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Updated: Aug 17, 2026
The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers
Published on: January 22, 2013
Network analysis of histopathological image features and genomics data improving prognosis performance in clear cell
Jianrui Ji1, Yunsong Liu1, Yongxing Bao1
1Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Introduction:
Clear cell renal cell carcinoma is the most common type of kidney cancer, but the prediction of prognosis remains a challenge.
Methods:
We collected whole-slide histopathological images, corresponding clinical and genetic information from the The Cancer Imaging Archive and The Cancer Genome Atlas databases and randomly divided patients into training (n = 197) and validation (n = 84) cohorts. After feature extraction by CellProfiler, we used 2 different machine learning techniques (Least Absolute Shrinkage and Selector Operation-regularized Cox and Support Vector Machine-Recursive Feature Elimination) and weighted gene co-expression network analysis to select prognosis-related image features and genes, respectively. These features and genes were integrated into a joint model using random forest and used to create a nomogram that combines other predictive indicators.
Results:
A total of 4 overlapped features were identified, represented by the computed histopathological risk score in the random forest model, and showed predictive value for overall survival (test set: 1-year area under the curves (AUC) = 0.726, 3-year AUC = 0.727, and 5-year AUC = 0.764). The histopathological-genetic risk score (HGRS) integrating the genetic information computed performed better than the model that used image features only (test set: 1-year AUC = 0.682, 3-year AUC = 0.734, and 5-year AUC = 0.78). The nomogram (gender, stage, and HGRS) achieved the highest net benefit according to decision curve analysis compared to HGRS or clinical model.
Conclusion:
This study developed a histopathological-genetic-related nomogram by combining histopathological features and clinical predictors, providing a more comprehensive prognostic assessment for clear cell renal cell carcinoma patients.
