Integrative Molecular Analyses of an Individual Transcription Factor-Based Genomic Model for Lung Cancer Prognosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34925645", "kind": "science-record", "revision": 3, "content_hash": "sha256:4b32c1d67d8d36d6d1a9e8e941ede0f98e49a2b66b19d9ab664169aa6abbdc54", "created_at": "2026-11-01T17:36:38Z", "updated_at": "2026-11-01T21:26:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrative Molecular Analyses of an Individual Transcription Factor-Based Genomic Model for Lung Cancer Prognosis.", "doi": "10.1155/2021/5125643", "pmid": "34925645", "authors": ["Rong Yao", "Leilei Zhou", "Zhongying Guo", "Dahong Zhang", "Tiecheng Zhang"], "journal": "Disease markers", "year": 2021, "abstract": "OBJECTIVE: Precision medicine with molecular profiles has revolutionized the management of lung cancer contributing to improved prognosis. Herein, we aimed to uncover the gene expression profiling of transcription factors (TFs) in lung cancer as well as to develop a TF-based genomic model.\nMETHODS: We retrospectively curated lung cancer patients from public databases. Through comparing mRNA expression profiling between lung cancer and normal specimens, specific TFs were determined. Thereafter, a TF genomic model was developed with univariate Cox regression and stepwise multivariable Cox analyses, which was verified through the GSE72094 dataset. Gene set enrichment analyses (GSEA) were presented. Downstream targets of TFs were predicted with ChEA, JASPAR, and MotifMap projects, and their biological significance was investigated through the clusterProfiler algorithm.\nRESULTS: In the TCGA cohort, we proposed a TF-based genomic model, comprised of SATB2, HLF, and NPAS2. Lung cancer individuals were remarkably stratified into high- and low-risk groups. Survival analyses uncovered that high-risk populations presented unfavorable survival outcomes. ROCs confirmed the excellent predictive potency in patients' prognosis. Additionally, this model was an independent prognostic indicator in accordance with multivariate analyses. The clinical implication of the model was well verified in an independent dataset. High risk score was in relation to carcinogenic pathways. Downstream targets were characterized by immune and carcinogenic activation.\nCONCLUSION: The proposed TF genomic model acts as a promising marker for estimation of lung cancer patients' outcomes. Prospective research is required for testing the clinical utility of the model in individualized management of lung cancer.", "claims": [{"id": "claim-method", "statement": "Further multivariate Cox regression analyses displayed that SATB2, HLF, and NPAS2 were independently predictive of lung cancer prognosis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34925645", "locator": "sections[14] \"3.3. Development of a TF Genomic Model for Lung Cancer Prognosis\" paragraph 2", "quote": "Further multivariate Cox regression analyses displayed that SATB2, HLF, and NPAS2 were independently predictive of lung cancer prognosis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used to support the findings of this study are included within the supplementary information files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34925645", "locator": "sections[22] \"Data Availability\" paragraph 2", "quote": "The data used to support the findings of this study are included within the supplementary information files."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The SATB2-HLF-NPAS2 three-transcription-factor genomic model showed prognostic predictive performance in the TCGA cohort, with an AUC of 0.676.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34925645", "locator": "sections[14] \"3.3. Development of a TF Genomic Model for Lung Cancer Prognosis\" paragraph 2", "quote": "Further ROC analyses were conducted for verifying the predictive potency of the TF genomic model in lung cancer outcomes."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The SATB2-HLF-NPAS2 three-transcription-factor genomic model showed prognostic predictive performance in the TCGA cohort, with an AUC of 0.676.", "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:34925645"], "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:GSE72094"], "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:GSE72094"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34925645", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2021/5125643", "sha256": 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