A radiogenomics biomarker based on immunological heterogeneity for non-invasive prognosis of renal clear cell carcinoma - Gao, Jiahao; Ye, Fangdie; Han, Fang; Jiang, Haowen; Zhang, Jiawen (Frontiers in Immunology, 2022)
Background: Tumor immunological heterogeneity potentially influences the prognostic disparities among patients with clear cell renal cell carcinoma (ccRCC); however, there is a lack of macroscopic imaging tools that can be used to predict immune-related gene expression in ccRCC. Methods: A novel non-invasive radiogenomic biomarker was constructed for immune-related gene expression in ccRCC. First, 520 ccRCC transcriptomic datasets from The Cancer Genome Atlas (TCGA) were analyzed using non-negative matrix decomposition (NMF) clustering to identify immune-related molecular subtypes. Immune-related prognostic genes were analyzed through Cox regression and Gene Set Enrichment Analysis (GSEA). We then built a risk model based on an immune-related gene subset to predict prognosis in patients with ccRCC. CT images corresponding to the ccRCC patients in the TCIA database were used to extract radiomic features. To stratify immune-related gene expression levels, extracted radiogenomic features were identified according to standard consecutive steps. A nomogram was built to combine radiogenomic and clinicopathological information through multi-variate logistic regression to further enhance the radiogenomic model. Mann–Whitney U test and ROC curves were used to assess the effectiveness. Results: NMF methods successfully clustered patients into diverse subtypes according to gene expression levels in the tumor microenvironment (TME). The relative abundance of 10 immune cell populations in each tissue were also analyzed. The tumor immune-related genomic signature (consisting of eight genes) was shown to be significantly associated with survival in patients with ccRCC in the TCGA database, delineated by grouping the signature expression as either low- or high-risk. Using the TCIA database, we constructed a radiogenomic biomarker consisting of 11 radiomic features that were optimal predictors of immune-related gene signature expression levels, which demonstrated AUC values of 0.76 and 0.72 in the training and validation groups, respectively. The nomogram built by combining radiomics and clinical pathological information could further improve the predictive efficacy of the radiogenomic model (AUC=0.81,074). Conclusions: The novel prognostic radiogenomic biomarker achieved excellent correlation with the immune-related gene expression status of patients with ccRCC and could successfully stratify the survival status of patients in the TCGA database. It is anticipated that this work will assist in selecting precise clinical treatment strategies.