Metabolic Signature-Based Subtypes May Pave Novel Ways for Low-Grade Glioma Prognosis and Therapy.
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ABSTRACT:
SUBMITTER: Li G
PROVIDER: S-EPMC8650219 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:AimLow 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.Methodssurvival-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.ResultsWe 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.ConclusionsThe 10-gene signature may become novel prognostic and diagnostic biomarkers to considerably improve the prognostic prediction in LGG.
| S-EPMC7876581 | biostudies-literature
Project description:Cuproptosis is a new type of cell death that is associated with mitochondrial respiration of the tricarboxylic acid cycle. Previous studies showed that long non-coding RNAs (lncRNAs) regulated low-grade glioma (LGG) progression. However, the potential applications of cuproptosis-related lncRNAs (CRLs) in LGG were not explored. A comprehensive analysis was performed in The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) cohorts. We first screened two distinct cuproptosis subtypes based on prognostic CRLs using consensus clustering. To facilitate individualized survival prediction in LGG, we constructed a prognostic signature (including CRNDE, HAR1A, and FAM181A-AS1) in the TCGA dataset. The prognostic signature exhibited excellent predictive ability and reliability, which was validated in the CGGA_325 and CGGA_693 datasets. Notably, patients in the high-risk group had increased immune cell infiltration and expression of immune checkpoints, which indicated that they may benefit more from immune checkpoint blockade (ICB) therapy. Finally, the prognostic signature screened the population with sensitivity to chemotherapy and ICB therapy. In summary, this study initially explored the mechanism of CRLs in LGG and provides some insights into chemotherapy and ICB therapy of LGG.
| S-EPMC9428515 | biostudies-literature
Project description:Gliomas make up ~80% of malignant brain tumors in adults and are responsible for the majority of deaths from primary brain tumors. The glioma tumor microenvironment (TME) is a dynamic, heterogeneous mixture of extracellular matrix and malignant and non-malignant cells. Several ongoing clinical trials are evaluating the efficacy of therapies that target non-malignant cells, particularly immune cells. Consequently, a better understanding of the TME in glioma is pertinent. We utilized several gene expression datasets to evaluate the relationship between immune-related genes (IRGs) and patient prognosis. We generated microglia signatures using single-cell RNAseq data from human and mouse glioma cells to infer microglia abundance. Lastly, we built a LASSO Cox regression model that predicts patient survival. We found that 428 IRGs were negatively associated with survival in glioma patients. Overall survival was significantly lower in those with a high level of microglia infiltration. In addition, we also found that microglia abundance was significantly associated with several common genomic aberrations, including IDH2 and TP53 mutations. Furthermore, we found that patients with high risk scores had significantly worse overall survival than those with low risk scores in several independent datasets. Altogether, we characterized immune features predictive of overall survival in glioma and found that microglia abundance is negatively associated with survival. We developed a 23-gene risk score that can significantly stratify patients into low- and high-risk categories.
| S-EPMC9564275 | biostudies-literature
Project description:BackgroundGlioma is one of the major health problems worldwide. Biomarkers for predicting the prognosis of Glioma are still needed.MethodsThe transcriptome data and clinic information on Glioma were obtained from the CGGA, TCGA, GDC, and GEO databases. The immune infiltration status in the clusters was compared. The genes with differential expression were identified, and a prognostic model was developed. Several assays were used to detect RPH3A's role in Glioma cells, including CCK-8, colony formation, wound healing, and transwell migration assay.ResultsLower Grade Glioma (LGG) was divided into two clusters. The immune infiltration difference was observed between the two clusters. We screened for genes that differed between the two groups. WGCNA was used to construct a co-expressed network using the DEGs, and four co-expressed modules were identified, which are blue, green, grey, and yellow modules. High-risk patients have a lower overall survival rate than low-risk patients. In addition, the risk score is associated with histological subtypes. Finally, the role of RPH3A was detected. The overexpression of RPH3A in LGG cells can significantly inhibit cell proliferation and migration and regulate EMT-regulated proteins.ConclusionOur study developed a metabolic-related model for the prognosis of Glioma cells. RPH3A is a potential therapeutic target for Glioma.
| S-EPMC9497112 | biostudies-literature
Project description:BackgroundAs immunotherapy has received attention as new treatments for brain cancer, the role of inflammation in the process of glioma is of particular importance. Increasing studies have further shown that long non-coding RNAs (lncRNAs) are important factors that promote the development of glioma. However, the relationship between inflammation-related lncRNAs and the prognosis of glioma patients remains unclear. The purpose of this study is to construct and validate an inflammation-related lncRNA prognostic signature to predict the prognosis of low-grade glioma patients.MethodsBy downloading and analyzing the gene expression data and clinical information of the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) patients with low-grade gliomas, we could screen for inflammatory gene-related lncRNAs. Furthermore, through Cox and the Least Absolute Shrinkage and Selection Operator regression analyses, we established a risk model and divided patients into high- and low-risk groups based on the median value of the risk score to analyze the prognosis. In addition, we analyzed the tumor mutation burden (TMB) between the two groups based on somatic mutation data, and explored the difference in copy number variations (CNVs) based on the GISTIC algorithm. Finally, we used the MCPCounter algorithm to study the relationship between the risk model and immune cell infiltration, and used gene set enrichment analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to explore the enrichment pathways and biological processes of differentially expressed genes between the high- and low-risk groups.ResultsA novel prognostic signature was constructed including 11 inflammatory lncRNAs. This risk model could be an independent prognostic predictor. The patients in the high-risk group had a poor prognosis. There were significant differences in TMB and CNVs for patients in the high- and low-risk groups. In the high-risk group, the immune system was activated more significantly, and the expression of immune checkpoint-related genes was also higher. The GSEA, GO, and KEGG analyses showed that highly expressed genes in the high-risk group were enriched in immune-related processes, while lowly expressed genes were enriched in neuromodulation processes.ConclusionThe risk model of 11 inflammation-related lncRNAs can serve as a promising prognostic biomarker for low-grade gliomas patients.
| S-EPMC8365518 | biostudies-literature
Project description:Background Low-grade glioma (LGG) is a common malignant tumor of the central nervous system. The clinical prognosis of different patients varies greatly, so exploring appropriate markers that affect the prognosis and treatment of LGG is important. The purpose of this study was to identify the potential effect of autophagy-related DNA methylation on the prognosis and immune microenvironment in LGG. Methods The methylation profile, transcription data and corresponding clinical information of 451 patients with LGG were obtained from The Cancer Genome Atlas (TCGA). Another methylation data and clinical information of 110 patients with LGG from Chinese Glioma Genome Atlas (CGGA) were used as the validation set. Through univariate and multivariate COX regression analysis, we identified the autophagy-related genes (ARGs) associated with methylation levels and prognosis, and established a risk assessment signature. The receiver operating characteristic (ROC) and Kaplan-Meier (KM) survival curve were used to verify the model’s effectiveness in predicting prognosis. Patients were divided into low- and high-risk groups based on risk scores. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were used to explore the differences in biological functions between the two groups. ESTIMATE and CIBERSORT algorithms were used to explore differences in immune infiltration and immunotherapy sites. Pearson correlation analysis was used to analyze the relative relationship between methylated cg sites and corresponding genes. Results A total of 6 ARGs (ARSB, CFLAR, WIPI2, RB1, ERN1, RAB24) were selected that were associated with methylation levels and prognosis. The area under the curve (AUC) =0.96, and the KM survival curve P<0.0001, which proves that the risk assessment model has a good effect in predicting the prognosis of LGG. GO and KEGG enrichment analysis showed that the model mainly involved major histocompatibility complex (MHC) II receptors, antigen processing and presentation, and immune cell differentiation. In addition, we also found differences in immune infiltration and immune checkpoints between high- and low-risk groups. Conclusions The methylation levels of these 6 ARGs have a strong predictive potential for LGG, and the methylation regulation of ARGs has an important impact on the immune microenvironment of LGGs.
| S-EPMC9372237 | biostudies-literature
Project description:BackgroundGrowing evidence has revealed that m6A modification of long noncoding RNAs (lncRNAs) dynamically controls tumor stemness and tumorigenesis-related processes. However, the prognostic significance of m6A-related lncRNAs and their associations with stemness in low-grade glioma (LGG) remain to be clarified.MethodsA multicenter transcriptome analysis of lncRNA expression in 1,247 LGG samples was performed in this study. The stemness landscape of LGG tumors was presented and associations with clinical features were revealed. The m6A-related lncRNAs were identified between stemness groups and were further prioritized via least absolute shrinkage and selection operator Cox regression analysis. A risk score model based on m6A-related lncRNAs was constructed and validated in external LGG datasets.ResultsBased on the expression of LINC02984, PFKP-DT, and CRNDE, a risk model and nomogram were constructed; they successfully predicted the survival of patients and were extended to external datasets. Significant correlations were observed between the risk score and tumor stemness. Moreover, patients in different risk groups exhibited distinct tumor immune microenvironments and immune signatures. We finally provided several potential compounds suitable for specific risk groups, which may aid in LGG treatment.ConclusionsThis novel signature presents noteworthy value in the prediction of prognosis and stemness status for LGG patients and will foster future research on the development of clinical regimens.
| S-EPMC10791469 | biostudies-literature
Project description:The binding of platelet-derived growth factor D (PDGF-DD) to the NKp44 receptor activates a distinct transcriptional program in primary IL-2 expanded human natural killer (NK) cells. We were interested in knowing if the PDGF-DD-NKp44 pathway of NK cell activation might play a clinically relevant role in anti-tumor immunity. In order to address this question, we determined transcriptional signatures unique to resting, IL-2 expanded, and PDGF-DD activated, NK cells, in addition to different T cell subsets, and established the abundance of these immune cell phenotypes in The Cancer Genome Atlas (TCGA) low-grade glioma (LGG) dataset using CIBERSORT. Our results show that LGG patient tumors enriched for either the PDGF-DD activated NK cell or memory CD8+ T cell phenotypes are associated with a more favorable prognosis. Combined cell phenotype analyses revealed that patients with LGG tumors enriched for the PDGF-DD activated NK cell phenotype and the CD4+ T helper cell phenotype had a more favorable prognosis. High expression of transcripts encoding members of the killer cell lectin-like receptor (KLR) family, such as KLRK1 and KLRC2, KLRC3 and KLRC4 in LGG tumors were associated with more favorable prognosis, suggesting that these NK cell family receptors may play a prominent role in LGG anti-tumor immunity. Finally, many of the TCGA findings were reciprocated in LGG patients from the Chinese Glioma Genome Atlas (CGGA) dataset. Our results provide transcriptomic evidence that PDGF-DD activated NK cells and KLR family receptors may play an important clinical role in immune surveillance of LGG.
| S-EPMC8444979 | biostudies-literature
Project description:Background:While recent genome-wide association studies have suggested novel low-grade glioma (LGG) stratification models based on a molecular classification, we explored the potential clinical utility of patient-derived cells. Specifically, we assayed glioma-associated stem cells (GASC) that are patient-derived and representative of the glioma microenvironment. Methods:By next-generation sequencing, we analyzed the transcriptional profile of GASC derived from patients who underwent anaplastic transformation either within 48 months (GASC-BAD) or ?7 years (GASC-GOOD) after surgery. Gene set enrichment and pathway enrichment analyses were applied. The prognostic role of a nuclear factor-kappaB (NF-?B) signature derived from GASC-BAD was tested in 530 newly diagnosed diffuse LGG patients comprised within The Cancer Genome Atlas (TCGA) database. The prognostic value of the GASC upstream regulator p65 NF-?B was assessed, by univariate and multivariate Cox analyses, in a single center case study, including 146 grade II LGGs. Results:The key elements differentiating the transcriptome of GASC isolated from LGG with different prognoses were mostly related to hallmarks of cancer (eg, inflammatory/immune process, NF-?B activation). Consistently, the NF-?B signature extrapolated from the GASC study was prognostic in the dataset of TCGA. Finally, the nuclear expression of the NF-kB-p65 protein, assessed using an inexpensive immunohistochemical method, was an independent predictor of both overall survival and malignant progression-free survival in 146 grade II LGGs. Conclusion:This study demonstrates for the first time the independent prognostic role of NF-kB activation in LGG and outlines the role of patient-based stem cell models as a tool for precision medicine approaches.
| S-EPMC5961156 | biostudies-literature
Project description:BackgroundLower-grade gliomas (LGGs) show highly metabolic heterogeneity and adaptability. To develop effective therapeutic strategies targeting metabolic processes, it is necessary to identify metabolic differences and define metabolic subtypes. Here, we aimed to develop a classification system based on metabolic gene expression profile in LGGs.MethodsThe metabolic gene profile of 402 diffuse LGGs from the Cancer Genome Atlas (TCGA) was used for consensus clustering to determine robust clusters of patients, and the reproducibility of the classification system was evaluated in three Chinese Glioma Genome Atlas (CGGA) cohorts. Then, the metadata set for clinical characteristics, immune infiltration, metabolic signatures and somatic alterations was integrated to characterise the features of each subtype.ResultsWe successfully identified and validated three highly distinct metabolic subtypes in LGGs. M2 subtype with upregulated carbohydrate, nucleotide and vitamin metabolism correlated with worse prognosis, whereas M1 subtype with upregulated lipid metabolism and immune infiltration showed better outcome. M3 subtype was associated with low metabolic activities and displayed good prognosis. Three metabolic subtypes correlated with diverse somatic alterations. Finally, we developed and validated a metabolic signature with better performance of prognosis prediction.ConclusionsOur study provides a new classification based on metabolic gene profile and highlights the metabolic heterogeneity within LGGs.
| S-EPMC8292364 | biostudies-literature