Scholarlink

Purpose <p>To develop and validate a novel risk stratification model for locally advanced nasopharyngeal carcinoma (LA-NPC) based on the "glyco-lipid-viral" axis, addressing the limitations of traditional TNM staging and single-modality biomarkers.</p> Methods <p>A total of 486 LA-NPC patients were retrospectively enrolled, with a metabolomics subcohort (n = 121) selected. PET/CT-derived glycolytic parameters (MTV, TLG), serum lipid biomarkers, and pretreatment EBV DNA were integrated via recursive partitioning analysis (RPA) to construct the model. Its performance was compared with TNM staging and single-modality markers.</p> Results <p>A multimodal model was built via RPA in 121 LA-NPC patients, using Lipsig-Score (lipid metabolite signature score), naso_MTV (PET/CT), and pretreatment EBV DNA. Three risk groups were defined: low-risk (Lipsig-Score &lt; 0.0127 + EBV DNA &lt; 500 copies/mL), intermediate-risk (Lipsig-Score &lt; 0.0127 + EBV DNA ≥ 500 copies/mL, or Lipsig-Score ≥ 0.0127 + naso_MTV &lt; 5.5), and high-risk (Lipsig-Score ≥ 0.0127 + naso_MTV ≥ 5.5). Kaplan–Meier analysis showed significant differences in PFS (log-rank <i>P</i> &lt; 0.0001) and OS (log-rank <i>P</i> &lt; 0.001) among groups, with the model outperforming TNM staging and single-modal markers.</p> Conclusion <p>The integrated model captures LA-NPC’s multi-dimensional biology, enabling precise risk stratification and providing a reliable basis for individualized treatment.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multimodal glyco-lipid-EBVDNA signature prognostic model for individualized risk stratification of locally advanced nasopharyngeal carcinoma

  • 期刊论文

  • 发表日期：

    2026年2月9日

查看全文
  • Jiayu Zhou,
  • Yuchen Hua,
  • Chuanrun Zhang,
  • Qin Liu,
  • Ying Deng,
  • Yuwen Kuang,
  • Jiahuan Lu,
  • Mengwen Wang,
  • Jie Gong,
  • Xiang Guo,
  • Xing Lv,
  • Zhenchong Yang,
  • Weixiong Xia

摘要

Purpose

To develop and validate a novel risk stratification model for locally advanced nasopharyngeal carcinoma (LA-NPC) based on the "glyco-lipid-viral" axis, addressing the limitations of traditional TNM staging and single-modality biomarkers.

Methods

A total of 486 LA-NPC patients were retrospectively enrolled, with a metabolomics subcohort (n = 121) selected. PET/CT-derived glycolytic parameters (MTV, TLG), serum lipid biomarkers, and pretreatment EBV DNA were integrated via recursive partitioning analysis (RPA) to construct the model. Its performance was compared with TNM staging and single-modality markers.

Results

A multimodal model was built via RPA in 121 LA-NPC patients, using Lipsig-Score (lipid metabolite signature score), naso_MTV (PET/CT), and pretreatment EBV DNA. Three risk groups were defined: low-risk (Lipsig-Score < 0.0127 + EBV DNA < 500 copies/mL), intermediate-risk (Lipsig-Score < 0.0127 + EBV DNA ≥ 500 copies/mL, or Lipsig-Score ≥ 0.0127 + naso_MTV < 5.5), and high-risk (Lipsig-Score ≥ 0.0127 + naso_MTV ≥ 5.5). Kaplan–Meier analysis showed significant differences in PFS (log-rank P < 0.0001) and OS (log-rank P < 0.001) among groups, with the model outperforming TNM staging and single-modal markers.

Conclusion

The integrated model captures LA-NPC’s multi-dimensional biology, enabling precise risk stratification and providing a reliable basis for individualized treatment.