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Prognostic and Predictive Value of an Immunoscore Signature in Glioblastoma Multiform.

ABSTRACT: Background:Although increasing evidence shows that immune infiltration plays an essential role in glioblastoma (GBM), current prognostic indicators do not accurately represent the risk of immune cells infiltration in patients. It is therefore critical to identify new prognostic markers for GBM. Here, we investigated the effectiveness of using immunoscore to improve risk stratification and prediction of prognosis in GBM patients receiving chemotherapy. Methods:Using mRNA microarrays and CIBERSORT, we analyzed 22 types of immune cell fractions in 517 GBM samples and characterized an immunoscore using the least absolute shrinkage and selection operator (LASSO) Cox regression model based on the fraction of immune cell types and patients' overall survival. The prognostic and predictive accuracy of immunoscore was verified in the validation cohort and the entire cohort. Results:Using the LASSO model, an immunoscore was developed to classify patients into High and Low immunoscore groups in the training cohort (P < 0.0001) based on the fraction of eight immune cell types. The immunoscore performance was validated in the validation cohort (P < 0.0001) and the entire cohort (P < 0.0001). Furthermore, a nomogram comprising age, IDH1 status, and immunoscore was generated to predict one- and three-year survival rates in the training cohort. The predictive value of the immunoscore was also confirmed in the validation cohort and the entire cohort (C-index: 0.66, 0.67, and 0.68, respectively). In addition, we concluded that patients in the low-immunoscore group may benefit from adjuvant chemotherapy for GBM. Conclusion:Immunoscore, an immune-infiltration-based signature, is a reliable prognostic and predictive tool for GBM.

SUBMITTER: Tang X

PROVIDER: S-EPMC7684008 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Prognostic and Predictive Value of an Immunoscore Signature in Glioblastoma Multiform.

Tang Xiangjun X Xu Pengfei P Chen Ann A Deng Gang G Zhang Shenqi S Gao Lun L Dai Longjun L Chen Qianxue Q

Frontiers in genetics 20201109

<h4>Background</h4>Although increasing evidence shows that immune infiltration plays an essential role in glioblastoma (GBM), current prognostic indicators do not accurately represent the risk of immune cells infiltration in patients. It is therefore critical to identify new prognostic markers for GBM. Here, we investigated the effectiveness of using immunoscore to improve risk stratification and prediction of prognosis in GBM patients receiving chemotherapy.<h4>Methods</h4>Using mRNA microarray ...[more]

PMID: 33244317

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Project description:BackgroundGliomas 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 methodsTo 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.ResultsAfter 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.ConclusionOur analytical study identified a set of potential biomarkers that could predict survival and may contribute to successful treatment of GBM patients.

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Prognostic and Predictive Value of an Immunoscore Signature in Glioblastoma Multiform.

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Prognostic and Predictive Value of an Immunoscore Signature in Glioblastoma Multiform.

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