Integrating Bulk and Single-Cell RNA-Seq Data to Identify Prognostic Features Related to Activated Dendritic Cells in Clear-Cell Renal-Cell Carcinoma

Abstract

Dendritic cells (DCs) serve as key regulators in tumor immunity, with activated DCs potentiating antitumor responses through the secretion of pro-inflammatory cytokines and the expression of co-stimulatory molecules. Most current studies focus on the relationship between DC subgroups and clear-cell renal-cell carcinoma (ccRCC), but there is limited research on the connection between DCs and ccRCC from the perspective of immune activation. In this study, activated DC genes were identified in both bulk and single-cell RNA-seq data. A prognostic model related to activated DCs was constructed using univariate, multivariate Cox regression and LASSO regression. The prognostic model was validated in three external validation sets: GSE167573, ICGC, and E-MTAB-1980. The prognostic model consists of five genes, PLCB2, XCR1, IFNG, HLA-DQB2, and SMIM24. The expression of these genes was validated in tissue samples using qRT-PCR. Stratified analysis revealed that the prognostic model was able to better predict outcomes in advanced ccRCC patients. The risk scores were associated with tumor progression, tumor mutation burden, immune cell infiltration, and adverse outcomes of immunotherapy. Notably, there was a strong correlation between the expression of the five genes and the sensitivity to JQ1, a BET inhibitor. Molecular docking indicated high-affinity binding of the proteins encoded by these genes with JQ1. In conclusion, our study reveals the crucial role of activated DCs in ccRCC, offering new insights into predicting immune response, targeted therapy effectiveness, and prognosis for ccRCC patients.

Keywords

• dendritic cells
• immunotherapy
• molecular docking
• immune activation
• prognostic model