Establishing a prognostic model of chromatin modulators and identifying potential drug candidates in renal clear cell patients Introduction

Abstract

Background: Renal carcinoma is a common malignant tumor of the urinary system. Advanced renal carcinoma has a low 5-year survival rate and a poor prognosis. More and more studies have confirmed that chromatin regulators(CRs) can regulate the occurrence and development of cancer. This article aims to investigate the functional and prognostic value of CRs in renal carcinoma patients. Methods: mRNA expression and clinical information were obtained from the TCGA(The Cancer Genome Atlas)database. Univariate Cox regression analysis and LASSO regression analysis were used to select prognostic chromatin-regulated genes and use them to construct a risk model for predicting the prognosis of renal cancer. Differences in prognosis between high-risk and low-risk groups were compared using Kaplan-Meier analysis. In addition, we analyzed the relationship between chromatin regulators and tumor immune infiltration, and explored differences in drug sensitivity between different risk groups. Results: We constructed a model consisting of 11 CRs to predict the prognosis of renal cancer patients, and not only successfully validated its feasibility, but we also found that the 11 CR-based model was an independent prognostic factor. Functional analysis showed that CRs were mainly enriched in cancer development-related signaling pathways. We also found through the TIMER database that CR-based models were also associated with immune cell infiltration and immune checkpoints.At the same time, the GDSC(The Genomics of Drug Sensitivity in Cancer database)database was used to analyze the commonly used drugs of KIRC(Renal clear cell carcinoma) patients. It was found that patients in the low-risk group were sensitive to drugs such as axitinib, pazopanib, sorafenib, and gemcitabine, while those in the high-risk group may be sensitive to sunitinib. Conclusion: The chromatin regulator-related prognostic model we constructed can be used to assess the prognostic risk of patients with clear cell renal cell carcinoma. The results of this study can bring new ideas for targeted therapy of clear cell renal carcinoma, helping doctors to take corresponding measures in advance for patients with different risks.