Abstract
Prostate cancer is one of the more heterogeneous tumour types. In recent years, with the rapid development of single-cell sequencing and spatial transcriptome technologies, researchers have gained a more intuitive and comprehensive understanding of the heterogeneity of prostate cancer. Tumour-associated epithelial cells; cancer-associated fibroblasts; the complexity of the immune microenvironment, and the heterogeneity of the spatial distribution of tumour cells and other cancer-promoting molecules play a crucial role in the growth, invasion, and metastasis of prostate cancer. Single-cell multi-omics biotechnology, especially single-cell transcriptome sequencing, reveals the expression level of single cells with higher resolution and finely dissects the molecular characteristics of different tumour cells. We reviewed the recent literature on prostate cancer cells, focusing on single-cell RNA sequencing. And we analysed the heterogeneity and spatial distribution differences of different tumour cell types. We discussed the impact of novel single-cell omics technologies, such as rich omics exploration strategies, multi-omics joint analysis modes, and deep learning models, on future prostate cancer research. In this review, we have constructed a comprehensive catalogue of single-cell omics studies in prostate cancer. This article aimed to provide a more thorough understanding of the diagnosis and treatment of prostate cancer. We summarised and proposed several key issues and directions on applying single-cell multi-omics and spatial transcriptomics to understand the heterogeneity of prostate cancer. Finally, we discussed single-cell omics trends and future directions in prostate cancer.
Introduction
According to the global Cancer data in 2020 released by the International Agency for Research on Cancer (IARC), Prostate cancer (PCa) is still the second most common cancer in men worldwide. Also, PCa is one of the main reasons for cancer-related deaths in men worldwide [1]. Cancer prediction data released by the American Cancer Society (ACS) show that by 2022, there will be about 260,000 new PCa cases in the new cancer cases in the United States, accounting for 27% of all cancer cases in men [2].In the formation of prostate cancer cells, gene mutations in normal epithelial cells are the primary way to induce PCa. However, the factors driving PCa progression are complex, and it is not enough to explore the causes of the cancer cells themselves [3, 4]. Previous studies have shown that the interaction between malignant epithelial cells and tumour microenvironment (TME) is a critical cause driving the progression of PCa [5, 6]. PCa progression is also a complex process that coordinates crosstalk between tumour cells and TME components [7]. Tumour cells can change and maintain their survival and development conditions through autocrine and paracrine, promoting cancer development and progression [8, 9]. In PCa, crosstalk between some components of TME promotes the malignant proliferation of tumour cells.
Traditional research methods are all aimed at specific cell populations. However, PCa is a tumour type characterised by high heterogeneity. Immunohistochemistry (IHC), immunofluorescence and other experimental methods are challenging to identify and analyse highly heterogeneous PCa. Therefore, it is difficult to provide complete information about tumour cells by traditional research methods alone. In recent years, the rapid development of single-cell omics has allowed us to understand the changes in a cell population, biochemical characteristics, and immune status of tumour tissues during disease progression [26, 27]. In recent years, the identification of subpopulations of CAFs has been completed by different experimental techniques such as immunohistochemistry, situ hybridisation, flow cytometry and fluorescence-activated cell sorting (FACS). However, the initial information and cellular origin of CAFs subsets still need to be clarified [28]. The advent of scRNA-seq has dramatically changed the field of study of CAFs and revealed additional complexities.
The first role of single-cell histology is to explore the heterogeneity of CAFs, that is, to classify subpopulations of CAFs by scRNA-seq. Bartoschek et al. [29] applied scRNA-seq to identify three subgroups of CAFs in breast cancer: vascular CAFs (vCAFs), matrix CAFs (mCAFs) and developmental CAFs (dCAFs). Each of the three subgroups of CAFs performs a different cellular function. Matrix CAFs can produce a diversity of matrix components in large quantities. However, vascular CAFs and developmental CAFs specialise in producing basement membrane products and paracrine signalling molecules, respectively [29]. An analysis of scRNA-seq data also detected two different cell clusters of CAFs, namely “Fibroblasts” and “Myofibroblasts/Mural cells” [30]. A second important role of single-cell omics is the identification of differential genes and specific markers associated with CAFs. Han Luo et al. combined the single-cell public database and their scRNA-seq data for a pan-cancer analysis of 10 solid cancers [ In addition, single-cell technology allows for precise risk assessment in multiple aspects of PCa patients, including diagnosis, stage treatment, and metastatic recurrence. ScRNA-seq of PCa cells and TME can better detect the development process of PCa, the risk of metastasis and recurrence, and drug response. It will better describe the heterogeneity of PCa and achieve precision therapy. The cellular and molecular expression heterogeneity between patients revealed by scRNA-seq will provide a deeper understanding of drug sensitivity and resistance in PCa. We will be able to discover actual therapeutic targets, leading to breakthroughs in develo** new drugs.
Availability of data and materials
All data generated or analysed during this study are included in this published article.
Abbreviations
- PCa:
-
Prostate cancer
- CRPC:
-
Castration-resistant prostate cancer
- CAFs:
-
Cancer-associated fibroblasts
- TME:
-
Tumour microenvironment
- TIME:
-
Tumour immune microenvironment
- TAECs:
-
Tumour-associated epithelial cells
- MDSCs:
-
Myeloid-derived suppressor cells
- scRNA-seq:
-
Single-cell RNA-sequencing
- ECM:
-
Extracellular matrix
- PCAFs:
-
Prostate cancer-associated fibroblasts
- CSF1R:
-
Colony stimulating factor 1 receptor
- CNV:
-
Copy number variation
- IHC:
-
Immunohistochemistry
- FACS:
-
Fluorescence-activated cell sorting
- OIS:
-
Oncogene-induced senescence
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Acknowledgements
We want to thank the authors who generously shared their data, all study participants, and the anonymous reviewers for their valuable comments on the manuscript. We want to thank the Figdraw platform for providing technical support. Figures 1 and 2 were drawn by Figdraw (www.figdraw.com).
Funding
This research was supported in part by [the Fundamental Research Funds for the Central Universities, Dongzhimen Hospital, Bei**g University of Chinese Medicine], [Bei**g Traditional Chinese Medicine "Torch Inheritance 3 + 3 Project"-the Wang Pei Famous Doctor Inheritance Workstation—Dongzhimen Hospital Branch], [the Wu Jie-** Medical Foundation Special Fund for Young people with TCM dominant diseases], and [Science and Technology Development Fund of Bei**g Traditional Chinese Medicine Hospital affiliated to Capital Medical University, No. LYYB202214].
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XDY and RJL participated in the conception and design of the review. XYW and WFG supervised the study. XDY wrote the manuscript. YSZ revised the manuscript. All authors contributed to the article and approved the submitted version. All authors read and approved the final manuscript.
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Yu, X., Liu, R., Gao, W. et al. Single-cell omics traces the heterogeneity of prostate cancer cells and the tumor microenvironment. Cell Mol Biol Lett 28, 38 (2023). https://doi.org/10.1186/s11658-023-00450-z
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DOI: https://doi.org/10.1186/s11658-023-00450-z