Development and Evaluation of High Risk Group Prediction Model in T1 Stage Renal Cell Cancer Using Molecular Biomarkers

Clear Renal Cell Cancer (< 7cm Size)

renal cell cancer, high risk group, bio-marker

For the appropriate individualized treatment of T1-stage renal cell carcinoma with heterogeneous biological features, the expression of PBRM1, SETD2, BAP1, KDM5C and the newly proposed FOXC2 and CLIP4, are compared with clinical features. The investigators evaluated the efficacy of FOXC2 and CLIP4 as prognostic biomarkers and developed a high risk prediction model based on these results. In a previous study, the investigators evaluated the efficacy of FOXC2 and CLIP4 as prognostic biomarkers and reported their association with synchronous metastasis in renal cell carcinomas less than 7 cm in size. The investigators analyzed the expression level of renal cell carcinoma according to the size and malignancy (Fuhrman grade) of renal cell carcinoma in T1-stage clear cell type renal cell carcinoma of tumor size less than 7cm. The aim of this study was to analyze the association of tumor recurrence or metastasis, cancer specific survival rate, overall survival rate, tumor size, malignancy and T stage in postoperative biopsy. For expression analysis, PCR amplification and bidirectional Sanger sequencing and mRNA expression analysis (qRT-PCR) were used. For statistical analysis, Fisher exact test, Wilcoxon exact 2-tailed test, Cox proportional hazard regression analysis and competing risk method were used. In this study, the investigators compared the expression of PBRM1, SETD2, BAP1, and KDM5 with newly proposed biomarkers, FOXC2 and CLIP4 and demonstrate the prognostic value of FOXC2 and CLIP4 as new prognostic biomarkers and compared the clinical outcomes with the clinical outcome. Based on these results, the investigators propose a high risk prediction model for individualized treatment of T1-stage renal cell carcinoma. This study is expected to establish a new prediction model and molecular biologic stage for risk stratification of T1-stage renal cell carcinoma patients and apply genetic test for selection of optimal tailored treatment for T1-stage renal cell carcinoma. In addition, it will be an important basic data of the molecular biologic mechanism of metastasis in early renal cell carcinoma and may be used as a basic data for the development and selection of customized therapeutic agents in patients with distant metastasis.

* collection of FFPE samples: collection of primary or metastatic site * 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

South Korea