Identification and validation of cigarette smoking-related genes in predicting prostate cancer development through bioinformatic analysis and experiments.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39625524", "kind": "science-record", "revision": 3, "content_hash": "sha256:156101e2fa9353a7da66d721b7f7ecc14d793b19e1e285f7faefd092dfcdd086", "created_at": "2026-11-01T20:29:53Z", "updated_at": "2026-11-01T22:13:14Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of cigarette smoking-related genes in predicting prostate cancer development through bioinformatic analysis and experiments.", "doi": "10.1007/s12672-024-01645-2", "pmid": "39625524", "authors": ["Duocheng Qian", "Xin'an Wang", "Tengfei Lv", "Dujian Li", "Xi Chen"], "journal": "Discover oncology", "year": 2024, "abstract": "The morbidity and mortality rates of prostate cancer (PCa) are high among elderly men worldwide. Several factors, such as heredity, obesity, and environment are associated with the occurrence of PCa. Cigarette smoking, which is also an important factor in the development of PCa, can lead to genetic alterations and consequently promote PCa development. However, the smoking-induced genetic alterations in PCa are unclear. This study aimed to identify the potential smoking-related genes associated with PCa development. The smoking-related differentially expressed genes (DEGs) were identified using the Gene Expression Omnibus (GEO) which included lots of PCa datasets. DEGs were subjected to protein-protein interaction (PPI) network analysis to identify the hub genes. The pathways in which these hub genes were enriched were identified. The Cancer Genome Atlas (TCGA) dataset was used to examine the expression of smoking-related genes in PCa samples and estimate their value in predicting tumor progression and prognosis. In total, 110 smoking-related DEGs were got from GSE68135 dataset which included microarray data of PCa patients with smoking or not and 14 smoking-related key genes associated with PCa were identified from PPI network. The expression of the following seven key genes was altered in TCGA PCa patients: EWSR1, SRSF6, COL6A3, FBLN1, DCN, CYP2J2, and PLA2G2A. EWSR1, SRSF6, FBLN1, and CYP2J2 also influenced PCa progression. Additionally, EWSR1 influenced disease-free survival. In the logistic regression model, CYP2J2, which exhibited the highest risk scores, was identified as the risk gene for PCa. We also found one of the smoking-related genes: EWSR1 was truly upregulated in clinical PCa patients and influenced PCa cells invasion and proliferation. This study identified the function of smoking-related genes involved in the progression of PCa.", "claims": [{"id": "claim-method", "statement": "The DEGs in the GSE68135 dataset were determined based on the following criteria: adjusted P-value < 0.05 and |log2 fold change (FC)| > 1. Next, these DEGs were represented using a volcano map, which was constructed using the R package “DESeq2” which can reflect the DEGs more directly [23]. A PPI network of the DEGs was constructed using the STRING online database (http://string-db.org/) and the raw data were downloaded from STRING [24]. Then the raw PPI data from STRING was reconstructed using Cytoscape software and the hub genes in the PPI network were identified using the plug-in: Molecular Complex Detection (MCODE) [25]. In MCODE, a gene which marked as a clustered or seed gene was indicated as a hub gene.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39625524", "locator": "sections[6] \"Identification of DEGs, construction of PPI network and mutation analysis\" paragraph 2", "quote": "The DEGs in the GSE68135 dataset were determined based on the following criteria: adjusted P-value < 0.05 and |log2 fold change (FC)| > 1. Next, these DEGs were represented using a volcano map, which was constructed using the R package “DESeq2” which can reflect the DEGs more directly [23]. A PPI network of the DEGs was constructed using the STRING online database (http://string-db.org/) and the raw data were downloaded from STRING [24]. Then the raw PPI data from STRING was reconstructed using Cytoscape software and the hub genes in the PPI network were identified using the plug-in: Molecular Complex Detection (MCODE) [25]. 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Other data can get from the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "In total, 110 DEGs were identified in the GSE68135 dataset (68 upregulated genes and 42 downregulated genes) (Fig. 1A).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39625524", "locator": "sections[16] \"Identification of hub genes associated with smoking in PCa\" paragraph 2", "quote": "In total, 110 DEGs were identified in the GSE68135 dataset (68 upregulated genes and 42 downregulated genes) (Fig. 1A)."}, {"asset": "source:pmid:39625524", "locator": "sections[16] \"Identification of hub genes associated with smoking in PCa\" paragraph 2", "quote": "Among the 110 DEGs, 14 hub genes were identified in the PPI network (Fig. 1B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "In total, 110 DEGs were identified in the GSE68135 dataset (68 upregulated genes and 42 downregulated genes) (Fig. 1A).", "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:39625524"], "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. 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Other data can get from the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The data from public databases can be acquired from TCGA (http://portal.gdc.cancer.gov), GEO (https://www.ncbi.nlm.nih.gov/gds), and CPGEA (http://www.cpgea.com/) databases. 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE68135"], "resolved_accessions": ["GSE68135"], "statements": ["The data from public databases can be acquired from TCGA (http://portal.gdc.cancer.gov), GEO (https://www.ncbi.nlm.nih.gov/gds), and CPGEA (http://www.cpgea.com/) databases. Other data can get from the corresponding author."], "basis": "The paper names GSE68135 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. 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The cell culture and transfection process was carried out as previous study de", "e constructed EWSR1 knck down lentivirus and transfected it into PC-3 PCa cells. We found that the le", "l of EWSR1 after EWSR1 knock down lentivirus transfected into PC-3 cells or not. D, E Cell proliferat"], "order_basis": "paper_text_order:2033", "id": "assay:1"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["were analyzed in clinical PCa tissues using immunohistochemical (IHC) analysis. B The IHC score of EWSR1. C", "ated in clinical PCa samples and the typical IHC images were shown (Fig. 7A, B). Then we trie", "nical PCa tissues using immunohistochemical (IHC) analysis. B The IHC score of EWSR1. C The p", "ng immunohistochemical (IHC) analysis. B The IHC score of EWSR1. C The protein level of EWSR1"], "order_basis": "paper_text_order:6191", "id": "assay:2"}, {"role": "data_generation", "definition_id": "apoptosis_assay", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["1 in PC-3 PCa cells (Fig. 7C). Then, we used CCK-8 experiments to find whether the cell prolife"], "order_basis": "paper_text_order:6501", "id": "assay:3"}, {"role": "analysis", "definition_id": "network_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The DEGs in the GSE68135 dataset were determined based on the following criteria: adjusted P-value < 0.05 and |log2 fold change (FC)| > 1. Next, these DEGs were represented using a volcano map, which was constructed using the R package “DESeq2” which can reflect the DEGs more directly [23]. A PPI network of the DEGs was constructed using the STRING online database (http://string-db.org/) and the raw data were downloaded from STRING [24]. Then the raw PPI data from STRING was reconstructed using Cytoscape software and the hub genes in the PPI network were identified using the plug-in: Molecular Complex Detection (MCODE) [25]. In MCODE, a gene which marked as a clustered or seed gene was indicated as a hub gene."], "order_basis": "consumes_earlier_output", "id": "assay:4"}]}