Prognostic and predictive value of an immune infiltration signature in diffuse lower-grade gliomas
Open Access
- 31 March 2020
- journal article
- research article
- Published by American Society for Clinical Investigation in JCI Insight
Abstract
BACKGROUND. Lower-grade gliomas (LGGs) vary widely in terms of the patient’s overall survival (OS). There is no current, valid method that could exactly predict the survival. The effects of intratumoral immune infiltration on clinical outcome have been widely reported. Thus, we aim to develop an immune infiltration signature to predict the survival of LGG patients. METHODS. We analyzed 1216 LGGs from 5 public data sets, including 2 RNA sequencing data sets and 3 microarray data sets. Least absolute shrinkage and selection operator (LASSO) Cox regression was used to select an immune infiltration signature and build a risk score. The performance of the risk score was assessed in the training set (329 patients), internal validation set (140 patients), and 4 external validation sets (405, 118, 88, and 136 patients). RESULTS. An immune infiltration signature consisting of 20 immune metagenes was used to generate a risk score. The performance of the risk score was thoroughly verified in the training and validation sets. Additionally, we found that the risk score was positively correlated with the expression levels of TGF-β and PD-L1, which were important targets of combination immunotherapy. Furthermore, a nomogram incorporating the risk score, patient’s age, and tumor grade was developed to predict the OS, and it performed well in all the training and validation sets (C-index: 0.873, 0.881, 0.781, 0.765, 0.721, and 0.753). CONCLUSION. The risk score based on the immune infiltration signature has reliable prognostic and predictive value for patients with LGGs and is a potential biomarker for the cotargeting immunotherapy. FUNDING. This work was supported by The National Natural Science Foundation of China (grant nos. 81472370 and 81672506), the Natural Science Foundation of Beijing (grant no. J180005), the National High Technology Research and Development Program of China (863 Program, grant no. 2014AA020610), and the National Basic Research Program of China (973 Program, grant no. 2014CB542006).Funding Information
- National Natural Science Foundation of China (81472370)
- National Natural Science Foundation of China (81672506)
- National High Technology Research and Development Program of China (2014AA020610)
- National Basic Research Program of China (2014CB542006)
- Natural Science Foundation of Beijing Municipality (J180005)
This publication has 48 references indexed in Scilit:
- Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samplesTheory in Biosciences, 2012
- Immune cell infiltrate differences in pilocytic astrocytoma and glioblastoma: evidence of distinct immunological microenvironments that reflect tumor biologyJournal of Neurosurgery, 2011
- Immunity, Inflammation, and CancerCell, 2010
- RNA-Seq gene expression estimation with read mapping uncertaintyBioinformatics, 2009
- Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than HistologyCancer Research, 2009
- IDH1andIDH2Mutations in GliomasThe New England Journal of Medicine, 2009
- Adaptive Lasso for Cox's proportional hazards modelBiometrika, 2007
- Low-grade gliomas: an update on pathology and therapyThe Lancet Neurology, 2005
- Engagement of the Pd-1 Immunoinhibitory Receptor by a Novel B7 Family Member Leads to Negative Regulation of Lymphocyte ActivationThe Journal of Experimental Medicine, 2000
- Flexible regression models with cubic splinesStatistics in Medicine, 1989