Dataset Information

Development and validation of an interferon signature predicting prognosis and treatment response for glioblastoma.

ABSTRACT: Background: Interferon treatment, as an important approach of anti-tumor immunotherapy, has been implemented in multiple clinical trials of glioma. However, only a small number of gliomas benefit from it. Therefore, it is necessary to investigate the clinical role of interferons and to establish robust biomarkers to facilitate its application. Materials and methods: This study reviewed 1,241 glioblastoma (GBM) and 1,068 lower grade glioma (LGG) patients from six glioma cohorts. The transcription matrix and clinical information were analyzed using R software, GraphPad Prism 7 and Medcalc, etc. Immunohistochemical (IHC) staining were performed for validation in protein level. Results: Interferon signaling was significantly enhanced in GBM. An interferon signature was developed based on five interferon genes with prognostic significance, which could reflect various interferon statuses. Survival analysis showed the signature could serve as an unfavorable prognostic factor independently. We also established a nomogram model integrating the risk signature into traditional prognostic factors, which increased the validity of survival prediction. Moreover, high-risk group conferred resistance to chemotherapy and high IFNB1 expression levels. Functional analysis showed that the high-risk group was associated with overloaded immune response. Microenvironment analysis and IHC staining found that high-risk group occupied a disorganized microenvironment which was characterized by an enrichment of M0 macrophages and neutrophils, but less infiltration of activated nature killing (NK) cells and M1 type macrophages. Conclusion: This interferon signature was an independent indicator for unfavorable prognosis and showed great potential for screening out patients who will benefit from chemotherapy and interferon treatment.

SUBMITTER: Zhu C

PROVIDER: S-EPMC6685507 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and validation of an interferon signature predicting prognosis and treatment response for glioblastoma.

Zhu Chen C Zou Cunyi C Guan Gefei G Guo Qing Q Yan Zihao Z Liu Tianqi T Shen Shuai S Xu Xiaoyan X Chen Chen C Lin Zhiguo Z Cheng Wen W Wu Anhua A

Oncoimmunology 20190612 9

<b>Background</b>: Interferon treatment, as an important approach of anti-tumor immunotherapy, has been implemented in multiple clinical trials of glioma. However, only a small number of gliomas benefit from it. Therefore, it is necessary to investigate the clinical role of interferons and to establish robust biomarkers to facilitate its application. <b>Materials and methods</b>: This study reviewed 1,241 glioblastoma (GBM) and 1,068 lower grade glioma (LGG) patients from six glioma cohorts. The ...[more]

PMID: 31428519

Similar Datasets

Project description:Purpose: Glioblastoma multiforme (GBM) is the most widely occurring brain malignancy. It is modulated by a variety of genes, and patients with GBM have a low survival ratio and an unsatisfactory treatment effect. The irregular regulation of RNA binding proteins (RBPs) is implicated in several malignant neoplasms and reported to exhibit an association with the occurrence and development of carcinoma. Thus, it is necessary to build a stable, multi-RBPs signature-originated model for GBM prognosis and treatment response prediction. Methods: Differentially expressed RBPs (DERBPs) were screened out based on the RBPs data of GBM and normal brain tissues from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression Program (GTEx) datasets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses on DERBPs were performed, followed by an analysis of the Protein-Protein Interaction network. Survival analysis of the DERBPs was conducted by univariate and multivariate Cox regression. Then, a risk score model was created on the basis of the gene signatures in various survival-associated RBPs, and its prognostic and predictive values were evaluated through Kaplan-Meier analysis and log-rank test. A nomogram on the basis of the hub RBPs signature was applied to estimate GBM patients' survival rates. Moreover, western blot was for the detection of the proteins. Results: BICC1, GNL3L, and KHDRBS2 were considered as prognosis-associated hub RBPs and then were applied in the construction of a prognostic model. Poor survival results appeared in GBM patients with a high-risk score. The area under the time-dependent ROC curve of the prognostic model was 0.723 in TCGA and 0.707 in Chinese Glioma Genome Atlas (CGGA) cohorts, indicating a good prognostic model. What was more, the survival duration of the high-risk group receiving radiotherapy or temozolomide chemotherapy was shorter than that of the low-risk group. The nomogram showed a great discriminating capacity for GBM, and western blot experiments demonstrated that the proteins of these 3 RBPs had different expressions in GBM cells. Conclusion: The identified 3 hub RBPs-derived risk score is effective in the prediction of GBM prognosis and treatment response, and benefits to the treatment of GBM patients.

Dataset Information

Development and validation of an interferon signature predicting prognosis and treatment response for glioblastoma.

Publications

Development and validation of an interferon signature predicting prognosis and treatment response for glioblastoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets