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: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 (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:Background: Hexokinase 2 not only plays a role in physiological function of human normal tissues and organs, but also plays a vital role in the process of glycolysis of tumor cells. However, there are few comprehensive studies on HK2 in esophageal carcinoma (ESCA) needs further study. Methods: Oncomine, Tumor Immune Estimation Resource (TIMER), The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were used to analyze the expression differences of HK2 in Pan-cancer and ESCA cohort, and to analyze the correlation between HK2 expression level and clinicopathological features of TCGA ESCA samples. GO/KEGG, GGI, and PPI analysis of HK2 was performed using R software, LinkedOmics, GeneMANIA and STRING online tools. The correlation between HK2 and ESCA immune infiltration was analyzed TIMER and TCGA ESCA cohort. The correlation between HK2 expression level and m6A modification of ESCA was analyzed by utilizing TCGA ESCA cohort. Results: HK2 is highly expressed in a variety of tumors, and its high expression level in ESCA is closely related to the weight, cancer stages, tumor histology and tumor grade of ESCA. The analysis results of GO/KEGG showed that HK2 was closely related to cell adhesion molecule binding, cell-cell junction, ameboidal-type cell migration, insulin signaling pathway, hif-1 signaling pathway, and insulin resistance. GGI showed that HK2 associated genes were mainly involved in the glycolytic pathway. PPI showed that HK2 was closely related to HK1, GPI, and HK3, all of which played an important role in tumor proliferation. The analysis results of TIMER and TCGA ESCA cohort indicated that the HK2 expression level was related to the infiltration of various immune cells. TCGA ESCA cohort analyze indicated that the HK2 expression level was correlated with m6A modification genes. Conclusion: HK2 is associated with tumor immune infiltration and m6A modification of ESCA, and can be used as a potential biological target for diagnosis and therapy of ESCA.

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.

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