{
  "abstract": "Background: Colorectal cancer (CRC) is a major cause of cancer-related mortality, with distant metastasis strongly associated with poor clinical outcomes. Integrating transcriptomic and epigenomic data through machine learning may improve the molecular characterization of metastatic CRC. Methods: We analyzed 518 primary tumors from the TCGA-COAD/READ cohort (436 non-metastatic [M0] and 82 metastatic [M1]) with matched RNA-seq and DNA methylation data. Five machine learning classifiers were evaluated for discrimination of metastatic status using stratified nested cross-validation. Model explainability was assessed using model coefficients for linear classifiers and SHAP-based feature importance for tree-based models. Differential expression, functional enrichment, methylation-expression correlation, immune-related transcriptional scoring, statistical mediation, and survival analyses were subsequently performed. Results: Integrated RNA-seq and DNA methylation showed the strongest discrimination between M0 and M1 tumors, with SVM reaching a ROC-AUC of 0.787 +/- 0.047. Six features - ARC, ASPDH, C13orf15, C4orf23, GPATCH3, and cg12040555 - ranked among the top 20 predictors across four models with consistent directions. cg10057218 methylation was inversely correlated with GSDMB expression (Spearman rho = -0.589) and increased in M1 tumors. Statistical mediation indicated a significant indirect association between M1 status and reduced GSDMB expression through cg10057218 methylation (indirect effect = -0.370; 95% CI [-0.519, -0.224]; proportion mediated = 82.1%). M1 tumors also exhibited reduced immune-related transcriptional activity. A 20-feature canonical molecular score separated M1 from M0 tumors (in-sample ROC-AUC = 0.918), while the corresponding Logistic Regression model achieved a nested cross-validated ROC-AUC of 0.781 +/- 0.046. Higher scores were associated with shorter overall survival (log-rank p = 1.67 x 10^-7). Conclusions: Transcriptomic and DNA methylation integration identified a consistently prioritized multiomics feature set associated with metastatic CRC. The findings highlight coordinated molecular and immune-related differences between M0 and M1 tumors and identify cg10057218-associated GSDMB repression as a candidate epigenetic feature of metastatic disease.",
  "abstract_shown": true,
  "affiliations": [
    "Universidade de Sao Paulo"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "M. d. S. Costa"
    },
    {
      "name": "H. I. Marcelo"
    },
    {
      "name": "G. A. Kafouri"
    },
    {
      "name": "V. De Camargo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/djceerbfjmmfg.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.01.748124",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.01.748124v1.full",
  "id": "djceerbfjmmfg",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.01.748124v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|Epigenetics and DNA Methylation"
    ]
  ],
  "openalex": "W7208818730",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.01.748124v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/djceerbfjmmfg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:35Z",
      "native_id": "10.64898/2026.09.01.748124",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.01.748124v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Interpretable Multiomics Machine Learning Identifies GSDMB-Associated Epigenetic Repression and Reduced Immune Activity in Metastatic Colorectal Cancer",
  "title_text": "Interpretable Multiomics Machine Learning Identifies GSDMB-Associated Epigenetic Repression and Reduced Immune Activity in Metastatic Colorectal Cancer",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:35Z",
  "url": "https://research.draconitic.com/paper/djceerbfjmmfg",
  "venue": "bioRxiv",
  "versions": []
}