RETRACTED ARTICLE: Renal enhanced CT images reveal the tandem mechanism between tumor cells and immunocytes based on bulk/single-cell RNA sequencing

• Published in: Functional & integrative genomics 2023-06, Vol.23 (2), p.88, Article 88
• Main Authors: Liang, Haote, Wu, Keming, Wu, Rongrong, Huang, KaTe, Deng, Zhexian, Chen, Hongde
• Format: Article
• Language: English
• Subjects: Animal Genetics and Genomics; Biochemistry; Bioinformatics; Biomedical and Life Sciences; Cell Biology; Life Sciences; Microbial Genetics and Genomics; Original Article; Plant Genetics and Genomics
• ISSN: 1438-793X; 1438-7948
• DOI: 10.1007/s10142-023-01011-5

Metabolic reprogramming is essential for establishing the tumor microenvironment (TME). Glutamine has been implicated in cancer metabolism, but its role in clear cell renal carcinoma (ccRCC) remains unknown. Transcriptome data of patients with ccRCC and single-cell RNA sequencing (scRNA-seq) data were obtained from The Cancer Genome Atlas (TCGA, 539 ccRCC samples and 59 normal samples) database and GSE152938 (5 ccRCC samples). Differentially expressed genes related to glutamine metabolism (GRGs) were obtained from the MSigDB database. Consensus cluster analysis distinguished metabolism-related ccRCC subtypes. LASSO-Cox regression analysis was used to construct a metabolism-related prognostic model. The ssGSEA and ESTIMATE algorithms evaluated the level of immune cell infiltration in the TME, and the immunotherapy sensitivity score was obtained from TIDE. Cell–cell communication analysis was used to observe the distribution and effects of the target genes in the cell subsets. An image genomics model was constructed using imaging feature extraction and a machine learning algorithm. Results: Fourteen GRGs were identified. Overall survival and progression-free survival rates were lower in metabolic cluster 2, compared with those in cluster 1. The matrix/ESTIMATE/immune score in C1 decreased, but tumor purity in C2 increased. Immune cells were more active in the high-risk group, in which CD8 + T cells, follicular helper T cells, Th1 cells, and Th2 cells were significantly higher than those in the low-risk group. The expression levels of immune checkpoints were also significantly different between the two groups. RIMKL mainly appeared in epithelial cells in the single-cell analysis. ARHGAP11B was sparsely distributed. The imaging genomics model proved effective in aiding with clinical decisions. Glutamine metabolism plays a crucial role in the formation of immune TMEs in ccRCC. It is effective in differentiating the risk and predicting survival in patients with ccRCC. Imaging features can be used as new biomarkers for predicting ccRCC immunotherapy.