Optimized screening of DNA methylation sites combined with gene expression analysis to identify diagnostic markers of colorectal cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37400791", "kind": "science-record", "revision": 3, "content_hash": "sha256:615e2d9ea777c63de71d8dba6a7724dae9b4bc676af38ae10bc5382a818ed04f", "created_at": "2026-11-01T19:02:10Z", "updated_at": "2026-11-01T21:31:06Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Optimized screening of DNA methylation sites combined with gene expression analysis to identify diagnostic markers of colorectal cancer.", "doi": "10.1186/s12885-023-10922-2", "pmid": "37400791", "authors": ["Zhen Ye", "Guangle Song", "Jianwei Liang", "Shuying Yi", "Yuqi Gao", "Hanming Jiang"], "journal": "BMC cancer", "year": 2023, "abstract": "BACKGROUND: The prognosis of patients with colorectal cancer is related to early detection. However, commonly used screening markers lack sensitivity and specificity. In this study, we identified diagnostic methylation sites for colorectal cancer.\nMETHODS: After screening the colorectal cancer methylation dataset, diagnostic sites were identified via survival analysis, difference analysis, and ridge regression dimensionality reduction. The correlation between the selected methylation sites and the estimation of immune cell infiltration was analyzed. The accuracy of the diagnosis was verified using different datasets and the 10-fold crossover method.\nRESULTS: According to Gene Ontology, the main enrichment pathways of genes with hypermethylation sites are axon development, axonogenesis, and pattern specification processes. However, the Kyoto Encyclopedia of Genes and Genomes (KEGG) suggests the following main enrichment pathways: neuroactive ligand-receptor interaction, calcium signaling, and cAMP signaling. In The Cancer Genome Atlas (TCGA) and GSE131013 datasets, the area under the curve of cg07628404 was > 0.95. For the NaiveBayes machine model of cg02604524, cg07628404, and cg27364741, the accuracies of 10-fold cross-validation in the GSE131013 and TCGA datasets were 95% and 99.4%, respectively. The survival prognosis of the hypomethylated group (cg02604524, cg07628404, and cg27364741) was better than that of the hypermethylated group. The mutation risk did not differ between the hypermethylated and hypomethylated groups. The correlation coefficient between the three loci and CD4 central memory T cells, hematological stem cells, and other immune cells was not high (p < 0.05).\nCONCLUSION: In cases of colorectal cancer, the main enrichment pathway of genes with hypermethylated sites was axon and nerve development. In the biopsy tissues, the hypermethylation sites were diagnostic for colorectal cancer, and the NaiveBayes machine model of the three loci showed good diagnostic performance. Site (cg02604524, cg07628404, and cg27364741) hypermethylation predicts poor survival for colorectal cancer. Three methylation sites were weakly correlated with individual immune cell infiltration. Hypermethylation sites may be a useful repository for diagnosing colorectal cancer.", "claims": [{"id": "claim-method", "statement": "We evaluated the accuracy of each model using 10-fold cross-validation, and the model with the highest accuracy had the best diagnostic performance.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37400791", "locator": "sections[8] \"Machine learning model and classification\" paragraph 2", "quote": "We evaluated the accuracy of each model using 10-fold cross-validation, and the model with the highest accuracy had the best diagnostic performance."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All the data were downloaded from TCGA (https://portal.gdc.cancer.gov/) and GEO websites (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131013), as well as from the Supplementary Materials.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37400791", "locator": "sections[31] \"Data Availability\" paragraph 2", "quote": "All the data were downloaded from TCGA (https://portal.gdc.cancer.gov/) and GEO websites (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131013), as well as from the Supplementary Materials."}], "state": "complete"}, {"id": "claim-target-result", "statement": "For the NaiveBayes machine model of cg02604524, cg07628404, and cg27364741, the accuracies of 10-fold cross-validation in the GSE131013 and TCGA datasets were 95% and 99.4%, respectively.", "kind": "quantitative", "target": {"value": 10.0, "unit": "fold change", "tolerance": null, "source": "For the NaiveBayes machine model of cg02604524, cg07628404, and cg27364741, the accuracies of 10-fold cross-validation in the GSE131013 and TCGA datasets were 95% and 99.4%, respectively."}, "evidence": [{"asset": "source:pmid:37400791", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "For the NaiveBayes machine model of cg02604524, cg07628404, and cg27364741, the accuracies of 10-fold cross-validation in the GSE131013 and TCGA datasets were 95% and 99.4%, respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "For the NaiveBayes machine model of cg02604524, cg07628404, and cg27364741, the accuracies of 10-fold cross-validation in the GSE131013 and TCGA datasets were 95% and 99.4%, respectively.", "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:37400791"], "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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