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Identification of Novel Transcriptome Signature as a Potential Prognostic Biomarker for Anti-Angiogenic Therapy in Glioblastoma Multiforme.

ABSTRACT: Glioblastoma multiforme (GBM) is the most common and devastating type of primary brain tumor, with a median survival time of only 15 months. Having a clinically applicable genetic biomarker would lead to a paradigm shift in precise diagnosis, personalized therapeutic decisions, and prognostic prediction for GBM. Radiogenomic profiling connecting radiological imaging features with molecular alterations will offer a noninvasive method for genomic studies of GBM. To this end, we analyzed over 3800 glioma and GBM cases across four independent datasets. The Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases were employed for RNA-Seq analysis, whereas the Ivy Glioblastoma Atlas Project (Ivy-GAP) and The Cancer Imaging Archive (TCIA) provided clinicopathological data. The Clinical Proteomic Tumor Analysis Consortium Glioblastoma Multiforme (CPTAC-GBM) was used for proteomic analysis. We identified a simple three-gene transcriptome signature-SOCS3, VEGFA, and TEK-that can connect GBM's overall prognosis with genes' expression and simultaneously correlate radiographical features of perfusion imaging with SOCS3 expression levels. More importantly, the rampant development of neovascularization in GBM offers a promising target for therapeutic intervention. However, treatment with bevacizumab failed to improve overall survival. We identified SOCS3 expression levels as a potential selection marker for patients who may benefit from early initiation of angiogenesis inhibitors.

SUBMITTER: Zheng S

PROVIDER: S-EPMC7957709 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.

Project description:Glioblastoma is the most aggressive brain tumor with a median survival ranging from 6.2 to 16.7 months. The complex interactions between the tumor and the cells of tumor microenvironment leads to tumor evolution which ultimately results in treatment failure. Immunotherapy has shown great potential in the treatment of solid tumors but has been less effective in treating glioblastoma. Failure of immunotherapy in glioblastoma has been attributed to low T-cell infiltration in glioblastoma and dysfunction of the T-cells that are present in the glioblastoma microenvironment. Recent advances in single-cell sequencing have increased our understanding of the transcriptional changes in the tumor microenvironment pre and post-treatment. Another treatment modality targeting the tumor microenvironment that has failed in glioblastoma has been anti-angiogenic therapy such as the VEGF neutralizing antibody bevacizumab, which did not improve survival in randomized clinical trials. Interestingly, the immunosuppressed microenvironment and abnormal vasculature of glioblastoma interact in ways that suggest the potential for synergy between these two therapeutic modalities that have failed individually. Abnormal tumor vasculature has been associated with immune evasion and the creation of an immunosuppressive microenvironment, suggesting that inhibiting pro-angiogenic factors like VEGF can increase infiltration of effector immune cells into the tumor microenvironment. Remodeling of the tumor vasculature by inhibiting VEGFR2 has also been shown to improve the efficacy of PDL1 cancer immunotherapy in mouse models of different cancers. In this review, we discuss the recent developments in our understanding of the glioblastoma tumor microenvironment specially the tumor vasculature and its interactions with the immune cells, and opportunities to target these interactions therapeutically. Combining anti-angiogenic and immunotherapy in glioblastoma has the potential to unlock these therapeutic modalities and impact the survival of patients with this devastating cancer.

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Identification of Novel Transcriptome Signature as a Potential Prognostic Biomarker for Anti-Angiogenic Therapy in Glioblastoma Multiforme.

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