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Risk Prediction Tool for Aggressive Tumors in Clinical T1 Stage Clear Cell Renal Cell Carcinoma Using Molecular Biomarkers

Authors
 Jee Soo Park  ;  Hyo Jung Lee  ;  Nam Hoon Cho  ;  Jongchan Kim  ;  Won Sik Jang  ;  Ji Eun Heo  ;  Won Sik Ham 
Citation
 COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, Vol.17 : 371-377, 2019 
Journal Title
 COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 
Issue Date
2019
Keywords
BAP1, BRCA1 associated protein-1 ; BMI, Body mass index ; Biomarker ; CLIP4, CAP-Gly, cytoskeleton-associated protein-glycine rich domain-containing linker protein family member 4 ; DNN, Deep neural network ; EDTA, Ethylenediaminetetraacetic acid ; FFPE, Formalin-fixed paraffin-embedded ; FOXC2, Forkhead box protein C2 ; KDM5C, Lysine-specific demethylase 5C ; MSKCC, Memorial Sloan Kettering Cancer Center ; PBRM1, Polybromo 1 ; PBS, Phosphate-buffered saline ; Prediction model ; Renal cell cancer ; SETD2, SET domain-containing 2 ; TNM, Tumor-node-metastasis ; ccRCC, Clear cell renal cell carcinoma ; qRT-PCR, Quantitative reverse transcription-polymerase chain reaction
Abstract
Some early-stage clear cell renal cell carcinomas (ccRCCs) of ≤7 cm are associated with a poor clinical outcome. In this study, we investigated molecular biomarkers associated with aggressive clinical T1 stage ccRCCs of ≤7 cm, which were used to develop a risk prediction tool toward guiding the decision of treatment. Among 1069 nephrectomies performed for ccRCC of ≤7 cm conducted between January 2008 and December 2014, 177 cases with available formalin-fixed paraffin-embedded tissue were evaluated. An aggressive tumor was defined as a tumor exhibiting synchronous metastasis, recurrence, or leading to cancer-specific death. Expression levels of six genes (FOXC2, CLIP4, PBRM1, BAP1, SETD2, and KDM5C) were measured by reverse-transcription polymerase chain reaction (qRT-PCR) and their relation to clinical outcomes was investigated. Immunohistochemistry was performed to validate the expression profiles of selected genes significantly associated with clinical outcomes in multivariate analysis. Using these genes, we developed a prediction model of aggressive ccRCC based on logistic regression and deep-learning methods. FOXC2, PBRM1, and BAP1 expression levels were significantly lower in aggressive ccRCC than non-aggressive ccRCC both in univariate and multivariate analysis. The immunohistochemistry result demonstrated the significant downregulation of FOXC2, PBRM1, and BAP1 expression in aggressive ccRCC. Adding immunohistochemical staining results to qRT-PCR, the aggressive ccRCC prediction models had the area under the curve (AUC) of 0.760 and 0.796 and accuracy of 0.759 and 0.852 using the logistic regression method and deep-learning method, respectively. Use of these biomarkers and the developed prediction model can help stratify patients with clinical T1 stage ccRCC.
DOI
10.1016/j.csbj.2019.03.005
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Urology (비뇨의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
Yonsei Authors
Kim, Jong Chan(김종찬) ORCID logo https://orcid.org/0000-0002-0022-6689
Park, Jee Soo(박지수) ORCID logo https://orcid.org/0000-0001-9976-6599
Jang, Won Sik(장원식) ORCID logo https://orcid.org/0000-0002-9082-0381
Cho, Nam Hoon(조남훈) ORCID logo https://orcid.org/0000-0002-0045-6441
Ham, Won Sik(함원식) ORCID logo https://orcid.org/0000-0003-2246-8838
Heo, Ji Eun(허지은) ORCID logo https://orcid.org/0000-0002-4184-8468
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/170020
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