Development and Evaluation of High Risk Group Prediction Model in T1 Stage Renal Cell Cancer Using Molecular Biomarkers
NCT ID: NCT03694912
Last Updated: 2019-09-17
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
426 participants
OBSERVATIONAL
2018-11-01
2023-09-22
Brief Summary
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Detailed Description
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* micro-dissection: only when the tumor contents are more than 90% are analyzed.
B. Data analysis and model development
* Development goals - Analysis of prospective biomarker expression in FFPE, and frozen tissue specimens
\- Development of a high-risk prediction model for post-surgical morbidity in T1-stage RCC
* Contents and scope
1. Expression analysis of prospective biomarkers in FFPE and frozen tissue samples of T1-stage clear cell type RCC : PBRM1, SETD2, BAP1, and KDM5C expressed as prognostic biomarkers of renal cell carcinoma in other studies
* The newly proposed FOXC2, CLIP4
1. Mutational analysis (Transcriptome sequencing with variant calling)
2. mRNA expression analysis (qRT-PCR) - only when the tumor contents are more than 90% are analyzed.
\- Analysis of tumor size and malignancy as a prognostic predictor of RCC
\- The expression of primary and metastatic lesions was analyzed by considering intratumor heterogeneity in RCC (Fisher exact test, Wilcoxon exact 2-tailed test)
* Analysis of association with postoperative local recurrence or distant metastasis, cancer-specific survival, and overall survival (Cox proportional hazard regression analysis: Time to recurrence and distant metastasis, overall survival, competing risk method: cancer specific survival)
2. Development of predictive model for high-risk molecular disease in T1-Stage RCC (survival rate) - elastic net Cox model in glmnet, version 1.7.3
\- prediction accuracy was evaluated using Harrel concordance probability (C-index), internal validation was performed using bootstrap
3. Development of predictive model for preoperative molecular high risk in T1- Stage RCC (for Poor Pathologic Findings)
* multivariate logistic/linear regression model
* prediction accuracy was evaluated using Harrel concordance probability (C-index), internal validation was performed using bootstrap
Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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Aggressive RCC Group
RCC with synchronous metastasis, recurrence, or cancer-specific death
Partial or radical nephrectomy
The investigators perform partial or radical nephrectomy those diagnosed as RCC.
Non-aggressive RCC Group
RCC without synchronous metastasis, recurrence, or cancer-specific death
Partial or radical nephrectomy
The investigators perform partial or radical nephrectomy those diagnosed as RCC.
Interventions
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Partial or radical nephrectomy
The investigators perform partial or radical nephrectomy those diagnosed as RCC.
Eligibility Criteria
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Inclusion Criteria
* Patients who have undergone partial or radical nephrectomy in Severance Hospital, Sinchon from 2018.11 and 2019.10
* Those who agree to give permission to use their human source information
* Those who agree with this study
Exclusion Criteria
* Those who don't agree with this study
20 Years
ALL
No
Sponsors
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Yonsei University
OTHER
Responsible Party
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Locations
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Department of Urology, Urological Science Institute, Yonsei University, Colleage of Medicine
Seoul, , South Korea
Countries
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Facility Contacts
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References
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Park JS, Jang WS, Kim J, Lee SH, Rha KH, Ham WS. Association between visceral adiposity and DDX11 as a predictor of aggressiveness of small clear-cell renal-cell carcinoma: a prospective clinical trial. Cancer Metab. 2021 Apr 6;9(1):15. doi: 10.1186/s40170-021-00251-y.
Park JS, Pierorazio PM, Lee JH, Lee HJ, Lim YS, Jang WS, Kim J, Lee SH, Rha KH, Cho NH, Ham WS. Gene Expression Analysis of Aggressive Clinical T1 Stage Clear Cell Renal Cell Carcinoma for Identifying Potential Diagnostic and Prognostic Biomarkers. Cancers (Basel). 2020 Jan 16;12(1):222. doi: 10.3390/cancers12010222.
Other Identifiers
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4-2018-0753
Identifier Type: -
Identifier Source: org_study_id
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