A Novel Immune-Related Gene Signature Predicts Prognosis of Lung Adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35437508", "kind": "science-record", "revision": 4, "content_hash": "sha256:b8ead58a27e3115147879c93fe3c105f891fe54613411c3b8cb2b752c955b4c1", "created_at": "2026-11-01T18:14:17Z", "updated_at": "2026-11-01T22:06:21Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Novel Immune-Related Gene Signature Predicts Prognosis of Lung Adenocarcinoma.", "doi": "10.1155/2022/4995874", "pmid": "35437508", "authors": ["Chao Ma", "Feng Li", "Ziming Wang", "Huan Luo"], "journal": "BioMed research international", "year": 2022, "abstract": "Background: Lung adenocarcinoma (LUAD) is the most common form of lung cancer, accounting for 30% of all cases and 40% of all non-small-cell lung cancer cases. Immune-related genes play a significant role in predicting the overall survival and monitoring the status of the cancer immune microenvironment. The present study was aimed at finding an immune-related gene signature for predicting LUAD patient outcomes.\nMethods: First, we chose the TCGA-LUAD project in the TCGA database as the training cohort for model training. For model validating, we found the datasets of GSE72094 and GSE68465 in the GEO database and took them as the candidate cohorts. We obtained 1793 immune-related genes from the ImmPort database and put them into a univariate Cox proportional hazard model to initially look for the genes with potential prognostic ability using the data of the training cohort. These identified genes then entered into a random survival forests-variable hunting algorithm for the best combination of genes for prognosis. In addition, the LASSO Cox regression model tested whether the gene combination can be further shrinkage, thereby constructing a gene signature. The Kaplan-Meier, Cox model, and ROC curve were deployed to examine the gene signature's prognosis in both cohorts. We conducted GSEA analysis to study further the mechanisms and pathways that involved the gene signature. Finally, we performed integrating analyses about the 22 TICs, fully interpreted the relationship between our signature and each TIC, and highlighted some TICs playing vital roles in the signature's prognostic ability.\nResults: A nine-gene signature was produced from the data of the training cohort. The Kaplan-Meier estimator, Cox proportional hazard model, and ROC curve confirmed the independence and predictive ability of the signature, using the data from the validation cohort. The GSEA analysis results illustrated the gene signature's mechanism and emphasized the importance of immune-related pathways for the gene signature. 22 TICs immune infiltration analysis revealed resting mast cells' key roles in contributing to gene signature's prognostic ability.\nConclusions: This study discovered a novel immune-related nine-gene signature (BTK, CCR6, S100A10, SEMA3C, GPI, SCG2, TNFRSF11A, CCL20, and DKK1) that predicts LUAD prognosis precisely and associates with resting mast cells strongly.", "claims": [{"id": "claim-method", "statement": "We obtained 1793 immune-related genes from the ImmPort database and put them into a univariate Cox proportional hazard model to initially look for the genes with potential prognostic ability using the data of the training cohort. These identified genes then entered into a random survival forests-variable hunting algorithm for the best combination of genes for prognosis. In addition, the LASSO Cox regression model tested whether the gene combination can be further shrinkage, thereby constructing a gene signature.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35437508", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We obtained 1793 immune-related genes from the ImmPort database and put them into a univariate Cox proportional hazard model to initially look for the genes with potential prognostic ability using the data of the training cohort. These identified genes then entered into a random survival forests-variable hunting algorithm for the best combination of genes for prognosis. In addition, the LASSO Cox regression model tested whether the gene combination can be further shrinkage, thereby constructing a gene signature."}], "state": "complete"}, {"id": "claim-data-access", "statement": "This study used the following publicly available datasets: TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35437508", "locator": "sections[18] \"Data Availability\" paragraph 2", "quote": "This study used the following publicly available datasets: TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "This study discovered a novel immune-related nine-gene signature (BTK, CCR6, S100A10, SEMA3C, GPI, SCG2, TNFRSF11A, CCL20, and DKK1) that predicts LUAD prognosis precisely and associates with resting mast cells strongly.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35437508", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "This study discovered a novel immune-related nine-gene signature (BTK, CCR6, S100A10, SEMA3C, GPI, SCG2, TNFRSF11A, CCL20, and DKK1) that predicts LUAD prognosis precisely and associates with resting mast cells strongly."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "This study discovered a novel immune-related nine-gene signature (BTK, CCR6, S100A10, SEMA3C, GPI, SCG2, TNFRSF11A, CCL20, and DKK1) that predicts LUAD prognosis precisely and associates with resting mast cells strongly.", "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:35437508"], "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:35437508", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/4995874", "sha256": "sha256:fa65c8667cf5801078fb5341f0cadb622c19963bb9d3a802de5aff7b6c2f48ea", "bytes": 27657, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1155/2022/4995874", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9013292", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "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": "This study used the following publicly available datasets: TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo/).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "This study used the following publicly available datasets: TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo/).", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1672281", "GSM1672282", "GSM1672283", "GSM1672284", "GSM1672285", "GSM1672286", "GSM1672287", "GSM1672288", "GSM1672289", "GSM1672290", "GSM1672291", "GSM1672292", "GSM1672293", "GSM1672294", "GSM1672295", "GSM1672296", "GSM1672297", "GSM1672298", "GSM1672299", "GSM1672300", "GSM1672301", "GSM1672302", "GSM1672303", "GSM1672304", "GSM1672305", "GSM1672306", "GSM1672307", "GSM1672308", "GSM1672309", "GSM1672310", "GSM1672311", "GSM1672312", "GSM1672313", "GSM1672314", "GSM1672315", "GSM1672316", "GSM1672317", "GSM1672318", "GSM1672319", "GSM1672320", "GSM1672321", "GSM1672322", "GSM1672323", "GSM1672324", "GSM1672325", "GSM1672326", "GSM1672327", "GSM1672328", "GSM1672329", "GSM1672330", "GSM1672331", "GSM1672332", "GSM1672333", "GSM1672334", "GSM1672335", "GSM1672336", "GSM1672337", "GSM1672338", "GSM1672339", "GSM1672340", "GSM1672341", "GSM1672342", "GSM1672343", "GSM1672344", "GSM1672345", "GSM1672346", "GSM1672347", "GSM1672348", "GSM1672349", "GSM1672350", "GSM1672351", "GSM1672352", "GSM1672353", "GSM1672354", "GSM1672355", "GSM1672356", "GSM1672357", "GSM1672358", "GSM1672359", "GSM1672360", "GSM1672361", "GSM1672362", "GSM1672363", "GSM1672364", "GSM1672365", "GSM1672366", "GSM1672367", "GSM1672368", "GSM1672369", "GSM1672370", "GSM1672371", "GSM1672372", "GSM1672373", "GSM1672374", "GSM1672375", "GSM1672376", "GSM1672377", "GSM1672378", "GSM1672379", "GSM1672380", "GSM1672381", "GSM1672382", "GSM1672383", "GSM1672384", "GSM1672385", "GSM1672386", "GSM1672387", "GSM1672388", "GSM1672389", "GSM1672390", "GSM1672391", "GSM1672392", "GSM1672393", "GSM1672394", "GSM1672395", "GSM1672396", "GSM1672397", "GSM1672398", "GSM1672399", "GSM1672400", "GSM1672401", "GSM1672402", "GSM1672403", "GSM1672404", "GSM1672405", "GSM1672406", "GSM1672407", "GSM1672408", "GSM1672409", "GSM1672410", "GSM1672411", "GSM1672412", "GSM1672413", "GSM1672414", "GSM1672415", "GSM1672416", "GSM1672417", "GSM1672418", "GSM1672419", "GSM1672420", "GSM1672421", "GSM1672422", "GSM1672423", "GSM1672424", "GSM1672425", "GSM1672426", "GSM1672427", "GSM1672428", "GSM1672429", "GSM1672430", "GSM1672431", "GSM1672432", "GSM1672433", "GSM1672434", "GSM1672435", "GSM1672436", "GSM1672437", "GSM1672438", "GSM1672439", "GSM1672440", "GSM1672441", "GSM1672442", "GSM1672443", "GSM1672444", "GSM1672445", "GSM1672446", "GSM1672447", "GSM1672448", "GSM1672449", "GSM1672450", "GSM1672451", "GSM1672452", "GSM1672453", "GSM1672454", "GSM1672455", "GSM1672456", "GSM1672457", "GSM1672458", "GSM1672459", "GSM1672460", "GSM1672461", "GSM1672462", "GSM1672463", "GSM1672464", "GSM1672465", "GSM1672466", "GSM1672467", "GSM1672468", "GSM1672469", "GSM1672470", "GSM1672471", "GSM1672472", "GSM1672473", "GSM1672474", "GSM1672475", "GSM1672476", "GSM1672477", "GSM1672478", "GSM1672479", "GSM1672480", "GSM1672481", "GSM1672482", "GSM1672483", "GSM1672484", "GSM1672485", "GSM1672486", "GSM1672487", "GSM1672488", "GSM1672489", "GSM1672490", "GSM1672491", "GSM1672492", "GSM1672493", "GSM1672494", "GSM1672495", "GSM1672496", "GSM1672497", "GSM1672498", "GSM1672499", "GSM1672500", "GSM1672501", "GSM1672502", "GSM1672503", "GSM1672504", "GSM1672505", "GSM1672506", "GSM1672507", "GSM1672508", "GSM1672509", "GSM1672510", "GSM1672511", "GSM1672512", "GSM1672513", "GSM1672514", "GSM1672515", "GSM1672516", "GSM1672517", "GSM1672518", "GSM1672519", "GSM1672520", "GSM1672521", "GSM1672522", "GSM1672523", "GSM1672524", "GSM1672525", "GSM1672526", "GSM1672527", "GSM1672528", "GSM1672529", "GSM1672530", "GSM1672531", "GSM1672532", "GSM1672533", "GSM1672534", "GSM1672535", "GSM1672536", "GSM1672537", "GSM1672538", "GSM1672539", "GSM1672540", "GSM1672541", "GSM1672542", "GSM1672543", "GSM1672544", "GSM1672545", "GSM1672546", "GSM1672547", "GSM1672548", "GSM1672549", "GSM1672550", "GSM1672551", "GSM1672552", "GSM1672553", "GSM1672554", "GSM1672555", "GSM1672556", "GSM1672557", "GSM1672558", "GSM1672559", "GSM1672560", "GSM1672561", "GSM1672562", "GSM1672563", "GSM1672564", "GSM1672565", "GSM1672566", "GSM1672567", "GSM1672568", "GSM1672569", "GSM1672570", "GSM1672571", "GSM1672572", "GSM1672573", "GSM1672574", "GSM1672575", "GSM1672576", "GSM1672577", "GSM1672578", "GSM1672579", "GSM1672580", "GSM1672581", "GSM1672582", "GSM1672583", "GSM1672584", "GSM1672585", "GSM1672586", "GSM1672587", "GSM1672588", "GSM1672589", "GSM1672590", "GSM1672591", "GSM1672592", "GSM1672593", "GSM1672594", "GSM1672595", "GSM1672596", "GSM1672597", "GSM1672598", "GSM1672599", "GSM1672600", "GSM1672601", "GSM1672602", "GSM1672603", "GSM1672604", "GSM1672605", "GSM1672606", "GSM1672607", "GSM1672608", "GSM1672609", "GSM1672610", "GSM1672611", "GSM1672612", "GSM1672613", "GSM1672614", "GSM1672615", "GSM1672616", "GSM1672617", "GSM1672618", "GSM1672619", "GSM1672620", "GSM1672621", "GSM1672622", "GSM1672623", "GSM1672624", "GSM1672625", "GSM1672626", "GSM1672627", "GSM1672628", "GSM1672629", "GSM1672630", "GSM1672631", "GSM1672632", "GSM1672633", "GSM1672634", "GSM1672635", "GSM1672636", "GSM1672637", "GSM1672638", "GSM1672639", "GSM1672640", "GSM1672641", "GSM1672642", "GSM1672643", "GSM1672644", "GSM1672645", "GSM1672646", "GSM1672647", "GSM1672648", "GSM1672649", "GSM1672650", "GSM1672651", "GSM1672652", "GSM1672653", "GSM1672654", "GSM1672655", "GSM1672656", "GSM1672657", "GSM1672658", "GSM1672659", "GSM1672660", "GSM1672661", "GSM1672662", "GSM1672663", "GSM1672664", "GSM1672665", "GSM1672666", "GSM1672667", "GSM1672668", "GSM1672669", "GSM1672670", "GSM1672671", "GSM1672672", "GSM1672673", "GSM1672674", "GSM1672675", "GSM1672676", "GSM1672677", "GSM1672678", "GSM1672679", "GSM1672680", "GSM1672681", "GSM1672682", "GSM1672683", "GSM1672684", "GSM1672685", "GSM1672686", "GSM1672687", "GSM1672688", "GSM1672689", "GSM1672690", "GSM1672691", "GSM1672692", "GSM1672693", "GSM1672694", "GSM1672695", "GSM1672696", "GSM1672697", "GSM1672698", "GSM1672699", "GSM1672700", "GSM1672701", "GSM1672702", "GSM1672703", "GSM1672704", "GSM1672705", "GSM1672706", "GSM1672707", "GSM1672708", "GSM1672709", "GSM1672710", "GSM1672711", "GSM1672712", "GSM1672713", "GSM1672714", "GSM1672715", "GSM1672716", "GSM1672717", "GSM1672718", "GSM1672719", "GSM1672720", "GSM1672721", "GSM1672722", "GSM1672723", "GSM1672724", "GSM1672725", "GSM1672726", "GSM1672727", "GSM1672728", "GSM1672729", "GSM1672730", "GSM1672731", "GSM1672732", "GSM1672733", "GSM1672734", "GSM1672735", "GSM1672736", "GSM1672737", "GSM1672738", "GSM1672739", "GSM1672740", "GSM1672741", "GSM1672742"], "sample_count": 462, "group_sizes": null, "group_sizes_basis": null, "platform": "96", "taxon": "Homo sapiens", "title": "caArray_jacob-00182: Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE72094", "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=GSE72094", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE72094", "version": null, "note": "This study used the following publicly available datasets: TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo/).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "This study used the following publicly available datasets: TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo/).", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1854797", "GSM1854798", "GSM1854799", "GSM1854800", "GSM1854801", "GSM1854802", "GSM1854803", "GSM1854804", "GSM1854805", "GSM1854806", "GSM1854807", "GSM1854808", "GSM1854809", "GSM1854810", "GSM1854811", "GSM1854812", "GSM1854813", "GSM1854814", "GSM1854815", "GSM1854816", "GSM1854817", "GSM1854818", "GSM1854819", "GSM1854820", "GSM1854821", "GSM1854822", "GSM1854823", "GSM1854824", "GSM1854825", "GSM1854826", "GSM1854827", "GSM1854828", "GSM1854829", "GSM1854830", "GSM1854831", "GSM1854832", "GSM1854833", "GSM1854834", "GSM1854835", "GSM1854836", "GSM1854837", "GSM1854838", "GSM1854839", "GSM1854840", "GSM1854841", "GSM1854842", "GSM1854843", "GSM1854844", "GSM1854845", "GSM1854846", "GSM1854847", "GSM1854848", "GSM1854849", "GSM1854850", "GSM1854851", "GSM1854852", "GSM1854853", "GSM1854854", "GSM1854855", "GSM1854856", "GSM1854857", "GSM1854858", "GSM1854859", "GSM1854860", "GSM1854861", "GSM1854862", "GSM1854863", "GSM1854864", "GSM1854865", "GSM1854866", "GSM1854867", "GSM1854868", "GSM1854869", "GSM1854870", "GSM1854871", "GSM1854872", "GSM1854873", "GSM1854874", "GSM1854875", "GSM1854876", "GSM1854877", "GSM1854878", "GSM1854879", "GSM1854880", "GSM1854881", "GSM1854882", "GSM1854883", "GSM1854884", "GSM1854885", "GSM1854886", "GSM1854887", "GSM1854888", "GSM1854889", "GSM1854890", "GSM1854891", "GSM1854892", "GSM1854893", "GSM1854894", "GSM1854895", "GSM1854896", "GSM1854897", "GSM1854898", "GSM1854899", "GSM1854900", "GSM1854901", "GSM1854902", "GSM1854903", "GSM1854904", "GSM1854905", "GSM1854906", "GSM1854907", "GSM1854908", "GSM1854909", "GSM1854910", "GSM1854911", "GSM1854912", "GSM1854913", "GSM1854914", "GSM1854915", "GSM1854916", "GSM1854917", "GSM1854918", "GSM1854919", "GSM1854920", "GSM1854921", "GSM1854922", "GSM1854923", "GSM1854924", "GSM1854925", "GSM1854926", "GSM1854927", "GSM1854928", "GSM1854929", "GSM1854930", "GSM1854931", "GSM1854932", "GSM1854933", "GSM1854934", "GSM1854935", "GSM1854936", "GSM1854937", "GSM1854938", "GSM1854939", "GSM1854940", "GSM1854941", "GSM1854942", "GSM1854943", "GSM1854944", "GSM1854945", "GSM1854946", "GSM1854947", "GSM1854948", "GSM1854949", "GSM1854950", "GSM1854951", "GSM1854952", "GSM1854953", "GSM1854954", "GSM1854955", "GSM1854956", "GSM1854957", "GSM1854958", "GSM1854959", "GSM1854960", "GSM1854961", "GSM1854962", "GSM1854963", "GSM1854964", "GSM1854965", "GSM1854966", "GSM1854967", "GSM1854968", "GSM1854969", "GSM1854970", "GSM1854971", "GSM1854972", "GSM1854973", "GSM1854974", "GSM1854975", "GSM1854976", "GSM1854977", "GSM1854978", "GSM1854979", "GSM1854980", "GSM1854981", "GSM1854982", "GSM1854983", "GSM1854984", "GSM1854985", "GSM1854986", "GSM1854987", "GSM1854988", "GSM1854989", "GSM1854990", "GSM1854991", "GSM1854992", "GSM1854993", "GSM1854994", "GSM1854995", "GSM1854996", "GSM1854997", "GSM1854998", "GSM1854999", "GSM1855000", "GSM1855001", "GSM1855002", "GSM1855003", "GSM1855004", "GSM1855005", "GSM1855006", "GSM1855007", "GSM1855008", "GSM1855009", "GSM1855010", "GSM1855011", "GSM1855012", "GSM1855013", "GSM1855014", "GSM1855015", "GSM1855016", "GSM1855017", "GSM1855018", "GSM1855019", "GSM1855020", "GSM1855021", "GSM1855022", "GSM1855023", "GSM1855024", "GSM1855025", "GSM1855026", "GSM1855027", "GSM1855028", "GSM1855029", "GSM1855030", "GSM1855031", "GSM1855032", "GSM1855033", "GSM1855034", "GSM1855035", "GSM1855036", "GSM1855037", "GSM1855038", "GSM1855039", "GSM1855040", "GSM1855041", "GSM1855042", "GSM1855043", "GSM1855044", "GSM1855045", "GSM1855046", "GSM1855047", "GSM1855048", "GSM1855049", "GSM1855050", "GSM1855051", "GSM1855052", "GSM1855053", "GSM1855054", "GSM1855055", "GSM1855056", "GSM1855057", "GSM1855058", "GSM1855059", "GSM1855060", "GSM1855061", "GSM1855062", "GSM1855063", "GSM1855064", "GSM1855065", "GSM1855066", "GSM1855067", "GSM1855068", "GSM1855069", "GSM1855070", "GSM1855071", "GSM1855072", "GSM1855073", "GSM1855074", "GSM1855075", "GSM1855076", "GSM1855077", "GSM1855078", "GSM1855079", "GSM1855080", "GSM1855081", "GSM1855082", "GSM1855083", "GSM1855084", "GSM1855085", "GSM1855086", "GSM1855087", "GSM1855088", "GSM1855089", "GSM1855090", "GSM1855091", "GSM1855092", "GSM1855093", "GSM1855094", "GSM1855095", "GSM1855096", "GSM1855097", "GSM1855098", "GSM1855099", "GSM1855100", "GSM1855101", "GSM1855102", "GSM1855103", "GSM1855104", "GSM1855105", "GSM1855106", "GSM1855107", "GSM1855108", "GSM1855109", "GSM1855110", "GSM1855111", "GSM1855112", "GSM1855113", "GSM1855114", "GSM1855115", "GSM1855116", "GSM1855117", "GSM1855118", "GSM1855119", "GSM1855120", "GSM1855121", "GSM1855122", "GSM1855123", "GSM1855124", "GSM1855125", "GSM1855126", "GSM1855127", "GSM1855128", "GSM1855129", "GSM1855130", "GSM1855131", "GSM1855132", "GSM1855133", "GSM1855134", "GSM1855135", "GSM1855136", "GSM1855137", "GSM1855138", "GSM1855139", "GSM1855140", "GSM1855141", "GSM1855142", "GSM1855143", "GSM1855144", "GSM1855145", "GSM1855146", "GSM1855147", "GSM1855148", "GSM1855149", "GSM1855150", "GSM1855151", "GSM1855152", "GSM1855153", "GSM1855154", "GSM1855155", "GSM1855156", "GSM1855157", "GSM1855158", "GSM1855159", "GSM1855160", "GSM1855161", "GSM1855162", "GSM1855163", "GSM1855164", "GSM1855165", "GSM1855166", "GSM1855167", "GSM1855168", "GSM1855169", "GSM1855170", "GSM1855171", "GSM1855172", "GSM1855173", "GSM1855174", "GSM1855175", "GSM1855176", "GSM1855177", "GSM1855178", "GSM1855179", "GSM1855180", "GSM1855181", "GSM1855182", "GSM1855183", "GSM1855184", "GSM1855185", "GSM1855186", "GSM1855187", "GSM1855188", "GSM1855189", "GSM1855190", "GSM1855191", "GSM1855192", "GSM1855193", "GSM1855194", "GSM1855195", "GSM1855196", "GSM1855197", "GSM1855198", "GSM1855199", "GSM1855200", "GSM1855201", "GSM1855202", "GSM1855203", "GSM1855204", "GSM1855205", "GSM1855206", "GSM1855207", "GSM1855208", "GSM1855209", "GSM1855210", "GSM1855211", "GSM1855212", "GSM1855213", "GSM1855214", "GSM1855215", "GSM1855216", "GSM1855217", "GSM1855218", "GSM1855219", "GSM1855220", "GSM1855221", "GSM1855222", "GSM1855223", "GSM1855224", "GSM1855225", "GSM1855226", "GSM1855227", "GSM1855228", "GSM1855229", "GSM1855230", "GSM1855231", "GSM1855232", "GSM1855233", "GSM1855234", "GSM1855235", "GSM1855236", "GSM1855237", "GSM1855238"], "sample_count": 442, "group_sizes": null, "group_sizes_basis": null, "platform": "15048", "taxon": "Homo sapiens", "title": "KRAS mutation-associated gene expression, p53 and STK11 mutations, proliferation and immune surveillance in lung adenocarcinoma", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE68465 not downloaded", "asset:data:geo:GSE68465 licence not read", "asset:data:geo:GSE72094 not downloaded", "asset:data:geo:GSE72094 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "We obtained 1793 immune-related genes from the ImmPort database and put them into a univariate Cox proportional hazard model to initially look for the genes with potential prognostic ability using the data of the training cohort. These identified genes then entered into a random survival forests-variable hunting algorithm for the best combination of genes for prognosis. In addition, the LASSO Cox regression model tested whether the gene combination can be further shrinkage, thereby constructing a gene signature."}}, {"id": "method-2", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.7, "band": "medium", "quote": "Kaplan-Meier"}}, {"id": "method-3", "family": "survival_analysis", "specific": "log_rank_test", "label": "Survival analysis", "dsl_operations": ["logrank_test"], "missing_operations": [], "expressible": true, "parameters": {"p_value": "ow-risk patients with a p value < 0"}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "log-rank"}}, {"id": "method-4", "family": "correlation_analysis", "specific": "spearman", "label": "Correlation analysis", "dsl_operations": ["spearman_rho"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Spearman's rank correlation"}}, {"id": "method-5", "family": "diagnostic_evaluation", "specific": "roc", "label": "Diagnostic test evaluation", "dsl_operations": ["confusion_counts", "roc_curve", "sensitivity_specificity", "solve_confusion_matrix", "classification_metrics", "published_precision_envelope"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["reference_standard_and_test"], "evidence": {"score": 0.7, "band": "medium", "quote": "ROC curve"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE72094", "GSE68465"], "resolved_accessions": ["GSE72094", "GSE68465"], "statements": ["This study used the following publicly available datasets: TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo/)."], "basis": "The paper names GSE72094, GSE68465 in GEO and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:35437508", "data:geo:GSE68465"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE68465"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T17:42:51Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 8287, "completion_tokens": 1749, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:35437508", "type": "paper", "label": "pmid:35437508"}, {"id": "source:pmid:35437508", "type": "manuscript", "label": "PMID 35437508 full text"}, {"id": "data:geo:GSE68465", "type": "dataset", "label": "GEO GSE68465"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:35437508", "type": "paper", "label": "pmid:35437508"}, {"id": "source:pmid:35437508", "type": "manuscript", "label": "PMID 35437508 full text"}, {"id": "data:geo:GSE68465", "type": "dataset", "label": "GEO GSE68465"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-35437508", "type": "extraction", "used": ["source:pmid:35437508"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE68465", "type": "accession_resolution", "used": ["data:geo:GSE68465"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:35437508", "parent_hash": "sha256:7367ef56874dde32ea1bf24ef77c77c95d37e93515d7a8fb63c018456c1840ce", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE68465 licence not read", "asset:data:geo:GSE68465 not downloaded", "asset:data:geo:GSE72094 licence not read", "asset:data:geo:GSE72094 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "clinical_cohort", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.85, "evidence": ["e differences between the high- and low-risk patients with a p value < 0.05.Figure S3: overall profiles"], "order_basis": "paper_text_order:3563", "id": "assay:1"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["We obtained 1793 immune-related genes from the ImmPort database and put them into a univariate Cox proportional hazard model to initially look for the genes with potential prognostic ability using the data of the training cohort. These identified genes then entered into a random survival forests-variable hunting algorithm for the best combination of genes for prognosis. In addition, the LASSO Cox regression model tested whether the gene combination can be further shrinkage, thereby constructing a gene signature."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}