Dataset Information

Predictive Analyses of Prognostic-Related Immune Genes and Immune Infiltrates for Glioblastoma.

ABSTRACT: Glioblastoma (GBM), the most common and aggressive brain tumor, has a very poor outcome and high tumor recurrence rate. The immune system has positive interactions with the central nervous system. Despite many studies investigating immune prognostic factors, there is no effective model to identify predictive biomarkers for GBM. Genomic data and clinical characteristic information of patients with GBM were evaluated by Kaplan-Meier analysis and proportional hazard modeling. Deseq2 software was used for differential expression analysis. Immune-related genes from ImmPort Shared Data and the Cistrome Project were evaluated. The model performance was determined based on the area under the receiver operating characteristic (ROC) curve. CIBERSORT was used to assess the infiltration of immune cells. The results of differential expression analyses showed a significant difference in the expression levels of 2942 genes, comprising 1338 upregulated genes and 1604 downregulated genes (p < 0.05). A population of 24 immune-related genes that predicted GBM patient survival was identified. A risk score model established on the basis of the expressions of the 24 immune-related genes was used to evaluate a favorable outcome of GBM. Further validation using the ROC curve confirmed the model was an independent predictor of GBM (AUC = 0.869). In the GBM microenvironment, eosinophils, macrophages, activated NK cells, and follicular helper T cells were associated with prognostic risk. Our study confirmed the importance of immune-related genes and immune infiltrates in predicting GBM patient prognosis.

SUBMITTER: Liang P

PROVIDER: S-EPMC7151008 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Predictive Analyses of Prognostic-Related Immune Genes and Immune Infiltrates for Glioblastoma.

Liang Ping P Chai Yi Y Zhao He H Wang Guihuai G

Diagnostics (Basel, Switzerland) 20200324 3

Glioblastoma (GBM), the most common and aggressive brain tumor, has a very poor outcome and high tumor recurrence rate. The immune system has positive interactions with the central nervous system. Despite many studies investigating immune prognostic factors, there is no effective model to identify predictive biomarkers for GBM. Genomic data and clinical characteristic information of patients with GBM were evaluated by Kaplan-Meier analysis and proportional hazard modeling. Deseq2 software was us ...[more]

PMID: 32214002

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Project description:BackgroundAdipocyte enhancer binding protein 1 (AEBP1) has been shown to be closely related to cancer progression; however research on its potential role in glioblastoma (GBM) remains limited.MethodsFollowing an expression analysis of AEBP1 in GBM through the Oncomine database, other critical findings were accessed via The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) databases. Specifically, in addition to identifying differentially expressed genes, the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) were further investigated. Additionally, a gene set enrichment analysis (GSEA) was performed to examine the enrichment pathways in the AEBP1 high-expression group. To examine the prognostic role of AEBP1 in GBM, survival information was obtained from the Chinese Glioma Genome Atlas (CGGA) database. Finally, the relationship between the expression of AEBP1 and immune infiltration in GBM was examined by using the "Gene Module", "Survival Module", and "SCNA Module" on the website "Tumor Immune Estimation Resource (TIMER)".ResultsThe Oncomine database revealed that AEBP1 was highly expressed in GBM. The prognostic analyses of 4 independent databases (i.e., TCGA, GTEx, Oncomine, and CGGA) revealed that AEBP1 was an independent predictable marker of GBM. The results of the GSEA showed that protein export, prion disease, cytokine receptor interaction, hematopoietic cell lineage, cell adhesion molecules, apoptosis, and the complement and coagulation cascades were differentially enriched in highly expressed AEBP1 phenotypes. Hence the conclusion is that the high expression of AEBP1 is closely correlated to poor prognosis of GBM. The immune analysis demonstrated that AEBP1 copy number alteration might affect immune infiltration in GBM tissues, and thus the survival outcomes of GBM patients.ConclusionsHigh AEBP1 expression in GBM is closely correlated to patient prognosis. AEBP1 is a potential therapeutic target for the inhibition of cancerous progression and the development of new immunotherapies for GBM.

Project description:Glioblastoma (GBM) is the most common type of malignant tumor of the central nervous system. The prognosis of patients with GBM is very poor, with a survival time of ~15 months. GBM is highly heterogeneous and highly aggressive. Surgical removal of intracranial tumors does provide a good advantage for patients as there is a high rate of recurrence. The understanding of this type of cancer needs to be strengthened, and the aim of the present study was to identify gene signatures present in GBM and uncover their potential mechanisms. The gene expression profiles of GSE15824 and GSE51062 were downloaded from the Gene Expression Omnibus database. Normalization of the data from primary GBM samples and normal samples in the two databases was conducted using R software. Then, joint analysis of the data was performed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, and the protein?protein interaction (PPI) network of the differentially expressed genes (DEGs) was constructed using Cytoscape software. Identification of prognostic biomarkers was conducted using UALCAN. In total, 9,341 DEGs were identified in the GBM samples, including 9,175 upregulated genes and 166 downregulated genes. The top 1,000 upregulated DEGs and all of the downregulated DEGs were selected for GO, KEGG and prognostic biomarker analyses. The GO results showed that the upregulated DEGs were significantly enriched in biological processes (BP), including immune response, cell division and cell proliferation, and the downregulated DEGs were also significantly enriched in BP, including cell growth, intracellular signal transduction and signal transduction by protein phosphorylation. KEGG pathway analysis showed that the upregulated DEGs were enriched in circadian entrainment, cytokine?cytokine receptor interaction and maturity onset diabetes of the young, while the downregulated DEGs were enriched in the TGF?? signaling pathway, MAPK signaling pathway and pathways in cancer. All of the downregulated genes and the top 1,000 upregulated genes were selected to establish the PPI network, and the sub?networks revealed that these genes were involved in significant pathways, including olfactory transduction, neuroactive ligand?receptor interaction and viral carcinogenesis. In total, seven genes were identified as good prognostic biomarkers. In conclusion, the identified DEGs and hub genes contribute to the understanding of the molecular mechanisms underlying the development of GBM and they may be used as diagnostic and prognostic biomarkers and molecular targets for the treatment of patients with GBM in the future.

Project description:BackgroundGlioblastoma multiforme (GBM) is a common malignant brain tumor with high mortality. It is urgently necessary to develop a new treatment because traditional approaches have plateaued.PurposeHere, we identified an immune-related gene (IRG)-based prognostic signature to comprehensively define the prognosis of GBM.MethodsGlioblastoma samples were selected from the Chinese Glioma Genome Atlas (CGGA). We retrieved IRGs from the ImmPort data resource. Univariate Cox regression and LASSO Cox regression analyses were used to develop our predictive model. In addition, we constructed a predictive nomogram integrating the independent predictive factors to determine the one-, two-, and 3-year overall survival (OS) probabilities of individuals with GBM. Additionally, the molecular and immune characteristics and benefits of ICI therapy were analyzed in subgroups defined based on our prognostic model. Finally, the proteins encoded by the selected genes were identified with liquid chromatography-tandem mass spectrometry and western blotting (WB).ResultsSix IRGs were used to construct the predictive model. The GBM patients were categorized into a high-risk group and a low-risk group. High-risk group patients had worse survival than low-risk group patients, and stronger positive associations with multiple tumor-related pathways, such as angiogenesis and hypoxia pathways, were found in the high-risk group. The high-risk group also had a low IDH1 mutation rate, high PTEN mutation rate, low 1p19q co-deletion rate and low MGMT promoter methylation rate. In addition, patients in the high-risk group showed increased immune cell infiltration, more aggressive immune activity, higher expression of immune checkpoint genes, and less benefit from immunotherapy than those in the low-risk group. Finally, the expression levels of TNC and SSTR2 were confirmed to be significantly associated with patient prognosis by protein mass spectrometry and WB.ConclusionHerein, a robust predictive model based on IRGs was developed to predict the OS of GBM patients and to aid future clinical research.

Project description:BackgroundGlioblastoma (GBM), which has a poor prognosis, accounts for 31% of all cancers in the brain and central nervous system. There is a paucity of research on prognostic indicators associated with the tumor immune microenvironment in GBM patients. Accurate tools for risk assessment of GBM patients are urgently needed.MethodsIn this study, we used weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) methods to screen out GBM-related genes among immune-related genes (IRGs). Then, we used survival analysis and Cox regression analysis to identify prognostic genes among the GBM-related genes to further establish a risk signature, which was validated using methods including ROC analysis, stratification analysis, protein expression level validation (HPA), gene expression level validation based on public cohorts, and RT-qPCR. In order to provide clinicians with a useful tool to predict survival, a nomogram based on an assessment of IRGs and clinicopathological features was constructed and further validated using DCA, time-dependent ROC curve, etc. Results: Three immune-related genes were found: PPP4C (p < 0.001, HR = 0.514), C5AR1 (p < 0.001, HR = 1.215), and IL-10 (p < 0.001, HR = 1.047). An immune-related prognostic signature (IPS) was built to calculate risk scores for GBM patients; patients classified into different risk groups had significant differences in survival (p = 0.006). Then, we constructed a nomogram based on an assessment of the IRG-based signature, which was validated as a potential prediction tool for GBM survival rates, showing greater accuracy than the nomogram without the IPS when predicting 1-year (0.35 < Pt < 0.50), 3-year (0.65 < Pt < 0.80), and 5-year (0.65 < Pt < 0.80) survival.ConclusionsIn conclusion, we integrated bioinformatics and experimental approaches to construct an IPS and a nomogram based on IPS for predicting GBM prognosis. The signature showed strong potential for prognostic prediction and could help in developing more precise diagnostic approaches and treatments for GBM.

Project description:Hepatocellular carcinoma is a malignant type of carcinoma with complicated pathogenesis. For HCC patients, there is not only a lack of valuable therapeutic targets, but also a lack of prognostic biomarker. The protein encoded by Aldehyde Dehydrogenase 2 Family Member (ALDH2) is a critical member of the aldehyde dehydrogenase family. Many researchers have found that ALDH2 mutations play an important role in the activation of hepatocellular carcinoma carcinogenic pathways. However, the clinicopathological meaning of ALDH2 in HCC and its relation with immune infiltration is still indistinguishable. In this study, we explored the expression of ALDH2 in 41 HCC tissues by immunohistochemistry. The clinicopathological meaning and molecular function of ALDH2 were analyzed and evaluated through comprehensive bioinformatics. ALDH2 expression in HCC was validated in TCGA, GEO and Oncomine databases, and a survival of ALDH2 based on TCGA database was analysed. LinkedOmics was used to classify the co-expressed genes of ALDH2 and its regulatory factors. The relation between ALDH2 and immune infiltration in HCC was further explored by TIMER. IHC results showed decreased levels of ALDH2 in HCC tumor tissues compared with corresponding normal liver tissues. The pathological grade and prognosis of patients with low expression of the ALDH2 gene were worse. Bioinformatics analysis results showed that ALDH2 was considerably down-regulated in cancer tissues compared with corresponding normal liver tissues in 8 GEO series and TCGA profile (all P<0.05). A nomogram was designed using expression of ALDH2 and clinical factors. ALDH2 was correlated with dendritic cells and macrophages in immune infiltration. In conclusion, ALDH2 has significant prognostic value in hepatocellular carcinoma and they may play key roles in regulating tumor progression and the immune cells infiltration. Our results suggest that ALDH2 may be a new type of tumor biomarker, which can be used to judge the prognosis, targeted therapy and immunotherapy of patients with HCC.

Project description:PurposeThe serine protease inhibitor clade E member 1 (SERPINE1) has been studied as a potential biomarker in a variety of cancers, but poorly studied in gastric cancer (GC). The purpose of this study was to explore the prognostic value of SERPINE1 in GC and primarily analyze its functions.MethodsWe analyzed the the prognostic value of SERPINE1 and studied the relationship with clinicopathologic biomarkers in gastric cancer. The expression of SERPINE1 was analyzed by GEO and TCGA databases. Moreover, we validated the results by immunohistochemistry. Next, the correlation analysis between SERPINE1 and the cuproptosis-related genes was analyzed by the "Spearman" method. CIBERSORT and TIMER algorithms were used to analyze the correlation of SERPINE1 with immune infiltration. Furthermore, GO and KEGG gene enrichment analyses were used to study the functions and pathways that SERPINE1 might be involved in. Then, drug sensitivity analysis was performed using CellMiner database. Finally, a cuproptosis-immune-related prognostic model was constructed using genes related to immune and cuproptosis, and verified against external datasets.ResultsSERPINE1 was up-regulated in gastric cancer tissues, which tends toward poor prognosis. Using immunohistochemistry experiment, the expression and prognostic value of SERPINE1 were verified. Then, we found that SERPINE1 was negatively correlated with cuproptosis-related genes FDX1, LIAS, LIPT1, and PDHA1. On the contrary, SERPINE1 was positively correlated with APOE. This indicates the effect of SERPINE1 on the cuproptosis process. Furthermore, by conducting immune-related analyses, it was revealed that SERPINE1 may promote the inhibitory immune microenvironment. The infiltration level of resting NK cells, neutrophils, activated mast cells, and macrophages M2 was positively correlated with SERPINE1. However, B cell memory and plasma cells were negatively correlated with SERPINE1. Functional analysis showed that SERPINE1 was closely related to angiogenesis, apoptosis, and ECM degradation. The KEGG pathway analysis showed that SERPINE1 may be associated with P53, Pi3k/Akt, TGF-β, and other signaling pathways. Drug sensitivity analysis showed that SERPINE1 could be also seen as a potential treatment target. The risk model based on SERPINE1 co-expression genes could better predict the survival of GC patients than SERPINE1 alone. We also verified the prognostic value of the risk score by GEO external datasets.ConclusionSERPINE1 is highly expressed in gastric cancer and related to poor prognosis. SERPINE1 may regulate cuproptosis and the immune microenvironment by a series of pathways. Therefore, SERPINE1 as a prognostic biomarker and potential therapeutic target deserves further study.

Dataset Information

Predictive Analyses of Prognostic-Related Immune Genes and Immune Infiltrates for Glioblastoma.

Publications

Predictive Analyses of Prognostic-Related Immune Genes and Immune Infiltrates for Glioblastoma.

Similar Datasets

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