Scholarlink

Background <p>Endometrial cancer (EC) is a heterogeneous malignancy in which clinicopathological, molecular, and immune characteristics each provide prognostic information but are insufficient when used independently. We aimed to identify prognostic determinants across these domains and develop an integrated, clinically applicable prognostic risk classification system.</p> Methods <p>This multicenter retrospective study included 200 EC patients from Peking University People's Hospital(Center 1) and 200 patients from Beijing obstetrics and Gynecology Hospital Affiliated to Capital Medical University(Center 2). Clinicopathological variables, mismatch repair (MMR) status, p53 status, and stromal CD3⁺ and CD8⁺ lymphocyte densities were analyzed. Cox regression was used to establish prognostic models. A comprehensive model integrating clinicopathological, molecular, and immune features (PKU-typing) was constructed using a nomogram and validated internally and externally.</p> Results <p>FIGO stage and histological grade were independent prognostic clinicopathological factors. Stromal CD3⁺ and CD8⁺ infiltration levels correlated with prognosis and defined three immune clusters with distinct outcomes. The PKU-typing model demonstrated strong predictive accuracy in the training cohort (C-index 0.899) and remained robust in external validation (C-index 0.765). Compared with single-dimension models, PKU-typing significantly improved risk classification (NRI and IDI across all comparisons).</p> Conclusions <p>By integrating clinicopathological, molecular, and immune characteristics, the PKU-typing risk classification system provides a biologically informed, practical framework for individualized risk stratification in EC. Its strong performance and reliance on accessible testing methods support its broad applicability in clinical practice.</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

Integrated typing system of endometrial cancer based on Chinese population - PKU typing: a retrospective, multicentre study

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月3日

查看全文
  • Liwei Li,
  • Dan Luo,
  • He Li,
  • Xingchen Li,
  • Yuan Cheng,
  • Jingyi Zhou,
  • Xuewu You,
  • Yibo Dai,
  • Xuji Jiang,
  • Yuanhe Zhai,
  • Jiaqi Wang,
  • Xiaobo Zhang,
  • Bo Han,
  • Weimin Kong,
  • Yangyang Dong,
  • Jianliu Wang

摘要

Background

Endometrial cancer (EC) is a heterogeneous malignancy in which clinicopathological, molecular, and immune characteristics each provide prognostic information but are insufficient when used independently. We aimed to identify prognostic determinants across these domains and develop an integrated, clinically applicable prognostic risk classification system.

Methods

This multicenter retrospective study included 200 EC patients from Peking University People's Hospital(Center 1) and 200 patients from Beijing obstetrics and Gynecology Hospital Affiliated to Capital Medical University(Center 2). Clinicopathological variables, mismatch repair (MMR) status, p53 status, and stromal CD3⁺ and CD8⁺ lymphocyte densities were analyzed. Cox regression was used to establish prognostic models. A comprehensive model integrating clinicopathological, molecular, and immune features (PKU-typing) was constructed using a nomogram and validated internally and externally.

Results

FIGO stage and histological grade were independent prognostic clinicopathological factors. Stromal CD3⁺ and CD8⁺ infiltration levels correlated with prognosis and defined three immune clusters with distinct outcomes. The PKU-typing model demonstrated strong predictive accuracy in the training cohort (C-index 0.899) and remained robust in external validation (C-index 0.765). Compared with single-dimension models, PKU-typing significantly improved risk classification (NRI and IDI across all comparisons).

Conclusions

By integrating clinicopathological, molecular, and immune characteristics, the PKU-typing risk classification system provides a biologically informed, practical framework for individualized risk stratification in EC. Its strong performance and reliance on accessible testing methods support its broad applicability in clinical practice.