Identification of an Immune-Related Prognostic Risk Model in Glioblastoma.
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ABSTRACT:
SUBMITTER: Lin Z
PROVIDER: S-EPMC9247349 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundGlioblastoma multiforme (GBM) is extensively genetically and transcriptionally heterogeneous, which poses challenges for classification and management. Long noncoding RNAs (lncRNAs) play a critical role in the development and progression of GBM, especially in tumor-associated immune processes. Therefore, it is necessary to develop an immune-related lncRNAs (irlncRNAs) signature.MethodsUnivariate and multivariate Cox regression analyses were utilized to construct a prognostic model. GBM-specific CeRNA and PPI network was constructed to predict lncRNAs targets and evaluate the interactions of immune mRNAs translated proteins. GO and KEGG pathway analyses were used to show the biological functions and pathways of CeRNA network-related immunity genes. Consensus Cluster Plus analysis was used for GBM gene clustering. Then, we evaluated GBM subtype-specific prognostic values, clinical characteristics, genes and pathways, immune infiltration access single cell RNA-seq data, and chemotherapeutics efficacy. The hub genes were finally validated.ResultsA total of 17 prognostically related irlncRNAs were screened to build a prognostic model signature based on six key irlncRNAs. Based on GBM-specific CeRNAs and enrichment analysis, PLAU was predicted as a target of lncRNA-H19 and mainly enriched in the malignant related pathways. GBM subtype-A displayed the most favorable prognosis, high proportion of genes (IDH1, ATRX, and EGFR) mutation, chemoradiotherapy, and low risk and was characterized by low expression of four high-risk lncRNAs (H19, HOTAIRM1, AGAP2-AS1, and AC002456.1) and one mRNA KRT8. GSs with poor survival were mainly infiltrated by mesenchymal stem cells (MSCs) and astrocyte, and were more sensitive to gefitinib and roscovitine. Among GSs, three hub genes KRT8, NGFR, and TCEA3, were screened and validated to potentially play feasible oncogenic roles in GBM.ConclusionConstruction of lncRNAs risk model and identification of GBM subtypes based on 17 irlncRNAs, which suggesting that irlncRNAs had the promising potential for clinical immunotherapy of GBM.
| S-EPMC8657607 | biostudies-literature
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.
| S-EPMC9366078 | biostudies-literature
Project description:PurposeWe focused on immune-related genes (IRGs) derived from transcriptomic studies, which had the potential to stratify patients' prognosis and to establish a risk assessment model in colorectal cancer.SummaryThis article examined our understanding of the molecular pathways associated with intratumoral immune response, which represented a critical step for the implementation of stratification strategies toward the development of personalized immunotherapy of colorectal cancer. More and more evidence shows that IRGs play an important role in tumors. We have used data analysis to screen and identify immune-related molecular biomarkers of colon cancer. We selected 18 immune-related prognostic genes and established models to assess prognostic risks of patients, which can provide recommendations for clinical treatment and follow-up. Colorectal cancer (CRC) is a leading cause of cancer-related death in human. Several studies have investigated whether IRGs and tumor immune microenvironment (TIME) could be indicators of CRC prognoses. This study aimed to develop an improved prognostic signature for CRC based on IRGs to predict overall survival (OS) and provide new therapeutic targets for CRC treatment. Based on the screened IRGs, the Cox regression model was used to build a prediction model based on 18-IRG signature. Cox regression analysis revealed that the 18-IRG signature was an independent prognostic factor for OS in CRC patients. Then, we used the TIMER online database to explore the relationship between the risk scoring model and the infiltration of immune cells, and the results showed that the risk model can reflect the state of TIME to a certain extent. In short, an 18-IRG prognostic signature for predicting CRC patients' survival was firmly established.
| S-EPMC7970128 | biostudies-literature
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.
| S-EPMC9562700 | biostudies-literature
Project description:Suppression of immune reactivity by increased expression of co-inhibitory receptors has been discussed as a major reason as to why the immune system fails to control tumor development. Elucidating the co-inhibitory expression pattern of tumor-infiltrating lymphocytes in different cancer types will help to develop future treatment strategies. We characterized markers reflecting and affecting T-cell functionality by flow cytometry on lymphocytes isolated from blood, ascites and tumor from advanced ovarian cancer patients (n = 35). Significantly higher proportions of CD4+ and CD8+ T-cells expressed co-inhibitory receptors LAG-3, PD-1 and TIM-3 in tumor and ascites compared to blood. Co-expression was predominantly observed among intratumoral CD8+ T-cells and the most common combination was PD-1 and TIM-3. Analysis of 26 soluble factors revealed highest concentrations of IP-10 and MCP-1 in both ascites and tumor. Correlating these results with clinical outcome revealed the proportion of CD8+ T-cells without expression of LAG-3, PD-1 and TIM-3 to be beneficial for overall survival. In total we identified eight immune-related risk factors associated with reduced survival. Ex vivo activation showed tumor-derived CD4+ and CD8+ T-cells to be functionally active, assessed by the production of IFN-γ, IL-2, TNF-α, IL-17 and CD107a. Blocking the PD-1 receptor resulted in significantly increased release of IFN-γ suggesting potential reinvigoration. The ovarian tumor environment exhibits an inflammatory milieu with abundant presence of infiltrating immune cells expressing inhibitory checkpoints. Importantly, we found subsets of CD8+ T-cells with double and triple expression of co-inhibitory receptors, supporting the need for multiple checkpoint-targeting agents to overcome T-cell dysfunction in ovarian cancer.
| S-EPMC6343785 | biostudies-literature
Project description: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.
| S-EPMC7151008 | biostudies-literature
Project description:Colon cancer is one of the leading malignancies with poor prognosis worldwide. Immune cell infiltration has a potential prognostic value for colon cancer. This study aimed to establish an immune-related prognostic risk model for colon cancer by bioinformatics analysis. A total of 1670 differentially expressed genes (DEGs), including 177 immune-related genes, were identified from The Cancer Genome Atlas (TCGA) dataset. A prognostic risk model was constructed based on six critical immune-related genes (C-X-C motif chemokine ligand 1 (CXCL1), epiregulin (EREG), C-C motif chemokine ligand 24 (CCL24), fatty acid binding protein 4 (FABP4), tropomyosin 2 (TPM2), and semaphorin 3G (SEMA3G)). This model was validated using the microarray dataset GSE35982. In addition, Cox regression analysis showed that age and clinical stage were correlated with prognostic risk scores. Kaplan-Meier survival analysis showed that high risk scores correlated with low survival probabilities in patients with colon cancer. Downregulated TPM2, FABP4, and SEMA3G levels were positively associated with the activated mast cells, monocytes, and macrophages M2. Upregulated CXCL1 and EREG were positively correlated with macrophages M1 and activated T cells CD4 memory, respectively. Based on these results, we can conclude that the proposed prognostic risk model presents promising novel signatures for the diagnosis and prognosis prediction of colon cancer. This model may provide therapeutic benefits for the development of immunotherapy for colon cancer.
| S-EPMC9440785 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) originates mainly from the mucous epithelium and glandular epithelium of the bronchi. It is the most common pathologic subtype of non-small cell lung cancer (NSCLC). At present, there is still a lack of clear criteria to predict the efficacy of immunotherapy. The 5-year survival rate for LUAD patients remains low.MethodsAll data were downloaded from The Cancer Genome Atlas (TCGA) database. We used Gene Set Enrichment Analysis (GSEA) database to obtain immune-related mRNAs. Immune-related lncRNAs were acquired by using the correlation test of the immune-related genes with R version 3.6.3 (Pearson correlation coefficient cor = 0.5, P < 0.05). The TCGA-LUAD dataset was divided into the testing set and the training set randomly. Based on the training set to perform univariate and multivariate Cox regression analyses, we screened prognostic immune-related lncRNAs and given a risk score to each sample. Samples were divided into the high-risk group and the low-risk group according to the median risk score. By the combination of Kaplan-Meier (KM) survival curve, the receiver operating characteristic (ROC) (AUC) curve, the independent risk factor analysis, and the clinical data of the samples, we assessed the accuracy of the risk model. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the differentially expressed mRNAs between the high-risk group and the low-risk group. The differentially expressed genes related to immune response between two risk groups were analyzed to evaluate the role of the model in predicting the efficacy and effects of immunotherapy. In order to explain the internal mechanism of the risk model in predicting the efficacy of immunotherapy, we analyzed the differentially expressed genes related to epithelial-mesenchymal transition (EMT) between two risk groups. We extracted RNA from normal bronchial epithelial cell and LUAD cells and verified the expression level of lncRNAs in the risk model by a quantitative real-time polymerase chain reaction (qRT-PCR) test. We compared our risk model with other published prognostic signatures with data from an independent cohort. We transfected LUAD cell with siRNA-LINC0253. Western blot analysis was performed to observed change of EMT-related marker in protein level.ResultsThrough univariate Cox regression analysis, 24 immune-related lncRNAs were found to be strongly associated with the survival of the TCGA-LUAD dataset. Utilizing multivariate Cox regression analysis, 10 lncRNAs were selected to establish the risk model. The K-M survival curves and the ROC (AUC) curves proved that the risk model has a fine predictive effect. The GO enrichment analysis indicated that the effect of the differentially expressed genes between high-risk and low-risk groups is mainly involved in immune response and intercellular interaction. The KEGG enrichment analysis indicated that the differentially expressed genes between high-risk and low-risk groups are mainly involved in endocytosis and the MAPK signaling pathway. The expression of genes related to the efficacy of immunotherapy was significantly different between the two groups. A qRT-PCR test verified the expression level of lncRNAs in LUAD cells in the risk model. The AUC of ROC of 5 years in the independent validation dataset showed that this model had superior accuracy. Western blot analysis verified the change of EMT-related marker in protein level.ConclusionThe immune lncRNA risk model established by us could better predict the prognosis of patients with LUAD.
| S-EPMC8044985 | biostudies-literature
Project description:BackgroundDespite receiving standard treatment, the prognosis of glioblastoma (GBM) patients is still poor. Considering the heterogeneity of each patient, it is imperative to identify reliable risk model that can effectively predict the prognosis of each GBM patient to guide the personalized treatment.MethodsTranscriptomic gene expression profiles and corresponding clinical data of GBM patients were downloaded from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Inflammatory response-related genes were extracted from Gene Set Enrichment Analysis (GSEA) website. Univariate Cox regression analysis was used for prognosis-related inflammatory genes (P<0.05). A polygenic prognostic risk model was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Validation was performed through CGGA cohort. Overall survival (OS) was compared by Kaplan-Meier analysis. A nomogram was plotted to accurately predict the prognosis for each patient. GSEA was used for the pathway enrichment analysis. The single sample GSEA (ssGSEA) algorithm was implemented to conduct the immune infiltration analysis. The potential role of oncostatin M receptor (OSMR) in GBM was investigated through the in vitro experiment.ResultsA prognostic risk model consisting of 4 genes (PTPRN, OSMR, MYD88, and EFEMP2) was developed. GBM patients in the high-risk group had worse OS. The time-dependent ROC curves showed an area under the curve (AUC) of 0.782, 0.765, and 0.784 for 1-, 2-, and 3-year survival in TCGA cohort, while the AUC in the CGGA cohort was 0.589, 0.684, and 0.785 at 1, 2, and 3 years, respectively. The risk score, primary-recurrent-secondary (PRS) type, and isocitrate dehydrogenase (IDH) mutation could predict the prognosis of GBM patients well. The nomogram accurately predicted the 1-, 2-, and 3-year OS for each patient. Immune cell infiltration was associated with the risk score and the model could predict immunotherapy responsiveness. The expression of the prognostic gene was correlated with the sensitivity to antitumor drugs. Interference of OSMR inhibited proliferation and migration and promoted apoptosis of GBM cells.ConclusionsThe prognostic model based on 4 inflammatory response-related genes had reliable predictive power to effectively predict clinical outcome in GBM patients and provided the guide for the personalized treatment.
| S-EPMC9929762 | biostudies-literature
Project description:BackgroundBreast cancer is one of the main malignant tumors that threaten the lives of women, which has received more and more clinical attention worldwide. There are increasing evidences showing that the immune micro-environment of breast cancer (BC) seriously affects the clinical outcome. This study aims to explore the role of tumor immune genes in the prognosis of BC patients and construct an immune-related genes prognostic index.MethodsThe list of 2498 immune genes was obtained from ImmPort database. In addition, gene expression data and clinical characteristics data of BC patients were also obtained from the TCGA database. The prognostic correlation of the differential genes was analyzed through Survival package. Cox regression analysis was performed to analyze the prognostic effect of immune genes. According to the regression coefficients of prognostic immune genes in regression analysis, an immune risk scores model was established. Gene set enrichment analysis (GSEA) was performed to probe the biological correlation of immune gene scores. P < 0.05 was considered to be statistically significant.ResultsIn total, 556 immune genes were differentially expressed between normal tissues and BC tissues (p < 0. 05). According to the univariate cox regression analysis, a total of 66 immune genes were statistically significant for survival risk, of which 30 were associated with overall survival (P < 0.05). Finally, a 15 immune genes risk scores model was established. All patients were divided into high- and low-groups. KM survival analysis revealed that high immune risk scores represented worse survival (p < 0.001). ROC curve indicated that the immune genes risk scores model had a good reliability in predicting prognosis (5-year OS, AUC = 0.752). The established risk model showed splendid AUC value in the validation dataset (3-year over survival (OS) AUC = 0.685, 5-year OS AUC = 0.717, P = 0.00048). Moreover, the immune risk signature was proved to be an independent prognostic factor for BC patients. Finally, it was found that 15 immune genes and risk scores had significant clinical correlations, and were involved in a variety of carcinogenic pathways.ConclusionIn conclusion, our study provides a new perspective for the expression of immune genes in BC. The constructed model has potential value for the prognostic prediction of BC patients and may provide some references for the clinical precision immunotherapy of patients.
| S-EPMC8011146 | biostudies-literature