Bioinformatic analysis of gene expression and methylation regulation in glioblastoma

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.

Development of a prognostic model for glioblastoma multiforme based on the expression levels of efferocytosis-related genes

Glioblastoma multiforme (GBM) is one of the most common and aggressive brain tumors. The microenvironment of GBM is characterized by its highly immunosuppressive nature with infiltration of immunosuppressive cells and the expression levels of cytokines. Efferocytosis is a biological process in which phagocytes remove apoptotic cells and vesicles from tissues. Efferocytosis plays a noticeable function in the formation of immunosuppressive environment. This study aimed to develop an efferocytosis-related prognostic model for GBM. The bioinformatic methods were utilized to analyze the transcriptomic data of GBM and normal samples. Clinical and RNA-seq data were sourced from TCGA database comprising 167 tumor samples and 5 normal samples, and 167 tumor samples for which survival information was available. Transcriptomic data of 1034 normal samples were collected from the Genotype-Tissue Expression (GTEx) database as a control sample supplement to the TCGA database. In the end, 167 tumor samples and 1039 normal samples were obtained for transcriptome analysis. Efferocytosis-related differentially expressed genes (ERDEGs) were obtained by intersecting 7487 differentially expressed genes (DEGs) between GBM and normal samples along with 1189 hub genes. Functional enrichment analyses revealed that ERDEGs were mainly involved in cytokine-mediated immune responses. Moreover, 9 prognosis-related genes (PRGs) were identified by the least absolute shrinkage and selection operator (LASSO) regression analysis, and a prognostic model was therefore developed. The nomogram combining age and risk score could effectively predict GBM patients' prognosis. GBM patients in the high-risk group had higher immune infiltration, invasion, epithelial-mesenchymal transition, angiogenesis scores and poorer tumor purity. In addition, the high-risk group exhibited higher half maximal inhibitory concentration (IC50) values for temozolomide, carmustine, and vincristine. Expression analysis indicated that PRGs were overexpressed in GBM cells. PDIA4 knockdown reduced efferocytosis in vitro. In summary, the proposed prognostic model for GBM based on efferocytosis-related genes exhibited a robust performance.

SPTSSA Is a Prognostic Marker for Glioblastoma Associated with Tumor-Infiltrating Immune Cells and Oxidative Stress

Background. SPTSSA encodes the small subunit A of serine palmitoyltransferase. It catalyzes the formation of sphingoid long-chain base backbone of sphingolipids. Its role in glioma prognosis and tumor-infiltrating immune cells remains unclear. Methods. We analyzed SPTSSA expression and association with clinical prognosis using GEPIA and CGGA database. Then, GSEA was performed to identify relevant biological functions of SPTSSA. The correlations between SPTSSA expression and tumor immune infiltrates were investigated using CIBERSORT and TIMER. Finally, IHC and IF were performed to confirm the value of prognosis and the correlation with immune infiltration. Results. SPTSSA expression was significantly upregulated in diffuse glioma compared to normal tissues and associated with poor survival in GEPIA and CGGA database. Then, we identified biological processes and signaling pathways associated with SPTSSA expression. The result showed that SPTSSA enriched in the GO term like oxidative stress. Finally, we showed that SPTSSA expression was significantly associated with tumor-infiltrating immune cells and overall survival via IHC. Conclusion. These findings suggest that SPTSSA expression might be used as a prognostic biomarker for glioma and potential target for novel glioma therapy.

Identification of 9 Gene Signatures by WGCNA to Predict Prognosis for Colon Adenocarcinoma

Background

A risk assessment model for prognostic prediction of colon adenocarcinoma (COAD) was established based on weighted gene co-expression network analysis (WGCNA).

Methods

From the Cancer Genome Atlas (TCGA) database, RNA-seq data and clinical data of COAD patients were retrieved. After screening of differentially expressed genes (DEGs), WGCNA was performed to identify gene modules and screen those associated with COAD progression. Then, via protein-protein interaction (PPI) network construction of module genes, hub genes were obtained, which were then subjected to the least absolute shrinkage and selection operator (LASSO) and Cox regression to build a hub gene-based prognostic scoring model. The receiver operating characteristic curve (ROC curve) was plotted for the optimal cutoff (OCO) of the risk score, based on which, patients were assigned to high or low-risk groups. Areas under the ROC curve (AUCs) were calculated, and model performance was visualized using Kaplan-Meier (KM) survival curves and verified in the external dataset GSE29621. Finally, the model's independent prognostic value was evaluated by univariate and multivariate Cox regression analyses, and a nomogram was built.

Results

Totally 2840 DEGs were screened from COAD dataset of TCGA, including 1401 upregulated ones and 1439 downregulated ones, which were divided into 10 modules by WGCNA. The eigenvalue of the black module was found to have a high correlation with COAD progression. PPI interaction networks were constructed for genes in the black module, and 34 hub genes were obtained by using the MCODE plug-in. A LASSO-Cox regression approach was utilized to analyze the hub genes, and a prognostic risk score model based on the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) was constructed. KM analysis identified shorter overall lower survival in the high-risk group. The model was verified to have favorable predictive ability through training set and validation set. The nomogram, composed of tumor node metastasis (TNM) staging and risk score, was of good predictability.

Conclusions

The COAD prognostic risk model constructed upon the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) can effectively predict the survival status of COAD patients.

DNA Methylation-Driven Genes for Developing Survival Nomogram for Low-Grade Glioma

Low-grade gliomas (LGG) are heterogeneous, and the current predictive models for LGG are either unsatisfactory or not user-friendly. The objective of this study was to establish a nomogram based on methylation-driven genes, combined with clinicopathological parameters for predicting prognosis in LGG. Differential expression, methylation correlation, and survival analysis were performed in 516 LGG patients using RNA and methylation sequencing data, with accompanying clinicopathological parameters from The Cancer Genome Atlas. LASSO regression was further applied to select optimal prognosis-related genes. The final prognostic nomogram was implemented together with prognostic clinicopathological parameters. The predictive efficiency of the nomogram was internally validated in training and testing groups, and externally validated in the Chinese Glioma Genome Atlas database. Three DNA methylation-driven genes, ARL9, CMYA5, and STEAP3, were identified as independent prognostic factors. Together with IDH1 mutation status, age, and sex, the final prognostic nomogram achieved the highest AUC value of 0.930, and demonstrated stable consistency in both internal and external validations. The prognostic nomogram could predict personal survival probabilities for patients with LGG, and serve as a user-friendly tool for prognostic evaluation, optimizing therapeutic regimes, and managing LGG patients.

CircASAP1 promotes the development of cervical cancer through sponging miR-338-3p to upregulate RPP25

Circular RNAs have been identified as vital regulators to regulate the development of human cancers, including cervical cancer. Therefore, this study was designed to clarify the underlying mechanism of circASAP1 in cervical cancer. The real-time quantitative PCR assay was applied to quantify the expression levels of circASAP1, microRNA (miR)-338-3p, and ribonuclease P and MRP subunit p25 (RPP25) in cervical cancer tissues and cells. The cell proliferation ability was measured by 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyl-2H-tetrazol-3-ium bromide and colony-forming assays. The protein expression levels of cyclin D1, proliferating cell nuclear antigen, and RPP25 were assessed by western blot assay. Flow cytometry assays were used to determine the apoptosis and cell cycle distribution of cervical cancer cells. The transwell assay was employed to test the migration and invasion abilities of cervical cancer cells. The interaction relationship between miR-338-3p and circASAP1 or RPP25 was confirmed by dual-luciferase reporter assay and RNA pull-down assay. The xenograft experiment was established to clarify the functional role of circASAP1 inhibition in vivo. CircASAP1 was overexpressed in cervical cancer tissues and cells compared with negative groups. Additionally, the loss-of-functional experiments implied that knockdown of circASAP1 impeded proliferation, migration, and invasion while induced apoptosis and cell cycle arrest in cervical cancer cells along with repressed tumor growth in vivo through regulation of miR-338-3p. In addition, RPP25 was a target mRNA of miR-338-3p, and overexpression of miR-338-3p suppressed proliferation, migration, and invasion while induced apoptosis and cell cycle arrest in cervical cancer cells by suppressing RPP25 expression. Mechanistically, circASAP1 could function as a sponge for miR-338-3p to increase the expression of RPP25, and further regulated proliferation, migration, invasion, apoptosis, and cell cycle program of cervical cancer cells, which might be potential markers for cervical cancer diagnosis.

ImaGene: a web-based software platform for tumor radiogenomic evaluation and reporting

Summary

Radiographic imaging techniques provide insight into the imaging features of tumor regions of interest, while immunohistochemistry and sequencing techniques performed on biopsy samples yield omics data. Relationships between tumor genotype and phenotype can be identified from these data through traditional correlation analyses and artificial intelligence (AI) models. However, the radiogenomics community lacks a unified software platform with which to conduct such analyses in a reproducible manner. To address this gap, we developed ImaGene, a web-based platform that takes tumor omics and imaging datasets as inputs, performs correlation analysis between them, and constructs AI models. ImaGene has several modifiable configuration parameters and produces a report displaying model diagnostics. To demonstrate the utility of ImaGene, we utilized data for invasive breast carcinoma (IBC) and head and neck squamous cell carcinoma (HNSCC) and identified potential associations between imaging features and nine genes (WT1, LGI3, SP7, DSG1, ORM1, CLDN10, CST1, SMTNL2, and SLC22A31) for IBC and eight genes (NR0B1, PLA2G2A, MAL, CLDN16, PRDM14, VRTN, LRRN1, and MECOM) for HNSCC. ImaGene has the potential to become a standard platform for radiogenomic tumor analyses due to its ease of use, flexibility, and reproducibility, playing a central role in the establishment of an emerging radiogenomic knowledge base.

Availability and implementation

www.ImaGene.pgxguide.org, https://github.com/skr1/Imagene.git.

Supplementary information

Supplementary data are available at https://github.com/skr1/Imagene.git.

Bioinformatic identification of differentially expressed genes regulated by DNA-methylation in glioblastoma

DNA methylation-driven differentially expressed genes (DEGs) play potentially important roles in glioblastoma (GBM). In the present study, we applied bioinformatic analyses to identify key methylation-regulated DEGs (MeDEGs) in glioblastoma and elucidate their functions. Gene expression and methylation profile data from glioblastoma samples along with clinical information were obtained from GEO and TCGA databases. A total of 65 genes were identified as MeDEGs from the aforementioned data. Subsequently, gene ontology and kyoto encyclopedia of genes and genomes enrichment analyses of these MeDEGs exhibited that MeDEGs were mostly enriched in several tumor-related terms such as "activation of cysteine-type endopeptidase activity involved in apoptotic process" and "phospholipid scrambling". Kaplan-Meier survival analysis demonstrated significant correlation of CASP1, CFH, and TTLL7 hyper-methylation with patient prognosis. Finally, CASP1 protein could indirectly interact with CFH protein, but interaction of TTLL7 protein with CASP1 or CFH protein was not evident. Based on gene set enrichment analysis, hyper-methylation of CASP1, CFH, and TTLL7 were found enriched in tumor-related KEGG terms, such as "RNA degradation", "apyruvate metabolism", and "nitrogen metabolism". Methylation levels of CASP1, CFH, and TTLL7 were addressed to negatively correlate with their mRNA levels in GBM cell lines. In sum, the present identification of MeDEGs associated with overall survival put forth potential molecular targets for translation towards improved diagnosis and treatment of GBM and specifically, methylation levels of CASP1, CFH, and TTLL7 genes could serve as key prognostic biomarkers in GBM.

Identification of key genes involved in the recurrence of glioblastoma multiforme using weighted gene co-expression network analysis and differential expression analysis

Glioblastoma multiforme (GBM) is the most fatal malignancy, and despite extensive treatment, tumors inevitably recur. This study aimed to identify recurrence-associated molecules in GBM. The gene expression profile GSE139533, containing 70 primary and 47 recurrent GBM tissues and their corresponding clinical traits, was downloaded from the Gene Expression Omnibus (GEO) database and used for weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis. After identifying the hub genes which differentially expressed in recurrent GBM tissues and in the gene modules correlated with recurrence, data from the Chinese Glioma Genome Atlas (CCGA) and The Cancer Genome Atlas (TCGA) databases were analyzed with GSE43378 to determine the relationship between hub genes and patient prognosis. The diagnostic value of the identified hub genes was verified using 52 GBM tissues. Three gene modules were correlated with recurrence and 2623 genes were clustered in these clinically significant modules. Among these, 13 genes - EHF, TRPM1, FXYD4, CDH15, LHX5, TP73, FBN3, TLX1, C1QL4, COL2A, SEC61G, NEUROD4 and GPR139 - were differentially expressed in recurrent GBM samples; low LHX5 and TLX1 expression predicted poor outcomes. LHX5 and TLX1 expression showed weak positive relationships with Karnofsky performance scale scores. Additionally, LHX5 and TLX1 expression was found to be decreased in our recurrent GBM samples compared with that in primary samples; these genes exhibited high diagnostic value in distinguishing recurrent samples from primary samples. Our findings indicate that LHX5 and TLX1 might be involved in GBM recurrence and act as potential biomarkers for this condition.

Construction of Novel Methylation-Driven Gene Model and Investigation of PARVB Function in Glioblastoma

Background

Glioblastoma multiforme (GBM) is characterized by widespread genetic and transcriptional heterogeneity. Aberrant DNA methylation plays a vital role in GBM progression by regulating gene expression. However, little is known about the role of methylation and its association with prognosis in GBM. Our aim was to explore DNA methylation-driven genes (DMDGs) and provide evidence for survival prediction and individualized treatment of GBM patients.

Methods

Use of the MethylMix R package identified DMDGs in GBM. The prognostic signature of DMDGs based on the risk score was constructed by multivariate Cox regression analysis. Receiver operating characteristics (ROC) curve and C-index were applied to assess the predictive performance of the DMDG prognostic signature. The predictive ability of the multigene signature model was validated in TCGA and CGGA cohorts. Finally, the role of DMDG β-Parvin (PARVB) was explored in vitro.

Results

The prognostic signature of DMDGs was constructed based on six genes (MDK, NMNAT3, PDPN, PARVB, SERPINB1, and UPP1). The low-risk cohort had significantly better survival than the high-risk cohort (p < 0.001). The area under the curve of the ROC of the six-gene signature was 0.832, 0.927, and 0.980 within 1, 2, and 3 years, respectively. The C-index of 0.704 indicated superior specificity and sensitivity. The six-gene model has been demonstrated to be an independent prognostic factor for GBM. In addition, joint survival analysis indicated that the MDK, NMNAT3, PARVB, SERPINB1, and UPP1 genes were significantly associated with prognosis and therapeutic targets for GBM. Importantly, our DMDG prognostic model was more suitable and accurate for low-grade gliomas. Finally, we verified that PARVB induced epithelial-mesenchymal transition partially through the JAK2/STAT3 pathway, which in turn promoted GBM cell proliferation, migration, and invasion.

Conclusion

This study demonstrated the potential value of the prognostic signature of DMDGs and provided important bioinformatic and potential therapeutic target data to facilitate individualized treatment for GBM, and to elucidate the specific mechanism by which PARVB promotes GBM progression.

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.

Development and validation of a risk prediction model and nomogram for colon adenocarcinoma based on methylation-driven genes

Evidence suggests that abnormal DNA methylation patterns play a crucial role in the etiology and pathogenesis of colon adenocarcinoma (COAD). In this study, we identified a total of 97 methylation-driven genes (MDGs) through a comprehensive analysis of the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Univariate Cox regression analysis identified four MDGs (CBLN2, RBM47, SLCO4C1, and TMEM220) associated with overall survival (OS) in COAD patients. A risk prediction model was then developed based on these four MDGs to predict the prognosis of COAD patients. We also created a nomogram that incorporated risk scores, age, and TNM stage to promote a personalized prediction of OS in COAD patients. Compared with the traditional TNM staging system, our new nomogram was better at predicting the OS of COAD patients. In cell experiments, we confirmed that the mRNA expression levels of CLBN2 and TMEM220 were regulated by the methylation of their promoter regions. Moreover, immunohistochemistry showed that CBLN2 and TMEM220 were potential prognostic biomarkers for COAD patients. In summary, we have established a risk prediction model and nomogram that might be effectively utilized to promote the prediction of OS in COAD patients.

Continuous MYD88 Activation Is Associated With Expansion and Then Transformation of IgM Differentiating Plasma Cells

Activating mutations of MYD88 (MYD88^L265P being the far most frequent) are found in most cases of Waldenström macroglobulinemia (WM) as well as in various aggressive B-cell lymphoma entities with features of plasma cell (PC) differentiation, such as activated B-cell type diffuse large B-cell lymphoma (DLBCL). To understand how MYD88 activation exerts its transformation potential, we developed a new mouse model in which the MYD88^L252P protein, the murine ortholog of human MYD88^L265P, is continuously expressed in CD19 positive B-cells together with the Yellow Fluorescent Protein (Myd88^L252P mice). In bone marrow, IgM B and plasma cells were expanded with a CD138 expression continuum from IgM^high CD138^low to IgM^low CD138^high cells and the progressive loss of the B220 marker. Serum protein electrophoresis (SPE) longitudinal analysis of 40 Myd88^L252P mice (16 to 56 weeks old) demonstrated that ageing was first associated with serum polyclonal hyper gammaglobulinemia (hyper Ig) and followed by a monoclonal immunoglobulin (Ig) peak related to a progressive increase in IgM serum levels. All Myd88^L252P mice exhibited spleen enlargement which was directly correlated with the SPE profile and was maximal for monoclonal Ig peaks. Myd88^L252P mice exhibited very early increased IgM PC differentiation. Most likely due to an early increase in the Ki67 proliferation index, IgM lymphoplasmacytic (LP) and plasma cells continuously expanded with age being first associated with hyper Ig and then with monoclonal Ig peak. This peak was consistently associated with a spleen LP-like B-cell lymphoma. Clonal expression of both membrane and secreted µ chain isoforms was demonstrated at the mRNA level by high throughput sequencing. The Myd88^L252P tumor transcriptomic signature identified both proliferation and canonical NF-κB p65/RelA activation. Comparison with MYD88^L265P WM showed that Myd88^L252P tumors also shared the typical lymphoplasmacytic transcriptomic signature of WM bone marrow purified tumor B-cells. Altogether these results demonstrate for the first time that continuous MYD88 activation is specifically associated with clonal transformation of differentiating IgM B-cells. Since MYD88^L252P targets the IgM PC differentiation continuum, it provides an interesting preclinical model for development of new therapeutic approaches to both WM and aggressive MYD88 associated DLBCLs.

PTRF/cavin-1 remodels phospholipid metabolism to promote tumor proliferation and suppress immune responses in glioblastoma by stabilizing cPLA2

BACKGROUND

Metabolism remodeling is a hallmark of glioblastoma (GBM) that regulates tumor proliferation and the immune microenvironment. Previous studies have reported that increased polymerase 1 and transcript release factor (PTRF) levels are associated with a worse prognosis in glioma patients. However, the biological role and the molecular mechanism of PTRF in GBM metabolism remain unclear.

METHODS

The relationship between PTRF and lipid metabolism in GBM was detected by nontargeted metabolomics profiling and subsequent lipidomics analysis. Western blotting, quantitative real-time PCR, and immunoprecipitation were conducted to explore the molecular mechanism of PTRF in lipid metabolism. A sequence of in vitro and in vivo experiments (both xenograft tumor and intracranial tumor mouse models) were used to detect the tumor-specific impacts of PTRF.

RESULTS

Here, we show that PTRF triggers a cytoplasmic phospholipase A2 (cPLA2)-mediated phospholipid remodeling pathway that promotes GBM tumor proliferation and suppresses tumor immune responses. Research in primary cell lines from GBM patients revealed that cells overexpressing PTRF show increased cPLA2 activity-resulting from increased protein stability-and exhibit remodeled phospholipid composition. Subsequent experiments revealed that PTRF overexpression alters the endocytosis capacity and energy metabolism of GBM cells. Finally, in GBM xenograft and intracranial tumor mouse models, we showed that inhibiting cPLA2 activity blocks tumor proliferation and prevents PTRF-induced reduction in CD8+ tumor-infiltrating lymphocytes.

CONCLUSIONS

The PTRF-cPLA2 lipid remodeling pathway promotes tumor proliferation and suppresses immune responses in GBM. In addition, our findings highlight multiple new therapeutic targets for GBM.

Analysis of the EGFR Amplification and CDKN2A Deletion Regulated Transcriptomic Signatures Reveals the Prognostic Significance of SPATS2L in Patients With Glioma

Purpose: This study was conducted in order to analyze the prognostic effects of epidermal growth factor receptor (EGFR) and CDKN2A alterations and determine the prognostic significance of EGFR and CDKN2A alterations on regulated genes in patients with glioblastoma (GBM) or lower grade glioma (LGG).

Methods: The alteration frequencies of EGFR and CDKN2A across 32 tumor types were derived from cBioPortal based on The Cancer Genome Atlas (TCGA) datasets. The Kaplan–Meier analysis was used to determine the prognostic significance of EGFR and CDKN2A alterations. EGFR and CDKN2A alterations on regulated expression signatures were identified from RNA-seq data in the TCGA GBM datasets. The prognostic significance of EGFR and CDKN2A alterations on regulated genes in patients with glioma was determined using the TCGA and the Chinese Glioma Genome Atlas (CGGA) datasets.

Results: Compared with the other 31 tumor types, EGFR amplification and CDKN2A deletion particularly occurred in patients with GBM. GBM patients with EGFR amplification or CDKN2A deletion demonstrated poor prognosis. Statistical analysis showed the coexistence of EGFR alteration and CDKN2A deletion in GBM patients. We identified 864 genes which were commonly regulated by EGFR amplification and CDKN2A deletion, and those genes were highly expressed in brain tissues and associated with the cell cycle, EBRR2, and MAPK signaling pathways. Spermatogenesis-associated serine-rich 2-like gene (SPATS2L) was upregulated in GBM patients with EGFR amplification or CDKN2A alteration. Higher expression levels of SPATS2L were associated with worse prognosis in patients with GBM in both TCGA and CGGA datasets. Moreover, the expression levels of SPATS2L were higher in patients with a mesenchymal subtype of GBM. Statistical analysis also showed that the coexistence of EGFR alteration and CDKN2A deletion was significant in patients with LGG. SPATS2L was upregulated in LGG patients with EGFR amplification or CDKN2A alteration. Furthermore, higher expression levels of SPATS2L were associated with worse prognosis in patients with LGG in both TCGA and CGGA datasets. The expression levels of SPATS2L were higher in patients with an astrocytoma subtype of LGG. Finally, the coexistence and unfavorable prognostic effects of EGFR amplification and CDKN2A alteration were validated using the Memorial Sloan Kettering Cancer Center (MSKCC) glioma datasets.

Conclusions: EGFR amplification and CDKN2A deletion of the regulated gene SPATS2L have significant prognostic effects in patients with GBM or LGG.

MYD88 Is a Potential Prognostic Gene and Immune Signature of Tumor Microenvironment for Gliomas

Purpose

To explore the profiles of immune and stromal components of the tumor microenvironment (TME) and their related key genes in gliomas.

Methods

We applied bioinformatic techniques to identify the core gene that participated in the regulation of the TME of the gliomas. And immunohistochemistry staining was used to calculate the gene expressions in clinical cases.

Results

The CIBERSORT and ESTIMATE were used to figure out the composition of TME in 698 glioma cases from The Cancer Genome Atlas (TCGA) database. Differential expression analysis identified 2103 genes between the high and the low-score group. Then the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, univariate Cox regression analysis, and protein–protein interaction (PPI) network construction were conducted based on these genes. MYD88 was identified as the key gene by the combination univariate Cox and PPI analysis. Furthermore, MYD88 expression was significantly associated with the overall survival and WHO grade of glioma patients. The genes in the high-expression MYD88 group were mainly in immune-related pathways in the Gene Set Enrichment Analysis (GSEA). We found that macrophage M2 accounted for the largest portion with an average of 27.6% in the glioma TIICs and was associated with high expression of MYD88. The results were verified in CGGA database and clinical cases in our hospital. Furthermore, we also found the MYD88 expression was higher in IDH1 wild types. The methylation rate was lower in high grade gliomas.

Conclusion

MYD88 had predictive prognostic value in glioma patients by influencing TIICs dysregulation especially the M2-type macrophages.

Abnormal expression of HOXD11 promotes the malignant behavior of glioma cells and leads to poor prognosis of glioma patients

Background

Homeobox D11 (HOXD11) plays an important role in a variety of cancers, but its precise role in gliomas remains unclear. This study aimed to explore the relationship between HOXD11 and gliomas by combining bioinformatics methods with basic experimental validation.

Materials and methods

Obtain gene expression information and clinical information of glioma and non-tumor brain tissue samples from multiple public databases such as TCGA (666 glioma samples), CGGA (749 glioma samples), GEPIA(163 glioblastoma samples and 207 normal control samples), GEO (GSE4290 and GSE15824). Nine cases of glioma tissue and five cases of normal control brain tissue were collected from the clinical department of Henan Provincial People’s Hospital for further verification. A series of bioinformatic analysis methods were used to confirm the relationship between HOXD11 expression and overall survival and clinical molecular characteristics of patients with glioma. RT-qPCR was used to verify the change of expression level of HOXD11 in glioma cells and tissues. MTT assay, colony formation assay, wound-healing assay, immunofluorescence staining, flow cytometry and western blotting were used to detect the effect of HOXD11 on the biological behavior of glioma cell line U251.

Results

The high expression of HOXD11 was significantly related to age, World Health Organization (WHO) grade, chemotherapy status, histological type, and even 1p19q codeletion data and isocitrate dehydrogenase (IDH) mutation. HOXD11, as an independent risk factor, reduces the overall survival of glioma patients and has diagnostic value for the prognosis of glioma. Gene Set Enrichment Analysis (GSEA) showed that HOXD11 was significantly enriched in cell signaling pathway such as cell cycle, DNA replication and so on. Finally, we confirmed that the knockout of HOXD11 can inhibit the proliferation and invasion of U251 glioma cells, and change the biological behavior of tumor cells by preventing the progression of cell cycle.

Conclusions

HOXD11 may be used as a candidate biomarker for the clinical application of targeted drug and prognostic assessment treatment of glioma. In addition, This study will help to explore the pathological mechanism of glioma.

A novel methylation signature predicts radiotherapy sensitivity in glioma

Glioblastoma (GBM) is the most common and malignant cancer of the central nervous system, and radiotherapy is widely applied in GBM treatment; however, the sensitivity to radiotherapy varies in different patients. To solve this clinical dilemma, a radiosensitivity prediction signature was constructed in the present study based on genomic methylation. In total, 1044 primary GBM samples with clinical and methylation microarray data were involved in this study. LASSO-COX, GSVA, Kaplan–Meier survival curve analysis, and COX regression were performed for the construction and verification of predictive models. The R programming language was used as the main tool for statistical analysis and graphical work. Via the integration analysis of methylation and the survival data of primary GBM, a novel prognostic and radiosensitivity prediction signature was constructed. This signature was found to be stable in prognosis prediction in the TCGA and CGGA databases. The possible mechanism was also explored, and it was found that this signature is closely related to DNA repair functions. Most importantly, this signature could predict whether GBM patients could benefit from radiotherapy. In summary, a radiosensitivity prediction signature for GBM patients based on five methylated probes was constructed, and presents great potential for clinical application.

FAM225B Is a Prognostic lncRNA for Patients with Recurrent Glioblastoma

Objective

The overall survival of patients with recurrent glioblastoma (rGBM) is quite different, so clinical outcome prediction is necessary to guide personalized clinical treatment for patients with rGBM. The expression level of lncRNA FAM225B was analyzed to determine its prognostic value in rGBMs.

Methods

We collected 109 samples of Chinese Glioma Genome Atlas (CGGA) RNA sequencing dataset and divided into training set and validation set. Then, we analyzed the expression of FAM225B, clinical characteristics, and overall survival (OS) information. Kaplan-Meier survival analysis was used to estimate the OS distributions. The prognostic value of FAM225B in rGBMs was tested by univariate and multivariate Cox regression analyses. Moreover, we analyzed the biological processes and signaling pathways of FAM225B.

Results

We found that FAM225B was upregulated in rGBMs (P = 0.0009). The expression of FAM225B increased with the grades of gliomas (P < 0.0001). The OS of rGBMs in the low-expression group was significantly longer than that in the high-expression group (P = 0.0041). Similar result was found in the training set (P = 0.0340) and verified in the validation set (P = 0.0292). In multivariate Cox regression analysis, FAM225B was identified to be an independent prognostic factor for rGBMs (P = 0.003). Biological process and KEGG pathway analyses implied FAM225B mainly played a functional role on transcription, regulation of transcription, cell migration, focal adhesion, etc.

Conclusions

FAM225B is expected to be as a new prognostic biomarker for the identification of rGBM patients with poor outcome. And our study provided a potential therapeutic target for rGBMs.

Methylation of MGMT promoter does not predict response to temozolomide in patients with glioblastoma in Donostia Hospital

O⁶-methylguanine-DNA methyltransferase (MGMT) promoter methylation status has been considered a prognostic factor in newly diagnosed glioblastoma (GBM). In this study, we evaluated the prognostic and predictive value of MGMT promoter methylation in patients with glioblastoma in Donostia Hospital. Surprisingly, methylation of MGMT promoter did not predict response to temozolomide in patients with glioblastoma in Donostia Hospital. Specifically, overall survival (OS) and progression-free survival (PFS) did not differ significantly by MGMT methylation status in our cohort. In contrast, both were longer in patients who received treatment, received more TMZ cycles, had a better general status and perform at least a partial resection. No association was detected between methylation of MGMT promoter and molecular markers such as ATRX, IDH, p53 and Ki67. These results indicate that MGMT methylation did not influence in patient survival in our cohort.

Downregulation of CyclophilinA/CD147 Axis Induces Cell Apoptosis and Inhibits Glioma Aggressiveness

Gliomas are the most common primary tumors in the brain with poor prognosis. Previous studies have detected high expression of Cyclophilin A (CyPA) and CD147, respectively, in glioma. However, the correlation between their expressions and glioma prognosis remains unclear. Here, we investigated the expression of CyPA and CD147 in different types of glioma and characterized their relationships with clinical features, prognosis, and cell proliferation. Results showed that CyPA and CD147 expressions were elevated in higher grade gliomas. Moreover, the knockdown of CyPA and CD147 by RNA interference significantly induced cell express apoptosis biomarkers such as Annexin V and inhibited proliferation biomarkers like EdU in glioma cells. In summary, our findings revealed that high expression of CyPA and CD147 correlated with glioma grades. Moreover, downregulation of the Cyclophilin A/CD147 axis induces cell apoptosis and inhibits glioma aggressiveness. Those indicating CyPA and CD147 could be used as both potential predictive biomarkers and a potential therapeutic target.

Identification of Key Differentially Expressed Transcription Factors in Glioblastoma

Glioblastoma (GBM) is the most frequent malignant brain tumor in adults. Our study focused on uncovering differentially expressed genes (DEGs) and their methylation in order to identify novel diagnostic biomarkers and potential treatment targets. Using GBM RNA-sequencing data from The Cancer Genome Atlas (TCGA) database, DEGs between GBM samples and paracancer tissue samples were analyzed. Enrichment analysis for DEGs and transcription factors (TFs) was performed. A total of 1029 upregulated genes and 1542 downregulated genes were identified, which were associated mainly with multiple tumor-related and immune-related pathways such as cell cycle, mitogen-activated protein kinase signaling pathway, leukocyte transendothelial migration, and autoimmune thyroid disease. These DEGs were enriched for 174 TFs, and six TFs were differentially expressed and identified as key TFs in GBM: HOXA3, EN1, ZIC1, and FOXD3 were upregulated, while HLF and EGR3 were downregulated. A total of 1978 DEGs were involved in the regulatory networks of the six key differentially expressed TFs. High expression of EN1 was associated with shorter overall survival, while high expression of EGR3 was associated with shorter recurrence-free survival. The six TFs were differentially methylated in GBM samples compared with paracancer tissues. Our study identifies numerous DEGs and their associated pathways as potential contributors to GBM, particularly the TFs EN1, EGR3, HOXA3, ZIC1, FOXD3, and HLF. The differential expression of these TFs may be unlikely driven by aberrant methylation. These TFs may be useful as diagnostic markers and treatment targets in GBM, and EN1 and EGR3 may have predictive prognostic value.

UBE2T promotes glioblastoma invasion and migration via stabilizing GRP78 and regulating EMT

Glioblastoma (GBM) generally has a dismal prognosis, and it is associated with a poor quality of life as the disease progresses. However, the development of effective therapies for GBM has been deficient. Ubiquitin-conjugating enzyme E2T (UBE2T) is a member of the E2 family in the ubiquitin-proteasome pathway and a vital regulator of tumour progression, but its role in GBM is unclear. In this study, we aimed to clarify the role of UBE2T in GBM. Bioinformatics analysis identified UBE2T as an independent risk factor for gliomas. Immunohistochemistry was used to measure UBE2T expression in GBM and normal tissue samples obtained from patients with GBM. The effects of UBE2T on GBM cell invasion and migration were analysed using the Transwell assay. BALB/c nude mice were used for the in vivo assays. Immunoblotting and immunoprecipitation were performed to determine the molecular mechanisms. UBE2T was highly expressed in GBM tissues, and its expression was linked to a poor prognosis. In vitro, depletion of UBE2T significantly suppressed cell invasion and migration. Moreover, UBE2T depletion suppressed the growth of GBM subcutaneous tumours in vivo. Further experiments revealed that UBE2T suppressed invasion and migration by regulating epithelial- mesenchymal transition (EMT) via stabilising GRP78 in GBM cells. We uncovered a novel UBE2T/GRP78/EMT regulatory axis that modulates the malignant progression and recurrence of GBM, indicating that the axis might be a valuable therapeutic target.

Integrated Analysis to Evaluate the Prognostic Value of Signature mRNAs in Glioblastoma Multiforme

Background

Gliomas are the most common intracranial tumors and are classified as I-IV. Among them, glioblastoma multiforme (GBM) is the most common invasive glioma with a poor prognosis. New molecular biomarkers that can predict clinical outcomes in GBM patients must be identified, which will help comprehend their pathogenesis and supply personalized treatment. Our research revealed four powerful survival indicators in GBM by reanalyzing microarray data and genetic sequencing data in public databases. Moreover, it unraveled new potential therapeutic targets which could help improve the survival time and quality of life of GBM patients.

Materials and Methods

To identify prognostic signatures in GBMs, we analyzed the gene profiling data of GBM and standard brain samples from the Gene Expression Omnibus, including four datasets and RNA sequencing data from The Cancer Genome Atlas (TCGA) containing 152 glioblastoma tissues. We performed the differential analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, weighted gene co-expression network analysis (WGCNA) and Cox regression analysis.

Results

After differential analysis in GSE12657, GSE15824, GSE42656 and GSE50161, overlapping differentially expressed genes were identified. We identified 110 up-regulated DEGs and 75 down-regulated DEGs in the GBM samples. Significantly enriched subclasses of the GO classification of these genes included mitotic sister chromatid separation, mitotic nuclear division and so on. In KEGG pathway analysis, the most abundant terms were ECM-receptor interaction and protein digestion and absorption. WGCNA analysis was performed on these 185 DEGs in 152 glioblastoma samples obtained from TCGA, and gene co-expression networks were constructed. We then performed a multivariate Cox analysis and established a Cox proportional hazards regression model using the top 20 genes significantly correlated with survival time. We identified a four-protein prognostic signature that could divide patients into high-risk and low-risk groups. Increased expression of SLC12A5, CCL2, IGFBP2, and PDPN was associated with increased risk scores. Finally, the K-M curves confirmed that these genes could be used as independent predictors of survival in patients with glioblastoma.

Conclusion

Our analytical study identified a set of potential biomarkers that could predict survival and may contribute to successful treatment of GBM patients.

Kernel Differential Subgraph Analysis to Reveal the Key Period Affecting Glioblastoma

Glioblastoma (GBM) is a fast-growing type of malignant primary brain tumor. To explore the mechanisms in GBM, complex biological networks are used to reveal crucial changes among different biological states, which reflect on the development of living organisms. It is critical to discover the kernel differential subgraph (KDS) that leads to drastic changes. However, identifying the KDS is similar to the Steiner Tree problem that is an NP-hard problem. In this paper, we developed a criterion to explore the KDS (CKDS), which considered the connectivity and scale of KDS, the topological difference of nodes and function relevance between genes in the KDS. The CKDS algorithm was applied to simulated datasets and three single-cell RNA sequencing (scRNA-seq) datasets including GBM, fetal human cortical neurons (FHCN) and neural differentiation. Then we performed the network topology and functional enrichment analyses on the extracted KDSs. Compared with the state-of-art methods, the CKDS algorithm outperformed on simulated datasets to discover the KDSs. In the GBM and FHCN, seventeen genes (one biomarker, nine regulatory genes, one driver genes, six therapeutic targets) and KEGG pathways in KDSs were strongly supported by literature mining that they were highly interrelated with GBM. Moreover, focused on GBM, there were fifteen genes (including ten regulatory genes, three driver genes, one biomarkers, one therapeutic target) and KEGG pathways found in the KDS of neural differentiation process from activated neural stem cells (aNSC) to neural progenitor cells (NPC), while few genes and no pathway were found in the period from NPC to astrocytes (Ast). These experiments indicated that the process from aNSC to NPC is a key differentiation period affecting the development of GBM. Therefore, the CKDS algorithm provides a unique perspective in identifying cell-type-specific genes and KDSs.

Low expression or hypermethylation of PLK2 might predict favorable prognosis for patients with glioblastoma multiforme

Background

As the most aggressive brain tumor, patients with glioblastoma multiforme (GBM) have a poor prognosis. Our purpose was to explore prognostic value of Polo-like kinase 2 (PLK2) in GBM, a member of the PLKs family.

Methods

The expression profile of PLK2 in GBM was obtained from The Cancer Genome Atlas database. The PLK2 expression in GBM was tested. Kaplan–Meier curves were generated to assess the association between PLK2 expression and overall survival (OS) in patients with GBM. Furthermore, to assess its prognostic significance in patients with primary GBM, we constructed univariate and multivariate Cox regression models. The association between PLK2 expression and its methylation was then performed. Differentially expressed genes correlated with PLK2 were identified by Pearson test and functional enrichment analysis was performed.

Results

Overall survival results showed that low PLK2 expression had a favorable prognosis of patients with GBM (P-value = 0.0022). Furthermore, PLK2 (HR = 0.449, 95% CI [0.243–0.830], P-value = 0.011) was positively associated with OS by multivariate Cox regression analysis. In cluster 5, DNA methylated PLK2 had the lowest expression, which implied that PLK2 expression might be affected by its DNA methylation status in GBM. PLK2 in CpG island methylation phenotype (G-CIMP) had lower expression than non G-CIMP group (P = 0.0077). Regression analysis showed that PLK2 expression was negatively correlated with its DNA methylation (P = 0.0062, Pearson r = −0.3855). Among all differentially expressed genes of GBM, CYGB (r = 0.5551; P < 0.0001), ISLR2 (r = 0.5126; P < 0.0001), RPP25 (r = 0.5333; P < 0.0001) and SOX2 (r = −0.4838; P < 0.0001) were strongly correlated with PLK2. Functional enrichment analysis results showed that these genes were enriched several biological processes or pathways that were associated with GBM.

Conclusion

Polo-like kinase 2 expression is regulated by DNA methylation in GBM, and its low expression or hypermethylation could be considered to predict a favorable prognosis for patients with GBM.

Identification of aberrantly methylated differentially expressed genes in glioblastoma multiforme and their association with patient survival

Glioblastoma multiforme (GBM) is the most malignant primary tumour type of the central nervous system with limited therapeutic options and poor prognosis, and its pathogenic mechanisms have remained to be fully elucidated. Aberrant DNA methylation is involved in multiple biological processes and may contribute to the occurrence and development of GBM by affecting the expression of certain genes. However, the specific molecular mechanisms remain to be fully elucidated. The present study focused on uncovering differentially expressed genes with altered methylation status in GBM and aimed to discover novel biomarkers for the diagnosis and treatment of GBM. These genes were identified by combined analysis of multiple gene expression and methylation datasets from gene expression omnibus (GSE16011, GSE50161 and GSE 50923) to increase the reliability. In addition, The Cancer Genome Atlas (TCGA) dataset for GBM was used to test the stability of the results. Overall, 251 hypomethylated upregulated genes (Hypo-UGs) and 199 hypermethylated downregulated genes (Hyper-DGs) were identified in the present study. Functional enrichment analysis revealed that the Hypo-UGs are involved in the regulation of immune- and infection-associated signalling, while the Hyper-DGs are involved in the regulation of synaptic transmission. The three hub genes for Hyper-DGs (somatostatin, neuropeptide Y and adenylate cyclase 2) and five hub genes for Hypo-UGs [interleukin-8, matrix metalloproteinase (MMP)9, cyclin-dependent kinase 1, 2′-5′-oligoadenylate synthetase 1, C-X-C motif chemokine ligand 10 and MMP2] were identified by protein-protein interaction network analysis. Among the Hypo-UGs and Hyper-DGs, overexpression of C-type lectin domain containing 5A, epithelial membrane protein 3, solute carrier family 43 member 3, STEAP3 metalloreductase, tumour necrosis factor α-induced protein 6 and apolipoprotein B mRNA editing enzyme catalytic subunit 3G was significantly associated with poor prognosis in the TCGA and GSE16011 datasets (P<0.001). In conclusion, the present study uncovered numerous novel aberrantly methylated genes and pathways associated with GBM. Methylation-based markers, including the hub genes and prognostic genes identified, may potentially serve as markers for the diagnosis of GBM and targets for its treatment.

Correlation of the invasive potential of glioblastoma and expression of caveola-forming proteins caveolin-1 and CAVIN1

INTRODUCTION

Glioblastoma (GBM) is the most common primary brain cancer. The average survival time for the majority of patients is approximately 15 months after diagnosis. A major feature of GBM that contributes to its poor prognosis is its high invasiveness. Caveolae are plasma membrane subdomains that participate in numerous biological functions. Caveolin-1 and Caveolae Associated Protein 1 (CAVIN1), formerly termed Polymerase I and Transcript Release Factor, are both necessary for caveola formation. We hypothesized that high expression of caveola-forming proteins in GBM promotes invasiveness via modulation of the production of matrix-degrading enzymes.

METHODS

The mRNA expression of caveola-forming proteins and matrix proteases in GBM samples, and survival after stratifying patients according to caveolin-1 or CAVIN1 expression, were analyzed from TCGA and REMBRANDT databases. The proteolytic profile of cell lines expressing or devoid of caveola-forming proteins was investigated using zymography and real-time qPCR. Invasion through basement membrane-like protein was investigated in vitro.

RESULTS

Expression of both caveolin-1 and CAVIN1 was increased in GBM compared to normal samples and correlated with expression of urokinase plasminogen activator (uPA) and gelatinases. High expression of caveola-forming proteins was associated with shorter survival time. GBM cell lines capable of forming caveolae expressed more uPA and matrix metalloproteinase-2 (MMP-2) and/or -9 (MMP-9) and were more invasive than GBM cells devoid of caveola-forming proteins. Experimental manipulation of caveolin-1 or CAVIN1 expression in GBM cells recapitulated some, but not all of these features. Caveolae modulate GBM cell invasion in part via matrix protease expression.

Identification of a multidimensional transcriptome signature for survival prediction of postoperative glioblastoma multiforme patients

Background

Glioblastoma multiform (GBM) is a devastating brain tumor with maximum surgical resection, radiotherapy plus concomitant and adjuvant temozolomide (TMZ) as the standard treatment. Diverse clinicopathological and molecular features are major obstacles to accurate predict survival and evaluate the efficacy of chemotherapy or radiotherapy. Reliable prognostic biomarkers are urgently needed for postoperative GBM patients.

Methods

The protein coding genes (PCGs) and long non-coding RNA (lncRNA) gene expression profiles of 233 GBM postoperative patients were obtained from The Cancer Genome Atlas (TCGA), TANRIC and Gene Expression Omnibus (GEO) database. We randomly divided the TCGA set into a training (n = 76) and a test set (n = 77) and used GSE7696 (n = 80) as an independent validation set. Survival analysis and the random survival forest algorithm were performed to screen survival associated signature.

Results

Six PCGs (EIF2AK3, EPRS, GALE, GUCY2C, MTHFD2, RNF212) and five lncRNAs (CTD-2140B24.6, LINC02015, AC068888.1, CERNA1, LINC00618) were screened out by a risk score model and formed a PCG-lncRNA signature for its predictive power was strongest (AUC = 0.78 in the training dataset). The PCG-lncRNA signature could divide patients into high- risk or low-risk group with significantly different survival (median 7.47 vs. 18.27 months, log-rank test P < 0.001) in the training dataset. Similar result was observed in the test dataset (median 11.40 vs. 16.80 months, log-rank test P = 0.001) and the independent set (median 8.93 vs. 16.22 months, log-rank test P = 0.007). Multivariable Cox regression analysis verified that it was an independent prognostic factor for the postsurgical patients with GBM. Compared with IDH mutation status, O-(6)-methylguanine DNA methyltransferase promoter methylation status and age, the signature was proved to have a superior predictive power. And stratified analysis found that the signature could further separated postoperative GBM patients who received TMZ-chemoradiation into high- and low-risk groups in TCGA and GEO dataset.

Conclusions

The PCG-lncRNA signature was a novel prognostic marker to predict survival and TMZ-chemoradiation response in GBM patients after surgery.

Electronic supplementary material

The online version of this article (10.1186/s12967-018-1744-8) contains supplementary material, which is available to authorized users.

Identification of glioblastoma gene prognosis modules based on weighted gene co-expression network analysis

Background

Glioblastoma multiforme, the most prevalent and aggressive brain tumour, has a poor prognosis. The molecular mechanisms underlying gliomagenesis remain poorly understood. Therefore, molecular research, including various markers, is necessary to understand the occurrence and development of glioma.

Method

Weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network in TCGA glioblastoma samples. Gene ontology (GO) and pathway-enrichment analysis were used to identify significance of gene modules. Cox proportional hazards regression model was used to predict outcome of glioblastoma patients.

Results

We performed weighted gene co-expression network analysis (WGCNA) and identified a gene module (yellow module) related to the survival time of TCGA glioblastoma samples. Then, 228 hub genes were calculated based on gene significance (GS) and module significance (MS). Four genes (OSMR + SOX21 + MED10 + PTPRN) were selected to construct a Cox proportional hazards regression model with high accuracy (AUC = 0.905). The prognostic value of the Cox proportional hazards regression model was also confirmed in GSE16011 dataset (GBM: n = 156).

Conclusion

We developed a promising mRNA signature for estimating overall survival in glioblastoma patients.

Electronic supplementary material

The online version of this article (10.1186/s12920-018-0407-1) contains supplementary material, which is available to authorized users.

Integrated profiling identifies caveolae-associated protein 1 as a prognostic biomarker of malignancy in glioblastoma patients

AIMS

Glioblastoma (GBM) is a lethal disease of the central nervous system with high mortality, and novel therapeutic targets and strategies for GBM are urgently needed. Caveolae-associated protein 1 (CAVIN1) is an essential caveolar component-encoding gene and has been poorly studied in glioma. To this end, in this study, we evaluated CAVIN1 expression in glioma tissue as well as the correlation between CAVIN1 expression and prognosis in glioma patients using the data collected from clinical samples or from the Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Rembrandt, and Gene Expression Omnibus (GEO) data sets.

METHODS

Survival analysis was performed with the Kaplan-Meier curve and log-rank test. The predictive role of CAVIN1 in progressive malignancy in glioma was evaluated by using a receiver operator characteristic (ROC) curve. Gene ontology (GO), Gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) methods were used to interpret the functions of CAVIN1 in GBM.

RESULTS

CAVIN1 expression was elevated in GBM compared with that in low-grade glioma and nontumor brain samples and was correlated with unfavorable outcomes in glioma patients. Additionally, CAVIN1 could serve as an independent predictive factor for progressive malignancy in GBM. Furthermore, CAVIN1 was associated with disrupted angiogenesis and immune response in the tumor microenvironment of GBM.

CONCLUSIONS

We identified CAVIN1 as a prognostic biomarker and potential target for developing novel therapeutic strategies against GBM.

Identification of the potential oncogenes in glioblastoma based on bioinformatic analysis and elucidation of the underlying mechanisms

Glioblastoma (GBM) is a common malignant tumour in the human brain, but its molecular mechanisms have not been systematically evaluated. The aim of this study was to identify potential key oncogenes associated with the progression of GBM and to elucidate their mechanisms. The gene expression profile of GSE50161, selected from the Gene Expression Omnibus database, was analysed to find cancer-associated genes and gene functions in GBM. In total, 486 differentially expressed genes, including 128 upregulated genes, were identified. The function and pathway enrichment of these genes were analysed through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Survival analysis for three selected partially upregulated genes, CDK1, CCNB1 and CDC20, showed that their high expression was significantly associated with poor survival in GBM. CDK1 was selected for validation of its function and molecular mechanism in GBM. This gene was significantly overexpressed in GBM cancer tissues and cells compared with normal control cells. In addition, knockdown of CDK1 clearly inhibited GBM cell proliferation. Notably, we demonstrated that CDK1 was involved in the Akt signalling pathway, where it promotes the process involved in GBM malignancy.