Identification of a differentiation-related prognostic nomogram based on single-cell RNA sequencing in clear cell renal cell carcinoma

Abstract

Renal cell carcinoma (RCC) is a kidney cancer that is originated from the lined proximal convoluted tubule, and its major histological subtype is clear cell RCC (ccRCC). This study aimed to retrospectively analyze single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database, to explore the correlation among the evolution of tumor microenvironment (TME), clinical outcomes, and potential immunotherapeutic responses in combination with bulk RNA-seq data from The Cancer Genome Atlas (TCGA) database, and to construct a differentiation-related genes (DRG)-based prognostic risk signature (PRS) and a nomogram to predict the prognosis of ccRCC patients. First, scRNA-seq data of ccRCC samples were systematically analyzed, and three subsets with distinct differentiation trajectories were identified. Then, ccRCC samples from TCGA database were divided into four DRG-based molecular subtypes, and it was revealed that the molecular subtypes were significantly correlated with prognosis, clinicopathological features, TME, and the expression levels of immune checkpoint genes (ICGs). A DRG-based PRS was constructed, and it was an independent prognostic factor, which could well predict the prognosis of ccRCC patients. Finally, we constructed a prognostic nomogram based on the PRS and clinicopathological characteristics, which exhibited a high accuracy and a robust predictive performance. This study highlighted the significance of trajectory differentiation of ccRCC cells and TME evolution in predicting clinical outcomes and potential immunotherapeutic responses of ccRCC patients, and the nomogram provided an intuitive and accurate method for predicting the prognosis of such patients.

PSAT1 enhances the efficacy of the prognosis estimation nomogram model in stage-based clear cell renal cell carcinoma

“…Various nomograms have been identified to determine the prognosis of ccRCC patients. For instance, Xia et al [ 12 ] constructed a prognostic nomogram based on the prognostic risk signature and clinicopathological characteristics, which exhibited high accuracy and a robust predictive performance. The study by Zhu et al [ 30 ] reported that combining methylation risk scores with conventional clinical covariates improved the prediction of clinical prognosis in ccRCC patients.…”

“…Nomograms are established based on Cox regression analysis results and are widely used for cancer prognosis, primarily because of their ability to reduce statistical predictive models to a single numerical estimate of the probability of an event, such as death or recurrence [ 10 , 11 ]. In ccRCC patients, a nomogram combining differentiation-related gene (DRG)-based risk score and prognostic clinicopathological variables was previously constructed to provide a visual method for determining prognosis [ 12 ]. However, another nomogram based on risk gene signature and clinical features may provide a practical method for recurrence prediction and facilitate personalized management of ccRCC patients after surgery [ 13 ].…”

Unraveling dynamic interactions between tumor-associated macrophages and consensus molecular subtypes in colorectal cancer: An integrative analysis of single-cell and bulk RNA transcriptome

“…Based on the marker genes in previous study, we annotated the two distinct SPP1+ TAM and C1QC + TAM subpopulations, and consistent with previous study, our trajectory analysis showed that an intermediate state of monocyte-like precursors gave rise to TAM subpopulations in tumor mass [ 18 ]. Several previous studies have utilized the DRGs to construct risk signatures for predicting the prognosis of liver cancer, lung cancer, renal cancer and osteosarcoma [ [35] , [36] , [37] , [38] ]. However, no study has extracted the genes expressed along TAM differentiation lineage for the construction of a prognostic signature for CRC.…”