Browsing by Subject "clear cell renal cell carcinoma"

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    Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis
    (AACR, 2017-11) Cheng, Jun; Zhang, Jie; Han, Yatong; Wang, Xusheng; Ye, Xiufen; Meng, Yuebo; Parwani, Anil; Han, Zhi; Feng, Qianjin; Huang, Kun; Medicine, School of Medicine
    In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with genomic data for predicting prognosis, as well as the relationships between them, has rarely been explored. In this study, we present an integrative genomics framework for constructing a prognostic model for clear cell renal cell carcinoma. We used patient data from The Cancer Genome Atlas (n = 410), extracting hundreds of cellular morphologic features from digitized whole-slide images and eigengenes from functional genomics data to predict patient outcome. The risk index generated by our model correlated strongly with survival, outperforming predictions based on considering morphologic features or eigengenes separately. The predicted risk index also effectively stratified patients in early-stage (stage I and stage II) tumors, whereas no significant survival difference was observed using staging alone. The prognostic value of our model was independent of other known clinical and molecular prognostic factors for patients with clear cell renal cell carcinoma. Overall, this workflow and the shared software code provide building blocks for applying similar approaches in other cancers.
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    A new survival model based on ferroptosis-related genes for prognostic prediction in clear cell renal cell carcinoma
    (Impact Journals, 2020-07-20) Wu, Guangzhen; Wang, Qifei; Xu, Yingkun; Li, Quanlin; Cheng, Liang; Pathology and Laboratory Medicine, School of Medicine
    In this study, we analyzed the clinical significance of ferroptosis-related genes (FRGs) in 32 cancer types in the GSCA database. We detected a 2-82% mutation rate among 36 FRGs. In clear cell renal cell carcinoma (ccRCC; n=539) tissues from the The Cancer Genome Atlas database, 30 of 36 FRGs were differentially expressed (up- or down-regulated) compared to normal kidney tissues (n=72). Consensus clustering analysis identified two clusters of FRGs based on similar co-expression in ccRCC tissues. We then used LASSO regression analysis to build a new survival model based on five risk-related FRGs (CARS, NCOA4, FANCD2, HMGCR, and SLC7A11). Receiver operating characteristic curve analysis confirmed good prognostic performance of the new survival model with an area under the curve of 0.73. High FANCD2, CARS, and SLC7A11 expression and low HMGCR and NCOA4 expression were associated with high-risk ccRCC patients. Multivariate analysis showed that risk score, age, stage, and grade were independent risk factors associated with prognosis in ccRCC. These findings demonstrate that this five risk-related FRG-based survival model accurately predicts prognosis in ccRCC patients, and suggest FRGs are potential prognostic biomarkers and therapeutic targets in several cancer types.