Highlights
- •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.
Abstract
Background
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.
Methods
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.
Results
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.
Conclusion
NKRPS had a strong predictive power on prognosis of ccRCC patients, which may facilitate
individualized treatment and medical decision making.
Keywords
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)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: November 18, 2022
Accepted:
November 14,
2022
Received in revised form:
November 12,
2022
Received:
August 23,
2022
Publication stage
In Press Journal Pre-ProofIdentification
Copyright
© 2022 Elsevier Inc. All rights reserved.
