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.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37304554", "kind": "science-record", "revision": 3, "content_hash": "sha256:2da27a6eb45d1a314548fbcb6bc8c001dc1afa9a5d06890682edcf79a92bea2b", "created_at": "2026-11-01T19:02:02Z", "updated_at": "2026-11-01T22:09:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "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.", "doi": "10.21037/tcr-22-2782", "pmid": "37304554", "authors": ["Ke Wang", "Mingyang Yu", "Zhouzhou Zhang", "Rong Yin", "Qifeng Chen", "Xuezhi Zhao", "Hongqi Yu"], "journal": "Translational cancer research", "year": 2023, "abstract": "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.\nMethods: 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.\nResults: 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.\nConclusions: 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.", "claims": [{"id": "claim-method", "statement": "The R package “glmnet” (version 4.1-4) was utilized to identify the prognostic hub genes from candidate genes by the least absolute shrinkage and selection operator (LASSO) Cox proportional hazards regression (27). Optimal value of tuning parameter (λ) was determined by 20-fold cross-validation with minimum criteria. “Step” function was applied to conduct the stepwise multivariate Cox regression analysis to determine the most predictive genes with P<0.1.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37304554", "locator": "sections[6] \"Construction and validation of prognostic signature based on NK cell marker genes\" paragraph 2", "quote": "The R package “glmnet” (version 4.1-4) was utilized to identify the prognostic hub genes from candidate genes by the least absolute shrinkage and selection operator (LASSO) Cox proportional hazards regression (27). Optimal value of tuning parameter (λ) was determined by 20-fold cross-validation with minimum criteria. “Step” function was applied to conduct the stepwise multivariate Cox regression analysis to determine the most predictive genes with P<0.1."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data used in our study were obtained from publicly available databases and did not require approval of the local ethics committee.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37304554", "locator": "sections[4] \"Data source and acquisition\" paragraph 2", "quote": "All data used in our study were obtained from publicly available databases and did not require approval of the local ethics committee."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Univariate Cox screening retained 22 of 52 NK cell marker genes, LASSO Cox regression retained 18 genes, and stepwise multivariate Cox regression produced a 7-gene NKMS.", "kind": "quantitative", "target": {"value": 0.05, "unit": "p-value", "tolerance": null, "source": "After performing a univariate Cox proportional regression analysis on the TCGA-KIRC training set, 22 out of 52 NK cell markers were exhibited significantly associated with OS (P<0.05, Figure S2C)."}, "evidence": [{"asset": "source:pmid:37304554", "locator": "sections[15] \"Construction of prognostic signature on NK cell marker genes\" paragraph 2", "quote": "After performing a univariate Cox proportional regression analysis on the TCGA-KIRC training set, 22 out of 52 NK cell markers were exhibited significantly associated with OS (P<0.05, Figure S2C)."}, {"asset": "source:pmid:37304554", "locator": "sections[15] \"Construction of prognostic signature on NK cell marker genes\" paragraph 2", "quote": "After applying the LASSO Cox regression model on prognostic genes, 18 genes with minimal λ were screened out (Figure 3A,3B)."}, {"asset": "source:pmid:37304554", "locator": "sections[15] \"Construction of prognostic signature on NK cell marker genes\" paragraph 2", "quote": "Subsequently, a stepwise multivariate Cox regression analysis was conducted to optimize the prognostic signature to include the 7 most predictive genes (CLEC2B, PLAC8, CD7, SH3BGRL3, CALM1, KLRF1, and JAK1) (Figure 3C)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Univariate Cox screening retained 22 of 52 NK cell marker genes, LASSO Cox regression retained 18 genes, and stepwise multivariate Cox regression produced a 7-gene NKMS.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:37304554"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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