In silico analysis identified miRNA‑based therapeutic agents against glioblastoma multiforme

Genomic and Epigenomic Features of Glioblastoma Multiforme and its Biomarkers

Glioblastoma multiforme is a serious and life-threatening tumor of central nervous system, characterized by aggressive behavior, poor prognosis, and low survival rate. Despite of the availability of aggressive antitumor therapeutic regimen for glioblastoma (radiotherapy followed by chemotherapeutic dose), recovery rate, and patients' survival ratio is attributed to the lack of selectivity of therapeutic drugs and less advancement in cancer therapeutics over last decade. Moreover, tools employed in conventional diagnosis of glioblastoma are more invasive and painful, making the process excruciating for the patients. These challenges urge for the need of novel biomarkers for diagnosis, prognosis, and prediction purpose with less invasiveness and more patient compliance. This article will explore the genetic biomarkers isocitrate dehydrogenase mutation, MGMT mutations, and EGFR that can be deployed as an analytical tool in diagnosis of disease and prognosis of a therapeutic course. The review also highlights the importance of employing novel microRNAs as prognostic biomarkers. Recent clinical advancements to treat GBM and to prevent relapse of the disease are also discussed in this article in the hope of finding a robust and effective method to treat GBM.

A rationally identified panel of microRNAs targets multiple oncogenic pathways to enhance chemotherapeutic effects in glioblastoma models

Glioblastoma (GBM) is the most common malignant brain tumor. Available treatments have limited success because most patients develop chemoresistance. Alternative strategies are required to improve anticancer effects of current chemotherapeutics while limiting resistance. Successful targeting of microRNAs (miRNAs) as regulators of gene expression can help reprogram GBM cells to better respond to chemotherapy. We aimed to identify a panel of miRNAs that target multiple oncogenic pathways to improve GBM therapy. We first identified differentially expressed miRNAs and tested if their target genes play central roles in GBM signaling pathways by analyzing data in the Gene Expression Omnibus and The Cancer Genome Atlas databases. We then studied the effects of different combinations of these miRNAs in GBM cells by delivering synthetic miRNAs using clinically compatible PLGA-PEG nanoparticles prior to treatment with temozolomide (TMZ) or doxorubicin (DOX). The successful miRNA panel was tested in mice bearing U87-MG cells co-treated with TMZ. We identified a panel of five miRNAs (miRNA-138, miRNA-139, miRNA-218, miRNA-490, and miRNA-21) and their oncogenic targets (CDK6, ZEB1, STAT3, TGIF2, and SMAD7) that cover four different signaling pathways (cell proliferation and apoptotic signaling, invasion and metastasis, cytokine signaling, and stemness) in GBM. We observed significant in vitro and in vivo enhancement of therapeutic efficiency of TMZ and DOX in GBM models. The proposed combination therapy using rationally selected miRNAs and chemotherapeutic drugs is effective owing to the ability of this specific miRNA panel to better target multiple genes associated with the hallmarks of cancer.

The Role and Mechanism of microRNA-1224 in Human Cancer

microRNAs (miRNAs) are a type of small endogenous non-coding RNAs composed of 20-22 nucleotides, which can regulate the expression of a gene by targeting 3’ untranslated region (3’-UTR) of mRNA. Many studies have reported that miRNAs are involved in the occurrence and progression of human diseases, including malignant tumors. miR-1224 plays significant roles in different tumors, including tumor proliferation, metastasis, invasion, angiogenesis, biological metabolism, and drug resistance. Mostly, it serves as a tumor suppressor. With accumulating proofs of miR-1224, it can act as a potential bio-indicator in the diagnosis and prognosis of patients with cancer. In this article, we review the characteristics and research progress of miR-1224 and emphasize the regulation and function of miR-1224 in different cancer. Furthermore, we conclude the clinical implications of miR-1224. This review may provide new horizons for deeply understanding the role of miR-1224 as biomarkers and therapeutic targets in human cancer.

Brain Regeneration Resembles Brain Cancer at Its Early Wound Healing Stage and Diverges From Cancer Later at Its Proliferation and Differentiation Stages

Gliomas are the most frequent type of brain cancers and characterized by continuous proliferation, inflammation, angiogenesis, invasion and dedifferentiation, which are also among the initiator and sustaining factors of brain regeneration during restoration of tissue integrity and function. Thus, brain regeneration and brain cancer should share more molecular mechanisms at early stages of regeneration where cell proliferation dominates. However, the mechanisms could diverge later when the regenerative response terminates, while cancer cells sustain proliferation. To test this hypothesis, we exploited the adult zebrafish that, in contrast to the mammals, can efficiently regenerate the brain in response to injury. By comparing transcriptome profiles of the regenerating zebrafish telencephalon at its three different stages, i.e., 1 day post-lesion (dpl)-early wound healing stage, 3 dpl-early proliferative stage and 14 dpl-differentiation stage, to those of two brain cancers, i.e., low-grade glioma (LGG) and glioblastoma (GBM), we reveal the common and distinct molecular mechanisms of brain regeneration and brain cancer. While the transcriptomes of 1 dpl and 3 dpl harbor unique gene modules and gene expression profiles that are more divergent from the control, the transcriptome of 14 dpl converges to that of the control. Next, by functional analysis of the transcriptomes of brain regeneration stages to LGG and GBM, we reveal the common and distinct molecular pathways in regeneration and cancer. 1 dpl and LGG and GBM resemble with regard to signaling pathways related to metabolism and neurogenesis, while 3 dpl and LGG and GBM share pathways that control cell proliferation and differentiation. On the other hand, 14 dpl and LGG and GBM converge with respect to developmental and morphogenetic processes. Finally, our global comparison of gene expression profiles of three brain regeneration stages, LGG and GBM exhibit that 1 dpl is the most similar stage to LGG and GBM while 14 dpl is the most distant stage to both brain cancers. Therefore, early convergence and later divergence of brain regeneration and brain cancer constitutes a key starting point in comparative understanding of cellular and molecular events between the two phenomena and development of relevant targeted therapies for brain cancers.

Estrogen Receptors as Molecular Targets of Endocrine Therapy for Glioblastoma

Hormonal factors may participate in the development and progression of glioblastoma, the most aggressive primary tumor of the central nervous system. Many studies have been conducted on the possible involvement of estrogen receptors (ERs) in gliomas. Since there is a tendency for a reduced expression of ERs as the degree of malignancy of such tumors increases, it is important to understand the role of these receptors in the progression and treatment of this disease. ERs belong to the family of nuclear receptors, although they can also be in the plasmatic membrane, cytoplasm and mitochondria. They are classified as estrogen receptors alpha and beta (ER⍺ and ERβ), each with different isoforms that have a distinct function in the organism. ERs regulate multiple physiological and pathological processes through the activation of genomic and nongenomic pathways in the cell. Nevertheless, the role of each isoform in the development and progression of glioblastoma is not completely clear. Diverse in vitro and in vivo studies have shown encouraging results for endocrine therapy as a treatment for gliomas. At the same time, many questions have arisen concerning the nature of ERs as well as the mechanism of action of the proposed drugs. Hence, the aim of the current review is to describe the drugs that could possibly be utilized in endocrine therapy for the treatment of high-grade gliomas, analyze their interaction with ERs, and explore the involvement of these drugs and receptors in resistance to standard chemotherapy.

Differential and Common Signatures of miRNA Expression and Methylation in Childhood Central Nervous System Malignancies: An Experimental and Computational Approach

Epigenetic modifications are considered of utmost significance for tumor ontogenesis and progression. Especially, it has been found that miRNA expression, as well as DNA methylation plays a significant role in central nervous system tumors during childhood. A total of 49 resected brain tumors from children were used for further analysis. DNA methylation was identified with methylation-specific MLPA and, in particular, for the tumor suppressor genes CASP8, RASSF1, MGMT, MSH6, GATA5, ATM1, TP53, and CADM1. miRNAs were identified with microarray screening, as well as selected samples, were tested for their mRNA expression levels. CASP8, RASSF1 were the most frequently methylated genes in all tumor samples. Simultaneous methylation of genes manifested significant results with respect to tumor staging, tumor type, and the differentiation of tumor and control samples. There was no significant dependence observed with the methylation of one gene promoter, rather with the simultaneous presence of all detected methylated genes' promoters. miRNA expression was found to be correlated to gene methylation. Epigenetic regulation appears to be of major importance in tumor progression and pathophysiology, making it an imperative field of study.

The Alterations and Potential Roles of MCMs in Breast Cancer

The minichromosome maintenance (MCM) protein family plays a key role in eukaryotic DNA replication and has been confirmed to be associated with the occurrence and progression of many tumors. However, the expression levels, functions, and prognostic values of MCMs in breast cancer (BC) have not been clearly and systematically explained. In this article, we studied the transcriptional levels of MCMs in BC based on the Oncomine database. Kaplan-Meier plotter was used to analyze prognostic value of MCMs in human BC patients. Furthermore, we constructed a MCM coexpression gene network and performed functional annotation analysis through DAVID to reveal the functions of MCMs and coexpressed genes. The data showed that the expression of MCM2–8 and MCM10 but not MCM1 and MCM9 was upregulated in BC. Kaplan-Meier plotter analysis revealed that high transcriptional levels of MCM2, MCM4–7, and MCM10 were significantly related to low relapse-free survival (RFS) in BC patients. In contrast, high levels of MCM1 and MCM9 predicted high RFS for BC patients. This study suggests that MCM2, MCM4–7, and MCM10 possess great potential to be valuable prognostic biomarkers for BC and that MCM1 and MCM9 may serve as potential treatment targets for BC patients.

The dysregulated expression and functional effect of CaMK2 in cancer

CaMK2 (calcium/calmodulin-dependent protein kinase 2), a multifunctional serine/threonine-protein kinase involved in diverse cellular processes, is vital for the transduction of the Ca²⁺ signaling cascade. Recently, research has highlighted the involvement of CaMK2 in cancer development. However, the specific effects of CaMK2 on cancer have not been fully elucidated. In this review, we summarize not only the altered expression of CaMK2 in a range of cancers, as evidenced by bioinformatics analysis, but also the significant role of CaMK2 in regulating cancer progression, such as proliferation and metastasis. In addition, we described the functional influence of CaMK2 on cancer stemness and resistance. Understanding the critical effects and mechanisms of CaMK2 in cancer would facilitate the development of a promising therapeutic strategy for cancer treatment.

Performance Comparison of Deep Learning Autoencoders for Cancer Subtype Detection Using Multi-Omics Data

A heterogeneous disease such as cancer is activated through multiple pathways and different perturbations. Depending upon the activated pathway(s), the survival of the patients varies significantly and shows different efficacy to various drugs. Therefore, cancer subtype detection using genomics level data is a significant research problem. Subtype detection is often a complex problem, and in most cases, needs multi-omics data fusion to achieve accurate subtyping. Different data fusion and subtyping approaches have been proposed over the years, such as kernel-based fusion, matrix factorization, and deep learning autoencoders. In this paper, we compared the performance of different deep learning autoencoders for cancer subtype detection. We performed cancer subtype detection on four different cancer types from The Cancer Genome Atlas (TCGA) datasets using four autoencoder implementations. We also predicted the optimal number of subtypes in a cancer type using the silhouette score and found that the detected subtypes exhibit significant differences in survival profiles. Furthermore, we compared the effect of feature selection and similarity measures for subtype detection. For further evaluation, we used the Glioblastoma multiforme (GBM) dataset and identified the differentially expressed genes in each of the subtypes. The results obtained are consistent with other genomic studies and can be corroborated with the involved pathways and biological functions. Thus, it shows that the results from the autoencoders, obtained through the interaction of different datatypes of cancer, can be used for the prediction and characterization of patient subgroups and survival profiles.

Potential Drug Prediction of Glioblastoma Based on Drug Perturbation-Induced Gene Expression Signatures

OBJECTIVES

Glioblastoma (GBM) is a malignant brain tumor which is the most common and aggressive type of central nervous system cancer, with high morbidity and mortality. Despite lots of systematic studies on the molecular mechanism of glioblastoma, the pathogenesis is still unclear, and effective therapies are relatively rare with surgical resection as the frequently therapeutic intervention. Identification of fundamental molecules and gene networks associated with initiation is critical in glioblastoma drug discovery. In this study, an approach for the prediction of potential drug was developed based on perturbation-induced gene expression signatures.

METHODS

We first collected RNA-seq data of 12 pairs of glioblastoma samples and adjacent normal samples from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by DESeq2, and coexpression networks were analyzed with weighted gene correlation network analysis (WGCNA). Furthermore, key driver genes were detected based on the differentially expressed genes and potential chemotherapeutic drugs and targeted drugs were found by correlating the gene expression profiles with drug perturbation database. Finally, RNA-seq data of glioblastoma from The Cancer Genome Atlas (TCGA) dataset was collected as an independent validation dataset to verify our findings.

RESULTS

We identified 1771 significantly DEGs with 446 upregulated genes and 1325 downregulated genes. A total of 24 key drivers were found in the upregulated gene set, and 81 key drivers were found in the downregulated gene set. We screened the Crowd Extracted Expression of Differential Signatures (CREEDS) database to identify drug perturbations that could reverse the key factors of glioblastoma, and a total of 354 drugs were obtained with p value < 10-10. Finally, 7 drugs that could turn down the expression of upregulated factors and 3 drugs that could reverse the expression of downregulated key factors were selected as potential glioblastoma drugs. In addition, similar results were obtained through the analysis of TCGA as independent dataset.

CONCLUSIONS

In this study, we provided a framework of workflow for potential therapeutic drug discovery and predicted 10 potential drugs for glioblastoma therapy.

Identification of Prognostic Model and Biomarkers for Cancer Stem Cell Characteristics in Glioblastoma by Network Analysis of Multi-Omics Data and Stemness Indices

The progression of most human cancers mainly involves the gradual accumulation of the loss of differentiated phenotypes and the sequential acquisition of progenitor and stem cell-like features. Glioblastoma multiforme (GBM) stem cells (GSCs), characterized by self-renewal and therapeutic resistance, play vital roles in GBM. However, a comprehensive understanding of GBM stemness remains elusive. Two stemness indices, mRNAsi and EREG-mRNAsi, were employed to comprehensively analyze GBM stemness. We observed that mRNAsi was significantly related to multi-omics parameters (such as mutant status, sample type, transcriptomics, and molecular subtype). Moreover, potential mechanisms and candidate compounds targeting the GBM stemness signature were illuminated. By combining weighted gene co-expression network analysis with differential analysis, we obtained 18 stemness-related genes, 10 of which were significantly related to survival. Moreover, we obtained a prediction model from both two independent cancer databases that was not only an independent clinical outcome predictor but could also accurately predict the clinical parameters of GBM. Survival analysis and experimental data confirmed that the five hub genes (CHI3L2, FSTL3, RPA3, RRM2, and YTHDF2) could be used as markers for poor prognosis of GBM. Mechanistically, the effect of inhibiting the proliferation of GSCs was attributed to the reduction of the ratio of CD133 and the suppression of the invasiveness of GSCs. The results based on an in vivo xenograft model are consistent with the finding that knockdown of the hub gene inhibits the growth of GSCs in vitro. Our approach could be applied to facilitate the development of objective diagnostic and targeted treatment tools to quantify cancer stemness in clinical tumors, and perhaps lead considerable benefits that could predict tumor prognosis, identify new stemness-related targets and targeted therapies, or improve targeted therapy sensitivity. The five genes identified in this study are expected to be the targets of GBM stem cell therapy.

CAMKIIγ is a targetable driver of multiple myeloma through CaMKIIγ/ Stat3 axis

Aberrant activation of CAMKIIγ has been linked to leukemia and T-cell lymphoma, but not multiple myeloma (MM). The purpose of this study was to explore the role of CaMKIIγ in the pathogenesis and therapy of MM. In this study, we found that CaMKIIγ was aberrantly activated in human MM and its expression level was positively correlated with malignant progression and poor prognosis. Ectopic expression of CaMKIIγ promoted cell growth, colony formation, cell cycle progress and inhibited apoptosis of MM cell lines, whereas, knockdown of CAMKIIγ expression suppressed MM cell growth in vitro and in vivo. Mechanically, we observed that CaMKIIγ overexpression upregulated p-ERK and p-Stat3 levels and suppression of CaMKIIγ had opposite effects. CaMKIIγ is frequently dysregulated in MM and plays a critical role in maintaining MM cell growth through upregulating STAT3 signaling pathway. Furthermore, our preclinical studies suggest that CaMKIIγ is a potential therapeutic target in MM, and could be intervened pharmacologically by small-molecule berbamine analogues.

miR-1224-5p inhibits the proliferation and invasion of ovarian cancer via targeting SND1

Emerging evidences have indicated that abnormal expression of microRNAs (miRNAs) contributed to carcinogenesis of ovarian cancer. However, the molecular mechanism of many aberrant expressed miRNAs was not known. Here, we discovered that miR-1224-5p was a downregulated miRNA in ovarian cancer via bioinformatic analysis and RT-qPCR. It was found that upregulation of miR-1224-5p inhibited cell proliferation and invasion ability of ovarian cancer cells. SND1, a well-characterized oncogene, was predicted as a target gene of miR-1224-5p. The western blotting, dual luciferase reporter assay, RNA-binding protein immunoprecipitation assay, and RT-qPCR demonstrated SND1 as a target gene of miR-1224-5p in ovarian cancer. MiR-1224-5p inhibited the expression of mesenchymal markers and increased the expression of epithelial markers in ovarian cancer cells via targeting SND1, indicating miR-1224-5p was involved in epithelial mesenchymal transition. The rescue assay manifested that miR-1224-5p-regulated cell proliferation and invasion mainly rely on downregulation of SND1 in ovarian cancer cells. In conclusion, our study revealed a direct regulatory association between miR-1224-5p and SND1 and their involvement in ovarian carcinogenesis.

Malignant Evaluation and Clinical Prognostic Values of m6A RNA Methylation Regulators in Glioblastoma

N6-methyladenosine (m6A) RNA methylation, the most common form of mRNA modification and regulated by the m6A RNA methylation regulators ("writers," "erasers," and "readers"), has been reported to be associated with the progression of the malignant tumor. However, its role in glioblastoma (GBM) has been poorly known. This study aimed to identify the expression, potential functions, and prognostic values of m6A RNA methylation regulators in GBM. Here, we revealed that the 13 central m6A RNA methylation regulators were firmly related to the clinical and molecular phenotype of GBM. Taking advantage of consensus cluster analysis, we obtained two categories of GBM samples and found malignancy-related processes of m6A methylation regulators and compounds that specifically targeted the malignant processes. Besides, we also obtained a list of genes with poor prognosis in GBM. Finally, we derived a risk-gene signature with three selected m6A RNA methylation regulators, which allowed us to extend the in-depth study and dichotomized the OS of patients with GBM into high- and low-risk subgroups. Notably, this risk-gene signature could be used as independent prognostic markers and accurate clinicopathological parameter predictors. In conclusion, m6A RNA methylation regulators are a type of vital participant in the malignant progression of GBM, with a critical potential in the prognostic stratification and treatment strategies of GBM.

Identification and analysis of long non-coding RNA related miRNA sponge regulatory network in bladder urothelial carcinoma

Background

The aim of this study was to investigate the regulatory network of lncRNAs as competing endogenous RNAs (ceRNA) in bladder urothelial carcinoma (BUC) based on gene expression data derived from The Cancer Genome Atlas (TCGA).

Materials and methods

RNA sequence profiles and clinical information from 414 BUC tissues and 19 non-tumor adjacent tissues were downloaded from TCGA. Differentially expressed RNAs derived from BUC and non-tumor adjacent samples were identified using the R package “edgeR”. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed using the “clusterProfiler” package. Gene ontology and protein–protein interaction (PPI) networks were analyzed for the differentially expressed mRNAs using the “STRING” database. The network for the dysregulated lncRNA associated ceRNAs was then constructed for BUC using miRcode, miRTarBase, miRDB, and TargetScan. Cox regression analysis was performed to identify independent prognostic RNAs associated with BUC overall survival (OS). Survival analysis for the independent prognostic RNAs within the ceRNA network was calculated using Kaplan–Meier curves.

Results

Based on our analysis, a total of 666, 1819 and 157 differentially expressed lncRNAs, mRNAs and miRNAs were identified respectively. The ceRNA network was then constructed and contained 59 lncRNAs, 23 DEmiRNAs, and 52 DEmRNAs. In total, 5 lncRNAs (HCG22, ADAMTS9-AS1, ADAMTS9-AS2, AC078778.1, and AC112721.1), 2 miRNAs (hsa-mir-145 and hsa-mir-141) and 6 mRNAs (ZEB1, TMEM100, MAP1B, DUSP2, JUN, and AIFM3) were found to be related to OS. Two lncRNAs (ADAMTS9-AS1 and ADAMTS9-AS2) and 4 mRNA (DUSP2, JUN, MAP1B, and TMEM100) were validated using GEPIA. Thirty key hub genes were identified using the ranking method of degree. KEGG analysis demonstrated that the majority of the DEmRNAs were involved in pathways associated with cancer.

Conclusion

Our findings provide an understanding of the important role of lncRNA–related ceRNAs in BUC. Additional experimental and clinical validations are required to support our findings.

MicroRNA based theranostics for brain cancer: basic principles

BACKGROUND

Because of the complexity of the blood-brain barrier (BBB), brain tumors, especially the most common and aggressive primary malignant tumor type arising from the central nervous system (CNS), glioblastoma, remain an essential challenge regarding diagnostic and treatment. There are no approved circulating diagnostic or prognostic biomarkers, nor novel therapies like immune checkpoint inhibitors for glioblastoma, and chemotherapy brings only minimal survival benefits. The development of molecular biology led to the discovery of new potential diagnostic tools and therapeutic targets, offering the premise to detect patients at earlier stages and overcome the current poor prognosis.

MAIN BODY

One potential diagnostic and therapeutic breakthrough might come from microRNAs (miRNAs). It is well-known that miRNAs play a role in the initiation and development of various types of cancer, including glioblastoma. The review aims to answer the following questions concerning the role of RNA theranostics for brain tumors: (1) which miRNAs are the best candidates to become early diagnostic and prognostic circulating biomarkers?; (2) how to deliver the therapeutic agents in the CNS to overcome the BBB?; (3) which are the best methods to restore/inhibit miRNAs?

CONCLUSIONS

Because of the proven roles played by miRNAs in gliomagenesis and of their capacity to pass from the CNS tissue into the blood or cerebrospinal fluid (CSF), we propose miRNAs as ideal diagnostic and prognostic biomarkers. Moreover, recent advances in direct miRNA restoration (miRNA mimics) and miRNA inhibition therapy (antisense oligonucleotides, antagomirs, locked nucleic acid anti-miRNA, small molecule miRNA inhibitors) make miRNAs perfect candidates for entering clinical trials for glioblastoma treatment.