Dataset Information

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.

SUBMITTER: Xia ZN

PROVIDER: S-EPMC9243004 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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

Xia Zhi-Nan ZN Wu Jing-Gen JG Yao Wen-Hao WH Meng Yu-Yang YY Jian Wen-Gang WG Wang Teng-Da TD Xue Wei W Yu Yi-Peng YP Cai Li-Cheng LC Wang Xing-Yuan XY Zhang Peng P Li Zhi-Yuan ZY Zhou Hao H Jiang Zhi-Cheng ZC Zhou Jia-Yu JY Zhang Cheng C

Scientific reports 20220629 1

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 Cance ...[more]

PMID: 35768519

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Project description:BackgroundClear cell renal cell carcinoma is a prototypical tumor characterized by metabolic reprogramming, which extends beyond tumor cells to encompass diverse cell types within the tumor microenvironment. Nonetheless, current research on metabolic reprogramming in renal cell carcinoma mostly focuses on either tumor cells alone or conducts analyses of all cells within the tumor microenvironment as a mixture, thereby failing to precisely identify metabolic changes in different cell types within the tumor microenvironment.MethodsGathering 9 major single-cell RNA sequencing databases of clear cell renal cell carcinoma, encompassing 195 samples. Spatial transcriptomics data were selected to conduct metabolic activity analysis with spatial localization. Developing scMet program to convert RNA-seq data into scRNA-seq data for downstream analysis.ResultsDiverse cellular entities within the tumor microenvironment exhibit distinct infiltration preferences across varying histological grades and tissue origins. Higher-grade tumors manifest pronounced immunosuppressive traits. The identification of tumor cells in the RNA splicing state reveals an association between the enrichment of this particular cellular population and an unfavorable prognostic outcome. The energy metabolism of CD8+ T cells is pivotal not only for their cytotoxic effector functions but also as a marker of impending cellular exhaustion. Sphingolipid metabolism evinces a correlation with diverse macrophage-specific traits, particularly M2 polarization. The tumor epicenter is characterized by heightened metabolic activity, prominently marked by elevated tricarboxylic acid cycle and glycolysis while the pericapsular milieu showcases a conspicuous enrichment of attributes associated with vasculogenesis, inflammatory responses, and epithelial-mesenchymal transition. The scMet facilitates the transformation of RNA sequencing datasets sourced from TCGA into scRNA sequencing data, maintaining a substantial degree of correlation.ConclusionsThe tumor microenvironment of clear cell renal cell carcinoma demonstrates significant metabolic heterogeneity across various cell types and spatial dimensions. scMet exhibits a notable capability to transform RNA sequencing data into scRNA sequencing data with a high degree of correlation.

Dataset Information

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

Publications

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

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