Identification of anoikis-related genes classification patterns and immune infiltration characterization in chronic rhinosinusitis with nasal polyps based on machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40894073", "kind": "science-record", "revision": 2, "content_hash": "sha256:5b0c139f6f876edc4e32c5b5d58c412a3fab78d926a0f92f36c6cfaa5491b089", "created_at": "2026-11-01T20:31:00Z", "updated_at": "2026-11-01T21:35:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of anoikis-related genes classification patterns and immune infiltration characterization in chronic rhinosinusitis with nasal polyps based on machine learning.", "doi": "10.3389/fmolb.2025.1624300", "pmid": "40894073", "authors": ["Ziqi Chen", "Lingmei Qu", "Qing Hao", "Shuang Teng", "Shuo Liu", "Qin Wu", "Hongtian Yi", "Xianji Shen", "Liang Li", "Zhaonan Xu", "Yanan Sun"], "journal": "Frontiers in molecular biosciences", "year": 2025, "abstract": "Introduction: Chronic rhinosinusitis with nasal polyps (CRSwNP) is characterized by stromal edema, albumin deposition, and pseudocyst formation. Anoikis, a process in which cells detach from the correct extracellular matrix, disrupts integrin junctions, thereby inhibiting improperly proliferating cells from growing or adhering to an inappropriate matrix. Although anoikis is implicated in immune regulation and CRSwNP pathogenesis, its specific mechanistic role remains poorly defined.\nMethods: The GSE136825 and GSE179625 datasets were obtained from the GEO database and 338 anoikis-related genes (ARGs) were extracted from the literature and databases. Immune cell infiltration was analysed using the CIBERSORT algorithm. CRSwNP samples were classified via consensus clustering. Key ARGs were identified through machine learning. The diagnostic performance of candidate genes was evaluated using Receiver Operating Characteristic (ROC) analysis. Functional annotation was performed based on Gene Ontology (GO) terms, and pathway enrichment analysis was conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Regulatory networks were visualized using NetworkAnalyst and Cytoscape. Experimental validation included quantitative real-time reverse-transcription PCR (qRT-PCR), immunohistochemistry (IHC), and immunofluorescence (IF) in human tissues.\nResults: Consensus clustering stratified CRSwNP patients into two distinct anoikis-related clusters. Machine learning identified four key genes: CDH3, PTHLH, PDCD4, and androgen receptor (AR). The nomogram model demonstrated high diagnostic accuracy, with an area under the receiver operating characteristic curve (AUC) >0.90. Immune infiltration analysis revealed differential immune microenvironments between clusters, with AR overexpressed in cluster 1 and PTHLH in cluster 2. Network analysis identified 862 drugs or compounds targeting AR. Experimental validation confirmed consistency between bioinformatics predictions and tissue-level expression patterns.\nConclusion: This study delineates two anoikis-related molecular subtypes of CRSwNP and identifies AR and PTHLH as cluster-specific biomarkers. These findings provide novel insights for personalized therapy, drug screening, and immunomodulatory strategies in CRSwNP.", "claims": [{"id": "claim-method", "statement": "To provide a more comprehensive depiction of the expression profile of ARGs in CRSwNP, we applied the consensus clustering algorithm to classify 42 CRSwNP samples based on the expression patterns of 35 ARGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40894073", "locator": "sections[20] \"3.3 Identification of anoikis-related clusters in CRSwNP\" paragraph 2", "quote": "To provide a more comprehensive depiction of the expression profile of ARGs in CRSwNP, we applied the consensus clustering algorithm to classify 42 CRSwNP samples based on the expression patterns of 35 ARGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material. Sequence data that support the findings of this study have been deposited in the European Nucleotide Archive with the primary accession code PRJNA563822 and PRJNA744406.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40894073", "locator": "sections[30] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material. Sequence data that support the findings of this study have been deposited in the European Nucleotide Archive with the primary accession code PRJNA563822 and PRJNA744406."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Consensus clustering stratified CRSwNP patients into two distinct anoikis-related clusters.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40894073", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Consensus clustering stratified CRSwNP patients into two distinct anoikis-related clusters."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Consensus clustering stratified CRSwNP patients into two distinct anoikis-related clusters.", "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:40894073"], "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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