Integrated analysis of the functions of RNA binding proteins in clear cell renal cell carcinoma

Xiaoliang Hua,Juan Chen,Shengdong Ge,Haibing Xiao,Li Zhang,Chaozhao Liang

doi:10.1016/j.ygeno.2020.10.016

Abstract

RNA binding proteins (RBPs) dysregulation is involved in the processes of various tumors. However, the roles of RBPs in clear cell renal cell carcinoma (ccRCC) remain poorly understand. In present study, we first performed consensus clustering and identified two clusters, of which cluster 2 was closely correlated with the malignancy of ccRCC. Differentially expressed RBPs between normal and tumor tissues were obtained, comprising 71 up-regulated and 44 down-regulated ones. Then, ten hub genes were selected and validated using The Human Protein Atlas database and receiver operating characteristic curves, showing good diagnostic value for cancers. Besides, we identified ten RBPs with the most useful prognostic values, and were used to construct a risk score model. The model could be used to stratify patients with different prognosis and phenotype distributions. The model showed good performance and can be used as a complementation for clinical factors to guide clinical practice in the future.

Highlights

RNA binding proteins (RBPs) dysregulation is involved in the process es of various tumor
We performed a systematic analysis of RNA binding proteins in clear cell renal cell carcinoma (ccRCC) patients
The results showed that these genes (RPLP0, RPS14, OASL, RPS20, RPL35, RPS2, RPL10, RPL30, RPS15 and RPL18) were highly expressed in ccRCC samples compared with normal samples (Fig. 5)

Summary

Introduction

RNA binding proteins (RBPs) dysregulation is involved in the process es of various tumor. The roles of RBPs in clear cell renal cell carcinoma (ccRCC) remain poorly understand. Systematic exploration of the roles of RBPs in ccRCC may provide new insights for the treatments of ccRCC. Renal cell carcinoma (RCC) is one of the most frequently diagnosed malignancies of the urinary system, and clear cell renal cell carcinoma (ccRCC) is the most common histologic subtype, that accounts for approximately 80–90% RCC[1]. The diagnostic methods of ccRCC mainly depend on image examination, histopathological examination and molecular diagnosis. Many molecular biomarkers have been uncovered based on next-generation sequencing technologies, and can be used to guide diagnosis and treatment of ccRCC. Identification of sensitive and specific biomarkers to predict tumor relapse and progression and construction of a more powerful stratification model to guide patients’ treatment are urgently needed

Methods

Results

Discussion

Conclusion