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Updated: Jun 29, 2025
The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers
Published on: January 22, 2013
Radiogenomic analysis based on lipid metabolism-related subset for non-invasive prediction for prognosis of renal
Haifeng He1, Yongzhi Xie1, Fulong Song1
1Department of Radiology, The Third Xiangya Hospital Central South University, Changsha, China.
Purpose:
Multiple lipid metabolism pathways alterations are associated with clear cell renal cell carcinoma (ccRCC) development and aggressiveness. In this study, we aim to develop a novel radiogenomics signature based on lipid metabolism-related genes (LMRGs) that may accurately predict ccRCC patients' survival.
Materials And Methods:
First, 327 ccRCC were used to screen survival-related LMRGs and construct a gene signature based on The Cancer Genome Atlas (TCGA) database. Then, 182 ccRCC were analyzed to establish radiogenomics signature linking LMRGs signature to radiomic features in The Cancer Imaging Archive (TCIA) database included enhanced CT images and transcriptome sequencing data. Lastly, we validated the prognostic power of the identified radiogenomics signature using these patients of TCIA and the Third Xiangya Hospital.
Results:
We identified the LMRGs signature, consisting of 13 genes, which could efficiently discriminate between low-risk and high-risk patients and serve as an independent and reliable predictor of overall survival (OS). Radiogenomics signature, comprised of 9 radiomic features, was created and could accurately predict the expression level of LMRGs signature (low- or high-risk) for patients. The predictive performance of this radiogenomics signature was demonstrated through AUC values of 0.75 and 0.74 for the training and validation sets (at a ratio of 7:3), respectively. Radiogenomics signature was proven to be an independent risk factor for OS by multivariable analysis (HR = 4.98, 95 % CI:1.72-14.43, P = 0.003).
Conclusions:
The LMRGs radiogenomics signature could serve as a novel prognostic predictor.
