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Background <p>Urothelial carcinoma (UC) is a common malignancy with a poor prognosis in advanced stages, characterized by high heterogeneity, recurrence risk, and chemotherapy resistance. There is a clinical lack of reliable prognostic markers. Human epidermal growth factor receptor 2 (HER2) is a key oncogene and therapeutic target. Its correlation with clinicopathological features in Chinese UC patients remains controversial, leading to unclear biomarker value and limiting the precise application of therapies like RC48-ADC. Therefore, clarifying the association between HER2 expression and UC pathological staging and prognosis is crucial for optimizing treatment.</p> Methods <p>Clinical data of 444 UC patients admitted to Fujian Provincial Hospital from January 2020 to August 2024 were retrospectively analyzed. HER2 expression was detected by immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH). Spearman’s rank correlation coefficient was used to clarify the association between HER2 and clinicopathological characteristics, and Kaplan-Meier method and Cox regression model were applied to analyze its prognostic value.</p> Results <p>The high HER2 expression rate (IHC 2+/3+) was 40.05%. High HER2 expression was significantly correlated with Age, History, Routine Urine Test indices, Pathological Grade, Ki67 index, p53 expression, and treatment options (all <i>P</i>&lt;0.05). Multivariate Cox regression analysis confirmed that high HER2 expression was an independent risk factor for UC Recurrence (HR=2.496, <i>P</i>=0.044) but not significantly associated with Metastasis (<i>P</i>=0.923).</p> Conclusion <p>HER2 is highly expressed in UC patients in the Fujian region, correlating with aggressive clinicopathological features. It can serve as a reliable biomarker for predicting recurrence risk, guiding postoperative management and targeted therapy selection.</p>

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Correlation analysis of HER2 expression with clinicopathological features and prognosis based on data from 444 patients with urothelial carcinoma

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  • 发表日期：

    2026年2月24日

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  • Jingyi Luo,
  • Ziyi Cheng,
  • Lihang Chen,
  • Xiaorui Li,
  • Zhixun Guo,
  • Jinfeng Wu,
  • Liefu Ye,
  • Haijian Huang,
  • Jiawen Wang,
  • Yongbao Wei

摘要

Background

Urothelial carcinoma (UC) is a common malignancy with a poor prognosis in advanced stages, characterized by high heterogeneity, recurrence risk, and chemotherapy resistance. There is a clinical lack of reliable prognostic markers. Human epidermal growth factor receptor 2 (HER2) is a key oncogene and therapeutic target. Its correlation with clinicopathological features in Chinese UC patients remains controversial, leading to unclear biomarker value and limiting the precise application of therapies like RC48-ADC. Therefore, clarifying the association between HER2 expression and UC pathological staging and prognosis is crucial for optimizing treatment.

Methods

Clinical data of 444 UC patients admitted to Fujian Provincial Hospital from January 2020 to August 2024 were retrospectively analyzed. HER2 expression was detected by immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH). Spearman’s rank correlation coefficient was used to clarify the association between HER2 and clinicopathological characteristics, and Kaplan-Meier method and Cox regression model were applied to analyze its prognostic value.

Results

The high HER2 expression rate (IHC 2+/3+) was 40.05%. High HER2 expression was significantly correlated with Age, History, Routine Urine Test indices, Pathological Grade, Ki67 index, p53 expression, and treatment options (all P<0.05). Multivariate Cox regression analysis confirmed that high HER2 expression was an independent risk factor for UC Recurrence (HR=2.496, P=0.044) but not significantly associated with Metastasis (P=0.923).

Conclusion

HER2 is highly expressed in UC patients in the Fujian region, correlating with aggressive clinicopathological features. It can serve as a reliable biomarker for predicting recurrence risk, guiding postoperative management and targeted therapy selection.