Identification of Dysregulated Competitive Endogenous RNA Networks Driven by Copy Number Variations in Malignant Gliomas

Identifying the driver miRNAs with somatic copy number alterations driving dysregulated ceRNA networks in cancers

BACKGROUND

MicroRNAs (miRNAs) play critical roles in cancer initiation and progression, which were critical components to maintain the dynamic balance of competing endogenous RNA (ceRNA) networks. Somatic copy number alterations (SCNAs) in the cancer genome could disturb the transcriptome level of miRNA to deregulate this balance. However, the driving effects of SCNAs of miRNAs were insufficiently understood.

METHODS

In this study, we proposed a method to dissect the functional roles of miRNAs under different copy number states and identify driver miRNAs by integrating miRNA SCNAs profile, miRNA-target relationships and expression profiles of miRNA, mRNA and lncRNA.

RESULTS

Applying our method to 813 TCGA breast cancer (BRCA) samples, we identified 29 driver miRNAs whose SCNAs significantly and concordantly regulated their own expression levels and further inversely dysregulated expression levels of their targets or disturbed the miRNA-target networks they directly involved. Based on miRNA-target networks, we further constructed dynamic ceRNA networks driven by driver SCNAs of miRNAs and identified three different patterns of SCNA interference in the miRNA-mediated dynamic ceRNA networks. Survival analysis of driver miRNAs showed that high-level amplifications of four driver miRNAs (including has-miR-30d-3p, has-mir-30b-5p, has-miR-30d-5p and has-miR-151a-3p) in 8q24 characterized a new BRCA subtype with poor prognosis and contributed to the dysfunction of cancer-associated hallmarks in a complementary way. The SCNAs of driver miRNAs across different cancer types contributed to the cancer development by dysregulating different components of the same cancer hallmarks, suggesting the cancer specificity of driver miRNA.

CONCLUSIONS

These results demonstrate the efficacy of our method in identifying driver miRNAs and elucidating their functional roles driven by endogenous SCNAs, which is useful for interpreting cancer genomes and pathogenic mechanisms.

Dysregulated ceRNA network modulated by copy number variation-driven lncRNAs in breast cancer: A comprehensive analysis

Breast cancer is a malignancy harmful to physical and mental health in women, with quite high mortality. Copy number variations (CNVs) are vital factors affecting the progression of breast cancer. Detecting CNVs in breast cancer to predict the prognosis of patients has become a promising approach to accurate treatment in recent years. The differential analysis was performed on CNVs of long noncoding RNAs (lncRNAs) as well as the expression of lncRNAs, microRNAs (miRNAs) and mRNAs in normal tissue and breast tumor tissue based on The Cancer Genome Atlas (TCGA) database. The CNV-driven lncRNAs were identified by the Kruskal-Wallis test. Meanwhile, a competitive endogenous RNA (ceRNA) network regulated by CNV-driven lncRNA was constructed. As the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed, the mRNAs in the dysregulated ceRNA network were mainly enriched in the biological functions and signaling pathways, including the Focal Adhesion-PI3K-Akt-mTOR-signaling pathway, the neuronal system, metapathway biotransformation Phase I and II and blood circulation, etc. The relationship between the CNVs of five lncRNAs and their gene expression in the ceRNA network was analyzed via a chi-square test, which confirmed that except for LINC00243, the expression of four lncRNAs was notably correlated with the CNVs. The survival analysis revealed that only the copy number gain of LINC00536 was evidently related to the poor prognosis of patients. The CIBERSORT algorithm showed that five lncRNAs were correlated with the abundance of immune cell infiltration and immune checkpoints. In a word, by analyzing CNV-driven lncRNAs and the ceRNA network regulated by these lncRNAs, this study explored the mechanism of breast cancer and provided novel insights into new biomarkers.

Multi-omics data integration for subtype identification of Chinese lower-grade gliomas: a joint similarity network fusion approach

Lower-grade gliomas (LGG), characterized by heterogeneity and invasiveness, originate from the central nervous system. Although studies focusing on molecular subtyping and molecular characteristics have provided novel insights into improving the diagnosis and therapy of LGG, there is an urgent need to identify new molecular subtypes and biomarkers that are promising to improve patient survival outcomes. Here, we proposed a joint similarity network fusion (Joint-SNF) method to integrate different omics data types to construct a fused network using the Joint and Individual Variation Explained (JIVE) technique under the SNF framework. Focusing on the joint network structure, a spectral clustering method was employed to obtain subtypes of patients. Simulation studies show that the proposed Joint-SNF method outperforms the original SNF approach under various simulation scenarios. We further applied the method to a Chinese LGG data set including mRNA expression, DNA methylation and microRNA (miRNA). Three molecular subtypes were identified and showed statistically significant differences in patient survival outcomes. The five-year mortality rates of the three subtypes are 80.8%, 32.1%, and 34.4%, respectively. After adjusting for clinically relevant covariates, the death risk of patients in Cluster 1 was 5.06 times higher than patients in other clusters. The fused network attained by the proposed Joint-SNF method enhances strong similarities, thus greatly improves subtyping performance compared to the original SNF method. The findings in the real application may provide important clues for improving patient survival outcomes and for precision treatment for Chinese LGG patients. An R package to implement the method can be accessed in Github at https://github.com/Sameerer/Joint-SNF.

Dissecting and analyzing the Subclonal Mutations Associated with Poor Prognosis in Diffuse Glioma

The prognostic and therapeutic implications in diffuse gliomas are still challenging. In this study, we first performed an integrative framework to infer the clonal status of mutations in glioblastomas (GBMs) and low-grade gliomas (LGGs) by using exome sequencing data from TCGA and observed both clonal and subclonal mutations for most mutant genes. Based on the clonal status of a given gene, we systematically investigated its prognostic value in GBM and LGG, respectively. Focusing on the subclonal mutations, our results showed that they were more likely to contribute to the poor prognosis, which could be hardly figured out without considering clonal status. These risk subclonal mutations were associated with some specific genomic features, such as genomic instability and intratumor heterogeneity, and their accumulation could enhance the prognostic value. By analyzing the regulatory mechanisms underlying the risk subclonal mutations, we found that the subclonal mutations of AHNAK and AHNAK2 in GBM and those of NF1 and PTEN in LGG could influence some important molecules and functions associated with glioma progression. Furthermore, we dissected the role of risk subclonal mutations in tumor evolution and found that advanced subclonal mutations showed poorer overall survival. Our study revealed the importance of clonal status in prognosis analysis, highlighting the role of the subclonal mutation in glioma prognosis.

Identification of dysregulated competing endogenous RNA networks in glioblastoma: A way toward improved therapeutic opportunities

Glioblastoma is recognized as one of the leading causes of death worldwide. Although there have been considerable advancements in understanding the causative molecular mechanisms of this malignancy, effective therapeutic strategies are still in limited use. It has been revealed that non-coding RNAs (ncRNAs) play critical roles in glioblastoma development, while interactions between the regulatory molecules such as long ncRNAs (lncRNAs), microRNAs (miRNAs), transcribed pseudogenes, and circular RNAs (circRNAs) remain to be fully deciphered. Over the recent years, researchers have discovered a new category of RNA molecules called competing endogenous RNA (ceRNA). This kind of RNA can contribute to molecular interactions in the form of ceRNA networks (ceRNETs). Multiple lines of evidence have demonstrated that dysregulation of various ceRNA networks is involved in glioblastoma development. Therefore, gaining insights into these dysregulations might offer potential for the early diagnosis of glioblastoma patients and identification of efficient therapeutic targets. In this review, we provide an overview of recent discoveries on ceRNA networks and the involvement of dysregulated networks in posing limitations to temozolomide therapy. We also describe signaling pathways relevant to the progression of glioblastoma.

Machine intelligence for precision oncology

Despite various advances in cancer research, the incidence and mortality rates of malignant diseases have remained high. Accurate risk assessment, prevention, detection, and treatment of cancer tailored to the individual are major challenges in clinical oncology. Artificial intelligence (AI), a field of applied computer science, has shown promising potential of accelerating evolution of healthcare towards precision oncology. This article focuses on highlights of the application of data-driven machine learning (ML) and deep learning (DL) in translational research for cancer diagnosis, prognosis, treatment, and clinical outcomes. ML-based algorithms in radiological and histological images have been demonstrated to improve detection and diagnosis of cancer. DL-based prediction models in molecular or multi-omics datasets of cancer for biomarkers and targets enable drug discovery and treatment. ML approaches combining radiomics with genomics and other omics data enhance the power of AI in improving diagnosis, prognostication, and treatment of cancer. Ethical and regulatory issues involving patient confidentiality and data security impose certain limitations on practical implementation of ML in clinical oncology. However, the ultimate goal of application of AI in cancer research is to develop and implement multi-modal machine intelligence for improving clinical decision on individualized management of patients.

Long Non-Coding RNA UBA6-AS1 Promotes the Malignant Properties of Glioblastoma by Competitively Binding to microRNA-760 and Enhancing Homeobox A2 Expression

Background

The dysregulation of long non-coding RNAs is a frequent finding in glioblastoma (GBM) and is considered as a crucial mechanism contributing to GBM oncogenesis and progression. The biological roles and underlying mechanisms of action of UBA6 antisense RNA 1 (UBA6-AS1) in GBM have been rarely investigated. Therefore, the aim of the present study was to investigate in detail the role of UBA6-AS1 in the modulation of the malignant properties of GBM and explore the possible underlying mechanism(s).

Methods

The expression of UBA6-AS1 in GBM was determined via reverse transcription-quantitative PCR. Cell Counting Kit-8 assay, flow cytometric analysis, Transwell migration and invasion assays, and in vivo tumorigenicity assay were applied to elucidate the biological effects of UBA6-AS1 on GBM cells. The possible biological events associated with UBA6-AS1 were investigated by luciferase reporter, RNA immunoprecipitation (RIP) and rescue assays.

Results

UBA6-AS1 was overexpressed in GBM, which was consistent with the data from The Cancer Genome Atlas database. In the case of UBA6-AS1 depletion, GBM cell proliferation, migration and invasion were notably decreased and cell apoptosis was enhanced in vitro. Additionally, knockdown of UBA6-AS1 suppressed the proliferation of GBM cells in vivo. Mechanistically, UBA6-AS1 functioned as a competing endogenous RNA by adsorbing miR-760 and, consequently, upregulating homeobox A2 (HOXA2) expression. Rescue experiments demonstrated that the UBA6-AS1 silencing-mediated regulatory effects on GBM cells were reversed by the decrease of miR-760 or restoration of HOXA2 expression.

Conclusion

Therefore, the results of the present study revealed that UBA6-AS1 promoted the malignant progression of GBM via targeting the miR-760/HOXA2 axis, thereby representing a promising effective target for the treatment of GBM.

A Five-lncRNAs Signature-Derived Risk Score Based on TCGA and CGGA for Glioblastoma: Potential Prospects for Treatment Evaluation and Prognostic Prediction

Accumulating studies have confirmed the crucial role of long non-coding RNAs (ncRNAs) as favorable biomarkers for cancer diagnosis, therapy, and prognosis prediction. In our recent study, we established a robust model which is based on multi-gene signature to predict the therapeutic efficacy and prognosis in glioblastoma (GBM), based on Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. lncRNA-seq data of GBM from TCGA and CGGA datasets were used to identify differentially expressed genes (DEGs) compared to normal brain tissues. The DEGs were then used for survival analysis by univariate and multivariate COX regression. Then we established a risk score model, depending on the gene signature of multiple survival-associated DEGs. Subsequently, Kaplan-Meier analysis was used for estimating the prognostic and predictive role of the model. Gene set enrichment analysis (GSEA) was applied to investigate the potential pathways associated to high-risk score by the R package “cluster profile” and Wiki-pathway. And five survival associated lncRNAs of GBM were identified: LNC01545, WDR11-AS1, NDUFA6-DT, FRY-AS1, TBX5-AS1. Then the risk score model was established and shows a desirable function for predicting overall survival (OS) in the GBM patients, which means the high-risk score significantly correlated with lower OS both in TCGA and CGGA cohort. GSEA showed that the high-risk score was enriched with PI3K-Akt, VEGFA-VEGFR2, TGF-beta, Notch, T-Cell pathways. Collectively, the five-lncRNAs signature-derived risk score presented satisfactory efficacies in predicting the therapeutic efficacy and prognosis in GBM and will be significant for guiding therapeutic strategies and research direction for GBM.

Gene regulation network analysis reveals core genes associated with survival in glioblastoma multiforme

Glioblastoma multiforme (GBM) is a very serious mortality of central nervous system cancer. The microarray data from GSE2223, GSE4058, GSE4290, GSE13276, GSE68848 and GSE70231 (389 GBM tumour and 67 normal tissues) and the RNA-seq data from TCGA-GBM dataset (169 GBM and five normal samples) were chosen to find differentially expressed genes (DEGs). RRA (Robust rank aggregation) method was used to integrate seven datasets and calculate 133 DEGs (82 up-regulated and 51 down-regulated genes). Subsequently, through the PPI (protein-protein interaction) network and MCODE/ cytoHubba methods, we finally filtered out ten hub genes, including FOXM1, CDK4, TOP2A, RRM2, MYBL2, MCM2, CDC20, CCNB2, MYC and EZH2, from the whole network. Functional enrichment analyses of DEGs were conducted to show that these hub genes were enriched in various cancer-related functions and pathways significantly. We also selected CCNB2, CDC20 and MYBL2 as core biomarkers, and further validated them in CGGA, HPA and CCLE database, suggesting that these three core hub genes may be involved in the origin of GBM. All these potential biomarkers for GBM might be helpful for illustrating the important role of molecular mechanisms of tumorigenesis in the diagnosis, prognosis and targeted therapy of GBM cancer.

Dissecting the Functional Mechanisms of Somatic Copy-Number Alterations Based on Dysregulated ceRNA Networks across Cancers

Somatic copy-number alterations (SCNAs) drive tumor growth and evolution. However, the functional roles of SCNAs across the genome are still poorly understood. We provide an integrative strategy to characterize the functional roles of driver SCNAs in cancers based on dysregulated competing endogenous RNA (ceRNA) networks. We identified 44 driver SCNAs in lower-grade glioma (LGG). The dysregulated patterns losing all correlation relationships dominated dysregulated ceRNA networks. Homozygous deletion of six genes in 9p21.3 characterized an LGG subtype with poor prognosis and contributed to the dysfunction of cancer-associated pathways in a complementary way. The pan-cancer analysis showed that different cancer types harbored different driver SCNAs through dysregulating the crosstalk with common ceRNAs. The same SCNAs destroyed their ceRNA networks through different miRNA-mediated ceRNA regulations in different cancers. Additionally, some SCNAs performed different functional mechanisms in different cancers, which added another layer of complexity to cancer heterogeneity. Compared with previous methods, our strategy could directly dissect functional roles of SCNAs from the view of ceRNA networks, which not only complemented the functions of protein-coding genes but also provided a new avenue to characterize the functions of noncoding RNAs. Also, our strategy could be applied to more types of cancers to identify pathogenic mechanism driven by the SCNAs.