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Assessment for prognostic value of differentially expressed genes in immune microenvironment of clear cell renal cell carcinoma

ABSTRACT: Tumor-infiltrating immune cells have been recognized to be associated with prognosis and response to immunotherapy; however, genes related to immune microenvironment of clear cell renal cell carcinoma (ccRCC) remains unclear. To better understand the effects of genes involved in immune and stromal cells on prognosis, we used Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC), DAVID database and ESTMATE algorithm, and divided the patients into low and high groups according to immune (median: 1038.45) and stromal scores (median: 667.945), respectively. We found the immune scores were significantly correlated with clinicopathological parameters and overall survival (OS). Based on immune scores, 890 DEGs were significantly associated with OS among the 1433 up-regulated genes. Based on top 10 DEGs (IL10RA, FCER1G, SASH3, TIGIT, RHOH, IL12RB1, AIF1, LPXN, LAPTM5 and SP140), cases with number of up-regulated genes ? 5 were associated poor OS (P = 0.002). In addition, the mean differences of percentages of CD8 T cells (11.32%), CD4 memory resting T cells (-4.52%) and mast resting cells (-3.55%) between low and high immune scores were the most significant. Thus, combination of these genes might use to predict the efficacy of immunotherapy. Further analyses of these genes were warrant to explore their potential association with the prognosis of ccRCC.

SUBMITTER: Yin X

PROVIDER: S-EPMC7540144 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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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.

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Assessment for prognostic value of differentially expressed genes in immune microenvironment of clear cell renal cell carcinoma

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