Identification Of Endothelial Cell Immune-related Gene Signature for Lung Adenocarcinoma by Integrated Analysis of Single-cell and Bulk RNA Sequencing Data.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38911372", "kind": "science-record", "revision": 3, "content_hash": "sha256:1049257122d925445d1a4d587298588cfb52dc9e0dc1655b4cb5bca8684e7845", "created_at": "2026-11-01T20:28:59Z", "updated_at": "2026-11-01T22:12:06Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification Of Endothelial Cell Immune-related Gene Signature for Lung Adenocarcinoma by Integrated Analysis of Single-cell and Bulk RNA Sequencing Data.", "doi": "10.7150/jca.94501", "pmid": "38911372", "authors": ["Zhuozheng Hu", "Jiajun Wu", "Weijun Zhou", "Kang Wang", "Wenxiong Zhang"], "journal": "Journal of Cancer", "year": 2024, "abstract": "Background: The role of endothelial cells in tumor progression is considerable, yet the effect of endothelial cell immune-related genes (EIRGs) is still unclear. This research aimed to scrutinize the prognostic value of EIRGs in lung adenocarcinoma (LUAD) and provide further insights into the abovementioned uncertainties. Methods: After single-cell RNA sequencing (scRNA-seq) samples were obtained from the Gene Expression Omnibus (GEO) database, they were integrated with bulk RNA sequencing data from The Cancer Genome Atlas (TCGA). Prognostic markers were determined and a prognostic model was developed. From this model, a nomogram was constructed. We analyzed the biological mechanism of the EIRGs in LUAD, including functional enrichment, tumor mutational burden (TMB), tumor microenvironment (TME) analyses and drug sensitivity. We validated the signature by validating the external cohort GSE31210 and RT-qPCR. Results: After analyzing the model constructed from eight EIRGs, we observed that high-risk group (HG) LUAD patients (a risk score exceeding 4.65) exhibited unfavorable outcomes according to Kaplan‒Meier survival curves. This outcome was confirmed by GSE31210. The nomogram based on the model demonstrated significant predictive value. HG was influenced primarily by steroid hormone biosynthesis and ECM receptor interactions. The TMB in HGs was greater than that in the LG. Analysis of drug sensitivity revealed the direction for individualized treatment for both risk cohorts. Variations in the expression of EIRGs have been confirmed via RT-qPCR in several LUAD cell lines. Conclusions: The prognostic model and nomogram above are valuable for determining the survival rate and treatment options for LUAD patients.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression analysis was performed to obtain EIRGs with significant prognostic correlation. On the basis of the \"glmnet\" package in R software, least absolute shrinkage and selection operator (LASSO) regression was conducted to reduce the number of model genes 13.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38911372", "locator": "sections[7] \"Construction of the prognostic model\" paragraph 2", "quote": "Univariate Cox regression analysis was performed to obtain EIRGs with significant prognostic correlation. On the basis of the \"glmnet\" package in R software, least absolute shrinkage and selection operator (LASSO) regression was conducted to reduce the number of model genes 13."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38911372", "locator": "sections[31] \"Availability of data and material\" paragraph 2", "quote": "The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "An eight-EIRG prognostic model classified LUAD patients into high- and low-risk groups, with high-risk patients having an unfavorable prognosis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38911372", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "After analyzing the model constructed from eight EIRGs, we observed that high-risk group (HG) LUAD patients (a risk score exceeding 4.65) exhibited unfavorable outcomes according to Kaplan‒Meier survival curves."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "An eight-EIRG prognostic model classified LUAD patients into high- and low-risk groups, with high-risk patients having an unfavorable prognosis.", "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:38911372"], "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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Methods: After single-cell RNA sequencing (scRNA-seq) samples were obtained from the G", "EO) database, they were integrated with bulk RNA sequencing data from The Cancer Genome Atlas (TCGA). Pr"], "order_basis": "paper_text_order:92", "id": "assay:1"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["abovementioned uncertainties. Methods: After single-cell RNA sequencing (scRNA-seq) samples were obtained from the G", ". Methods: After single-cell RNA sequencing (scRNA-seq) samples were obtained from the Gene Express", "ge in R software was utilized to convert the scRNA-seq data into seurat objects 8. 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