A radiogenomics signature for predicting the clinical outcome of bladder urothelial carcinoma
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To determine the integrative value of contrast-enhanced computed tomography (CECT), transcriptomics data and clinicopathological data for predicting the survival of bladder urothelial carcinoma (BLCA) patients.
RNA sequencing data, radiomics features and clinical parameters of 62 BLCA patients were included in the study. Then, prognostic signatures based on radiomics features and gene expression profile were constructed by using least absolute shrinkage and selection operator (LASSO) Cox analysis. A multi-omics nomogram was developed by integrating radiomics, transcriptomics and clinicopathological data. More importantly, radiomics risk score–related genes were identified via weighted correlation network analysis and submitted to functional enrichment analysis.
The radiomics and transcriptomics signatures significantly stratified BLCA patients into high- and low-risk groups in terms of the progression-free interval (PFI). The two risk models remained independent prognostic factors in multivariate analyses after adjusting for clinical parameters. A nomogram was developed and showed an excellent predictive ability for the PFI in BLCA patients. Functional enrichment analysis suggested that the radiomics signature we developed could reflect the angiogenesis status of BLCA patients.
The integrative nomogram incorporated CECT radiomics, transcriptomics and clinical features improved the PFI prediction in BLCA patients and is a feasible and practical reference for oncological precision medicine.
• Our radiomics and transcriptomics models are proved robust for survival prediction in bladder urothelial carcinoma patients.
• A multi-omics nomogram model which integrates radiomics, transcriptomics and clinical features for prediction of progression-free interval in bladder urothelial carcinoma is established.
• Molecular functional enrichment analysis is used to reveal the potential molecular function of radiomics signature.
KeywordsMultidetector computed tomography Urinary bladder neoplasms Artificial intelligence Prognosis
Area under the curve
Bladder urothelial carcinoma
Contrast-enhanced computed tomography
Kyoto Encyclopedia of Genes and Genomes
Least absolute shrinkage and selection operator
Receiver operating characteristic
The regions of interest
The Cancer Genome Atlas
The Cancer Imaging Archive
Trans per million
Weighted gene co-expression network analysis
The authors would like to thank the TCGA and TCIA databases for the availability of the data.
This study has received funding by grants from the Guangxi Science and Technology Program (grant no. GuiKeAB17195020), the Fund of National Natural Science Foundation of China (grant no. NSFC81260222) and Innovation Project of Guangxi Graduate Education (grant no. YCSW2018104).
Compliance with ethical standards
The scientific guarantor of this publication is Hong Yang.
Conflict of interest
The authors declare that they have no conflict of interest.
Statistics and biometry
One of the authors (Ling Chen) has significant statistical expertise.
Written informed consent was not required for this study because all patients’ data included in this study are publicly and freely available for scientific purposes (The Cancer Genome Atlas Bladder Urothelial Carcinoma [TCGA-BLCA]).
Institutional Review Board approval was not required because all patients’ data included in this study are publicly and freely available for scientific purposes (The Cancer Genome Atlas-Bladder Urothelial Carcinoma [TCGA-BLCA]).
• diagnostic or prognostic study
• performed at one institution
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