Single cell transcriptomic analysis reveals tumor immune infiltration by NK cells gene signature in lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39071697", "kind": "science-record", "revision": 2, "content_hash": "sha256:fe811fe6ec7475864e3c38db04767ca18318b249891d654b3c18802d01c4dba9", "created_at": "2026-11-01T20:29:08Z", "updated_at": "2026-11-01T21:33:32Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Single cell transcriptomic analysis reveals tumor immune infiltration by NK cells gene signature in lung adenocarcinoma.", "doi": "10.1016/j.heliyon.2024.e33928", "pmid": "39071697", "authors": ["Yimin Zhu", "Xiuhua Wu", "Yunjiao Zhang", "Jie Gu", "Rongwei Zhou", "Zhong Guo"], "journal": "Heliyon", "year": 2024, "abstract": "Background: Natural Killer (NK) cells are vital components of the innate immune system, crucial for combating infections and tumor growth, making them pivotal in cancer prognosis and immunotherapy. We sought to understand the diverse characteristics of NK cells within lung adenocarcinoma (LUAD) by conducting single-cell RNA sequencing analyses.\nMethods: Using the scRNA-seq dataset for multiple primary lung cancers (MPLCs), we examined two major NK cell groups, NK1 and NK2, comparing the expression profiles of 422 differentially expressed NK signature genes. We identified eight genes (SPON2, PLEKHG3, CAMK2N1, RAB27B, CTBP2, EFHD2, GOLM1, and PLOD1) that distinguish NK1 from NK2 cells. A prognostic signature, the NK gene signature (NKGS) score, was established through LASSO Cox regression. High NKGS scores were linked to poorer overall survival in TCGA-LUAD patients and consistently validated in other datasets (GSE31210 and GSE14814).\nResults: Functional analysis revealed an enrichment of genes related to the TGF-β signaling pathway in the high NKGS score group. Moreover, a high NKGS score correlated with an immunosuppressive tumor microenvironment (TME) driven by immune evasion mechanisms. We also observed reduced T-cell receptor (TCR) repertoire diversity in the high-risk NKGS group, indicating a negative association between inflammation and risk score.\nConclusion: This study introduced the innovative NKGS score, differentiating NK1 from NK2 cells. High NKGS scores were associated with the TGF-β pathway and provided insights into LUAD prognosis and immune activities.", "claims": [{"id": "claim-method", "statement": "These genes were then evaluated using the least absolute shrinkage and selection operator (LASSO) Cox proportional hazards regression via the “glmnet” R package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39071697", "locator": "sections[14] \"Construction and validation of the prognostic signature\" paragraph 2", "quote": "These genes were then evaluated using the least absolute shrinkage and selection operator (LASSO) Cox proportional hazards regression via the “glmnet” R package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The single-cell RNA-seq dataset was sourced from the GEO database using the accession number GSE200972. We obtained the TCGA-LUAD cohort dataset from The Cancer Genome Atlas (TCGA) portal (https://portal.gdc.cancer.gov/). Additionally, we retrieved datasets GSE31210 and GSE14814 from the GEO database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39071697", "locator": "sections[25] \"Data availability statement\" paragraph 2", "quote": "The single-cell RNA-seq dataset was sourced from the GEO database using the accession number GSE200972. We obtained the TCGA-LUAD cohort dataset from The Cancer Genome Atlas (TCGA) portal (https://portal.gdc.cancer.gov/). Additionally, we retrieved datasets GSE31210 and GSE14814 from the GEO database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The high NKGS risk group exhibited significantly poorer overall survival than the low-risk group in TCGA-LUAD (HR = 1.76, P = 0.00016).", "kind": "quantitative", "target": {"value": 1.76, "unit": "hazard ratio", "tolerance": null, "source": "Kaplan–Meier (KM) analysis illustrated that high-risk patients exhibited markedly diminished overall survival (OS) compared to their low-risk counterparts (Fig. 4A, HR = 1.76, P = 0.00016)."}, "evidence": [{"asset": "source:pmid:39071697", "locator": "sections[5] \"Construction of the prognostic signature based on NK cell signature genes\" paragraph 3", "quote": "Kaplan–Meier (KM) analysis illustrated that high-risk patients exhibited markedly diminished overall survival (OS) compared to their low-risk counterparts (Fig. 4A, HR = 1.76, P = 0.00016)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The high NKGS risk group exhibited significantly poorer overall survival than the low-risk group in TCGA-LUAD (HR = 1.76, P = 0.00016).", "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:39071697"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE14814"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE14814"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39071697", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.heliyon.2024.e33928", "sha256": "sha256:98247047073af1f4e2246ba94b4a26a21ee4e3276d11dc4710efb2a3c4779590", "bytes": 38040, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1016/j.heliyon.2024.e33928", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11283104", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE14814", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14814", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE14814", "version": null, "note": "The single-cell RNA-seq dataset was sourced from the GEO database using the accession number GSE200972. 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