Transcriptomic Landscape of Lower Grade Glioma Based on Age-Related Non-Silent Somatic Mutations.
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ABSTRACT:
SUBMITTER: Park Y
PROVIDER: S-EPMC8293196 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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:Somatic copy-number alterations (SCNAs) are major contributors to cancer development that are pervasive and highly heterogeneous in human cancers. However, the driver roles of SCNAs in cancer are insufficiently characterized. We combined network propagation and linear regression models to design an integrative strategy to identify driver SCNAs and dissect the functional roles of SCNAs by integrating profiles of copy number and gene expression in lower-grade glioma (LGG). We applied our strategy to 511 LGG patients and identified 98 driver genes that dysregulated 29 cancer hallmark signatures, forming 143 active gene-hallmark pairs. We found that these active gene-hallmark pairs could stratify LGG patients into four subtypes with significantly different survival times. The two new subtypes with similar poorest prognoses were driven by two different gene sets (one including EGFR, CDKN2A, CDKN2B, INFA8, and INFA5, and the other including CDK4, AVIL, and DTX3), respectively. The SCNAs of the two gene sets could disorder the same cancer hallmark signature in a mutually exclusive manner (including E2F_TARGETS and G2M_CHECKPOINT). Compared with previous methods, our strategy could not only capture the known cancer genes and directly dissect the functional roles of their SCNAs in LGG, but also discover the functions of new driver genes in LGG, such as IFNA5, IFNA8, and DTX3. Additionally, our method can be applied to a variety of cancer types to explore the pathogenesis of driver SCNAs and improve the treatment and diagnosis of cancer.
| S-EPMC8215700 | biostudies-literature
Project description:Background:Glioma is the most common and lethal tumor in the central nervous system (CNS). More than 70% of WHO grade II/III gliomas were found to harbor isocitrate dehydrogenase (IDH) mutations which generated targetable metabolic vulnerabilities. Focusing on the metabolic vulnerabilities, some targeted therapies, such as NAMPT, have shown significant effects in preclinical and clinical trials. Methods:We explored the TCGA as well as CGGA database and analyzed the RNA-seq data of lower grade gliomas (LGG) with the method of weighted correlation network analysis (WGCNA). Differential expressed genes were screened, and coexpression relationships were grouped together by performing average linkage hierarchical clustering on the topological overlap. Clinical data were used to conduct Kaplan-Meier analysis. Results:In this study, we identified ACAA2 as a prognostic factor in IDH mutation lower grade glioma with the method of weighted correlation network analysis (WGCNA). The difference of ACAA2 gene expressions between the IDH wild-type (IDH-WT) group and the IDH mutant (IDH-MUT) group suggested that there may be different potential targeted therapies based on the fatty acid metabolic vulnerabilities, which promoted the personalized treatment for LGG patients.
| S-EPMC7115055 | biostudies-literature
Project description:Aging is associated with an increased risk of cancer, possibly in part because of an age-related increase in mutations in normal tissues. Due to their extremely low abundance, somatic mutations in normal tissues frequently escape detection. Tumors, as clonal expansions of single cells, can provide information about the somatic mutations present in these cells prior to tumorigenesis. Here, we used data from The Cancer Genome Atlas (TCGA), to systematically study the frequency and spectrum of somatic mutations in a total of 6,969 patients and 34 different tumor types as a function of the age of the patient. After using linear modeling to control for the age structure of different tumor types, we found that the number of identified somatic mutations increases exponentially with age. Using additional data from the literature, we found that accumulation of somatic mutations is associated with cell division rate, cancer risk and cigarette smoking, with the latter also associated with a distinct spectrum of mutations. Our results confirm that aging is associated with the accumulation of somatic mutations, and strongly suggest that the level of genome instability of normal cells, modified by both endogenous and environmental factors, is the main risk factor for cancer.
| S-EPMC4694783 | biostudies-literature
Project description:BackgroundMalignant high-grade gliomas (HGGs), including the most aggressive form, glioblastoma multiforme, show significant clinical and genomic heterogeneity. Despite recent advances, the overall survival of HGGs and their response to treatment remain poor. In order to gain further insight into disease pathophysiology by correlating genomic landscape with clinical behavior, thereby identifying distinct HGG molecular subgroups associated with improved prognosis, we performed a comprehensive genomic analysis.MethodsWe analyzed and compared 720 exome-sequenced gliomas (136 from Yale, 584 from The Cancer Genome Atlas) based on their genomic, histological, and clinical features.ResultsWe identified a subgroup of HGGs (6 total, 4 adults and 2 children) that harbored a statistically significantly increased number of somatic mutations (mean = 9257.3 vs 76.2, P = .002). All of these "ultramutated" tumors harbored somatic mutations in the exonuclease domain of the polymerase epsilon gene (POLE), displaying a distinctive genetic profile, characterized by genomic stability and increased C-to-A transversions. Histologically, they all harbored multinucleated giant or bizarre cells, some with predominant infiltrating immune cells. One adult and both pediatric patients carried homozygous germline mutations in the mutS homolog 6 (MSH6) gene. In adults, POLE mutations were observed in patients younger than 40 years and were associated with a longer progression-free survival.ConclusionsWe identified a genomically, histologically, and clinically distinct subgroup of HGGs that harbored somatic POLE mutations and carried an improved prognosis. Identification of distinctive molecular and pathological HGG phenotypes has implications not only for improved classification but also for potential targeted treatments.
| S-EPMC4578578 | biostudies-literature
Project description:Previous studies have shown that the prognosis of patients with lower-grade glioma (LGG) is closely related to the infiltration of immune cells and the expression of long non-coding RNAs (lncRNAs). In this paper, we applied single-sample gene set enrichment analysis (ssGSEA) algorithm to evaluate the expression level of immune genes from tumor tissues in The Cancer Genome Atlas (TCGA) database, and divided patients into the high immune group and the low immune group, which were separately analyzed for differential expression. Venn analysis was taken to select 36 immune-related lncRNAs. To construct a prognostic model of LGG based on immune-related lncRNAs, we divided patients into a training set and a verification set at a ratio of 2:1. Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) regression were performed to select 11 immune-related lncRNAs associated with the prognosis of LGG, and based on these selected lncRNAs, the risk scoring model was constructed. Through Kaplan-Meier analysis, the overall survival (OS) of patients in the high-risk group was significantly lower than that of the low-risk group. Then, established a nomogram including age, gender, neoplasm histologic grade, and risk score. Meanwhile, the predictive performance of the model was evaluated by calculating the C-index, drawing the calibration chart, the clinical decision curve as well as the Receiver Operating Characteristic (ROC) curve. Similar results were obtained by utilizing the validation data to verify the above consequences. Based on the TIMER database, the correlation analysis showed that the 11 immune-related lncRNAs risk score of LGG were in connection with the infiltration of the subtypes of immune cells. Subsequently, we performed enrichment analysis, whose results showed that these immune-related lncRNAs played important roles in the progress of LGG. In conclusion, these 11 immune-related lncRNAs have the potential to predict the prognosis of patients with LGG, which may play a key role in the development of LGG.
| S-EPMC7719803 | biostudies-literature
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:LncRNA SET-binding factor 2 (SBF2) antisense RNA1 (SBF2-AS1) has been proven to play an oncogenic role in various types of tumors, but the prognostic role of SBF2-AS1 in tumors, especially in diffuse lower-grade glioma (LGG), is still unclear. Here, we aimed to investigate the prognostic value of SBF2-AS1 in LGG. The LGG expression profiles from The Cancer Genome Atlas (TCGA, n = 524) and Chinese Glioma Genome Atlas (CGGA, n = 431) were mined by Kaplan-Meier analysis, Cox regression analysis, Chi-square test and GSEA analysis. Through Kaplan-Meier analysis, we found the prognosis of LGG patients with high expression of SBF2-AS1 were worse than that of patients with low expression (Log Rank P < 0.001). Cox analysis showed SBF2-AS1 was an independent prognostic factor for poorer overall survival in LGG (P < 0.05). SBF2-AS1 was found to be significantly related to IDH mutation status and SBF2-AS1 was highly expressed in IDH wildtype group. GSEA analysis obtained a total of 126 GO terms and 6 KEGG pathways that were significantly enriched in SBF2-AS1 high expression phenotype (NOM P value < 0.05). We found these 126 GO terms and KEGG pathways were mainly related to immunity. In conclusion, lncRNA SBF2-AS1 expression is an immune-related lncRNA associated with unfavorable overall survival in LGG. SBF2-AS1 could be a reliable prognostic biomarker for patients with LGG.
| S-EPMC8226362 | biostudies-literature
Project description:Cuproptosis is the most recently discovered type of regulated cell death and is mediated by copper ions. Studies show that cuproptosis plays a significant role in cancer development and progression. Lower-grade gliomas (LGGs) are slow-growing brain tumors. The majority of LGGs progress to high-grade glioma, which makes it difficult to predict the prognosis. However, the prognostic value of cuproptosis-related genes (CRGs) in LGG needs to be further explored. mRNA expression profiles and clinical data of LGG patients were collected from public sources for this study. Univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression model were used to build a multigene signature that could divide patients into different risk groups. The differences in clinical pathological characteristics, immune infiltration characteristics, and mutation status were evaluated in risk subgroups. In addition, drug sensitivity and immune checkpoint scores were estimated in risk subgroups to provide LGG patients with precision medication. We found that all CRGs were differentially expressed in LGG and normal tissues. Patients were divided into high- and low-risk groups based on the risk score of the CRG signature. Patients in the high-risk group had a considerably lower overall survival rate than those in the low-risk group. According to functional analysis, pathways related to the immune system were enriched, and the immune state differed across the two risk groups. Immune characteristic analysis showed that the immune cell proportion and immune scores were different in the different groups. High-risk group was characterized by low sensitivity to chemotherapy but high sensitivity to immune checkpoint inhibitors. The current study revealed that the novel CRG signature was related to the prognosis, clinicopathological features, immune characteristics, and treatment perference of LGG.
| S-EPMC9640744 | biostudies-literature
Project description:BackgroundHypoxia-related genes are demonstrated to correlate with the prognosis of various cancers. However, the role of hypoxia-related long non-coding RNAs (HRLs) in lower-grade glioma (LGG) remains unclear.MethodsA total of 700 LGG samples were extracted from TCGA and CGGA databases. Pearson correlation analysis was used to identify HRLs. Lasso analysis was adopted to construct the HRL signature. TIDE algorithm was used to predict responses to immune checkpoint inhibitors. Cell proliferation was estimated by cell counting kit-8 assay, colony formation assay, and EdU assay.ResultsWe identified 340 HRLs and constructed a novel risk signature composed of 19 HRLs. The risk score exhibited potent value in predicting the prognosis of LGG patients and was significantly associated with the prognosis of LGG patients. Moreover, HRL signature could distinguish patients with similar expression levels of immune checkpoints and might predict the efficacy of immune checkpoint inhibitors. Additionally, hypoxia-related pathways and immune pathways were enriched in high-risk group, and high risk score indicated low tumor purity and high immune infiltration. Two major HRLs, LINC00941 and BASP1-AS1, could significantly affect the proliferation of glioma cells.ConclusionsOur study constructed a novel HRL signature that could predict the prognosis and immunotherapy response of LGG patients. HRLs could be novel biomarkers to predict the prognosis of LGG patients and potential targets for LGG treatment.
| S-EPMC8514865 | biostudies-literature