Integrated analysis of single-cell and bulk RNA-sequencing reveals a novel signature based on NK cell marker genes to predict prognosis and immunotherapy response in gastric cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38561388", "kind": "science-record", "revision": 2, "content_hash": "sha256:139fde24ed9e71de2f5dc8426b36c94ead7c3c1aa4a5d8e90f857fb39aca5493", "created_at": "2026-11-01T19:20:31Z", "updated_at": "2026-11-01T21:32:50Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated analysis of single-cell and bulk RNA-sequencing reveals a novel signature based on NK cell marker genes to predict prognosis and immunotherapy response in gastric cancer.", "doi": "10.1038/s41598-024-57714-7", "pmid": "38561388", "authors": ["Jian-Rong Sun", "Chen-Fan Kong", "Yi-Xiang Ye", "Qin Wang", "Xiang-Ke Qu", "Li-Qun Jia", "Song Wu"], "journal": "Scientific reports", "year": 2024, "abstract": "Natural killer (NK) cells play essential roles in the tumor development, diagnosis, and prognosis of tumors. In this study, we aimed to establish a reliable signature based on marker genes in NK cells, thus providing a new perspective for assessing immunotherapy and the prognosis of patients with gastric cancer (GC). We analyzed a total of 1560 samples retrieved from the public database. We performed a comprehensive analysis of single-cell RNA-sequencing (scRNA-seq) data of gastric cancer and identified 377 marker genes for NK cells. By performing Cox regression analysis, we established a 12-gene NK cell-associated signature (NKCAS) for the Cancer Genome Atlas (TCGA) cohort, that assigned GC patients into a low-risk group (LRG) or a high-risk group (HRG). In the TCGA cohort, the areas under curve (AUC) value were 0.73, 0.81, and 0.80 at 1, 3, and 5 years. External validation of the predictive ability for the signature was then validated in the Gene Expression Omnibus (GEO) cohorts (GSE84437). The expression levels of signature genes were measured and validated in GC cell lines by real-time PCR. Moreover, NKCAS was identified as an independent prognostic factor by multivariate analysis. We combined this with a variety of clinicopathological characteristics (age, M stage, and tumor grade) to construct a nomogram to predict the survival outcomes of patients. Moreover, the LRG showed higher immune cell infiltration, especially CD8+ T cells and NK cells. The risk score was negatively associated with inflammatory activities. Importantly, analysis of the independent immunotherapy cohort showed that the LRG had a better prognosis and immunotherapy response when compared with the HRG. The identification of NK cell marker genes in this study suggests potential therapeutic targets. Additionally, the developed predictive signatures and nomograms may aid in the clinical management of GC.", "claims": [{"id": "claim-method", "statement": "Finally, based on the genes screened by LASSO Cox regression analysis, we conducted the multivariate Cox regression analysis to generate a prognostic signature.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38561388", "locator": "sections[5] \"Establishment and verification of the NK cell-associated signature (NKCAS)\" paragraph 2", "quote": "Finally, based on the genes screened by LASSO Cox regression analysis, we conducted the multivariate Cox regression analysis to generate a prognostic signature."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data that support this study are openly available in online repositories including the TCGA database (https://portal.gdc.cancer.gov/repository), GEO database (https://www.ncbi.nlm.nih.gov/geo/), and IMvigor210 cohort (http://research-pub.gene.com/IMvigor210CoreBiologies).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38561388", "locator": "sections[30] \"notes\" paragraph 1", "quote": "The data that support this study are openly available in online repositories including the TCGA database (https://portal.gdc.cancer.gov/repository), GEO database (https://www.ncbi.nlm.nih.gov/geo/), and IMvigor210 cohort (http://research-pub.gene.com/IMvigor210CoreBiologies)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Finally, multivariate Cox regression analysis was conducted to establish an optimal prognostic signature based on the 12 most predictive NK cell signature genes (CXCR4, RDH8, MAGEA11, CYP19A1, SHOX2, GRB14, SLC35E4, NEK5, AKAP5, MSI2, KYNU, and PLCL1).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38561388", "locator": "sections[15] \"Establishment and validation of a NKCAS for the prognostic assessment of GC\" paragraph 2", "quote": "Finally, multivariate Cox regression analysis was conducted to establish an optimal prognostic signature based on the 12 most predictive NK cell signature genes (CXCR4, RDH8, MAGEA11, CYP19A1, SHOX2, GRB14, SLC35E4, NEK5, AKAP5, MSI2, KYNU, and PLCL1)."}, {"asset": "source:pmid:38561388", "locator": "sections[15] \"Establishment and validation of a NKCAS for the prognostic assessment of GC\" paragraph 3", "quote": "The AUCs of the training set at 1-, 3-, and 5-years were 0.730, 0.809, and 0.804, respectively (Fig. 2D)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Finally, multivariate Cox regression analysis was conducted to establish an optimal prognostic signature based on the 12 most predictive NK cell signature genes (CXCR4, RDH8, MAGEA11, CYP19A1, SHOX2, GRB14, SLC35E4, NEK5, AKAP5, MSI2, KYNU, and PLCL1).", "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:38561388"], "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": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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