Lin Yingying, Ma Yuefei, Chen Yan, Huang Yepei, Lin Jinchuan, Xiao Zhenzhou, Cui Zhaolei
Laboratory of Biochemistry and Molecular Biology Research, Department of Clinical Laboratory, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, Fujian, China.
Department of Laboratory Medicine, the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China.
BMC Cancer. 2025 Apr 17;25(1):721. doi: 10.1186/s12885-025-14025-y.
A significant proportion, ranging from 20 to 40%, of individuals with hepatocellular carcinoma (HCC) do not exhibit elevated Alpha-fetoprotein (AFP) levels. This study aimed to evaluate the utility of serum glypican-3 (GPC3) and protein induced by vitamin K absence or antagonist II (PIVKA-II) in an AFP-negative HCC (N-HCC) population, and to develop nomogram diagnostic and prognostic prediction models utilizing GPC3 and PIVKA-II.
Serum GPC3 and PIVKA-II levels were measured in this case-control study, followed by the establishment of a receiver operating characteristic (ROC) curve, restricted cubic spline (RCS), and Kaplan-Meier survival curve. Additionally, a diagnostic prediction nomogram was constructed using univariate and multivariate logistic regression. Furthermore, we utilized least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression to develop a prognostic prediction nomogram. The performance of these models was evaluated using ROC curve analysis and decision curve analysis (DCA).
Serum GPC3 and PIVKA-II expression levels were significantly elevated in untreated patients with N-HCC (especially stageI and tumor size < 3 cm) compared to those with AFP-negative benign liver disease (N-BLD). Derived from ROC analysis, the diagnostic cutoff points for GPC3 and PIVKA-II were set at 0.100 ng/mL and 40.00 mAU/mL, respectively. PIVKA-II demonstrated sensitivity and specificity of 84.62% and 90.38%, surpassing GPC3's 76.92% and 73.08%. The area under the ROC curve (AUC) for a diagnostic prediction nomogram incorporating GPC3, PIVKA-II, and gamma-glutamyltransferase (GGT) was 0.943 (95% CI: 0.912-0.974), superior to models using GPC3 or PIVKA-II alone. This model showed 95.20% sensitivity and 81.70% specificity in differentiating N-HCC from N-BLD. Stratifying patients into high-risk and low-risk groups using cutoff values established by RCS for GPC3 (0.124 ng/mL) and PIVKA-II (274 mAU/mL) revealed significant associations between these risk stratifications and patient survival. Finally, the use of GPC3-highrisk, cirrhosis, albumin (ALB), portal venous thrombosis (PVT), and surgical treatment as five parameters in the nomogram prognostic prediction model effectively differentiated between high- and low-risk prognostic patients with N-HCC with relatively high accuracy.
Serum GPC3 and PIVKA-II demonstrate clinical significance in the timely detection and prognosis assessment of N-HCC. The application of nomogram prediction models based on GPC3 and PIVKA-II stands as an important adjunctive tool for diagnosing and prognosticating N-HCC.
肝细胞癌（HCC）患者中有20%至40%的人甲胎蛋白（AFP）水平未升高。本研究旨在评估血清磷脂酰肌醇蛋白聚糖-3（GPC3）和维生素K缺乏或拮抗剂II诱导蛋白（PIVKA-II）在AFP阴性HCC（N-HCC）人群中的应用价值，并利用GPC3和PIVKA-II建立列线图诊断和预后预测模型。
在本病例对照研究中测量血清GPC3和PIVKA-II水平，随后建立受试者工作特征（ROC）曲线、限制性立方样条（RCS）曲线和Kaplan-Meier生存曲线。此外，使用单因素和多因素逻辑回归构建诊断预测列线图。此外，我们利用最小绝对收缩和选择算子（LASSO）回归和多因素Cox回归建立预后预测列线图。使用ROC曲线分析和决策曲线分析（DCA）评估这些模型的性能。
与AFP阴性良性肝病（N-BLD）患者相比，未经治疗的N-HCC患者（尤其是I期和肿瘤大小<3 cm）血清GPC3和PIVKA-II表达水平显著升高。根据ROC分析，GPC3和PIVKA-II的诊断临界值分别设定为0.100 ng/mL和40.00 mAU/mL。PIVKA-II的敏感性和特异性分别为84.62%和90.38%，超过GPC3的76.92%和73.08%。纳入GPC3、PIVKA-II和γ-谷氨酰转移酶（GGT）的诊断预测列线图的ROC曲线下面积（AUC）为0.943（95%CI：0.912-0.974），优于单独使用GPC3或PIVKA-II的模型。该模型在区分N-HCC和N-BLD方面显示出95.20%的敏感性和81.70%的特异性。使用RCS确定的GPC3（0.124 ng/mL）和PIVKA-II（274 mAU/mL）临界值将患者分为高风险和低风险组，结果显示这些风险分层与患者生存之间存在显著关联。最后，在列线图预后预测模型中使用GPC3高风险、肝硬化、白蛋白（ALB）、门静脉血栓形成（PVT）和手术治疗作为五个参数，能够以相对较高的准确性有效区分N-HCC高风险和低风险预后患者。
血清GPC3和PIVKA-II在N-HCC的早期检测和预后评估中具有临床意义。基于GPC3和PIVKA-II的列线图预测模型的应用是诊断和预测N-HCC的重要辅助工具。
