Long noncoding RNA profiles reveal three molecular subtypes in glioma

High LYRM4-AS1 predicts poor prognosis in patients with glioma and correlates with immune infiltration

Background

Many researches proved that non-coding RNAs are important in glioma development. We screened the differentially expressed genes through The Cancer Genome Atlas (TCGA) database and identified the molecule LYRM4-AS1 associated with prognosis. As a lncRNA, the expression level and role of LYRM4-AS1 in glioma are inconclusive. Therefore, we attempted to assess the clinical significance, expression and related mechanisms of LYRM4-AS1 in glioma by employing cell experiments and an integrative in silico methodology.

Methods

RNA-seq data were obtained from UCSC XENA and TCGA datasets. The Gene Expression Omnibus (GEO) database was used to download glioma-related expression profile data. The LYRM4-AS1 expression level was evaluated. Survival curves were constructed by the Kaplan-Meier method. Cox regression analysis was used to analyze independent variables. Patients were divided into high and low expression group base on the median LYRM4-AS1 expression value in glioma tissues. The DESeq2 R package was used to identify differentially expressed genes (DEGs) between two different expression LYRM4-AS1 groups. Gene set enrichment analysis (GSEA) was conducted. Next, the single-sample Gene Set Enrichment Analysis (ssGSEA) was done to quantify the immune infiltration of immune cells in glioma tissues. Gene expression profiles for glioma tumor tissues were used to quantify the relative enrichment score for each immune cell. Spearman correlation analysis was used to analyze the correlation between LYRM4-AS1 and biomarkers of immune cells as well as immune checkpoints in glioma. Finally, assays for cell apoptosis, cell viability and wound healing were conducted to evaluate the function on U87 MG and U251 cells after knocking down LYRM4-AS1.

Results

We found that LYRM4-AS1 was upregulated and related to the grade and malignancy of glioma. Survival analyses showed that high expression LYRM4-AS1 patients had poor clinical outcomes (P < 0.01). Cox regression analyses demonstrated that LYRM4-AS1 was an independent risk factor for overall survival (OS) in glioma (HR: 274 1.836; CI [1.278-2.639]; P = 0.001). Enrichment and immune infiltration analysis showed interferon signaling and cytokine-cytokine receptor interaction enriched in the LYRM4-AS1 high-expression phenotype, and LYRM4-AS1 showed significantly positively related to immune infiltration as well as immune checkpoints (P < 0.01). The knockdown of LYRM4-AS1 in U87 MG and U251 cells can inhibit migration and proliferation of cells (P < 0.05).

Conclusions

These findings indicated that the increased LYRM4-AS1 may be useful for the diagnosis and prognosis of glioma and might participate in the immune infiltration.

Integrative analysis of a novel super-enhancer-associated lncRNA prognostic signature and identifying LINC00945 in aggravating glioma progression

BACKGROUND

Super-enhancers (SEs), driving high-level expression of genes with tumor-promoting functions, have been investigated recently. However, the roles of super-enhancer-associated lncRNAs (SE-lncRNAs) in tumors remain undetermined, especially in gliomas. We here established a SE-lncRNAs expression-based prognostic signature to choose the effective treatment of glioma and identify a novel therapeutic target.

METHODS

Combined analysis of RNA sequencing (RNA-seq) data and ChIP sequencing (ChIP-seq) data of glioma patient-derived glioma stem cells (GSCs) screened SE-lncRNAs. Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets served to construct and validate SE-lncRNA prognostic signature. The immune profiles and potential immuno- and chemotherapies response prediction value of the signature were also explored. Moreover, we verified the epigenetic activation mechanism of LINC00945 via the ChIP assay, and its effect on glioma was determined by performing the functional assay and a mouse xenograft model.

RESULTS

6 SE-lncRNAs were obtained and identified three subgroups of glioma patients with different prognostic and clinical features. A risk signature was further constructed and demonstrated to be an independent prognostic factor. The high-risk group exhibited an immunosuppressive microenvironment and was higher enrichment of M2 macrophage, regulatory T cells (Tregs), and Cancer-associated fibroblasts (CAFs). Patients in the high-risk group were better candidates for immunotherapy and chemotherapeutics. The SE of LINC00945 was further verified via ChIP assay. Mechanistically, BRD4 may mediate epigenetic activation of LINC00945. Additionally, overexpression of LINC00945 promoted glioma cell proliferation, EMT, migration, and invasion in vitro and xenograft tumor formation in vivo.

CONCLUSION

Our study constructed the first prognostic SE-lncRNA signature with the ability to optimize the choice of patients receiving immuno- and chemotherapies and provided a potential therapeutic target for glioma.

Systematic Investigation of the Diagnostic and Prognostic Impact of LINC01087 in Human Cancers

(1) Background: Long non-coding RNAs may constitute epigenetic biomarkers for the diagnosis, prognosis, and therapeutic response of a variety of tumors. In this context, we aimed at assessing the diagnostic and prognostic value of the recently described long intergenic non-coding RNA 01087 (LINC01087) in human cancers. (2) Methods: We studied the expression of LINC01087 across 30 oncological indications by interrogating public resources. Data extracted from the TCGA and GTEx databases were exploited to plot receiver operating characteristic curves (ROC) and determine the diagnostic performance of LINC01087. Survival data from TCGA and KM-Plotter directories allowed us to graph Kaplan-Meier curves and evaluate the prognostic value of LINC01087. To investigate the function of LINC01087, gene ontology (GO) annotation and Kyoto Encyclopedia of Gene and Genomes (KEGG) enrichment analyses were performed. Furthermore, interactions between LINC01087 and both miRNA and mRNA were studied by means of bioinformatics tools. (3) Results: LINC01087 was significantly deregulated in 7 out of 30 cancers, showing a predominant upregulation. Notably, it was overexpressed in breast (BC), esophageal (ESCA), and ovarian (OV) cancers, as well as lung squamous cell carcinoma (LUSC), stomach adenocarcinoma (STAD), and uterine carcinosarcoma (UCS). By contrast, LINC01087 displayed downregulation in testicular germ cell tumors (TGCT). ROC curve analyses identified LINC01087 as a potential diagnostic indicator in BC, ESCA, OV, STAD, and TGCT. Moreover, high and low expression of LINC01087 predicted a favorable prognosis in BC and papillary cell carcinoma, respectively. In silico analyses indicated that deregulation of LINC01087 in cancer was associated with a modulation of genes related to ion channel, transporter, and peptide receptor activity. (4) Conclusions: the quantification of an altered abundance of LINC01087 in tissue specimens might be clinically useful for the diagnosis and prognosis of some hormone-related tumors, including BC, OV, and TGCT, as well as other cancer types such as ESCA and STAD. Moreover, our study revealed the potential of LINC01087 (and perhaps other lncRNAs) to regulate neuroactive molecules in cancer.

Next-Generation Grade and Survival Expression Biomarkers of Human Gliomas Based on Algorithmically Reconstructed Molecular Pathways

In gliomas, expression of certain marker genes is strongly associated with survival and tumor type and often exceeds histological assessments. Using a human interactome model, we algorithmically reconstructed 7494 new-type molecular pathways that are centered each on an individual protein. Each single-gene expression and gene-centric pathway activation was tested as a survival and tumor grade biomarker in gliomas and their diagnostic subgroups (IDH mutant or wild type, IDH mutant with 1p/19q co-deletion, MGMT promoter methylated or unmethylated), including the three major molecular subtypes of glioblastoma (proneural, mesenchymal, classical). We used three datasets from The Cancer Genome Atlas and the Chinese Glioma Genome Atlas, which in total include 527 glioblastoma and 1097 low grade glioma profiles. We identified 2724 such gene and 2418 pathway survival biomarkers out of total 17,717 genes and 7494 pathways analyzed. We then assessed tumor grade and molecular subtype biomarkers and with the threshold of AUC > 0.7 identified 1322/982 gene biomarkers and 472/537 pathway biomarkers. This suggests roughly two times greater efficacy of the reconstructed pathway approach compared to gene biomarkers. Thus, we conclude that activation levels of algorithmically reconstructed gene-centric pathways are a potent class of new-generation diagnostic and prognostic biomarkers for gliomas.

A Novel Defined Pyroptosis-Related Gene Signature for Predicting Prognosis and Treatment of Glioma

Pyroptosis, a form of programmed cell death, that plays a significant role in the occurrence and progression of tumors, has been frequently investigated recently. However, the prognostic significance and therapeutic value of pyroptosis in glioma remain undetermined. In this research, we revealed the relationship of pyroptosis-related genes to glioma by analyzing whole transcriptome data from The Cancer Genome Atlas (TCGA) dataset serving as the training set and the Chinese Glioma Genome Atlas (CGGA) dataset serving as the validation set. We identified two subgroups of glioma patients with disparate prognostic and clinical features by performing consensus clustering analysis on nineteen pyroptosis-related genes that were differentially expressed between glioma and normal brain tissues. We further derived a risk signature, using eleven pyroptosis-related genes, that was demonstrated to be an independent prognostic factor for glioma. Furthermore, we used Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to implement functional analysis of our gene set, and the results were closely related to immune and inflammatory responses in accordance with the characteristics of pyroptosis. Moreover, Gene Set Enrichment Analysis (GSEA) results showed that that the high-risk group exhibited enriched characteristics of malignant tumors in accordance with its poor prognosis. Next, we analyzed different immune cell infiltration between the two risk groups using ssGSEA. Finally, CASP1 was identified as a core gene, so we subsequently selected an inhibitor targeting CASP1 and simulated molecular docking. In addition, the inhibitory effect of belnacasan on glioma was verified at the cellular level. In conclusion, pyroptosis-related genes are of great significance for performing prognostic stratification and developing treatment strategies for glioma.

Identification of Key Genes Related With Aspartic Acid Metabolism and Corresponding Protein Expression in Human Colon Cancer With Postoperative Prognosis and the Underlying Molecular Pathways Prediction

Objective: Colon cancer is one of the most frequent and lethal neoplasias. Altered metabolic activity is a well-known hallmark for cancer. The present study is aiming to screen key genes associated with tumor metabolism and construct a prognostic signature of colon cancer patients.

Methods: Glutamine- and UC- metabolism related genes were downloaded from GSEA MsigDB. Three key genes were screened by Cox regression analysis with data samples downloaded from TCGA and GSE29623 database. Consistent clustering based on the prognostic genes identified was employed to divide the colon cancer samples into two clusters with significant OS differences. The mRNA and protein expression of the key genes in colon tissues and matched adjacent noncancerous tissues of 16 patients were detected by IHC, qPCR, and Western blot to validate the constructed clustering model. GO, GSVA, and IPA were used to predict the relevant metabolic pathways.

Results: According to the three key genes identified, i.e., ASNS, CEBPA, and CAD, the cohort can be divided into two clusters with prognosis differences. Clinical specimen results confirmed that the risk model established was effective, and the different expression pattern of ASNS and CEBPA was correlated with TNM stage and lymph node metastasis, whilst that of CAD was correlated with post-operative tumor metastasis and recurrence. Molecular mechanism prediction indicated that CREB, insulin, and RNA Pol II were the key nodes affecting CEBPA and ASNS expression. Moreover, TIDE algorithm reflected the better immune response of the cluster with shorter OS. Further immune infiltration and checkpoints analyses provided important reference for clinicians to perform individualized immunotherapy.

Conclusion: Differential expression profile of three aspartic acid metabolic-associated genes, ASNS, CEBPA, and CAD, can be considered as a risk model with a good evaluation effect on the prognosis of colon cancer patients.

Large-Scale Transcriptomics-Driven Approach Revealed Overexpression of CRNDE as a Poor Survival Prognosis Biomarker in Glioblastoma

Glioblastoma is the most common and malignant brain malignancy worldwide, with a 10-year survival of only 0.7%. Aggressive multimodal treatment is not enough to increase life expectancy and provide good quality of life for glioblastoma patients. In addition, despite decades of research, there are no established biomarkers for early disease diagnosis and monitoring of patient response to treatment. High throughput sequencing technologies allow for the identification of unique molecules from large clinically annotated datasets. Thus, the aim of our study was to identify significant molecular changes between short- and long-term glioblastoma survivors by transcriptome RNA sequencing profiling, followed by differential pathway-activation-level analysis. We used data from the publicly available repositories The Cancer Genome Atlas (TCGA; number of annotated cases = 135) and Chinese Glioma Genome Atlas (CGGA; number of annotated cases = 218), and experimental clinically annotated glioblastoma tissue samples from the Institute of Pathology, Faculty of Medicine in Ljubljana corresponding to 2-58 months overall survival (n = 16). We found one differential gene for long noncoding RNA CRNDE whose overexpression showed correlation to poor patient OS. Moreover, we identified overlapping sets of congruently regulated differential genes involved in cell growth, division, and migration, structure and dynamics of extracellular matrix, DNA methylation, and regulation through noncoding RNAs. Gene ontology analysis can provide additional information about the function of protein- and nonprotein-coding genes of interest and the processes in which they are involved. In the future, this can shape the design of more targeted therapeutic approaches.

The Role of lncRNAs in the Stem Phenotype of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma is one of the deadliest tumors. This neoplasia is characterized by an important cellular and phenotypic heterogeneity. In particular, it has been shown that at least two subtypes can be found: basal-like, which presents stem-like properties, and classical. Cancer stem cells have been isolated and characterized from these tumors, showing their dependance on general and tissue-specific stem transcription factors and signaling pathways. Nevertheless, little is known about their tissue microenvironment and cell non-autonomous regulators, such as long-non-coding RNAs. (lncRNAs). In this review, we summarize the current knowledge about the positive and negative effects of lncRNAs in the stemness phenotype of pancreatic ductal adenocarcinoma cancer (PDAC).

Identification of a three-long non-coding RNA signature for predicting survival of temozolomide-treated isocitrate dehydrogenase mutant low-grade gliomas

Temozolomide (TMZ) is the major chemotherapy agent in glioma, and isocitrate dehydrogenase (IDH) is a well-known prognostic marker in glioma. O6-methylguanine-DNA methyltransferase promoter methylation (MGMTmethyl) is a predictive biomarker in overall gliomas rather than in IDH mutant gliomas. To discover effective biomarkers that could predict TMZ efficacy in IDH mutant low-grade gliomas (LGGs), we retrieved data of IDH mutant LGGs from TMZ arm of the EORTC22033-26033 trial as the training-set (n = 83), analyzed correlations between long non-coding RNAs (lncRNAs) and progression-free survival (PFS) using Lasso-Cox regression, and created a risk score (RS) to stratify patients. We identified a three-lncRNA signature in TMZ-treated IDH mutant LGGs. All of the three lncRNAs, as well as the RS derived, were significantly correlated with PFS. Patients were classified into high-risk and low-risk groups according to RS. PFS of the high-risk group was significantly worse than that of the low-risk group (P < 0.001). AUCs of the three-, four-, and five-year survival probability predicted by RS were 0.73, 0.79, and 0.76, respectively. The predictive role of the three-lncRNA signature was further validated in an independent testing-set, the TCGA-LGGs, which resulted in a significantly worse PFS (P < 0.001) in the high-risk group. Three-, four-, and five-year survival probabilities predicted by RS were 0.65, 0.69, and 0.84, respectively. Functions of these three lncRNAs involve cell proliferation and differentiation, predicted by their targeting cancer genes. Conclusively, we created a scoring model based on the expression of three lncRNAs, which can effectively predict the survival of IDH mutant LGGs treated with TMZ.

Non-coding RNAs in Brain Tumors, the Contribution of lncRNAs, circRNAs, and snoRNAs to Cancer Development-Their Diagnostic and Therapeutic Potential

Brain tumors are one of the most frightening ailments that afflict human beings worldwide. They are among the most lethal of all adult and pediatric solid tumors. The unique cell-intrinsic and microenvironmental properties of neural tissues are some of the most critical obstacles that researchers face in the diagnosis and treatment of brain tumors. Intensifying the search for potential new molecular markers in order to develop new effective treatments for patients might resolve this issue. Recently, the world of non-coding RNAs (ncRNAs) has become a field of intensive research since the discovery of their essential impact on carcinogenesis. Some of the most promising diagnostic and therapeutic regulatory RNAs are long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), and small nucleolar RNAs (snoRNAs). Many recent reports indicate the important role of these molecules in brain tumor development, as well as their implications in metastasis. In the following review, we summarize the current state of knowledge about regulatory RNAs, namely lncRNA, circRNAs, and snoRNAs, and their impact on the development of brain tumors in children and adults with particular emphasis on malignant primary brain tumors-gliomas and medulloblastomas (MB). We also provide an overview of how these different ncRNAs may act as biomarkers in these tumors and we present their potential clinical implications.

Coding of Glioblastoma Progression and Therapy Resistance through Long Noncoding RNAs

Glioblastoma is the most aggressive and lethal primary brain malignancy, with an average patient survival from diagnosis of 14 months. Glioblastoma also usually progresses as a more invasive phenotype after initial treatment. A major step forward in our understanding of the nature of glioblastoma was achieved with large-scale expression analysis. However, due to genomic complexity and heterogeneity, transcriptomics alone is not enough to define the glioblastoma “fingerprint”, so epigenetic mechanisms are being examined, including the noncoding genome. On the basis of their tissue specificity, long noncoding RNAs (lncRNAs) are being explored as new diagnostic and therapeutic targets. In addition, growing evidence indicates that lncRNAs have various roles in resistance to glioblastoma therapies (e.g., MALAT1, H19) and in glioblastoma progression (e.g., CRNDE, HOTAIRM1, ASLNC22381, ASLNC20819). Investigations have also focused on the prognostic value of lncRNAs, as well as the definition of the molecular signatures of glioma, to provide more precise tumor classification. This review discusses the potential that lncRNAs hold for the development of novel diagnostic and, hopefully, therapeutic targets that can contribute to prolonged survival and improved quality of life for patients with glioblastoma.

Clinical and Biological Significances of a Methyltransferase-Related Signature in Diffuse Glioma

Methylation of DNA, RNA or protein is a reversible modification. The proteins and genes that regulate this modification can be a candidate target for tumor therapy. However, the characteristics of methyltransferase related genes in glioma remain obscure. In this study, we systematically analyzed the relationship between methyltransferase-related genes expression profiles and outcomes in glioma patients based on The Cancer Genome Atlas and Chinese Glioma Genome Atlas RNA sequencing datasets. Consensus clustering identified two robust groups with significantly different pathological features and prognosis. Then a methyltransferase-related risk signature was built by a Cox proportional hazards model with elastic net penalty. Moreover, the risk score is associated with patients' clinical and molecular features and can be used as an independent prognostic indicator for patients with glioma. Furthermore, genes associated with the high-risk group were involved in various aspects of the malignant progression of glioma via Gene Ontology analysis and Gene Set Enrichment Analysis. In summary, our study identified a methyltransferase-related risk signature for predicting the prognosis of gliomas.

Identification of an energy metabolism-related signature associated with clinical prognosis in diffuse glioma

Now, numerous exciting findings have been yielded in the field of energy metabolism within glioma cells. In addition to aerobic glycolysis, multiple catabolic pathways are employed for energy production. However, the prognostic significance of energy metabolism in glioma remains obscure. Here, we explored the relationship between energy metabolism gene profile and outcome of diffuse glioma patients using The Cancer Genome Altas (TCGA) and Chinese Glioma Genome Altas (CGGA) datasets. Based on the gene expression profile, consensus clustering identified two robust clusters of glioma patients with distinguished prognostic and molecular features. With the Cox proportional hazards model with elastic net penalty, an energy metabolism-related signature was built to evaluate patients’ prognosis. Kaplan-Meier analysis found that the acquired signature could differentiate the outcome of low and high-risk groups of patients in both cohorts. Moreover, the signature, significantly associated with the clinical and molecular features, could serve as an independent prognostic factor for glioma patients. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) showed that gene sets correlated with high-risk group were involved in immune and inflammatory response, with the low-risk group were mainly related to glutamate receptor signaling pathway. Our results provided new insight into energy metabolism role in diffuse glioma.

Comparing biological information contained in mRNA and non-coding RNAs for classification of lung cancer patients

BACKGROUND

Deciphering the meaning of the human DNA is an outstanding goal which would revolutionize medicine and our way for treating diseases. In recent years, non-coding RNAs have attracted much attention and shown to be functional in part. Yet the importance of these RNAs especially for higher biological functions remains under investigation.

METHODS

In this paper, we analyze RNA-seq data, including non-coding and protein coding RNAs, from lung adenocarcinoma patients, a histologic subtype of non-small-cell lung cancer, with deep learning neural networks and other state-of-the-art classification methods. The purpose of our paper is three-fold. First, we compare the classification performance of different versions of deep belief networks with SVMs, decision trees and random forests. Second, we compare the classification capabilities of protein coding and non-coding RNAs. Third, we study the influence of feature selection on the classification performance.

RESULTS

As a result, we find that deep belief networks perform at least competitively to other state-of-the-art classifiers. Second, data from non-coding RNAs perform better than coding RNAs across a number of different classification methods. This demonstrates the equivalence of predictive information as captured by non-coding RNAs compared to protein coding RNAs, conventionally used in computational diagnostics tasks. Third, we find that feature selection has in general a negative effect on the classification performance which means that unfiltered data with all features give the best classification results.

CONCLUSIONS

Our study is the first to use ncRNAs beyond miRNAs for the computational classification of cancer and for performing a direct comparison of the classification capabilities of protein coding RNAs and non-coding RNAs.

Estrogen administration reduces the risk of pulmonary arterial hypertension by modulating the miR-133a signaling pathways in rats

We aimed to investigate how estrogen (ES) is implicated in the pathogenesis of pulmonary arterial hypertension (PAH) potentially by reducing the extent of vascular remodeling in females. HE assay, Western Blot, IHC, and real-time PCR were carried out to observe the role of ES in regulating miR-133a expression and the levels of MYOSLID, SRF, CTGF, and vascular remodeling in rats. In addition, MTT assay and flow cytometry were utilized to observe how ES affects cell proliferation and cell cycle in PAH. Moreover, luciferase assays were carried out to clarity the regulatory relationship between miR-133a and its downstream targets. ES administration relieved the deregulation of miR-133a, MYOSLID, SRF, and CTGF in PAH rats. In addition, ES also reduced the thickening of blood vessels in PAH rats. ES could activate miR-133a promoter and arrest the cells in the G0/G1 cycle, thus dose-dependently suppressing the proliferation of cells. In addition, the presence of ES, MYOSLID siRNA, or miR-133a precursor all altered the expression of MYOSLID, SP1, SRF, and CTGF, thus establishing a molecular signaling pathway among these factors. Furthermore, miR-133a could bind to SP1, MYOSLID, SRF, and CTGF to reduce their expression. Moreover, SRF was proved to function as an activator of miR-133a promoter. Two feedback loops were established in this study: a negative feedback loop between SRF and miR-133a, and a positive loop among miR-133a/SRF/MLK1/MYOSLID. ES treatment upregulates miR-133a expression and reduces the incidence of PAH and vascular remodeling.

A novel lncRNA-LINC01116 regulates tumorigenesis of glioma by targeting VEGFA

Brain glioma is the most common malignant tumor of the central nervous system, and one of the leading causes of death in patients with intracranial tumors. The clinical outcome of glioma is usually poor due to abundant vascularity, fast growth and susceptibility of invasion to normal brain tissues. Our microarray study showed that lncRNA-LINC01116 was significantly upregulated in glioma tissues and played an important role in cell proliferation, cycle, migration, invasion and angiogenesis. In addition, vascular endothelial growth factor (VEGFA) may be the major target genes in the downstream of lncRNA-LINC01116. Dual luciferase assay showed that LINC01116 and VEGFA both contained a miR-31-5p binding site, and LINC01116 could regulate the expression of VEGFA through competitive absorption of miR-31-5p. RNA immunoprecipitation indicated that LINC01116 and VEGFA were present in the miR-31-5p-RISC complex, and biotinylated miR-31-5p pull-down assay suggested that there was a competitive relationship between LINC01116 and VEGFA to bind with miR-31-5p. Collectively, our study has identified a novel lncRNA-LINC01116 and clarified the role and mechanism of LINC01116 in the tumorigenesis of glioma. LINC01116 may prove to be a potential target for the clinical diagnosis and treatment of glioma.

Review: Peering through a keyhole: liquid biopsy in primary and metastatic central nervous system tumours

Tumour molecular profiling by liquid biopsy is being investigated for a wide range of research and clinical purposes. The possibility of repeatedly interrogating the tumour profile using minimally invasive procedures is helping to understand spatial and temporal tumour heterogeneity, and to shed a light on mechanisms of resistance to targeted therapies. Moreover, this approach has been already implemented in clinical practice to address specific decisions regarding patients’ follow‐up and therapeutic management. For central nervous system (CNS) tumours, molecular profiling is particularly relevant for the proper characterization of primary neoplasms, while CNS metastases can significantly diverge from primary disease or extra‐CNS metastases, thus compelling a dedicated assessment. Based on these considerations, effective liquid biopsy tools for CNS tumours are highly warranted and a significant amount of data have been accrued over the last few years. These results have shown that liquid biopsy can provide clinically meaningful information about both primary and metastatic CNS tumours, but specific considerations must be taken into account, for example, when choosing the source of liquid biopsy. Nevertheless, this approach is especially attractive for CNS tumours, as repeated tumour sampling is not feasible. The aim of our review was to thoroughly report the state‐of‐the‐art regarding the opportunities and challenges posed by liquid biopsy in both primary and secondary CNS tumours.

A Prognostic Signature for Lower Grade Gliomas Based on Expression of Long Non-Coding RNAs

Diffuse low-grade and intermediate-grade gliomas (together known as lower grade gliomas, WHO grade II and III) develop in the supporting glial cells of brain and are the most common types of primary brain tumor. Despite a better prognosis for lower grade gliomas, 70% of patients undergo high-grade transformation within 10 years, stressing the importance of better prognosis. Long non-coding RNAs (lncRNAs) are gaining attention as potential biomarkers for cancer diagnosis and prognosis. We have developed a computational model, UVA8, for prognosis of lower grade gliomas by combining lncRNA expression, Cox regression, and L1-LASSO penalization. The model was trained on a subset of patients in TCGA. Patients in TCGA, as well as a completely independent validation set (CGGA) could be dichotomized based on their risk score, a linear combination of the level of each prognostic lncRNA weighted by its multivariable Cox regression coefficient. UVA8 is an independent predictor of survival and outperforms standard epidemiological approaches and previous published lncRNA-based predictors as a survival model. Guilt-by-association studies of the lncRNAs in UVA8, all of which predict good outcome, suggest they have a role in suppressing interferon-stimulated response and epithelial to mesenchymal transition. The expression levels of eight lncRNAs can be combined to produce a prognostic tool applicable to diverse populations of glioma patients. The 8 lncRNA (UVA8) based score can identify grade II and grade III glioma patients with poor outcome, and thus identify patients who should receive more aggressive therapy at the outset.

Long Non-Coding RNAs in Gliomas: From Molecular Pathology to Diagnostic Biomarkers and Therapeutic Targets

Gliomas are the most common malignancies of the central nervous system. Because of tumor localization and the biological behavior of tumor cells, gliomas are characterized by very poor prognosis. Despite significant efforts that have gone into glioma research in recent years, the therapeutic efficacy of available treatment options is still limited, and only a few clinically usable diagnostic biomarkers are available. More and more studies suggest non-coding RNAs to be promising diagnostic biomarkers and therapeutic targets in many cancers, including gliomas. One of the largest groups of these molecules is long non-coding RNAs (lncRNAs). LncRNAs show promising potential because of their unique tissue expression patterns and regulatory functions in cancer cells. Understanding the role of lncRNAs in gliomas may lead to discovery of the novel molecular mechanisms behind glioma biological features. It may also enable development of new solutions to overcome the greatest obstacles in therapy of glioma patients. In this review, we summarize the current knowledge about lncRNAs and their involvement in the molecular pathology of gliomas. A conclusion follows that these RNAs show great potential to serve as powerful diagnostic, prognostic, and predictive biomarkers as well as therapeutic targets.

Long non-coding RNAs in brain tumours: Focus on recent epigenetic findings in glioma

Glioma biology is a major focus in tumour research, primarily due to the aggressiveness and high mortality rate of its most aggressive form, glioblastoma. Progress in understanding the molecular mechanisms behind poor prognosis of glioblastoma, regardless of treatment approaches, has changed the classification of brain tumours after nearly 100 years of relying on anatomopathological criteria. Expanding knowledge in genetic, epigenetic and translational medicine is also beginning to contribute to further elucidating molecular dysregulation in glioma. Long non‐coding RNAs (lncRNAs) and their main representatives, large intergenic non‐coding RNAs (lincRNAs), have recently been under scrutiny in glioma research, revealing novel mechanisms of pathogenesis and reinforcing others. Among those confirmed was the reactivation of events significant for foetal brain development and neuronal commitment. Novel mechanisms of tumour suppression and activation of stem‐like behaviour in tumour cells have also been examined. Interestingly, these processes involve lncRNAs that are present both during normal brain development and in brain malignancies and their reactivation might be explained by epigenetic mechanisms, which we discuss in detail in the present review. In addition, the review discusses the lncRNAs‐induced changes, as well as epigenetic changes that are consequential for tumour formation, affecting, in turn, the expression of various types of lncRNAs.

Exploring Long Noncoding RNAs in Glioblastoma: Regulatory Mechanisms and Clinical Potentials

Gliomas are primary brain tumors presumably derived from glial cells. The WHO grade IV glioblastoma (GBM), characterized by rapid cell proliferation, easily recrudescent, high morbidity, and mortality, is the most common, devastating, and lethal gliomas. Molecular mechanisms underlying the pathogenesis and progression of GBMs with potential diagnostic and therapeutic value have been explored industriously. With the advent of high-throughput technologies, numerous long noncoding RNAs (lncRNAs) aberrantly expressed in GBMs were discovered recently, some of them probably involved in GBM initiation, malignant progression, relapse and resistant to therapy, or showing diagnostic and prognostic value. In this review, we summarized the profile of lncRNAs that has been extensively investigated in glioma research, with a focus on their regulatory mechanisms. Then, their diagnostic, prognostic, and therapeutic implications were also discussed.

Genetic landscape of long noncoding RNA (lncRNAs) in glioblastoma: identification of complex lncRNA regulatory networks and clinically relevant lncRNAs in glioblastoma

The major part of the genome that was previously called junk DNA has been shown to be dynamically transcribed to produce non-coding RNAs. Among them, the long non-coding RNAs (lncRNA) play diverse roles in the cellular context and are therefore involved in various diseases like cancer. LncRNA transcript profiling of glioblastoma (n = 19) and control brain samples (n = 9) identified 2,774 and 5,016 lncRNAs to be upregulated and downregulated in GBMs respectively. Correlation analysis of differentially regulated lncRNAs with mRNA and lncRNA identified several lncRNAs that may potentially regulate many tumor relevant mRNAs and lncRNAs both at nearby locations (cis) and far locations (trans). Integration of our data set with TCGA GBM RNA-Seq data (n = 172) revealed many lncRNAs as a host as well as decoy for many tumor regulated miRNAs. The expression pattern of seven lncRNAs- HOXD-AS2, RP4-792G4.2, CRNDE, ANRIL, RP11-389G6.3, RP11-325122.2 and AC123886.2 was validated by TCGA RNA-Seq data and RT-qPCR. Silencing ANRIL, a GBM upregulated lncRNA, inhibited glioma cell proliferation and colony growth. Cox regression analysis identified several prognostic lncRNAs. An lncRNA risk score derived from five lnRNAs-RP6-99M1.2, SOX21-AS1, CTD-2127H9.1, RP11-375B1.3 and RP3-449M8.9 predicted survival independent of all other markers. Multivariate cox regression analysis involving G-CIMP, IDH1 mutation, MGMT promoter methylation identified lncRNA risk score to be an independent poor predictor of GBM survival. The lncRNA risk score also stratified GBM patients into low and high risk with significant survival difference. Thus our study demonstrates the importance of lncRNA in GBM pathology and underscores the potential possibility of targeting lncRNA for GBM therapy.

The novel long non-coding RNA TALNEC2, regulates tumor cell growth and the stemness and radiation response of glioma stem cells

Despite advances in novel therapeutic approaches for the treatment of glioblastoma (GBM), the median survival of 12-14 months has not changed significantly. Therefore, there is an imperative need to identify molecular mechanisms that play a role in patient survival. Here, we analyzed the expression and functions of a novel lncRNA, TALNEC2 that was identified using RNA seq of E2F1-regulated lncRNAs. TALNEC2 was localized to the cytosol and its expression was E2F1-regulated and cell-cycle dependent. TALNEC2 was highly expressed in GBM with poor prognosis, in GBM specimens derived from short-term survivors and in glioma cells and glioma stem cells (GSCs). Silencing of TALNEC2 inhibited cell proliferation and arrested the cells in the G1\S phase of the cell cycle in various cancer cell lines. In addition, silencing of TALNEC2 decreased the self-renewal and mesenchymal transformation of GSCs, increased sensitivity of these cells to radiation and prolonged survival of mice bearing GSC-derived xenografts. Using miRNA array analysis, we identified specific miRNAs that were altered in the silenced cells that were associated with cell-cycle progression, proliferation and mesenchymal transformation. Two of the downregulated miRNAs, miR-21 and miR-191, mediated some of TALNEC2 effects on the stemness and mesenchymal transformation of GSCs. In conclusion, we identified a novel E2F1-regulated lncRNA that is highly expressed in GBM and in tumors from patients of short-term survival. The expression of TALNEC2 is associated with the increased tumorigenic potential of GSCs and their resistance to radiation. We conclude that TALNEC2 is an attractive therapeutic target for the treatment of GBM.

Long non-coding RNAs: potential molecular biomarkers for gliomas diagnosis and prognosis

The current grade classification system of gliomas is based on the histopathological features of these tumors and has great significance in defining groups of patients for clinical assessment. However, this classification system is also associated with a number of limitations, and as such, additional clinical assessment criteria are required. Long non-coding RNAs (lncRNAs) play a critical role in cellular functions and are currently regarded as potential biomarkers for glioma diagnosis and prognosis. Therefore, the molecular classification of glioma based on lncRNA expression may provide additional information to assist in the systematic identification of glioma. In the present paper, we review the emerging evidence indicating that specific lncRNAs may have the potential for use as key novel biomarkers and thus provide a powerful tool for the systematic diagnosis of glioma.

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

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

A Potential Prognostic Long Noncoding RNA Signature to Predict Recurrence among ER-positive Breast Cancer Patients Treated with Tamoxifen

Limited predictable long noncoding RNA (lncRNA) signature was reported in tamoxifen resistance among estrogen receptor (ER)-positive breast cancer (BC) patients. The aim of this study was to identify and assess prognostic lncRNA signature to predict recurrence among ER-positive BC patients treated with tamoxifen. Cohorts from Gene Expression Omnibus (GEO) (n = 298) and The Cancer Genome Atlas (TCGA) (n = 160) were defined as training and validation cohort, respectively. BC relapse associated lnRNAs was identify within training cohort, and the predictable value of recurrence was assessed in both cohorts. A total of 11lncRNAs were recognized to be associated with relapse free survival (RFS) of ER-positive BC patients receiving tamoxifen, who were divided into low-risk and high-risk group on basis of relapse risk scores (RRS). Multivariate cox regression analyses revealed that the RRS is an independent prognostic biomarker in the prediction of ER-positive BC patients' survival. GSEA indicated that high-risk group was associated with several signaling pathways in processing of BC recurrence and metastasis such as PI3K-Akt and Wnt signaling. Our 11-lncRNA based classifier is a reliable prognostic and predictive tool for disease relapse in BC patients receiving tamoxifen.

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

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

Serum HOTAIR and GAS5 levels as predictors of survival in patients with glioblastoma

Circulating long non-coding RNAs (lncRNAs) are a new class of cancer biomarkers. However, their significance in predicting outcomes in glioblastoma patients is unclear. We measured the levels of six known oncogenic lncRNAs-CRNDE, GAS5, H19, HOTAIR, MALAT1, and TUG1 in serum samples from 106 patients with primary glioblastoma and analyzed their association with outcomes. High levels of HOTAIR were associated with decreased probability of 2-year overall survival (adjusted hazard ratio [HR] = 2.04; 95% confidence interval [CI] = 1.08-9.76), and disease-free survival (adjusted HR = 1.82; 95% CI = 1.04-6.17). High levels of GAS5 were associated with increased probability of 2-year overall survival (adjusted HR = 0.44; 95% CI = 0.18-0.99), and disease-free survival (adjusted HR = 0.46; 95% CI = 0.16-0.98). HOTAIR and GAS5 levels could serve as reciprocal prognostic predictors of survival and disease progression in patients with glioblastoma.

MALAT1 is a prognostic factor in glioblastoma multiforme and induces chemoresistance to temozolomide through suppressing miR-203 and promoting thymidylate synthase expression

Glioblastoma multiforme (GBM) is the most malignant brain tumor with limited therapeutic options. Temozolomide (TMZ) is a novel cytotoxic agent used as first-line chemotherapy for GBM, however, some individual cells can't be isolated for surgical resection and show treatment-resistance, thus inducing poor prognosis. By using the HiSeq sequencing and bioinformatics methods, we identified lncRNAs showing different expression levels in TMZ-resistant and non-resistant patients. RT-qPCR was then performed in tissues and serum samples, and lncRNA MALAT1 was finally identified to show considerable discriminating potential to identify responding patients from non-responding patients. Moreover, high serum MALAT1 expression was associated with poor chemoresponse and survival in GBM patients receiving TMZ treatment. Subsequently, the TMZ resistant cell lines were established, and the CCK8 assay showed that lncRNA MALAT1 knockdown significantly reversed TMZ resistance in GBM cells. The gain and loss-function experiments revealed that miR-203 was down-regulated by MALAT1 and this interaction has reciprocal effects. Besides, thymidylate synthase (TS) mRNA was identified as a direct target of miR-203. LncRNA MALAT1 inhibition re-sensitized TMZ resistant cells through up-regulating miR-203 and down-regulating TS expression. On the other hand, MALAT1 overexpression promoted resistance by suppressing miR-203 and promoting TS expression. In conclusion, our integrated approach demonstrates that enhanced expression of lncRNA MALAT1 confers a potent poor therapeutic efficacy and inhibition of MALAT1 levels could be a future direction to develop a novel therapeutic strategy to overcome TMZ resistance in GBM patients.

Long noncoding RNA FTX is upregulated in gliomas and promotes proliferation and invasion of glioma cells by negatively regulating miR-342-3p

Gliomas remain a major public health challenge, posing a high risk for brain tumor-related morbidity and mortality. However, the mechanisms that drive the development of gliomas remain largely unknown. Emerging evidence has shown that long noncoding RNAs are key factors in glioma pathogenesis. qRT-PCR analysis was used to assess the expression of FTX and miR-342-3p in the different stages of gliomas in tissues. Bioinformatics tool DIANA and TargetSCan were used to predict the targets of FTX and miR-342-3p, respectively. Pearson's correlation analysis was performed to test the correlation between the expression levels of FTX, miR-342-3p, and astrocyte-elevated gene-1 (AEG-1). To examine the role of FTX in regulating proliferation and invasion of glioma cells, specific siRNA was used to knockdown FTX, and MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) and transwell assays were performed. Furthermore, rescue experiments were performed to further confirm the regulation of miR-342-3p by FTX. We then found that the expression of FTX and miR-342-3p was associated with progression of gliomas. FTX directly inhibited the expression of miR-342-3p, which subsequently regulates the expression of AEG-1. Collectively, FTX is critical for proliferation and invasion of glioma cells by regulating miR-342-3p and AEG-1. Our findings indicate that FTX and miR-342-3p may serve as a biomarker of glioma diagnosis, and offer potential novel therapeutic targets of treatment of gliomas.

An Insight into the Increasing Role of LncRNAs in the Pathogenesis of Gliomas

Long non-coding RNAs (LncRNAs) are essential epigenetic regulators with critical roles in tumor initiation and malignant progression. However, the roles and mechanisms of aberrantly expressed lncRNAs in the pathogenesis of gliomas are not fully understood. With the development of deep sequencing analyses, an extensive amount of functional non-coding RNAs has been discovered in glioma tissues and cell lines. Additionally, the contributions of several lncRNAs, such as Hox transcript antisense intergenic RNA, H19 and Colorectal neoplasia differentially expressed, previously reported to be involved in other pathogenesis and processes to the oncogenesis of glioblastoma are currently addressed. Thus, lncRNAs detected in tumor tissues could serve as candidate diagnostic biomarkers and therapeutic targets for gliomas. To understand the potential function of lncRNAs in gliomas, in this review, we briefly describe the profile of lncRNAs in human glioma research and therapy. Then, we discuss the individual lncRNA that has been under intensive investigation in glioma research, and the focus is its mechanism and clinical implication.

A Comprehensive Review of Genomics and Noncoding RNA in Gliomas

Glioblastoma (GBM) is the most malignant primary adult brain tumor. In spite of our greater understanding of the biology of GBMs, clinical outcome of GBM patients remains poor, as their median survival with best available treatment is 12 to 18 months. Recent efforts of The Cancer Genome Atlas (TCGA) have subgrouped patients into 4 molecular/transcriptional subgroups: proneural, neural, classical, and mesenchymal. Continuing efforts are underway to provide a comprehensive map of the heterogeneous makeup of GBM to include noncoding transcripts, genetic mutations, and their associations to clinical outcome. In this review, we introduce key molecular events (genetic and epigenetic) that have been deemed most relevant as per studies such as TCGA, with a specific focus on noncoding RNAs such as microRNAs (miRNA) and long noncoding RNAs (lncRNA). One of our main objectives is to illustrate how miRNAs and lncRNAs play a pivotal role in brain tumor biology to define tumor heterogeneity at molecular and cellular levels. Ultimately, we elaborate how radiogenomics-based predictive models can describe miRNA/lncRNA-driven networks to better define heterogeneity of GBM with clinical relevance.

Discovery and validation of immune-associated long non-coding RNA biomarkers associated with clinically molecular subtype and prognosis in diffuse large B cell lymphoma

Background

Diffuse large B-cell lymphoma (DLBCL) is an aggressive and complex disease characterized by wide clinical, phenotypic and molecular heterogeneities. The expression pattern and clinical implication of long non-coding RNAs (lncRNAs) between germinal center B-cell-like (GCB) and activated B-cell-like (ABC) subtypes in DLBCL remain unclear. This study aims to determine whether lncRNA can serve as predictive biomarkers for subtype classification and prognosis in DLBCL.

Methods

Genome-wide comparative analysis of lncRNA expression profiles were performed in a large number of DLBCL patients from Gene Expression Omnibus (GEO), including GSE31312 cohort (N = 426), GSE10846 (N = 350) cohort and GSE4475 cohort (N = 129). Novel lncRNA biomarkers associated with clinically molecular subtype and prognosis were identified in the discovery cohort using differential expression analyses and weighted voting algorithm. The predictive value of the lncRNA signature was then assessed in two independent cohorts. The functional implication of lncRNA signature was also analyzed by integrative analysis of lncRNA and mRNA.

Results

Seventeen of the 156 differentially expressed lncRNAs between GCB and ABC subtypes were identified as candidate biomarkers and integrated into form a lncRNA-based signature (termed SubSigLnc-17) which was able to discriminate between GCB and ABC subtypes with AUC of 0.974, specificity of 89.6% and sensitivity of 92.5%. Furthermore, subgroups of patients characterized by the SubSigLnc-17 demonstrated significantly different clinical outcome. The reproducible predictive power of SubSigLnc-17 in subtype classification and prognosis was successfully validated in the internal validation cohort and another two independent patient cohorts. Integrative analysis of lncRNA-mRNA suggested that these candidate lncRNA biomarkers were mainly related to immune-associated processes, such as T cell activation, leukocyte activation, lymphocyte activation and Chemokine signaling pathway.

Conclusions

Our study uncovered differentiated lncRNA expression pattern between GCB and ABC DLBCL and identified a 17-lncRNA signature for subtype classification and prognosis prediction. With further prospective validation, our study will improve the understanding of underlying molecular heterogeneities in DLBCL and provide candidate lncRNA biomarkers in DLBCL classification and prognosis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12943-017-0580-4) contains supplementary material, which is available to authorized users.

Down-regulation of long non-coding RNA FOXD3 antisense RNA 1 (FOXD3-AS1) inhibits cell proliferation, migration, and invasion in malignant glioma cells

Growing evidence indicates that long non-coding RNAs (lncRNAs) play key roles in cancer initiation and progression. However, little is known about the therapeutic significance of lncRNAs in glioma. In this study, we explored the tumorigenic role of a classical lncRNA, FOXD3 antisense RNA 1 (FOXD3-AS1) in glioma. Systemic analysis of the patient specimens and clinical data showed that FOXD3-AS1 was markedly up-regulated in high-grade glioma tissues (WHO grade III-IV) compared with that in low-grade glioma (WHO grade I-II) and normal brain tissues (both P<0.01), and patients with low FOXD3-AS1 expression had grater survival probability. Multivariate regression analysis showed that increased FOXD3-AS1 expression was a significant independent indicator of poor prognosis in glioma patients (P=0.034). To understand the tumorigenic mechanism of FOXD3-AS1, the expression pattern and functional role of FOXD3-AS1 in glioma were detected using real-time PCR and Smart Silencer-mediated knockdown study. In related cell biological assays, we discovered that FOXD3-AS1 knockdown significantly inhibited cell proliferation, induced cell cycle S-phase arrest, and impaired cell migration and invasion in malignant glioma cells. As expected, we also found that the expression of FOXD3-AS1 was positively correlated with FOXD3 mRNA. Knockdown of FOXD3-AS1 reduced the protein level of FOXD3 in cultured U251 and A172 cell lines. These results suggest that FOXD3-AS1 is an oncogenic lncRNA, which may promote the occurrence and development of glioma through transcriptional regulation of FOXD3.

Differentially Expressed Long Non-Coding RNAs Were Predicted to Be Involved in the Control of Signaling Pathways in Pediatric Astrocytoma

Expression changes for long non-coding RNAs (lncRNAs) have been identified in adult glioblastoma multiforme (GBM) and in a mixture of adult and pediatric astrocytoma. Since adult and pediatric astrocytomas are molecularly different, the mixture of both could mask specific features in each. We determined the global expression patterns of lncRNAs and messenger RNA (mRNAs) in pediatric astrocytoma of different histological grades. Transcript expression changes were determined with an HTA 2.0 array. lncRNA interactions with microRNAs and mRNAs were predicted by using an algorithm and the LncTar tool, respectively. Interactomes were constructed with the HIPPIE database and visualized with the Cytoscape platform. The array showed expression changes in 156 and 207 lncRNAs in tumors (versus the control) and in pediatric GBM (versus low-grade astrocytoma), respectively. Predictions identified lncRNAs that have putative microRNA binding sites, which might suggest that they function as sponges in these tumors. Also, lncRNAs were shown to interact with many mRNAs, such as Pleckstrin homology-like domain, family A, member 1 (PHLDA1) and sulfatase 2 (SULF2). For example, qPCR found long intergenic non-coding RNA regulator of reprogramming (linc-RoR) expression levels upregulated in pediatric GBM when they were compared with control tissues or with low-grade tumors. Meanwhile, PHLDA1 and ELAV-like RNA binding protein 1 (ELAV1) showed expression changes in tumors relative to the control. Our data showed many lncRNAs with expression changes in pediatric astrocytoma, which might be involved in the regulation of different signaling pathways.

Long Noncoding RNA miR210HG as a Potential Biomarker for the Diagnosis of Glioma

BACKGROUND

Glioma remains a diagnostic challenge because of its variable clinical presentation and a lack of reliable screening tools. Long noncoding RNAs (lncRNAs) regulate gene function in a wide range of pathophysiological processes and are therefore emerging biomarkers for prostate cancer, hepatic cancer, and other tumor diseases. However, the effective use of lncRNAs as biomarkers for the diagnosis of glioma remains unproven.

METHODS

This study included 42 glioma patients and 10 healthy controls. lncRNA and mRNA microarray chips were used to identify dysregulated lncRNAs in tumor tissue and tumor-adjacent normal tissue, and SYBR Green-based miRNA quantitative real-time reverse transcription polymerase chain reactions were used to validate upregulated lncRNAs. A receiver operating characteristic curve analysis was conducted to evaluate the diagnostic accuracy of the lncRNA identified as the candidate biomarker.

RESULTS

miR210HG levels were significantly higher in tumor tissue than in tumor-adjacent normal tissue in participating glioma patients. Serum miR210HG levels were also significantly higher in glioma patients than in healthy controls. The receiver operating characteristic curve showed that serum miR210HG was a specific diagnostic predictor of acute pulmonary embolism with an area under the curve of 0.8323 (95% confidence interval, 0.7347 to 0.9299, p < 0.001).

CONCLUSION

Our findings indicate that miR210HG could be an important biomarker for the diagnosis of glioma, and, as such, large-scale investigations are urgently needed to pave the way from basic research to clinical use.

A long non-coding RNA signature to improve prognosis prediction of gastric cancer

BACKGROUND

Increasing evidence suggests long non-coding RNAs (lncRNAs) are frequently aberrantly expressed in cancers, however, few related lncRNA signatures have been established for prediction of cancer prognosis. We aimed at developing alncRNA signature to improve prognosis prediction of gastric cancer (GC).

METHODS

Using a lncRNA-mining approach, we performed lncRNA expression profiling in large GC cohorts from Gene Expression Ominus (GEO), including GSE62254 data set (N = 300) and GSE15459 data set (N = 192). We established a set of 24-lncRNAs that were significantly associated with the disease free survival (DFS) in the test series.

RESULTS

Based on this 24-lncRNA signature, the test series patients could be classified into high-risk or low-risk subgroup with significantly different DFS (HR = 1.19, 95 % CI = 1.13-1.25, P < 0.0001). The prognostic value of this 24-lncRNA signature was confirmed in the internal validation series and another external validation series, respectively. Further analysis revealed that the prognostic value of this signature was independent of lymph node ratio (LNR) and postoperative chemotherapy. Gene set enrichment analysis (GSEA) indicated that high risk score group was associated with several cancer recurrence and metastasis associated pathways.

CONCLUSIONS

The identification of the prognostic lncRNAs indicates the potential roles of lncRNAs in GC biogenesis. Our results may provide an efficient classification tool for clinical prognosis evaluation of GC.

Cancer subtypes classification using long non-coding RNA

Inter-tumor heterogeneity might explain divergent clinical evolution of cancers bearing similar pathological features. In the last decade, genomic has highly improved tumor subtypes classification through the identification of oncogenic or tumor suppressor drivers. In addition, epigenetics and long non-coding RNAs (lncRNAs) are emerging as new fields for investigation, which might also account for tumor heterogeneity. There is growing evidence that modifications of lncRNA expression profiles are involved in cancer progression through epigenetic regulation, activation of pro-oncogenic pathways and crosstalks with other RNA subtypes. Consequently, the study of lncRNA expression profile will be a key factor in the future for charting cancer subtype classifications as well as defining prognostic and progression biomarkers. Herein we discuss the interest of lncRNA as potent prognostic and predictive biomarkers, and provide a glimpse on the impact of emerging cancer subtypes classification based on lncRNAs.

Sample Level Enrichment Analysis of KEGG Pathways Identifies Clinically Relevant Subtypes of Glioblastoma

Background: Glioblastoma is the most lethal primary brain tumor in adults. Aberrant signal transduction pathways, associated with the progression of glioblastoma, have been identified recently and may offer a potential gene therapy strategy. Methods and Findings: We first used the sample level enrichment analysis to transfer gene expression profile of TCGA dataset into pathway enrichment z-score matrix. Then, we classified glioblastoma into five subtypes (Cluster A to Cluster E) by the consensus clustering and silhouette analysis. Principle component analysis showed the five subtype could be separated by first three principle components. Integrative omics data showed that mesenchymal subtype was rich in Cluster A, neural subtype was centered in Cluster D and proneural subtype was gathered in Cluster E, while Cluster E showed a high percentage of G-CIMP subtype. Additionally, according to analyze the overall survival and progression free survival of each subtype by Kaplan-Merie analysis and Cox hazard proportion model, we identified Cluster D and Cluster E received a better prognosis. Conclusions: We report a clinically relevant classification of glioblastoma based on sample level KEGG pathway enrichment profile and this novel classification system provided new insights into the heterogeneity of glioblastoma, and may be used as an important clinical tool to predict the prognosis.

A long noncoding RNA AB073614 promotes tumorigenesis and predicts poor prognosis in ovarian cancer

Long noncoding RNA (lncRNA) profiles in ovarian cancer (OC) remain largely unknown. In the present study, we screened AB073614 as a new candidate lncRNA which promotes development of OC, in two independent datasets (GSE18521 and GSE38666) from the Gene Expression Omnibus (GEO). The level of AB073614 was then detected in 75 paired OC tissues and adjacent normal tissues by qRT-PCR. Results showed that AB073614 expression was significantly up-regulated in 85.3% (64/75) cancerous tissues compared with normal counterparts (P < 0.01). Further, the 5-year overall survival (OS) in OC patients with high expression of AB073614 was inferior to that with low expression (17.2 months vs 30.0 months, P = 0.0025). To investigate the functional role of AB073614, AB073614 siRNA was transfected into OC cell lines. Knockdown of AB073614 expression significantly inhibited cell proliferation and invasion, resulted in cell arrest in G₁ phase of cell cycle and a dramatic increase of apoptosis, both in HO-8910 and OVCAR3 cells. In vivo experiment also revealed that knockdown AB073614 inhibited OVCAR3 cells proliferation. Finally, western blot assays indicated that lncRNA AB073614 may exert its function by targeting ERK1/2 and AKT-mediated signaling pathway. In conclusion, our study suggests that lncRNA AB073614 acts as a functional oncogene in OC development.

Up-Regulation of Long Non-Coding RNA AB073614 Predicts a Poor Prognosis in Patients with Glioma

Dysregulated long noncoding RNAs (lncRNAs) have been found in human diseases, especially in cancer. Emerging evidence indicates that dysregulated lncRNAs are implicated in tumorigenesis and cancer progression. LncRNA AB073614 characterized as a new candidate lncRNA promotes the development of ovarian cancer. However, the role of lncRNA AB073614 in human gliomas remains unknown. The expression of AB073614 was detected in 65 glioma tissues and 13 normal brain tissues by qRT-PCR, showing that lncRNA AB073614 expression was significantly up-regulated in cancerous tissues compared with normal brain tissues (p < 0.001), and it was positively correlated with tumor grade (I-II grades vs. III-IV grades, p = 0.013) in glioma patients. Kaplan-Meier analysis demonstrated that increased AB073614 expression contributed to poor overall survival (HR (hazard ratio) = 1.952, 95%CI: 1.202-3.940, p = 0.0129). Further, univariate Cox regression analysis indicated that lncRNA AB073614 overexpression was an unfavorable prognostic factor in gliomas (HR = 1.997, 95%CI: 1.135-3.514, p = 0.016), regardless of the tumor grade (I-II grades vs. III-IV grades, HR = 1.902, 95%CI: 1.066-3.391, p = 0.029). Finally, after adjustment with age, sex, tumor grade and tumor location, multivariate Cox regression analysis suggested that both highly expressed lncRNA AB073614 (HR = 2.606, 95%CI: 1.408-4.824, p = 0.002) and high tumor grade (III-IV grades, HR = 2.720, 95%CI: 1.401-5.282, p = 0.003) could be considered independent poor prognostic indicators for glioma patients. In conclusion, our study suggested that increased lncRNA AB073614 expression may be identified as a poor prognostic biomarker in gliomas.

Systematic characterization of lncRNAs' cell-to-cell expression heterogeneity in glioblastoma cells

Glioblastoma (GBM) is the most common malignant adult brain tumor generally associated with high level of cellular heterogeneity and a dismal prognosis. Long noncoding RNAs (lncRNAs) are emerging as novel mediators of tumorigenesis. Recently developed single-cell RNA-seq provides an unprecedented way for analysis of the cell-to-cell variability in lncRNA expression profiles. Here we comprehensively examined the expression patterns of 2,003 lncRNAs in 380 cells from five primary GBMs and two glioblastoma stem-like cell (GSC) lines. Employing the self-organizing maps, we displayed the landscape of the lncRNA expression dynamics for individual cells. Further analyses revealed heterogeneous nature of lncRNA in abundance and splicing patterns. Moreover, lncRNA expression variation is also ubiquitously present in the established GSC lines composed of seemingly identical cells. Through comparative analysis of GSC and corresponding differentiated cell cultures, we defined a stemness signature by the set of 31 differentially expressed lncRNAs, which can disclose stemness gradients in five tumors. Additionally, based on known classifier lncRNAs for molecular subtypes, each tumor was found to comprise individual cells representing four subtypes. Our systematic characterization of lncRNA expression heterogeneity lays the foundation for future efforts to further understand the function of lncRNA, develop valuable biomarkers, and enhance knowledge of GBM biology.

Aberrant NEAT1 expression is associated with clinical outcome in high grade glioma patients

Long noncoding RNA (lncRNA) NEAT1 has been reported to play critical roles in various human tumor entities and related to the survival of patients with malignancies. However, little is known about the role of lncRNA NEAT1 in glioma patients. The aim of this study was to investigate the expression of NEAT1 in human glioma and its correlation with clinicopathological features and prognosis in human glioma; we analyzed the relationship of lncRNA NEAT1 expression with clinicopathological characteristics and prognosis in glioma patients. In our results, the relative level of NEAT1 expression was higher in cancer tissues compared with adjacent noncancerous tissues (p < 0.001). High NEAT1 expression was observed to be closely correlated with larger tumor size (p = 0.023), higher WHO grade (p = 0.005), and recurrence (p = 0.011). Kaplan-Meier curves showed that patients with high NEAT1 expression showed unfavorable overall survival (OS) than the low NEAT1 expression group (p = 0.002). Multivariate analysis results revealed that NEAT1 overexpression was an independent prognostic factor for OS in addition to postoperative chemoradiotherapy and WHO grade. Moreover, high NEAT1 expression in patients with stage III~IV disease and postoperative chemoradiotherapy conferred unfavorable OS (stage III~IV p = 0.002, postoperative chemoradiotherapy p = 0.000). This study supports NEAT1 as a potential prognostic predictor with its high expression in cancer tissues and its association with carcinogenesis and progression in glioma.

Breast cancer intrinsic subtype classification, clinical use and future trends

Breast cancer is composed of multiple subtypes with distinct morphologies and clinical implications. The advent of microarrays has led to a new paradigm in deciphering breast cancer heterogeneity, based on which the intrinsic subtyping system using prognostic multigene classifiers was developed. Subtypes identified using different gene panels, though overlap to a great extent, do not completely converge, and the avail of new information and perspectives has led to the emergence of novel subtypes, which complicate our understanding towards breast tumor heterogeneity. This review explores and summarizes the existing intrinsic subtypes, patient clinical features and management, commercial signature panels, as well as various information used for tumor classification. Two trends are pointed out in the end on breast cancer subtyping, i.e., either diverging to more refined groups or converging to the major subtypes. This review improves our understandings towards breast cancer intrinsic classification, current status on clinical application, and future trends.

IDH1 mutation-associated long non-coding RNA expression profile changes in glioma

Isocitrate dehydrogenase 1 (IDH1) mutation is an important prognostic marker in glioma. However, its downstream effect remains incompletely understood. Long non-coding RNAs (lncRNAs) are emerging as important regulators of tumorigenesis in a number of human malignancies, including glioma. Here, we investigated whether and how lncRNA expression profiles would differ between gliomas with or without IDH1 mutation. By using our previously reported lncRNA mining approach, we performed lncRNA profiling in three public glioma microarray datasets. The differential lncRNA expression analysis was then conducted between mutant-type and wild-type IDH1 glioma samples. Comparison analysis identified 14 and 9 lncRNA probe sets that showed significantly altered expressions in astrocytic and oligodendroglial tumors, respectively (fold change ≥ 1.5, false discovery rate ≤ 0.1). Moreover, the differential expressions of these lncRNAs could be confirmed in the independent testing sets. Functional exploration of the lncRNAs by analyzing the lncRNA-protein interactions revealed that these IDH1 mutation-associated lncRNAs were involved in multiple tumor-associated cellular processes, including metabolism, cell growth and apoptosis. Our data suggest the potential roles of lncRNA in gliomagenesis, and may help to understand the pathogenesis of gliomas associated with IDH1 mutation.

Exploring miRNA-Associated Signatures with Diagnostic Relevance in Glioblastoma Multiforme and Breast Cancer Patients

The growing attention that non-coding RNAs have attracted in the field of cancer research in recent years is undeniable. Whether investigated as prospective therapeutic targets or prognostic indicators or diagnostic biomarkers, the clinical relevance of these molecules is starting to emerge. In addition, identification of non-coding RNAs in a plethora of body fluids has further positioned these molecules as attractive non-invasive biomarkers. This review will first provide an overview of the synthetic cascade that leads to the production of the small non-coding RNAs microRNAs (miRNAs) and presents their strengths as biomarkers of disease. Our interest will next be directed at exploring the diagnostic utility of miRNAs in two types of cancer: the brain tumor glioblastoma multiforme (GBM) and breast cancer. Finally, we will discuss additional clinical implications associated with miRNA detection as well as introduce other non-coding RNAs that have generated recent interest in the cancer research community.

The Use of Three Long Non-Coding RNAs as Potential Prognostic Indicators of Astrocytoma

Long noncoding RNAs (lncRNAs) are pervasively transcribed and play a key role in tumorigenesis. The aim of the study was to determine the lncRNA expression profile in astrocytomas and to assess its potential clinical value. We performed a three-step analysis to establish the lncRNA profile for astrocytoma: a) the lncRNA expression was examined on 3 astrocytomas as well as 3 NATs (normal adjacent tissues) using the lncRNA microarray; b) the top-hits were validated in 40 astrocytomas (WHO grade II-IV) by quantitative real time-PCR (qRT-PCR); c) the hits with significant differences were re-evaluated using qRT-PCR in 90 astrocytomas. Finally, 7 lncRNAs were found to have a significantly different expression profile in astrocytoma samples compared to the NAT samples. Unsupervised clustering analysis further revealed the potential of the 7-lncRNA profile to differentiate between tumors and NAT samples. The upregulation of ENST00000545440 and NR_002809 was associated with advanced clinical stages of astrocytoma. Using Kaplan-Meier survival analysis, we showed that the low expression of BC002811 or XLOC_010967, or the high expression of NR_002809 was significantly associated with poor patient survival. Moreover, Cox proportional hazard regression analysis revealed that this prognostic impact was independent of other clinicopathological factors. Our results indicate that the lncRNA profile may be a potential prognostic biomarker for the prediction of post-surgical outcomes.