Exploring functions of long noncoding RNAs across multiple cancers through co-expression network

TP63-TRIM29 axis regulates enhancer methylation and chromosomal instability in prostate cancer

BACKGROUND

Prostate adenocarcinoma (PRAD) is the second leading cause of cancer-related deaths in men. High variability in DNA methylation and a high rate of large genomic rearrangements are often observed in PRAD.

RESULTS

To investigate the reasons for such high variance, we integrated DNA methylation, RNA-seq, and copy number alterations datasets from The Cancer Genome Atlas (TCGA), focusing on PRAD, and employed weighted gene co-expression network analysis (WGCNA). Our results show that only single cluster of co-expressed genes is associated with genomic and epigenomic instability. Within this cluster, TP63 and TRIM29 are key transcription regulators and are downregulated in PRAD. We discovered that TP63 regulates the level of enhancer methylation in prostate basal epithelial cells. TRIM29 forms a complex with TP63 and together regulates the expression of genes specific to the prostate basal epithelium. In addition, TRIM29 binds DNA repair proteins and prevents the formation of the TMPRSS2:ERG gene fusion typically observed in PRAD.

CONCLUSION

Our study demonstrates that TRIM29 and TP63 are important regulators in maintaining the identity of the basal epithelium under physiological conditions. Furthermore, we uncover the role of TRIM29 in PRAD development.

The roles of lncRNAs in Th17-associated diseases, with special focus on JAK/STAT signaling pathway

One of the most crucial T cell subsets in a variety of autoimmune and chronic inflammatory illnesses is T helper (Th) 17 cells. Th17 cells appear to have an essential role in the clearance of extracellular pathogens during infections. However, Th17 cells are also involved in inflammation and have been implicated in the pathogenesis of several autoimmune diseases and human inflammatory conditions. Due to the involvement of Th17 cells in the onset of Th17-associated diseases, understanding molecular mechanisms of Th17 cell functions may open the door to developing tailored therapies to address these difficult disorders. However, the molecular mechanisms governing Th17 differentiation in various diseases are still not well understood. The JAK/STAT signaling pathway plays a critical role in immune responses and has been linked to various aspects of Th17 cell differentiation and function. In this article, we conducted a comprehensive review of various molecular mechanisms (JAK/STAT, microRNAs, etc.), that can affect the differentiation of Th17 cells in various Th17-associated diseases.

Isolation of Cells and Exosomes from Glioblastoma Tissue to Investigate the Effects of Ascorbic Acid on the c-Myc, HIF-1α, and Lnc-SNHG16 Genes

Glioblastoma multiforme (GBM) is incurable with routine treatments. Ascorbic acid (Asc) has antioxidant and anti-cancer properties. However, its specific anti-cancer mechanisms are only partially understood. In this study, the effect of Asc on the c-Myc, HIF-1α, and lnc-SNHG16 genes in GBM cells and their exosomes was investigated. Cells isolated from the tissue were characterized by the immunocytochemistry method (GFAP+). The cell-doubling time was determined, and FBS-free medium supplemented with Asc (5 mM) was added to the cells. The extracted exosomes in the cell culture medium were scanned by electron microscopy, Zetasizer, and BCA assay. The expression of lnc-SNHG16 in the exosomes and c-Myc and HIF-1α in the treated and control cells was evaluated by real-time PCR. The interactions between Asc and the c-Myc and HIF-1α proteins were studied using the molecular docking method. The cells showed 90-100% GFAP+ in passage 4, with a cell-doubling time of 4.8 days. Exosomal vesicles measuring 98.25-105.9 were observed. Zetasizer results showed a sharp pick at 90 nm. Protein quantitation showed 3.812 µg/ml protein in the exosomes. Lnc-SNHG16 expression was reduced (P = 0.041), and c-Myc was upregulated (P = 0.002). The expression of HIF-1α was not significant in the treated cells. Also, Asc was able to interact and affect c-Myc and HIF-1α. Asc exerts its effect by reducing lnc-SNHG16 expression in exosomes, upregulating c-Myc in GBM cells, and interacting with HIF-1α and c-Myc. Further research is necessary to achieve a full understanding of these findings.

Identification of a Novel Long Non-coding RNA, lnc-ATMIN-4:2, and its Clinicopathological and Prognostic Significance in Advanced Gastric Cancer

BACKGROUND/AIM

Long non-coding RNAs (lncRNAs) are emerging as significant regulators of gene expression and a novel promising biomarker for cancer diagnosis and prognosis. This study identified a novel, differentially expressed lncRNA in advanced gastric cancer (AGC), Inc-ATMIN-4:2, and evaluated its clinicopathological and prognostic significance.

PATIENTS AND METHODS

Whole transcriptome sequencing was performed to identify differentially expressed lncRNAs in AGC tissue samples. We also analyzed lnc-ATMIN-4:2 expression in 317 patients with AGC using RNA in situ hybridization.

RESULTS

High (>30 dots) lnc-ATMIN-4:2 expression significantly correlated with younger age, poorly differentiated histology, diffuse type, deeper invasion depth, perineural invasion, lymph node metastasis, and higher stage group. In addition, high lnc-ATMIN-4:2 expression was significantly associated with worse overall survival in patients with AGC.

CONCLUSION

This study elucidated the significance of lncRNAs in AGC and indicated the value of lnc-ATMIN-4:2 expression as a predictive biomarker for the overall survival of patients with AGC.

Prognostic Roles of Glucose to Lymphocyte Ratio and Modified Glasgow Prognosis Score in Patients With Non-small Cell Lung Cancer

Background

Non-small cell lung cancer (NSCLC) is among the most prevalent malignancies worldwide. Previous studies have shown that the status of inflammation, nutrition and immune are closely related to overall survival (OS) of patients with NSCLC, but little is known about their interactive and combined roles. Hence, we chose glucose to lymphocyte ratio (GLR) and modified Glasgow Prognosis Score (mGPS) as prognostic factors and assessed the prognostic values of them for patients with NSCLC.

Methods

Baseline clinicopathologic and laboratory characteristics of 862 patients with NSCLC were obtained from a multicenter prospective cohort. The Cox proportional hazard regression models were used to determine prognostic values of the clinical factors. A nomogram was also constructed integrating the clinical factors with clinical significance or independent prognostic values. Concordance index (C-index) was utilized to evaluate the prediction accuracy of the TNM stage and the nomogram.

Results

Multivariate analyses demonstrated that GLR [Hazard ratio (HR) = 1.029, 95% confidence interval (CI) = 1.004–1.056, P = 0.023] and mGPS (score of 1: HR = 1.404, 95% CI = 1.143–1.726, P = 0.001; score of 2: HR = 1.515, 95% CI = 1.159–1.980, P = 0.002) were independent prognostic factors for patients with NSCLC. The C-indexes of the TNM stage and the nomogram were 0.642 (95% CI = 0.620–0.663) and 0.694 (95% CI = 0.671–0.717), respectively.

Conclusion

GLR and mGPS were independent prognostic factors for patients with NSCLC. Moreover, our constructed nomogram might be superior in predicting prognosis of patients with NSCLC compared with the TNM stage.

Screening of key methylation-driven genes CDO1 in breast cancer based on WGCNA

BACKGROUND

With the rapid development of genomics and molecular biology, not only have biochemical indicators been used as tumour markers, but many new molecular markers have emerged. Epigenetic abnormalities are a new type of molecular marker, and DNA methylation is an important part of epigenetics.

OBJECTIVE

This study used weighted gene coexpression network analysis (WGCNA) to analyse key methylation-driven genes in breast cancer.

METHODS

The RNA-seq transcriptome data, DNA methylation data, and clinical information data of breast cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database, and the MethylMix R package was used to screen methylation-driven genes in breast cancer. The ClusterProfiler package and enrichplot package in R software were used to further analyse the function and signalling pathway of methylation-driven genes. Through univariate and multivariate Cox regression analyses, methylation-driver genes related to prognostic were obtained, a prognostic model was constructed and prognostic characteristics were analysed.

RESULTS

The 17 methylation-driven genes related to prognosis were obtained by the WGCNA method in breast cancer, and the prognostic significance of these methylation-driven genes was determined by transcriptome and methylation combined survival analysis. Analysis of functions and signalling pathways showed that these genes were mainly enriched in biological processes and signalling pathway. Through univariate and multivariate Cox regression analyses, a prognostic model of 5 methylation-driven genes was constructed.

CONCLUSIONS

The AUC of the receiver operating characteristic (ROC) curve of this model was 0.784, showing that the model had a good prediction effect. Based on WGCNA screening, it was found that only CDO1 was the key methylation-driven gene for prognosis in breast cancer, indicating that CDO1 may be an important indicator of the prognosis of breast cancer patients.

Lantern: an integrative repository of functional annotations for lncRNAs in the human genome

BACKGROUND

With advancements in omics technologies, the range of biological processes where long non-coding RNAs (lncRNAs) are involved, is expanding extensively, thereby generating the need to develop lncRNA annotation resources. Although, there are a plethora of resources for annotating genes, despite the extensive corpus of lncRNA literature, the available resources with lncRNA ontology annotations are rare.

RESULTS

We present a lncRNA annotation extractor and repository (Lantern), developed using PubMed's abstract retrieval engine and NCBO's recommender annotation system. Lantern's annotations were benchmarked against lncRNAdb's manually curated free text. Benchmarking analysis suggested that Lantern has a recall of 0.62 against lncRNAdb for 182 lncRNAs and precision of 0.8. Additionally, we also annotated lncRNAs with multiple omics annotations, including predicted cis-regulatory TFs, interactions with RBPs, tissue-specific expression profiles, protein co-expression networks, coding potential, sub-cellular localization, and SNPs for ~ 11,000 lncRNAs in the human genome, providing a one-stop dynamic visualization platform.

CONCLUSIONS

Lantern integrates a novel, accurate semi-automatic ontology annotation engine derived annotations combined with a variety of multi-omics annotations for lncRNAs, to provide a central web resource for dissecting the functional dynamics of long non-coding RNAs and to facilitate future hypothesis-driven experiments. The annotation pipeline and a web resource with current annotations for human lncRNAs are freely available on sysbio.lab.iupui.edu/lantern.

Prognostic Value of Long Non-Coding RNA Long Intergenic Non-Protein Coding RNA 1586 in Pancreatic Cancer

This study investigated the prognostic value of the long non-coding RNA (lncRNA) LINC01586 in pancreatic cancer. Three pancreatic cancer patients who received pancreaticoduodenectomies in our department in January 2019 were retrospectively selected. Cancer tissue and adjacent tissue samples were collected for high-throughput sequencing of lncRNAs. Among them, 221 lncRNAs were up-regulated and 235 were down-regulated. The expression of LINC01586 was decreased in pancreatic cancer patients (logFC = -3.308). An additional 74 tissue specimens were collected from pancreatic cancer patients from January 2011 to December 2016 for low-throughput validation. Patient samples were categorized into overexpression and low expression groups, based on the median LINC01586 expression level. The LINC01586 low expression group exhibited larger tumor size than the overexpression group (P < 0.001), while the low expression group exhibited a lower cancer-related survival rate than the overexpression group (one-year cancer-related survival rate: 55.6% vs. 89.2%, P < 0.001). Further analysis confirmed that low expression of LINC01586 was associated with poor prognosis for pancreatic cancer patients (OR = 0.169, 95% CI 0.066-0.437, P = 0.000). KEGG signaling pathway analysis was used to enrich LINC01586 target genes, and were mainly related to two metabolic pathways: insulin secretion (P = 0.011) and dopaminergic synapses (P = 0.0129), with SNAP25 as the core gene. The expression of LINC01586 and SNAP25were positively correlated in pancreatic cancer (R = 0.81 and P < 0.001). Together, our results indicate that LINC01586 may be used as a biomarker for prognosis predictions in pancreatic cancer patients, and its low expression is associated with poor prognosis.

Long non-coding RNA (lncRNA) and epithelial-mesenchymal transition (EMT) in colorectal cancer: a systematic review

BACKGROUND

Colorectal cancer (CRC) is a leading cause of cancer-related death. Epithelial-mesenchymal transition (EMT) is a major process in tumor metastasis development. This systematic review aims to describe the role of long non-coding RNA (lncRNA) in EMT in CRC.

METHODS

The electronic databases, PubMed, Cochrane, and EMBASE, were searched from January1990 to June 2019 to identify studies examining lncRNA and their role in mediating EMT in CRC. Studies examining clinical specimens and/or in vitro experiments were included.

RESULTS

In 61 identified studies, 54 lncRNAs were increased in CRC compared to normal colorectal epithelium. Increased lncRNA expression was frequently associated with worse survival. Many lncRNAs mediate their effect through competitive endogenous RNA or transcription factor regulation. The ZEB1, 2/E-cadherin, Wnt/β-catenin signaling, and chromatin remodeling pathways are discussed in particular.

CONCLUSIONS

lncRNAs are major regulators of EMT and predictor adverse outcome in CRC patients. Future research must focus on delineating lncRNA function prior to potential clinical use.

CORAZON: a web server for data normalization and unsupervised clustering based on expression profiles

Objective

Data normalization and clustering are mandatory steps in gene expression and downstream analyses, respectively. However, user-friendly implementations of these methodologies are available exclusively under expensive licensing agreements, or in stand-alone scripts developed, reflecting on a great obstacle for users with less computational skills.

Results

We developed an online tool called CORAZON (Correlations Analyses Zipper Online), which implements three unsupervised learning methods to cluster gene expression datasets in a friendly environment. It allows the usage of eight gene expression normalization/transformation methodologies and the attribute’s influence. The normalizations requiring the gene length only could be performed to RNA-seq, meanwhile the others can be used with microarray and/or NanoString data. Clustering methodologies performances were evaluated through five models with accuracies between 92 and 100%. We applied our tool to obtain functional insights of non-coding RNAs (ncRNAs) based on Gene Ontology enrichment of clusters in a dataset generated by the ENCODE project. The clusters where the majority of transcripts are coding genes were enriched in Cellular, Metabolic, Transports, and Systems Development categories. Meanwhile, the ncRNAs were enriched in the Detection of Stimulus, Sensory Perception, Immunological System, and Digestion categories. CORAZON source-code is freely available at https://gitlab.com/integrativebioinformatics/corazon and the web-server can be accessed at http://corazon.integrativebioinformatics.me.

Unsupervised discovery of phenotype-specific multi-omics networks

MOTIVATION

Complex diseases often involve a wide spectrum of phenotypic traits. Better understanding of the biological mechanisms relevant to each trait promotes understanding of the etiology of the disease and the potential for targeted and effective treatment plans. There have been many efforts towards omics data integration and network reconstruction, but limited work has examined the incorporation of relevant (quantitative) phenotypic traits.

RESULTS

We propose a novel technique, sparse multiple canonical correlation network analysis (SmCCNet), for integrating multiple omics data types along with a quantitative phenotype of interest, and for constructing multi-omics networks that are specific to the phenotype. As a case study, we focus on miRNA-mRNA networks. Through simulations, we demonstrate that SmCCNet has better overall prediction performance compared to popular gene expression network construction and integration approaches under realistic settings. Applying SmCCNet to studies on chronic obstructive pulmonary disease (COPD) and breast cancer, we found enrichment of known relevant pathways (e.g. the Cadherin pathway for COPD and the interferon-gamma signaling pathway for breast cancer) as well as less known omics features that may be important to the diseases. Although those applications focus on miRNA-mRNA co-expression networks, SmCCNet is applicable to a variety of omics and other data types. It can also be easily generalized to incorporate multiple quantitative phenotype simultaneously. The versatility of SmCCNet suggests great potential of the approach in many areas.

AVAILABILITY AND IMPLEMENTATION

The SmCCNet algorithm is written in R, and is freely available on the web at https://cran.r-project.org/web/packages/SmCCNet/index.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification and Validation of Novel Long Non-coding RNA Biomarkers for Early Diagnosis of Oral Squamous Cell Carcinoma

Long non-coding RNAs (lncRNAs) are recently emerging as a novel promising biomarker for cancer diagnosis and prognosis. Despite these previous investigations, the expression pattern and diagnostic role of lncRNAs in oral squamous cell carcinoma (OSCC) remain unclear. In this study, we identified six novel lncRNA biomarkers (LINC01697, LINC02487, LOC105376575, AC005083.1, SLC8A1-AS1, and U62317.1) from a list of 29 differentially expressed lncRNAs using the least absolute shrinkage and selection operator (LASSO) method in the discovery dataset of 167 OSCC samples and 45 normal oral tissues. Using the logistic regression method, we constructed a six lncRNAs-based diagnostic risk model (6lncRNAScore) which was able to differentiate between OSCC samples and control samples with high performance with AUC of 0.995 and high diagnostic specificity of 88.9% and sensitivity of 98.2% in the discovery dataset. The diagnostic performance of the 6lncRNAScore was further validated in another two independent OSCC dataset with AUC of 0.968 and 1.0. Functional enrichment analysis for lncRNA biomarkers-related mRNAs suggested that lncRNAs biomarkers tended to be involved in the lipid metabolic process. Together, our study highlighted the importance of lncRNAs in OSCC and demonstrated the utility of lncRNA expression as a promising biomarker for early diagnosis of OSCC.

Recounting the FANTOM CAGE-Associated Transcriptome

Long noncoding RNAs (lncRNAs) have emerged as key coordinators of biological and cellular processes. Characterizing lncRNA expression across cells and tissues is key to understanding their role in determining phenotypes, including human diseases. We present here FC-R2, a comprehensive expression atlas across a broadly defined human transcriptome, inclusive of over 109,000 coding and noncoding genes, as described in the FANTOM CAGE-Associated Transcriptome (FANTOM-CAT) study. This atlas greatly extends the gene annotation used in the original recount2 resource. We demonstrate the utility of the FC-R2 atlas by reproducing key findings from published large studies and by generating new results across normal and diseased human samples. In particular, we (a) identify tissue-specific transcription profiles for distinct classes of coding and noncoding genes, (b) perform differential expression analysis across thirteen cancer types, identifying novel noncoding genes potentially involved in tumor pathogenesis and progression, and (c) confirm the prognostic value for several enhancer lncRNAs expression in cancer. Our resource is instrumental for the systematic molecular characterization of lncRNA by the FANTOM6 Consortium. In conclusion, comprised of over 70,000 samples, the FC-R2 atlas will empower other researchers to investigate functions and biological roles of both known coding genes and novel lncRNAs.

Computational models for lncRNA function prediction and functional similarity calculation

From transcriptional noise to dark matter of biology, the rapidly changing view of long non-coding RNA (lncRNA) leads to deep understanding of human complex diseases induced by abnormal expression of lncRNAs. There is urgent need to discern potential functional roles of lncRNAs for further study of pathology, diagnosis, therapy, prognosis, prevention of human complex disease and disease biomarker detection at lncRNA level. Computational models are anticipated to be an effective way to combine current related databases for predicting most potential lncRNA functions and calculating lncRNA functional similarity on the large scale. In this review, we firstly illustrated the biological function of lncRNAs from five biological processes and briefly depicted the relationship between mutations or dysfunctions of lncRNAs and human complex diseases involving cancers, nervous system disorders and others. Then, 17 publicly available lncRNA function-related databases containing four types of functional information content were introduced. Based on these databases, dozens of developed computational models are emerging to help characterize the functional roles of lncRNAs. We therefore systematically described and classified both 16 lncRNA function prediction models and 9 lncRNA functional similarity calculation models into 8 types for highlighting their core algorithm and process. Finally, we concluded with discussions about the advantages and limitations of these computational models and future directions of lncRNA function prediction and functional similarity calculation. We believe that constructing systematic functional annotation systems is essential to strengthen the prediction accuracy of computational models, which will accelerate the identification process of novel lncRNA functions in the future.

Inferring disease and pathway associations of long non-coding RNAs using heterogeneous information network model

Recent findings from biological experiments demonstrate that long non-coding RNAs (lncRNAs) are actively involved in critical cellular processes and are associated with innumerable diseases. Computational prediction of lncRNA-disease association draws tremendous research attention nowadays. This paper proposes a machine learning model that predicts lncRNA-disease associations using Heterogeneous Information Network (HIN) of lncRNAs and diseases. A Support Vector Machine classifier is developed using the feature set extracted from a meta-path-based parameter, Association Index derived from the HIN. Performance of the model is validated using standard statistical metrics and it generated an AUC value of 0.87, which is better than the existing methods in the literature. Results are further validated using the recent literature and many of the predicted lncRNA-disease associations are identified as actually existing. This paper also proposes an HIN-based methodology to associate lncRNAs with pathways in which they may have biological influence. A case study on the pathway associations of four well-known lncRNAs (HOTAIR, TUG1, NEAT1, and MALAT1) has been conducted. It has been observed that many times the same lncRNA is associated with more than one biologically related pathways. Further exploration is needed to substantiate whether such lncRNAs have any role in determining the pathway interplay. The script and sample data for the model construction is freely available at http://bdbl.nitc.ac.in/LncDisPath/index.html.

miR-92b-3p Functions As A Key Gene In Esophageal Squamous Cell Cancer As Determined By Co-Expression Analysis

Background

Esophageal squamous cell carcinoma (ESCC) is a highly aggressive malignancy. The aims of the present study were to screen the critical miRNA and corresponding target genes that related to development of ESCC by weighted gene correlation network analysis (WGCNA) and investigate the functions by experimental validation.

Methods

Datasets of mRNA and miRNA expression data were downloaded from GEO. The R software was used for data preprocessing and differential expression gene analysis. The differentially expressed protein-coding genes (DEGs) and miRNAs (DEMs) were selected (FDR <0.05 or |Fold Change (FC)| >1.5). Meanwhile, 81 expression data of ESCC patients in TCGA combined with clinic information were applied by WGCNA to create networks. The correlational analyses between each module and clinical parameters were conducted, and enrichment analyses of GO and KEGG were subsequently performed. Then, a series of experiments were conducted in ESCC cells by use of miRNA mimics.

Results

In total, 4,023 DEGs and 328 DEMs were screened. After checking good genes and samples, 3,841 genes (3,696 DEGs and 145 DEMs) were used for WGCNA. As a consequence, altogether 11 gene modules were found. Among them, the brown modules were found to be strongly inversely associated with pathological grade. Meanwhile, has-mir-92b, the only miRNA in brown module, had a positive correlation with grade and negatively correlated with potential target gene (KFL4 and DCS2) in the same module. Furthermore, an increased expression of miR-92b-3p and down-regulated KLF4 and DSC2 protein was detected in the ESCC clinical samples. Up-regulated miR-92b-3p shortened G0/G1 phase and promote ESCC cells invasion and migration. Furthermore, we verified that DSC2 and KFL4 was target genes of miR-92b-3p by luciferase report assay.

Conclusion

WGCNA is an efficient approach to system biology. By this procedure, miR-92b-3p was identified as an ESCC-promoting gene by target KLF4 and DCS2.

Functional impact of the long non-coding RNA MEG3 deletion by CRISPR/Cas9 in the human triple negative metastatic Hs578T cancer cell line

Long non-coding RNAs (lncRNAs) serve critical roles in regulating cellular homeostasis, and their deregulated expression/activity is associated with neoplastic transformation. The maternally expressed gene 3 (MEG3) has been extensively described as a tumor suppressor gene in different types of cancer, including breast cancer. Interestingly, using a panel of seven different breast cancer cell lines, the present study revealed that MEG3 is highly expressed in the triple negative metastatic human Hs578T breast cancer cell line, which is refractory to different therapeutic approaches. Therefore, the present study aimed to investigate the phenotypic impact of MEG3 deletion in this cell line. Using the CRISPR/Cas9 system, complete knockout (KO) of MEG3 was achieved. Deletion was confirmed by genomic PCR and reverse transcription-quantitative PCR. The MEG3_KO cell population displaying the highest efficiency of genomic editing was selected for phenotypic in vitro assays, including wound scratch and Transwell assays, flow cytometry and immunofluorescence. The results demonstrated that MEG3 deletion increased cell proliferation, anchorage-independent cell growth and cell motility, which was consistent with its well-known tumor suppressor function. However, the present study revealed that MEG3_KO also lead to decreased cell invasiveness ability, supporting previous evidence that MEG3 modulates epithelial-to-mesenchymal inducing factors. The present study demonstrated that deletion of MEG3 promoted an increase in transforming growth factor β and N-cadherin protein levels and significant reduction in matrix metallopeptidase 2, zinc-finger E-box binding homeobox 1 and collagen type III α1 chain gene expression levels. Additionally, MEG3_KO cells displayed significant resistance to doxorubicin treatment, demonstrating the role of this lncRNA in cancer cell survival by regulating apoptosis. The present study highlighted the utility of CRISPR/Cas9 for anticancer studies of intergenic lncRNAs and demonstrated that, although Hs578T cells express MEG3 at high levels, these cells display mechanisms to escape the growth suppression effects of this lncRNA. Notably, the detailed pathological mechanisms of MEG3 concerning tumor metastasis remain to be elucidated prior to applying MEG3 expression/activation in future therapeutic approaches for breast cancer treatment.

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dysregulated in tumors, but only a handful are known to play pathophysiological roles in cancer. We inferred lncRNAs that dysregulate cancer pathways, oncogenes, and tumor suppressors (cancer genes) by modeling their effects on the activity of transcription factors, RNA-binding proteins, and microRNAs in 5,185 TCGA tumors and 1,019 ENCODE assays. Our predictions included hundreds of candidate onco- and tumor-suppressor lncRNAs (cancer lncRNAs) whose somatic alterations account for the dysregulation of dozens of cancer genes and pathways in each of 14 tumor contexts. To demonstrate proof of concept, we showed that perturbations targeting OIP5-AS1 (an inferred tumor suppressor) and TUG1 and WT1-AS (inferred onco-lncRNAs) dysregulated cancer genes and altered proliferation of breast and gynecologic cancer cells. Our analysis indicates that, although most lncRNAs are dysregulated in a tumor-specific manner, some, including OIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergistically dysregulate cancer pathways in multiple tumor contexts.

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Hundreds of lncRNAs target cancer genes and pathways in each tumor context

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lncRNA copy numbers are predictive of target cancer gene dysregulation

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Most lncRNAs are predicted to be transcriptional or post-transcriptional specialists

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lncRNAs are predicted to synergistically regulate proliferation pathways in cancer

A nomogram based on 9-lncRNAs signature for improving prognostic prediction of clear cell renal cell carcinoma

Background

Abnormal expressions of long noncoding RNAs (lncRNAs) are very common in clear cell renal cell carcinoma (ccRCC), and some of these have been reported to be highly correlated with prognosis of ccRCC patients.

Methods

“edgeR” AND “DEseq” R packages were used to explore differentially expressed genes (DEGs) between normal and tumor tissues of ccRCC samples from The Cancer Genome Atlas (TCGA). Univariable Cox survival analysis, robust likelihood-based survival model and multivariable Cox regression analysis were used to identify prognostic lncRNAs and construct lncRNAs signature. Finally, a graphic nomogram based on the lncRNAs signature was developed to predict 1-, 3- and 5-year survival probability of ccRCC patients by using rms R package.

Results

8413 DEGs including 2740 lncRNAs and 4530 mRNAs were identified between normal and tumor tissues. 395 lncRNAs were found to be associated with prognosis of ccRCC patients (P < 0.05). Among these 395 prognostic lncRNAs, 9 key prognostic lncRNAs (RP13-463N16.6, CTD-2201E18.5, RP11-430G17.3, AC005785.2, RP11-2E11.9, TFAP2A-AS1, RP11-133F8.2, RP11-297L17.2 and RP11-348J24.2) were identified by using robust likelihood-based survival model. A 9-lncRNAs signature was constructed by using estimated regression coefficients of the 9 key prognostic lncRNAs. Results of χ²-test or Fisher’s exact test indicated that the 9-lncRNAs signature was significantly associated with clinicopathological characteristics such as tumor grade, T stage, N stage, M stage, TNM stage and survival outcome of ccRCC patients. Multivariate analysis showed that the 9-lncRNAs signature, age and M stage were independent prognostic factors. Finally, a graphic nomogram based on the lncRNAs signature, age and M stage was developed to predict 1-, 3- and 5-year survival probability of ccRCC patients by using rms R package.

Conclusions

A 9-lncRNAs signature associated with prognosis of ccRCC patients was constructed and a promising prognostic nomogram based on the 9-lncRNAs signature was developed for 1-, 3- and 5-year OS prediction of ccRCC patients in this study.

Whole transcriptome analysis reveals correlation of long noncoding RNA ZEB1-AS1 with invasive profile in melanoma

Melanoma is the deadliest form of skin cancer, and little is known about the impact of deregulated expression of long noncoding RNAs (lncRNAs) in the progression of this cancer. In this study, we explored RNA-Seq data to search for lncRNAs associated with melanoma progression. We found distinct lncRNA gene expression patterns across melanocytes, primary and metastatic melanoma cells. Also, we observed upregulation of the lncRNA ZEB1-AS1 (ZEB1 antisense RNA 1) in melanoma cell lines. Data analysis from The Cancer Genome Atlas (TCGA) confirmed higher ZEB1-AS1 expression in metastatic melanoma and its association with hotspot mutations in BRAF (B-Raf proto-oncogene, serine/threonine kinase) gene and RAS family genes. In addition, a positive correlation between ZEB1-AS1 and ZEB1 (zinc finger E-box binding homeobox 1) gene expression was verified in primary and metastatic melanomas. Using gene expression signatures indicative of invasive or proliferative phenotypes, we found an association between ZEB1-AS1 upregulation and a transcriptional profile for invasiveness. Enrichment analysis of correlated genes demonstrated cancer genes and pathways associated with ZEB1-AS1. We suggest that the lncRNA ZEB1-AS1 could function by activating ZEB1 gene expression, thereby influencing invasiveness and phenotype switching in melanoma, an epithelial-to-mesenchymal transition (EMT)-like process, which the ZEB1 gene has an essential role.

Survival analysis and functional annotation of long non-coding RNAs in lung adenocarcinoma

Long non-coding RNAs (lncRNAs) are a subclass of non-protein coding transcripts that are involved in several regulatory processes and are considered as potential biomarkers for almost all cancer types. This study aims to investigate the prognostic value of lncRNAs for lung adenocarcinoma (LUAD), the most prevalent subtype of lung cancer. To this end, the processed data of The Cancer Genome Atlas LUAD were retrieved from GEPIA and circlncRNAnet databases, matched with each other and integrated with the analysis results of a non-small cell lung cancer plasma RNA-Seq study. Then, the data were filtered in order to separate the differentially expressed lncRNAs that have a prognostic value for LUAD. Finally, the selected lncRNAs were functionally annotated using a bioinformatic and systems biology approach. Accordingly, we identified 19 lncRNAs as the novel LUAD prognostic lncRNAs. Also, based on our results, all 19 lncRNAs might be involved in lung cancer-related biological processes. Overall, we suggested several novel biomarkers and drug targets which could help early diagnosis, prognosis and treatment of LUAD patients.

Syndecan-4 involves in the pathogenesis of rheumatoid arthritis by regulating the inflammatory response and apoptosis of fibroblast-like synoviocytes

Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease, and the pathogenesis of RA is still unknown. Rheumatoid arthritis fibroblast-like synoviocytes (RA-FLSs) are of significance in the pathogenesis of RA. In this study, three microarray profiles (GSE55457, GSE55584, and GSE55235) of human joint FLSs from 33 RA patients and 20 normal controls were extracted from the Gene Expression Omnibus Dataset and analyzed to investigate the underlying pathogenesis of RA. As analyzed by the differently expressed genes, gene ontology, Kyoto Encyclopedia of Genes and Genomes pathway enrichment, and protein-protein interaction network analysis, syndecan-4 (SDC4), a receptor of multiple cytokines and chemokines, which played a key role in the regulation of inflammatory response, was found to be an essential regulator in RA. To further validate these results, the levels of SDC4, reactive oxygen species (ROS), nitric oxide (NO), inflammation, and apoptosis in RA-FLSs were examined. SDC4-silenced RA-FLSs were also used. The results demonstrated that SDC4 and the level of ROS, NO, and inflammation were highly expressed while the apoptosis was decreased in RA-FLSs compared with normal FLSs. SDC4 silencing significantly suppressed the levels of ROS, NO, and inflammation; elevated the expression of nuclear factor erythroid 2-related factor 2; and promoted the apoptosis of RA-FLSs. Collectively, our results demonstrated a new mechanism of SDC4 in initiating the inflammation and inhibiting the apoptosis of RA-FLSs and that a potential target for the diagnosis and treatment of RA in the clinic might be developed.

Detection of novel biomarkers for early detection of Non-Muscle-Invasive Bladder Cancer using Competing Endogenous RNA network analysis

Bladder Cancer (BC) is one of the most common cancers in the world. Recent studies show that non-coding RNAs such as lncRNAs and circRNAs play critical roles in the progression of this cancer, but their regulatory relationships and functions are still largely unknown. As a new regulatory process within the cell, the coding and non-coding RNAs compete with each other to sponge their target miRNAs. This mechanism is described as "the competing endogenous RNA (ceRNA) hypothesis" which provides a new perspective to understand the regulation of gene expression in health and diseases such as cancer. In this study, to investigate the role of non-coding RNAs in BC, a new approach was used to reconstruct the ceRNA network for Non-Muscle Invasive Bladder Cancer (NMIBC) based on the expression data of coding and non-coding genes. Analysis of ceRNA networks in the early stage of BC led to the detection of an important module containing the lncRNA MEG3 as the central gene. The results show that the lncRNAs CARMN, FENDRR and ADAMTS9-AS2 may regulate MEG3 in NMIBC through sponging some important miRNAs such as miR-143-3p, miR-106a-5p and miR-34a-3p. Also, the lncRNA AC007608.2 is shown to be a potential BC related lncRNA for the first time based on ceRNA stage-specific network analysis. Furthermore, hub and altered genes in stage-specific and between stage networks led to the detection of hsa_circ_0017586 and hsa_circ_0001741 as novel potential circRNAs related to NMIBC. Finally, the hub genes in the networks were shown to be valuable candidates as biomarkers for the early stage diagnosis of BC.

Identification of lncRNAs associated with early-stage breast cancer and their prognostic implications

Breast cancer is the most common malignancy among women, with the highest incidence rate worldwide. Dysregulation of long noncoding RNAs during the preliminary stages of breast carcinogenesis is poorly understood. In this study, we performed RNA sequencing to identify long noncoding RNA expression profiles associated with early-stage breast cancer. RNA sequencing was performed on six invasive ductal carcinoma (IDC) tissues along with paired normal tissue samples, seven ductal carcinoma in situ tissues, and five apparently normal breast tissues. We identified 375 differentially expressed lncRNAs (DElncRNAs) in IDC tissues compared to paired normal tissues. Antisense transcripts (~ 58%) were the largest subtype among DElncRNAs. About 20% of the 375 DElncRNAs were supported by typical split readings leveraging their detection confidence. Validation was performed in n = 52 IDC and paired normal tissue by qRT-PCR for the identified targets (ADAMTS9-AS2, EPB41L4A-AS1, WDFY3-AS2, RP11-295M3.4, RP11-161M6.2, RP11-490M8.1, CTB-92J24.3, and FAM83H-AS1). We evaluated the prognostic significance of DElncRNAs based on TCGA datasets and report that overexpression of FAM83H-AS1 was associated with patient poor survival. We confirmed that the downregulation of ADAMTS9-AS2 in breast cancer was due to promoter hypermethylation through in vitro silencing experiments and pyrosequencing.

Long noncoding RNA CCAT2 promotes hepatocellular carcinoma proliferation and metastasis through up-regulation of NDRG1

BACKGROUND

Emerging studies demonstrate that long noncoding RNAs (lncRNAs) play crucial roles in hepatocarcinogenesis through various mechanisms. LncRNA CCAT2 was a newly discovered lncRNA and amplified in several cancers. However, the mechanisms involved in function of CCAT2 in hepatocellular carcinoma (HCC) remain to be explored.

METHODS

CCAT2 expressions in HCC tissues and cell lines were measured by RT-qPCR. MTS assay, colony formation assay, wound-healing assay and transwell assay were used to explore the biological functions of CCAT2 on HCC cells proliferation and metastasis. Experiments in vivo were carried out to confirm these effects. The underlying mechanisms were analyzed by western blot and dual-luciferase reporter assay.

RESULTS

In this study, we found that CCAT2 were significantly elevated in HCC tissues and cell lines, and it promoted HCC cells proliferation and metastasis both in vitro and in vivo. Additionally, we identified that NDRG1 was a downstream target of CCAT2. Meanwhile, depletion of CCAT2 inhibited cellular proliferation and metastasis behaviors induced by NDRG1- overexpression. Analysis of mechanism underlying these effects revealed that CCAT2 increased the expression of NDRG1 by enhancing its promoter activity. Furthermore, the active region between CCAT2 and NDRG1 promoter was confirmed by dual-luciferase reporter assay.

CONCLUSIONS

All these observations demonstrate that CCAT2 acts as an oncogene by up-regulating NDRG1, which may have the potential to be used as a promising prognostic biomarker and therapeutic target for HCC.

Systematical Identification of Breast Cancer-Related Circular RNA Modules for Deciphering circRNA Functions Based on the Non-Negative Matrix Factorization Algorithm

Circular RNA (circRNA), a kind of special endogenous RNA, has been shown to be implicated in crucial biological processes of multiple cancers as a gene regulator. However, the functional roles of circRNAs in breast cancer (BC) remain to be poorly explored, and relatively incomplete knowledge of circRNAs handles the identification and prediction of BC-related circRNAs. Towards this end, we developed a systematic approach to identify circRNA modules in the BC context through integrating circRNA, mRNA, miRNA, and pathway data based on a non-negative matrix factorization (NMF) algorithm. Thirteen circRNA modules were uncovered by our approach, containing 4164 nodes (80 circRNAs, 2703 genes, 63 miRNAs and 1318 pathways) and 67,959 edges in total. GO (Gene Ontology) function screening identified nine circRNA functional modules with 44 circRNAs. Within them, 31 circRNAs in eight modules having direct relationships with known BC-related genes, miRNAs or disease-related pathways were selected as BC candidate circRNAs. Functional enrichment results showed that they were closely related with BC-associated pathways, such as ‘KEGG (Kyoto Encyclopedia of Genes and Genomes) PATHWAYS IN CANCER’, ‘REACTOME IMMUNE SYSTEM’ and ‘KEGG MAPK SIGNALING PATHWAY’, ‘KEGG P53 SIGNALING PATHWAY’ or ‘KEGG WNT SIGNALING PATHWAY’, and could sever as potential circRNA biomarkers in BC. Comparison results showed that our approach could identify more BC-related functional circRNA modules in performance. In summary, we proposed a novel systematic approach dependent on the known disease information of mRNA, miRNA and pathway to identify BC-related circRNA modules, which could help identify BC-related circRNAs and benefits treatment and prognosis for BC patients.

PLAIDOH: a novel method for functional prediction of long non-coding RNAs identifies cancer-specific LncRNA activities

BACKGROUND

Long non-coding RNAs (lncRNAs) exhibit remarkable cell-type specificity and disease association. LncRNA's functional versatility includes epigenetic modification, nuclear domain organization, transcriptional control, regulation of RNA splicing and translation, and modulation of protein activity. However, most lncRNAs remain uncharacterized due to a shortage of predictive tools available to guide functional experiments.

RESULTS

To address this gap for lymphoma-associated lncRNAs identified in our studies, we developed a new computational method, Predicting LncRNA Activity through Integrative Data-driven 'Omics and Heuristics (PLAIDOH), which has several unique features not found in other methods. PLAIDOH integrates transcriptome, subcellular localization, enhancer landscape, genome architecture, chromatin interaction, and RNA-binding (eCLIP) data and generates statistically defined output scores. PLAIDOH's approach identifies and ranks functional connections between individual lncRNA, coding gene, and protein pairs using enhancer, transcript cis-regulatory, and RNA-binding protein interactome scores that predict the relative likelihood of these different lncRNA functions. When applied to 'omics datasets that we collected from lymphoma patients, or to publicly available cancer (TCGA) or ENCODE datasets, PLAIDOH identified and prioritized well-known lncRNA-target gene regulatory pairs (e.g., HOTAIR and HOX genes, PVT1 and MYC), validated hits in multiple lncRNA-targeted CRISPR screens, and lncRNA-protein binding partners (e.g., NEAT1 and NONO). Importantly, PLAIDOH also identified novel putative functional interactions, including one lymphoma-associated lncRNA based on analysis of data from our human lymphoma study. We validated PLAIDOH's predictions for this lncRNA using knock-down and knock-out experiments in lymphoma cell models.

CONCLUSIONS

Our study demonstrates that we have developed a new method for the prediction and ranking of functional connections between individual lncRNA, coding gene, and protein pairs, which were validated by genetic experiments and comparison to published CRISPR screens. PLAIDOH expedites validation and follow-on mechanistic studies of lncRNAs in any biological system. It is available at https://github.com/sarahpyfrom/PLAIDOH .

Workflow Development for the Functional Characterization of ncRNAs

During the last decade, ncRNAs have been investigated intensively and revealed their regulatory role in various biological processes. Worldwide research efforts have identified numerous ncRNAs and multiple RNA subtypes, which are attributed to diverse functionalities known to interact with different functional layers, from DNA and RNA to proteins. This makes the prediction of functions for newly identified ncRNAs challenging. Current bioinformatics and systems biology approaches show promising results to facilitate an identification of these diverse ncRNA functionalities. Here, we review (a) current experimental protocols, i.e., for Next Generation Sequencing, for a successful identification of ncRNAs; (b) sequencing data analysis workflows as well as available computational environments; and (c) state-of-the-art approaches to functionally characterize ncRNAs, e.g., by means of transcriptome-wide association studies, molecular network analyses, or artificial intelligence guided prediction. In addition, we present a strategy to cover the identification and functional characterization of unknown transcripts by using connective workflows.

Upregulation of Long Non-Coding RNA DRAIC Correlates with Adverse Features of Breast Cancer

DRAIC (also known as LOC145837 and RP11-279F6.1), is a long non-coding RNA associated with several types of cancer including prostate cancer, lung cancer, and breast cancer. Its expression is elevated in tumor tissues compared to adjacent benign tissues in breast cancer patients and is regulated by estrogen treatment in breast cancer cells. In addition, expression analysis of DRAIC in more than 100 cell lines showed that DRAIC expression is high in luminal and basal subtypes compared to claudin low subtype, suggesting a prognostic value of DRAIC expression in breast cancer. In the present study, we analyzed DRAIC expression in 828 invasive breast carcinomas and 105 normal samples of RNA sequencing datasets from The Cancer Genome Atlas (TCGA) and found that DRAIC expression was correlated with estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status, and is increased in cancerous tissues. Additionally, higher DRAIC expression was associated with poorer survival of patients, especially in ER positive breast cancer. DRAIC was also investigated in the Oncomine database and we found that DRAIC expression predicted patients’ response to paclitaxel and FEC as well as lapatinib, which are commonly used therapy options for breast cancer. Finally, DRAIC expression in breast cancer was negatively correlated with immune cell infiltration. These results reinforce the importance of DRAIC in breast cancer.

Long non‑coding RNA MLK7‑AS1 promotes proliferation in human colorectal cancer via downregulation of p21 expression

Current studies have highlighted long non‑coding RNAs (lncRNAs) as critical regulators in various cancers, including colorectal cancer (CRC). By utilizing publicly available data from The Cancer Genome Atlas dataset, MLK7 antisense RNA 1 (MLK7‑AS1) was identified as a novel lncRNA that correlated with CRC progression. The results of reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR) revealed a significant upregulation of MLK7‑AS1 in both CRC tissue samples and cell lines. In addition, a positive correlation was observed between increased MLK7‑AS1 expression and several clinicopathological factors in patients with CRC. Importantly, MLK7‑AS1 knockdown suppressed CRC cell proliferation and promoted G1/G0 phase arrest and apoptosis in vitro, whereas MLK7‑AS1 overexpression exhibited opposite effects. Consistently, decreased MLK7‑AS1 expression inhibited tumor growth in vivo. Furthermore, RT‑qPCR and western blot assays revealed that p21 may be a potential downstream target of MLK7‑AS1. To the best of the authors' knowledge, this is the first study to report that MLK7‑AS1 has potential as a biomarker and may promote proliferation in CRC partially through downregulating p21 expression.

Comparison of rheumatoid arthritis (RA) and osteoarthritis (OA) based on microarray profiles of human joint fibroblast-like synoviocytes

The purpose of the present study was to investigate the underlying molecular mechanism of osteoarthritis (OA) and rheumatoid arthritis (RA) based on microarray profiles. Three human joint fibroblast-like synoviocytes (FLSs) microarray profiles including 26 OA samples, 33 RA samples, and 20 healthy control (HC) samples were downloaded from the GEO database. Differentially expressed genes (DEGs) between OA and HC (DEGsOA) and RA and HC (DEGsRA) were identified. Co-expressed and specific genes were analysed between DEGsOA and DEGsRA. Gene ontology, KEGG pathway enrichment, PPI network, and GSEA were performed to predict the function of DEGs. Two hundred seventy-six and 410 differential genes in DEGsOA and DEGsRA were observed. One hundred fifty coexpressed genes and 126 OA-specific genes (SELE, SERPINE1, and NFKBIA were the key genes) between DEGsOA and DEGsRA were enriched in the tumour necrosis factor (TNF) signalling pathway. However, 260 RA-specific genes of which the key genes were CCR5, CCR7, CXCR4, CCL5, and CCR4 were enriched in chemokine signalling pathway. Therefore, FLSs might exert an inflammatory effect by regulating TNF signalling pathway, targeting SELE, SERPINE1, and NFKBIA during the process of OA. Although TNF signalling pathway was also involved in the synovitis of RA, chemokine signalling pathway played the key role in RA FLSs mediating cell migration, invasion, and release of chemotaxis. In addition, CCR5, CCR7, CXCR4, CCL5, and CCR4 might be hub genes in RA. The different biomarkers and pathways identified in OA and RA may provide references for further study. SIGNIFICANCE OF THE STUDY: This study revealed the similar and different mechanisms of FLSs and different biomarkers that might with important regulatory effects on RA and OA. In OA, FLSs played an inflammatory role through TNF signalling pathway, targeting SELE, SERPINE1, and NFKBIA. Although TNF signalling pathway was also involved in the synovitis of RA, chemokine signalling pathway was a crucial pathway in mediating FLSs migration, invasion, and release of chemotaxis. CCR5, CCR7, CXCR4, CCL5, and CCR4 might be keys genes in RA. We expect that our results will bring more comprehensively understanding between RA and OA for researchers.

si-TP73-AS1 suppressed proliferation and increased the chemotherapeutic response of GC cells to cisplatin

Previous studies have revealed that long noncoding RNAs (lncRNAs) function as crucial regulators in various biological processes, including tumorigenesis. Although the expression of lncRNA TP73-antisense RNA1 (AS1) has been identified in hepatocellular carcinoma and glioma, the biological function of TP73-AS1 in gastric cancer (GC) remains unclear. Thus, the present study employed a comprehensive analysis on the function of lncRNA TP73-AS1 in GC. The aim of the present study was to determine the clinical significance and biological function of TP73-AS1 in human GC tissues and cells. Additionally, the expression of TP73-AS1 was increased in GC tissues and cell lines and increased expression level of TP73-AS1 was associated with poor prognosis in patients with GC. Functional assays revealed that silencing of TP73-AS1 may suppress cell proliferation and enhance the chemotherapeutic response of GC cells to cisplatin through targeting the high mobility group 1/receptor for advanced glycation endproducts signaling pathway. Collectively, the results of the present study demonstrated that TP73-AS1 may be a novel lncRNA for the clinical prognosis of GC and a potential therapeutic target for the treatment of GC.