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.