Validation of Potential Protein Markers Predicting Chemoradioresistance in Early Cervical Cancer by Immunohistochemistry

Background: In a previous study, a proteomic panel consisting of BCL-2, HER2, CD133, CAIX, and ERCC1 significantly predicted survival in patients with locally advanced cervical cancer. However, the prognostic significance of these proteins has not been assessed in early cervical cancer. The present study investigated the clinical significance and chemoradioresistance prediction power of these proteins in patients with early-stage cervical cancer. Materials and Methods: BCL-2, HER2, CD133, CAIX, and ERCC1 expression was determined by the immunohistochemical staining of 336 cervical cancer tissue microarrays. The associations of these proteins with clinicopathologic characteristics and disease progression were assessed. Results: There was a trend of low CAIX expression (p=0.082) and high ERCC1 expression (p=0.059) in patients with a favorable response to adjuvant radiation. High HER2 expression was significantly associated with shorter disease-free survival (DFS) in the total group (5-year DFS of 80.1% vs. 92.2%, p=0.004). A prognostic significance remained in multivariate analysis (Hazard ratio, HR=2.10, p=0.029). In the adjuvant radiation group, low CAIX and high ERCC1 expression indicated significantly unfavorable DFS (75.0% vs. 89.0%, p=0.026 and 76.8% vs. 88.6%, p=0.022, respectively). Low CAIX expression remained an independent prognostic marker in multivariate analysis (HR=0.45, p=0.037). The combined molecular-clinical model using random survival forest method predicted DFS with improved power compared with that of the clinical variable model (C-index 0.77 vs. 0.71, p=0.006). Conclusion: HER2, CAIX, and ERCC1 expression can be predictive protein markers for clinical outcomes in early cervical cancer patients treated primarily with radical surgery with or without adjuvant radiation.

Automated summary

The study investigated the clinical significance and chemoradioresistance prediction power of BCL-2, HER2, CD133, CAIX, and ERCC1 in early-stage cervical cancer. The findings suggest that HER2, CAIX, and ERCC1 expression can serve as predictive protein markers for clinical outcomes in this patient population. The combined molecular-clinical model using random survival forest method showed improved power in predicting disease-free survival compared to the clinical variable model.