Bioinformatic Profiling of Prognosis-Related Genes in Malignant Glioma Microenvironment

Li, Yong; Deng, Gang; Qi, Yangzhi; Zhang, Huikai; Gao, Lun; Jiang, Hongxiang; Ye, Zhang; Liu, Baohui

doi:10.12659/msm.924054

Cited by 21 publications

(17 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In addition, high expression of EGFLAM in GBM is associated with poor prognosis, and silencing EGFLAM can inhibit proliferation, invasion, and migration of GBM cells by negatively regulating the PI3K/AKT pathway [ 55 ]. A study by Li et al showed that LILRB2, a TME-related gene, is a potential prognostic biomarker in malignant glioma [ 56 ]. SAA2 has a unique role in promoting Th17 cell-mediated inflammatory disease [ 57 ] and plays an important role in many cancers, including GBM [ 57 , 58 ].…”

Section: Resultsmentioning

confidence: 99%

Tumor antigens and immune subtypes of glioblastoma: the fundamentals of mRNA vaccine and individualized immunotherapy development

Qin

Long

et al. 2022

J Big Data

View full text Add to dashboard Cite

Purpose Glioblastoma (GBM) is the most common primary brain tumor in adults and is notorious for its lethality. Given its limited therapeutic measures and high heterogeneity, the development of new individualized therapies is important. mRNA vaccines have exhibited promising performance in a variety of solid tumors, those designed for glioblastoma (GBM) need further development. The aim of this study is to explore tumor antigens for the development of mRNA vaccines against GBM and to identify potential immune subtypes of GBM to identify the patients suitable for different immunotherapies. Methods RNA-seq data and the clinical information of 143 GBM patients was extracted from the TCGA database; microarray data and the clinical information of 181 GBM patients was obtained from the REMBRANDT cohort. A GBM immunotherapy cohort of 17 patients was obtained from a previous literature. GEPIA2, cBioPortal, and TIMER2 were used to identify the potential tumor antigens. Immune subtypes and gene modules were identified using consensus clustering; immune landscape was constructed using graph-learning-based dimensionality reduction analysis. Results Nine potential tumor antigens associated with poor prognosis and infiltration of antigen-presenting cells were identified in GBM: ADAMTSL4, COL6A1, CTSL, CYTH4, EGFLAM, LILRB2, MPZL2, SAA2, and LSP1. Four robust immune subtypes and seven functional gene modules were identified and validated in an independent cohort. Immune subtypes had different cellular and molecular characteristics, with IS1, an immune cold phenotype; IS2, an immune hot and immunosuppressive phenotype; IS3, a relatively immune cold phenotype, second only to IS1; IS4, having a moderate tumor immune microenvironment. Immune landscape revealed the immune distribution of the GBM patients. Additionally, the potential value of immune subtypes for individualized immunotherapy was demonstrated in a GBM immunotherapy cohort. Conclusions ADAMTSL4, COL6A1, CTSL, CYTH4, EGFLAM, LILRB2, MPZL2, SAA2, and LSP1 are the candidate tumor antigens for mRNA vaccine development in GBM, and IS1 GBM patients are best suited for mRNA vaccination, IS2 patients are best suited for immune checkpoint inhibitor. This study provides a theoretical framework for GBM mRNA vaccine development and individualized immunotherapy strategies.

show abstract

Section: Resultsmentioning

confidence: 99%

Tumor antigens and immune subtypes of glioblastoma: the fundamentals of mRNA vaccine and individualized immunotherapy development

Qin

Long

et al. 2022

J Big Data

View full text Add to dashboard Cite

show abstract

“…Immune and stromal scores were calculated to evaluate cell infiltration levels in CRC. Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm ( https://bioinformatics.mdanderson.org/estimate/ ) was adopted to measure stromal level (stromal scores), immunocyte infiltration degree (immune scores), and tumor purity using “estimate” R package ( Li et al, 2020b ; Guo et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Identification of a novel lipid metabolism-related gene signature for predicting colorectal cancer survival

et al. 2022

View full text Add to dashboard Cite

Colorectal cancer (CRC) is a common malignant tumor worldwide. Lipid metabolism is a prerequisite for the growth, proliferation and invasion of cancer cells. However, the lipid metabolism-related gene signature and its underlying molecular mechanisms remain unclear. The aim of this study was to establish a lipid metabolism signature risk model for survival prediction in CRC and to investigate the effect of gene signature on the immune microenvironment. Lipid metabolism-mediated genes (LMGs) were obtained from the Molecular Signatures Database. The consensus molecular subtypes were established using “ConsensusClusterPlus” based on LMGs and the cancer genome atlas (TCGA) data. The risk model was established using univariate and multivariate Cox regression with TCGA database and independently validated in the international cancer genome consortium (ICGC) datasets. Immune infiltration in the risk model was developed using CIBERSORT and xCell analyses. A total of 267 differentially expressed genes (DEGs) were identified between subtype 1 and subtype 2 from consensus molecular subtypes, including 153 upregulated DEGs and 114 downregulated DEGs. 21 DEGs associated with overall survival (OS) were selected using univariate Cox regression analysis. Furthermore, a prognostic risk model was constructed using the risk coefficients and gene expression of eleven-gene signature. Patients with a high-risk score had poorer OS compared with patients in the low-risk score group (p = 3.36e-07) in the TCGA cohort and the validationdatasets (p = 4.03e-05). Analysis of immune infiltration identified multiple T cells were associated with better prognosis in the low-risk group, including Th2 cells (p = 0.0208), regulatory T cells (p = 0.0425), and gammadelta T cells (p = 0.0112). A nomogram integrating the risk model and clinical characteristics was further developed to predict the prognosis of patients with CRC. In conclusion, our study revealed that the expression of lipid-metabolism genes were correlated with the immune microenvironment. The eleven-gene signature might be useful for prediction the prognosis of CRC patients.

show abstract

“…They found that grade III or IDH1 wild type gliomas had both higher immune and stromal scores (31).They also found that ESTIMATE algorithms-based scores were meaningful in subtype classification of glioblastomas and affected prognosis. Li Yong et al reported that increased immune and stromal scores were closely related with advanced glioma grade and poor prognosis (32). And the GO and KEGG analyses revealed that the majority of the DEGs were involved in immunologic process.…”

Section: Tme Characteristics Of Gliomamentioning

confidence: 99%

MYD88 Is a Potential Prognostic Gene and Immune Signature of Tumor Microenvironment for Gliomas

2021

View full text Add to dashboard Cite

PurposeTo explore the profiles of immune and stromal components of the tumor microenvironment (TME) and their related key genes in gliomas.MethodsWe applied bioinformatic techniques to identify the core gene that participated in the regulation of the TME of the gliomas. And immunohistochemistry staining was used to calculate the gene expressions in clinical cases.ResultsThe CIBERSORT and ESTIMATE were used to figure out the composition of TME in 698 glioma cases from The Cancer Genome Atlas (TCGA) database. Differential expression analysis identified 2103 genes between the high and the low-score group. Then the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, univariate Cox regression analysis, and protein–protein interaction (PPI) network construction were conducted based on these genes. MYD88 was identified as the key gene by the combination univariate Cox and PPI analysis. Furthermore, MYD88 expression was significantly associated with the overall survival and WHO grade of glioma patients. The genes in the high-expression MYD88 group were mainly in immune-related pathways in the Gene Set Enrichment Analysis (GSEA). We found that macrophage M2 accounted for the largest portion with an average of 27.6% in the glioma TIICs and was associated with high expression of MYD88. The results were verified in CGGA database and clinical cases in our hospital. Furthermore, we also found the MYD88 expression was higher in IDH1 wild types. The methylation rate was lower in high grade gliomas.ConclusionMYD88 had predictive prognostic value in glioma patients by influencing TIICs dysregulation especially the M2-type macrophages.

show abstract

Bioinformatic Profiling of Prognosis-Related Genes in Malignant Glioma Microenvironment

Cited by 21 publications

References 50 publications

Tumor antigens and immune subtypes of glioblastoma: the fundamentals of mRNA vaccine and individualized immunotherapy development

Tumor antigens and immune subtypes of glioblastoma: the fundamentals of mRNA vaccine and individualized immunotherapy development

Identification of a novel lipid metabolism-related gene signature for predicting colorectal cancer survival

MYD88 Is a Potential Prognostic Gene and Immune Signature of Tumor Microenvironment for Gliomas

Contact Info

Product

Resources

About