Novel Biomarker Genes for Prognosis of Survival and Treatment of Glioma.
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ABSTRACT:
SUBMITTER: Zhu X
PROVIDER: S-EPMC8714878 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundGlioma is the most common primary intracranial tumor and is associated with poor prognosis. Identifying effective biomarkers for glioma is particularly important. MXRA5, a secreted glycoprotein, is involved in cell adhesion and extracellular matrix remodeling and has been reported to be expressed in many cancers. However, the role and mechanism of action of MXRA5 in gliomas remain unclear. This study was aimed at investigating the role of MXRA5 at the transcriptome level and its clinical prognostic value.MethodsIn this study, RNA microarray data of 301 glioma patients from the Chinese Glioma Genome Atlas (CGGA) were collected as a training cohort and RNA-seq data of 702 glioma samples from The Cancer Genome Atlas (TCGA) were used for validation. We analyzed the clinical and molecular characteristics as well as the prognostic value of MXRA5 in glioma. In addition, the expression level of MXRA was evaluated in 28 glioma tissue samples.ResultsWe found that MXRA5 expression was significantly upregulated in high-grade gliomas and IDH wild-type gliomas compared to controls. Receiver operating characteristic (ROC) analysis showed that MXRA5 is a potential marker of the mesenchymal subtype of glioblastoma multiforme (GBM). We found that MXRA5 expression is highly correlated with immune checkpoint molecule expression levels and tumor-associated macrophage infiltration. High MXRA5 expression could be used as an independent indicator of poor prognosis in glioma patients.ConclusionOur study suggests that MXRA5 expression is associated with the clinicopathologic features and poor prognosis of gliomas. MXRA5 may play an important role in the immunosuppressive microenvironment of glioma. As a secreted glycoprotein, MXRA5 is a potential circulating biomarker for glioma, deserving further investigation.
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Project description:BackgroundHepatocellular carcinoma (HCC) is closely associated with the immune microenvironment. To identify the effective population before administering treatment, the establishment of prognostic immune biomarkers is crucial for early HCC diagnosis and treatment.ResultsA total of 335 IRGs identified from 788 overlapping IRGs were associated with the survival of HCC. A prognostic immunoscore model was identified. The Kaplan-Meier survival curves and time-dependent ROC analysis revealed a powerful prognostic performance of immunoscore signature via multi validation. Besides, the immunoscore signature exhibited a better predictive power compared to other prognostic signatures. Gene set enrichment analysis showed multiple signaling differences between the high and low immunoscore group. Furthermore, immunoscore was significantly associated with multiple immune cells and immune infiltration in the tumor microenvironment.ConclusionsWe identified the immunoscore as a robust marker for predicting HCC patient survival.MethodsThree sets of immune-related genes (IRGs) were integrated to identify the overlapping IRGs. Weighted gene co-expression network analysis was performed to obtain the survival-related IRGs. Further, the prognostic immunoscore model was constructed via LASSO-penalized Cox regression analysis. Then the prognostic performance of immunoscore was evaluated. In addition, ESTIMATE and CIBERSORT algorithms were applied to explore the relationship between immunoscore and tumor immune microenvironment.
| S-EPMC7834986 | biostudies-literature
Project description:Bradykinin receptor B2 (BDKRB2) has been reported as an oncogene in several malignancies. In glioma, the role of BDKRB2 remains unknown. This study aimed at investigating its clinical significance and biological function in glioma at the transcriptional level. We selected 301 glioma patients with microarray data from CGGA database and 697 with RNAseq data from TCGA database. Transcriptome and clinical data of 998 samples were analyzed. Statistical analysis and figure generating were performed with R language. BDKRB2 expression showed a positive correlation with the WHO grade of glioma. BDKRB2 was increased in IDH wildtype and mesenchymal subtype of glioma. Gene ontology analysis demonstrated that BDKRB2 was profoundly associated with extracellular matrix organization in glioma. GSEA analysis revealed that BDKRB2 was particularly correlated with epithelial-to-mesenchymal transition (EMT). GSVA analysis showed that BDKRB2 was significantly paralleled with several EMT signaling pathways, including PI3K/AKT, hypoxia, and TGF-β. Moreover, BDKRB2 expression was significantly correlated with key biomarkers of EMT, especially with N-cadherin, snail, slug, vimentin, TWIST1, and TWIST2. Finally, higher BDKRB2 indicated significantly shorter survival for glioma patients. In conclusion, BDKRB2 was associated with more aggressive phenotypes of gliomas. Furthermore, BDKRB2 was involved in the EMT process and could serve as an independent prognosticator in glioma.
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Project description:BackgroundGlioma is the most common central nervous malignancy. Due to its poor survival outcomes, it is essential to identify novel individualized therapy. Oncolytic virus (OV) treatment is a key therapy regulating tumor microenvironment in malignant glioma. Herein, we aim to identify the key genes after OV infection and its role in glioma.MethodsPerforming an RNA-seq analysis, the differentially expressed genes (DEGs) between EV-A71-infection and mock group were screened with GFold values. DAVID online analysis was performed to identify the functional classification. Overall survival (OS) or disease-free survival (DFS) was evaluated to analyze the relation between PTBP1 expression levels and prognosis of glioma patients. Additionally, the ssGSEA and TIMER algorithms were applied for evaluating immune cell infiltration in glioma.ResultsFollowing EV-A71 infection in glioma cells, PTBP1, one of the downregulated DEGs, was found to be associated with multiple categories of GO and KEGG enrichment analysis. We observed elevated expression levels of PTBP1 across various tumor grades of glioma in comparison to normal brain samples. High PTBP1 expression had a notable impact on the OS of patients with low-grade glioma (LGG). Furthermore, we observed an obvious association between PTBP1 levels and immune cell infiltration in LGG. Notably, PTBP1 was regarded as an essential prognostic biomarker in immune cells of LGG.ConclusionOur research uncovered a critical role of PTBP1 in outcomes and immune cell infiltration of glioma patients, particularly in those with LGG.
| S-EPMC10556503 | biostudies-literature
Project description:The composition and abundance of immune and stromal cells in the tumor microenvironment (TME) dramatically affect prognosis. Infiltration of immunosuppressive tumor-associated fibroblasts (TAFs) is a hallmark of glioma. However, the mechanisms regulating TAF infiltration and the prognostic value of TAF-related genes in glioma remain unclear. In this study, we analyzed TAF infiltration by Estimating the Proportion of Immune and Cancer cells (EPIC) algorithm based on multiple glioma databases, including Glioblastoma and low-grade glioma merged cohort from The Cancer Genome Atlas (TCGA GBMLGG) cohort, the Chinese Glioma Genome Atlas (CGGA) #325 cohort, and the CGGA #693 cohort. TAF infiltration was increased in glioblastoma (GBM), and elevated TAF infiltration predicted poorer survival in gliomas. Gene enrichment analyses revealed that differentially expressed genes (DEGs) between low-grade glioma (LGG) and GBM were significantly enriched in the extracellular matrix (ECM) remodeling-related signaling, which may contribute to immune escape and resistance to immune checkpoint blockers (ICBs). To identify co-expression modules and candidate hub genes that may be associated with TAF infiltration, we performed weighted correlation network analysis (WGCNA) of DEGs. Afterward, univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analyses were performed to screen the positive prognostic hub genes. Finally, a high-efficacy prediction signature was constructed based on the expression of S100A4, PLAUR, and EMP3. The signature correlated with the abundance of TAF infiltration in glioma and was an independent risk factor for glioma. In conclusion, our findings suggested that the TAF-related signature was a valuable prognostic biomarker in glioma and provided potential targets for integrative therapy of gliomas.
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Project description:BackgroundMelanoma is a cancer of the skin with potential to spread to other organs and is responsible for most deaths due to skin cancer. It is imperative to identify immune biomarkers for early melanoma diagnosis and treatment.Results63 immune-related genes of the total 1039 unique IRGs retrieved were associated with overall survival of melanoma. A multi-IRGs classifier constructed using eight IRGs showed a powerful predictive ability. The classifier had better predictive power compared with the current clinical data. GSEA analysis showed multiple signaling differences between high and low risk score group. Furthermore, biomarker was associated with multiple immune cells and immune infiltration in tumor microenvironment.ConclusionsThe immune-related genes prognosis biomarker is an effective potential prognostic classifier in the immunotherapies and surveillance of melanoma.MethodsMelanoma samples of genes were retrieved from TCGA and GEO databases while the immune-related genes (IRGs) were retrieved from the ImmPort database. WGCNA, Cox regression analysis and LASSO analysis were used to classify melanoma prognosis. ESTIMATE and CIBERSORT algorithms were used to explore the relationship between risk score and tumor immune microenvironment. GSEA analysis was performed to explore the biological signaling pathway.
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Project description:ObjectiveDespite several clinicopathological factors being integrated as prognostic biomarkers, the individual variants and risk stratification have not been fully elucidated in lower grade glioma (LGG). With the prevalence of gene expression profiling in LGG, and based on the critical role of the immune microenvironment, the aim of our study was to develop an immune-related signature for risk stratification and prognosis prediction in LGG.MethodsRNA-sequencing data from The Cancer Genome Atlas (TCGA), Genome Tissue Expression (GTEx), and Chinese Glioma Genome Atlas (CGGA) were used. Immune-related genes were obtained from the Immunology Database and Analysis Portal (ImmPort). Univariate, multivariate cox regression, and Lasso regression were employed to identify differentially expressed immune-related genes (DEGs) and establish the signature. A nomogram was constructed, and its performance was evaluated by Harrell's concordance index (C-index), receiver operating characteristic (ROC), and calibration curves. Relationships between the risk score and tumor-infiltrating immune cell abundances were evaluated using CIBERSORTx and TIMER.ResultsNoted, 277 immune-related DEGs were identified. Consecutively, 6 immune genes (CANX, HSPA1B, KLRC2, PSMC6, RFXAP, and TAP1) were identified as risk signature and Kaplan-Meier curve, ROC curve, and risk plot verified its performance in TCGA and CGGA datasets. Univariate and multivariate Cox regression indicated that the risk group was an independent predictor in primary LGG. The prognostic signature showed fair accuracy for 3- and 5-year overall survival in both internal (TCGA) and external (CGGA) validation cohorts. However, predictive performance was poor in the recurrent LGG cohort. The CIBERSORTx algorithm revealed that naïve CD4+ T cells were significant higher in low-risk group. Conversely, the infiltration levels of M1-type macrophages, M2-type macrophages, and CD8+T cells were significant higher in high-risk group in both TCGA and CGGA cohorts.ConclusionThe present study constructed a robust six immune-related gene signature and established a prognostic nomogram effective in risk stratification and prediction of overall survival in primary LGG.
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Project description:In many cancers, cells undergo re-programming of metabolism, cell survival and anti-apoptotic defense strategies, with the proteins mediating this reprogramming representing potential biomarkers. Here, we searched for novel biomarker proteins in chronic lymphocytic leukemia (CLL) that can impact diagnosis, treatment and prognosis by comparing the protein expression profiles of peripheral blood mononuclear cells from CLL patients and healthy donors using specific antibodies, mass spectrometry and binary logistic regression analyses and other bioinformatics tools. Mass spectrometry (LC-HR-MS/MS) analysis identified 1,360 proteins whose expression levels were modified in CLL-derived lymphocytes. Some of these proteins were previously connected to different cancer types, including CLL, while four other highly expressed proteins were not previously reported to be associated with cancer, and here, for the first time, DDX46 and AK3 are linked to CLL. Down-regulation expression of two of these proteins resulted in cell growth inhibition. High DDX46 expression levels were associated with shorter survival of CLL patients and thus can serve as a prognosis marker. The proteins with modified expression include proteins involved in RNA splicing and translation and particularly mitochondrial proteins involved in apoptosis and metabolism. Thus, we focused on several metabolism- and apoptosis-modulating proteins, particularly on the voltage-dependent anion channel 1 (VDAC1), regulating both metabolism and apoptosis. Expression levels of Bcl-2, VDAC1, MAVS, AIF and SMAC/Diablo were markedly increased in CLL-derived lymphocytes. VDAC1 levels were highly correlated with the amount of CLL-cancerous CD19+/CD5+ cells and with the levels of all other apoptosis-modulating proteins tested. Binary logistic regression analysis demonstrated the ability to predict probability of disease with over 90% accuracy. Finally, based on the changes in the levels of several proteins in CLL patients, as revealed from LC-HR-MS/MS, we could distinguish between patients in a stable disease state and those who would be later transferred to anti-cancer treatments. The over-expressed proteins can thus serve as potential biomarkers for early diagnosis, prognosis, new targets for CLL therapy, and treatment guidance of CLL, forming the basis for personalized therapy.
| S-EPMC4831809 | biostudies-literature
Project description:Y box protein 1 (YB-1) is a multifunctional cellular protein expressed in various cancers, and is a potential target in cancer therapy. Although there is evidence showing that YB-1 plays a role in human cancers, the clinical significance of YB-1 expression in glioma has not been established. In the present study, we investigated the YB-1 level in glioma tumors and analyzed the relationship between the YB-1 level and the grade of malignant glioma, with the aim of providing new ideas for the diagnosis and treatment of gliomas in clinical and basic research settings. A total of 108 patients, comprising 14, 31, 30, and 33 with gliomas of Grades I, II, III, and IV, respectively, were included in this study. The mRNA and protein levels of YB-1 were found to be significantly different between Grade IV and lower-grade tumors. The YB-1 levels in cerebrospinal fluid were significantly higher in Grades III and IV glioma patients than in Grades I and II patients. Immunofluorescence staining was used to detect the levels of YB-1 in the cytoplasm and the nucleus, and results indicated that the intracellular distribution was significantly associated with the pathological grade of glioma. A higher level of YB-1 was associated with shortened survival, suggesting that YB-1 plays a role in the progression of human glioma.
| S-EPMC4886251 | biostudies-literature
Project description:BackgroundGlioma is a highly aggressive brain cancer with a poor prognosis. Necroptosis is a form of programmed cell death occurring during tumor development and in immune microenvironments. The prognostic value of necroptosis in glioma is unclear. This study aimed to develop a prognostic glioma model based on necroptosis.MethodsA necroptosis-related risk model was constructed by Cox regression analysis based on The Cancer Genome Atlas (TCGA) training set, validated in two Chinese Glioma Genome Atlas (CGGA) validation sets. We explored the differences in immune infiltration and immune checkpoint genes between low and high risk groups and constructed a nomogram. Moreover, we compiled a third validation cohort including 43 glioma patients. The expression of necroptosis-related genes was verified in matched tissues using immunochemical staining in the third cohort, and we analyzed their relationship to clinicopathological features.ResultsThree necroptosis-related differentially expressed genes (EZH2, LEF1, and CASP1) were selected to construct the prognostic model. Glioma patients with a high risk score in the TCGA and CGGA cohorts had significantly shorter overall survival. The necroptosis-related risk model and nomogram exhibited good predictive performance in the TCGA training set and the CGGA validation sets. Furthermore, patients in the high risk group had higher immune infiltration status and higher expression of immune checkpoint genes, which was positively correlated with poorer outcomes. In the third validation cohort, the expression levels of the three proteins encoded by EZH2, LEF1, and CASP1 in glioma tissues were significantly higher than those from paracancerous tissues. They were also closely associated with disease severity and prognosis.ConclusionsOur necroptosis-related risk model can be used to predict the prognosis of glioma patients and improve prognostic accuracy, which may provide potential therapeutic targets and a theoretical basis for treatment.
| S-EPMC9640834 | biostudies-literature