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

Coding, or non-coding, that is the question

The advent of high-throughput sequencing uncovered that our genome is pervasively transcribed into RNAs that are seemingly not translated into proteins. It was also found that non-coding RNA transcripts outnumber canonical protein-coding genes. This mindboggling discovery prompted a surge in non-coding RNA research that started unraveling the functional relevance of these new genetic units, shaking the classic definition of "gene". While the non-coding RNA revolution was still taking place, polysome/ribosome profiling and mass spectrometry analyses revealed that peptides can be translated from non-canonical open reading frames. Therefore, it is becoming evident that the coding vs non-coding dichotomy is way blurrier than anticipated. In this review, we focus on several examples in which the binary classification of coding vs non-coding genes is outdated, since the same bifunctional gene expresses both coding and non-coding products. We discuss the implications of this intricate usage of transcripts in terms of molecular mechanisms of gene expression and biological outputs, which are often concordant, but can also surprisingly be discordant. Finally, we discuss the methodological caveats that are associated with the study of bifunctional genes, and we highlight the opportunities and challenges of therapeutic exploitation of this intricacy towards the development of anticancer therapies.

Long non-coding RNA (CMR) involved in autoprotection in S. aureus mastitis in dairy cows by regulating miR-877/FOXM1

Long non-coding RNAs (LncRNAs) are dysregulated in a variety of human diseases and are highly involved in the development and progression of tumors. Studies on lncRNAs associated with cow mastitis have been lagging behind compared to humans or model animals, therefore, the aim of this study was to explore the mechanism of LncRNAs (CMR) involved in autoprotection against S. aureus mastitis in Bovine Mammary Epithelial Cells (BMECs). First, qRT-PCR was used to examine the relative expression of CMR in a S. aureus mastitis model of BMECs. Then, cell proliferation and apoptosis were detected by EdU and apoptosis assay. Finally, the targeting relationship between miRNAs and mRNA/LncRNAs was determined by dual luciferase reporter gene, qRT-PCR and western blotting techniques. The results showed that CMR was upregulated in the S. aureus mastitis model of BMECs and promoted the expression of inflammatory factors, and SiRNA-mediated CMR inhibited the proliferation of mammary epithelial cells and induced apoptosis. Mechanistically, CMR acts as a competitive endogenous RNA (ceRNA) sponge miR-877, leading to upregulation of FOXM1, a target of miR-877. Importantly, either miR-877 overexpression or FOXM1 inhibition abrogated CMR knockdown-induced apoptosis promoting cell proliferation and reducing inflammatory factor expression levels. In summary, CMR is involved in the regulation of autoprotection against S. aureus mastitis through the miR-877/FOXM1 axis in BMECs and induces immune responses in mammary tissues and cells of dairy cows, providing an important reference for subsequent prevention and control of cow mastitis and the development of targeted drugs.

Exosomal noncoding RNAs in central nervous system diseases: biological functions and potential clinical applications

Central nervous system (CNS) disease is a general term for a series of complex and diverse diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), multiple sclerosis (MS), CNS tumors, stroke, epilepsy, and amyotrophic lateral sclerosis (ALS). Interneuron and neuron-glia cells communicate with each other through their homeostatic microenvironment. Exosomes in the microenvironment have crucial impacts on interneuron and neuron-glia cells by transferring their contents, such as proteins, lipids, and ncRNAs, constituting a novel form of cell-to-cell interaction and communication. Exosomal noncoding RNAs (ncRNAs), including microRNAs (miRNAs), long noncoding RNAs (lncRNAs), circular RNAs (circRNAs), and PIWI-interacting RNAs (piRNAs), regulate physiological functions and maintain CNS homeostasis. Exosomes are regarded as extracellular messengers that transfer ncRNAs between neurons and body fluids due to their ability to cross the blood-brain barrier. This review aims to summarize the current understanding of exosomal ncRNAs in CNS diseases, including prospective diagnostic biomarkers, pathological regulators, therapeutic strategies and clinical applications. We also provide an all-sided discussion of the comparison with some similar CNS diseases and the main limitations and challenges for exosomal ncRNAs in clinical applications.

CircRNAs in Tumor Radioresistance

Circular RNAs (circRNAs) are endogenous, non-coding RNAs, which are derived from host genes that are present in several species and can be involved in the progression of various diseases. circRNAs’ leading role is to act as RNA sponges. In recent years, the other roles of circRNAs have been discovered, such as regulating transcription and translation, regulating host genes, and even being translated into proteins. As some tumor cells are no longer radiosensitive, tumor radioresistance has since become a challenge in treating tumors. In recent years, circRNAs are differentially expressed in tumor cells and can be used as biological markers of tumors. In addition, circRNAs can regulate the radiosensitivity of tumors. Here, we list the mechanisms of circRNAs in glioma, nasopharyngeal carcinoma, and non-small cell lung cancer; further, these studies also provide new ideas for the purposes of eliminating radioresistance in tumors.

Identification and Validation of Key Genes of Differential Correlations in Gastric Cancer

Background: Gastric cancer (GC) is aggressive cancer with a poor prognosis. Previously bulk transcriptome analysis was utilized to identify key genes correlated with the development, progression and prognosis of GC. However, due to the complexity of the genetic mutations, there is still an urgent need to recognize core genes in the regulatory network of GC.

Methods: Gene expression profiles (GSE66229) were retrieved from the GEO database. Weighted correlation network analysis (WGCNA) was employed to identify gene modules mostly correlated with GC carcinogenesis. R package ‘DiffCorr’ was applied to identify differentially correlated gene pairs in tumor and normal tissues. Cytoscape was adopted to construct and visualize the gene regulatory network.

Results: A total of 15 modules were detected in WGCNA analysis, among which three modules were significantly correlated with GC. Then genes in these modules were analyzed separately by “DiffCorr”. Multiple differentially correlated gene pairs were recognized and the network was visualized by the software Cytoscape. Moreover, GEMIN5 and PFDN2, which were rarely discussed in GC, were identified as key genes in the regulatory network and the differential expression was validated by real-time qPCR, WB and IHC in cell lines and GC patient tissues.

Conclusions: Our research has shed light on the carcinogenesis mechanism by revealing differentially correlated gene pairs during transition from normal to tumor. We believe the application of this network-based algorithm holds great potential in inferring relationships and detecting candidate biomarkers.

Long Noncoding Competing Endogenous RNA Networks in Pancreatic Cancer

Pancreatic cancer (PC) is a highly malignant disease characterized by insidious onset, rapid progress, and poor therapeutic effects. The molecular mechanisms associated with PC initiation and progression are largely insufficient, hampering the exploitation of novel diagnostic biomarkers and development of efficient therapeutic strategies. Emerging evidence recently reveals that noncoding RNAs (ncRNAs), including long ncRNAs (lncRNAs) and microRNAs (miRNAs), extensively participate in PC pathogenesis. Specifically, lncRNAs can function as competing endogenous RNAs (ceRNAs), competitively sequestering miRNAs, therefore modulating the expression levels of their downstream target genes. Such complex lncRNA/miRNA/mRNA networks, namely, ceRNA networks, play crucial roles in the biological processes of PC by regulating cell growth and survival, epithelial-mesenchymal transition and metastasis, cancer stem cell maintenance, metabolism, autophagy, chemoresistance, and angiogenesis. In this review, the emerging knowledge on the lncRNA-associated ceRNA networks involved in PC initiation and progression will be summarized, and the potentials of the competitive crosstalk as diagnostic, prognostic, and therapeutic targets will be comprehensively discussed.

Identification of Mutation Landscape and Immune Cell Component for Liver Hepatocellular Carcinoma Highlights Potential Therapeutic Targets and Prognostic Markers

Liver hepatocellular carcinoma (LIHC) is a primary malignancy, and there is a lack of effective treatment for advanced patients. Although numerous studies exist to reveal the carcinogenic mechanism of LIHC, few studies have integrated multi-omics data to systematically analyze pathogenesis and reveal potential therapeutic targets. Here, we integrated genomic variation data and RNA-seq profiles obtained by high-throughput sequencing to define high- and low-genomic instability samples. The mutational landscape was reported, and the advanced patients of LIHC were characterized by high-genomic instability. We found that the tumor microenvironment underwent metabolic reprograming driven by mutations accumulate to satisfy tumor proliferation and invasion. Further, the co-expression network identifies three mutant long non-coding RNAs as potential therapeutic targets, which can promote tumor progression by participating in specific carcinogenic mechanisms. Then, five potential prognostic markers (RP11-502I4.3, SPINK5, CHRM3, SLC5A12, and RP11-467L13.7) were identified by examining the association of genes and patient survival. By characterizing the immune landscape of LIHC, loss of immunogenicity was revealed as a key factor of immune checkpoint suppression. Macrophages were found to be significantly associated with patient risk scores, and high levels of macrophages accelerated patient mortality. In summary, the mutation-driven mechanism and immune landscape of LIHC revealed by this study will serve precision medicine.

Genome-Wide Analysis of LncRNA in Bovine Mammary Epithelial Cell Injuries Induced by Escherichia Coli and Staphylococcus Aureus

Escherichia coli and Staphylococcus aureus are two common pathogenic microorganisms that cause mastitis in dairy cows. They can cause clinical mastitis and subclinical mastitis. In recent studies, lncRNAs have been found to play an important role in the immune responses triggered by microbial inducers. However, the actions of lncRNAs in bovine mastitis remain unclear. The purpose of this study was to investigate the effects of bovine mammary epithelial cell injuries induced by treatment with E. coli and S. aureus, and to explore the lncRNA profile on cell injuries. The lncRNA transcriptome analysis showed a total of 2597 lncRNAs. There were 2234 lncRNAs differentially expressed in the E. coli group and 2334 in the S. aureus group. Moreover, we found that the E. coli and S. aureus groups of maternal genes targeted signaling pathways with similar functions according to KEGG and GO analyses. Two lncRNA-miRNA-mRNA interaction networks were constructed in order to predict the potential molecular mechanisms of regulation in the cell injuries. We believe that this is the first report demonstrating the dysregulation of lncRNAs in cells upon E. coli and S. aureus infections, suggesting that they have the potential to become important diagnostic markers and to provide novel insights into controlling and preventing mastitis.

Non-coding RNAs as Novel Biomarkers in Cancer Drug Resistance

Chemotherapy is often the primary and most effective anticancer treatment; however, drug resistance remains a major obstacle to it being curative. Recent studies have demonstrated that non-coding RNAs (ncRNAs), especially microRNAs and long non-coding RNAs, are involved in drug resistance of tumor cells in many ways, such as modulation of apoptosis, drug efflux and metabolism, epithelial-to-mesenchymal transition, DNA repair, and cell cycle progression. Exploring the relationships between ncRNAs and drug resistance will not only contribute to our understanding of the mechanisms of drug resistance and provide ncRNA biomarkers of chemoresistance, but will also help realize personalized anticancer treatment regimens. Due to the high cost and low efficiency of biological experimentation, many researchers have opted to use computational methods to identify ncRNA biomarkers associated with drug resistance. In this review, we summarize recent discoveries related to ncRNA-mediated drug resistance and highlight the computational methods and resources available for ncRNA biomarkers involved in chemoresistance.

A Seven-Long Non-coding RNA Signature Improves Prognosis Prediction of Lung Adenocarcinoma: An Integrated Competing Endogenous RNA Network Analysis

Early and precise prediction is an important way to reduce the poor prognosis of lung adenocarcinoma (LUAD) patients. Nevertheless, the widely used tumor, node, and metastasis (TNM) staging system based on anatomical information only often could not achieve adequate performance on foreseeing the prognosis of LUAD patients. This study thus aimed to examine whether the long non-coding RNAs (lncRNAs), known highly involved in the tumorigenesis of LUAD through the competing endogenous RNAs (ceRNAs) mechanism, could provide additional information to improve prognosis prediction of LUAD patients. To prove the hypothesis, a dataset consisting of both RNA sequencing data and clinical pathological data, obtained from The Cancer Genome Atlas (TCGA) database, was analyzed. Then, differentially expressed RNAs (DElncRNAs, DEmiRNAs, and DEmRNAs) were identified and a lncRNA-miRNA-mRNA ceRNA network was constructed based on those differentially expressed RNAs. Functional enrichment analysis revealed that this ceRNA network was highly enriched in some cancer-associated signaling pathways. Next, lasso-Cox model was run 1,000 times to recognize the potential survival-related combinations of the candidate lncRNAs in the ceRNA network, followed by the "best subset selection" to further optimize these lncRNA-based combinations, and a seven-lncRNA prognostic signature with the best performance was determined. Based on the median risk score, LUAD patients could be well distinguished into high-/low-risk subgroups. The Kaplan-Meier survival curve showed that LUAD patients in the high-risk group had significantly shorter overall survival than those in the low-risk group (log-rank test P = 4.52 × 10^-9). The ROC curve indicated that the clinical genomic model including both the TNM staging system and the signature had a superior performance in predicting the patients' overall survival compared to the clinical model with the TNM staging system only. Further stratification analysis suggested that the signature could work well in the different strata of the stage, gender, or age, rendering it to be a wide application. Finally, a ceRNA subnetwork related to the signature was extracted, demonstrating its high involvement in the tumorigenesis mechanism of LUAD. In conclusion, the present study established a lncRNA-based molecular signature, which can significantly improve prognosis prediction for LUAD patients.

Differential Expression and Bioinformatic Analysis of the circRNA Expression in Migraine Patients

Background

CircRNAs are noncoding RNA molecules that have recently been described and shown to regulate miRNA functionality. While recent studies have suggested such circRNAs to be associated with pain related diseases in humans, no comprehensive migraine-related circRNA profiles have been generated, and there is currently no clear understanding of whether they can serve as regulators of migraine pathology.

Methods

We initially conducted a circRNA microarray analysis of the plasma of migraine patients and healthy controls. Based upon these data, we then selected 8 differentially expressed circRNAs and confirmed their expression in more migraine patient plasma samples via real-time PCR. We then performed functional and pathway enrichment analyses. Lastly, using a robust rank aggregation approach, we constructed a ceRNA network according to predicted circRNA-miRNA and miRNA-mRNA pairs in these migraine patient samples.

Results

We were able to detect 2039 circRNAs in our patient samples, with 794 of 1245 these circRNAs being up- and downregulated in migraine patients relative to controls, respectively (fold change ≥ 1.5, p < 0.01). A qRT-PCR analysis confirmed that the expression of hsa_circRNA_100236, hsa_circRNA_102413, and hsa_circRNA_000367 was significantly enhanced in migraine patients, whereas the expression of hsa_circRNA_103809, hsa_circRNA_103670, and hsa_circRNA_101833 was significantly reduced in these individuals relative to healthy controls. We found these differentially regulated circRNAs to be associated with numerous predicted biological processes, with enrichment analyses suggesting that they may modulate the PI3K-Akt signaling so as to promote inflammation to drive migraine development. However, further research will be needed to formally test these mechanistic possibilities and to validate these circRNAs as potential biomarkers of migraine patients.

Conclusions

Our results offer new potential insights into the mechanistic basis of this condition and suggest that hsa_circRNA_000367 and hsa_circRNA_102413 may offer value as regulators of migraine pathology.

Inferences of individual differences in response to tripterysium glycosides across patients with Rheumatoid arthritis using a novel ceRNA regulatory axis

BACKGROUND

To identify biomarkers for guiding therapy and predicting clinical response of Tripterysium Glycosides Tablets (TGT) treatment is an urgent task due to individual differences in TGT response across rheumatoid arthritis (RA) patients. Competing endogenous RNA (ceRNA) regulatory system may influence drug response with involvement in diverse biological processes. Herein, we aimed to identify a TGT response-related ceRNA axis.

METHODS

A TGT response-related ceRNA axis was screened according to clinical cohort-based RNA expression profiling, lncRNA-mRNA coexpression, and ceRNA network analyses. Its clinical relevance was evaluated by computational modeling. Regulatory mechanisms of ceRNA axis were also experimentally investigated.

RESULTS

The ceRNA regulatory axis combined with lncRNA ENST00000494760, miR-654-5p, and C1QC was identified as a candidate biomarker for RA patients' response to TGT. Both ENST00000494760 and C1QC mRNA expression were significantly lower, while miR-654-5p expression was dramatically higher in TGT responders than nonresponders. Its clinical relevance was verified by computational modeling based on both independent clinical validation cohort and collagen-induced arthritis (CIA) mice. Mechanistically, miR-654-5p directly bound to the 3'-untranslated region of both ENST00000494760 and C1QC mRNA to inhibit their expression. Moreover, miR-654-5p suppressed C1QC mRNA expression, but ENST00000494760 bound to miR-654-5p and relieved its repression on C1QC mRNA, leading to RA aggressive progression and weak TGT response.

CONCLUSIONS

LncRNA ENST00000494760 overexpression may sponge miR-654-5p to promote C1QC expression in RA patients. This novel ceRNA axis may serve as a biomarker for screening the responsive RA patients to TGT treatment, which will allow improved personalized healthcare.

Identification of a Six-lncRNA Signature With Prognostic Value for Breast Cancer Patients

Breast cancer (BRCA) is the most common cancer and a major cause of death in women. Long non-coding RNAs (lncRNAs) are emerging as key regulators and have been implicated in carcinogenesis and prognosis. In this study, we aimed to develop a lncRNA signature of BRCA patients to improve risk stratification. In the training cohort (GSE21653, n = 232), 17 lncRNAs were identified by univariate Cox proportional hazards regression, which were significantly associated with patients’ survival. The least absolute shrinkage and selection operator-penalized Cox proportional hazards regression analysis was used to identify a six-lncRNA signature. According to the median of the signature risk score, patients were divided into a high-risk group and a low-risk group with significant disease-free survival differences in the training cohort. A similar phenomenon was observed in validation cohorts (GSE42568, n = 101; GSE20711, n = 87). The six-lncRNA signature remained as independent prognostic factors after adjusting for clinical factors in these two cohorts. Furthermore, this signature significantly predicted the survival of grade III patients and estrogen receptor-positive patients. Furthermore, in another cohort (GSE19615, n = 115), the low-risk patients that were treated with tamoxifen therapy had longer disease-free survival than those who underwent no therapy. Overall, the six-lncRNA signature can be a potential prognostic tool used to predict disease-free survival of patients and to predict the benefits of tamoxifen treatment in BRCA, which will be helpful in guiding individualized treatments for BRCA patients.

Human Plasma-Derived 3D Cultures Model Breast Cancer Treatment Responses and Predict Clinically Effective Drug Treatment Concentrations

Lack of efficacy and a low overall success rate of phase I-II clinical trials are the most common failures when it comes to advancing cancer treatment. Current drug sensitivity screenings present several challenges including differences in cell growth rates, the inconsistent use of drug metrics, and the lack of translatability. Here, we present a patient-derived 3D culture model to overcome these limitations in breast cancer (BCa). The human plasma-derived 3D culture model (HuP3D) utilizes patient plasma as the matrix, where BCa cell lines and primary BCa biopsies were grown and screened for drug treatments. Several drug metrics were evaluated from relative cell count and growth rate curves. Correlations between HuP3D metrics, established preclinical models, and clinical effective concentrations in patients were determined. HuP3D efficiently supported the growth and expansion of BCa cell lines and primary breast cancer tumors as both organoids and single cells. Significant and strong correlations between clinical effective concentrations in patients were found for eight out of ten metrics for HuP3D, while a very poor positive correlation and a moderate correlation was found for 2D models and other 3D models, respectively. HuP3D is a feasible and efficacious platform for supporting the growth and expansion of BCa, allowing high-throughput drug screening and predicting clinically effective therapies better than current preclinical models.

Identification of a competing endogenous RNA network associated with prognosis of pancreatic adenocarcinoma

Background

Emerging evidence suggests that competing endogenous RNAs plays a crucial role in the development and progress of pancreatic adenocarcinoma (PAAD). The objective was to identify a new lncRNA-miRNA-mRNA network as prognostic markers, and develop and validate a multi-mRNAs-based classifier for predicting overall survival (OS) in PAAD.

Methods

Data on pancreatic RNA expression and clinical information of 445 PAAD patients and 328 normal subjects were downloaded from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Genotype-Tissue Expression (GTEx). The weighted correlation network analysis (WGCNA) was used to analyze long non-coding RNA (lncRNA) and mRNA, clustering genes with similar expression patterns. MiRcode was used to predict the sponge microRNAs (miRNAs) corresponding to lncRNAs. The downstream targeted mRNAs of miRNAs were identified by starBase, miRDB, miRTarBase and Targetscan. A multi-mRNAs-based classifier was develop using least absolute shrinkage and selection operator method (LASSO) COX regression model, which was tested in an independent validation cohort.

Results

A lncRNA-miRNA-mRNA co-expression network which consisted of 60 lncRNAs, 3 miRNAs and 3 mRNAs associated with the prognosis of patients with PAAD was established. In addition, we constructed a 14-mRNAs-based classifier based on a training cohort composed of 178 PAAD patients, of which the area under receiver operating characteristic (AUC) in predicting 1-year, 3-year, and 5-year OS was 0.719, 0.806 and 0.794, respectively. The classifier also shown good prediction function in independent verification cohorts, with the AUC of 0.604, 0.639 and 0.607, respectively.

Conclusions

A novel competitive endogenous RNA (ceRNA) network associated with progression of PAAD could be used as a reference for future molecular biology research.

Circular RNA circPITX1 knockdown inhibits glycolysis to enhance radiosensitivity of glioma cells by miR-329-3p/NEK2 axis

Background

Numerous circular RNAs (circRNAs) have been recognized as vital modulators of human malignancies, including glioma. Whereas, the functional role of circRNA Pituitary Homeo Box 1 (circPITX1) in the radioresistance of glioma cells remains largely uncertain.

Methods

Quantitative real-time PCR (qRT-PCR) or western blot analysis was employed to examine the expression of circPITX1, microRNA (miR)-329-3p and NIMA-related kinase 2 (NEK2). 3-(4, 5-dimethylthiazol-2-y1)-2, 5-diphenyl tetrazolium bromide (MTT) assay was used to determine cell viability. Glycolysis was assessed by commercial kits and western blot analysis. Colony formation assay was conducted to analyze cell survival and clonogenicity capacity. The relationship among circPITX1, miR-329-3p and NEK2 was confirmed via dual-luciferase reporter assay. The in vivo function of circPITX1 was evaluated by tumor xenograft assay.

Results

Expression of circPITX1 and NEK2 was up-regulated in glioma tissues and cells, while miR-329-3p exhibited reverse trend. CircPITX1 knockdown repressed viability, glycolysis and colony formation, but promoted radiosensitivity of glioma cells, as well as inhibited tumor growth in vivo. MiR-329-3p was a target miRNA of circPITX1 and miR-329-3p deficiency reversed knockdown of circPITX1-mediated glycolysis inhibition and radioresistance reduction. MiR-329-3p exerted inhibitory effects on glycolysis and radioresistance of glioma cells by targeting NEK2. CircPITX1 facilitated NEK2 expression by sponging miR-329-3p. Glycolytic inhibitor 2-deoxy-d-glucose (2-DG) disposition weakened the promoted impact on glycolysis caused by circPITX1.

Conclusion

CircPITX1 knockdown reduced glycolysis to contribute to radiosensitivity in glioma through miR-329-3p/NEK2 axis, providing a possible mechanism of circPITX1 in the development of glioma.

Competing endogenous RNA (ceRNA) hypothetic model based on comprehensive analysis of long non-coding RNA expression in lung adenocarcinoma

BACKGROUND

Non-small cell lung cancer (NSCLC) is a major subtype of lung cancer with high malignancy and bad prognosis, consisted of lung adenocarcinomas (LUAD) and lung squamous cell carcinomas (LUSC) chiefly. Multiple studies have indicated that competing endogenous RNA (ceRNA) network centered long noncoding RNAs (lncRNAs) can regulate gene expression and the progression of various cancers. However, the research about lncRNAs-mediated ceRNA network in LUAD is still lacking.

METHODS

In this study, we analyzed the RNA-seq database from The Cancer Genome Atlas (TCGA) and obtained dysregulated lncRNAs in NSCLC, then further identified survival associated lncRNAs through Kaplan-Meier analysis. Quantitative real time PCR (qRT-PCR) was performed to confirm their expression in LUAD tissues and cell lines. The ceRNA networks were constructed based on DIANA-TarBase and TargetScan databases and visualized with OmicShare tools. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to investigate the potential function of ceRNA networks.

RESULTS

In total, 1,437 and 1,699 lncRNAs were found to be up-regulated in LUAD and LUSC respectively with 895 lncRNAs overlapping (|log2FC| > 3, adjusted P value <0.01). Among which, 222 lncRNAs and 46 lncRNAs were associated with the overall survival (OS) of LUAD and LUSC, and 18 out of 222 up-regulated lncRNAs were found to have inverse correlation with LUAD patients' OS (|log2FC| > 3, adjusted P value < 0.02). We selected 3 lncRNAs (CASC8, LINC01842 and VPS9D1-AS1) out of these 18 lncRNAs and confirmed their overexpression in lung cancer tissues and cells. CeRNA networks were further constructed centered CASC8, LINC01842 and VPS9D1-AS1 with 3 miRNAs and 100 mRNAs included respectively.

CONCLUSION

Through comprehensively analyses of TCGA, our study identified specific lncRNAs as candidate diagnostic and prognostic biomarkers for LUAD. The novel ceRNA network we created provided more insights into the regulatory mechanisms underlying LUAD.

Systematic Characterization of Circular RNA-Associated CeRNA Network Identified Novel circRNA Biomarkers in Alzheimer's Disease

Alzheimer's disease (AD), a degenerative disease of the central nervous system, is the most common form of dementia in old age. The complexity and behavior of circular RNA (circRNA)-associated competing endogenous RNA (ceRNA) network remained poorly characterized in AD. The aim of this study was to elucidate the regulatory networks of dysregulated circRNAs from ceRNA view and identify potential risk circRNAs involved in AD pathogenesis. Consistent differentially expressed genes (CDEGs) were obtained using meta-analysis for multiple microarrays, and differentially expressed miRNAs (DEmiRs) were identified using empirical Bayes method. The circRNA-associated ceRNA network (cirCeNET) was constructed based on “ceRNA hypothesis” using an integrated system biology method. A total of 1,872 CDEGs and 48 DEmiRs were screened across different datasets. By mapping CDEGs and DEmiRs into the cirCeNET, an AD-related circRNA-associated ceRNA network (ADcirCeNET) was constructed, including 3,907 edges and 1,407 nodes (276 circRNAs, 14 miRNAs and 1,117 mRNAs). By prioritizing AD risk circRNA-associated ceRNAs, we found that the circRNA KIAA1586 occurred most frequently in the AD risk circRNA-associated ceRNAs and function as a ceRNA that operates by competitively binding three known AD-risk miRNAs. In silico functional analysis suggested that circRNA KIAA1586-related ceRNA network was significantly enriched in known AD-associated biological processes. Our study provided a global view and systematic dissection of circRNA-associated ceRNA network. The identified circRNA KIAA1586 may be a key risk factor involved in AD pathogenesis.

A network-based analysis for mining the risk pathways in glioblastoma

The most malignant type of brain tumour is glioblastoma multiforme (GBM). Patients with GBM often have a poor prognosis, as a result of incomplete or inaccurate diagnoses. Regulatory pathways have been demonstrated to serve important roles in complex human diseases. Therefore, deciphering these risk pathways may shed light on the molecular mechanisms underlying GBM progression. In the present study, differentially expressed genes and microRNAs (miRNAs) in a publicly available database were identified between normal and tumour samples. To determine the pathophysiology and molecular mechanisms underlying GBM, integrated network analysis was performed to mine GBM-specific risk pathways. Specifically, a GBM-specific regulatory network was constructed that integrated manually curated GBM-associated transcription and post-transcriptional data resources, including transcription factors and miRNAs. A total of 1,827 differentially expressed genes and 30 miRNAs were identified. The differentially expressed genes were significantly enriched in a number of immune response-associated functions. Based on the GBM-specific regulatory network, 15 risk regulatory pathways containing not only known regulators, but also potential novel targets that might be involved in tumourigenesis were identified. Network analysis provides a strategy for leveraging genomic data to identify potential oncogenic pathways and molecular targets for GBM.

Detecting the long non‑coding RNA signature related to spinal cord ependymal tumor subtype using a genome‑wide methylome analysis approach

Ependymoma is a type of intramedullary tumor that tends to occur in the adult spinal cord. Ependymoma affects the nervous system and has significant impacts on the quality of life, and it may lead to mortality. Previous studies have performed molecular classification of spinal cord ependymal tumors at the DNA methylation level. However, the DNA methylation status of non-coding regions in spinal cord ependymal tumors remains unclear. In the present study, a genome-wide methylome method was used to characterize the DNA methylation landscape of long non-coding RNAs (lncRNAs) in spinal cord ependymal tumor samples. The present study identified lncRNA signatures associated with tumor subtypes based on the methylation status of lncRNA promoters. The present results suggested that the identified lncRNA signatures were associated with cancer- or nervous system-related protein-coding genes. The majority of the identified lncRNAs was hypomethylated, and may have a role in spinal cord development. The present findings suggested that detection of tumor subtype-specific lncRNAs may facilitate the identification of novel diagnostic and therapeutic strategies to treat patients with spinal cord ependymal tumor.

Noncoding RNA:RNA Regulatory Networks in Cancer

Noncoding RNAs (ncRNAs) constitute the majority of the human transcribed genome. This largest class of RNA transcripts plays diverse roles in a multitude of cellular processes, and has been implicated in many pathological conditions, especially cancer. The different subclasses of ncRNAs include microRNAs, a class of short ncRNAs; and a variety of long ncRNAs (lncRNAs), such as lincRNAs, antisense RNAs, pseudogenes, and circular RNAs. Many studies have demonstrated the involvement of these ncRNAs in competitive regulatory interactions, known as competing endogenous RNA (ceRNA) networks, whereby lncRNAs can act as microRNA decoys to modulate gene expression. These interactions are often interconnected, thus aberrant expression of any network component could derail the complex regulatory circuitry, culminating in cancer development and progression. Recent integrative analyses have provided evidence that new computational platforms and experimental approaches can be harnessed together to distinguish key ceRNA interactions in specific cancers, which could facilitate the identification of robust biomarkers and therapeutic targets, and hence, more effective cancer therapies and better patient outcome and survival.