Abstract
BACKGROUND: Epigenetic alterations driven by chromatin regulators (CRs) are well-recognized cancer hallmarks. Growing evidence suggests that the imbalance of CRs may lead to the occurrence of various diseases including tumors. However, the role and prognostic value of CRs in clear cell renal cell carcinoma (ccRCC) remain undefined. METHODS: Consensus clustering analysis was used to identify different subtypes. Univariate and multivariate Cox regression analysis were performed to identify prognosis-related CRs and constructed a risk model. Transcriptome sequencing was used to verify gene expression levels. Kaplan-Meier survival analysis was used to compare overall survival (OS) between high- and low-risk groups. The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve was used to evaluate the performance of the model. The ESTIMATE algorithm and single-sample gene set enrichment analysis (ssGSEA) were executed to evaluate the immune characteristics of samples. Correlation analysis was used to assess the relationship between risk score and immune checkpoint genes, the relationship between expression levels of CRs and immune cell infiltration and drug therapeutic response. Finally, we also compared differences in drug sensitivity between low- and high-risk groups. RESULTS: We identified three CRs-related subtypes with different characteristics. A prognostic model was built with four CRs and can precisely predict the OS of patients in different risk groups. The model has good stability and applicability and was further verified in the internal and external dataset. The transcriptomic levels of the four CRs were also validated, and the risk score was an independent prognostic factor for ccRCC. Obvious differences in the immune microenvironment and the expression levels of immune checkpoints were observed in low- and high-risk group. Higher immune activity and immune cell infiltration were found in the high-risk group. Besides, the expression levels of CRs were associated with drug therapeutic response. Patients with high-risk score may be more sensitive to gemcitabine, vinblastine, paclitaxel, axitinib, sunitinib, and temsirolimus. CONCLUSIONS: CRs were strongly associated with the occurrence and development of ccRCC. Targeting CRs may become a new therapeutic strategy for ccRCC. Besides, CRs-related gene signature can predict the prognosis and therapeutic significance of ccRCC, which provides an important reference for clinical decision-making.