Integrated analysis of single-cell and bulk transcriptome identifies a signature based on NK cell marker genes to predict prognosis and therapeutic response in clear cell renal cell carcinoma.

Background: Accumulating evidence has highlighted the effects of natural killer (NK) cells on shaping anti-tumor immunity. This study aimed to construct an NK cell marker gene signature (NKMS) to predict prognosis and therapeutic response of clear cell renal cell carcinoma (ccRCC) patients. Methods: Publicly available single-cell and bulk RNA profiles with matched clinical information of ccRCC patients were collected from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), ArrayExpress, and International Cancer Genome Consortium (ICGC) databases. A novel NKMS was constructed, and its prognostic value, associated immunogenomic features and predictive capability to immune checkpoint inhibitors (ICIs) and anti-angiogenic therapies were evaluated in ccRCC patients. Results: We identified 52 NK cell marker genes by single-cell RNA-sequencing (scRNA-seq) analysis in GSE152938 and GSE159115. After least absolute shrinkage and selection operator (LASSO) and Cox regression, the most prognostic 7 genes (CLEC2B, PLAC8, CD7, SH3BGRL3, CALM1, KLRF1, and JAK1) composed NKMS using bulk transcriptome from TCGA. Survival and time-dependent receiver operating characteristic (ROC) analysis exhibited exceptional predictive capability of the signature in the training set and two independent validation cohorts (E-MTAB-1980 and RECA-EU cohorts). The seven-gene signature was able to identify patients within high Fuhrman grade (G3-G4) and American Joint Committee on Cancer (AJCC) stage (III-IV). Multivariate analysis confirmed the independent prognostic value of the signature, and a nomogram was built for clinical utility. The high-risk group was characterized by a higher tumor mutation burden (TMB) and greater infiltration of immunocytes, particularly CD8+ T cells, regulatory T (Treg) cells and follicular helper T (Tfh) cells, in parallel with higher expression of genes negatively regulating anti-tumor immunity. Moreover, high-risk tumors exhibited higher richness and diversity of T-cell receptor (TCR) repertoire. In two therapy cohorts of ccRCC patients (PMID32472114 and E-MTAB-3267), we demonstrated that high-risk group showed greater sensitivity to ICIs, whereas the low-risk group was more likely to benefit from anti-angiogenic therapy. Conclusions: We identified a novel signature that can be utilized as an independent predictive biomarker and a tool for selecting the individualized treatment for ccRCC patients.