Identification of an early diagnostic biomarker of lung adenocarcinoma based on co-expression similarity and construction of a diagnostic model.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:30029648", "kind": "science-record", "revision": 8, "content_hash": "sha256:98a11d3a17dd0d3ba121bc9255f16bed8dca4972e30c8a7d3540f904a0df68ce", "created_at": "2026-11-01T15:40:35Z", "updated_at": "2026-11-01T22:43:28Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of an early diagnostic biomarker of lung adenocarcinoma based on co-expression similarity and construction of a diagnostic model.", "doi": "10.1186/s12967-018-1577-5", "pmid": "30029648", "authors": ["Zhirui Fan", "Wenhua Xue", "Lifeng Li", "Chaoqi Zhang", "Jingli Lu", "Yunkai Zhai", "Zhenhe Suo", "Jie Zhao"], "journal": "Journal of translational medicine", "year": 2018, "abstract": "BACKGROUND: The purpose of this study was to achieve early and accurate diagnosis of lung cancer and long-term monitoring of the therapeutic response.\nMETHODS: We downloaded GSE20189 from GEO database as analysis data. We also downloaded human lung adenocarcinoma RNA-seq transcriptome expression data from the TCGA database as validation data. Finally, the expression of all of the genes underwent z test normalization. We used ANOVA to identify differentially expressed genes specific to each stage, as well as the intersection between them. Two methods, correlation analysis and co-expression network analysis, were used to compare the expression patterns and topological properties of each stage. Using the functional quantification algorithm, we evaluated the functional level of each significantly enriched biological function under different stages. A machine-learning algorithm was used to screen out significant functions as features and to establish an early diagnosis model. Finally, survival analysis was used to verify the correlation between the outcome and the biomarkers that we found.\nRESULTS: We screened 12 significant biomarkers that could distinguish lung cancer patients with diverse risks. Patients carrying variations in these 12 genes also presented a poor outcome in terms of survival status compared with patients without variations.\nCONCLUSIONS: We propose a new molecular-based noninvasive detection method. According to the expression of the stage-specific gene set in the peripheral blood of patients with lung cancer, the difference in the functional level is quantified to realize the early diagnosis and prediction of lung cancer.", "claims": [{"id": "claim-method", "statement": "We take 12 genes as features, using an SVM (support vector machine) to construct the classification model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30029648", "locator": "sections[22] \"Risk diagnostic model construction based on early screening genes\" paragraph 2", "quote": "We take 12 genes as features, using an SVM (support vector machine) to construct the classification model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analyzed during this study are included either in this article or in the supplementary information files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30029648", "locator": "sections[29] \"Availability of data and materials\" paragraph 2", "quote": "All data generated or analyzed during this study are included either in this article or in the supplementary information files."}], "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:30029648"], "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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