Dataset Information

The role of an immune signature for prognosis and immunotherapy response in endometrial cancer.

ABSTRACT: Immunotherapy is a practical and promising treatment for advanced and recurrent endometrial cancer (EC). In this study, we identified an immune-related gene (IRG) signature to predict the overall survival (OS) and response to immune checkpoints inhibitors (ICIs) in patients with EC. The RNA expression profiles of EC were obtained from The Cancer Genome Atlas database and then were filtered for IRGs based on the Immport database. Using the conjoint Cox regression model, an immune signature consisting of seven risk IRGs (CBLC, PLA2G2A, TNF, NR3C1, APOD, TNFRSF18, and LTB) was developed. The immune signature was independent of other clinical factors and was superior to the traditional staging method for OS prediction in EC. Immunohistochemistry staining from the Human Protein Atlas database and quantitative real-time PCR analysis of EC samples were also performed to validate the expression levels of risk IRGs. By further analyzing the tumor microenvironment in EC, patients in the low-risk subgroup showed a higher immune cell infiltration status, which was associated with a better prognosis. Moreover, the tumor mutational burden and immunophenoscore analysis demonstrated that the low-risk subgroup was more sensitive to ICI-based immunotherapy. These findings might shed light on the development of targeted treatment and novel biomarkers for patients with EC.

SUBMITTER: Meng Y

PROVIDER: S-EPMC7868845 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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The role of an immune signature for prognosis and immunotherapy response in endometrial cancer.

Meng Yue Y Yang Yuebo Y Zhang Yu Y Yang Xiaohui X Li Xiaomao X Hu Chuan C

American journal of translational research 20210215 2

Immunotherapy is a practical and promising treatment for advanced and recurrent endometrial cancer (EC). In this study, we identified an immune-related gene (IRG) signature to predict the overall survival (OS) and response to immune checkpoints inhibitors (ICIs) in patients with EC. The RNA expression profiles of EC were obtained from The Cancer Genome Atlas database and then were filtered for IRGs based on the Immport database. Using the conjoint Cox regression model, an immune signature consis ...[more]

PMID: 33594308

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Project description:BackgroundUterus corpus endometrial cancer (UCEC) is the main malignant tumor in gynecology, with a high degree of heterogeneity, especially in terms of prognosis and immunotherapy efficacy. DNA methylation is one of the most important epigenetic modifications. Studying DNA methylation can help predict the prognosis of cancer patients and provide help for clinical treatment. Our research aims to discover whether abnormal DNA methylation can predict the prognosis of UCEC and reflect the patient's tumor immune microenvironment.Patients and methodsThe clinical data, DNA methylation data, gene expression data and somatic mutation data of UCEC patients were all downloaded from the TCGA database. The MethylMix algorithm was used to integrate DNA methylation data and mRNA expression data. Univariate Cox regression analysis, Multivariate Cox regression analysis, and Lasso Cox regression analysis were used to determine prognostic DNA methylation-driven genes and to construct an independent prognostic index (MDS). ROC curve analysis and Kaplan-Meier survival curve analysis were used to evaluate the predictive ability of MDS. GSEA analysis was used to explore possible mechanisms that contribute to the heterogeneity of the prognosis of UCEC patients.Results3 differential methylation-driven genes (DMDGs) (PARVG, SYNE4 and CDO1) were considered as predictors of poor prognosis in UCEC. An independent prognostic index was finally established based on 3 DMDGs. From the results of ROC curve analysis and survival curve analysis, MDS showed excellent prognostic ability in TCGA-UCEC. A new nomogram based on MDS and other prognostic clinical indicators has also been successfully established. The C-index of the nomogram for OS prediction was 0.764 (95% CI = 0.702-0.826). GSEA analysis suggests that there were differences in immune-related pathways among patients with different prognosis. The abundance of M2 macrophages and M0 macrophages were significantly enhanced in the high-risk group while T cells CD8, Eosinophils and Neutrophils were markedly elevated in the low-risk group. Meanwhile, patients in the low-risk group had higher levels of immunosuppressant expression, higher tumor mutational burden and immunophenoscore (IPS) scores. Joint survival analysis revealed that 7 methylation-driven genes could be independent prognostic factors for overall survival for UCEC.ConclusionWe have successfully established a risk model based on 3 DMDGs, which could accurately predict the prognosis of patients with UCEC and reflect the tumor immune microenvironment.

Project description:BackgroundEsophageal cancer (EC) is one of the deadliest solid malignancies, mainly consisting of esophageal squamous cell carcinoma (ESCC) and adenocarcinoma (EAC). Robust biomarkers that can improve patient risk stratification are needed to optimize cancer management. We sought to establish potent prognostic signatures with immune-related gene (IRG) pairs for ESCC and EAC.MethodsWe obtained differentially expressed IRGs by intersecting the Immunology Database and Analysis Portal (ImmPort) with the transcriptome data set of The Cancer Genome Atlas (TCGA)-ESCC and EAC cohorts. A novel rank-based pairwise comparison algorithm was applied to select effective IRG pairs (IRGPs), followed by constructing a prognostic IRGP signature via the least absolute shrinkage and selection operator (LASSO) regression model. We assessed the predictive power of the IRGP signatures on prognosis, tumor-infiltrating immune cells, and immune checkpoint inhibitor (ICI) efficacy in EC. Kaplan-Meier survival analysis and receiver operating characteristic curves (ROC) were used to evaluate the clinical significance of IRGPs. Univariate and multivariate Cox regression analyses were performed to investigate the association of overall survival (OS) with IRGPs and clinical characteristics.ResultsWe built a 19-IRGP signature for ESCC (n=75) and a 17-IRGP signature for EAC (n=78), with an area under the ROC curve (AUC) of 0.931 and 0.803, respectively. IRGP signature-derived risk scores stratified patients into low- and high-risk groups with significantly different OS in ESCC and EAC (P<0.001). Nomogram and decision curve analysis were used to evaluate the clinical relevance of the prognostic signatures, achieving a C-index of 0.973 in ESCC and 0.880 in EAC. The risk scores were associated with immune and ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) scores and the composition of immune cells in the tumor microenvironment. The association between risk score and human leukocyte antigens (HLAs), mismatch repair (MMR) genes, and immune checkpoint molecules demonstrated its predictive value for ICI response. Differential immune characteristics and predictive value of the risk score were observed in EAC.ConclusionsThe established immune signatures showed great promise in predicting prognosis, tumor immunogenicity, and immunotherapy response in ESCC and EAC.

Dataset Information

The role of an immune signature for prognosis and immunotherapy response in endometrial cancer.

Publications

The role of an immune signature for prognosis and immunotherapy response in endometrial cancer.

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