Single-Cell RNA Sequencing in Multiple Pathologic Types of Renal Cell Carcinoma Revealed Novel Potential Tumor-Specific Markers.
IF: 5.738
Cited by: 3


Renal cell carcinoma (RCC) is the most common type of kidney cancer. Studying the pathogenesis of RCC is particularly important, because it could provide a direct guide for clinical treatment. Given that tumor heterogeneity is probably reflected at the mRNA level, the study of mRNA in RCC may reveal some potential tumor-specific markers, especially single-cell RNA sequencing (scRNA-seq). We performed an exploratory study on three pathological types of RCC with a small sample size. This study presented clear-cell RCC (ccRCC), type 2 pRCC, and chRCC in a total of 30,263 high-quality single-cell transcriptome information from three pathological types of RCC. In addition, scRNA-seq was performed on normal kidneys. Tumor characteristics were well identified by the comparison between different pathological types of RCC and normal kidneys at the scRNA level. Some new tumor-specific markers for different pathologic types of RCC, such as SPOCK1, PTGIS, REG1A, CP and SPAG4 were identified and validated. We also discovered that NDUFA4L2 both highly expressed in tumor cells of ccRCC and type 2 pRCC. The presence of two different types of endothelial cells in ccRCC and type 2 pRCC was also identified and verified. An endothelial cell in ccRCC may be associated with fibroblasts and significantly expressed fibroblast markers, such as POSTN and COL3A1. At last, by applying scRNA-seq results, the activation of drug target pathways and sensitivity to drug responses was predicted in different pathological types of RCC. Taken together, these findings considerably enriched the single-cell transcriptomic information for RCC, thereby providing new insights into the diagnosis and treatment of RCC.


cancer-associated fibroblasts
endothelial cells
renal cell carcinoma
single-cell RNA sequencing
tumor-immune microenvironment
tumor-specific markers


Su, Cheng
Lv, Yufang
Lu, Wenhao
Yu, Zhenyuan
Ye, Yu
Guo, Bingqian
Liu, Deyun
Yan, Haibiao
Li, Tianyu
Zhang, Qingyun
Cheng, Jiwen
Mo, Zengnan

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