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Recognition of a Novel Gene Signature for Human Glioblastoma.

ABSTRACT: Glioblastoma (GBM) is one of the most common malignant and incurable brain tumors. The identification of a gene signature for GBM may be helpful for its diagnosis, treatment, prediction of prognosis and even the development of treatments. In this study, we used the GSE108474 database to perform GSEA and machine learning analysis, and identified a 33-gene signature of GBM by examining astrocytoma or non-GBM glioma differential gene expression. The 33 identified signature genes included the overexpressed genes COL6A2, ABCC3, COL8A1, FAM20A, ADM, CTHRC1, PDPN, IBSP, MIR210HG, GPX8, MYL9 and PDLIM4, as well as the underexpressed genes CHST9, CSDC2, ENHO, FERMT1, IGFN1, LINC00836, MGAT4C, SHANK2 and VIPR2. Protein functional analysis by CELLO2GO implied that these signature genes might be involved in regulating various aspects of biological function, including anatomical structure development, cell proliferation and adhesion, signaling transduction and many of the genes were annotated in response to stress. Of these 33 signature genes, 23 have previously been reported to be functionally correlated with GBM; the roles of the remaining 10 genes in glioma development remain unknown. Our results were the first to reveal that GBM exhibited the overexpressed GPX8 gene and underexpressed signature genes including CHST9, CSDC2, ENHO, FERMT1, IGFN1, LINC00836, MGAT4C and SHANK2, which might play crucial roles in the tumorigenesis of different gliomas.

SUBMITTER: Lu CH

PROVIDER: S-EPMC9029857 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Recognition of a Novel Gene Signature for Human Glioblastoma.

Lu Chih-Hao CH Wei Sung-Tai ST Liu Jia-Jun JJ Chang Yu-Jen YJ Lin Yu-Feng YF Yu Chin-Sheng CS Chang Sunny Li-Yun SL

International journal of molecular sciences 20220409 8

Glioblastoma (GBM) is one of the most common malignant and incurable brain tumors. The identification of a gene signature for GBM may be helpful for its diagnosis, treatment, prediction of prognosis and even the development of treatments. In this study, we used the GSE108474 database to perform GSEA and machine learning analysis, and identified a 33-gene signature of GBM by examining astrocytoma or non-GBM glioma differential gene expression. The 33 identified signature genes included the overex ...[more]

PMID: 35456975

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Project description:IntroductionGlioblastoma (GBM) is one of the most frequent primary intracranial malignancies, with limited treatment options and poor overall survival rates. Alternated glucose metabolism is a key metabolic feature of tumour cells, including GBM cells. However, due to high cellular heterogeneity, accurately predicting the prognosis of GBM patients using a single biomarker is difficult. Therefore, identifying a novel glucose metabolism-related biomarker signature is important and may contribute to accurate prognosis prediction for GBM patients.MethodsIn this research, we performed gene set enrichment analysis and profiled four glucose metabolism-related gene sets containing 327 genes related to biological processes. Univariate and multivariate Cox regression analyses were specifically completed to identify genes to build a specific risk signature, and we identified ten mRNAs (B4GALT7, CHST12, G6PC2, GALE, IL13RA1, LDHB, SPAG4, STC1, TGFBI, and TPBG) within the Cox proportional hazards regression model for GBM.ResultsDepending on this glucose metabolism-related gene signature, we divided patients into high-risk (with poor outcomes) and low-risk (with satisfactory outcomes) subgroups. The results of the multivariate Cox regression analysis demonstrated that the prognostic potential of this ten-gene signature is independent of clinical variables. Furthermore, we used two other GBM databases (Chinese Glioma Genome Atlas (CGGA) and REMBRANDT) to validate this model. In the functional analysis results, the risk signature was associated with almost every step of cancer progression, such as adhesion, proliferation, angiogenesis, drug resistance, and even an immune-suppressed microenvironment. Moreover, we found that IL31RA expression was significantly different between the high-risk and low-risk subgroups.ConclusionThe 10 glucose metabolism-related gene risk signatures could serve as an independent prognostic factor for GBM patients and might be valuable for the clinical management of GBM patients. The differential gene IL31RA may be a potential treatment target in GBM.

Project description:BackgroundSeveral studies have established Glioblastoma Multiforme (GBM) prognostic and predictive models based on age and Karnofsky Performance Status (KPS), while very few studies evaluated the prognostic and predictive significance of preoperative MR-imaging. However, to date, there is no simple preoperative GBM classification that also correlates with a highly prognostic genomic signature. Thus, we present for the first time a biologically relevant, and clinically applicable tumor Volume, patient Age, and KPS (VAK) GBM classification that can easily and non-invasively be determined upon patient admission.MethodsWe quantitatively analyzed the volumes of 78 GBM patient MRIs present in The Cancer Imaging Archive (TCIA) corresponding to patients in The Cancer Genome Atlas (TCGA) with VAK annotation. The variables were then combined using a simple 3-point scoring system to form the VAK classification. A validation set (N?=?64) from both the TCGA and Rembrandt databases was used to confirm the classification. Transcription factor and genomic correlations were performed using the gene pattern suite and Ingenuity Pathway Analysis.ResultsVAK-A and VAK-B classes showed significant median survival differences in discovery (P?=?0.007) and validation sets (P?=?0.008). VAK-A is significantly associated with P53 activation, while VAK-B shows significant P53 inhibition. Furthermore, a molecular gene signature comprised of a total of 25 genes and microRNAs was significantly associated with the classes and predicted survival in an independent validation set (P?=?0.001). A favorable MGMT promoter methylation status resulted in a 10.5 months additional survival benefit for VAK-A compared to VAK-B patients.ConclusionsThe non-invasively determined VAK classification with its implication of VAK-specific molecular regulatory networks, can serve as a very robust initial prognostic tool, clinical trial selection criteria, and important step toward the refinement of genomics-based personalized therapy for GBM patients.

Project description:BackgroundGlioblastoma (GBM) is a highly lethal brain tumor with a complex tumor microenvironment (TME) and poor prognosis. This study aimed to develop and validate a novel immune-related prognostic model for GBM patients to enhance personalized prognosis prediction and develop effective therapeutic strategies.MethodsRNA sequencing and clinical data for GBM patients were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) (GSE83300). Single-sample gene set enrichment analysis (ssGSEA) was performed using the gene set variation analysis (GSVA) package in R to classify the samples into high and low immune infiltration clusters based on 29 immune cell subtypes. Clustering validations included differential analysis of immune scores and comparison of human leukocyte antigen (HLA) family expression and immune cell subtypes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology (GO) analysis compared molecular mechanisms and cellular functions between clusters. Differentially expressed immune-related genes between the high and low immune infiltration clusters were screened out, and the prognostic immune-related genes (PIGs) were identified using univariate Cox regression. Co-expression analysis between PIGs and transcription factors (TFs) (Cistrome) was conducted, and a protein-protein interaction (PPI) network (STRING) was constructed. Least absolute shrinkage and selection operator (LASSO) regression constructed a prognostic model. Correlation analyses between PIGs, immune infiltrates, and GBM-related genes were performed. Tumor mutation burden (TMB) analysis and a nomogram incorporating age, gender, and risk score were developed for individualized prognosis prediction.ResultsA total of 312 differentially expressed immune-related genes were identified between high and low immune infiltration clusters. Of these, 28 genes were correlated with GBM prognosis. LASSO regression identified 10 genes (CLCF1, PTX3, TNFRSF14, SDC2, VGF, AREG, PLAUR, GRN, AQP9, and IGLV6-57) for the prognostic model. Patients were divided into high-risk and low-risk groups based on risk scores. Survival analysis showed significantly better overall survival (OS) for the low-risk group (P<0.05). The prognostic signature was validated as an independent prognostic factor. Correlation analyses demonstrated significant associations between the prognostic model, immune cell infiltrates, GBM-related genes, and immune checkpoint-related genes. A nomogram incorporating age, gender, and risk score was developed for personalized prognosis prediction.ConclusionsIn summary, our study provided a novel prognostic model based on ssGSEA for GBM patients and offered potential insights for understanding the tumor immune and molecular mechanisms of the disease.

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Recognition of a Novel Gene Signature for Human Glioblastoma.

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Recognition of a Novel Gene Signature for Human Glioblastoma.

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