Establishment of an Endocytosis-Related Prognostic Signature for Patients With Low-Grade Glioma

BackgroundLow-grade glioma (LGG) is a heterogeneous tumor that might develop into high-grade malignant glioma, which markedly reduces patient survival time. Endocytosis is a cellular process responsible for the internalization of cell surface proteins or external materials into the cytosol. Dysregulated endocytic pathways have been linked to all steps of oncogenesis, from initial transformation to late invasion and metastasis. However, endocytosis-related gene (ERG) signatures have not been used to study the correlations between endocytosis and prognosis in cancer. Therefore, it is essential to develop a prognostic model for LGG based on the expression profiles of ERGs.MethodsThe Cancer Genome Atlas and the Genotype-Tissue Expression database were used to identify differentially expressed ERGs in LGG patients. Gene ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene set enrichment analysis methodologies were adopted for functional analysis. A protein-protein interaction (PPI) network was constructed and hub genes were identified based on the Search Tool for the Retrieval of Interacting Proteins database. Univariate and multivariate Cox regression analyses were used to develop an ERG signature to predict the overall survival (OS) of LGG patients. Finally, the association between the ERG signature and gene mutation status was further analyzed.ResultsSixty-two ERGs showed distinct mRNA expression patterns between normal brain tissues and LGG tissues. Functional analysis indicated that these ERGs were strikingly enriched in endosomal trafficking pathways. The PPI network indicated that EGFR was the most central protein. We then built a 29-gene signature, dividing patients into high-risk and low-risk groups with significantly different OS times. The prognostic performance of the 29-gene signature was validated in another LGG cohort. Additionally, we found that the mutation scores calculated based on the TTN, PIK3CA, NF1, and IDH1 mutation status were significantly correlated with the endocytosis-related prognostic signature. Finally, a clinical nomogram with a concordance index of 0.881 predicted the survival probability of LGG patients by integrating clinicopathologic features and ERG signatures.ConclusionOur ERG-based prediction models could serve as an independent prognostic tool to accurately predict the outcomes of LGG.

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Elevated TYROBP expression predicts poor prognosis and high tumor immune infiltration in patients with low-grade glioma

BMC Cancer ◽

10.1186/s12885-021-08456-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jiajie Lu ◽

Yuecheng Peng ◽

Rihong Huang ◽

Zejia Feng ◽

Yongyang Fan ◽

...

Keyword(s):

Dendritic Cells ◽

Signaling Pathway ◽

Low Grade Glioma ◽

Gene Set Enrichment Analysis ◽

Low Grade ◽

Ppi Network ◽

Myeloid Dendritic Cells ◽

Immune Infiltration ◽

Normal Tissues ◽

Worse Prognosis

Abstract Background Tyrosine protein tyrosine kinase binding protein (TYROBP) binds non-covalently to activated receptors on the surface of various immune cells, and mediates signal transduction and cellular activation. It is dysregulated in various malignancies, although little is known regarding its role in low-grade glioma. The aim of this study is to explore the clinicopathological significance, prognostic value and immune signature of TYROBP expression in low-grade glioma (LGG). Methods The differentially expressed genes (DEGs) between glioma samples and normal tissues were identified from two GEO microarray datasets using the limma package. The DEGs overlapping across both datasets were functionally annotated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. STRING database was used to establish the protein-protein interaction (PPI) of the DEGs. The PPI network was visualized by Cytoscape and cytoHubba, and the core module and hub genes were identified. The expression profile of TYROBP and patient survival were validated in the Oncomine, GEPIA2 and CGGA databases. The correlation between TYROBP expression and the clinicopathologic characteristics were evaluated. Gene Set Enrichment Analysis (GSEA) and single-sample GSEA (ssGSEA) were performed by R based on the LGG data from TCGA. The TIMER2.0 database was used to determine the correlation between TYROBP expression and tumor immune infiltrating cells in the LGG patients. Univariate and multivariate Cox regression analyses were performed to determine the prognostic impact of clinicopathological factors via TCGA database. Results Sixty-two overlapping DEGs were identified in the 2 datasets, and were mainly enriched in the response to wounding, focal adhesion, GTPase activity and Parkinson disease pathways. TYROBP was identified through the PPI network and cytoHubba. TYROBP expression levels were significantly higher in the LGG tissues compared to the normal tissues, and was associated with worse prognosis and poor clinicopathological parameters. In addition, GSEA showed that TYROBP was positively correlated to neutrophil chemotaxis, macrophage activation, chemokine signaling pathway, JAK-STAT signaling pathway, and negatively associated with gamma aminobutyric acid signaling pathway, neurotransmitter transport, neuroactive ligand receptor intersection etc. TIMER2.0 and ssGSEA showed that TYROBP expression was significantly associated with the infiltration of neutrophils, macrophages, myeloid dendritic cells and monocytes. The infiltration of the M2 phenotype macrophages, cancer-associated fibroblasts and myeloid dendritic cells correlated to worse prognosis in LGG patients. Finally, multivariate analysis showed that elevated TYROBP expression is an independent risk factor for LGG. Conclusion TYROBP is dysregulated in LGG and correlates with immune infiltration. It is a potential therapeutic target and prognostic marker for LGG.

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A bioinformatics study of autophagy biomarkers associated with the prognosis of endometrial carcinoma

10.21203/rs.3.rs-20960/v1 ◽

2020 ◽

Author(s):

Wei Liu ◽

Rui Sun ◽

Yixuan Bai ◽

Yunkai Xie ◽

Changzhong Li

Keyword(s):

Functional Analysis ◽

Endometrial Carcinoma ◽

Expression Analysis ◽

Critical Role ◽

Curve Analysis ◽

Gene Set Enrichment Analysis ◽

Ppi Network ◽

Prognostic Signature ◽

Roc Curve Analysis ◽

Limma Package

Abstract Background: Autophagy plays a critical role in endometrial carcinoma (EC), but prognosis studies of differentially expressed autophagy-related genes (DEARGs) in EC are lacking. This study aimed to access the prognostic value of autophagy-related genes (ARGs) in EC and to identify the potential characteristics of ARGs in predicting patient survival and guiding treatment.Methods: The RNA-Seq data and clinical data were obtained from TCGA databases. The DEARGs were identified by “limma” package in R software. Clusterprofler was used for functional analysis. Using STRING databases to construct a protein-protein interaction (PPI) network and plug-in MCODE to screen hub modules in Cytoscape. The degrees method of cytoHubba was used to select important hub genes. Co-expression analysis was performed using “limma” package and Metascape for functional analysis. Univariate and multivariate Cox proportional hazards regression analyses were performed to construct a prognostic signature. Kaplan–Meier curve analysis, ROC curve analysis and Gene set enrichment analysis (GSEA) were also performed. Validation was executed by UALCAN, CCLE and HPA databases.Results: In total, 45 DEARGs were identified. Functional analysis showed that DEARGs were strikingly enriched in autophagy pathway. PPI network showed that CASP3 was the most central protein. CASP3 co-expression analysis revealed that co-expressed genes were mostly enriched in cell cycle. We identified a novel prognostic signature consisting of 3 genes (CDKN2A, PTK6 and GRID2). The risk score based on the prognostic signature was able to classify patients into high-risk and low-risk groups with significantly different overall survival. Furthermore, the prognostic signature is an independent prognostic predictor of survival and demonstrates superior prognostic performance compared with the clinicopathologic features for predicting 5-year survival. CDKN2A and PTK6 were further validated in UALCAN. GSEA suggested that the two genes played crucial roles in drug metabolism.Conclusion: Using bioinformatics analyses, we identified DEARGs and determined CDKN2A, PTK6 and DRID2 are tumor-associated genes and can be utilized as possible biomarkers in EC treatment.

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A Novel 10-Gene Signature Predicts Poor Prognosis in Low Grade Glioma

Technology in Cancer Research & Treatment ◽

10.1177/1533033821992084 ◽

2021 ◽

Vol 20 ◽

pp. 153303382199208

Author(s):

Wentao Liu ◽

Jiaxuan Zou ◽

Rijun Ren ◽

Jingping Liu ◽

Gentang Zhang ◽

...

Keyword(s):

Risk Score ◽

Poor Prognosis ◽

Cox Regression ◽

Gene Signature ◽

Low Grade Glioma ◽

The Cancer Genome Atlas ◽

Low Grade ◽

Normal Brain ◽

Cox Regression Analysis ◽

Genome Atlas

Aim: Low grade glioma (LGG) is a lethal brain cancer with relatively poor prognosis in young adults. Thus, this study was performed to develop novel molecular biomarkers to effectively predict the prognosis of LGG patients and finally guide treatment decisions. Methods: survival-related genes were determined by Kaplan-Meier survival analysis and multivariate Cox regression analysis using the expression and clinical data of 506 LGG patients from The Cancer Genome Atlas (TCGA) database and independently validated in a Chinese Glioma Genome Atlas (CGGA) dataset. A prognostic risk score was established based on a linear combination of 10 gene expression levels using the regression coefficients of the multivariate Cox regression models. GSEA was performed to analyze the altered signaling pathways between the high and low risk groups stratified by median risk score. Results: We identified a total of 1489 genes significantly correlated with patients’ prognosis in LGG. The top 5 protective genes were DISP2, CKMT1B, AQP7, GPR162 and CHGB, the top 5 risk genes were SP1, EYA3, ZSCAN20, ITPRIPL1 and ZNF217 in LGG. The risk score was predictive of poor overall survival and relapse-free survival in LGG patients. Pathways of small cell lung cancer, pathways in cancer, chronic myeloid leukemia, colorectal cancer were the top 4 most enriched pathways in the high risk group. SP1, EYA3, ZSCAN20, ITPRIPL1, ZNF217 and GPR162 were significantly up-regulated, while DISP2, CKMT1B, AQP7 were down-regulated in 523 LGG tissues as compared to 1141 normal brain controls. Conclusions: The 10-gene signature may become novel prognostic and diagnostic biomarkers to considerably improve the prognostic prediction in LGG.

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Identification of DNA-Repair-Related Five-Gene Signature to Predict Prognosis in Patients with Esophageal Cancer

Pathology & Oncology Research ◽

10.3389/pore.2021.596899 ◽

2021 ◽

Vol 27 ◽

Author(s):

Lin Wang ◽

Xueping Li ◽

Lan Zhao ◽

Longyang Jiang ◽

Xinyue Song ◽

...

Keyword(s):

Esophageal Cancer ◽

Dna Repair ◽

Poor Prognosis ◽

Prognostic Model ◽

Expression Profiles ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Risk Of Cancer

Esophageal cancer (ESCA) is a leading cause of cancer-related mortality, with poor prognosis worldwide. DNA damage repair is one of the hallmarks of cancer. Loss of genomic integrity owing to inactivation of DNA repair genes can increase the risk of cancer progression and lead to poor prognosis. We aimed to identify a novel gene signature related to DNA repair to predict the prognosis of ESCA patients. Based on gene expression profiles of ESCA patients from The Cancer Genome Atlas and gene set enrichment analysis, 102 genes related to DNA repair were identified as candidates. After stepwise Cox regression analysis, we established a five-gene prognostic model comprising DGCR8, POM121, TAF9, UPF3B, and BCAP31. Kaplan-Meier survival analysis confirmed a strong correlation between the prognostic model and survival. Moreover, we verified the clinical value of the prognostic signature under the influence of different clinical parameters. We found that small-molecule drugs (trametinib, selumetinib, and refametinib) could help to improve patient survival. In summary, our study provides a novel and promising prognostic signature based on DNA-repair-related genes to predict survival of patients with ESCA. Systematic data mining provides a theoretical basis for further exploring the molecular pathogenesis of ESCA and identifying therapeutic targets.

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Elevated TYROBP expression predicts poor prognosis and high tumor immune infiltration in patients with low‑grade glioma

10.21203/rs.3.rs-376032/v1 ◽

2021 ◽

Author(s):

Jiajie Lu ◽

Yuecheng Peng ◽

Rihong Huang ◽

Zejia Feng ◽

Yongyang Fan ◽

...

Keyword(s):

Dendritic Cells ◽

Signaling Pathway ◽

Low Grade Glioma ◽

Gene Set Enrichment Analysis ◽

Low Grade ◽

Ppi Network ◽

Myeloid Dendritic Cells ◽

Immune Infiltration ◽

Normal Tissues ◽

Worse Prognosis

Abstract Background: Tyrosine protein tyrosine kinase binding protein (TYROBP) binds non-covalently to activated receptors on the surface of various immune cells, and mediates signal transduction and cellular activation. It is dysregulated in various malignancies, although little is known regarding its role in low‑grade glioma. The aim of this study is to explore the clinicopathological significance, prognostic value and immune signature of TYROBP expression in low‑grade glioma (LGG).Methods: The differentially expressed genes (DEGs) between glioma samples and normal tissues were identified from two GEO microarray datasets using the limma package. The DEGs overlapping across both datasets were functionally annotated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. STRING database was used to establish the protein-protein interaction (PPI) of the DEGs. The PPI network was visualized by Cytoscape and cytoHubba, and the core module and hub genes were identified. The expression profile of TYROBP and patient survival were validated in the Oncomine, GEPIA2 and CGGA databases. The correlation between TYROBP expression and the clinicopathologic characteristics were evaluated. Gene Set Enrichment Analysis (GSEA) and single-sample GSEA (ssGSEA) were performed by R based on the LGG data from TCGA. The TIMER2.0 database was used to determine the correlation between TYROBP expression and tumor immune infiltrating cells in the LGG patients. Univariate and multivariate Cox regression analyses were performed to determine the prognostic impact of clinicopathological factors via TCGA database. Results: Sixty-two overlapping DEGs were identified in the 2 datasets, and were mainly enriched in the response to wounding, focal adhesion, GTPase activity and Parkinson disease pathways. TYROBP was identified through the PPI network and cytoHubba. TYROBP expression levels were significantly higher in the LGG tissues compared to the normal tissues, and was associated with worse prognosis and poor clinicopathological parameters. In addition, GSEA showed that TYROBP was positively correlated to neutrophil chemotaxis, macrophage activation, chemokine signaling pathway, JAK-STAT signaling pathway, and negatively associated with gamma aminobutyric acid signaling pathway, neurotransmitter transport, neuroactive ligand receptor intersection etc. TIMER2.0 and ssGSEA showed that TYROBP expression was significantly associated with the infiltration of neutrophils, macrophages, myeloid dendritic cells and monocytes. The infiltration of the M2 phenotype macrophages, cancer-associated fibroblasts and myeloid dendritic cells correlated to worse prognosis in LGG patients. Finally, multivariate analysis showed that elevated TYROBP expression is an independent risk factor for LGG. Conclusion: TYROBP is dysregulated in LGG and correlates with immune infiltration. It is a potential therapeutic target and prognostic marker for LGG.

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P04.16 TP53 mutations in codon 273 is predictive of overall survival in astrocytoma patients

Neuro-Oncology ◽

10.1093/neuonc/noz126.111 ◽

2019 ◽

Vol 21 (Supplement_3) ◽

pp. iii32-iii32

Author(s):

H Noor ◽

R Rapkins ◽

K McDonald

Keyword(s):

Overall Survival ◽

Tp53 Mutation ◽

Progression Free Survival ◽

Low Grade Glioma ◽

Low Grade ◽

Wild Type ◽

Mutation Status ◽

Free Survival ◽

Tp53 Mutations ◽

Fresh Frozen

Abstract BACKGROUND Tumour Protein 53 (TP53) is a tumour suppressor gene that is mutated in at least 50% of human malignancies. The prevalence of TP53 mutation is much higher in astrocytomas with reports of up to 75% TP53 mutant cases. Rare cases of TP53 mutation also exist in oligodendroglial tumours (10–13%). P53 pathway is therefore an important factor in low-grade glioma tumorigenesis. Although the prognostic impact of TP53 mutations has been studied previously, no concrete concordance were reached between the studies. In this study, we investigated the prognostic effects of TP53 mutation in astrocytoma and oligodendroglioma. MATERIAL AND METHODS A cohort of 65 matched primary and recurrent fresh frozen tumours were sequenced to identify hotspot exons of TP53 mutation. Exons 1 to 10 were sequenced and pathogenic mutations were mostly predominant between Exons 4 and 8. The cohort was further expanded with 78 low grade glioma fresh frozen tissues and hotspot exons were sequenced. Selecting only the primary tumour from 65 matched tumours, a total of 50 Astrocytoma cases and 51 oligodendroglioma cases were analysed for prognostic effects of TP53. Only pathogenic TP53 mutations confirmed through COSMIC and NCBI databases were included in the over survival and progression-free survival analysis. RESULTS 62% (31/50) of astrocytomas and 16% (8/51) of oligodendrogliomas harboured pathogenic TP53 mutations. Pathogenic hotspot mutations in codon 273 (c.817 C>T and c.818 G>A) was prevalent in astrocytoma with 58% (18/31) of tumours with these mutations. TP53 mutation status was maintained between primary and recurrent tumours in 93% of cases. In astrocytoma, overall survival of TP53 mutant patients was longer compared to TP53 wild-type patients (p<0.01) but was not significant after adjusting for age, gender, grade and IDH1 mutation status. In contrast, astrocytoma patients with specific TP53 mutation in codon 273 showed significantly better survival compared to other TP53 mutant and TP53 wild-type patients combined (p<0.01) in our multivariate analysis. Time to first recurrence (progression-free survival) of TP53 mutant patients was significantly longer than TP53 wild-type patients (p<0.01) after adjustments were made, while TP53 mutation in codon 273 was not prognostic for progression-free survival. In oligodendroglioma patients, TP53 mutations did not significantly affect overall survival and progression-free survival. CONCLUSION In agreement with others, TP53 mutation is more prevalent in Astrocytoma and mutations in codon 273 are significantly associated with longer survival.

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Identification of Core Genes and Screening of Potential Targets in Glioblastoma Multiforme by Integrated Bioinformatic Analysis

Frontiers in Oncology ◽

10.3389/fonc.2020.615976 ◽

2021 ◽

Vol 10 ◽

Author(s):

Ji’an Yang ◽

Qian Yang

Keyword(s):

Glioblastoma Multiforme ◽

Enrichment Analysis ◽

Bioinformatic Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Low Grade ◽

Hypergeometric Test ◽

Ppi Network ◽

Neuropsychiatric Diseases ◽

Core Genes

Glioblastoma multiforme is the most common primary intracranial malignancy, but its etiology and pathogenesis are still unclear. With the deepening of human genome research, the research of glioma subtype screening based on core molecules has become more in-depth. In the present study, we screened out differentially expressed genes (DEGs) through reanalyzing the glioblastoma multiforme (GBM) datasets GSE90598 from the Gene Expression Omnibus (GEO), the GBM dataset TCGA-GBM and the low-grade glioma (LGG) dataset TCGA-LGG from the Cancer Genome Atlas (TCGA). A total of 150 intersecting DEGs were found, of which 48 were upregulated and 102 were downregulated. These DEGs from GSE90598 dataset were enriched using the overrepresentation method, and multiple enriched gene ontology (GO) function terms were significantly correlated with neural cell signal transduction. DEGs between GBM and LGG were analyzed by gene set enrichment analysis (GSEA), and the significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways involved in synapse signaling and oxytocin signaling pathways. Then, a protein-protein interaction (PPI) network was constructed to assess the interaction of proteins encoded by the DEGs. MCODE identified 2 modules from the PPI network. The 11 genes with the highest degrees in module 1 were designated as core molecules, namely, GABRD, KCNC1, KCNA1, SYT1, CACNG3, OPALIN, CD163, HPCAL4, ANK3, KIF5A, and MS4A6A, which were mainly enriched in ionic signaling-related pathways. Survival analysis of the GSE83300 dataset verified the significant relationship between expression levels of the 11 core genes and survival. Finally, the core molecules of GBM and the DrugBank database were assessed by a hypergeometric test to identify 10 drugs included tetrachlorodecaoxide related to cancer and neuropsychiatric diseases. Further studies are required to explore these core genes for their potentiality in diagnosis, prognosis, and targeted therapy and explain the relationship among ionic signaling-related pathways, neuropsychiatric diseases and neurological tumors.

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Identification of a Prognostic Gene Signature Associated with Microenvironment in Acute Myeloid Leukemia

10.21203/rs.3.rs-91594/v1 ◽

2020 ◽

Author(s):

Zhixiang Chen ◽

Luya Ye ◽

Xuechun Wang ◽

Fuquan Tu ◽

Xuezhen Li ◽

...

Keyword(s):

Acute Myeloid Leukemia ◽

Myeloid Leukemia ◽

Expression Profiles ◽

Critical Role ◽

Enrichment Analysis ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Risk Scores ◽

Acute Myeloid

Abstract Background: Acute myeloid leukemia (AML) is a common hematologic malignancy with poor prognosis. Accumulating reports have indicated that the tumor microenvironment (TME) performs a critical role in the progress of the disease and the clinical outcomes of patients. To date, the role of TME in AML remains clouded due to the complex regulatory mechanisms in it. In this study, We identified key prognostic genes relate to TME in AML and developed a novel gene signature for individualized prognosis assessment. Methods: The expression profiles of AML samples with clinical information were obtained from the Cancer Genome Atlas (TCGA). The ESTIMATE algorithm was applied to calculate the TME relevant immune and stromal scores. The differentially expressed genes (DEGs) were selected based on the immune and stromal scores. Then, the survival analysis was applied to select prognostic DEGs, and these genes were annotated by functional enrichment analysis. A TME relevant gene signature with predictive capability was constructed by a series of regression analyses and performed well in another cohort from the Gene Expression Omnibus (GEO) database. Moreover, we also developed a nomogram with the integration of the gene signature and clinical indicators to establish an individually quantified risk-scoring system. Results: In the AML microenvironment, a total of 181 DEGs with prognostic value were clarified. Then a seven-gene ( IL1R2, MX1, S100A4, GNGT2, ZSCAN23, PLXNB1 and DPY19L2 ) signature with robust prediction was identified, and was validated by an independent cohort of AML samples from the GSE71014. Gene set enrichment analysis (GSEA) of genes in the gene signature revealed these genes mainly enriched in the immune and inflammatory related processes. The correlation between the signature-calculated risk scores and the clinical features indicated that patients with high risk scores were accompanied by adverse survival. Finally, a nomogram with clinical utility was constructed. Conclusion: Our study explored and identified a novel TME relevant seven-gene signature, which could serve as a prognostic indicator for AML. Meanwhile, we also establish a nomogram with clinical significance. These findings might provide new insights into the diagnosis, treatment and prognosis of AML.

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Malignant Tumor Purity Reveals the Driven and Prognostic Role of CD3E in Low-Grade Glioma Microenvironment

Frontiers in Oncology ◽

10.3389/fonc.2021.676124 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xiuqin Lu ◽

Chuanyu Li ◽

Wenhao Xu ◽

Yuanyuan Wu ◽

Jian Wang ◽

...

Keyword(s):

Cox Regression ◽

Complex Structure ◽

Clinical Information ◽

Prognostic Indicator ◽

Low Grade Glioma ◽

Low Grade ◽

Normal Brain ◽

Bioinformatics Analyses ◽

Tumor Purity ◽

The Impact

The tumor microenvironment (TME) contributes to the initiation and progression of many neoplasms. However, the impact of low-grade glioma (LGG) purity on carcinogenesis remains to be elucidated. We selected 509 LGG patients with available genomic and clinical information from the TCGA database. The percentage of tumor infiltrating immune cells and the tumor purity of LGG were evaluated using the ESTIMATE and CIBERSORT algorithms. Stromal-related genes were screened through Cox regression, and protein-protein interaction analyses and survival-related genes were selected in 487 LGG patients from GEO database. Hub genes involved in LGG purity were then identified and functionally annotated using bioinformatics analyses. Prognostic implications were validated in 100 patients from an Asian real-world cohort. Elevated tumor purity burden, immune scores, and stromal scores were significantly associated with poor outcomes and increased grade in LGG patients from the TCGA cohort. In addition, CD3E was selected with the most significant prognostic value (Hazard Ratio=1.552, P<0.001). Differentially expressed genes screened according to CD3E expression were mainly involved in stromal related activities. Additionally, significantly increased CD3E expression was found in 100 LGG samples from the validation cohort compared with adjacent normal brain tissues. High CD3E expression could serve as an independent prognostic indicator for survival of LGG patients and promotes malignant cellular biological behaviors of LGG. In conclusion, tumor purity has a considerable impact on the clinical, genomic, and biological status of LGG. CD3E, the gene for novel membrane immune biomarker deeply affecting tumor purity, may help to evaluate the prognosis and develop individual immunotherapy strategies for LGG patients. Evaluating the ratio of differential tumor purity and CD3E expression levels may provide novel insights into the complex structure of the LGG microenvironment and targeted drug development.

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