Identification of novel diagnostic biomarkers associated with liver metastasis in colon adenocarcinoma by machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39390264", "kind": "science-record", "revision": 3, "content_hash": "sha256:974257419aafca6fc7d776854d94e5079d8b8cb1c9f8fc91fdc87f1d39dafebe", "created_at": "2026-11-01T20:29:36Z", "updated_at": "2026-11-01T22:12:53Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of novel diagnostic biomarkers associated with liver metastasis in colon adenocarcinoma by machine learning.", "doi": "10.1007/s12672-024-01398-y", "pmid": "39390264", "authors": ["Long Yang", "Ye Tian", "Xiaofei Cao", "Jiawei Wang", "Baoyang Luo"], "journal": "Discover oncology", "year": 2024, "abstract": "BACKGROUND: Liver metastasis is one of the primary causes of poor prognosis in colon adenocarcinoma (COAD) patients, but there are few studies on its biomarkers.\nMETHODS: The Cancer Genome Atlas (TCGA)-COAD, GSE41258, and GSE49355 datasets were acquired from the public database. Differentially expressed genes (DEGs) between liver metastasis and primary tumor samples in COAD were identified by limma, and functional enrichment analysis were performed. MuTect2 and maftools were used to measure somatic mutation rates, while ADTEx was used to measure copy number variations (CNVs). The intersection of three machine learning methods, support vector machine (SVM), Random Forest, and least absolute shrinkage and selection operator (LASSO), is utilized to screen biomarkers, and their diagnostic performance is subsequently validated. The correlation between biomarkers and immune cells infiltration was analyzed by Spearman method.\nRESULTS: 47 DEGs between liver metastasis and primary tumor samples in COAD were obtained, which were mainly enriched in the complement and coagulation, extracellular matrix (ECM), and peptidase regulator activity, etc. 38 out of 47 DEGs had mutations and exhibited a high frequency of CNV amplification or deletion. Furthermore, 3 biomarkers (MMP3, MAB21L2, and COLEC11) were screened, which showed good diagnostic performance. The proportion of multiple immune cells, such as B cells naive, T cells CD4 naive, Monocytes, and Dendritic cells resting, was higher in liver metastasis samples than that in primary tumor samples. Meanwhile, MMP3, MAB21L2, and COLEC11 exhibited an outstanding correlation with immune cells infiltration.\nCONCLUSION: In short, 3 biomarkers with good diagnostic efficacy were identified, providing a new perspective of therapeutic targets for liver metastasis in COAD.", "claims": [{"id": "claim-method", "statement": "The Wilcoxon rank sum test was applied to compare the difference between two groups, and p < 0.05 indicated statistically significant.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39390264", "locator": "sections[9] \"Statistical analysis\" paragraph 2", "quote": "The Wilcoxon rank sum test was applied to compare the difference between two groups, and p < 0.05 indicated statistically significant."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and/or analyzed during the current study are available in the [GSE41258] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE41258] and [GSE49355] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE49355].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39390264", "locator": "sections[23] \"Data availability\" paragraph 2", "quote": "The datasets generated and/or analyzed during the current study are available in the [GSE41258] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE41258] and [GSE49355] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE49355]."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The comprehensive model of 3 biomarkers showed good diagnostic efficacy with the area under ROC curve (AUC) of 0.987 in the training set GSE41258 and AUC of 0.974 in the validation set GSE49355.", "kind": "quantitative", "target": {"value": 0.974, "unit": "auc", "tolerance": null, "source": "The comprehensive model of 3 biomarkers showed good diagnostic efficacy with the area under ROC curve (AUC) of 0.987 in the training set GSE41258 (Fig. 4B) and AUC of 0.974 in the validation set GSE49355 (Fig. 4C)."}, "evidence": [{"asset": "source:pmid:39390264", "locator": "sections[14] \"Three biomarkers were identified and exhibited good diagnostic performance\" paragraph 2", "quote": "The comprehensive model of 3 biomarkers showed good diagnostic efficacy with the area under ROC curve (AUC) of 0.987 in the training set GSE41258 (Fig. 4B) and AUC of 0.974 in the validation set GSE49355 (Fig. 4C)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The comprehensive model of 3 biomarkers showed good diagnostic efficacy with the area under ROC curve (AUC) of 0.987 in the training set GSE41258 and AUC of 0.974 in the validation set GSE49355.", "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:39390264"], "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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