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논문 기본 정보

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학술저널
저자정보
김경 (충북대학교병원) 변영준 (충북대학교) Chuang-Ming Zheng (충북대학교) 문성민 (충북대학교병원) 조수정 (충북대학교병원) 강호원 (충북대학교병원) 김원태 (충북대학교) 최용현 (동의대학교) 문성권 (중앙대학교) 김원재 ((주)유로테크) 박현미 (충북대학교) 윤석중 (충북대학교)
저널정보
대한비뇨기과학회 Investigative and Clinical Urology Investigative and Clinical Urology Vol.65 No.1
발행연도
2024.1
수록면
94 - 103 (10page)
DOI
https://doi.org/10.4111/icu.20230227

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Purpose: T1 high grade (T1HG) bladder cancer (BC) is a type of non-muscle invasive BC (NMIBC) that is recognized as an aggressive subtype with a heightened propensity for progression. Current risk stratification methods for NMIBC rely on clinicopathological indicators; however, these approaches do not adequately capture the aggressive nature of T1HG BC. Thus, new, more accurate biomarkers for T1HG risk stratification are needed. Here, we enrolled three different patient cohorts and investigated expression of collagen type VI alpha 1 (COL6A1), a key component of the extracellular matrix, at different stages and grades of BC, with a specific focus on T1HG BC. Materials and Methods: Samples from 298 BC patients were subjected to RNA sequencing and real-time polymerase chain reaction. Results: We found that T1HG BC and muscle invasive BC (MIBC) exhibited comparable expression of COL6A1, which was significantly higher than that by other NMIBC subtypes. In particular, T1HG patients who later progressed to MIBC had considerably higher expression of COL6A1 than Ta, T1 low grade patients, and patients that did not progress, highlighting the aggressive nature and higher risk of progression associated with T1HG BC. Moreover, Cox and Kaplan–Meier survival analyses revealed a significant association between elevated expression of COL6A1 and poor progression-free survival of T1HG BC patients (multivariate Cox hazard ratio, 16.812; 95% confidence interval, 3.283–86.095; p=0.001 and p=0.0002 [log-rank test]). Conclusions: These findings suggest that COL6A1 may be a promising biomarker for risk stratification of T1HG BC, offering valuable insight into disease prognosis and guidance of personalized treatment decisions.

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