The prognostic value of immune escape-related genes in lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38988926", "kind": "science-record", "revision": 3, "content_hash": "sha256:d607d8c052e7c1d28c9ee13c4ba6c70341e14350c04f4ba28a01717208b7baaf", "created_at": "2026-11-01T20:29:03Z", "updated_at": "2026-11-01T22:12:12Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The prognostic value of immune escape-related genes in lung adenocarcinoma.", "doi": "10.21037/tcr-23-2295", "pmid": "38988926", "authors": ["Hao Ran Jia", "Wen Chao Li", "Lin Wu"], "journal": "Translational cancer research", "year": 2024, "abstract": "Background: Lung cancer is one of the most common cancers in humans, and lung adenocarcinoma (LUAD) has become the most common histological type of lung cancer. Immune escape promotes progression of LUAD from the early to metastatic late stages and is one of the main obstacles to improving clinical outcomes for immunotherapy targeting immune detection points. Our study aims to explore the immune escape related genes that are abnormally expressed in lung adenocarcinoma, providing assistance in predicting the prognosis of lung adenocarcinoma and targeted.\nMethods: RNA data and related clinical details of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) database. Through weighted gene coexpression network analysis (WGCNA), 3112 key genes were screened and intersected with 182 immune escape genes obtained from a previous study to identify the immune escape-related genes (IERGs). The role of IERGs in LUAD was systematically explored through gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analyses, which were used to enrich the relevant pathways of IERGs. The least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analysis were used to identify the key prognostic genes, and a prognostic risk model was constructed. Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data (ESTIMATE) and microenvironment cell populations (MCP) counter methods (which can accurately assess the amount of eight immune cell populations and two stromal cell groups) were used to analyze the tumor immune status of the high and low risk subgroups. The protein expression level of the differentially expressed genes in lung cancer samples was determined by using the Human Protein Atlas (HPA) database. A nomogram was constructed, and the prognostic risk model was verified via the Gene Expression Omnibus (GEO) datasets GSE72094 and GSE30219.\nResults: Twenty differentially expressed IERGs were obtained. GO analysis of these 20 IERGs revealed that they were mainly associated with the regulation of immune system processes, immune responses, and interferon-γ enrichment in mediating signaling pathways and apoptotic signaling pathways; meanwhile, KEGG analysis revealed that IERGs were associated with necroptosis, antigen processing and presentation, programmed cell death ligand 1 (PD-L1) expression and programmed cell death 1 (PD-1) pathway in tumors, cytokine-cytokine receptor interactions, T helper cell 1 (Th1) and Th2 differentiation, and tumor necrosis factor signaling pathways. Using LASSO and Cox regression analysis, we constructed a four-gene model that could predict the prognosis of patients with LUAD, and the model was validated with a validation cohort. The immunohistochemical results of the HPA database showed that AHSA1 and CEP55 had low expression in normal lung tissue but high expression in lung cancer tissue.\nConclusions: We constructed an IERG-based model for predicting the prognosis of LUAD. Among the genes identified, CEP55 and AHSA1 may be potential prognostic and therapeutic targets, and reducing their expression may represent a novel approach in the treatment of LUAD.", "claims": [{"id": "claim-method", "statement": "Survival status, survival time, and gene expression data were integrated into the R software package “glmnet”, and regression analysis was performed, with the least absolute shrinkage and selection operator (LASSO) Cox method being used to further screen for key prognostic genes related to the IERGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38988926", "locator": "sections[7] \"Establishment of a prognostic risk model\" paragraph 2", "quote": "Survival status, survival time, and gene expression data were integrated into the R software package “glmnet”, and regression analysis was performed, with the least absolute shrinkage and selection operator (LASSO) Cox method being used to further screen for key prognostic genes related to the IERGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "RNA data and related clinical details of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38988926", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "RNA data and related clinical details of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "To evaluate the risk score and the reliability of the model, time-dependent ROC curves were plotted, and the final AUC values for 1 year and 3 years were 0.70 and 0.63, respectively (Figure 4E).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38988926", "locator": "sections[16] \"Construction of a risk model\" paragraph 2", "quote": "To evaluate the risk score and the reliability of the model, time-dependent ROC curves were plotted, and the final AUC values for 1 year and 3 years were 0.70 and 0.63, respectively (Figure 4E)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "To evaluate the risk score and the reliability of the model, time-dependent ROC curves were plotted, and the final AUC values for 1 year and 3 years were 0.70 and 0.63, respectively (Figure 4E).", "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:38988926"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE30219"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE30219"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38988926", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-23-2295", "sha256": 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