The identification of genes associated T-cell exhaustion and construction of prognostic signature to predict immunotherapy response in lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37592010", "kind": "science-record", "revision": 3, "content_hash": "sha256:f1d49dd8cf50c232fe2e6de97601b2c8eb2ef8344d14bd7a90a55871e2c32b61", "created_at": "2026-11-01T19:02:23Z", "updated_at": "2026-11-01T22:10:07Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The identification of genes associated T-cell exhaustion and construction of prognostic signature to predict immunotherapy response in lung adenocarcinoma.", "doi": "10.1038/s41598-023-40662-z", "pmid": "37592010", "authors": ["Yahua Wu", "Bin Du", "Mingqiang Lin", "Xiaohui Ji", "Chengliu Lv", "Jinhuo Lai"], "journal": "Scientific reports", "year": 2023, "abstract": "T-cell exhaustion (Tex) is considered to be a reason for immunotherapy resistance and poor prognosis in lung adenocarcinoma. Therefore, we used weighted correlation network analysis to identify Tex-related genes in the cancer genome atlas (TCGA). Unsupervised clustering approach based on Tex-related genes divided patients into cluster 1 and cluster 2. Then, we utilized random forest and the least absolute shrinkage and selection operator to identify nine key genes to construct a riskscore. Patients were classified as low or high-risk groups. The multivariate cox analysis showed the riskscore was an independent prognostic factor in TCGA and GSE72094 cohorts. Moreover, patients in cluster 2 with high riskscore had the worst prognosis. The immune response prediction analysis showed the low-risk group had higher immune, stromal, estimate scores, higher immunophenscore (IPS), and lower tumor immune dysfunction and exclusion score which suggested a better response to immune checkpoint inhibitors (ICIs) therapy in the low-risk group. In the meantime, we included two independent immunotherapy cohorts that also confirmed a better response to ICIs treatment in the low-risk group. Besides, we discovered differences in chemotherapy and targeted drug sensitivity between two groups. Finally, a nomogram was built to facilitate clinical decision making.", "claims": [{"id": "claim-method", "statement": "Finally, we further analyzed the relationship between the prognostic signature and clinical characteristics (including age, gender and stage) to clarify the independent prognostic value of riskscore using multivariate Cox regression analyses.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37592010", "locator": "sections[8] \"Development and verification of prognostic signature\" paragraph 3", "quote": "Finally, we further analyzed the relationship between the prognostic signature and clinical characteristics (including age, gender and stage) to clarify the independent prognostic value of riskscore using multivariate Cox regression analyses."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and analyzed during the current study are available in The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/), and Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/). Access to both databases is not required accession number. The datasets analyzed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37592010", "locator": "sections[28] \"Data availability\" paragraph 2", "quote": "The datasets generated and analyzed during the current study are available in The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/), and Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/). Access to both databases is not required accession number. The datasets analyzed during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The nine-gene riskscore was an independent prognostic factor for overall survival in both the TCGA-LUAD and GSE72094 cohorts.", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "In addition, when the riskscore was included in multivariate cox analysis combined with age, gender, and stage, it still was demonstrated as an independent prognostic factor for OS (HR = 1.904; 95% CI 1.389–2.612; P < 0.001; Fig. 6D)."}, "evidence": [{"asset": "source:pmid:37592010", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The multivariate cox analysis showed the riskscore was an independent prognostic factor in TCGA and GSE72094 cohorts."}, {"asset": "source:pmid:37592010", "locator": "sections[18] \"Construction of prognostic signature\" paragraph 2", "quote": "In addition, when the riskscore was included in multivariate cox analysis combined with age, gender, and stage, it still was demonstrated as an independent prognostic factor for OS (HR = 1.904; 95% CI 1.389–2.612; P < 0.001; Fig. 6D)."}, {"asset": "source:pmid:37592010", "locator": "sections[18] \"Construction of prognostic signature\" paragraph 2", "quote": "To further confirmed the reliability of the riskscore, we used the same formula to calculate the riskscore for patients in the GSE72094 cohort and used it for external validation. Similar to the previous results (Fig. 6E–H), the riskscore was an independent prognostic factor (HR = 1.990; 95% CI: 1.321–2.997; P < 0.001) as well and had good predictive power with an AUC at 1-, 2-, 3-year of 0.692, 0.709, 0.715."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The nine-gene riskscore was an independent prognostic factor for overall survival in both the TCGA-LUAD and GSE72094 cohorts.", "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:37592010"], "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:GSE135222"], "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:GSE135222"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37592010", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1038/s41598-023-40662-z", "sha256": "sha256:ec2d87d982007c8a5f273a58a1a1d04910e51e4c86c0f3f9689c295c93509b79", "bytes": 53607, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1038/s41598-023-40662-z", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10435542", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE135222", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE135222", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE135222", "version": null, "note": "The datasets generated and analyzed during the current study are available in The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/), and Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/). 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