Dataset Information

Identification of a Six-Gene Prognostic Signature Characterized by Tumor Microenvironment Immune Profiles in Clear Cell Renal Cell Carcinoma.

ABSTRACT: Clear cell renal cell carcinoma (ccRCC) is widely acknowledged to be extremely sensitive to immunotherapy, emphasizing the tremendous impacts on which the tumor microenvironment (TME) has shown. However, the molecular subgroups characterized by the TME features scarcely serve as the risk stratification guides in clinical practice for survival outcomes and immunotherapy response prediction. This study generated fresh insights into a novel TME-related prognostic signature derived from The Cancer Genome Atlas database using integrated bioinformatics analyses. Subsequently, Kaplan-Meier survival analysis, receiver operating characteristic analysis, and univariate and multivariate Cox regression analysis were performed to evaluate and validate the efficacy and the accuracy of the signature in ccRCC prognosis. Furthermore, we discovered that the risk score presented an increased likelihood of correlation with miscellaneous clinicopathological characteristics, natural killer cell-mediated cytotoxicity, immune cell infiltration levels, and immune checkpoint expression. These findings highlighted the notion that the six-gene signature characterized by the TME features may have implications on the risk stratification for personalized and precise immunotherapeutic management.

SUBMITTER: Zhang L

PROVIDER: S-EPMC8637193 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Identification of a Six-Gene Prognostic Signature Characterized by Tumor Microenvironment Immune Profiles in Clear Cell Renal Cell Carcinoma.

Zhang Lu L Li Jianlong J Zhang Mengzhao M Wang Lu L Yang Tao T Shao Qiuya Q Liang Xiao X Ma Minghai M Zhang Nan N Jing Minxuan M Song Rundong R Fan Jinhai J

Frontiers in genetics 20211118

Clear cell renal cell carcinoma (ccRCC) is widely acknowledged to be extremely sensitive to immunotherapy, emphasizing the tremendous impacts on which the tumor microenvironment (TME) has shown. However, the molecular subgroups characterized by the TME features scarcely serve as the risk stratification guides in clinical practice for survival outcomes and immunotherapy response prediction. This study generated fresh insights into a novel TME-related prognostic signature derived from The Cancer G ...[more]

PMID: 34868201

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Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is the main component of renal cell carcinoma, and advanced ccRCC often predicts a poor prognosis. In recent years, research has revealed the critical role of Cys2His2 zinc finger genes (CHZFs) and long non-coding RNAs (lncRNAs) in the development of cancer. Currently, little is known about the prognostic value of the lncRNAs linked to Cys2His2 (C2H2) zinc finger proteins (ZFPs) in ccRCC. The aim of this study was to construct a prognostic model for C2H2-associated lncRNAs to assist in the selection of clinical therapy.MethodsRNA-sequencing data, and related clinical and prognostic information were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analyses were conducted to identify Cys2His2 zinc finger-associated long non-coding RNAs (CHZFLs) and build prediction signatures. A receiver operating characteristic (ROC) curve analysis was performed to validate the risk model. The prognosis of the groups was analyzed using the Kaplan-Meier method. The independent prognostic significance of these signatures was evaluated by univariate and multivariate Cox regression analyses. The relationship between the CHZFL signature and ccRCC tumor immunity was confirmed by a differential analysis of immune function and immunological checkpoints.ResultsA signature composed of five lncRNAs (AL117336.2, AC026401.3, AC124854.1, DBH-AS1, and LINC02100) was constructed. The results revealed a strong correlation between the CHZFLs signature and the prognosis of ccRCC patients. Prognostic characteristics of CHZFLs are independent prognostic factors in ccRCC patients. The diagnostic efficacy of the predictive signature was higher than that of individual clinicopathologic variables, and it had a ROC area under the curve (AUC) of 0.775. The results of the clinical subgroup analysis showed that the high-risk group had shorter overall survival (OS) than the low-risk group. Common chemotherapy medications, including vinorelbine, cytarabine, epirubicin, and gemcitabine, caused increased sensitivity in the high-risk group. Additionally, the single-sample gene set enrichment analysis (ssGSEA) revealed that the immunological state of the ccRCC patients was substantially linked with the predictive parameters.ConclusionsThe five CHZFL signature can help predict the prognosis of ccRCC patients, and assist in selecting immunotherapy and chemotherapy regimens in clinical practice.

Project description:Background: The tumor microenvironment affects the occurrence and development of cancers, including clear cell renal cell carcinoma (ccRCC). However, how the immune contexture interacts with the cancer phenotype remains unclear. Methods: We identified and evaluated immunophenotyping clusters in ccRCC using machine-learning algorithms. Analyses for functional enrichment, DNA variation, immune cell distribution, association with independent clinicopathological features, and predictive responses for immune checkpoint therapies were performed and validated. Results: Three immunophenotyping clusters with gradual levels of immune infiltration were identified. The intermediate and high immune infiltration clusters (Clusters B and C) were associated with a worse prognosis for ccRCC patients. Tumors in the immune-hot Clusters B and C showed pro-tumorigenic immune infiltration, and these patients showed significantly worse survival compared with patients in the immune-cold Cluster A in the training and testing cohorts (n = 422). In addition to distinct immune cell infiltrations of immunophenotyping, we detected significant differences in DNA variation among clusters, suggesting a high degree of genetic heterogeneity. Furthermore, expressions of multiple immune checkpoint molecules were significantly increased. Clusters B and C predicted favorable outcomes in 64 ccRCC patients receiving immune checkpoint therapies from the FUSCC cohort. In 360 ccRCC patients from the FUSCC validation cohort, Clusters B and C significantly predicted worse prognosis compared with Cluster A. After immunophenotyping of ccRCC was confirmed, significantly increased tertiary lymphatic structures, aggressive phenotype, elevated glycolysis and PD-L1 expression, higher abundance of CD8+ T cells, and TCRn cell infiltration were found in the immune-hot Clusters B and C. Conclusion: This study described immunophenotyping clusters that improved the prognostic accuracy of the immune contexture in the ccRCC microenvironment. Our discovery of the novel independent prognostic indicators in ccRCC highlights the relationship between tumor phenotype and immune microenvironment.

Project description:Background: Necroptosis, a cell death of caspase-independence, plays a pivotal role in cancer biological regulation. Although necroptosis is closely associated with oncogenesis, cancer metastasis, and immunity, there remains a lack of studies determining the role of necroptosis-related genes (NRGs) in the highly immunogenic cancer type, kidney renal clear cell carcinoma (KIRC). Methods: The information of clinicopathology and transcriptome was extracted from TCGA database. Following the division into the train and test cohorts, a three-NRGs (TLR3, FASLG, ZBP1) risk model was identified in train cohort by LASSO regression. The overall survival (OS) comparison was conducted between different risk groups through Kaplan-Meier analysis, which was further validated in test cohort. The Cox proportional hazards regression model was introduced to assess its impact of clinicopathological factors and risk score on survival. ESTIMATE and CIBERSORT algorithms were introduced to evaluate immune microenvironment, while enrichment analysis was conducted to explore the biological significance. Correlation analysis was applied for the correlation assessment between checkpoint gene expression and risk score, between gene expression and therapeutic response. Gene expressions from TCGA were verified by GEO datasets and immunohistochemistry (IHC) analysis. Results: This NRGs-related signature predicted poorer OS in high-risk group, which was also verified in test cohort. Risk score could also independently predict survival outcome of KIRC. Significant changes were also found in immune microenvironment and checkpoint gene expressions between different risk groups, with immune functional enrichment in high-risk group. Interestingly, therapeutic response was correlated with the expressions of NRGs. The expressions of NRGs from TCGA were consistent with those from GEO datasets and IHC analysis. Conclusion: The NRGs-related signature functions as a novel prognostic predictor of immune microenvironment and therapeutic response in KIRC.

Dataset Information

Identification of a Six-Gene Prognostic Signature Characterized by Tumor Microenvironment Immune Profiles in Clear Cell Renal Cell Carcinoma.

Publications

Identification of a Six-Gene Prognostic Signature Characterized by Tumor Microenvironment Immune Profiles in Clear Cell Renal Cell Carcinoma.

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