A novel anoikis resistance-associated gene model for prognostic prediction and immune microenvironment characterization in lung squamous cell carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41530460", "kind": "science-record", "revision": 3, "content_hash": "sha256:af99bf38f2a06d8d6549693f1a50379c796ae817796f13c49312b1c0644dfe1e", "created_at": "2026-11-01T20:31:29Z", "updated_at": "2026-11-01T22:15:17Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A novel anoikis resistance-associated gene model for prognostic prediction and immune microenvironment characterization in lung squamous cell carcinoma.", "doi": "10.1007/s12672-026-04395-5", "pmid": "41530460", "authors": ["Dong Ou", "HongPing Wang", "Yi Liu", "Jin Nie", "Daishun Liu"], "journal": "Discover oncology", "year": 2026, "abstract": "PURPOSE: Advanced lung squamous cell carcinoma (LUSC) has poor prognosis due to local invasion, metastasis, therapeutic resistance, and a dynamic tumor microenvironment. While anoikis resistance contributes to its malignancy, its influence on the tumor immune microenvironment (TIME) and clinical outcomes in LUSC remains unclear.\nMETHODS: Anoikis resistance-associated genes (ARGs) in LUSC were identified using LASSO regression and univariate Cox proportional hazards analysis. A prognostic signature was constructed and validated in both internal (TCGA-LUSC) and external (GSE73403, GSE74777) cohorts. Functional characterization of signature genes was performed using single-cell transcriptomics and pathway enrichment analysis. Comprehensive immune profiling was conducted to explore the relationship between risk stratification and TIME features. Core genes were experimentally validated to confirm their biological relevance.\nRESULTS: The ARG-based model stratified patients into high- and low-risk subgroups with significant overall survival differences (log-rank p < 0.001). The risk score was an independent prognostic factor (HR: 3.91; 95% CI: 2.08-7.34). The model demonstrated robust predictive performance across external datasets (AUC: >0.7). High-risk patients displayed immunosuppressive TIME characteristics, including reduced CD8 + T cell infiltration (p = 0.004), increased stromal content (p < 0.01), and lower predicted immunotherapy responsiveness (p = 0.007). Three key ARGs-SDCBP, RPS6KA1, and ITGA3-were identified as critical regulators of anoikis resistance, with functional assays confirming SDCBP's oncogenic role in promoting malignancy.\nCONCLUSION: This validated ARG-based prognostic model effectively predicts survival outcomes and revealed strong associations between anoikis resistance, immune landscape alterations, and immunotherapy response, offering a framework for risk stratification and precision immunotherapy in LUSC clinical management.", "claims": [{"id": "claim-method", "statement": "A prognostic signature based on ARGs was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41530460", "locator": "sections[5] \"Construction and validation of the model\" paragraph 2", "quote": "A prognostic signature based on ARGs was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analysed during this study are included in this published article [and its supplementary information files].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41530460", "locator": "sections[25] \"Data availability\" paragraph 2", "quote": "All data generated or analysed during this study are included in this published article [and its supplementary information files]."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The ARG-based model stratified patients into high- and low-risk subgroups with significant overall survival differences (log-rank p < 0.001).", "kind": "quantitative", "target": {"value": 0.001, "unit": "p-value", "tolerance": null, "source": "The ARG-based model stratified patients into high- and low-risk subgroups with significant overall survival differences (log-rank p < 0.001)."}, "evidence": [{"asset": "source:pmid:41530460", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The ARG-based model stratified patients into high- and low-risk subgroups with significant overall survival differences (log-rank p < 0.001)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The ARG-based model stratified patients into high- and low-risk subgroups with significant overall survival differences (log-rank p < 0.001).", "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:41530460"], "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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Comprehensi"], "order_basis": "paper_text_order:780", "id": "assay:1"}, {"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.69, "evidence": ["nature genes was performed using single-cell transcriptomics and pathway enrichment analysis. Comprehensi"], "order_basis": "paper_text_order:792", "id": "assay:2"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["rse transcription polymerase chain reaction (RT-qPCR). For functional validation, SDCBP-overexpre", "of SDCBP was systematically validated using RT-qPCR. g Comparison of TUNEL assay apoptosis level", "transcription polymerase chain reaction (RT-qPCR). For functional validation, SDCBP-overexpre", "SDCBP was systematically validated using RT-qPCR. g Comparison of TUNEL assay apoptosis level"], "order_basis": "paper_text_order:2663", "id": "assay:3"}, {"role": "data_generation", "definition_id": "transfection", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ementary Fig. S1. Twenty-four hours prior to transfection, cells were trypsinized and seeded to achiev", "to achieve 70–90% confluency at the time of transfection. Transfection was performed using Lipo8000™", "–90% confluency at the time of transfection. Transfection was performed using Lipo8000™ transfection r", ". Transfection was performed using Lipo8000™ transfection reagent (C0533, Beyotime Biotechnology, Shan"], "order_basis": "paper_text_order:3008", "id": "assay:4"}, {"role": "data_generation", "definition_id": "apoptosis_assay", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["after 48 h incubation in serum-free medium; Apoptosis was assessed using TUNEL assay kits (Procell", "icated that elevated SDCBP levels suppressed apoptosis while enhancing cell migration and wound hea", "d using RT-qPCR. g Comparison of TUNEL assay apoptosis levels between SDCBP-overexpressing and cont"], "order_basis": "paper_text_order:3756", "id": "assay:5"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["A prognostic signature based on ARGs was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression."], "order_basis": "consumes_earlier_output", "id": "assay:6"}]}