A radiogenomics biomarker based on immunological heterogeneity for non-invasive prognosis of renal clear cell carcinoma

Abstract

BackgroundTumor 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.MethodsA novel non-invasive radiogenomics biomarker was constructed for immune-related gene expression in ccRCC. First, 520 ccRCC transcriptomic datasets from The Cancer Genome Atlas (TCGA) were analyzed using a 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 Cancer Imaging Archive (TCIA) database were used to extract radiomic features. To stratify immune-related gene expression levels, extracted radiogenomics features were identified according to standard consecutive steps. A nomogram was built to combine radiogenomics and clinicopathological information through multivariate logistic regression to further enhance the radiogenomics model. Mann–Whitney U test and ROC curves were used to assess the effectiveness of the radiogenomics marker.ResultsNMF 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 was also analyzed. The immune-related genomic signature (consisting of eight genes) of the tumor was shown to be significantly associated with survival in patients with ccRCC in TCGA database. The immune-related genomic signature was delineated by grouping the signature expression as either low- or high-risk. Using TCIA database, we constructed a radiogenomics biomarker consisting of 11 radiomic features that were optimal predictors of immune-related gene signature expression levels, which demonstrated AUC (area under the ROC curve) 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 radiogenomics model (AUC = 0.81, 074).ConclusionsThe novel prognostic radiogenomics 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 TCGA database. It is anticipated that this work will assist in selecting precise clinical treatment strategies. This study may also lead to precise theranostics for patients with ccRCC in the future.

Radiogenomics study to predict the nuclear grade of renal clear cell carcinoma

“…Furthermore, the Ras signalling pathway, which is related to the HIF pathway, has been reported to be involved in the regulation of ccRCC progression and drug resistance [36] . CT radiomic features correlate with genes associated with clinical outcomes, such as postoperative distant metastasis, overall survival, and immune subtypes of ccRCC [10] , [33] , [37] . Radiomic features-associated genes was enriched in critical pathways for ccRCC development, suggesting that four radiomic features was related to the MAPK, HIF-1 and Ras signalling pathways.…”

“…Studies evaluating the ccRCC nuclear grade prediction revealed that performance of a CT-radiomics-based model was higher than that of a The Cancer Genomics Atlas (TCGA) transcriptomics-based model, as indicated by the AUCs in the validation set which were 0.90 and 0.82, respectively [7] , [8] . Radiogenomic studies for prognosis and prediction of ccRCC suggested the existence of correlations between radiomic features and prognosis-related pathways such as cell cycle and microvascular infiltration [9] , [10] , [11] . The answer to predicting nuclear grade of ccRCC may lie in deciphering the association between nuclear grade-related radiomic features and genomics.…”

Transcriptome mapping of renal clear cell carcinoma revealed by machine learning algorithm based on enhanced computed tomography images

“…However, there are still some drawbacks in its clinical application, such as some patients cannot obtain tissue samples, resulting in the loss of transcriptome data, and then the model built in the present study cannot be used to predict the prognosis of patients with ccRCC. It is gratifying that, in recent years, models based on transcriptomics combined with radiomics have been constructed for the prognostic analysis of various cancers including ccRCC 47–49 . These studies aimed to predict the prognosis of cancer patients by radiomics signatures, and achieved encouraging results.…”

“…Radiomics is a non‐invasive method that uses advanced algorithms and image processors 47 . This greatly simplifies data acquisition.…”

“…It is gratifying that, in recent years, models based on transcriptomics combined with radiomics have been constructed for the prognostic analysis of various cancers including ccRCC. [47][48][49] These studies aimed to predict the prognosis of cancer patients by radiomics signatures, and achieved encouraging results. This greatly broadens our research horizon.…”