Identification of a Sixteen-gene Prognostic Biomarker for Lung Adenocarcinoma Using a Machine Learning Method.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31956375", "kind": "science-record", "revision": 5, "content_hash": "sha256:eb64b264503becb4ce0a0e96201dab3fa07b463b9d17f2c2974e8aacce1f531b", "created_at": "2026-11-01T15:40:55Z", "updated_at": "2026-11-01T21:22:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a Sixteen-gene Prognostic Biomarker for Lung Adenocarcinoma Using a Machine Learning Method.", "doi": "10.7150/jca.34585", "pmid": "31956375", "authors": ["Baoshan Ma", "Yao Geng", "Fanyu Meng", "Ge Yan", "Fengju Song"], "journal": "Journal of Cancer", "year": 2020, "abstract": "Objectives: Lung adenocarcinoma (LUAD) accounts for a majority of cancer-related deaths worldwide annually. The identification of prognostic biomarkers and prediction of prognosis for LUAD patients is necessary. Materials and Methods: In this study, LUAD RNA-Seq data and clinical data from the Cancer Genome Atlas (TCGA) were divided into TCGA cohort I (n = 338) and II (n = 168). The cohort I was used for model construction, and the cohort II and data from Gene Expression Omnibus (GSE72094 cohort, n = 393; GSE11969 cohort, n = 149) were utilized for validation. First, the survival-related seed genes were selected from the cohort I using the machine learning model (random survival forest, RSF), and then in order to improve prediction accuracy, the forward selection model was utilized to identify the prognosis-related key genes among the seed genes using the clinically-integrated RNA-Seq data. Second, the survival risk score system was constructed by using these key genes in the cohort II, the GSE72094 cohort and the GSE11969 cohort, and the evaluation metrics such as HR, p value and C-index were calculated to validate the proposed method. Third, the developed approach was compared with the previous five prediction models. Finally, bioinformatics analyses (pathway, heatmap, protein-gene interaction network) have been applied to the identified seed genes and key genes. Results and Conclusion: Based on the RSF model and clinically-integrated RNA-Seq data, we identified sixteen key genes that formed the prognostic gene expression signature. These sixteen key genes could achieve a strong power for prognostic prediction of LUAD patients in cohort II (HR = 3.80, p = 1.63e-06, C-index = 0.656), and were further validated in the GSE72094 cohort (HR = 4.12, p = 1.34e-10, C-index = 0.672) and GSE11969 cohort (HR = 3.87, p = 6.81e-07, C-index = 0.670). The experimental results of three independent validation cohorts showed that compared with the traditional Cox model and the use of standalone RNA-Seq data, the machine-learning-based method effectively improved the prediction accuracy of LUAD prognosis, and the derived model was also superior to the other five existing prediction models. KEGG pathway analysis found eleven of the sixteen genes were associated with Nicotine addiction. Thirteen of the sixteen genes were reported for the first time as the LUAD prognosis-related key genes. In conclusion, we developed a sixteen-gene prognostic marker for LUAD, which may provide a powerful prognostic tool for precision oncology.", "claims": [{"id": "claim-data-access", "statement": "The RNA-Seq data and the corresponding clinical information for LUAD were downloaded from the publicly available TCGA database. After filtering out the missing data, a total of 506 LUAD patients were kept as our study samples. The 506 LUAD patients were further randomly assigned to a training cohort (TCGA cohort I, n = 338) and an internal validation cohort (TCGA cohort II, n = 168).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31956375", "locator": "sections[3] \"Data collection and preprocessing\" paragraph 2", "quote": "The RNA-Seq data and the corresponding clinical information for LUAD were downloaded from the publicly available TCGA database. After filtering out the missing data, a total of 506 LUAD patients were kept as our study samples. The 506 LUAD patients were further randomly assigned to a training cohort (TCGA cohort I, n = 338) and an internal validation cohort (TCGA cohort II, n = 168)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:31956375"], "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:GSE11969"], "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": {"hazard_ratio": "cantly worse prognosis (HR = 1"}, "inputs": ["data:geo:GSE11969"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:31956375", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7150/jca.34585", "sha256": "sha256:c0dcbfe9765f332186a3a79863c63ba95d58146a1e759a75c2d4661b22671b06", "bytes": 41220, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.7150/jca.34585", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC6959071", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE11969", "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=GSE11969", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE11969", "version": null, "note": "The RNA-Seq data and the corresponding clinical information for LUAD were downloaded from the publicly available TCGA database. 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"caption": "Comparison of the sixteen-gene prognostic signatures to the five published lung cancer prognostic signatures", "rows": [["studies", "TCGA cohort II", "GSE72094 cohort", "GSE11969 cohort"], ["HR (95% CI)", "p", "C-index", "HR (95% CI)", "p", "C-index", "HR (95% CI)", "p", "C-index"], ["Present study16-gene signature", "3.80 (2.20-6.55)", "1.63e-06", "0.656", "4.12 (2.68-6.35)", "1.34e-10", "0.672", "3.87 (2.27-6.61)", "6.81e-07", "0.670"], ["Shukla et al.4-gene signature", "2.24 (1.36-3.68)", "1.48e-03", "0.613", "3.01 (2.00-4.51)", "1.07e-07", "0.639", "3.02 (1.85-4.92)", "9.42e-06", "0.641"], ["Boutros et al.6-gene signature", "1.70 (1.04-2.77)", "3.49e-02", "0.561", "3.17 (2.10-4.79)", "3.84e-08", "0.656", "3.09 (1.89-5.03)", "6.10e-06", "0.646"], ["Chen et al.5-gene signature", "2.20 (1.34-3.61)", "1.93e-03", "0.625", "2.83 (1.88-4.27)", "7.07e-07", "0.631", "3.09 (1.89-5.03)", "6.27e-06", "0.644"], ["Lau et al.3-gene signature", "1.80 (1.10-2.94)", "1.88e-02", "0.588", "2.81 (1.88-4.22)", "5.55e-07", "0.625", "2.62 (1.62-4.23)", "7.90e-05", "0.628"], ["Bianchi et al.10-gene signature", "3.13 (1.84-5.34)", "2.78e-05", "0.619", "2.92 (1.94-4.39)", "2.53e-07", "0.640", "3.41 (2.08-5.59)", "1.23e-06", "0.655"]], "row_count": 8, "column_count": 10, "numeric_cells": 65, "header_rows": 2, "locator": "section[17]/table-wrap[1]#T2"}}], "gaps": ["asset:data:geo:GSE11969 not downloaded", "asset:data:geo:GSE11969 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": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {"hazard_ratio": "cantly worse prognosis (HR = 1"}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "KM"}}, {"id": "method-2", "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": 0.7, "band": "medium", "quote": "Cox model"}}], "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": ["survival_km"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: kaplan_meier."}, "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", "GSE11969"], "resolved_accessions": ["GSE72094", "GSE11969"], "statements": ["The RNA-Seq data and the corresponding clinical information for LUAD were downloaded from the publicly available TCGA database. After filtering out the missing data, a total of 506 LUAD patients were kept as our study samples. The 506 LUAD patients were further randomly assigned to a training cohort (TCGA cohort I, n = 338) and an internal validation cohort (TCGA cohort II, n = 168)."], "basis": "The paper names GSE72094, GSE11969 in Gene Expression Omnibus (GEO) database and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["survival_km"], "assets": ["source:pmid:31956375", "data:geo:GSE11969", "asset:table:pmid:31956375:1", "asset:table:pmid:31956375:2"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE11969"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-10-31T18:55:38Z", "independence": "single_reader"}, "evidence_check": {"method": null, "availability": null}, "calls": 3, "usage": {"prompt_tokens": 32936, "completion_tokens": 1576, "cached_tokens": 640}}, "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:31956375", "type": "paper", "label": "pmid:31956375"}, {"id": "source:pmid:31956375", "type": "manuscript", "label": "PMID 31956375 full text"}, {"id": "data:geo:GSE11969", "type": "dataset", "label": "GEO GSE11969"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:31956375", "type": "paper", "label": "pmid:31956375"}, {"id": "source:pmid:31956375", "type": "manuscript", "label": "PMID 31956375 full text"}, {"id": "data:geo:GSE11969", "type": "dataset", "label": "GEO GSE11969"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-31956375", "type": "extraction", "used": ["source:pmid:31956375"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE11969", "type": "accession_resolution", "used": ["data:geo:GSE11969"], "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:31956375", "parent_hash": "sha256:43797facaf2c5fcd759e8cdf507f8a3e9d78e6925a5d93f6b7e0ee6bd91012b9", "fork_of": null, "generation": 4}, "gaps": ["asset:data:geo:GSE11969 licence not read", "asset:data:geo:GSE11969 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": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": [". Materials and Methods: In this study, LUAD RNA-Seq data and clinical data from the Cancer Genom", "e seed genes using the clinically-integrated RNA-Seq data. Second, the survival risk score system", "d on the RSF model and clinically-integrated RNA-Seq data, we identified sixteen key genes that f", "ditional Cox model and the use of standalone RNA-Seq data, the machine-learning-based method effe"], "order_basis": "paper_text_order:321", "id": "assay:1"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["survival_km"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.8, "evidence": ["KM"], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}