Intercellular Communication-Related Molecular Subtypes and a Gene Signature Identified by the Single-Cell RNA Sequencing Combined with a Transcriptomic Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35620271", "kind": "science-record", "revision": 3, "content_hash": "sha256:cacd5cf12dbc49a9ab8bbbc8581ef1adf0d211ea9e4566274426efc83990f15c", "created_at": "2026-11-01T18:14:32Z", "updated_at": "2026-11-01T21:27:57Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Intercellular Communication-Related Molecular Subtypes and a Gene Signature Identified by the Single-Cell RNA Sequencing Combined with a Transcriptomic Analysis.", "doi": "10.1155/2022/6837849", "pmid": "35620271", "authors": ["Pin Guan", "Wentao Cai", "Ke Wu", "Fan Jiang", "Jinchan Wu", "Xin Zhai", "Min Zeng"], "journal": "Disease markers", "year": 2022, "abstract": "Background: The tumor microenvironment (TME) of lung adenocarcinoma (LUAD) comprise various cell types that communicate with each other through ligand-receptor interactions. This study focused on the identification of cell types in LUAD by single-cell RNA sequencing (scRNA-seq) data and screening of intercellular communication-related genes.\nMethods: The Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo) provided the RNA-seq data of LUAD patients in the GSE149655, GSE31210, and GSE72094 datasets. Quality control of the scRNA-seq data in GSE149655 was performed by the Seurat package (http://seurat.r-forge.r-project.org) for identifying highly variable genes for principal component analysis (PCA) and cell clustering. The CellPhoneDB (http://www.cellphonedb.org) was used for filtering intercellular communication-related ligand-receptor pairs. According to ligand and receptor expressions, LUAD samples were clustered using ConsensusClusterPlus (https://www.bioconductor.org/packages/release/bioc/html/ConsensusClusterPlus). Additionally, the identification of prognosis-related ligand and receptor genes was conducted along with the development of a risk prediction model by the least absolute shrinkage and selection operator (LASSO) Cox regression analysis.\nResults: This study identified twelve cell types in 8170 cells of LUAD tissues along with 219 ligand and receptor genes. LUAD was classified into three different molecular subtypes, among which cluster 3 (C3) had the longest overall survival (OS) time and cluster (C1) had the shortest OS time. In comparison with the other two molecular subtypes, it was observed that C1 had a higher rate of somatic mutations and lower levels of infiltrating immune cells and immune scores. Ten genes were screened from the total ligand and receptor genes to construct a risk model, which showed a strong prediction power in the prognosis of patients with LUAD.\nConclusion: The results of this study revealed cell types specific to LUAD, which were classified into different molecular subtypes according to intercellular communication-related genes. A novel prognostic risk model was developed in this study, providing new insights into prognostic assessment models for LUAD.", "claims": [{"id": "claim-method", "statement": "Additionally, the identification of prognosis-related ligand and receptor genes was conducted along with the development of a risk prediction model by the least absolute shrinkage and selection operator (LASSO) Cox regression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35620271", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Additionally, the identification of prognosis-related ligand and receptor genes was conducted along with the development of a risk prediction model by the least absolute shrinkage and selection operator (LASSO) Cox regression analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analysed during this study are included in this article.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35620271", "locator": "sections[22] \"Data Availability\" paragraph 2", "quote": "All data generated or analysed during this study are included in this article."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A novel prognostic risk model was developed in this study, providing new insights into prognostic assessment models for LUAD.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35620271", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "A novel prognostic risk model was developed in this study, providing new insights into prognostic assessment models for LUAD."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A novel prognostic risk model was developed in this study, providing new insights into prognostic assessment models for LUAD.", "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:35620271"], "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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