Identification and Validation of Prognostic Markers for Lung Squamous Cell Carcinoma Associated with Chronic Obstructive Pulmonary Disease.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36035311", "kind": "science-record", "revision": 3, "content_hash": "sha256:881f89bf9b6d2e0857cdb024e9abb7d75d7a9d5893ade43e8a107f4938ccec43", "created_at": "2026-11-01T18:15:12Z", "updated_at": "2026-11-01T21:28:46Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and Validation of Prognostic Markers for Lung Squamous Cell Carcinoma Associated with Chronic Obstructive Pulmonary Disease.", "doi": "10.1155/2022/4254195", "pmid": "36035311", "authors": ["Zheng Li", "Dan Xu", "Jing Jing", "Jing Wang", "Min Jiang", "Fengsen Li"], "journal": "Journal of oncology", "year": 2022, "abstract": "Background: Globally, the incidence and associated mortality of chronic obstructive pulmonary disease (COPD) and lung carcinoma are showing a worsening trend. There is increasing evidence that COPD is an independent risk factor for the occurrence and progression of lung carcinoma. This study aimed to identify and validate the gene signatures associated with COPD, which may serve as potential new biomarkers for the prediction of prognosis in patients with lung carcinoma.\nMethods: A total of 111 COPD patient samples and 40 control samples were obtained from the GSE76925 cohort, and a total of 4933 genes were included in the study. The weighted gene coexpression network analysis (WGCNA) was performed to identify the modular genes that were significantly associated with COPD. The KEGG pathway and GO functional enrichment analyses were also performed. The RNAseq and clinicopathological data of 490 lung squamous cell carcinoma patients were obtained from the TCGA database. Further, univariate Cox regression and Lasso analyses were performed to screen for marker genes and construct a survival analysis model. Finally, the Human Protein Atlas (HPA) database was used to assess the gene expression in normal and tumor tissues of the lungs.\nResults: A 6-gene signature (DVL1, MRPL4, NRTN, NSUN3, RPH3A, and SNX32) was identified based on the Cox proportional risk analysis to construct the prognostic RiskScore survival model associated with COPD. Kaplan-Meier survival analysis indicated that the model could significantly differentiate between the prognoses of patients with lung carcinoma, wherein higher RiskScore samples were associated with a worse prognosis. Additionally, the model had a good predictive performance and reliability, as indicated by a high AUC, and these were validated in both internal and external sets. The 6-gene signature had a good predictive ability across clinical signs and could be considered an independent factor of prognostic risk. Finally, the protein expressions of the six genes were analyzed based on the HPA database. The expressions of DVL1, MRPL4, and NSUN3 were relatively higher, while that of RPH3A was relatively lower in the tumor tissues. The expression of SNX32 was high in both the tumor and paracarcinoma tissues. Results of the analyses using TCGA and GSE31446 databases were consistent with the expressions reported in the HPA database.\nConclusion: Novel COPD-associated gene markers for lung carcinoma were identified and validated in this study. The genes may be considered potential biomarkers to evaluate the prognostic risk of patients with lung carcinoma. Furthermore, some of these genes may have implications as new therapeutic targets and can be used to guide clinical applications.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression analysis was performed to select the significant candidate genes (p < 0.05), followed by the Lasso-Cox regression analysis. Finally, a multivariate Cox regression analysis was performed to prioritize the significant genes (p < 0.05) as the markers.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36035311", "locator": "sections[6] \"2.4. Machine Learning-Based Variable Screening and Model Construction\" paragraph 2", "quote": "Univariate Cox regression analysis was performed to select the significant candidate genes (p < 0.05), followed by the Lasso-Cox regression analysis. Finally, a multivariate Cox regression analysis was performed to prioritize the significant genes (p < 0.05) as the markers."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We have provided detailed information about the materials and methods in our manuscript.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36035311", "locator": "sections[21] \"Data Availability\" paragraph 2", "quote": "We have provided detailed information about the materials and methods in our manuscript."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A 6-gene signature (DVL1, MRPL4, NRTN, NSUN3, RPH3A, and SNX32) was identified based on the Cox proportional risk analysis to construct the prognostic RiskScore survival model associated with COPD.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36035311", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A 6-gene signature (DVL1, MRPL4, NRTN, NSUN3, RPH3A, and SNX32) was identified based on the Cox proportional risk analysis to construct the prognostic RiskScore survival model associated with COPD."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A 6-gene signature (DVL1, MRPL4, NRTN, NSUN3, RPH3A, and SNX32) was identified based on the Cox proportional risk analysis to construct the prognostic RiskScore survival model associated with COPD.", "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:36035311"], "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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