Characterization of the Immune Microenvironmental Landscape of Lung Squamous Cell Carcinoma with Immune Cell Infiltration.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36411824", "kind": "science-record", "revision": 2, "content_hash": "sha256:ea1ff907b4681b655ebfb7eb9811f296e7de772d167a0e0faeaace5ea1b4089b", "created_at": "2026-11-01T19:00:52Z", "updated_at": "2026-11-01T21:29:30Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Characterization of the Immune Microenvironmental Landscape of Lung Squamous Cell Carcinoma with Immune Cell Infiltration.", "doi": "10.1155/2022/2361507", "pmid": "36411824", "authors": ["Chunji Chen", "Dongfang Tang", "Chang Gu", "Bin Wang", "Yuanshan Yao", "Rui Wang", "Huibiao Zhang", "Wen Gao"], "journal": "Disease markers", "year": 2022, "abstract": "Background: Increasing evidence supports that immune cell infiltration (ICI) patterns play a key role in the tumor progression of lung squamous cell carcinoma (LUSC). However, to date, the immune infiltration picture of LUSC has not been elucidated.\nMethod: TCGA was used to download multiomics data from LUSC samples. At the same time, we included two datasets on lung squamous cell carcinoma, GSE17710 and GSE157010. To reveal the landscape of tumor immune microenvironment (TIME), the ESTIMATE algorithm, ssGSEA approach, and CIBERSORT analysis are used. To quantify the ICI pattern in a single tumor, consistent clustering is used to determine the LUSC subtype based on the ICI pattern, and principal component analysis (PCA) is used to obtain the ICI score. The prognostic value of the Kaplan-Meier curves is confirmed. GSEA (Gene Set Enrichment Analysis) was used to perform functional annotation. To investigate the immunotherapeutic effects of the ICI score, the immunophenotyping score (IPS) is used. Finally, analyze the mutation data with the \"maftools\" R package.\nResults: We identified four different immune infiltration patterns with different prognosis and biological characteristics in 792 LUSC samples. The identification of ICI patterns in individual tumors developed under ICI-related characteristic genes based on the ICI score helps to analyze the biological process, clinical results, immune cell infiltration, immunotherapy effects, and genetic variation. Immune failure is indicated by a high ICI score subtype marked by immunosuppression. Patients with low ICI scores have an abundance of efficient immune cells, which corresponds to the immunological activation phenotype and may have therapeutic benefits. The immunophenotypic score was used as a surrogate indicator of immunotherapy results, and samples with low ICI scores obtained significantly higher immunophenotypic scores. Finally, the relationship between the ICI score and tumor mutation burden (TMB) was proven.\nConclusion: This study fully clarified the indispensable role of the ICI model in the complexity and diversity of TIME. The quantitative identification of ICI patterns in a single tumor will help draw the picture of TIME and further optimize precision immunotherapy.", "claims": [{"id": "claim-method", "statement": "Four separate subtypes of ICI patterns were identified using a consensus clustering approach. In addition, to identify ICI patterns in individual samples and evaluate immunotherapy response, an ICI-based scoring scheme was developed.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36411824", "locator": "sections[1] \"1. Introduction\" paragraph 4", "quote": "Four separate subtypes of ICI patterns were identified using a consensus clustering approach. In addition, to identify ICI patterns in individual samples and evaluate immunotherapy response, an ICI-based scoring scheme was developed."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The simulation experiment data used to support the findings of this study are available from the corresponding authors upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36411824", "locator": "sections[16] \"Data Availability\" paragraph 2", "quote": "The simulation experiment data used to support the findings of this study are available from the corresponding authors upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We identified four different immune infiltration patterns with different prognosis and biological characteristics in 792 LUSC samples.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36411824", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We identified four different immune infiltration patterns with different prognosis and biological characteristics in 792 LUSC samples."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We identified four different immune infiltration patterns with different prognosis and biological characteristics in 792 LUSC samples.", "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:36411824"], "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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