Machine learning and bioinformatics analysis of diagnostic biomarkers associated with the occurrence and development of lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39071134", "kind": "science-record", "revision": 3, "content_hash": "sha256:1e74fec51466955992e8888687a3c48667717acc1ec1183ff6d5197dacf96f07", "created_at": "2026-11-01T20:29:08Z", "updated_at": "2026-11-01T22:12:19Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Machine learning and bioinformatics analysis of diagnostic biomarkers associated with the occurrence and development of lung adenocarcinoma.", "doi": "10.7717/peerj.17746", "pmid": "39071134", "authors": ["Yong Li", "Yunxiang Cai", "Longfei Ji", "Binyu Wang", "Danfei Shi", "Xinmin Li"], "journal": "PeerJ", "year": 2024, "abstract": "Objective: Lung adenocarcinoma poses a major global health challenge and is a leading cause of cancer-related deaths worldwide. This study is a review of three molecular biomarkers screened by machine learning that are not only important in the occurrence and progression of lung adenocarcinoma but also have the potential to serve as biomarkers for clinical diagnosis, prognosis evaluation and treatment guidance.\nMethods: Differentially expressed genes (DEGs) were identified using comprehensive GSE1987 and GSE18842 gene expression databases. A comprehensive bioinformatics analysis of these DEGs was conducted to explore enriched functions and pathways, relative expression levels, and interaction networks. Random Forest and LASSO regression analysis techniques were used to identify the three most significant target genes. The TCGA database and quantitative polymerase chain reaction (qPCR) experiments were used to verify the expression levels and receiver operating characteristic (ROC) curves of these three target genes. Furthermore, immune invasiveness, pan-cancer, and mRNA-miRNA interaction network analyses were performed.\nResults: Eighty-nine genes showed increased expression and 190 genes showed decreased expression. Notably, the upregulated DEGs were predominantly associated with organelle fission and nuclear division, whereas the downregulated DEGs were mainly associated with genitourinary system development and cell-substrate adhesion. The construction of the DEG protein-protein interaction network revealed 32 and 19 hub genes with the highest moderate values among the upregulated and downregulated genes, respectively. Using random forest and LASSO regression analyses, the hub genes were employed to identify three most significant target genes.TCGA database and qPCR experiments were used to verify the expression levels and ROC curves of these three target genes, and immunoinvasive analysis, pan-cancer analysis and mRNA-miRNA interaction network analysis were performed.\nConclusion: Three target genes identified by machine learning: BUB1B, CENPF, and PLK1 play key roles in LUAD development of lung adenocarcinoma.", "claims": [{"id": "claim-method", "statement": "In this study, the screening of DEGs was conducted using R software (version 4.2.1; R Core Team, 2022) with criteria set at P < 0.05 and log2FC ≥ 1.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39071134", "locator": "sections[4] \"Data processing of DEGs\" paragraph 2", "quote": "In this study, the screening of DEGs was conducted using R software (version 4.2.1; R Core Team, 2022) with criteria set at P < 0.05 and log2FC ≥ 1."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw measurements are available in the Supplementary Files. The gene expression data is available at GEO: GSE1987 and GSE18842.The TCGA-LUAD data used is available at Zenodo: LI, . yong . (2024). Machine learning and bioinformatics analysis of diagnostic biomarkers associated with the occurrence and development of lung adenocarcinoma [Data set]. Zenodo. https://doi.org/10.5281/zenodo.12561085.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39071134", "locator": "sections[26] \"Additional Information and Declarations\" paragraph 5", "quote": "The raw measurements are available in the Supplementary Files. The gene expression data is available at GEO: GSE1987 and GSE18842.The TCGA-LUAD data used is available at Zenodo: LI, . yong . (2024). Machine learning and bioinformatics analysis of diagnostic biomarkers associated with the occurrence and development of lung adenocarcinoma [Data set]. Zenodo. https://doi.org/10.5281/zenodo.12561085."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using GEO2R on GSE1987 and GSE18842, the study identified 279 DEGs and selected BUB1B, CENPF, and PLK1 as machine-learning target genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39071134", "locator": "sections[14] \"Identification of differentially expressed genes\" paragraph 2", "quote": "Subsequently, DEGs were identified in the two datasets using the GEO2R online tool."}, {"asset": "source:pmid:39071134", "locator": "sections[14] \"Identification of differentially expressed genes\" paragraph 2", "quote": "A total of 279 DEGs were identified, comprising 89 upregulated and 190 downregulated genes in lung cancer specimens compared to the corresponding non-cancer specimens (Fig. 1)."}, {"asset": "source:pmid:39071134", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Three target genes identified by machine learning: BUB1B, CENPF, and PLK1 play key roles in LUAD development of lung adenocarcinoma."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Using GEO2R on GSE1987 and GSE18842, the study identified 279 DEGs and selected BUB1B, CENPF, and PLK1 as machine-learning target genes.", "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:39071134"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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