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Immune Characteristics of LYN in Tumor Microenvironment of Gliomas.

ABSTRACT: The prognosis of gliomas is poor and there are limited therapeutic approaches. Immunotherapy has become a promising treatment for gliomas. Here, we explored the expression pattern of Lck/yes-related protein tyrosine kinase (LYN) in gliomas and assessed its value as an immunotherapy biomarker. Transcriptional data was mined from two publicly available datasets, TCGA and CGGA, and used to investigate the correlation between LYN and clinical characteristics including patient prognosis, somatic mutation, and immune infiltrating features in gliomas. Besides, the correlation between LYN and classical immune checkpoint molecules was explored. Glioma samples obtained from the Xiangya Hospital cohort were used for immunohistochemistry staining. High expression level of LYN was observed in advanced gliomas and other cancer types, which predicted a worse prognosis. LYN stratified patients' survival in the Xiangya cohort and was also significantly associated with infiltrating immune cell types and inflammatory activities in the tumor microenvironment. LYN was involved in tumor mutation, correlated with the regulation of oncogenic genes, and also showed a significant positive correlation with PD-L1. LYN can be a potential diagnostic marker and immunotherapy marker in gliomas.

SUBMITTER: Jiang C

PROVIDER: S-EPMC8847791 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Immune Characteristics of LYN in Tumor Microenvironment of Gliomas.

Jiang Chonghua C Zhang Hao H Wu Wantao W Wang Zeyu Z Dai Ziyu Z Zhang Liyang L Liu Zhixiong Z Cheng Quan Q

Frontiers in cell and developmental biology 20220202

The prognosis of gliomas is poor and there are limited therapeutic approaches. Immunotherapy has become a promising treatment for gliomas. Here, we explored the expression pattern of Lck/yes-related protein tyrosine kinase (LYN) in gliomas and assessed its value as an immunotherapy biomarker. Transcriptional data was mined from two publicly available datasets, TCGA and CGGA, and used to investigate the correlation between LYN and clinical characteristics including patient prognosis, somatic muta ...[more]

PMID: 35186945

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Project description:BackgroundDiffuse gliomas possess a kind of malignant brain tumor with high mortality. Glutamine represents the most abundant and versatile amino acid in the body. Glutamine not only plays an important role in cell metabolism but also involves in cell survival and malignancies progression. Recent studies indicate that glutamine could also affect the metabolism of immune cells in the tumor microenvironment (TME).Materials and methodsThe transcriptome data and clinicopathological information of patients with glioma were acquired from TCGA, CGGA, and West China Hospital (WCH). The glutamine metabolism-related genes (GMRGs) were retrieved from the Molecular Signature Database. Consensus clustering analysis was used to discover expression patterns of GMRGs, and glutamine metabolism risk scores (GMRSs) were established to model tumor aggressiveness-related GMRG expression signature. ESTIMATE and CIBERSORTx were applied to depict the TME immune landscape. The tumor immunological phenotype analysis and TIDE were utilized for predicting the therapeutic response of immunotherapy.ResultsA total of 106 GMRGs were retrieved. Two distinct clusters were established by consensus clustering analysis, which showed a close association with the IDH mutational status of gliomas. In both IDH-mutant and IDH-wildtype gliomas, cluster 2 had significantly shorter overall survival compared with cluster 1, and the differentially expressed genes between the two clusters enriched in pathways related to malignant transformation as well as immunity. In silico TME analysis of the two IDH subtypes revealed not only significantly different immune cell infiltrations and immune phenotypes between the GMRG expression clusters but also different predicted responses to immunotherapy. After the screening, a total of 10 GMRGs were selected to build the GMRS. Survival analysis demonstrated the independent prognostic role of GMRS. Prognostic nomograms were established to predict 1-, 2-, and 3-year survival rates in the four cohorts.ConclusionDifferent subtypes of glutamine metabolism could affect the aggressiveness and TME immune features of diffuse glioma, despite their IDH mutational status. The expression signature of GMRGs could not only predict the outcome of patients with glioma but also be combined into an accurate prognostic nomogram.

Project description:Introduction High-grade glioma (HGG) defines a group of brain gliomas characterized by contrast enhancement, high tumor heterogeneity, and poor clinical outcome. Disturbed reduction–oxidation (redox) balance has been frequently associated with the development of tumor cells and their microenvironment (TME). Methods To study the influence of redox balance on HGGs and their microenvironment, we collected mRNA-sequencing and clinical data of HGG patients from TCGA and CGGA databases and our own cohort. Redox-related genes (ROGs) were defined as genes in the MSigDB pathways with keyword “redox” that were differentially expressed between HGGs and normal brain samples. Unsupervised clustering analysis was used to discover ROG expression clusters. Over-representation analysis (ORA), gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were also employed to understand the biological implication of differentially expressed genes between HGG clusters. CIBERSORTx and ESTIMATE were used to profile the immune TME landscapes of tumors, and TIDE was used to evaluated the potential response to immune checkpoint inhibitors. Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression was used to construct HGG-ROG expression risk signature (GRORS). Results Seventy-five ROGs were found and consensus clustering using the expression profile of ROGs divided the both IDH-mutant (IDHmut) and IDH-wildtype (IDHwt) HGGs into subclusters with different prognosis. Functional enrichment analysis revealed that the differential aggressiveness between redox subclusters in IDHmut HGGs were significantly associated with cell cycle regulation pathways, while IDHwt HGG redox subclusters showed differentially activated immune-related pathways. In silico TME analysis on immune landscapes in the TME showed that the more aggressive redox subclusters in both IDHmut and IDHwt HGGs may harbor a more diverse composition of tumor-infiltrating immune cells, expressed a higher level of immune checkpoints and were more likely to respond to immune checkpoint blockade. Next, we established a GRORS which showed AUCs of 0.787, 0.884, and 0.917 in predicting 1–3-year survival of HGG patients in the held-out validation datasets, and the C-index of a nomogram combining the GRORS and other prognostic information reached 0.835. Conclusion Briefly, our results suggest that the expression pattern of ROGs was closely associated with the prognosis as well as the TME immune profile of HGGs, and may serve as a potential indicator for their response to immunotherapies.

Project description:BackgroundGlioma is the most common primary brain tumor in adults and accounts for more than 70% of brain malignancies. Lipids are crucial components of biological membranes and other structures in cells. Accumulating evidence has supported the role of lipid metabolism in reshaping the tumor immune microenvironment (TME). However, the relationship between the immune TME of glioma and lipid metabolism remain poorly described.Materials and methodsThe RNA-seq data and clinicopathological information of primary glioma patients were downloaded from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA). An independent RNA-seq dataset from the West China Hospital (WCH) also included in the study. Univariate Cox regression and LASSO Cox regression model was first to determine the prognostic gene signature from lipid metabolism-related genes (LMRGs). Then a risk score named LMRGs-related risk score (LRS) was established and patients were stratified into high and low risk groups according to LRS. The prognostic value of the LRS was further demonstrated by construction of a glioma risk nomogram. ESTIMATE and CIBERSORTx were used to depicted the TME immune landscape. Tumor Immune Dysfunction and Exclusion (TIDE) was utilized to predict the therapeutic response of immune checkpoint blockades (ICB) among glioma patients.ResultsA total of 144 LMRGs were differentially expressed between gliomas and brain tissue. Finally, 11 prognostic LMRGs were included in the construction of LRS. The LRS was demonstrated to be an independent prognostic predictor for glioma patients, and a nomogram consisting of the LRS, IDH mutational status, WHO grade, and radiotherapy showed a C-index of 0.852. LRS values were significantly associated with stromal score, immune score, and ESTIMATE score. CIBERSORTx indicated remarkable differences in the abundance of TME immune cells between patients with high and low LRS risk levels. Based on the results of TIDE algorithm, we speculated that the high-risk group had a greater chance of benefiting from immunotherapy.ConclusionThe risk model based upon LMRGs could effectively predict prognosis in patients with glioma. Risk score also divided glioma patients into different groups with distinct TME immune characteristics. Immunotherapy is potentially beneficial to glioma patients with certain lipid metabolism profiles.

Dataset Information

Immune Characteristics of LYN in Tumor Microenvironment of Gliomas.

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Immune Characteristics of LYN in Tumor Microenvironment of Gliomas.

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