the performance of TSCCA using a set of simulated data and matched miRNA/gene expression data across 33 cancer types from the TCGA database. We uncover several dysfunctional miRNA-gene modules with important biological functions and statistical significance. These modules can advance our understanding of miRNA regulatory mechanisms of cancer and provide insights into miRNA-based treatments for cancer.

MicroRNAs (miRNAs) are a class of small non-coding RNAs. Previous studies have revealed that miRNA-gene regulatory modules play key roles in the occurrence and development of cancer. However, little has been done to discover miRNA-gene regulatory modules from a pan-cancer view. Thus, it is urgently needed to develop new methods to explore the complex cancer-related miRNA-gene patterns by integrating multi-omics data of multi-cancers. To build the connections between miRNA-gene regulatory modules across different cancer types, we propose a tensor sparse canonical correlation analysis (TSCCA) method. Our specific contributions are two-fold: (1) We propose a sparse statistical learning model TSCCA and an efficient block-coordinate descent algorithm to solve it. (2) We apply TSCCA to a multi-omics data set of 33 cancer types from TCGA and identify some cancer-related miRNA-gene modules with important biological functions and statistical significance.

SNRPC promotes hepatocellular carcinoma cell motility by inducing epithelial-mesenchymal transition

The therapeutic outcome of hepatocellular carcinoma (HCC) remains unsatisfactory because of poor response and acquired drug resistance. To better elucidate the molecular mechanisms of HCC, here we used three Gene Expression Omnibus datasets to identify potential oncogenes, and thereby identified small nuclear ribonucleoprotein polypeptide C (SNRPC). We report that SNRPC is highly up‐regulated in HCC tissues as determined using immunohistochemistry assays of samples from a cohort of 224 patients with HCC, and overexpression of SNRPC was correlated with multiple tumors, advanced stage, and poor outcome. Kaplan–Meier analysis confirmed that patients with high SNRPC expression exhibited shorter survival in four independent HCC cohorts (all P < 0.05). Furthermore, SNRPC mutations are significantly more frequent in HCC tissues than in normal liver tissues and are an early event in the development of HCC. Functional network analysis suggested that SNRPC is linked to the regulation of ribosome, spliceosome, and proteasome signaling. Subsequently, gain‐ and loss‐of‐function assays showed that SNRPC promotes the motility and epithelial–mesenchymal transition of HCC cells in vitro. SNRPC expression was negatively correlated with the infiltration of CD4⁺ T cells, macrophage cells, and neutrophil cells (all P < 0.05), as determined by analyzing the TIMER (Tumor IMmune Estimation Resource) database. In conclusion, our findings suggest that SNRPC has a potential role in epithelial–mesenchymal transition and motility in HCC.

Comprehensive characterization of a drug-resistance-related ceRNA network across 15 anti-cancer drug categories

Cancer is still a major health problem around the world. The treatment failure of cancer has largely been attributed to drug resistance. Competitive endogenous RNAs (ceRNAs) are involved in various biological processes and thus influence the drug sensitivity of cancers. However, a comprehensive characterization of drug-sensitivity-related ceRNAs has not yet been performed. In the present study, we constructed 15 ceRNA networks across 15 anti-cancer drug categories, involving 217 long noncoding RNAs (lncRNAs), 158 microRNAs (miRNAs), and 1,389 protein coding genes (PCGs). We found that these ceRNAs were involved in hallmark processes such as “self-sufficiency in growth signals,” “insensitivity to antigrowth signals,” and so on. We then identified an intersection ceRNA network (ICN) across the 15 anti-cancer drug categories. We further identified interactions between genes in the ICN and clinically actionable genes (CAGs) by analyzing the co-expressions, protein-protein interactions, and transcription factor-target gene interactions. We found that certain genes in the ICN are correlated with CAGs. Finally, we found that genes in the ICN were aberrantly expressed in tumors, and some were associated with patient survival time and cancer stage.

PGM5-AS1 impairs miR-587-mediated GDF10 inhibition and abrogates progression of prostate cancer

Background

Prostate cancer (PCa) is a leading cause of cancer-related death in males. Aberrant expression of long non-coding RNAs (lncRNAs) has been implicated in various human malignancies, including PCa. This study aims to clarify the inhibitory role of human PGM5 antisense RNA 1 (PGM5-AS1) in the proliferation and apoptosis of PCa cells.

Methods

The regulatory network of PGM5-AS1/microRNA-587 (miR-587)/growth and differentiation factor 10 (GDF10) axis was examined by dual-luciferase reporter gene assay, RNA-binding protein immunoprecipitation, and RNA pull down assay. We manipulated the expression of PGM5-AS1, miR-587 and GDF10 by transducing expression vectors, mimic, inhibitor, or short hairpin RNA into PCa cells, thus establishing their functions in cell proliferation and apoptosis. Additionally, we measured the tumorigenicity of PCa cells xenografted in nude mice.

Results

PGM5-AS1 is expressed at low levels in PCa cell lines. Forced overexpression of PGM5-AS1 restricted proliferation and facilitated apoptosis of PCa cells, manifesting in suppressed xenograft tumor growth in nude mice. Notably, PGM5-AS1 competitively bound to miR-587, which directly targets GDF10. We further validated that the anti-cancer role of PGM5-AS1 in PCa cells was achieved by binding to miR-587 to promote the expression of GDF10.

Conclusion

PGM5-AS1 upregulates GDF10 gene expression by competitively binding to miR-587, thus inhibiting proliferation and accelerating apoptosis of PCa cells.

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.

LINC00968 can inhibit the progression of lung adenocarcinoma through the miR-21-5p/SMAD7 signal axis

Background: Long non-coding RNAs (LncRNAs) have been associated with several types of cancer. However, little is known about their role in lung adenocarcinoma (LUAD).

Results: LINC00968 was significantly differentially expressed in LUAD tissues. Downregulated LINC00968 was associated with clinicopathological features of LUAD. LINC00968 inhibited cell growth and metastasis by regulating the Hippo signaling pathway We demonstrated that LINC00968 acts as a ceRNA to consume miR-21-5p, enhancing the accumulation of SMAD7, a miR-21-5p target.

Conclusions: LINC00968 limits LUAD progression via the miR-21-5p/SMAD7 axis and may serve as a prognostic biomarker and therapeutic target for LUAD.

Methods: We conducted comprehensive data mining on LINC00968 based on the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. The expression of LINC00968 in LUAD cells was determined using in situ hybridization. We detected LINC00968 function in LUAD cells using the MTT, clone formation, and transwell assays, and tumor xenografts. Label-free quantitative proteomics, western blotting, a dual-luciferase reporter assay, immunofluorescence, and RNA immunoprecipitation assays were used to determine the correlations among LINC00968, miR-21-5p, and SMAD7. Gain- and loss-function approaches were used to explore the effects of LINC00968, miR-21-5p, and SMAD7 on cell proliferation, migration, and invasion.

Knockdown of LncRNA DLEU2 Inhibits Cervical Cancer Progression via Targeting miR-128-3p

Objective

Cervical cancer is one of the most common female malignancies worldwide and represents a major global health challenge. The fast growth of tumor and high rates of metastasis still lead to a poor prognosis of cervical cancer patients. It is urgent to clarify the mechanism and identify predictive biomarkers for the treatment of cervical cancer. Long non-coding RNAs (LncRNAs) have been identified in cervical cancer and are related to malignant phenotypes of cervical cancer cells. However, the roles and mechanism of LncRNA deleted in lymphocytic leukemia (DLEU2) in the tumorigenesis and progression of cervical cancer remain unknown.

Materials and Methods

qPCR was performed to analyze the expression of DLEU2, Cyclin D1, CDK4, Bax, Bcl2 and mi-128-3p. Western blot was performed to detect the cell cycle hallmarks expression. CCK8 was used to examine cell proliferation. Cellular apoptosis was analyzed by Hoechst 33,258 staining and AV/PI staining with flow cytometry. Cell cycle was analyzed by flow cytometry. The xenograft model in nude mice was used to elucidate the function of DLEU2 in vivo. Bioinformatics analysis and luciferase reporter assay were proceeded to clarify whether miR-128-3p directly binds with lncRNA DLEU2. Pull‑down assay and RNA-binding protein immunoprecipitation assay were used for exploring the relationship between DLEU2 and miR-128-3p.

Results

We demonstrated that DLEU2 was upregulated in cervical cancer tumor tissues. Downregulation of DLEU2 inhibited cell proliferation, induced apoptosis and cell cycle arrest at G2/M phase of cervical cancer cells in vitro, and suppressed tumor growth in vivo. Further, LncRNA DLEU2 is one of the targets of miR-128-3p. miR-128-3p inhibitor abrogated the cell proliferation suppressed by knockdown of DLEU2, apoptosis induced by knockdown of DLEU2 and reversed the expression of cell cycle hallmarks regulated by knockdown of DLEU2.

Conclusion

Taken together, these results suggested knockdown of DLEU2 inhibited cervical cancer progression via targeting miR-128-3p.

FOXD2-AS1 promotes migration and invasion of head and neck squamous cell carcinoma and predicts poor prognosis

Aim: To investigate the role of long noncoding RNA FOXD2-AS1 in head and neck squamous cell carcinoma (HNSCC). Materials & methods: The expression and clinical significance of FOXD2-AS1 were analyzed using data from public databases. Transwell assays were used to examine the function of FOXD2-AS1 in HNSCC. The molecular mechanism of FOXD2-AS1 was probed by western blotting. Results: The expression of FOXD2-AS1 was upregulated in HNSCC; it was positively related with the pathological stage as well as with poor prognosis in HNSCC patients. FOXD2-AS1 silencing inhibited HNSCC cell migration and invasion, also influenced the expression of epithelial-mesenchymal transition-related molecules. Conclusion: FOXD2-AS1 was a prognostic marker in patients with HNSCC and may be a favorable novel treatment target for HNSCC.

Characterizing methylation regulated miRNA in carcinoma of the human uterine cervix

Gene regulatory mechanisms determine the multistep carcinogenesis process. Two aspects of epigenetics are microRNA (miRNAs) and DNA methylation that regulate distinct biological mechanisms such as metastasis, apoptosis cell proliferation and induction of senescence. Although critical, the interplay between these two epigenetic mechanisms is yet to be completely understood, particularly in cervical cancer. To study the DNA methylation regulation of miRNAs and its potential role in cervical cancer, we investigated the differential methylation pattern of two candidate miRNAs (miR-375 and miR-196a-1) during cervical cancer progression against normal cervical epithelium (NCE) by bisulfite DNA sequencing. miR-375 and miR-196a-1 were hypermethylated in Squamous Cell Carcinoma (SCC) against NCE and Cervical Intra-Epithelial Neoplasia (CIN) (p < 0.05). Treatment with demethylating agent reactivated the miR-375 and miR-196a-1 expression in SiHa, HeLa and CaSki cells. In vitro artificial methylation by M.SssI followed by dual luciferase assay confirmed miR-375 and miR-196a-1 as methylation regulated miRNAs (P < 0.05). miR-375 and miR-196a-1 expression levels were negatively correlated with methylation levels in clinical specimens. We further identified Replication Factor C Subunit 3 (RFC3) and High Mobility Group AT-Hook 1 (HMGA1) as targets of miR-375 and miR-196a-1 respectively by dual luciferase reporter assay. Our analysis indicates that miR-375 and miR-196a-1 are DNA methylation regulated miRNAs whose deregulation may facilitate pathophysiology of cervical cancer.

Analysis of miRNA-mRNA network reveals miR-140-5p as a suppressor of breast cancer glycolysis via targeting GLUT1

Aim: Aerobic glycolysis is characteristic of breast cancer. Comprehensive expression profiles of key proteins, their prognosis and detailed relationships between miRNAs and mRNAs remain unclear. Materials & methods: Oncomine database, Kaplan-Meier overall survival and miRNA-mRNA network analysis were performed. A key miRNA was identified and explored in vitro and in vivo. Results & conclusion: Eleven key glycolytic proteins were found with higher expression and poor prognosis: GLUT1, SLC2A5, HK1, PFKP, ALDOA, TPI1, GAPDH, PGK1, ENO1, GOT1 and GOT2. Seven miRNAs were predicted targeting 11 key glycolytic proteins: miR-140-5p, miR-3064-5p, miR-152-3p, miR-449b-5p, miR-449a, miR-194-5p and miR-34a-5p. Among them, miR-140-5p was found to be downregulated in breast cancer and directly targeted GLUT1, resulting in an antiglycolytic and antiproliferative effect.

miRspongeR: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules

BACKGROUND

A microRNA (miRNA) sponge is an RNA molecule with multiple tandem miRNA response elements that can sequester miRNAs from their target mRNAs. Despite growing appreciation of the importance of miRNA sponges, our knowledge of their complex functions remains limited. Moreover, there is still a lack of miRNA sponge research tools that help researchers to quickly compare their proposed methods with other methods, apply existing methods to new datasets, or select appropriate methods for assisting in subsequent experimental design.

RESULTS

To fill the gap, we present an R/Bioconductor package, miRspongeR, for simplifying the procedure of identifying and analyzing miRNA sponge interaction networks and modules. It provides seven popular methods and an integrative method to identify miRNA sponge interactions. Moreover, it supports the validation of miRNA sponge interactions and the identification of miRNA sponge modules, as well as functional enrichment and survival analysis of miRNA sponge modules.

CONCLUSIONS

This package enables researchers to quickly evaluate their new methods, apply existing methods to new datasets, and consequently speed up miRNA sponge research.

PPARγ inhibition regulates the cell cycle, proliferation and motility of bladder cancer cells

Peroxisome proliferator-activated receptor gamma (PPARγ) is a member of the nuclear receptor family of ligand-activated transcription factors and plays an important role in regulating cell proliferation, inflammation and lipid and glucose homeostasis. Our results revealed that PPARγ was up-regulated in human bladder cancer (BCa) tissues both at transcriptional and translational levels. Moreover, down-regulation of PPARγ mRNA or inhibition of PPARγ function (using GW9662, antagonist of PPARγ) could significantly suppress the proliferation of BCa cells. Furthermore, the cell cycle arrested in G0/G1 phase was also induced by the down-regulated PPARγ possibly through AKT-mediated up-regulation of p21/p27, whereas no significant transformation of apoptosis was observed. In addition, knockdown or inhibition of PPARγ might reduce the invasion and migration of BCa cells by affecting epithelial-mesenchymal transition-related proteins through AKT/GSK3β signalling pathway. Additionally, in vivo studies showed that BCa cell proliferation was significantly suppressed by GW9662. In conclusion, our results indicated that PPARγ might be crucial for BCa tumorigenesis by interfering with the motility and viability of BCa cells.

Characterization of Transcriptome Transition Associates Long Noncoding RNAs with Glioma Progression

Long noncoding RNAs (lncRNAs) have been implicated in cancer biogenesis and prognosis. However, we still lack knowledge on their function during glioma progression. In this study, we analyzed the lncRNA expression profile across 907 glioma patients in grades II, III, and IV. Widespread dynamic expression of lncRNAs during glioma progression was revealed, and we identified 33 onco-lncRNAs and 61 tumor suppressor lncRNAs. We found that the expression of these oncogenic lncRNAs is regulated by grade-specific expressed transcription factors. Based on the “guilt by association” rule, we predicted the potential functions of oncogenic lncRNAs, and the majority of these lncRNAs are involved in cancer hallmarks. Especially we found that CARD8-AS1 regulates the metastatic potential of glioma cell lines in vitro. Integrating clinical information, we identified the 12 protective and 8 risk lncRNAs (such as PWAR6 and CARD8-AS1) in glioma. Finally, an lncRNA-gene functional module was identified to be associated with the survival of patients. The predictive ability of this module signature was further validated in an independent dataset. Our results revealed the dynamic transcriptome transition during glioma progression, indicating that the lncRNA signature could be a useful biomarker that may improve upon our understanding of the molecular mechanisms underlying glioma progression.