{
  "abstract": "Accurate risk stratification is essential for guiding treatment decisions and preventing over treatment of prostate cancer, which remains one of the most prevalent cancers among adult men. While the Gleason score, obtained from prostate biopsies, is routinely used to assess tumor aggressiveness, the biopsy procedure carries risks such as pain, infection, and, in some cases, serious complications such as sepsis. In this study, we proposed an artificial intelligence-based framework that integrates mRNA expression profiles with functional interaction networks to classify prostate cancer patients into low-, medium-, and high-risk groups defined by Gleason scores. The pipeline comprised five steps: (1) data collection from The Cancer Genome Atlas (TCGA), (2) preprocessing of gene expression data, (3) two-stage feature selection to identify informative biomarkers, (4) risk classification using a dual-branch graph neural network (GNN) that combines gene-gene interaction graphs with sample-level expression features, and (5) model interpretation using SHAP to quantify feature contributions. Differentially expressed genes were identified in the High (ASPN, GMNN, PEBP4, C2, KNCK17), Medium (C2, IGSF1, ASPN, CDKN3, AMH), and Low (TNMD, VWA5B2, ST6GALNAC5, CYP3A5, PHGR1) risk groups, underscoring the molecular heterogeneity of disease progression. On an independent held-out test set, the model achieved AUCs of 0.86, 0.88, and 0.95 for the low-, medium-, and high-risk groups, respectively, with an overall accuracy of 80%. These results suggest that combining GNN-based modeling with explainable AI can capture both global and local molecular patterns relevant to tumor aggressiveness. However, as the model was developed and evaluated solely on the TCGA cohort, the findings should be regarded as exploratory, and external validation will be required to establish generalizability. Within these limitations, the proposed framework highlights the potential of molecular profiling and graph-based deep learning to support more precise, potentially less invasive, risk assessment and individualized treatment planning in prostate cancer.",
  "abstract_shown": true,
  "affiliations": [
    "Neurosciences Research Center, Tabriz University of Medical Sciences, Tabriz, Iran",
    "Faculty of Electronic and Computer Engineering Department, K.N. Toosi University of Technology, Tehran, Iran",
    "Clinical Research Development Unit of Tabriz Valiasr Hospital, Tabriz University of Medical Sciences, Tabriz, Iran",
    "Kidney Research Center, Tabriz University of Medical Sciences, Tabriz, Iran"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Saeed Pirmoradi"
    },
    {
      "affs": [
        1
      ],
      "name": "Sevil Vaghefi Moghaddam"
    },
    {
      "affs": [
        2
      ],
      "name": "Mohammadreza Ardalan"
    },
    {
      "affs": [
        3
      ],
      "name": "Mir Mohsen Sharifi Bonab"
    },
    {
      "name": "Mohammad Teshnehlab"
    },
    {
      "name": "Sepideh Zununi Vahed"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7tqgd11mshsqc.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0356727",
  "fulltext_url": null,
  "id": "7tqgd11mshsqc",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0356727",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Gene expression and cancer classification"
    ],
    [
      "plos",
      "/Biology and life sciences/Anatomy/Exocrine glands/Prostate gland"
    ],
    [
      "plos",
      "/Biology and life sciences/Genetics/Gene expression"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Support vector machines"
    ],
    [
      "plos",
      "/Medicine and health sciences/Anatomy/Exocrine glands/Prostate gland"
    ],
    [
      "plos",
      "/Medicine and health sciences/Epidemiology/Medical risk factors/Cancer risk factors"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Basic cancer research/Metastasis"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancer risk factors"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancer treatment"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Genitourinary tract tumors/Prostate cancer"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Metastasis"
    ],
    [
      "plos",
      "/Medicine and health sciences/Urology/Prostate diseases/Prostate cancer"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Statistics/Statistical methods/Multivariate analysis/Principal component analysis"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Multivariate analysis/Principal component analysis"
    ]
  ],
  "openalex": "W7211959212",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0356727&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/7tqgd11mshsqc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:56Z",
      "native_id": "10.1371/journal.pone.0356727",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0356727"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "plos:oncology",
      "subfield": "oncology"
    }
  ],
  "title": "Graph neural network-based risk stratification of prostate cancer using gene expression and SHAP interpretability",
  "title_text": "Graph neural network-based risk stratification of prostate cancer using gene expression and SHAP interpretability",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:56Z",
  "url": "https://research.draconitic.com/paper/7tqgd11mshsqc",
  "venue": "PLOS One",
  "versions": []
}