A more novel and powerful prognostic gene signature of lung adenocarcinoma determined from the immune cell infiltration landscape.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36311939", "kind": "science-record", "revision": 4, "content_hash": "sha256:e2822beb40ee11b2b45ca625c04824762027d6c11c90f9dbac535bd2ce37dfbd", "created_at": "2026-11-01T18:15:44Z", "updated_at": "2026-11-01T22:07:59Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A more novel and powerful prognostic gene signature of lung adenocarcinoma determined from the immune cell infiltration landscape.", "doi": "10.3389/fsurg.2022.1015263", "pmid": "36311939", "authors": ["Chao Ma", "Feng Li", "Zhanfeng He", "Song Zhao"], "journal": "Frontiers in surgery", "year": 2022, "abstract": "Background: Lung adenocarcinoma (LUAD) is the leading histological subtype of lung cancer worldwide, causing high mortality each year. The tumor immune cell infiltration (ICI) is closely associated with clinical outcome with LUAD patients. The present study was designed to construct a gene signature based on the ICI of LUAD to predict prognosis.\nMethods: Downloaded the raw data of three cohorts of the TCGA-LUAD, GSE72094, and GSE68465 and treat them as training cohort, validation cohort one, and validation cohort two for this research. Unsupervised clustering detailed grouped LUAD cases of the training cohort based on the ICI profile. The univariate Cox regression and Kaplan-Meier was adopted to identify potential prognostic genes from the differentially expressed genes recognized from the ICI clusters. A risk score-based prognostic signature was subsequently developed using LASSO-penalized Cox regression analysis. The Kaplan-Meier analysis, Cox analysis, ROC, IAUC, and IBS were constructed to assess the ability to predict the prognosis and effects of clinical variables in another two independent validation cohorts. More innovatively, we searched similar papers in the most recent year and made comprehensive comparisons with ours. GSEA was used to discover the related signaling pathway. The immune relevant signature correlation identification and immune infiltrating analysis were used to evaluate the potential role of the signature for immunotherapy and recognize the critical immune cell that can influence the signature's prognosis capability.\nResults: A signature composed of thirteen gene including ABCC2, CCR2, CERS4, CMAHP, DENND1C, ECT2, FKBP4, GJB3, GNG7, KRT6A, PCDH7, PLK1, and VEGFC, was identified as significantly associated with the prognosis in LUAD patients. The thirteen-gene signature exhibited independence in evaluating the prognosis of LUAD patients in our training and validation cohorts. Compared to our predecessors, our model has an advantage in predictive power. Nine well know immunotherapy targets, including TBX2, TNF, CTLA4, HAVCR2, GZMB, CD8A, PRF1, GZMA, and PDCD1 were recognized correlating with our signature. The mast cells were found to play vital parts in backing on the thirteen-gene signature's outcome predictive capacity.\nConclusions: Collectively, the current study indicated a robust thirteen-gene signature that can accurately predict LUAD prognosis, which is superior to our predecessors in predictive ability. The immune relevant signatures, TBX2, TNF, CTLA4, HAVCR2, GZMB, CD8A, PRF1, GZMA, PDCD1, and mast cells infiltrating were found closely correlate with the thirteen-gene signature's power.", "claims": [{"id": "claim-method", "statement": "A risk score-based prognostic signature was subsequently developed using LASSO-penalized Cox regression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36311939", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "A risk score-based prognostic signature was subsequently developed using LASSO-penalized Cox regression analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: The following publicly available datasets were used in this study: TCGA-LUAD: https://gdc.xenahubs.net; GSE72094: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE72094; GSE68465: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE68465.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36311939", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: The following publicly available datasets were used in this study: TCGA-LUAD: https://gdc.xenahubs.net; GSE72094: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE72094; GSE68465: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE68465."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A signature composed of thirteen gene including ABCC2, CCR2, CERS4, CMAHP, DENND1C, ECT2, FKBP4, GJB3, GNG7, KRT6A, PCDH7, PLK1, and VEGFC, was identified as significantly associated with the prognosis in LUAD patients.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36311939", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A signature composed of thirteen gene including ABCC2, CCR2, CERS4, CMAHP, DENND1C, ECT2, FKBP4, GJB3, GNG7, KRT6A, PCDH7, PLK1, and VEGFC, was identified as significantly associated with the prognosis in LUAD patients."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A signature composed of thirteen gene including ABCC2, CCR2, CERS4, CMAHP, DENND1C, ECT2, FKBP4, GJB3, GNG7, KRT6A, PCDH7, PLK1, and VEGFC, was identified as significantly associated with the prognosis in LUAD patients.", "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:36311939"], "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:GSE68465"], "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:GSE68465"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36311939", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fsurg.2022.1015263", "sha256": "sha256:50c4e2556b70240abe191062862590aaab7f13ffec25ae48ca89c1b7cf32c885", "bytes": 56134, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fsurg.2022.1015263", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9606711", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE68465", "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=GSE68465", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE68465", "version": null, "note": "Publicly available datasets were analyzed in this study. This data can be found here: The following publicly available datasets were used in this study: TCGA-LUAD: https://gdc.xenahubs.net; GSE72094: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE72094; GSE68465: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE68465.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. 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