{
  "abstract": "Objective To develop and validate a venous thromboembolism (VTE) risk prediction model integrating genetic polymorphisms and routine clinical indicators in a cohort of cancer patients from Xinjiang, China. Methods A retrospective study was conducted on 1135 cancer patients (179 with VTE, 956 without) at Affiliated Cancer Hospital of Xinjiang Medical University between March 2024 and May 2025. Six gene polymorphisms ( SERPINE1 , SERPINC1 , PROC , MTHFR , F5 , F2 ) and clinical predictors were assessed. Chi-square tests and logistic regression identified independent predictors for model construction. Model performance was evaluated using ROC analysis, and three clinically optimized cut-off values were defined. Results The SERPINE1 4G allele was identified as a strong genetic risk factor for VTE (4G/4G genotype: OR=2.330, 95%CI: 1.378-3.942; 4G/5G genotype: OR=2.399, 95%CI: 1.411-4.078). No significant associations were found for the other five genes. Age (OR=1.022, 95%CI: 1.008-1.037), D-dimer level (OR=1.288, 95%CI: 1.186-1.399), and hemoglobin level (OR=0.987, 95%CI: 0.979-0.995) were independent clinical predictors ( P <0.01). The combined model achieved an area under the ROC curve (AUC) of 0.769 (95%CI: 0.731-0.807). Three optimized cut-off values were derived for different clinical contexts: high-sensitivity (90.5%), balanced (Youden index 0.434), and high-specificity (90.1%). Conclusion The SERPINE1 4G allele independently predicts VTE risk in this cohort of cancer patients from Xinjiang. A prediction model integrating this genetic marker with age, D-dimer, and hemoglobin demonstrates good discriminatory performance. The three derived cut-off values may facilitate risk-stratified thromboprophylaxis in clinical practice; however, these findings are based on a retrospective, single-center cohort and internally validated only by ROC analysis.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "name": "Fan Guo"
    },
    {
      "name": "Yiliminuer Keremu"
    },
    {
      "name": "Gang Zhao"
    },
    {
      "name": "Huiting Hu"
    },
    {
      "name": "Yufei Wu"
    },
    {
      "name": "Xiaolu Yu"
    },
    {
      "name": "Chongxiao Hao"
    },
    {
      "name": "Weina Kong"
    },
    {
      "name": "Xiumin Ma"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/btfcs3q4tqn6r.bib",
  "cited_by": 0,
  "doi": "10.1177/10760296261503703",
  "fulltext_url": "https://doi.org/10.1177/10760296261503703",
  "id": "btfcs3q4tqn6r",
  "issns": [
    "1938-2723",
    "1076-0296"
  ],
  "landing_url": "https://doi.org/10.1177/10760296261503703",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "RC666-701"
    ]
  ],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "SAGE Publishing",
  "ris": "https://research.draconitic.com/paper/btfcs3q4tqn6r.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T14:21:15Z",
      "native_id": "20ff830a41084f1cb7b1c7dd5385001e",
      "source": "doaj",
      "url": "https://doi.org/10.1177/10760296261503703"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:RC666-701",
      "subfield": "cardiovascular"
    }
  ],
  "title": "Integrating Genotype With Clinical Indicators Improves Venous Thromboembolism Risk Prediction in Cancer Patients: A Retrospective Cohort Study From Xinjiang",
  "title_text": "Integrating Genotype With Clinical Indicators Improves Venous Thromboembolism Risk Prediction in Cancer Patients: A Retrospective Cohort Study From Xinjiang",
  "type": "peer-reviewed",
  "updated_at": "2026-11-02T14:21:15Z",
  "url": "https://research.draconitic.com/paper/btfcs3q4tqn6r",
  "venue": "Clinical and Applied Thrombosis/Hemostasis",
  "versions": []
}