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The integrate profiling of single-cell and spatial transcriptome RNA-seq reveals tumor heterogeneity, therapeutic targets, and prognostic subtypes in ccRCC

Cancer Gene Therapy (2024)Cite this article

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Abstract

Clear-cell renal cell carcinoma (ccRCC) is the most common type of RCC; however, the intratumoral heterogeneity in ccRCC remains unclear. We first identified markers and biological features of each cell cluster using bioinformatics analysis based on single-cell and spatial transcriptome RNA-sequencing data. We found that gene copy number loss on chromosome 3p and amplification on chromosome 5q were common features in ccRCC cells. Meanwhile, NNMT and HILPDA, which are associated with the response to hypoxia and metabolism, are potential therapeutic targets for ccRCC. In addition, CD8+ exhausted T cells (LAG3+ HAVCR2+), CD8+ proliferated T cells (STMN+), and M2-like macrophages (CD68+ CD163+ APOC1+), which are closely associated with immunosuppression, played vital roles in ccRCC occurrence and development. These results were further verified by whole exome sequencing, cell line and xenograft experiments, and immunofluorescence staining. Finally, we divide patients with ccRCC into three subtypes using unsupervised cluster analysis. and generated a classifier to reproduce these subtypes using the eXtreme Gradient Boosting algorithm. Our classifier can help clinicians evaluate prognosis and design personalized treatment strategies for ccRCC. In summary, our work provides a new perspective for understanding tumor heterogeneity and will aid in the design of antitumor therapeutic strategies for ccRCC.

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Fig. 1: The landscape of single cells derived from ccRCC and para-tumor tissues.
Fig. 2: Hypoxia and metabolism disorder are the molecular features in ccRCC Cancer Cells.
Fig. 3: The NNMT and HILPDA were potential therapeutic targets in ccRCC.
Fig. 4: M2 polarization of macrophage in ccRCC.
Fig. 5: The cell differentiation trajectory of tumor-driven CD8+ T cells in ccRCC.
Fig. 6: Fibroblasts and endothelial cells in ccRCC.
Fig. 7: Prognostic role of cell clusters in ccRCC.
Fig. 8: Construction and validation of a ccRCC subtype classifier.

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Data availability

The public datasets used and/or analyzed during the current study are available from The Mendeley Data (https://data.mendeley.com/datasets/nc9bc8dn4m/1 and https://doi.org/10.17632/g67bkbnhhg.1), Gene Expression Omnibus (GSE129253), The mRNA microarray data (E-MTAB-1980: https://www.ebi.ac.uk/arrayexpress/) and University of California Santa Cruz (UCSC) Xena browser (https://xenabrowser.net) database. The WES data produced in our study was uploaded in supplementary materials (Tables S25 and S26). The raw WES data can be obtained by asking the authors. The code of “ccRCCcluster” was published on GitHub (https://github.com/ZylRpackage2023/ccRCCcluster). The shinyAPP to Visualize scRNA-seq data stored in https://drive.google.com/file/d/15H0GNV6EwmvBrhiFsBhgpjfODDK6XOwc/view?usp=sharing.

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Acknowledgements

We would like to thank all the investigators for participating in the present study. This work was supported by the Ministry of Science and Technology of People’s Republic of China (2023YFC2306003), the National Natural Science Foundation of China (No. 81972652 and No. 32270635) and the National Natural Science Foundation of Beijing Municipality (No. 7232082).

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  1. These authors contributed equally: Yanlong Zhang, Xuefeng Huang, Minghang Yu.

Authors and Affiliations

  1. National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China

    Yanlong Zhang, Xuefeng Huang, Minghang Yu, Menghan Zhang & Xi Wang

  2. Shanxi Medical University, Shanxi Bethune Hospital, Taiyuan, Shanxi, China

    Yanlong Zhang, Li Zhao & Liyun Zhang

  3. Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China

    Yanlong Zhang, Xuefeng Huang, Minghang Yu & Xi Wang

  4. Beijing Institute of Infectious Diseases, Beijing, 100015, China

    Yanlong Zhang, Xuefeng Huang, Minghang Yu & Xi Wang

  5. National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China

    Yanlong Zhang, Xuefeng Huang, Minghang Yu & Xi Wang

  6. Department of Urology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China

    Yanlong Zhang & Yong Yan

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Contributions

YZ, XH, and MY designed and conducted the study. YZ, XH, and MY drafted the manuscript. XW, YY, and LZ revised the manuscript. YZ, XH, and MY performed the data analysis. YZ, XH, MY, MZ, and LZ conduct experimental operations. YZ, XH, and MY contributed equally to this work. XW, YY, and LZ contributed equally to this work. All authors have read and approved the final manuscript.

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Correspondence to Yong Yan, Liyun Zhang or Xi Wang.

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The studies involving human participants were reviewed and approved by the ethics committee of Shanghai OUTDO Biotech Corporation Limited and Shanxi Bethune Hospital. The patients/participants provided their written informed consent to participate in this study.

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Zhang, Y., Huang, X., Yu, M. et al. The integrate profiling of single-cell and spatial transcriptome RNA-seq reveals tumor heterogeneity, therapeutic targets, and prognostic subtypes in ccRCC. Cancer Gene Ther (2024). https://doi.org/10.1038/s41417-024-00755-x

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