A novel diagnostic model for predicting immune microenvironment subclass based on costimulatory molecules in lung squamous carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36588791", "kind": "science-record", "revision": 4, "content_hash": "sha256:ef72c4264461861d43b520cb7834c8c1def44470ce34352cba8ed56f0f96ca67", "created_at": "2026-11-01T19:01:08Z", "updated_at": "2026-11-01T22:50:58Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A novel diagnostic model for predicting immune microenvironment subclass based on costimulatory molecules in lung squamous carcinoma.", "doi": "10.3389/fgene.2022.1078790", "pmid": "36588791", "authors": ["Fangfang Duan", "Weisen Wang", "Wenyu Zhai", "Junye Wang", "Zerui Zhao", "Lie Zheng", "Bingyu Rao", "Yuheng Zhou", "Hao Long", "Yaobin Lin"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "There is still no ideal predictive biomarker for immunotherapy response among patients with non-small cell lung cancer. Costimulatory molecules play a role in anti-tumor immune response. Hence, they can be a potential biomarker for immunotherapy response. The current study comprehensively investigated the expression of costimulatory molecules in lung squamous carcinoma (LUSC) and identified diagnostic biomarkers for immunotherapy response. The costimulatory molecule gene expression profiles of 627 patients were obtained from the The Cancer Genome Atlas, GSE73403, and GSE37745 datasets. Patients were divided into different clusters using the k-means clustering method and were further classified into two discrepant tumor microenvironment (TIME) subclasses (hot and cold tumors) according to the immune score of the ESTIMATE algorithm. A high proportion of activated immune cells, including activated memory CD4 T cells, CD8 T cells, and M1 macrophages. Five CMGs (FAS, TNFRSF14, TNFRSF17, TNFRSF1B, and TNFSF13B) were considered as diagnostic markers using the Least Absolute Shrinkage and Selection Operator and the Support Vector Machine-Recursive Feature Elimination machine learning algorithms. Based on the five CMGs, a diagnostic nomogram for predicting individual tumor immune microenvironment subclasses in the TCGA dataset was developed, and its predictive performance was validated using GSE73403 and GSE37745 datasets. The predictive accuracy of the diagnostic nomogram was satisfactory in all three datasets. Therefore, it can be used to identify patients who may benefit more from immunotherapy.", "claims": [{"id": "claim-method", "statement": "Five CMGs (FAS, TNFRSF14, TNFRSF17, TNFRSF1B, and TNFSF13B) were considered as diagnostic markers using the Least Absolute Shrinkage and Selection Operator and the Support Vector Machine-Recursive Feature Elimination machine learning algorithms.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36588791", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Five CMGs (FAS, TNFRSF14, TNFRSF17, TNFRSF1B, and TNFSF13B) were considered as diagnostic markers using the Least Absolute Shrinkage and Selection Operator and the Support Vector Machine-Recursive Feature Elimination machine learning algorithms."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: TCGA databases (https://tcga-data.nci.nih.gov/tcga/) and the GEO databases (https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36588791", "locator": "sections[20] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: TCGA databases (https://tcga-data.nci.nih.gov/tcga/) and the GEO databases (https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The AUC of TCGA, GSE73403, and GSE37745 was 0.984, 0.968, and 0.882, respectively, which demonstrated a good diagnostic efficacy of the nomogram.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36588791", "locator": "sections[16] \"Development and validation of the diagnostic nomogram based on CMG biomarkers\" paragraph 4", "quote": "The AUC of TCGA, GSE73403, and GSE37745 was 0.984, 0.968, and 0.882, respectively, which demonstrated a good diagnostic efficacy of the nomogram."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The AUC of TCGA, GSE73403, and GSE37745 was 0.984, 0.968, and 0.882, respectively, which demonstrated a good diagnostic efficacy of the nomogram.", "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:36588791"], "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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