scholarly journals Development and Verification of Glutamatergic Synapse-Associated Prognosis Signature for Lower-Grade Gliomas

2021 ◽  
Vol 14 ◽  
Author(s):  
Liguo Ye ◽  
Yang Xu ◽  
Ping Hu ◽  
Long Wang ◽  
Ji’an Yang ◽  
...  
Keyword(s):  
Risk Score ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
Normal Brain ◽  
Lower Grade ◽  
Glutamatergic Synapse ◽  
Hub Genes ◽  
Gene Set ◽  
Genome Atlas

Background: Lower-grade glioma (LGG) is the most common histology identified in gliomas, a heterogeneous tumor that may develop into high-grade malignant glioma that seriously shortens patient survival time. Recent studies reported that glutamatergic synapses might play an essential role in the progress of gliomas. However, the role of glutamatergic synapse-related biomarkers in LGG has not been systemically researched yet.Methods: The mRNA expression data of glioma and normal brain tissue were obtained from The Cancer Genome Atlas database and Genotype-Tissue Expression, respectively, and the Chinese Glioma Genome Atlas database was used as a validation set. Difference analysis was performed to evaluate the expression pattern of glutamatergic synapse-related genes (GSRGs) in LGG. The least absolute shrinkage and selection operator (LASSO) Cox regression was applied to construct the glutamatergic synapse-related risk signature (GSRS), and the risk score of each LGG sample was calculated based on the coefficients and expression value of selected GSRGs. Univariate and multivariate Cox regression analyses were used to investigate the prognostic value of risk score. Immunity profile and single-sample gene set enrichment analysis (ssGSEA) were performed to explore the association between risk score and the characters of tumor microenvironment in LGG. Gene set variation analysis (GSVA) was performed to investigate the potential pathways related to GSRS. The HPA database and real-time PCR were used to identify the expression of hub genes identified in GSRS.Results: A total of 22 genes of 39 GSRGs were found differentially expressed among normal and LGG samples. Through the LASSO algorithm, 14-genes GSRS constructed were associated with the prognosis and clinicopathological features of patients with LGG. Furthermore, the risk score level was significantly positively correlated with the infiltrating level of immunosuppressive cells, including M2 macrophages and regulatory T cells. GSVA identified a series of cancer-related pathways related to GSRS, such as P13K-AKT and P53 pathways. Moreover, ATAD1, NLGN2, OXTR, and TNR, hub genes identified in GSRS, were considered as potential prognostic biomarkers in LGG.Conclusion: A 14-genes GSRS was constructed and verified in this study. We provided a novel insight into the role of GSRS in LGG through a series of bioinformatics methods.

scholarly journals A Five-lncRNAs Signature-Derived Risk Score Based on TCGA and CGGA for Glioblastoma: Potential Prospects for Treatment Evaluation and Prognostic Prediction

2020 ◽  
Vol 10 ◽  
Author(s):  
Xuegang Niu ◽  
Jiangnan Sun ◽  
Lingyin Meng ◽  
Tao Fang ◽  
Tongshuo Zhang ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Therapeutic Efficacy ◽  
Cox Regression ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Normal Brain ◽  
Score Model ◽  
Genome Atlas

Accumulating studies have confirmed the crucial role of long non-coding RNAs (ncRNAs) as favorable biomarkers for cancer diagnosis, therapy, and prognosis prediction. In our recent study, we established a robust model which is based on multi-gene signature to predict the therapeutic efficacy and prognosis in glioblastoma (GBM), based on Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. lncRNA-seq data of GBM from TCGA and CGGA datasets were used to identify differentially expressed genes (DEGs) compared to normal brain tissues. The DEGs were then used for survival analysis by univariate and multivariate COX regression. Then we established a risk score model, depending on the gene signature of multiple survival-associated DEGs. Subsequently, Kaplan-Meier analysis was used for estimating the prognostic and predictive role of the model. Gene set enrichment analysis (GSEA) was applied to investigate the potential pathways associated to high-risk score by the R package “cluster profile” and Wiki-pathway. And five survival associated lncRNAs of GBM were identified: LNC01545, WDR11-AS1, NDUFA6-DT, FRY-AS1, TBX5-AS1. Then the risk score model was established and shows a desirable function for predicting overall survival (OS) in the GBM patients, which means the high-risk score significantly correlated with lower OS both in TCGA and CGGA cohort. GSEA showed that the high-risk score was enriched with PI3K-Akt, VEGFA-VEGFR2, TGF-beta, Notch, T-Cell pathways. Collectively, the five-lncRNAs signature-derived risk score presented satisfactory efficacies in predicting the therapeutic efficacy and prognosis in GBM and will be significant for guiding therapeutic strategies and research direction for GBM.

scholarly journals Identification and Validation of an Energy Metabolism-Related lncRNA-mRNA Signature for Lower-Grade Glioma

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jingwei Zhao ◽  
Le Wang ◽  
Bo Wei
Keyword(s):  
Energy Metabolism ◽  
Risk Score ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Coexpression Network ◽  
Normal Brain ◽  
Lower Grade ◽  
Prognostic Signature ◽  
Lower Grade Glioma ◽  
Genome Atlas

Energy metabolic processes play important roles for tumor malignancy, indicating that related protein-coding genes and regulatory upstream genes (such as long noncoding RNAs (lncRNAs)) may represent potential biomarkers for prognostic prediction. This study will develop a new energy metabolism-related lncRNA-mRNA prognostic signature for lower-grade glioma (LGG) patients. A GSE4290 dataset obtained from Gene Expression Omnibus was used for screening the differentially expressed genes (DEGs) and lncRNAs (DELs). The Cancer Genome Atlas (TCGA) dataset was used as the prognosis training set, while the Chinese Glioma Genome Atlas (CGGA) was for the validation set. Energy metabolism-related genes were collected from the Molecular Signatures Database (MsigDB), and a coexpression network was established between energy metabolism-related DEGs and DELs to identify energy metabolism-related DELs. Least absolute shrinkage and selection operator (LASSO) analysis was performed to filter the prognostic signature which underwent survival analysis and nomogram construction. A total of 1613 DEGs and 37 DELs were identified between LGG and normal brain tissues. One hundred and ten DEGs were overlapped with energy metabolism-related genes. Twenty-seven DELs could coexpress with 67 metabolism-related DEGs. LASSO regression analysis showed that 9 genes in the coexpression network were the optimal signature and used to construct the risk score. Kaplan-Meier curve analysis showed that patients with a high risk score had significantly worse OS than those with a low risk score (TCGA: HR=3.192, 95%CI=2.182‐4.670; CGGA: HR=1.922, 95%CI=1.431‐2.583). The predictive accuracy of the risk score was also high according to the AUC of the ROC curve (TCGA: 0.827; CGGA: 0.806). Multivariate Cox regression analyses revealed age, IDH1 mutation, and risk score as independent prognostic factors, and thus, a prognostic nomogram was established based on these three variables. The excellent prognostic performance of the nomogram was confirmed by calibration and discrimination analyses. In conclusion, our findings provided a new biomarker for the stratification of LGG patients with poor prognosis.

A Hypoxia-related LncRNA Signature Predicts Survival of Primary Lower-grade glioma

2021 ◽  
Author(s):  
Yanjia Hu ◽  
Jing Zhang ◽  
Jing Chen
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Lower Grade ◽  
Prognostic Signature ◽  
Gene Set Enrichment

Abstract Background Hypoxia-related long non-coding RNAs (lncRNAs) have been proven to play a role in multiple cancers and can serve as prognostic markers. Lower-grade gliomas (LGGs) are characterized by large heterogeneity. Methods This study aimed to construct a hypoxia-related lncRNA signature for predicting the prognosis of LGG patients. Transcriptome and clinical data of LGG patients were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). LGG cohort in TCGA was chosen as training set and LGG cohorts in CGGA served as validation sets. A prognostic signature consisting of fourteen hypoxia-related lncRNAs was constructed using univariate and LASSO Cox regression. A risk score formula involving the fourteen lncRNAs was developed to calculate the risk score and patients were classified into high- and low-risk groups based on cutoff. Kaplan-Meier survival analysis was used to compare the survival between two groups. Cox regression analysis was used to determine whether risk score was an independent prognostic factor. A nomogram was then constructed based on independent prognostic factors and assessed by C-index and calibration plot. Gene set enrichment analysis and immune cell infiltration analysis were performed to uncover further mechanisms of this lncRNA signature. Results LGG patients with high risk had poorer prognosis than those with low risk in both training and validation sets. Recipient operating characteristic curves showed good performance of the prognostic signature. Univariate and multivariate Cox regression confirmed that the established lncRNA signature was an independent prognostic factor. C-index and calibration plots showed good predictive performance of nomogram. Gene set enrichment analysis showed that genes in the high-risk group were enriched in apoptosis, cell adhesion, pathways in cancer, hypoxia etc. Immune cells were higher in high-risk group. Conclusion The present study showed the value of the 14-lncRNA signature in predicting survival of LGGs and these 14 lncRNAs could be further investigated to reveal more mechanisms involved in gliomas.

Clinicopathological signature and prognostic value of RNA:m5C methyltransferases in gliomas

2020 ◽  
Author(s):  
Peng Wang ◽  
Kai Huang ◽  
Miaojing Wu ◽  
Qing Hu ◽  
Chuming Tao ◽  
...  
Keyword(s):  
Overall Survival ◽  
Risk Score ◽  
Prognostic Value ◽  
Cox Regression ◽  
Roc Curves ◽  
Clinicopathological Features ◽  
Gene Set Enrichment Analysis ◽  
Consensus Clustering ◽  
Score Model ◽  
Genome Atlas

Abstract Background: Glioma is the most common primary intracranial tumor, accounting for the vast majority of intracranial malignant tumors. Aberrant expression of RNA:5-methylcytosine(m5C) methyltransferases has recently been the focus of research relating to the occurrence and progression of tumors. However, the prognostic value of RNA:m5C methyltransferases in glioma remains unclear. This study investigated RNA: m5C methyltransferase expression and defined its clinicopathological signature and prognostic value in gliomas. Methods: We systematically studied the RNA-sequence data of RNA:m5C methyltransferases underlying gliomas in the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets and identified different subtypes using Consensus clustering analysis. Gene Ontology (GO) and Gene Set Enrichment analysis (GSEA) was used to annotate the function of these genes. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm analyses were performed to construct the risk score model. Kaplan-Meier method and Receiver operating characteristic (ROC) curves were used to assess the overall survival of glioma patients. Additionally, Cox proportional regression model analysis was developed to address the connections between the risk scores and clinical factors. Results: Consensus clustering of RNA:m5C methyltransferases identified three clusters of gliomas with different prognostic and clinicopathological features. Meanwhile, Functional annotations demonstrated that RNA:m5C methyltransferases were significantly associated with the malignant progression of gliomas. Thereafter, five RNA:m5C methyltransferase genes were screened to construct a risk score model which can be used to predict not only overall survival but also clinicopathological features in gliomas. ROC curves revealed the significant prognostic ability of this signature. In addition, Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for glioma outcome. Conclusion: We demonstrated the role of RNA:m5C methyltransferases in the initiation and progression of glioma. We have expanded on the understanding of the molecular mechanism involved, and provided a unique approach to predictive biomarkers and targeted therapy.

scholarly journals Identification of Seven Novel Ferroptosis-Related Long Non - Coding RNA Signatures as a Diagnostic Biomarker for Acute Myeloid Leukemia

2021 ◽  
Author(s):  
Zhiyuan Zheng ◽  
Wei Wu ◽  
Zehang Lin ◽  
Shuhan Liu ◽  
Qiaoqian Chen ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Risk Score ◽  
Myeloid Leukemia ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Kaplan Meier ◽  
Acute Myeloid

Abstract Background: Ferroptosis is a newly discovered type of programmed cell death that participates in the biological processes of various cancers. However, the mechanism by which ferroptosis modulates acute myeloid leukemia (AML) remains unclear. This study aimed to investigate the role of ferroptosis-related long non-coding RNAs (lncRNAs) in AML and establish a corresponding prognostic model.Methods: RNA-sequencing data and clinicopathological characteristics were obtained from The Cancer Genome Atlas database, and ferroptosis-related genes were obtained from the FerrDb database. The “limma” R package, Cox regression, and the least absolute shrinkage and selection operator were used to determine the ferroptosis-related lncRNA signature with the lowest Akaike information criteria (AIC). The risk score of ferroptosis-related lncRNAs was calculated and patients with AML were divided into high- and low-risk groups based on the median risk score. The Kaplan-Meier curve and Cox regression were used to evaluate the prognostic value of the risk score. Finally, gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA) were performed to explore the biological functions of the ferroptosis-related lncRNAs.Results: Seven ferroptosis-related lncRNA signatures were identified in the training group, and Kaplan-Meier and Cox regression analyses confirmed that risk scores were independent prognostic predictors of AML in both the training and validation groups (All P < 0.05). In addition, the area under the curve (AUC) analysis confirmed that the signatures had a good predictive ability for the prognosis of AML. GSEA and ssGSEA showed that the seven ferroptosis-related lncRNAs were related to glutathione metabolism and tumor immunity.Conclusions: In this study, seven novel ferroptosis-related lncRNA signatures (AP001266.2, AC133961.1, AF064858.3, AC007383.2, AC008906.1, AC026771.1, and KIF26B-AS1) were established. These signatures were shown to accurately predict the prognosis of AML, which would provide new insights into strategies for the development of new AML therapies.

scholarly journals TEAD4 Serves as a Prognostic Biomarker and Correlates With Immune Phenotype in Lower-Grade Gliomas

2021 ◽  
Author(s):  
Mu Chen ◽  
Bingsong Huang ◽  
Lei Zhu ◽  
Kui Chen ◽  
Hao Lian ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Clinical Outcome ◽  
Immune Cells ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
Lower Grade ◽  
Ppi Network ◽  
Mesenchymal Transition ◽  
Cox Regression Analysis ◽  
Genome Atlas

Abstract Background: Tumor-infiltrating immune cells (TIICs), which play a pivotal role in the tumor microenvironment, are intimately related to tumor progression and clinical outcome. It remains unclear which factors influence tumor immune infiltration in lower-grade gliomas (LGGs). TEAD4 (TEA Domain Transcription Factor 4) is an essential member of the Hippo pathway that is involved in cancer progression, epithelial-mesenchymal transition, metastasis, and cancer stem cell function across multiple types of cancers. However, the prognostic value of TEAD4 and its association with TIICs in LGG have been hardly studied. Methods: LGG data were obtained from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA). TEAD4 expression between different groups was compared by R and survival analysis was implemented by Kaplan–Meier curves. In Virto experiments were conducted to investigate the role of TEAD4 in glioma cells. Gene set enrichment analysis (GSEA) and protein-protein interaction (PPI) network were used to investigate the differential biological processes and signaling pathways. Multiple computational methods were employed to estimate the association between TEAD4 expression and tumor microenvironment in LGG. Correlations were analyzed by Spearman correlationResults: TEAD4 expression was up-regulated in higher-grade gliomas and correlated with a poorer clinical outcome. Glioma cell proliferation and migration were promoted by TEAD4 overexpression. GSEA and PPI network indicated that multiple immune-related pathways and hub genes were closely associated with TEAD4 expression in LGG specimens. TEAD4 expression was negatively associated with glioma purity. Multivariate Cox regression analysis indicated that TEAD4 expression and tumor purity were independent prognostic factors in LGG. TEAD4 expression was positively correlated with the infiltration of multiple immune cells, including plasma cells, CD8+ T cells, and macrophages M1 and M2. Correlation analysis showed that the TEAD4 level can predict the efficacy of immune checkpoint blockade therapy. Conclusions: TEAD4 is highly related to glioma malignancy grades and multiple immune cell infiltration, suggesting TEAD4 can serve as a new biomarker for anti-cancer therapies in LGG.

scholarly journals Establishing and Validating an Aging-Related Prognostic Four-Gene Signature in Colon Adenocarcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Lian Zheng ◽  
Yang Yang ◽  
Xiaorong Cui
Keyword(s):  
High Risk ◽  
B Cell ◽  
Gene Mutation ◽  
Risk Score ◽  
Cox Regression ◽  
Colon Adenocarcinoma ◽  
Oncological Outcome ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Gene Set

Background. Aging is a process that biological changes accumulate with time and lead to increasing susceptibility to diseases like cancer. This study is aimed at establishing an aging-related prognostic signature in colon adenocarcinoma (COAD). Methods. The transcriptome data and clinical variables of COAD patients were downloaded from TCGA database. The genes in GOBP_AGING gene set was used for prognostic evaluation by the univariate and multivariate Cox regression analyses. The model was presented by a nomogram and assessed by the Kaplan-Meier curves and calibration curves. The drug response and gene mutation were also performed to implicate the clinical significance. The GO and KEGG analyses were employed to unravel the potential functional mechanism. Results. The Gene Set Enrichment Analysis result indicates that GOBP_AGING pathway is significantly enriched in COAD samples. Four aging-related genes are finally used to construct the aging-related prognostic signature: FOXM1, PTH1R, KL, and CGAS. The COAD patients with high risk score have much shorter overall survival in both train cohort and test cohort. The nomogram is then assembled to predict 1-year, 3-year, and 5-year survival. Patients with high risk score have elevated infiltrating B cell naïve and attenuated cisplatin sensitivity. The mutation landscape shows that the TTN, FAT4, ZFHX4, APC, and OBSCN gene mutation are different between high risk score patients and low risk score patients. The differentially expressed genes between patients with high score and low score are enriched in B cell receptor signaling pathway. Conclusion. We constructed an aging-related signature in COAD patients, which can predict oncological outcome and optimize therapeutic strategy.

Development and validation of an immune-related gene pairs signature in lower-grade glioma

2020 ◽  
Author(s):  
Xu Zhang ◽  
Shuai Ping ◽  
Rui Zhang ◽  
Can Li ◽  
Caibin Gao ◽  
...  
Keyword(s):  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Lower Grade ◽  
Related Gene ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Immune Related Gene ◽  
Lower Grade Glioma ◽  
Functional Pathways ◽  
Genome Atlas

Abstract Background Lower-grade gliomas (LGG) are the prevalent primary intracerebral malignancy tumor. Increasing evidence indicated an association between immune signature and LGG prognosis. Thus, we aim to develop an immune-related gene pairs (IRGPs) signature that can predict prognosis for LGG. Method: Gene expression levels and clinical information of LGG patients (LGGs) were collected from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. The two databases were divided into training cohort (n = 515) and an independent validation cohort (n = 604). IGRPs significantly associated with prognosis were selected by Cox regression. Gene set enrichment analysis and filtration were performed on IGRPs. Results Within 1991 immune genes, an 8 IRGPs signature including 15 unique genes was constructed, which had a significant association with survival. In the validation dataset, the IRGPs signature significantly stratified LGGs into low- and high-risk groups (P < 0.001), and it remained an independent prognostic factor in univariate and multivariate analyses (P < 0.001). Additionally, 26 functional pathways were filtrated through the intersection of Gene set enrichment analysis (GSEA) and gene ontology (GO) enrichment analysis. Conclusion The IGRPs signature demonstrated good prognostic value in lower-grade glioma, which may provide new insights into individual treatment for glioma patients. And the IGRPs might take effect through these filtrated 26 functional pathways.

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

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.

scholarly journals Fam20C Overexpression Predicts Poor Outcomes and is a Diagnostic Biomarker in Lower-Grade Glioma

2021 ◽  
Vol 12 ◽  
Author(s):  
Jing Feng ◽  
Jinping Zhou ◽  
Lin Zhao ◽  
Xinpeng Wang ◽  
Danyu Ma ◽  
...  
Keyword(s):  
Prognostic Value ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Research Field ◽  
Gene Set Enrichment Analysis ◽  
Lower Grade ◽  
Kaplan Meier ◽  
Lower Grade Glioma ◽  
Poor Outcomes

Glioma is a relatively low aggressive brain tumor. Although the median survival time of patients for lower-grade glioma (LGG) was longer than that of patients for glioblastoma, the overall survival was still short. Therefore, it is urgent to find out more effective molecular prognostic markers. The role of the Fam20 kinase family in different tumors was an emerging research field. However, the biological function of Fam20C and its prognostic value in brain tumors have rarely been reported. This study aimed to evaluate the value of Fam20C as a potential prognostic marker for LGG. A total of 761 LGG samples (our cohort, TCGA and CGGA) were included to investigate the expression and role of Fam20C in LGG. We found that Fam20C was drastically overexpressed in LGG and was positively associated with its clinical progression. Kaplan-Meier analysis and a Cox regression model were employed to evaluate its prognostic value, and Fam20C was found as an independent risk factor in LGG patients. Gene set enrichment analysis also revealed the potential signaling pathways associated with Fam20C gene expression in LGG; these pathways were mainly enriched in extracellular matrix receptor interactions, cell adhesion, cell apoptosis, NOTCH signaling, cell cycle, etc. In summary, our findings provide insights for understanding the potential role of Fam20C and its application as a new prognostic biomarker for LGG.

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