Dataset Information

Bioinformatic Profiling of Prognosis-Related Genes in Malignant Glioma Microenvironment.

ABSTRACT: BACKGROUND Gliomas are the most common primary tumors of the brain and spinal cord. The tumor microenvironment (TME) is the cellular environment in which tumors exist. This study aimed to identify the role of the TME and the effects of genes involved in the TME of malignant glioma. MATERIAL AND METHODS The ESTIMATE algorithms in the R package were used to calculate the immune and stromal scores of samples in the TCGA and GSE4290 datasets. The associations of stromal and immune scores with clinicopathological characteristics and overall survival of malignant glioma patients were assessed by analysis of variance and Kaplan-Meier analysis. Differentially expressed genes (DEGs) were obtained through the median immune and stromal score using the R package "limma". Functional enrichment analysis and the PPI network MCODE were used to analyze DEGs. RESULTS Increased immune and stromal scores were closely related with advanced glioma grade and poor prognosis (all P<0.01). In total, 558 DEGs were found and most were related to tumor prognosis. Functional enrichment analysis showed that DEGs were associated with cell-matrix regulation and immune response. Four hub modules related to tumor angiogenesis, collagen formation, and immune response were found and analyzed. Previously overlooked microenvironment-related genes such as LAMB1, FN1, ACTN1, TRIM, SERPINH1, CYBA, LAIR1, and LILRB2 showed prognostic values in malignant glioma patients. CONCLUSIONS The glioma stromal/immune scores are closely related to glioma grade, histology, and survival time. Some glioma microenvironment-related genes including LAMB1, FN1, ACTN1, TRIM6, SERPINH1, CYBA, LAIR1, and LILRB2 show prognostic values in malignant gliomas and serve as potential biomarkers.

SUBMITTER: Li Y

PROVIDER: S-EPMC7780890 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Bioinformatic Profiling of Prognosis-Related Genes in Malignant Glioma Microenvironment.

Li Yong Y Deng Gang G Qi Yangzhi Y Zhang Huikai H Gao Lun L Jiang Hongxiang H Ye Zhang Z Liu Baohui B Chen Qianxue Q

Medical science monitor : international medical journal of experimental and clinical research 20200826

BACKGROUND Gliomas are the most common primary tumors of the brain and spinal cord. The tumor microenvironment (TME) is the cellular environment in which tumors exist. This study aimed to identify the role of the TME and the effects of genes involved in the TME of malignant glioma. MATERIAL AND METHODS The ESTIMATE algorithms in the R package were used to calculate the immune and stromal scores of samples in the TCGA and GSE4290 datasets. The associations of stromal and immune scores with clinic ...[more]

PMID: 32843610

Similar Datasets

Project description:Endometrial carcinoma (EC) is a common malignancy of female genital system which exhibits a unique immune profile. It is a promising strategy to quantify immune patterns of EC for predicting prognosis and therapeutic efficiency. Here, we attempted to identify the possible immune microenvironment-related prognostic markers of EC. We obtained the RNA sequencing and corresponding clinical data of EC from TCGA database. Then, 3 immune scores based on the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm were computed. Correlation between above ESTIMATE scores and other immune-related scores, molecular subtypes, prognosis, and gene mutation status (including BRCA and TP53) were further analyzed. Afterwards, gene modules associated with the ESTIMATE scores were screened out through hierarchical clustering analysis and weighted gene co-expression network analysis (WGCNA). Differentially expressed analysis was performed and genes shared by the most relevant modules were found out. KEGG pathway enrichment analysis was conducted to explore the biological functions of those genes. Survival analysis was carried out to identify prognostic immune-related genes and GSE17025 database was further used to confirm the correlation between immune-related genes and the ImmuneScore. The immune-related scores based on ESTIMATE algorithm was closely related to the immune microenvironment of EC. 3 gene modules that had the closest correlations with 3 ESTIMATE scores were obtained. 109 immune-related genes were preliminarily found out and 29 pathways were significantly enriched, most of which were associated with immune response. Univariate survival analysis revealed that there were 14 genes positively associated with both OS and PFS. Among which, 11 genes showed marked correlations with ImmuneScore values in GSE17025 database. Our current study profiled the immune status and identified 14 novel immune-related prognostic biomarkers for EC. Our findings may help to investigate the complicated tumor microenvironment and develop novel individualized therapeutic targets for EC.

Project description:Glioma is the most common malignant tumor in the central nervous system with no significant therapeutic breakthrough in recent years. Most attempts to apply immunotherapy in glioma have failed. Tryptophan and its metabolism can regulate malignant features of cancers and reshape immune microenvironment of tumors. However, the role of tryptophan metabolism in glioma remains unclear. In current study, we explored the relationships between the expression pattern of tryptophan metabolism-related genes (TrMGs) and tumor characteristics, including prognosis and tumor microenvironment of gliomas through analyzing 1,523 patients' samples from multiple public databases and our own cohort. Based on expression of TrMGs, K-means clustering analysis stratified all glioma patients into two clusters with significantly different TrMG expression patterns, clinicopathological features and immune microenvironment. Furthermore, we constructed a tryptophan metabolism-related genes signature (TrMRS) based on seven essential TrMGs to classify the patients into TrMRS low- and high-risk groups and validated the prognostic value of the TrMRS in multiple cohorts. Higher TrMRS represented for potentially more active tryptophan catabolism, which could subsequently lead to less tryptophan in tumor. The TrMRS high-risk group presented with shorter overall survival, and further analysis confirmed TrMRS as an independent prognostic factor in gliomas. The nomograms uniting TrMRS with other prognostic factors manifested with satisfactory efficacy in predicting the prognosis of glioma patients. Additionally, analyses of tumor immune landscapes demonstrated that higher TrMRS was correlated with more immune cell infiltration and "hot" immunological phenotype. TrMRS was also demonstrated to be positively correlated with the expression of multiple immunotherapy targets, including PD1 and PD-L1. Finally, the TrMRS high-risk group manifested better predicted response to immune checkpoint inhibitors. In conclusion, our study illustrated the relationships between expression pattern of TrMGs and characteristics of gliomas, and presented a novel model based on TrMRS for prognosis prediction in glioma patients. The association between TrMRS and tumor immune microenvironment of gliomas indicated an important role of tryptophan and its metabolism in reshaping immune landscape and the potential ability to guide the application of immunotherapy for gliomas.

Dataset Information

Bioinformatic Profiling of Prognosis-Related Genes in Malignant Glioma Microenvironment.

Publications

Bioinformatic Profiling of Prognosis-Related Genes in Malignant Glioma Microenvironment.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets