In Anticancer research
BACKGROUND/AIM : Bladder cancer is the most common urinary tract cancer. Patients diagnosed with advanced T-stage/muscle-invasive bladder cancer through transurethral resection of bladder tumors (TURBT) are treated with total radical cystectomy; however, there is a high chance of recurrence. Nevertheless, markers for predicting this recurrence are not currently available. Here, we evaluated the chronological change of ephrin type-A receptor 2 (EPHA2) expression, a molecule known for its role in cell adhesion, to predict bladder cancer recurrence after cystectomy, using TURBT and cystectomy specimens.
MATERIALS AND METHODS : An immunostaining evaluation method that combines whole-slide images and image analysis software was developed to quantify and evaluate stainability objectively. We assessed the correlation between EPHA2 expression and bladder cancer recurrence using this novel immunostaining method and chronological changes in target protein expression in TURBT and radical cystectomy samples.
RESULTS : In TURBT specimens, the number of cases with a high N-terminal/C-terminal EPHA2 ratio in the group with recurrence was significantly higher than in the non-recurrent group (p=0.019). The number of cases with a high level of C-terminal EPHA2 positivity in the radical cystectomy specimen when compared to the TURBT specimen obtained from the same patient was significantly higher in the recurrent group than in the non-recurrent group (p=0.0034).
CONCLUSION : EPHA2 appears to be a promising marker for bladder tumor recurrence after cystectomy and its evaluation may enable the selection of appropriate cases for adjuvant therapy among patients undergoing radical cystectomy. Further studies, including mass-scale analysis, are required to confirm these results.
Koizumi Mitsuyuki, Sato Shinya, Yoshihara Mitsuyo, Nakamura Yoshiyasu, Terao Hideyuki, Okubo Yoichiro, Washimi Kota, Yoshioka Emi, Yokose Tomoyuki, Kishida Takeshi, Koshikawa Naohiko, Miyagi Yohei
2022-Dec
Bladder cancer, EPHA2, adipocytes, biomarker, cancer recurrence, machine learning
