Screening of autophagy genes as prognostic indicators for glioma patients
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ABSTRACT:
SUBMITTER: Qu S
PROVIDER: S-EPMC7540153 | biostudies-literature | 2020 Jan
REPOSITORIES: biostudies-literature
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Project description:Lower-grade glioma (LGG) is characterized by genetic and transcriptional heterogeneity, and a dismal prognosis. Iron metabolism is considered central for glioma tumorigenesis, tumor progression and tumor microenvironment, although key iron metabolism-related genes are unclear. Here we developed and validated an iron metabolism-related gene signature LGG prognosis. RNA-sequence and clinicopathological data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were downloaded. Prognostic iron metabolism-related genes were screened and used to construct a risk-score model via differential gene expression analysis, univariate Cox analysis, and the Least Absolute Shrinkage and Selection Operator (LASSO)-regression algorithm. All LGG patients were stratified into high- and low-risk groups, based on the risk score. The prognostic significance of the risk-score model in the TCGA and CGGA cohorts was evaluated with Kaplan-Meier (KM) survival and receiver operating characteristic (ROC) curve analysis. Risk- score distributions in subgroups were stratified by age, gender, the World Health Organization (WHO) grade, isocitrate dehydrogenase 1 (IDH1) mutation status, the O6-methylguanine-DNA methyl-transferase (MGMT) promoter-methylation status, and the 1p/19q co-deletion status. Furthermore, a nomogram model with a risk score was developed, and its predictive performance was validated with the TCGA and CGGA cohorts. Additionally, the gene set enrichment analysis (GSEA) identified signaling pathways and pathological processes enriched in the high-risk group. Finally, immune infiltration and immune checkpoint analysis were utilized to investigate the tumor microenvironment characteristics related to the risk score. We identified a prognostic 15-gene iron metabolism-related signature and constructed a risk-score model. High risk scores were associated with an age of > 40, wild-type IDH1, a WHO grade of III, an unmethylated MGMT promoter, and 1p/19q non-codeletion. ROC analysis indicated that the risk-score model accurately predicted 1-, 3-, and 5-year overall survival rates of LGG patients in the both TCGA and CGGA cohorts. KM analysis showed that the high-risk group had a much lower overall survival than the low-risk group (P < 0.0001). The nomogram model showed a strong ability to predict the overall survival of LGG patients in the TCGA and CGGA cohorts. GSEA analysis indicated that inflammatory responses, tumor-associated pathways, and pathological processes were enriched in high-risk group. Moreover, a high risk score correlated with the infiltration immune cells (dendritic cells, macrophages, CD4+ T cells, and B cells) and expression of immune checkpoint (PD1, PDL1, TIM3, and CD48). Our prognostic model was based on iron metabolism-related genes in LGG, can potentially aid in LGG prognosis, and provides potential targets against gliomas.
| S-EPMC8458946 | biostudies-literature
Project description:Objective: To analyze the prognostic effects of age in different tumor types and determine the prognostic significance of age related genes in patients with lower grade glioma (LGG). Methods: The relationships between age and tumor overall survival were determined by Kaplan-Meier survival analysis using The Cancer Genome Atlas (TCGA) dataset. The age related genes were identified using TCGA RNA-seq data. Univariate and multivariate cox regression were used to determine the prognostic significance of age related genes. The results derived from TCGA dataset were further validated using Gene Expression Omnibus (GEO) and Chinese Glioma Genome Atlas (CGGA) datasets. Results: Age at initial pathologic diagnosis was most associated with the overall survival of LGG patients than other types of tumor patients. Age related genes EMP3, IGFBP2, TIMP1 and SERPINE1 were highly expressed in old LGG patients. The hypo-methylations of EMP3 and SERPINE1 were contributing to the high expressions of EMP3 and SERPINE1 in old LGG patients. Also, EMP3, IGFBP2, TIMP1 and SERPINE1 were highly expressed in LGG tumor tissues, compared with normal brain tissues. Moreover, high expressions of IGFBP2, EMP3, TIMP1 and SERPINE1 were associated with the worse prognosis of LGG patients. Furthermore, we demonstrated that EMP3 and SERPINE1 were connected with each other and the combination of EMP3 and SERPINE1 had better prognostic effects in glioma patients. Conclusions: Age related genes IGFBP2, EMP3, TIMP1 and SERPINE1 have significant prognostic effects in LGG patients.
| S-EPMC7171497 | biostudies-literature
Project description:ObjectiveTo screen glycolytic genes linked to the glioma prognosis and construct the prognostic model.MethodsThe relevant data of glioma were downloaded from TCGA and GTEx databases. GSEA of glycolysis-related pathways was carried out, and enriched differential genes were extracted. Screening out prognostic-related genes with conspicuous significance and construction of the prognostic model were conducted by multivariate Cox regression analysis and Lasso regression analysis. The model was evaluated, and cBioPortal was used to analyze the mutation of the model gene. The expression of the model gene in tumor and normal colon tissue was analyzed. The model was used to evaluate the prognosis of patients in different groups to verify the applicability of the model.Results339 differentially glycolytic-related genes were enriched in REACTOME_GLYCOLYSIS, GLYCOLYTIC_PROCESS, HALLMARK_GLYCOLYSIS, and other pathways. We obtained 9 key prognostic genes and constructed the prognostic evaluation model. The 3-year AUC values of the ROC curve display model are greater than 0.75, which indicates that the accuracy of the model is good. The relation of age and risk score to prognosis is shown by univariate and multivariate Cox analysis. The expression of SRD5A3, MDH2, and B3GAT3 genes was significantly upregulated in the tumor tissues, while the HDAC4 and G6PC2 genes were downregulated. The mutation rate of MDH2 and HDAC4 genes was the highest. This model could effectively distinguish the risk of poor prognosis of patients in any age stage.ConclusionThe prognostic assessment models based on glycolysis-related nine-gene signature could accurately predict the prognosis of patients with GBM.
| S-EPMC8225435 | biostudies-literature
Project description:Autophagy is a complex process of autodigestion in conditions of cellular stress, and it might play an important role in the pathophysiology during carcinogenesis. We hypothesize that genetic variants of the autophagy pathway may influence clinical outcomes in prostate cancer patients. We genotyped 40 tagging single-nucleotide polymorphisms (SNPs) from 7 core autophagy pathway genes in 458 localized prostate cancer patients. Multivariate Cox regression was performed to evaluate the independent association of each SNP with disease progression. Positive findings were then replicated in an independent cohort of 504 advanced prostate cancer patients. After adjusting for known clinicopathologic factors, the association between ATG16L1 rs78835907 and recurrence in localized disease [hazard ratio (HR) 0.70, 95% confidence interval (CI) 0.54-0.90, P = 0.006] was replicated in more advanced disease (HR 0.78, 95% CI 0.64-0.95, P = 0.014). Additional integrated in silico analysis suggests that rs78835907 tends to affect ATG16L1 expression, which in turn is correlated with tumor aggressiveness and patient prognosis. In conclusion, genetic variants of the autophagy pathway contribute to the variable outcomes in prostate cancer, and discovery of these novel biomarkers might help stratify patients according to their risk of disease progression.
| S-EPMC4568463 | biostudies-literature
Project description:In this study, we identified 74 differentially expressed autophagy-related genes in glioma patients. Analysis using a Cox proportional hazard regression model showed that MAPK8IP1 and SH3GLB1, two autophagy-related genes, were associated with the prognostic signature for glioma. Glioma patients from the CGGA batches 1 and 2, GSE4412 and TCGA datasets could be divided into high- and low-risk groups with different survival times based on levels of MAPK8IP1 and SH3GLB1 expression. The autophagy-related signature was an independent predictor of survival outcomes in glioma patients. MAPK8IP1 overexpression and SH3GLB1 knockdown inhibited glioma cell proliferation, migration and invasion, and improved Temozolomide sensitivity. These findings suggest autophagy-related genes like MAPK8IP1 and SH3GLB1 could be potential therapeutic targets in glioma.
| S-EPMC5617400 | biostudies-literature
Project description:Purpose: Metabolic alterations are crucial for tumor progression and response to therapy. The comprehensive model of combined central carbon metabolism-associated genes that contribute to the outcomes of glioma and astrocytoma is not well understood. Method: We studied the profiles of 63 genes involved in central carbon metabolism in 514 relatively low-grade glioma patients. The different distributions of gene expression in gliomas and astrocytoma were identified. The differential gene expression between each cohort and the correlations with prognosis were detected. Finally, we built a tentative model to detect the prognostic roles of carbon metabolism-associated genes in astrocytoma. Result: Two primary clusters and four subclusters with significantly different overall survival were identified in low-grade glioma. The differences of histological diagnoses, grade, tumor site, and age were detected between each cluster. Comparing with other histological types, patients with astrocytoma exhibited the worst prognosis. Between astrocytoma patients with poor and favorable prognoses, expression profiles of 11 genes were significantly discrepant. We detected that 18 genes were respectively correlated with overall survival in astrocytoma; moreover, four genes (RAF1, AKT3, IDH1, and FGFR1) were detected as dependent variables for the prediction of the survival status of astrocytoma patients and were capable to predict the survival. Conclusion: Central carbon metabolism-associated genes are differentially expressed in all patients with glioma and histological subtype astrocytoma. The gene expression profile is significantly associated with clinical manifestations. These results suggested that both the multigene expression patterns and individual central carbon metabolism-associated genes were potentially capable to predict the prognosis of patients with low-grade glioma.
| S-EPMC6759923 | biostudies-literature
Project description:ObjectiveTo screen key autophagy genes in colon cancer and construct an autophagy gene model to predict the prognosis of patients with colon cancer.MethodsThe colon cancer data from the TCGA were downloaded as the training set, data chip of GSE17536 as the validation set. The differential genes of the training set were obtained and were analyzed for enrichment and protein network. Acquire autophagy genes from Human Autophagy Database www.autophagy.lu/project.html. Autophagy genes in differentially expressed genes were extracted using R-packages limma. Using LASSO/Cox regression analysis combined with clinical information to construct the autophagy gene risk scoring model and divide the samples into high and low risk groups according to the risk value. The Nomogram assessment model was used to predict patient outcomes. CIBERSORT was used to calculate the infiltration of immune cells in the samples and study the relationship between high and low risk groups and immune checkpoints.ResultsNine hundred seventy-six differentially expressed genes were screened from training set, including five hundred sixty-eight up-regulated genes and four hundred eight down regulated genes. These differentially expressed genes were mainly involved: the regulation of membrane potential, neuroactive ligand-receptor interaction. We identified eight autophagy genes CTSD, ULK3, CDKN2A, NRG1, ATG4B, ULK1, DAPK1, and SERPINA1 as key prognostic genes and constructed the model after extracting the differential autophagy genes in the training set. Survival analysis showed significant differences in sample survival time after grouping according to the model. Nomogram assessment showed that the model had high reliability for predicting the survival of patients with colon cancer in the 1, 3, 5 years. In the high-risk group, the infiltration degrees of nine types of immune cells are different and the samples can be well distinguished according to these nine types of immune cells. Immunological checkpoint correlation results showed that the expression levels of CTLA4, IDO1, LAG3, PDL1, and TIGIT increased in high-risk groups.ConclusionThe prognosis prediction model based on autophagy gene has a good evaluation effect on the prognosis of colon cancer patients. Eight key autophagy genes can be used as prognostic markers for colon cancer.
| S-EPMC7726244 | biostudies-literature
Project description:BackgroundMalignant glioma, especially glioblastoma, is a highly aggressive disease with a dismal prognosis. Vacuole membrane protein 1 (VMP1) is a critical autophagy-associated protein with roles in oncogenesis and tumor progression. However, the contribution of VMP1 to glioma development as well as its prognostic value has not been established.MethodsThe expression of VMP1 and clinicopathologic data for 1996 glioma samples were collected from authoritative public databases to explore its prognostic value. Lentiviral CRISPR-Cas9 gene editing system was performed to deplete VMP1 expression. Apoptosis assays, cell cycle assays, colony formation assays, and EdU incorporation analysis were conducted to validate the biological function of VMP1. Transmission electron microscopy was used to determine the role of VMP1 in regulating autophagy.ResultsVMP1 overexpression was associated with advanced disease and had a poor prognosis in patients with glioma. The depletion of VMP1 by CRISPR-Cas9 gene editing significantly inhibited cell proliferation, increased cell death, and induced cell cycle arrest. Mechanistically, VMP1 knockout blocked autophagic flux and thus sensitized glioma cells to radiotherapy and chemotherapy. Moreover, a nomogram model showed that VMP1 expression has high prognostic value for determining survival in glioma.ConclusionsOur results provide insights into the pathological and biological functions of VMP1, including its roles in promoting tumor growth and progression, and support its value as a new diagnostic and prognostic biomarker for glioma.
| S-EPMC8311950 | biostudies-literature
Project description:The current glioma classification could be optimized to cover such a separate and individualized prognosis ranging from a few months to over ten years. Considering its highly conserved role and potential in therapies, autophagy might be a promising element to be incorporated as a refinement for improved survival prognostication. The expression and RNA-seq data of 881 glioma patients from the Gene Expression Omnibus and The Cancer Genome Atlas were included, mapped with autophagy-related genes. Weighted gene coexpression network analysis and Cox regression analysis were used for the autophagy signature establishment, which composed of MUL1, NPC1, and TRIM13. Validations were represented by Kaplan-Meier plots and receiver operating curves (ROC). Cluster analysis suggested the IDH1 mutant involved in the favorable prognosis of the signature clusters. The signature was also immune-related shown by the Gene Ontology analysis and the Gene Set Enrichment Analysis. The high signature risk group held a higher ESTIMATE score (p = 2.6e - 11) and stromal score (p = 1.8e - 10). CD276 significantly correlated with the signature (r = 0.51, p < 0.05). The final nomogram integrated with the autophagy signature, IDH1 mutation, and pathological grade was built with accuracy and discrimination (1-year survival AUC = 0.812, 5-year survival AUC = 0.822, and 10-year survival AUC = 0.834). Its prognostic value and clinical utility were well-defined by the superiority in the comparisons with the current World Health Organization glioma classification in ROC (p < 0.05) and decision curve analysis. The autophagy signature-based IDH1 mutation and grade nomogram refined glioma classification for a more individualized and clinically applicable survival estimation and inspired potential autophagy-related therapies.
| S-EPMC7492900 | biostudies-literature
Project description:Ubiquitination is a dynamic and reversible process of a specific modification of target proteins catalyzed by a series of ubiquitination enzymes. Because of the extensive range of substrates, ubiquitination plays a crucial role in the localization, metabolism, regulation, and degradation of proteins. Although the treatment of glioma has been improved, the survival rate of patients is still not satisfactory. Therefore, we explore the role of ubiquitin proteasome in glioma. Survival-related ubiquitination related genes (URGs) were obtained through analysis of the Genotype-Tissue Expression (GTEx) and the Cancer Genome Atlas (TCGA). Cox analysis was performed to construct risk model. The accuracy of risk model is verified by survival, Receiver operating characteristic (ROC) and Cox analysis. We obtained 36 differentially expressed URGs and found that 25 URGs were related to patient prognosis. We used the 25 URGs to construct a model containing 8 URGs to predict glioma patient risk by Cox analysis. ROC showed that the accuracy rate of this model is 85.3%. Cox analysis found that this model can be used as an independent prognostic factor. We also found that this model is related to molecular typing markers. Patients in the high-risk group were enriched in multiple tumor-related signaling pathways. In addition, we predicted TFs that may regulate the risk model URGs and found that the risk model is related to B cells, CD4 T cells, and neutrophils.
| S-EPMC8084191 | biostudies-literature