Identification of prognostic biomarkers of smoking-related lung cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38505085", "kind": "science-record", "revision": 3, "content_hash": "sha256:c0260cbd7d5653d18c42a5996aef5209cad41d3e5072f1391ebcffd5ca98326b", "created_at": "2026-11-01T19:20:26Z", "updated_at": "2026-11-01T21:32:43Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of prognostic biomarkers of smoking-related lung cancer.", "doi": "10.21037/jtd-23-1890", "pmid": "38505085", "authors": ["Chen Liang", "Wei Pan", "Zhijun Zhou", "Xiaomin Liu"], "journal": "Journal of thoracic disease", "year": 2024, "abstract": "Background: The early diagnosis and effective prognostic treatment measures for lung cancer are still limited, leading to a 5-year survival rate of less than 15% for these patients. Smoking is one of the causes of lung cancer, but it is not the initial carcinogenic factor. It is not clear what specific mechanism cigarette induces lung cancer, and there is a lack of research on the relationship between related genes and the prognosis of patients with smoking lung cancer. The objective of this study was to provide new theoretical evidence and potential therapeutic targets for the mechanisms of smoking-related lung cancer formation.\nMethods: The gene expression profile data from the GSE12428 dataset which includes 63 lung cancer and normal tissue pairs were downloaded from the Gene Expression Omnibus (GEO) database, and data from smokers with lung cancer [both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC)] from The Cancer Genome Atlas (TCGA) database were analyzed. The differential genes in smokers with lung cancer were screened using the linear model for microarray data via R software. The differential gene enrichment analysis was performed using the online analysis software Database for Annotation, Visualization and Integrated Discovery (DAVID). The expression levels of differential genes and their correlation with patient tumor clinical stage were analyzed using gene expression profiling interactive analysis (GEPIA). The overall survival rate was analyzed using Kaplan-Meier curves.\nResults: In the GSE12428 dataset, 225 upregulated genes and 565 downregulated genes were identified in cancer tissues; based on smoking status, 1 upregulated gene and 4 downregulated genes were identified. Among smokers who also had lung cancer, 4 genes were downregulated, namely CSH1, BPIFA1, SLPI, and SCGB3A1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that these genes were mainly associated with biological functions such as antibacterial response, humoral immune response, and response to external stimuli. Among them, BPIFA1, SLPI, and SCGB3A1 expression was decreased in lung cancer tissues, with SCGB3A1 showing significant differences. Additionally, high expression of SCGB3A1 was associated with favorable prognosis in patients with lung cancer.\nConclusions: Three genes BPIFA1, SLPI and SCGB3A1, were identified as being associated with smokers with lung cancer, with SCGB3A1 showing a close correlation with patient prognosis. These findings provide potential new targets for the treatment of lung cancer. Certainly, this study needs to more investigate the mechanism of these genes regulation.", "claims": [{"id": "claim-method", "statement": "The Kaplan-Meier plotter (https://kmplot.com/analysis/) was used for online analysis of the relationship between the differential gene mRNA expression levels obtained from lung cancer (LUAD and LUSC) tissues and normal tissues and survival data.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38505085", "locator": "sections[8] \"Prognostic analysis of differentially expressed genes\" paragraph 2", "quote": "The Kaplan-Meier plotter (https://kmplot.com/analysis/) was used for online analysis of the relationship between the differential gene mRNA expression levels obtained from lung cancer (LUAD and LUSC) tissues and normal tissues and survival data."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The messenger RNA (mRNA) expression profile data from the GSE12428 dataset were downloaded from the GEO database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38505085", "locator": "sections[4] \"Dataset selection\" paragraph 2", "quote": "The messenger RNA (mRNA) expression profile data from the GSE12428 dataset were downloaded from the GEO database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Additionally, high expression of SCGB3A1 was associated with favorable prognosis in patients with lung cancer.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38505085", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Additionally, high expression of SCGB3A1 was associated with favorable prognosis in patients with lung cancer."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Additionally, high expression of SCGB3A1 was associated with favorable prognosis in patients with lung cancer.", "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:38505085"], "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:GSE12428"], "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:GSE12428"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38505085", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/jtd-23-1890", "sha256": "sha256:3468ba654b01f3112c340ed1934ac03d79c921a0004d8ad5741cbacf8559c8db", "bytes": 28455, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/jtd-23-1890", "version": null, "note": "Full article fetched from PMC; 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