Scholarlink

Aim <p>Microvascular invasion (MVI) is a key risk factor for hepatocellular carcinoma (HCC) recurrence. There is a lack of methods to diagnose MVI preoperatively. The objective of this study was to develop a model for preoperative prediction of MVI in HCC.</p> Method <p>The training cohort data were obtained from our previous study. One hundred and fourteen liver transplant patients with HCC were enrolled for validation. The serum level of soluble tumor necrosis factor receptor-2 (sTNFR2) was detected by ELISA. The Kaplan-Meier method was used for survival analysis. The multivariate logistic regression analysis was used to identify independent predictors of MVI, and a nomogram was constructed for visualization.</p> Result <p>The recipients with MVI had significantly poorer outcomes than those without MVI both in the training cohort (<i>n</i> = 83, <i>P</i> &lt; 0.001) and the validation cohort (<i>P</i> &lt; 0.001). The inflammatory profiling from the training cohort data indicated that the serum level of B-cell activating factor (<i>P</i> = 0.014) and sTNFR2 (<i>P</i> = 0.013) significantly elevated, and the serum level of osteocalcin (<i>P</i> = 0.002) decreased in patients with MVI. Multivariate logistic analysis showed that the Milan criteria and the serum sTNFR2 were independent predictors for the presence of MVI, and a nomogram was constructed. The nomogram demonstrated an area under the receiver operating characteristic curve (AUROC) of 0.821 for MVI and distinct stratification for tumor recurrence (<i>P</i> &lt; 0.001). Furthermore, the data in the validation cohort revealed an acceptable discriminative ability of confirmed MVI (AUROC = 0.702) and a notable discriminating capability for tumor recurrence (<i>P</i> = 0.043).</p> Conclusion <p>The non-invasive model based on sTNFR2 could effectively predict preoperative MVI in HCC. And the nomogram could discriminate the tumor recurrence risk for HCC.</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

A serum TNFR2-based model effectively predicates preoperative microvascular invasion and stratifies the tumor recurrence risk in hepatocellular carcinoma

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月27日

查看全文
  • Peiru Zhang,
  • Jianyong Zhuo,
  • Huigang Li,
  • Modan Yang,
  • Jinyan Chen,
  • Xudong Yang,
  • Chenghao Cao,
  • Shusen Zheng,
  • Xiao Xu,
  • Di Lu

摘要

Aim

Microvascular invasion (MVI) is a key risk factor for hepatocellular carcinoma (HCC) recurrence. There is a lack of methods to diagnose MVI preoperatively. The objective of this study was to develop a model for preoperative prediction of MVI in HCC.

Method

The training cohort data were obtained from our previous study. One hundred and fourteen liver transplant patients with HCC were enrolled for validation. The serum level of soluble tumor necrosis factor receptor-2 (sTNFR2) was detected by ELISA. The Kaplan-Meier method was used for survival analysis. The multivariate logistic regression analysis was used to identify independent predictors of MVI, and a nomogram was constructed for visualization.

Result

The recipients with MVI had significantly poorer outcomes than those without MVI both in the training cohort (n = 83, P < 0.001) and the validation cohort (P < 0.001). The inflammatory profiling from the training cohort data indicated that the serum level of B-cell activating factor (P = 0.014) and sTNFR2 (P = 0.013) significantly elevated, and the serum level of osteocalcin (P = 0.002) decreased in patients with MVI. Multivariate logistic analysis showed that the Milan criteria and the serum sTNFR2 were independent predictors for the presence of MVI, and a nomogram was constructed. The nomogram demonstrated an area under the receiver operating characteristic curve (AUROC) of 0.821 for MVI and distinct stratification for tumor recurrence (P < 0.001). Furthermore, the data in the validation cohort revealed an acceptable discriminative ability of confirmed MVI (AUROC = 0.702) and a notable discriminating capability for tumor recurrence (P = 0.043).

Conclusion

The non-invasive model based on sTNFR2 could effectively predict preoperative MVI in HCC. And the nomogram could discriminate the tumor recurrence risk for HCC.