Whole-genome mRNA expression profiling identifies functional and prognostic signatures in patients with mesenchymal glioblastoma multiforme

Machine learning-based identification of a cell death-related signature associated with prognosis and immune infiltration in glioma

Accumulating evidence suggests that a wide variety of cell deaths are deeply involved in cancer immunity. However, their roles in glioma have not been explored. We employed a logistic regression model with the shrinkage regularization operator (LASSO) Cox combined with seven machine learning algorithms to analyse the patterns of cell death (including cuproptosis, ferroptosis, pyroptosis, apoptosis and necrosis) in The Cancer Genome Atlas (TCGA) cohort. The performance of the nomogram was assessed through the use of receiver operating characteristic (ROC) curves and calibration curves. Cell-type identification was estimated by using the cell-type identification by estimating relative subsets of known RNA transcripts (CIBERSORT) and single sample gene set enrichment analysis methods. Hub genes associated with the prognostic model were screened through machine learning techniques. The expression pattern and clinical significance of MYD88 were investigated via immunohistochemistry (IHC). The cell death score represents an independent prognostic factor for poor outcomes in glioma patients and has a distinctly superior accuracy to that of 10 published signatures. The nomogram performed well in predicting outcomes according to time-dependent ROC and calibration plots. In addition, a high-risk score was significantly related to high expression of immune checkpoint molecules and dense infiltration of protumor cells, these findings were associated with a cell death-based prognostic model. Upregulated MYD88 expression was associated with malignant phenotypes and undesirable prognoses according to the IHC. Furthermore, high MYD88 expression was associated with poor clinical outcomes and was positively related to CD163, PD-L1 and vimentin expression in the in-horse cohort. The cell death score provides a precise stratification and immune status for glioma. MYD88 was found to be an outstanding representative that might play an important role in glioma.

mRNA markers for survival prediction in glioblastoma multiforme patients: a systematic review with bioinformatic analyses

BACKGROUND

Glioblastoma multiforme (GBM) is a type of fast-growing brain glioma associated with a very poor prognosis. This study aims to identify key genes whose expression is associated with the overall survival (OS) in patients with GBM.

METHODS

A systematic review was performed using PubMed, Scopus, Cochrane, and Web of Science up to Journey 2024. Two researchers independently extracted the data and assessed the study quality according to the New Castle Ottawa scale (NOS). The genes whose expression was found to be associated with survival were identified and considered in a subsequent bioinformatic study. The products of these genes were also analyzed considering protein-protein interaction (PPI) relationship analysis using STRING. Additionally, the most important genes associated with GBM patients' survival were also identified using the Cytoscape 3.9.0 software. For final validation, GEPIA and CGGA (mRNAseq_325 and mRNAseq_693) databases were used to conduct OS analyses. Gene set enrichment analysis was performed with GO Biological Process 2023.

RESULTS

From an initial search of 4104 articles, 255 studies were included from 24 countries. Studies described 613 unique genes whose mRNAs were significantly associated with OS in GBM patients, of which 107 were described in 2 or more studies. Based on the NOS, 131 studies were of high quality, while 124 were considered as low-quality studies. According to the PPI network, 31 key target genes were identified. Pathway analysis revealed five hub genes (IL6, NOTCH1, TGFB1, EGFR, and KDR). However, in the validation study, only, the FN1 gene was significant in three cohorts.

CONCLUSION

We successfully identified the most important 31 genes whose products may be considered as potential prognosis biomarkers as well as candidate target genes for innovative therapy of GBM tumors.

Diagnostic Utility of Immunohistochemical Detection of MEOX2, SOX11, INSM1 and EGFR in Gliomas

Histological identification of dispersed glioma cells in small biopsies can be challenging, especially in tumours lacking the IDH1 R132H mutation or alterations in TP53. We postulated that immunohistochemical detection of proteins expressed preferentially in gliomas (EGFR, MEOX2, CD34) or during embryonal development (SOX11, INSM1) can be used to distinguish reactive gliosis from glioma. Tissue microarrays of 46 reactive glioses, 81 glioblastomas, 34 IDH1-mutant diffuse gliomas, and 23 gliomas of other types were analysed. Glial neoplasms were significantly more often (p < 0.001, χ2) positive for EGFR (34.1% vs. 0%), MEOX2 (49.3% vs. 2.3%), SOX11 (70.5% vs. 20.4%), and INSM1 (65.4% vs. 2.3%). In 94.3% (66/70) of the glioblastomas, the expression of at least two markers was observed, while no reactive gliosis showed coexpression of any of the proteins. Compared to IDH1-mutant tumours, glioblastomas showed significantly higher expression of EGFR, MEOX2, and CD34 and significantly lower positivity for SOX11. Non-diffuse gliomas were only rarely positive for any of the five markers tested. Our results indicate that immunohistochemical detection of EGFR, MEOX2, SOX11, and INSM1 can be useful for detection of glioblastoma cells in limited histological samples, especially when used in combination.

A novel cuproptosis-related gene signature of prognosis and immune microenvironment in head and neck squamous cell carcinoma cancer

BACKGROUND

Cuproptosis is a novel form of cell death that is highly related to mitochondrial metabolism and mediated by protein lipoacetylation. This study systematically assessed the differential expression and genetic alterations of cuproptosis-related genes (CRGs) in head and neck squamous-cell carcinoma (HNSCC) and constructed CRG risk models to predict survival in patients with HNSCC.

METHODS

We investigated the expression of 19 CRGs in HNSCC and noncancerous tissues, and the relationship between mutation load, immune infiltration, and clinical features was examined based on data from public databases. CRG risk models were constructed by univariate Cox analysis and lasso regression and validated by independent datasets for their accuracy in predicting survival outcomes in patients with HNSCC. The expression distribution of CRGs in HNSCC cells was further explored in the HNSCC single-cell sequencing dataset.

RESULTS

NFE2L2, ATP7A, FDX1, LIAS, DLD, DLAT, PDHB, MTF1 and DBT were highly expressed in noncancerous samples, while GLS, CDKN2A and DLST were highly expressed in HNSCC samples (p < 0.05). Gene copy number variation frequency (CNV) revealed CDKN2A, FDX1 and DLAT copy number deletions and LIPT2 and NFE2L2 copy number increases. Ten CRGs were used to construct a risk model to predict overall survival (OS) in HNSCC, yielding reduced OS in the high-risk group compared to the low-risk group, training group (p = 9.733e - 05), and testing group (p = 0.040). The CRG risk model was significantly correlated with various immune cells, regulatory T cells (Tregs) and memory B cells were significantly negatively correlated (p = 0.027, p = 0.00084), and resting CD4 memory T cells was significantly positively correlated (p = 9e - 04). Most CRGs significantly affected the clinical characteristics of HNSCC. NFE2L2, SLC31A1, PDHA1, CDKN2A and DBT were highly expressed in epithelial cells of HNSCC, while SLC31A1, DBT and NFE2L2 were highly expressed in T cells, and SLC31A1 in B cells. In monocytes, NFE2L2, SLC31A1 and PDHA1 were highly expressed.

CONCLUSION

The CRG risk model can be used as a potential prognostic factor for HNSCC patients and may provide new insights into cancer treatment from the perspective of copper metabolism.

A Novel Multi-Omics Analysis Model for Diagnosis and Survival Prediction of Lower-Grade Glioma Patients

Background

Lower-grade gliomas (LGGs) are characterized by remarkable genetic heterogeneity and different clinical outcomes. Classification of LGGs is improved by the development of molecular stratification markers including IDH mutation and 1p/19q chromosomal integrity, which are used as a hallmark of survival and therapy sensitivity of LGG patients. However, the reproducibility and sensitivity of the current classification remain ambiguous. This study aimed to construct more accurate risk-stratification approaches.

Methods

According to bioinformatics, the sequencing profiles of methylation and transcription and imaging data derived from LGG patients were analyzed and developed predictable risk score and radiomics score. Moreover, the performance of predictable models was further validated.

Results

In this study, we determined a cluster of 6 genes that were correlated with IDH mutation/1p19q co-deletion status. Risk score model was calculated based on 6 genes and showed gratifying sensitivity and specificity for survival prediction and therapy response of LGG patients. Furthermore, a radiomics risk score model was established to noninvasively assist judgment of risk score in pre-surgery. Taken together, a predictable nomogram that combined transcriptional signatures and clinical characteristics was established and validated to be preferable to the histopathological classification. Our novel multi-omics nomograms showed a satisfying performance. To establish a user-friendly application, the nomogram was further developed into a web-based platform: https://drw576223193.shinyapps.io/Nomo/, which could be used as a supporting method in addition to the current histopathological-based classification of gliomas.

Conclusions

Our novel multi-omics nomograms showed the satisfying performance of LGG patients and assisted clinicians to draw up individualized clinical management.

MEOX2 Regulates the Growth and Survival of Glioblastoma Stem Cells by Modulating Genes of the Glycolytic Pathway and Response to Hypoxia

Simple Summary

Glioblastoma is the most common incurable primary brain tumor in adults, typically leading to death within 15 months of diagnosis. Although there is an ongoing debate in the scientific community about the precise cellular origin of this tumor, glioblastoma stem cells (GSCs), which are able to self-renew, yield a full tumor mass, and determine chemo- and radio-resistance, are recognized to have a pivotal role. Our research aims to understand the role of the mesenchyme homeobox 2 (MEOX2) transcription factor in GSCs where it is strongly and specifically expressed. We have found that MEOX2 is indeed important for the survival of these cells. In fact, when we reduce its expression in two different GSC lines, they undergo a massive death accompanied by the inhibition of key genes of the glycolytic metabolism, the main source of energy for these cells. Our results reveal a novel function for MEOX2 in glioblastoma and suggest a mechanism through which GSCs may survive even in unfavorable conditions.

Abstract

The most widely accepted hypothesis for the development of glioblastoma suggests that glioblastoma stem-like cells (GSCs) are crucially involved in tumor initiation and recurrence as well as in the occurrence of chemo- and radio-resistance. Mesenchyme homeobox 2 (MEOX2) is a transcription factor overexpressed in glioblastoma, whose expression is negatively correlated with patient survival. Starting from our observation that MEOX2 expression is strongly enhanced in six GSC lines, we performed shRNA-mediated knock-down experiments in two different GSC lines and found that MEOX2 depletion resulted in the inhibition of cell growth and sphere-forming ability and an increase in apoptotic cell death. By a deep transcriptome analysis, we identified a core group of genes modulated in response to MEOX2 knock-down. Among these genes, the repressed ones are largely enriched in genes involved in the hypoxic response and glycolytic pathway, two strictly related pathways that contribute to the resistance of high-grade gliomas to therapies. An in silico study of the regulatory regions of genes differentially expressed by MEOX2 knock-down revealed that they mainly consisted of GC-rich regions enriched for Sp1 and Klf4 binding motifs, two main regulators of metabolism in glioblastoma. Our results show, for the first time, the involvement of MEOX2 in the regulation of genes of GSC metabolism, which is essential for the survival and growth of these cells.

The potential role of miR-1290 in cancer progression, diagnosis, prognosis, and treatment: An oncomiR or onco-suppressor microRNA?

Cancer is one of the leading causes of death in humans because of the lack of early diagnosis, distant metastases, and the resistance to adjuvant therapies, including chemotherapy and radiotherapy. In addition to playing an essential role in tumor progression and development, microRNAs (miRNAs) can be used as a robust biomarker in the early detection of cancer. MiR-1290 was discovered for the first time in human embryonic stem cells, and under typical physiological situations, plays an essential role in neuronal differentiation and neural stem cell proliferation. Its coding sequence is located at the 1p36.13 regions in the first intron of the aldehyde dehydrogenase 4 gene member A1. miR-1290 is out of control in many cancers such as breast cancer, colorectal cancer, esophageal squamous cell carcinoma, gastric cancer, lung cancer, pancreatic cancer, and plays a vital role in their development. Therefore, it is suggested that miR-1290 can be considered as a potential diagnostic and therapeutic target in many cancers. In addition to the importance of miR-1290 in the noninvasive diagnosis of various cancers, this systematic review study discussed the role of miR-1290 in altering the expression of different genes involved in cancer development and chemo-radiation resistance. Moreover, it considered the regulatory effect of natural products on miR-1290 expression and the interaction of lncRNAs by miR-1290.

MEOX2 Transcription Factor Is Involved in Survival and Adhesion of Glioma Stem-like Cells

Simple Summary

Glioblastoma is the most common and lethal primary brain tumor for which no curative treatment currently exists. In our previous work, we showed that MEOX2 was associated with a poor patient prognosis but its biological involvement in tumor development remains ill defined. To this purpose, the aim of our study was to investigate the role of MEOX2 in patient-derived glioblastoma cell cultures. We unraveled the MEOX2 contribution to cell viability and growth and its potential involvement in phenotype and adhesion properties of glioblastoma cells. This work paves the way toward a better understanding of the role of MEOX2 in the pathophysiology of primary brain tumors.

Abstract

The high expression of MEOX2 transcription factor is closely associated with poor overall survival in glioma. MEOX2 has recently been described as an interesting prognostic biomarker, especially for lower grade glioma. MEOX2 has never been studied in glioma stem-like cells (GSC), responsible for glioma recurrence. The aim of our study was to investigate the role of MEOX2 in GSC. Loss of function approach using siRNA was used to assess the impact of MEOX2 on GSC viability and stemness phenotype. MEOX2 was localized in the nucleus and its expression was heterogeneous between GSCs. MEOX2 expression depends on the methylation state of its promoter and is strongly associated with IDH mutations. MEOX2 is involved in cell proliferation and viability regulation through ERK/MAPK and PI3K/AKT pathways. MEOX2 loss of function correlated with GSC differentiation and acquisition of neuronal lineage characteristics. Besides, inhibition of MEOX2 is correlated with increased expression of CDH10 and decreased pFAK. In this study, we unraveled, for the first time, MEOX2 contribution to cell viability and proliferation through AKT/ERK pathway and its potential involvement in phenotype and adhesion properties of GSC.

Promoting Prognostic Model Application: A Review Based on Gliomas

Malignant neoplasms are characterized by poor therapeutic efficacy, high recurrence rate, and extensive metastasis, leading to short survival. Previous methods for grouping prognostic risks are based on anatomic, clinical, and pathological features that exhibit lower distinguishing capability compared with genetic signatures. The update of sequencing techniques and machine learning promotes the genetic panels-based prognostic model development, especially the RNA-panel models. Gliomas harbor the most malignant features and the poorest survival among all tumors. Currently, numerous glioma prognostic models have been reported. We systematically reviewed all 138 machine-learning-based genetic models and proposed novel criteria in assessing their quality. Besides, the biological and clinical significance of some highly overlapped glioma markers in these models were discussed. This study screened out markers with strong prognostic potential and 27 models presenting high quality. Conclusively, we comprehensively reviewed 138 prognostic models combined with glioma genetic panels and presented novel criteria for the development and assessment of clinically important prognostic models. This will guide the genetic models in cancers from laboratory-based research studies to clinical applications and improve glioma patient prognostic management.

Promoting Prognostic Model Application: A Review Based on Gliomas

Malignant neoplasms are characterized by poor therapeutic efficacy, high recurrence rate, and extensive metastasis, leading to short survival. Previous methods for grouping prognostic risks are based on anatomic, clinical, and pathological features that exhibit lower distinguishing capability compared with genetic signatures. The update of sequencing techniques and machine learning promotes the genetic panels-based prognostic model development, especially the RNA-panel models. Gliomas harbor the most malignant features and the poorest survival among all tumors. Currently, numerous glioma prognostic models have been reported. We systematically reviewed all 138 machine-learning-based genetic models and proposed novel criteria in assessing their quality. Besides, the biological and clinical significance of some highly overlapped glioma markers in these models were discussed. This study screened out markers with strong prognostic potential and 27 models presenting high quality. Conclusively, we comprehensively reviewed 138 prognostic models combined with glioma genetic panels and presented novel criteria for the development and assessment of clinically important prognostic models. This will guide the genetic models in cancers from laboratory-based research studies to clinical applications and improve glioma patient prognostic management.

Transcriptomic Profiling Identifies a DNA Repair-Related Signature as a Novel Prognostic Marker in Lower Grade Gliomas

BACKGROUND

Gliomas are the most common and malignant intracranial tumors. The standard therapy is surgical resection combined with radiotherapy and chemotherapy. However, the emergence of radioresistance and chemoresistance, which is largely due to DNA damage repair, limits the therapeutic efficacy. Therefore, we identified a high-efficiency DNA damage repair-related risk signature as a predictor for prognosis in lower grade glioma.

METHODS

The signature was developed and validated in two independent datasets of the Chinese Glioma Genome Atlas (172 samples) and The Cancer Genome Atlas (451 samples). The time-dependent ROC curve, Cox regression, Nomogram, and Kaplan-Meier analyses were performed to evaluate the prognostic performance of the risk signature. The Metascape and IHC staining were performed to reveal the potential biological mechanism. GraphPad prism, SPSS, and R language were used for statistical analysis and graphical work.

RESULTS

This signature could distinguish the prognosis of patients, and patients with high-risk scores exhibited short survival time. The time-dependent ROC curve, Cox regression, and Nomogram model indicated the independent prognostic performance and high prognostic accuracy of the signature for survival. Combined with the IDH mutation status, this risk signature could further subdivide patients with distinct survival. Functional analysis of associated genes revealed signature-related biological process of cell cycle and DNA repair. These mechanisms were confirmed in patient samples.

CONCLUSIONS

The DNA damage repair-related signature was an independent and powerful prognostic biomarker in lower grade glioma.

IMPACT

The signature may potentially improve risk stratification of patients and provide a more accurate assessment of personalized treatment in clinic.

Prognostic significance of MEOX2 in gliomas

Gliomas are the most common malignant primary tumors in the central nervous system and have variable predictive clinical courses. Glioblastoma, the most aggressive form of glioma, is a complex disease with unsatisfactory therapeutic solutions and a very poor prognosis. Some processes at stake in gliomagenesis have been discovered but little is known about the role of homeobox genes, even though they are highly expressed in gliomas, particularly in glioblastoma. Among them, the transcription factor Mesenchyme Homeobox 2 (MEOX2) had previously been associated with malignant progression and clinical prognosis in lung cancer and hepatocarcinoma but never studied in glioma. The aim of our study was to investigate the clinical significance of MEOX2 in gliomas. We assessed the expression of MEOX2 according to IDH1/2 molecular profile and patient survival among three different public datasets: The Cancer Genome Atlas (TCGA), The Chinese Glioma Genome Atlas (CGGA) and the US National Cancer Institute Repository for Molecular Brain Neoplasia Data (Rembrandt). We then evaluated the prognostic significance of MEOX2 protein expression on 112 glioma clinical samples including; 56 IDH1 wildtype glioblastomas, 7 IDH1 wild-type lower grade gliomas, 49 IDH1 mutated lower grade gliomas. Survival rates were estimated by the Kaplan-Meier method followed by uni/multivariate analyses. We demonstrated that MEOX2 was one of the transcription factors most closely associated with overall survival in glioma. Moreover, MEOX2 expression was associated with IDH1/2 wildtype molecular subtype and was significantly correlated with overall survival of all gliomas and, more interestingly, in lower grade glioma. To conclude, our results may be the first to provide insight into the clinical significance of MEOX2 in gliomas, which is a factor closely related to patient outcome. MEOX2 could constitute an interesting prognostic biomarker, especially for lower grade glioma.

ALDH1A3 induces mesenchymal differentiation and serves as a predictor for survival in glioblastoma

As aldehyde dehydrogenase (ALDH) is a novel stem cell marker, increasing studies have confirmed that high ALDH activity promotes tumorigenesis and progression in cancers. Some preliminary studies have found that ALDH1A3 may play an important role in glioma malignant progression, but so far there was no conclusive conclusion. The purpose of our study was to elucidate the mechanisms by which ALDH1A3 regulated in glioma and to provide practical tools for clinical application. Aldefluor, flow cytometry sorting and qRT-PCR were performed to verify the role of ALDH1A3 in ALDH activity maintenance. Transwell, immunofluorescence, glycolytic assays, and orthotopic xenograft models were used to explore ALDH1A3 bio-functions in GBM. LASSO-COX, COX survival analysis and Kaplan–Meier analysis were used to establish the prognostic evaluation system and predict postoperative chemotherapy sensitivity of GBMs. Our integrated study found that (1) ALDH1A3 associates with mesenchymal differentiation of GBM in Eastern and Western world patients. (2) ALDH1A3 plays a critical role in ALDH activity maintenance. (3) ALDH1A3 is an activator of mesenchymal transformation in GBM. (4) ALDH1A3-derived PMT markers’ molecular signature can predict 1-, 2-, and 3-year survival rates of GBMs precisely. In conclusion, ALDH1A3 was a major contributor to ALDH activity and a key driver in triggering mesenchymal transformation in GBM. ALDH1A3-based molecular classification scheme can help to improve guidance for prognosis forecasting and individualized treatment decision making for GBM patients.

MiR-1290 promotes proliferation, migration, and invasion of glioma cells by targeting LHX6

To investigate the interaction of miR-1290 and LHX6 in gliomas, and their influence on the propagation and metastasis of glioma cells. The differential expression of miR-1290 in glioma cells was identified by chip screening. The expression level of miR-1290 and LHX6 were determined by qRT-PCR and Western blot. The influence of miR-1290 on propagation of glioma cells were analyzed by MTT assay, EdU incorporation, and colony formation, while the impact of miR-1290 on metastasis was assessed by transwell assay. The relationship between LHX6 and miR-1290 was testified by luciferase reporter assay. The gliomas orthotopic implantation model of nude mice was established to investigate the influence of miR-1290 and LHX6 on tumor growth. Tumor volumes were evaluated by photon density, and the expression of Ki67 protein was analyzed by immunohistochemistry. MiR-1290 presented a higher expression in glioma cells and tissues. MiR-1290 overexpression significantly promoted propagation and metastasis of glioma cells, while miR-1290 knockdown inhibited glioma development. MiR-1290 suppressed LHX6 expression, facilitating development of glioma cells. The orthotopic implantation model showed that miR-1290 overexpression promoted tumor growth while LHX6 overexpression inhibited it. MiR-1290 could promote glioma cell propagation and metastasis by inhibiting LHX6.

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.

Molecular mechanisms underlying gliomas and glioblastoma pathogenesis revealed by bioinformatics analysis of microarray data

The aim of this study was to identify key genes associated with gliomas and glioblastoma and to explore the related signaling pathways. Gene expression profiles of three glioma stem cell line samples, three normal astrocyte samples, three astrocyte overexpressing 4 iPSC-inducing and oncogenic factors (myc(T58A), OCT-4, p53DD, and H-Ras(G12V)) samples, three astrocyte overexpressing 7 iPSC-inducing and oncogenic factors (OCT4, H-Ras(G12V), myc(T58A), p53DD, cyclin D1, CDK4(RC24) and hTERT) samples and three glioblastoma cell line samples were downloaded from the ArrayExpress database (accession: E-MTAB-4771). The differentially expressed genes (DEGs) in gliomas and glioblastoma were identified using FDR and t tests, and protein-protein interaction (PPI) networks for these DEGs were constructed using the protein interaction network analysis. The GeneTrail2 1.5 tool was used to identify potentially enriched biological processes among the DEGs using gene ontology (GO) terms and to identify the related pathways using the Kyoto Encyclopedia of Genes and Genomes, Reactome and WikiPathways pathway database. In addition, crucial modules of the constructed PPI networks were identified using the PEWCC1 plug-in, and their topological properties were analyzed using NetworkAnalyzer, both available from Cytoscape. We also constructed microRNA-target gene regulatory network and transcription factor-target gene regulatory network for these DEGs were constructed using the miRNet and binding and expression target analysis. We identified 200 genes that could potentially be involved in the gliomas and glioblastoma. Among them, bioinformatics analysis identified 137 up-regulated and 63 down-regulated DEGs in gliomas and glioblastoma. The significant enriched pathway (PI3K-Akt) for up-regulated genes such as COL4A1, COL4A2, EGFR, FGFR1, LAPR6, MYC, PDGFA, SPP1 were selected as well as significant GO term (ear development) for up-regulated genes such as CELSR1, CHRNA9, DDR1, FGFR1, GLI2, LGR5, SOX2, TSHR were selected, while the significant enriched pathway (amebiasis) for down-regulated gene such as COL3A1, COL5A2, LAMA2 were selected as well as significant GO term (RNA polymerase II core promoter proximal region sequence-specific binding (5) such as MEIS2, MEOX2, NR2E1, PITX2, TFAP2B, ZFPM2 were selected. Importantly, MYC and SOX2 were hub proteins in the up-regulated PPI network, while MET and CDKN2A were hub proteins in the down-regulated PPI network. After network module analysis, MYC, FGFR1 and HOXA10 were selected as the up-regulated coexpressed genes in the gliomas and glioblastoma, while SH3GL3 and SNRPN were selected as the down-regulated coexpressed genes in the gliomas and glioblastoma. MicroRNA hsa-mir-22-3p had a regulatory effect on the most up DEGs, including VSNL1, while hsa-mir-103a-3p had a regulatory effect on the most down DEGs, including DAPK1. Transcription factor EZH2 had a regulatory effect on the both up and down DEGs, including CD9, CHI3L1, MEIS2 and NR2E1. The DEGs, such as MYC, FGFR1, CDKN2A, HOXA10 and MET, may be used for targeted diagnosis and treatment of gliomas and glioblastoma.

RNA processing as an alternative route to attack glioblastoma

Genomic analyses have become an important tool to identify new avenues for therapy. This is especially true for cancer types with extremely poor outcomes, since our lack of effective therapies offers no tangible clinical starting point to build upon. The highly malignant brain tumor glioblastoma (GBM) exemplifies such a refractory cancer, with only 15 month average patient survival. Analyses of several hundred GBM samples compiled by the TCGA (The Cancer Genome Atlas) have produced an extensive transcriptomic map, identified prevalent chromosomal alterations, and defined important driver mutations. Unfortunately, clinical trials based on these results have not yet delivered an improvement on outcome. It is, therefore, necessary to characterize other regulatory routes known for playing a role in tumor relapse and response to treatment. Alternative splicing affects more than 90% of the human coding genes and it is an important source for transcript variation and gene regulation. Mutations and alterations in splicing factors are highly prevalent in multiple cancers, demonstrating the potential for splicing to act as a tumor driver. As a result, numerous genes are expressed as cancer-specific splicing isoforms that are functionally distinct from the canonical isoforms found in normal tissue. These include genes that regulate cancer-critical pathways such as apoptosis, DNA repair, cell proliferation, and migration. Splicing defects can even induce genomic instability, a common characteristic of cancer, and a driver of tumor evolution. Importantly, components of the splicing machinery are targetable; multiple drugs can inhibit splicing factors or promote changes in splicing which could be exploited to begin improving clinical outcomes. Here, we review the current literature and present a case for exploring RNA processing as therapeutic route for the treatment of GBM.

Joint Covariate Detection on Expression Profiles for Selecting Prognostic miRNAs in Glioblastoma

An important application of expression profiles is to stratify patients into high-risk and low-risk groups using limited but key covariates associated with survival outcomes. Prior to that, variables considered to be associated with survival outcomes are selected. A combination of single variables, each of which is significantly related to survival outcomes, is always regarded to be candidates for posterior patient stratification. Instead of individually significant variables, a combination that contains not only significant but also insignificant variables is supposed to be concentrated on. By means of bottom-up enumeration on each pair of variables, we propose a joint covariate detection strategy to select candidates that not only correspond to close association with survival outcomes but also help to make a clear stratification of patients. Experimental results on a publicly available dataset of glioblastoma multiforme indicate that the selected pair composed of an individually significant and an insignificant miRNA keeps a better performance than the combination of significant single variables. The selected miRNA pair is ultimately regarded to be associated with the prognosis of glioblastoma multiforme by further pathway analysis.

Identification of high risk anaplastic gliomas by a diagnostic and prognostic signature derived from mRNA expression profiling

Anaplastic gliomas are characterized by variable clinical and genetic features, but there are few studies focusing on the substratification of anaplastic gliomas. To identify a more objective and applicable classification of anaplastic gliomas, we analyzed whole genome mRNA expression profiling of four independent datasets. Univariate Cox regression, linear risk score formula and receiver operating characteristic (ROC) curve were applied to derive a gene signature with best prognostic performance. The corresponding clinical and molecular information were further analyzed for interpretation of the different prognosis and the independence of the signature. Gene ontology (GO), Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA) were performed for functional annotation of the differences. We found a three-gene signature, by applying which, the anaplastic gliomas could be divided into low risk and high risk groups. The two groups showed a high concordance with grade II and grade IV gliomas, respectively. The high risk group was more aggressive and complex. The three-gene signature showed diagnostic and prognostic value in anaplastic gliomas.

Evaluating the Prognostic Accuracy of Biomarkers for Glioblastoma Multiforme Using The Cancer Genome Atlas Data

Background:

Glioblastoma multiforme (GBM) is the most common and aggressive primary brain tumor. Previous studies on GBM biomarkers focused on the effect of the biomarkers on overall survival (OS). Until now, no study has been published that evaluates the performance of biomarkers for prognosing OS. We examined the performance of microRNAs, gene expressions, gene signatures, and methylation that were previously identified to be prognostic. In addition, we investigated whether using clinical risk factors in combination with biomarkers can improve the prognostic performance.

Methods:

The Cancer Genome Atlas, which provides both biomarkers and OS information, was used in this study. The time-dependent receiver operating characteristic (ROC) curve was used to evaluate the prognostic accuracy.

Results:

For prognosis of OS by 2 years from diagnosis, the area under the ROC curve (AUC) of microRNAs, Mir21 and Mir222, was 0.550 and 0.625, respectively. When age was included in the risk prediction score of these biomarkers, the AUC increased to 0.719 and 0.701, respectively. The SAMSN1 gene expression attains an AUC of 0.563, and the “8-gene” signature identified by Bao achieves an AUC of 0.613.

Conclusions:

Although some biomarkers are significantly associated with OS, the ability of these biomarkers for prognosing OS events is limited. Incorporating clinical risk factors, such as age, can greatly improve the prognostic performance.

Identification of hub genes and regulatory factors of glioblastoma multiforme subgroups by RNA-seq data analysis

Glioblastoma multiforme (GBM) is the most common malignant brain tumor. This study aimed to identify the hub genes and regulatory factors of GBM subgroups by RNA sequencing (RNA-seq) data analysis, in order to explore the possible mechanisms responsbile for the progression of GBM. The dataset RNASeqV2 was downloaded by TCGA-Assembler, containing 169 GBM and 5 normal samples. Gene expression was calculated by the reads per kilobase per million reads measurement, and nor malized with tag count comparison. Following subgroup classification by the non-negative matrix factorization, the differentially expressed genes (DEGs) were screened in 4 GBM subgroups using the method of significance analysis of microarrays. Functional enrichment analysis was performed by DAVID, and the protein-protein interaction (PPI) network was constructed based on the HPRD database. The subgroup-related microRNAs (miRNAs or miRs), transcription factors (TFs) and small molecule drugs were predicted with predefined criteria. A cohort of 19,515 DEGs between the GBM and control samples was screened, which were predominantly enriched in cell cycle- and immunoreaction-related pathways. In the PPI network, lymphocyte cytosolic protein 2 (LCP2), breast cancer 1 (BRCA1), specificity protein 1 (Sp1) and chromodomain-helicase-DNA-binding protein 3 (CHD3) were the hub nodes in subgroups 1–4, respectively. Paired box 5 (PAX5), adipocyte protein 2 (aP2), E2F transcription factor 1 (E2F1) and cAMP-response element-binding protein-1 (CREB1) were the specific TFs in subgroups 1–4, respectively. miR-147b, miR-770-5p, miR-220a and miR-1247 were the particular miRNAs in subgroups 1–4, respectively. Natalizumab was the predicted small molecule drug in subgroup 2. In conclusion, the molecular regulatory mechanisms of GBM pathogenesis were distinct in the different subgroups. Several crucial genes, TFs, miRNAs and small molecules in the different GBM subgroups were identified, which may be used as potential markers. However, further experimental validations may be required.

Comparative Analysis of Matrix Metalloproteinase Family Members Reveals That MMP9 Predicts Survival and Response to Temozolomide in Patients with Primary Glioblastoma

Background

Glioblastoma multiform (GBM) is the most common malignant primary brain tumor in adults. Radiotherapy plus concomitant and adjuvant TMZ chemotherapy is the current standard of care for patients with GBM. Matrix metalloproteinases (MMPs), a family of zinc-dependent endopeptidases, are key modulators of tumor invasion and metastasis due to their ECM degradation capacity. The aim of the present study was to identify the most informative MMP member in terms of prognostic and predictive ability for patients with primary GBM.

Method

The mRNA expression profiles of all MMP genes were obtained from the Chinese Glioma Genome Atlas (CGGA), the Repository for Molecular Brain Neoplasia Data (REMBRANDT) and the GSE16011 dataset. MGMT methylation status was also examined by pyrosequencing. The correlation of MMP9 expression with tumor progression was explored in glioma specimens of all grades. Kaplan–Meier analysis and Cox proportional hazards regression models were used to investigate the association of MMP9 expression with survival and response to temozolomide.

Results

MMP9 was the only significant prognostic factor in three datasets for primary glioblastoma patients. Our results indicated that MMP9 expression is correlated with glioma grade (p<0.0001). Additionally, low expression of MMP9 was correlated with better survival outcome (OS: p = 0.0012 and PFS: p = 0.0066), and MMP9 was an independent prognostic factor in primary GBM (OS: p = 0.027 and PFS: p = 0.032). Additionally, the GBM patients with low MMP9 expression benefited from temozolomide (TMZ) chemotherapy regardless of the MGMT methylation status.

Conclusions

Patients with primary GBMs with low MMP9 expression may have longer survival and may benefit from temozolomide chemotherapy.

Microarray Analysis in Glioblastomas

Microarray analysis in glioblastomas is done using either cell lines or patient samples as starting material. A survey of the current literature points to transcript-based microarrays and immunohistochemistry (IHC)-based tissue microarrays as being the preferred methods of choice in cancers of neurological origin. Microarray analysis may be carried out for various purposes including the following: i. To correlate gene expression signatures of glioblastoma cell lines or tumors with response to chemotherapy (DeLay et al., Clin Cancer Res 18(10):2930-2942, 2012). ii. To correlate gene expression patterns with biological features like proliferation or invasiveness of the glioblastoma cells (Jiang et al., PLoS One 8(6):e66008, 2013). iii. To discover new tumor classificatory systems based on gene expression signature, and to correlate therapeutic response and prognosis with these signatures (Huse et al., Annu Rev Med 64(1):59-70, 2013; Verhaak et al., Cancer Cell 17(1):98-110, 2010). While investigators can sometimes use archived tumor gene expression data available from repositories such as the NCBI Gene Expression Omnibus to answer their questions, new arrays must often be run to adequately answer specific questions. Here, we provide a detailed description of microarray methodologies, how to select the appropriate methodology for a given question, and analytical strategies that can be used. Experimental methodology for protein microarrays is outside the scope of this chapter, but basic sample preparation techniques for transcript-based microarrays are included here.

AJAP1 is dysregulated at an early stage of gliomagenesis and suppresses invasion through cytoskeleton reorganization

AIMS

Down-regulation of AJAP1 in glioblastoma multiforme (GBM) has been reported. However, the expression profiles of AJAP1 in gliomas and the underlying mechanisms of AJAP1 function on invasion are still poorly understood.

METHODS

The gene profiles of AJAP1 in glioma patients were studied among four independent cohorts. Confocal imaging was used to analyze the AJAP1 localization. After AJAP1 overexpression in GBM cell lines, cellular polarity, cytoskeleton distribution, and antitumor effect were investigated in vitro and in vivo.

RESULTS

AJAP1 expression was significantly decreased in gliomas compared with normal brain in REMBRANDT and CGCA cohorts. Additionally, low AJAP1 expression was associated with worse survival in GBMs in REMBRANDT and TCGA U133A cohorts and was significantly associated with classical and mesenchymal subtypes of GBMs among four cohorts. Confocal imaging indicated AJAP1 localized in cell membranes in low-grade gliomas and AJAP1-overexpressing GBM cells, but difficult to assess in high-grade gliomas due to its absence. AJAP1 overexpression altered the cytoskeleton and cellular polarity in vitro and inhibited the tumor growth in vivo.

CONCLUSIONS

AJAP1 is dysregulated at an early stage of gliomagenesis and may suppress glioma cell invasion and proliferation, which suggests that AJAP1 may be a potential diagnostic and prognostic marker for gliomas.

Long noncoding RNA profiles reveal three molecular subtypes in glioma

BACKGROUND

Gliomas are the most lethal type of primary brain tumor in adult. Long noncoding RNAs (lncRNAs), which are involved in the progression of various cancers, may offer a potential gene therapy target in glioma.

METHODS AND FINDINGS

We first classified gliomas into three molecular subtypes (namely LncR1, LncR2 and LncR3) in Rembrandt dataset using consensus clustering. Survival analysis indicated that LncR3 had the best prognosis, while the LncR1 subtype showed the poorest overall survival rate. The results were further validated in an independent glioma dataset GSE16011. Additionally, we collected and merged data of the two databases (Rembrandt and GSE16011 dataset) and analyzed prognosis of each subtype in WHO II, III and IV gliomas. The similar results were obtained. Gene Set Variation Analysis (GSVA) demonstrated that LncR1 subtype enriched cultured astroglia's gene signature, while LncR2 subtype was characterized by neuronal gene signature. Oligodendrocytic was rich in LncR3. In addition, IDH1 mutation and 1p/19q LOH were found rich with LncR3, and EGFR amplification showed high percentage in LncR1 in GSE16011 dataset.

CONCLUSIONS

We report a novel molecular classification of glioma based on lncRNA expression profiles and believe that it would provide a potential platform for future studies on gene treatment for glioma and lead to more individualized therapies to improve survival rates.