A Novel Prognostic Signature Revealed the Interaction of Immune Cells in Tumor Microenvironment Based on Single-Cell RNA Sequencing for Lung Adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35812244", "kind": "science-record", "revision": 4, "content_hash": "sha256:2973ad4b4eb5e1e0e70cd180db64859535e8349da4e76a25062294c862302ff1", "created_at": "2026-11-01T18:14:52Z", "updated_at": "2026-11-01T22:06:59Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Novel Prognostic Signature Revealed the Interaction of Immune Cells in Tumor Microenvironment Based on Single-Cell RNA Sequencing for Lung Adenocarcinoma.", "doi": "10.1155/2022/6555810", "pmid": "35812244", "authors": ["Xing Jin", "Zhengyang Hu", "Qihai Sui", "Mengnan Zhao", "Jiaqi Liang", "Zhenyu Liao", "Yuansheng Zheng", "Hao Wang", "Yu Shi"], "journal": "Journal of immunology research", "year": 2022, "abstract": "Background: The tumor immune microenvironment (TIME) played an important role in immunotherapy prognosis and treatment response. Immune cells constitute a large part of the tumor microenvironment and regulate tumor progression. Our research is dedicated to studying the infiltrating immune cell in lung adenocarcinoma (LUAD) and seeking potential targets.\nMethods: The scRNA-seq data were collected from our FDZSH and two public datasets. The code for cell-type mapping algorithms was downloaded from the CIBERSORTx portal. The bioinformatics data of LUAD patients could be approached from The Cancer Genome Atlas (TCGA) portal. Weighted gene coexpression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) analyses were performed to construct a risk model. TIMER2 and TIDE helped with the immune infiltration estimation, while PROGENy helped the cancer-related pathways' enrichment analysis. GSE31210 dataset and IMVigor ICB therapy cohort validated our findings as the external validation datasets.\nResults: We clustered the scRNA-seq dataset (integrating our FDZSH datasets and other public datasets) into 23 subpopulations. After curated cell annotation, we implemented Cibersort and WGCNA analysis to anchor the brown module and natural killer cell cluster1 due to the most relationship with tumor trait. The overlap of the brown module gene, natural killer cell signature, and DEGs of tumor and adjacent normal samples was screened by LASSO Cox regression. The obtained 5-gene risk model showed an excellent prognostic performance in the validation dataset. Furthermore, there was a correlation between risk score and tumor-infiltrating immune cells and tumor genomics abnormity. Patients with higher risk scores had a significantly lower immunotherapy response rate.\nConclusion: Our observations implied that immune cells played a pivotal role in TIME and established a 5-gene signature (including IDH2, ADRB2, SFTPC, CCDC69, and CCND2) on the basement of nature killer markers targeted by WGCNA analysis. The significance of clinical outcome and immunotherapy response prediction was validated robustly.", "claims": [{"id": "claim-method", "statement": "The LASSO Cox model was conducted to build a prognostic model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35812244", "locator": "sections[13] \"3.3. 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Other data are available from the corresponding authors upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The five-gene risk model showed excellent prognostic performance in the validation dataset.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35812244", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The obtained 5-gene risk model showed an excellent prognostic performance in the validation dataset."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The five-gene risk model showed excellent prognostic performance in the validation dataset.", "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:35812244"], "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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