Single-cell multiomics analysis reveals regulatory programs in clear cell renal cell carcinoma

Long, Zhilin; Sun, Chengfang; Tang, Min; Wang, Yin; Ma, Jiayan; Yu, Jichuan; Wei, Jingchao; Ma, Jianzhu; Wang, Bohan; Xie, Qi; Wen, Jiaming

doi:10.1038/s41421-022-00415-0

Cited by 52 publications

(38 citation statements)

References 106 publications

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“…Previous studies have established CA9 as a marker for ccRCC tumor cells [15,[31][32][33]. Consistently, our ndings showed that CA9 + nephrons originated from tumor tissue and exhibited a distinct distribution compared to adjacent normal tissue (Fig.…”

Section: The Differential Tumor Microenviroment Of Primary Lesion Bet...supporting

confidence: 89%

Deciphering Tumor Metastasis and Immune Inhibitory Signature of Clear Cell Renal Cell Carcinoma by Single-Cell Transcriptome Analysis

Yin,

Wang,

Wang

et al. 2023

Preprint

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Clear cell renal cell carcinoma (ccRCC) is known for its high heterogeneity and tendency to metastasize through the bloodstream, leading to limited treatment options and poor overall survival rates. To overcome these challenges, it is crucial to acquire a comprehensive understanding of the underlying biology of ccRCC. Therefore, we conducted a thorough analysis using single-cell RNA sequencing data obtained from samples of non-metastatic and metastatic ccRCC. Our analysis revealed significant differences in the composition of infiltrating immune cells within the primary tumor lesions between metastatic and non-metastatic ccRCC cases. Additionally, we identified two distinct tumor cell states, particularly proximal tubule cells, which exhibited significant enrichment in metastatic ccRCC cases. We found that MDK was highly expressed in metastatic ccRCC and exhibited significant prognostic value for patients. In metastaic ccRCC, we observed enhanced interactions between tumor cells and macrophages mediated by MDK, resulting in the polarization of macrophages towards an angiogenic and immune-suppressive M2-like phenotype. Furthermore, we observed notable differences in the interactions between macrophages and CD8 + T cells in non-metastatic and metastatic ccRCC. Metastatic ccRCC exhibited stronger interactions mediated by immune inhibitory molecules such as SPP1 and CD24, potentially contributing to immune suppression within tumor microenvironment. These dignificant findings provide valuable insights into the molecular and cellular signatures associated with metastatic ccRCC. Moreover, they open up promising opportunities for the development of novel biomarkers and therapeutic targets, specifically tailored to address the challenges posed by metastatic ccRCC.

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Section: The Differential Tumor Microenviroment Of Primary Lesion Bet...supporting

confidence: 89%

Deciphering Tumor Metastasis and Immune Inhibitory Signature of Clear Cell Renal Cell Carcinoma by Single-Cell Transcriptome Analysis

Yin,

Wang,

Wang

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Moreover, single-cell multi-omics approaches enable simultaneously profiling a couple of omics in identical cells [288], providing information on both regulatory elements and consequential gene expression levels for individual cells. The datasets generated by these technologies may help biomedical researchers to discover disease-specific regulatory programs, possibly in the subset of certain cell types [289]. Furthermore, although still in the developmental stage, spatial transcriptomics is a promising technique for considering the cellular context in molecular features of a particular cell [290].…”

Section: Discussionmentioning

confidence: 99%

Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications

Pan

Chen

et al. 2022

Military Med Res

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The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.

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“…Single-cell mRNA sequence (scRNA-seq) data from 4 ccRCC patients with 4 tumor samples (1 sample is assigned to 1 patient), 1 VHL-mutated sample and 3 VHL-wild samples, were collected from Young, et al cohort [ 13 ] and another dataset from Long cohort [ 14 ] including 4 samples from 4 ccRCC patients, all the patients were VHL mutated in Long cohort. After preliminary sample integration and quality control of scRNA-seq data sets, we generated a gene expression matrix with 76,118 cells.…”

Section: Methodsmentioning

confidence: 99%

Cell–cell communications shape tumor microenvironment and predict clinical outcomes in clear cell renal carcinoma

et al. 2023

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Background Cell–cell communications of various cell populations within tumor microenvironment play an essential role in primary tumor growth, metastasis evolution, and immune escape. Nevertheless, comprehensive investigation of cell–cell communications in the ccRCC (Clear cell renal carcinoma) microenvironment and how this interplay affects prognosis still remains limited. Methods Intercellular communications were characterized by single-cell data. Firstly, we employed “CellChat” package to characterize intercellular communications across all types of cells in microenvironment in VHL mutated and non-mutated samples from 8 patients, respectively. And pseudotime trajectory analyses were performed with monocle analyses. Finally clinical prognosis and immunotherapy efficacy with different landscapes of intercellular interplay are evaluated by TCGA-KIRC and immunotherapy cohort. Results Firstly, the VHL phenotype may be related to the intercellular communication landscape. And trajectory analysis reveals the potential relationship of cell–cell communication molecules with T cells and Myeloid cells differentiation. Furthermore, those molecules also correlate with the infiltration of T cells and Myeloid cells. A tumor cluster with highly expressed ligands was defined by quantitative analysis and transcription factor enrichment analysis, which was identified to be pivotal for intercellular communications in tumor microenvironment. Finally, bulk data indicates bulk that different clusters with different intercellular communications have significant predictive value for prognosis and distinguished immunotherapy efficiency. Conclusions The intercellular communication landscapes of VHL wild and VHL mutant ccRCC vary. Intercellular communications within the tumor microenvironment also influence T cell and myeloid cell development and infiltration, as well as predict clinical prognosis and immunotherapy efficacy in ccRCC.

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Single-cell multiomics analysis reveals regulatory programs in clear cell renal cell carcinoma

Cited by 52 publications

References 106 publications

Deciphering Tumor Metastasis and Immune Inhibitory Signature of Clear Cell Renal Cell Carcinoma by Single-Cell Transcriptome Analysis

Deciphering Tumor Metastasis and Immune Inhibitory Signature of Clear Cell Renal Cell Carcinoma by Single-Cell Transcriptome Analysis

Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications

Cell–cell communications shape tumor microenvironment and predict clinical outcomes in clear cell renal carcinoma

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