Radiomic profiling of clear cell renal cell carcinoma reveals subtypes with distinct prognoses and molecular pathways

Autor: Yun He, Hui Qin, Rong Wen, Hong Yang, Peng Lin, Yi-qun Lin, Rui-Zhi Gao
Jazyk: angličtina
Rok vydání: 2021
Předmět:
0301 basic medicine
Oncology
Clear cell renal cell carcinoma
Cancer Research
medicine.medical_specialty
CDF
cumulative distribution function
ROI
region of interest
Genomics
Computed tomography
Biology
Gene mutation
Transcriptome
OS
overall survival
03 medical and health sciences
0302 clinical medicine
Radiomics
GSEA
gene set enrichment analysis
Internal medicine
medicine
CcRCC
Clear cell renal cell carcinoma
Clinical significance
K–M
Kaplan–Meier
PFI
progression-free interval
RC254-282
Original Research
CT
Computer Tomography
LASSO
least absolute shrinkage and selection operator
medicine.diagnostic_test
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
medicine.disease
030104 developmental biology
030220 oncology & carcinogenesis
Clinicopathological features
TCGA
The Cancer Genome Atlas
Zdroj: Translational Oncology
Translational Oncology, Vol 14, Iss 7, Pp 101078-(2021)
ISSN: 1936-5233
Popis: Highlights • Radiomics profile of clear cell renal cell carcinoma is heterogeneity. • Multi-scale Radiogenomics could link molecular features and images. • Radiomic subtypes could be used for risk stratification.
Background To identify radiomic subtypes of clear cell renal cell carcinoma (ccRCC) patients with distinct clinical significance and molecular characteristics reflective of the heterogeneity of ccRCC. Methods Quantitative radiomic features of ccRCC were extracted from preoperative CT images of 160 ccRCC patients. Unsupervised consensus cluster analysis was performed to identify robust radiomic subtypes based on these features. The Kaplan–Meier method and chi-square test were used to assess the different clinicopathological characteristics and gene mutations among the radiomic subtypes. Subtype-specific marker genes were identified, and gene set enrichment analyses were performed to reveal the specific molecular characteristics of each subtype. Moreover, a gene expression-based classifier of radiomic subtypes was developed using the random forest algorithm and tested in another independent cohort (n = 101). Results Radiomic profiling revealed three ccRCC subtypes with distinct clinicopathological features and prognoses. VHL, MUC16, FBN2, and FLG were found to have different mutation frequencies in these radiomic subtypes. In addition, transcriptome analysis revealed that the dysregulation of cell cycle-related pathways may be responsible for the distinct clinical significance of the obtained subtypes. The prognostic value of the radiomic subtypes was further validated in another independent cohort (log-rank P = 0.015). Conclusion In the present multi-scale radiogenomic analysis of ccRCC, radiomics played a central role. Radiomic subtypes could help discern genomic alterations and non-invasively stratify ccRCC patients.
Databáze: OpenAIRE
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