Development and Verification of the Amino Metabolism-Related and Immune-Associated Prognosis Signature in Gliomas.
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ABSTRACT:
SUBMITTER: Xu Y
PROVIDER: S-EPMC8602207 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: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.
| S-EPMC8581158 | biostudies-literature
Project description:As the core element of material and energy metabolism pathways, the biological functions and prognostic significance of ATP metabolism in diffuse gliomas have so far remained unclear. Based on comprehensive analysis of ATP metabolism-related gene expression profiles, we constructed an ATP metabolism-related risk signature to determine the role of ATP metabolism. We found that this ATP metabolism-related gene expression profile could divide patients into 2 robust groups with distinct clinical characteristics and prognosis. Patients in the high-risk group tended to be predicted as malignant entities, indicating that the activation of ATP metabolism may promote the malignant progress of diffuse gliomas. Cox regression and Kaplan-Meier analyses suggested that this risk signature was an independent predictor for prognosis. Furthermore, we constructed an individualized prognosis prediction model through nomogram and time-dependent receiver operating characteristic (ROC) curve analyses. Functional analysis suggested that, in addition to material and energy metabolism, ATP metabolism also played an essential role in the regulation of the tumor immune microenvironment. In brief, the ATP metabolism-related signature was tightly associated with regulation of the tumor immune microenvironment and could serve as an independent prognostic biomarker in diffuse gliomas.
| S-EPMC7385348 | biostudies-literature
Project description:Glioma is one of the leading causes of death from cancer, and autophagy-related genes (ARGs) play an important role in glioma occurrence, progression, and treatment. In this study, the gene expression profiles and clinical data of glioma patients were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), respectively. ARGs were obtained from the Human Autophagy Database. We analyzed the expression of the ARGs in glioma and found that 73 ARGs were differentially expressed in tumor and normal tissues. Univariate Cox regression analysis was used to identify prognostic differentially expressed ARGs (PDEARGs). Least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were performed on the PDEARGs to determine the risk genes; and BRIC5, NFE2L2, GABARAP, IKBKE, BID, MAPK3, FKBP1B, MAPK8IP1, PRKCQ, CX3CL1, NPC1, HSP90AB1, DAPK2, SUPT20H, and PTEN were selected to establish a prognostic risk score model for TCGA and CGGA cohorts. This model accurately stratified patients with different survival outcomes, and the autophagy-related signature was also appraised as being an independent prognostic factor. We also constructed a prognostic nomogram using risk score, age, gender, WHO grade, isocitrate dehydrogenase (IDH) mutation status, and 1p/19q co-deletion status; and the calibration plots showed excellent prognostic performance. Finally, Pearson correlation analysis suggested that the ARG signature also played an essential role in the tumor immune microenvironment. In summary, we constructed and verified a novel autophagy-related signature that was tightly associated with the tumor immune microenvironment and could serve as an independent prognostic biomarker in gliomas.
| S-EPMC7604433 | biostudies-literature
Project description:BackgroundLower-grade gliomas (LGGs) have more favorable outcomes than glioblastomas; however, LGGs often progress to process glioblastomas within a few years. Numerous studies have proven that the tumor microenvironment (TME) is correlated with the prognosis of glioma.MethodsLGG RNA-Sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were extracted and then divided into training and testing cohorts, respectively. Immune-related differentially expressed genes (DEGs) were screened to establish a prognostic signature by a multivariate Cox proportional hazards regression model. The immune-related risk score and clinical information, such as age, sex, World Health Organization (WHO) grade, and isocitrate dehydrogenase 1 (IDH1) mutation, were used to independently validate and develop a prognostic nomogram. GO and KEGG pathway analyses to DEGs between immune-related high-risk and low-risk groups were performed.ResultsSixteen immune-related genes were screened for establishing a prognostic signature. The risk score had a negative correlation with prognosis, with an area under the receiver operating characteristic (ROC) curve of 0.941. The risk score, age, grade, and IDH1 mutation were identified as independent prognostic factors in patients with LGGs. The hazard ratios (HRs) of the high-risk score were 5.247 [95% confidence interval (CI) = 3.060-8.996] in the multivariate analysis. A prognostic nomogram of 1-, 3-, and 5-year survival was established and validated internally and externally. Go and KEGG pathway analyses implied that immune-related biological function and pathways were involved in the TME.ConclusionThe immune-related prognostic signature and the prognostic nomogram could accurately predict survival.
| S-EPMC7753319 | biostudies-literature
Project description:IntroductionTo better understand the role of immune escape and cancer-associated fibroblasts (CAFs) in colon adenocarcinoma (COAD), an integrative analysis of the tumor microenvironment was performed using a set of 12 immune- and CAF-related genes (ICRGs).MethodsUnivariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used to establish a prognostic signature based on the expression of these 12 genes (S1PR5, AEN, IL20RB, FGF9, OSBPL1A, HSF4, PCAT6, FABP4, KIF15, ZNF792, CD1B and GLP2R). This signature was validated in both internal and external cohorts and was found to have a higher C-index than previous COAD signatures, confirming its robustness and reliability. To make use of this signature in clinical settings, a nomogram incorporating ICRG signatures and key clinical parameters, such as age and T stage, was developed. Finally, the role of S1PR5 in the immune response of COAD was validated through in vitro cytotoxicity experiments.ResultsThe developed nomogram exhibited slightly improved predictive accuracy compared to the ICRG signature alone, as indicated by the areas under the receiver operating characteristic curves (AUC, nomogram:0.838; ICRGs:0.807). The study also evaluated the relationships between risk scores (RS) based on the expression of the ICRGs and other key immunotherapy variables, including immune checkpoint expression, immunophenoscore (IPS), and microsatellite instability (MSI). Integration of these variables led to more precise prediction of treatment efficacy, enabling personalized immunotherapy for COAD patients. Knocking down S1PR5 can enhance the efficacy of PD-1 monoclonal antibody, promoting the cytotoxicity of T cells against HCT116 cells ((p<0.05).DiscussionThese findings indicate that the ICRG signature may be a valuable tool for predicting prognostic risk, evaluating the efficacy of immunotherapy, and tailoring personalized treatment options for patients with COAD.
| S-EPMC10834644 | biostudies-literature
Project description:BackgroundGlioma cells have increased intake and metabolism of methionine, which can be monitored with 11 C-L-methionine. However, a short half-life of 11 C (~ 20 min) limits its application in clinical practice. It is necessary to develop a methionine metabolism genes-based prediction model for a more convenient prediction of glioma survival.MethodsWe evaluated the patterns of 29 methionine metabolism genes in glioma from the Cancer Genome Atlas (TCGA). A risk model was established using Lasso regression analysis and Cox regression. The reliability of the prognostic model was validated in derivation and validation cohorts (Chinese Glioma Genome Atlas; CGGA). GO, KEGG, GSEA and ESTIMATE analyses were performed for biological functions and immune characterization.ResultsOur results showed that a majority of the methionine metabolism genes (25 genes) were involved in the overall survival of glioma (logrank p and Cox p < 0.05). A 7-methionine metabolism prognostic signature was significantly related to a poor clinical prognosis and overall survival of glioma patients (C-index = 0.83). Functional analysis revealed that the risk model was correlated with immune responses and with epithelial-mesenchymal transition. Furthermore, the nomogram integrating the signature of methionine metabolism genes manifested a strong prognostic ability in the training and validation groups.ConclusionsThe current model had the potential to improve the understanding of methionine metabolism in gliomas and contributed to the development of precise treatment for glioma patients, showing a promising application in clinical practice.
| S-EPMC10699061 | biostudies-literature
Project description:With the increasing prevalence of Hepatocellular carcinoma (HCC) and the poor prognosis of immunotherapy, reliable immune-related gene pairs (IRGPs) prognostic signature is required for personalized management and treatment of patients. Gene expression profiles and clinical information of HCC patients were obtained from the TCGA and ICGC databases. The IRGPs are constructed using immune-related genes (IRGs) with large variations. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct IRGPs signature. The IRGPs signature was verified through the ICGC cohort. 1,309 IRGPs were constructed from 90 IRGs with high variability. We obtained 50 IRGPs that were significantly connected to the prognosis and constructed a signature that included 17 IRGPs. In the TCGA and ICGC cohorts, patients were divided into high and low-risk patients by the IRGPs signature. The overall survival time of low-risk patients is longer than that of high-risk patients. After adjustment for clinical and pathological factors, multivariate analysis showed that the IRGPs signature is an independent prognostic factor. The Receiver operating characteristic (ROC) curve confirmed the accuracy of the signature. Besides, gene set enrichment analysis (GSEA) revealed that the signature is related to immune biological processes, and the immune microenvironment status is distinct in different risk patients. The proposed IRGPs signature can effectively assess the overall survival of HCC, and provide the relationship between the signature and the reactivity of immune checkpoint therapy and the sensitivity of targeted drugs, thereby providing new ideas for the diagnosis and treatment of the disease.
| S-EPMC8517445 | biostudies-literature
Project description:As a cold tumor, malignant glioma has strong immunosuppression and immune escape characteristics. The tumor microenvironment (TME) provides the "soil" for the survival of malignant tumors, and cancer-associated fibroblasts (CAFs) are the architects of matrix remodeling in TME. Therefore, CAFs have potent regulatory effects on the recruitment and functional differentiation of immune cells, whereby they synthesize and secrete numerous collagens, cytokines, chemokines, and other soluble factors whose interaction with tumor cells creates an immunosuppressive TME. This consequently facilitates the immune escape of tumor cells. Targeting CAFs would improve the TME and enhance the efficacy of immunotherapy. Thus, regulation of CAFs and CAFs-related genes holds promise as effective immunotherapies for gliomas. Here, by analyzing the Chinese Glioma Genome Atlas and the Cancer Genome Atlas database, the proportion of CAFs in the tumor was revealed to be associated with clinical and immune characteristics of gliomas. Moreover, a risk model based on the expression of CAFs-related six-gene for the assessment of glioma patients was constructed using the least absolute shrinkage and selection operator and the results showed that a high-risk group had a higher expression of the CAFs-related six-genes and lower overall survival rates compared with those in the low-risk group. Additionally, patients in the high-risk group exhibited older age, high tumor grade, isocitrate dehydrogenase wildtype, 1p/19q non-codeletion, O-6-methylguanine-DNA methyltransferase promoter unmethylation and poor prognosis. The high-risk subtype had a high proportion CAFs in the TME of glioma, and a high expression of immune checkpoint genes. Analysis of the Submap algorithm indicated that the high-risk patients could show potent response to anti-PD-1 therapy. The established risk prediction model based on the expression of six CAFs-related genes has application prospects as an independent prognostic indicator and a predictor of the response of patients to immunotherapy.
| S-EPMC8586504 | biostudies-literature
Project description:BackgroundThe close relationship between histone deacetylase 9 (HDAC9) and immunity has attracted attention. We constructed an immune signature for HDAC9, a vital epigenetic modification, to predict the survival status and treatment benefits in bladder cancer (BC).MethodsAn exhaustive analysis of HDAC9 and immunology via the tumor and immune system interaction database (TISIDB) was performed, and an immune prognostic risk signature was developed based on genes enriched in the top five immune-related pathways under high HDAC9 status. Comprehensive analysis of survival curves and Cox regression were used to estimate the effectiveness of the risk signature. The relationship between immunological characteristics and the risk score was evaluated, and the mechanisms were also explored.ResultsIn the TISIDB, HDAC9 was closely related to various immunological characteristics. The risk signature was obtained based on genes related to prognosis enriched in the top five immune-related pathways under high HDAC9 status. The survival rate of the high-risk BC patients was poor. The risk score was closely related to multiple immunological characteristics, drug sensitivity, immunotherapy benefits and biofunctions.ConclusionAn immune-related prognostic signature established for HDAC9 expression status could independently predict the prognosis of BC patients. The use of this signature could help clinicians make personalized treatment decisions.
| S-EPMC8893690 | biostudies-literature
Project description:Melanoma remains a potentially deadly malignant tumor. The incidence of melanoma continues to rise. Immunotherapy has become a new treatment method and is widely used in a variety of tumors. Original melanoma data were downloaded from TCGA. ssGSEA was performed to classify them. GSVA software and the "hclust" package were used to analyze the data. The ESTIMATE algorithm screened DEGs. The edgeR package and Venn diagram identified valid immune-related genes. Univariate, LASSO and multivariate analyses were used to explore the hub genes. The "rms" package established the nomogram and calibrated the curve. Immune infiltration data were obtained from the TIMER database. Compared with that of samples in the high immune cell infiltration cluster, we found that the tumor purity of samples in the low immune cell infiltration cluster was higher. The immune score, ESTIMATE score and stromal score in the low immune cell infiltration cluster were lower. In the high immune cell infiltration cluster, the immune components were more abundant, while the tumor purity was lower. The expression levels of TIGIT, PDCD1, LAG3, HAVCR2, CTLA4 and the HLA family were also higher in the high immune cell infiltration cluster. Survival analysis showed that patients in the high immune cell infiltration cluster had shorter OS than patients in the low immune cell infiltration cluster. IGHV1-18, CXCL11, LTF, and HLA-DQB1 were identified as immune cell infiltration-related DEGs. The prognosis of melanoma was significantly negatively correlated with the infiltration of CD4+ T cells, CD8+ T cells, dendritic cells, neutrophils and macrophages. In this study, we identified immune-related melanoma core genes and relevant immune cell subtypes, which may be used in targeted therapy and immunotherapy of melanoma.
| S-EPMC7874014 | biostudies-literature