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Integrative Analysis of DNA Methylation and Transcriptome Identifies a Predictive Epigenetic Signature Associated With Immune Infiltration in Gliomas.

ABSTRACT: Glioma is the most common primary brain tumor with poor prognosis and high mortality. The purpose of this study was to use the epigenetic signature to predict prognosis and evaluate the degree of immune infiltration in gliomas. We integrated gene expression profiles and DNA methylation data of lower-grade glioma and glioblastoma to explore epigenetic differences and associated differences in biological function. Cox regression and lasso analysis were used to develop an epigenetic signature based on eight DNA methylation sites to predict prognosis of glioma patients. Kaplan-Meier analysis showed that the overall survival time of high- and low-risk groups was significantly separated, and ROC analysis verified that the model had great predictive ability. In addition, we constructed a nomogram based on age, sex, 1p/19q status, glioma type, and risk score. The epigenetic signature was obviously associated with tumor purity, immune checkpoints, and tumor-immune infiltrating cells (CD8+ T cells, gamma delta T cells, M0 macrophages, M1 macrophages, M2 macrophages, activated NK cells, monocytes, and activated mast cells) and thus, it may find application as a guide for the evaluation of immune infiltration or in treatment decisions in immunotherapy.

SUBMITTER: Zhang J

PROVIDER: S-EPMC8203203 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Integrative Analysis of DNA Methylation and Transcriptome Identifies a Predictive Epigenetic Signature Associated With Immune Infiltration in Gliomas.

Zhang Jianlei J Yin Jiang J Luo Liyun L Huang Danqing D Zhai Dongfeng D Wang Ge G Xu Ning N Yang Mingqiang M Song Ying Y Zheng Guopei G Zhang Qiong Q

Frontiers in cell and developmental biology 20210531

Glioma is the most common primary brain tumor with poor prognosis and high mortality. The purpose of this study was to use the epigenetic signature to predict prognosis and evaluate the degree of immune infiltration in gliomas. We integrated gene expression profiles and DNA methylation data of lower-grade glioma and glioblastoma to explore epigenetic differences and associated differences in biological function. Cox regression and lasso analysis were used to develop an epigenetic signature based ...[more]

PMID: 34136486

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Project description:BACKGROUNDLower-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.METHODSWe 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).RESULTSAn 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).CONCLUSIONThe 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.FUNDINGThis 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).

Project description:BackgroundGliomas are the most common primary tumors of the central nervous system, with high heterogeneity and highly variable survival rates. Accurate classification and prognostic assessment are key to the selection of treatment strategies. One hallmark of the tumor is resistance to cell death. PANoptosis, a novel mode of programmed cell death, has been frequently reported to be involved in the innate immunity associated with pathogen infection and played an important role in cancers. However, the intrinsic association of PANoptosis with glioma requires deeper investigation.MethodsThe genetics and expression of the 17 reported PANoptosome-related genes were analyzed in glioma. Based on these genes, patients were divided into two subtypes by consensus clustering analysis. After obtaining the differentially expressed genes between clusters, a prognostic model called PANopotic score was constructed after univariate Cox regression, LASSO regression, and multivariate Cox regression. The expression of the 5 genes included in the PANopotic score was also examined by qPCR in our cohort. The prognostic differences, clinical features, TME infiltration status, and immune characteristics between PANoptotic clusters and score groups were compared, some of which even extended to pan-cancer levels.ResultsGene mutations, CNVs and altered gene expression of PANoptosome-related genes exist in gliomas. Two PANoptotic clusters were significantly different in prognosis, clinical features, immune characteristics, and mutation landscapes. The 5 genes included in the PANopotic score had significantly altered expression in glioma samples in our cohort. The high PANoptotic score group was inclined to show an unfavorable prognosis, lower tumor purity, worse molecular genetic signature, and distinct immune characteristics related to immunotherapy. The PANoptotic score was considered as an independent prognostic factor for glioma and showed superior prognostic assessment efficacy over several reported models. PANopotic score was included in the nomogram constructed for the potential clinical prognostic application. The associations of PANoptotic score with prognostic assessment and tumor immune characteristics were also reflected at the pan-cancer level.ConclusionMolecular subtypes of glioma based on PANoptosome-related genes were proposed and PANoptotic score was constructed with different clinical characteristics of anti-tumor immunity. The potential intrinsic association between PANoptosis and glioma subtypes, prognosis, and immunotherapy was revealed.

Project description:Background: Tumor microenvironment, especially infiltrating immune cell, is crucial for solid tumors including glioma. However, the hub genes as well as their effects on patient prognosis and immunotherapy efficacy remain obscure. Methods: We employed a total of 952 lower grade glioma (LGG) patients from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases, and 24 samples in our hospital for subsequent analyses. Abundances of immune infiltrates were evaluated using CIBERSORT and ImmuCellAI. Their correlations with prognosis were assessed by log-rank test. Immune infiltration-related hub genes were obtained from overlapped differential expressed genes (DEGs) in various subsets of survival-related immune cell types. The risk signature was constructed by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis. The functional analyses were estimated by GVSA and Gene Set Enrichment Analysis (GSEA) algorithms. And protein-protein interaction enrichment analysis was carried out with the Metascape database integrating STRING, BioGrid, OmniPath, and InWeb_IM. Results: Among the 21 infiltrates, the abundances of five immune infiltrates were correlated with overall survival (OS) in LGG patients. Higher abundances of naïve CD4+ T cells (p = 0.002), activated mast cells (p = 0.015), and monocytes (p = 0.014) were correlated with better prognosis, while higher abundances of resting memory CD4+ T cells (p = 0.015) and M1 macrophages (p = 0.020) correlated with poorer OS. We finally obtained 44 hub genes and constructed an immune infiltration-related signature (IIRS). The IIRS correlates with clinicopathological characteristics and exhibited potential power in predicting the immunotherapy efficacy. The IRRS correlates with cancer related pathways, especially "epithelial-mesenchymal transition (EMT)," and cytotoxic T lymphocytes. Conclusion: Our study constructed and validated a novel signature for risk stratification and prediction of immunotherapy response in grade II and III gliomas, which was closely associated with glioma immune microenvironment and could serve as a promising prognostic biomarker for glioma patients.

Project description:BackgroundThe tumor-associated microenvironment plays important roles in tumor progression and drug resistance. However, systematic investigations of macrophage-tumor cell interactions to identify novel macrophage-related gene signatures in gliomas for predicting patient prognoses and responses to targeted therapies are lacking.MethodsWe developed a multicellular gene network approach to investigating the prognostic role of macrophage-tumor cell interactions in tumor progression and drug resistance in gliomas. Multicellular gene networks connecting macrophages and tumor cells were constructed from re-grouped drug-sensitive and drug-resistant samples of RNA-seq data in mice gliomas treated with BLZ945 (a CSF1R inhibitor). Subsequently, a differential network-based COX regression model was built to identify the risk signature using a cohort of 310 glioma samples from the Chinese Glioma Genome Atlas database. A large independent validation set of 690 glioma samples from The Cancer Genome Atlas database was used to test the prognostic significance and accuracy of the gene signature in predicting prognosis and targeted therapeutic response of glioma patients.ResultsA macrophage-related gene signature was developed consisting of twelve genes (ANPEP, DPP4, PRRG1, GPNMB, TMEM26, PXDN, CDH6, SCN3A, SEMA6B, CCDC37, FANCA, NETO2), which was tested in the independent validation set to examine its prognostic significance and accuracy. The generation of 1000 random gene signatures by a bootstrapping scheme justified the non-random nature of the macrophage-related gene signature. Moreover, the discovered gene signature was verified to be predictive of the sensitivity or resistance of glioma patients to molecularly targeted therapeutics and outperformed other existing gene signatures. Additionally, the macrophage-related gene signature was an independent and the strongest prognostic factor when adjusted for clinicopathologic risk factors and other existing gene signatures.ConclusionThe multicellular gene network approach developed herein indicates profound roles of the macrophage-mediated tumor microenvironment in the progression and drug resistance of gliomas. The identified macrophage-related gene signature has good prognostic value for predicting resistance to targeted therapeutics and survival of glioma patients, implying that combining current targeted therapies with new macrophage-targeted therapy may be beneficial for the long-term treatment outcomes of glioma patients.

Dataset Information

Integrative Analysis of DNA Methylation and Transcriptome Identifies a Predictive Epigenetic Signature Associated With Immune Infiltration in Gliomas.

Publications

Integrative Analysis of DNA Methylation and Transcriptome Identifies a Predictive Epigenetic Signature Associated With Immune Infiltration in Gliomas.

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