- •This article constructed a NKRPS based on 9 NK cells related genes.
- •NKRPS were applied to quantify the immune infiltration landscape of ccRCC.
- •High and low-risk groups showed differences in immune cell landscape.
- •NKRPS can be used to screen ccRCC patients suitable for immunotherapy.
Natural killer (NK) cells are a key factor affecting progression and immune surveillance of clear-cell renal cell carcinoma (ccRCC). This study sought to construct a natural killer cell-related prognostic signature (NKRPS) to predict the outcome of ccRCC patients and to furnish guidance for finding appropriate treatment strategies.
From the TCGA and ArrayExpress databases, transcriptomic profiles and relevant clinical information of ccRCC patients were downloaded for the TCGA cohort (n = 515) and the E-MTAB-1980 cohort (n = 101). With the univariate Cox analysis and LASSO-Cox regression algorithm, a NKRPS was built to evaluate patients’ prognosis. Receiver operating characteristic (ROC) curves and calibration curves were drawn to estimate the predictive power of the prognostic model. Then, tumor microenvironment (TME), tumor mutational burden (TMB), sensitization to immune checkpoint inhibitors (ICIs) therapy and targeted drug treatment were analyzed in ccRCC patients.
Nine genes (BID, CCL7, CSF2, IL23A, KNSTRN, RHBDD3, PIK3R3, RNF19B and VAV3) were identified to construct a NKRPS. High-risk group displayed undesirable survival compared to low-risk group (P < .05). Moreover, the area under the curve (AUC) of ROC at 1-, 3- and 5-year were 0.766, 0.755, and 0.757, respectively. High-risk group was characterized by superior immune infiltration, higher TMB, and higher expression of 5 ICI-related genes. Additionally, this model enabled to predict the sensitivity of patients to chemotherapy drugs.
NKRPS had a strong predictive power on prognosis of ccRCC patients, which may facilitate individualized treatment and medical decision making.
Abbreviations:NK (natural killer), ccRCC (clear-cell renal cell carcinoma), NKRPS (natural killer cell-related prognostic signature), TCGA (The Cancer Genome Altas), LASSO (least absolute shrinkage and selection operator), ROC (receiver operating characteristic), TME (tumor microenvironment), TMB (tumor mutational burden), ICIs (immune checkpoint inhibitors), AUC (area under the curve), RCC (renal cell carcinoma), PD-1 (programmed death 1), PD-L1 (programmed death ligand 1), CTLA-4 (cytotoxic T-lymphocyte-associated antigen 4), K-M (Kaplan-Meier), ssGSEA (single sample Gene set enrichment analysis), IPS (immunophenoscore), IC50 (half-maximal inhibitory concentration), GDSC (Genomics of Drug Sensitivity in Cancer), PH (proportional hazard), OS (overall survival), Tregs (T cells regulatory), MHC (major histocompatibility complex), IFN (interferon), MICA (MHC class I chain-related A)
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Published online: November 18, 2022
Accepted: November 14, 2022
Received in revised form: November 12, 2022
Received: August 23, 2022
Publication stageIn Press Journal Pre-Proof
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