
==== Front
Discov Oncol
Discov Oncol
Discover Oncology
2730-6011
Springer US New York

39259257
1303
10.1007/s12672-024-01303-7
Analysis
A new exploration: characterization of the differentiation trajectory of prostate cancer cells
Yang Jiyu 1
Wang Changyou 2
Ma Xiao 1
Li Jie 1
Yuan Haoran 3
Tan Renzhen 3
Ling Ling 3
Zhou Xianchun xczhou@ybu.edu.cn

13
1 https://ror.org/037ve0v69 grid.459480.4 0000 0004 1758 0638 Department of Oncology, Yanbian University Hospital, Yanji, China
2 https://ror.org/039xnh269 grid.440752.0 0000 0001 1581 2747 College of Foreign Language, Yanbian University, Yanji, China
3 https://ror.org/037ve0v69 grid.459480.4 0000 0004 1758 0638 Department of General Medicine, Yanbian University Hospital, Yanji, China
11 9 2024
11 9 2024
12 2024
15 42630 5 2024
2 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Prostate cancer is one of the most common malignant tumors in men, and in-depth study of its gene expression patterns is crucial for understanding the formation and development of prostate cancer. Although single-cell transcriptomics has deeply explored the heterogeneous expression characteristics of prostate cancer, given that normal epithelial cells themselves have different states of differentiation, these normal differentiation characteristics may lead to confusion with heterogeneous tumor characteristics. In this study, we used single-cell data from the GEO database to analyze in detail the heterogeneity of prostate cancer tumor cells/tumor-associated epithelium cells (TAECs), with a particular focus on the differentiation state of epithelial cells in matching normal tissue. We found that after subtype pairing analysis of normal tissue and tumor tissue epithelium based on differentiation status, the characteristics identified later were not consistent with the general characteristics originally exhibited by different TAECs subpopulations. Among them, all TAECs subpopulations showed P53 enrichment and downregulation of the apoptotic pathway, and expressed higher levels of EGFR, ERBB2, interferon receptors, MIF, and cell adhesion-related signals; through transcription factor regulatory network analysis, we observed that YY1, NKX3-1, and EHF had higher transcriptional activity in TAECs subpopulations than normal epithelial cells at the same differentiation stage, while ATF3 was the opposite. Among them, YY1 may act as an upstream regulator of the MIF signaling pathway, and ATF3 is a key upstream transcriptional regulator of differentially expressed genes in the P53 and apoptotic pathways. Immune infiltration analysis showed that the above four transcription factors were significantly correlated with the infiltration of immune cells in prostate cancer, and pan-cancer analysis showed that their expression-related survival risks were widely present in different cancers. It is worth noting that this is merely a preliminary, exploratory study, which inevitably has some deficiencies and limitations. Despite this, this study is committed to bringing a novel and unique perspective to the field through this work, with the hope of opening up new levels of understanding and stimulating more in-depth research and discussion.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-024-01303-7.

Keywords

Prostate cancer
Single-cell transcriptomics
Heterogeneity
Transcription factors
Cell communication
issue-copyright-statement© Springer Science+Business Media, LLC 2024
==== Body
pmcIntroduction

Prostate cancer is one of the most common malignant tumors in men, accounting for approximately 29% of cases [1]. Although its five-year survival rate is relatively high, once the tumor becomes invasive, it can quickly metastasize and become difficult to treat. Currently, the main treatments for prostate cancer include surgical resection, radiation therapy, chemotherapy, and hormone therapy. Although these treatment methods could control the growth and spread of the tumor to some extent, there are still certain limitations, such as unstable treatment effects and the development of drug resistance [2]. Therefore, a deep understanding of the occurrence and development mechanisms of prostate cancer is of great significance for improving treatment effectiveness and developing new treatment strategies.

While cell proliferation and differentiation in normal tissues are stringently controlled, tumors exhibit unusual alterations in this regulatory process, which governs the immune reaction, thereby preventing immune system assaults. The complexity in treating tumors arises from the diversity in differentiation, which means that different cells exhibit different phenotypes and functions, leading to varied responses to the treatment from different cells [3–5].

Single-cell transcriptomics offers significant advantages over bulk transcriptomics by providing insights into gene expression at the individual cell level. Unlike bulk transcriptomics, which averages gene expression across a population of cells, single-cell approaches allow for the analysis of gene expression variations among distinct cells. This high-resolution perspective is crucial for understanding cellular heterogeneity and identifying subtle differences that might be masked in bulk analyses [8, 9]. Additionally, single-cell transcriptomics is instrumental in discovering novel cell types. The ability to profile the transcriptome of individual cells allows for the identification of previously uncharacterized cell types and states. This capability is particularly valuable in expanding our knowledge of cellular diversity and function, leading to new insights into biological processes and potential therapeutic targets [10]. Furthermore, single-cell transcriptomics facilitates the study of diseases and individual differences at a finer scale. By examining gene expression patterns in individual cells, researchers can uncover how specific cell types contribute to disease progression or exhibit unique characteristics in different individuals. This detailed resolution helps in understanding the molecular mechanisms underlying various conditions and enables more precise identification of disease-associated cellular subpopulations [11–13].

Although single-cell transcriptomics has deeply explored the heterogeneous expression characteristics of prostate cancer, given that normal epithelial cells themselves have different states of differentiation, these normal differentiation characteristics may lead to confusion with heterogeneous tumor characteristics. In this study, we combined non-negative matrix factorization clustering and graph-based clustering techniques to jointly confirm the reliability of clustering influence. Additionally, this study utilized anchor integration algorithms to identify specific epithelial cells in peritumoral tissues that are consistent with the differentiation state of tumor cells, providing a benchmark for subsequent research to more accurately detect irregular expressions among various tumor cell subpopulations.

Patients and methods

Preprocessing of single-cell data

Single-cell prostate data were cited from the GEO public database (GSE193337) as regards to single-cell and bulk RNA sequence analysis of human prostate adenocarcinoma and normal benign prostate samples [14].

The above data were imported through the R language (version 4.3.1), converted to Seurat objects using the Seurat R package (version 4.3.0.1) [15], normalized and linear regression of the Seurat objects through the NormalizeData function and ScaleData function, normalized the gene expression matrix to total cell read counts, and used the IntegrateData function to perform anchor-based integration of single-cell data. Mass filtration was then performed to remove cells with genes less than 201 or greater than 8000 or total genes greater than 30,000 or greater than 20% from the mitochondrial genome Unique Molecular Identifiers (UMIs).

The processed expression matrix mentioned above was further regressed against some potential confounding factors (such as "S.Score", "G2M.Score", and "percent.mt"), and then subjected to principal component analysis (PCA) using the RunPCA function to reduce the dimensionality of the data. Furthermore, RunTSNE functions were used to perform t-SNE dimensionality reduction analyses, incorporating the first 15 components of the data, which optimally balanced the expression characteristics of cells and avoided background noise. Constructed a neighbor plot for clustering analysis, and selected a 0.5 resolution parameter to identify small differences between cell groups.

Cell type identification

The pretreated single-cell data were compared to the reference dataset using the SingleR function package [16], and the cell type annotation information for each single-cell sample was obtained by correcting the results of the SingleR analysis according the characteristic genes for each cluster. Given the potential difficulty in precisely distinguishing normal epithelium from tumor epithelium in tumor tissues through algorithms, and the possibility that normal epithelium within tumor tissues may exhibit characteristics similar to tumor cells due to the influence of the tumor microenvironment, we have cautiously defined normal epithelial cells and tumor-associated epithelial cells (TAECs) based on the source of the samples. In the single-cell dataset, monocytes/macrophages (CD14,CD68,CD163), T cells (CD3D,CD3E,CD4), and B cells (CD19,CD79A,CD79B) were isolated and uploaded to Azimuth (http://azimuth.satijalab.org/app/azimuth), where identification of cell types was based on gene expression resemblance and characteristic alignment, followed by manual proofreading, culminating in more comprehensive cell annotation outcomes. The proportion of cell types was calculated.

Heterogeneous clustering of TAECs cells and Preliminary enrichment analysis

Using the NMF R package (version 0.26) [17], 3434 TAECs epithelial cells from 4 tumor patients underwent unsupervised NMF clustering. By choosing k = 4 as the factor number, 16 distinct patterns of metagene expression were discovered across four tumors samples. Then the four sets of characteristic genes were artificially identified by hierarchical clustering and comparing these 16 metagene expression patterns. The total load of repeat genes was summarized for each set of characteristic genes. The original load of the unique genes was retained, wherein the second group of characteristic gene sets retained the characteristic genes of T cell subsets (CD4, CD8) and with a low number of cells (164/3434), which was considered to be doublets. Ultimately retained the remaining three characteristic gene sets as prostate TAECs heterogeneous subsets. Then visualized the difference in the expression of TAECs subsets in each sample. A TSNE map was constructed to visualize the expression of characteristic genes of cell subsets. The differential genes between the three subpopulations and the total normal epithelial cells were calculated, and the functional enrichment analysis was performed based on these differential genes.

Cell trajectory analysis, matching of TAECs to normal epithelial cells, and functional enrichment analysis of TAECs subsets

The Monocle2 R package (v2.8.0) [18] was used to extract and construct CDS (Cell Data Set) objects from Seurat objects, select high-variable genes with average expression greater than 10%, constructed quasi-timeline trajectories, and arranged cells in quasi-temporal and visualize.

Based on anchor point integration, paired normal epithelial cells with the same TSNE neighbor graph position was identified for each of the three TAECs subpopulations. These three normal epithelial clusters were used as controls for the three subpopulations in the same differentiation state. Subsequently, the differential genes of three paired samples of TAECs and normal epithelium in the same differentiated state were calculated. For these differential genes, the "fgsea" function [19] was used for enrichment analysis to find the co-expressed genes of the common enrichment pathway of the three paired samples.

Cell communication analysis

Cell communication analysis was performed using the "CellChat" R package [20] (http://www.cellchat.org/). Using network analysis and pattern recognition techniques, we deduced and examined intercellular communication networks from scRNA-seq data, illustrating the varied ligand-receptor interactions and their signaling routes in normal tissue epithelial cells.

Transcription factor regulatory network analysis

SCENIC analysis (version 1.3.1) [21, 22] was performed using the motif databases of RcisTarget and GRNboost with default parameters (version 1.3.1, corresponding to RcisTarget 1.2.0.0 and AUCell 1.22.0) to construct the transcription factor regulatory network. Transcription factor activity scores were calculated for three TAECs subtypes compared to their corresponding normal epithelial cells. Upstream regulatory factors of the co-expressed differential genes of the common enrichment pathway (P53, apoptosis signaling pathways) of the three paired samples (TAECs1-N1, TAECs2-N2, TAECs3-N3), were identified.

Immune infiltration analysis

Downloaded TCGA-PRAD data from the UCSC database [23], retaining transcriptome data of 482 malignant tumor samples with complete clinical information for subsequent analysis. The proportion of 22 tumor-infiltrating immune cells in each sample was determined using 'CIBERSORT' (R package). CIBERSORT was employed to predict the proportions of 22 tumor-infiltrating immune cells in various tissues, namely: B cells naive, B cells memory, Plasma cells, T cells CD8, T cells CD4 naive, T cells CD4 memory resting, T cells CD4 memory activated, T cells follicular helper, T cells regulatory (Tregs),T cells gamma delta, NK cells resting, NK cells activated, Monocytes, Macrophages M0, Macrophages M1, Macrophages M2, Dendritic cells resting, Dendritic cells activated, Mast cells resting, Mast cells activated, Eosinophils, Neutrophils. The correlation between the expression levels of target genes (YY1, EHF, NKX3-1, ATF3) and tumor immune cell infiltration was calculated.

Pan-cancer analysis

The debatch-effecting and normalized pan-cancer gene expression RNA sequencing data were downloaded from the UCSC database. The data were processed using R software, and wilcox tests were performed in tumor tissues and adjacent tissues of various cancers for YY1, EHF, NKX3-1, and ATF3 genes to compare their expression differences. A COX proportional hazards model was established by univariate COX regression to evaluate the risk ratios of disease-free survival (DFI), overall survival (OS) and progression-free survival (PFI) in each cancer type with differences in the expression of YY1, EHF, NKX3-1 and ATF3, respectively.

Results

Machine annotation combined with manual annotation used to identify cell types

A total of 7 cell clusters, epithelial cells (EPCAM), fibroblasts (DCN), vascular endothelial cells (VWF), T cells (CD3E), B cells (CD79B), mast cells (KIT), and monocytes/macrophages (CD68), were tentatively identified according to the genetic profile of the cells and the typical marker genes (NKG7 was used to validate NK cells that are subsequently identified). Subsequently, Azimuth assisted in acquiring more comprehensive annotation outcomes (Fig. 1A–C). A range of lymphocyte subtypes were identified from immune cells, including T cells, B cells, and monocytes/macrophages. Furthermore, NK cells, DC cells, and a few other blood cells were isolated from these cells (Fig. 1A–C). Ultimately, calculations were made for the cell count ratios of various cell types (Fig. 1D), and the expression of characteristic genes representative of specific cell types was depicted (Fig. 1E) (Supplementary Figure 1A). The identification of characteristic genes demonstrates the dependability in classifying cell types (Fig. 1E) (Supplementary Figure 1A). Despite the varied distribution of cell types across samples, each sample exhibits a relatively comprehensive range of cell type expressions (Fig. 1C, D).Fig. 1 A-C t-distribution random neighbor embedding (t-SNE) plot of cells from 4 patients, colored by cell types (A), specific cell types with fine cell subpopulations (B), sample sources (C). D Relative proportions of cell types per prostate cancer patient. E t-SNE plots of marker genes for each cell subset.

Heterogeneous clustering of TAECs and their general characteristics

TAECs cells were extracted from epithelial cells, and the 16 metagene expression patterns obtained by non-negative matrix decomposition (NMF) were characterized by four gene expression patterns (Fig. 2A), of which one gene expression pattern with low confidence was eliminated. Three heterogeneous TAECs subpopulations with different gene expression patterns (TAECs1, TAECs2, and TAECs3) were finally determined (Fig. 2A, B). Hierarchical clustering reveals significant variation among the three subpopulations, with the TAECs2 and TAECs3 subpopulations showing closer affinity (Fig. 2A). Either a TSNE map or the hierarchical cluster diagram reveals clear divisions among these subpopulations, demonstrating that the outcomes of the clustering effectively depict the diversity present in TAECs (Fig. 2A, B). Furthermore, each sample contained three subsets of TAECs, indicating that these heterogeneities were not derived from individual differences (Fig. 2C).Fig. 2 A Heat map depicting pairwise correlations of 16 tumor metagene expression patterns from 4 tumor patients. Clustering identified 4 coherent feature sets across tumors samples. B t-distribution random neighbor embedding (t-SNE) plots of tumor cells from 4 patients, colored by TAECs subpopulations. C Relative proportion of TAECs types per prostate cancer patient. D t-SNE plots of characteristic gene expression for three heterogeneous TAECs subpopulations. E GSEA enrichment analysis of TAECs1 subsets versus total normal epithelial cells. F GSEA enrichment analysis of TAECs2 subsets versus total normal epithelial cells. G GSEA enrichment analysis of TAECs3 subsets versus total normal epithelial cells.

By examining the expression of some characteristic genes, we found that the TAECs2 subpopulation overexpressed the basal cell marker genes TP63, KRT5, KRT14, and KRT19 (Fig. 2D); the TAECs1 subpopulation overexpressed hormone and secretion-related genes associated with differentiated terminal epithelial cells, such as AR, NPY, KLK3, and KLK4 (Fig. 2D), which indicates that the three subpopulations might have time-dependent differentiation development features similar to normal epithelial cells. The gene expression heatmap was used to further characterize the expression features of the three subgroups (Supplementary Figure 1B).

Enrichment analysis was performed for each subpopulation compared to total normal epithelial cells, revealing that the TAECs1 subpopulation was enriched in the androgen receptor pathway (Fig. 2E); the TAECs2 subpopulation was enriched in the epithelial-mesenchymal transition and extracellular matrix pathways (Fig. 2F); and the TAECs3 subpopulation was enriched in the interferon pathway and various antigen processing and presentation, and vesicle transport pathways (Fig. 2G). Notably, this might be the case that three subpopulations exhibit biological characteristics associated with different stages of differentiation from normal epithelial cells, rather than true tumor behavior.

Pseudo-time analysis and paired enrichment analysis

Analysis of cell differentiation and developmental trajectories using monocle 2 validates the hypothesis that TAECs follow the trajectories of TAECs2, TAECs3 to TAECs1 (Fig. 3A–C), for which TAECs3 subpopulation closely related to the TAECs2 subpopulation (Fig. 3A–C). The characteristic gene expression trends of the three subpopulations were consistent with the inferred differentiation trajectories (Fig. 3D, E).Fig. 3 A–C TAECs cell developmental trajectories inferred using monocle2 colored by TAECs subpopulations (A), inferred differentiation state (B), pseudo time (C). D Curve plots showing expression changes of characteristic genes related to differentiation state along pseudo time. Point colors correspond to cell state colors in B. E The distribution of TAECs is shown along pseudo time, Heatmap showing dynamic expression changes of selected genes and related pathways along pseudo time, clustering was carried out according to the dynamic expression changes of genes with pseudo-time. F Differential enrichment analysis (GSEA) of the TAECs1 subpopulation with its matched N 1 normal epithelium. G Differential enrichment analysis (GSEA) of the TAECs2 subpopulation with its matched N 2 normal epithelium. H Differential enrichment analysis (GSEA) of the TAECs3 subpopulation with its matched N 3 normal epithelium.

Given possible problems with previous enrichment analysis, following earlier findings on dimensionality reduction clustering from anchor point integration, clusters of normal epithelial cells, identical in differentiation to TAECs, were pinpointed. (N1, N2, N3) (Supplementary Figure 2A-C). The paired enrichment study was conducted on groups of three TAECs subtypes along with their matched normal epithelial cells, subpopulations in each pair were at an identical stage of differentiation (Fig. 3F–H). Findings indicated that each of the three subtypes possessed more potent anti-apoptosis properties than their corresponding normal epithelial cells, as demonstrated by the inverse enhancement in the P53, apoptosis, and NFKB-driven tumor necrosis factor signaling pathways (Fig. 3F–H). Additionally, the TAECs1 subpopulation shows an down-regulated enhancement in epithelial-mesenchymal transition, interferon signaling, and specific immune response routes (Fig. 2F), indicating its function as a final differentiation phase and immune avoidance. The TAECs2 subpopulation, resembling basal cells, show enhanced epithelial-mesenchymal transition activity compared to matched normal cells (Fig. 3G), suggesting possible stromal infiltration, though this is not histologically verified. TAECs3 subpopulations exhibit enhanced secretion capabilities (Fig. 3H).

Cell communication analysis

TAECs cells are the major senders of MIF signals (Fig. 4A–C). Compared with matched normal epithelial cells at the same differentiation stage, they show higher levels of expression of EGFR, ERBB2, and interferon receptors (Fig. 4D–F). In addition, TAECs express VEGF signaling at an earlier stage of differentiation than normal epithelial cells and might undergo a partial expression shift from VEGFA to VEGFB signaling (Fig. 4G).Fig. 4 A Cell-to-cell communication mediated by the MIF signaling pathway. B Expression of all signaling genes associated with the MIF signaling pathway. C Heatmap visualization of the calculated centrality score to identify the main signaling role of the cell population in the MIF pathway. D Cell-to-cell communication mediated by EREG_EGFR_ERBB2 signaling pathways. E Expression of all signaling genes associated with the EGFR signaling pathway. F Expression of all signaling genes associated with the interferon signaling pathway. G Expression of all signaling genes associated with the VEGF signaling pathway. H Transcription factor activity heat map of tumor subsets and their paired normal epithelial cells at the same TSNE neighbor plot location based on anchor integration.

Tumor cells abnormally expressed cell adhesion related signals (SEMA4A, OCLN, NECTIN, MPZL1, JAG, ALCAM, CD46, CD99) (Supplementary Figure 3A), which were closely related to tumor survival and invasion, angiogenesis, and tumor immunity. Compared to matched normal epithelial cells, the expression of MHC molecules related to antigen presentation is higher in TAECs2 and TAECs3 subpopulation (Supplementary Figure 3A).

Compared to matched normal epithelial cells, the TAECs2 and TAECs3 subpopulations expressed higher levels of SEMA3C, CXCL16 (Supplementary Figure 4A-C), and WNT5B signal (Supplementary Figure 4D); the TAECs1 and TAECs3 subpopulations showed higher levels of BAG signal (Supplementary Figure 4E, F).

The TAECs2 subpopulation showed higher expression of LAMB3 and LAMA5 molecules compared to matched normal epithelial cells (Supplementary Figure 3B). Also, it, as well as TAECs3 subpopulation, exhibited higher expression of matrix-related receptors (SDC1, SDC4, ITG) (Supplementary Figure 3C), indicating a closer association with the extracellular matrix.

Compared to matched normal epithelial cells, the TAECs1 subpopulation had higher expression of FZD5 and LRP6 (Supplementary Figure 5A, B), which could serve as receptors for WNT10A signal in plasma cells; the TAECs2 subpopulation specifically expressed ADM signal, acting on endothelial cells (Supplementary Figure 5C); the TAECs3 subpopulation showed higher expression of TNFRSF15, acting on MAIT cells and ILC cells (Supplementary Figure 5D).

Transcription factor regulatory network analysis

Using scenic to infer the transcription factor regulatory network in epithelial cells (Supplementary Table 1), we calculated the transcription factor activity score (Supplementary Table 2) for three TAECs subpopulations with reference to matched normal epithelial cells at the same stage of differentiation. We extracted the top 20 transcription factor regulatory units with high and low transcriptional activity to draw the transcription factor activity heatmap (Supplementary Table 3) (Fig. 4H). We found that in the three TAECs subpopulations, the transcriptional activity of the transcription factors YY1, NKX3-1, and EHF was higher than in the matched normal epithelium at the same differentiation stage (YY1 regulatory unit ranked in the top 10) (Supplementary Table3).

As previously noted, the MIF signaling pathway is overexpressed in various TAECs subpopulations (Fig. 4A–C). The scenic-inferred transcription factor regulatory network suggests that YY1 acts as a dependable transcriptional upstream regulator for MIF (Supplementary Table 1), with its target genes containing diverse genetic activity elements, underscoring its vital function in tumor growth and progression. Since it was previously found that the functional enrichment of the three TAECs subpopulations were weakened in the apoptotic related pathway (Fig. 3F–H), we further intersected the differential genes enriched by the apoptotic and P53 pathway of the three subpopulations (Supplementary Tables 4), and found that ATF3 was the upstream regulator of most of these genes (Supplementary Tables 4), and the transcriptional activity of ATF3 in normal epithelial cells was higher than that of its paired TAECs (Supplementary Table3), suggesting that TAECs might inhibit ATF3 by some mechanism to escape apoptosis.

Correlations of prostate cancer immune infiltration with YY1, EHF, NKX3-1 and ATF3 expressions

Correlation analysis showed that the expression of YY1 was significantly positively correlated with the immune infiltration of CD4 resting memory T cells, resting dendritic cells and M1 macrophages (P<0.001, Cor= 0.30000817, 0.22245352, 0.18262690) (Supplementary Figure 6 A).

The expression of NKX3-1 was significantly positively correlated with the immune infiltration of resting mast cells and M1 macrophages (P<0.001, Cor= 0.163012839, 0.154186876). The expression of NKX3-1 was significantly negatively correlated with the immune infiltration of suppressor T cells (Tregs) and large chemical cells (P<0.001, Cor= 0.246266240, 0.218359890) (Supplementary Figure 6B).

The expression of EHF was significantly positively correlated with the immune infiltration of CD4 resting memory T cells and M1 macrophages (P<0.001, Cor= 0.298198031, 0.154686150). It was negatively correlated with the immune infiltration of inhibitory regulatory T cells (Tregs), CD8 T cells and activated natural killer cells (P<0.001, Cor= 303334670, 0.210507210, 0.155223607) (Supplementary Figure 6C).

The expression of ATF3 was significantly positively correlated with the immune infiltration of large chemical cells, neutrophils, naïve B cells and follicular helper T cells (P<0.001, Cor= 0.491918316, 0.241133868, 0.218357002, 0.177083614). It was significantly negatively correlated with the immune infiltration of resting mast cells and memory B cells (P<0.001, Cor= 0.391263842, 0.154324034) (Supplementary Figure 6D).

Pan-cancer analysis revealing the expression and prognosis of YY1, NKX3-1, EHF and ATF3

In order to further explore the prognosis of several transcription factors with aberrant transcriptional activity in prostate cancer, we subsequently performed a pan-cancer analysis (Supplementary Figure 7), and the results showed that YY1 and NKX3-1 were significantly overexpressed in tumor tissues of prostate cancer and ATF3 was significantly underexpressed in tumor tissues relative to adjacent tissues, which was consistent with the results obtained by single-cell analysis. Among them, the hazard ratio of YY1 in overall survival of prostate cancer was 11.9 (95% confidence interval (1.76−81.2, P = 0.0112), the hazard ratio of ATF3 in prostate cancer was 0.791 (95% confidence interval 0.631−0.991, P = 0.0411), and the risk ratio of ATF3 in prostate cancer was 0.88 (95% confidence interval 0.778−0.995, P = 0.0413). In addition, these four transcription factors were associated with an increased or decreased risk of poor prognosis in a variety of cancers, among which YY1, EHF, and NKX3-1 all showed a significant increase in the risk of consistent adverse prognosis in pancreatic cancer (YY1: DFI HR 8.11 95% confidence interval 2.18−30.1, P = 0.00177, OS HR 3.17 95% confidence interval 1.68−5.97, P = 0.000357, PFI). HR 2.63 (95% CI 1.41−4.89), P = 0.00232; EHF: DFI HR 1.57 95% CI 1.07−2.32, P = 0.0224), OS HR 1.26 95% CI 1.05−1.5, P = 0.0106, PFI HR 1.24 95% CI 1.06−1.46, P = 0.00875; NKX3-1: DFI HR 2.46 95% CI 1.41−4.28, P = 0.00145, OS HR 1.3 95% The confidence interval was 1.05−1.61, P = 0.0152, PFI HR 1.61, 95% confidence interval was 1.3−1.99, P = 0.106*10-6). <0.001), high expression of YY1 showed a consistent increase in the risk of poor prognosis of renal papillary cell carcinoma (DFI HR 3.71 95% confidence interval 1.27−10.9, P = 0.0166, OS HR 9.1 95% confidence interval 3.78−21.9, P = 0.832*10-8, PFI HR 4.19 95% confidence interval 2−8.78, P = 0.000149) (Supplementary Figure 8).

Discussion

Cell annotation is an essential process in single-cell analysis. Once cell types are initially identified in the dataset, the absence of dependable feature genes complicates the use of these genes for further cell annotation, especially if a more intricate categorization of specific cell types is needed. The selection of methods based on comparison with reference datasets for the automatic annotation of cells often leads to less accurate results [24, 25]. During our analysis of subsequent annotation of the preliminary identified immune cells, we compared the PBMC demonstration dataset on Azimuth, achieving a more detailed cell annotation. Investigations into the expression of CD4, CD8, and HLA molecules linked to antigen presentation revealed alignment with Azimuth's automated annotation findings.

M1 macrophages are generally considered tumor-killing macrophages, mainly involved in anti-tumor and immune promotion, while M2 macrophages exhibit immunosuppression, promoting tissue repair and tumor initiation and development [26]. However, in actual disease, M1 and M2 are not necessarily clearly defined, and M1 and M2 features are not mutually exclusive but often coexist [27, 28]. Furthermore, the impact of M1 or M2 stimuli must be considered for their dynamic intricacy, and the interplay among numerous signals necessitates additional in vivo and in vitro research [26]. The research revealed no notable polarization in the expression of polarization markers, possibly because the cancer samples were in the disease's initial phase. Consequently, we decided to cease additional labeling of macrophages' polarized state to ensure the reliability of the annotation.

In single-cell analysis, two prevalent clustering techniques are NMF and Graph·-based clustering. NMF clustering, also known as Non-negative Matrix Factorization clustering, utilizes matrix factorization for clustering. It's apt for single-cell non-negative data like gene expression and offers pattern-oriented clustering outcomes. Each cluster is depicted as a linear amalgamation of various gene expression patterns, enhancing interpretability [17]. Consequently, NMF clustering is typically chosen in tumor heterogeneity studies to distinguish different cell groups and gene expression trends, aiding in a profound comprehension of tumor heterogeneity traits. The Seurat clustering Graph·-based clustering technique, rooted in graph theory, enhances visualization and visually represents clustering outcomes by charting the cellular similarity network [15]. In contrast to NMF clustering, Seurat clustering Graph·-based clustering shows greater sensitivity to the choice of parameters, and varying parameter configurations can yield diverse clustering outcomes. Noise and batch effects can impact Seurat clustering Graph·-based clustering, necessitating pre-processing measures to mitigate these impacts [29, 30]. The research employed the NMF algorithm for grouping diverse TAECs, identifying three distinct TAECs subpopulations. Post-clustering annotations were then integrated into Seurat's visualization outcomes, revealing clear distinctions among these subpopulations in the TSNE diagram. This indicates that there is significant heterogeneity among the three subpopulations. The outcomes of clustering demonstrate dependability and consistency. Concurrently, it's proposed that NMF and graph theory-based clustering could yield comparable outcomes, provided the batch effect [29] is efficiently managed and the quality control outcomes across samples are consistent and consistent.

The heterogeneity characteristics inside TAECs and the differences exhibited by epithelial cells following the normal differentiation trajectory could both be observed in single-cell clustering. Although the two concepts seem to overlap, it is necessary to distinguish between them to find their respective different tumor features among the subtypes of tumor heterogeneity; Otherwise, certain characteristics might be overlooked or exaggerated.

Enrichment analysis was performed on the trio of tumor subpopulations identified through clustering, with each subpopulation being matched against the aggregate of normal epithelial cells. Research revealed that the TAECs1 subpopulation was enriched in the androgen receptor pathway (Fig. 2E); the TAECs2 subpopulation was enriched in the epithelial-mesenchymal transition and extracellular matrix pathways (Fig. 2F); the TAECs3 subpopulation was enriched in the interferon pathway, various antigen processing and presentation, and vesicle transport (Fig. 2G). Nonetheless, our suspicion is that these enhanced traits might be present in epithelial cells during various differentiation phases, which doesn't automatically imply they have tumor characteristics. Consequently, utilizing anchor points, we pinpointed typical epithelial cells aligning with tumor cell differentiation levels, serving as a benchmark for comparing tumor subpopulations at identical differentiation phases. Subsequently, we performed another enrichment analysis, which revealed results that were inconsistent with earlier research outcomes.

Despite the high expression of AR in highly differentiated luminal epithelial cells, the paired enrichment analysis revealed that TAECs showed increased activity in the androgen receptor pathway at the onset of differentiation. Indeed, prior research has revealed that irregular expression of AR receptors [31] on the basal membrane of prostate cancer cells, along with the fundamental function of the AR signaling pathway in various phases of prostate cancer development. Despite the development of endocrine resistance in numerous advanced cancer stages, tumors still maintain their responsiveness to hormones [32, 33]. Research indicates that the pathogenesis of these tumors is often associated with overactive downstream signaling, the presence of various AR mutations, and the activity and expression of AR co-regulatory factors [32, 33]. Our research revealed unusually elevated levels of EGFR and ERBB2 receptors in TAECs, as determined by examining intercellular communication. This discovery is consistent with the previous research reports on the detection of growth factor-related receptors in dysfunctional cavity epithelial cells and malignant cells of prostate cancer [34]. The research additionally uncovered a TAECs2 subpopulation during the initial differentiation phase of basal cells, exhibiting epithelial-mesenchymal transition activities that exceeded those of typical basal cells and demonstrated a more intimate interaction with extracellular matrix ligand receptors. This result aligns with earlier studies that noted an increased expression of the IV-type collagenase, which breaks down the basement membrane, compared to regular prostate epithelial tissue [34], thereby boosting the cells invasive capacity, which is consistent with one another. Furthermore, through a pairwise enrichment study, it was noted that every TAECs subset exhibited a down-regulated enhancement in the P53 signaling route, apoptosis process, and the NFKB-driven tumor necrosis factor signaling pathway, in contrast to their corresponding normal epithelial cells. This suggests that the trio of TAECs variants possess an enhanced ability to withstand apoptosis. Subsequent studies into the transcription factor regulatory network have identified ATF3 as a crucial regulatory element. By blocking ATF3, tumor cells might avoid apoptosis, and YY1 could be involved in controlling MIF signal expression, aiding in reshaping the tumor's microenvironment. In summary, it is entirely justified and crucial to carry out paired study at different stages of differentiation.

Additional studies have noted an increased presence of tumor subpopulations regarding antigen presentation and immune/inflammatory reactions, which are believed to correlate with improved prognoses [35]. Conversely, some research indicates these subpopulations might be the focal points of inflammatory responses close to cancerous tissues [36]. In the course of our study, through the analysis of cellular communication, we observed a significant increase in the expression of HLA-related molecules in epithelial cells in the early stages of their differentiation (N2, N3, TAECs2, TAECs3), especially in the N3 and TAECs3 subsets. Within cancerous tissues (TAECs1, TAECs2), the heightened expression was notably more pronounced, yet our pairwise enrichment analysis revealed no significant enrichment of the TAECs3 subset in the antigen presentation pathway compared to the N3 subset in normal epithelial cells. Numerous studies have been reported on the expression of HLA-related antigens in various tissues, including tumor cells [37]. Still, no definitive studies have revealed their specific roles and significance in re-epithelial tissues, although some researchers have suggested that HLA-G might be involved in the induction of immune tolerance [38]. Thus, there may exist a hypothesis that epithelial cells might start from their basal cell state in the early stage of differentiation, briefly express HLA molecules, and exert antigen display functions, which helps immune cells carry out immune monitoring and thus prevent cancer during subsequent growth. When in a low differentiation state, the invasion of prostate cancer cells seems to be more advantageous because their connection with the stroma is closer; on the other hand, epithelial cells in the early differentiation stage exhibit a higher level of HLA molecule expression and are clustered on the antigen presentation pathway, which might be related to a better prognosis. This appears to be a contradictory occurrence. Our hypothesis is that, as the disease progresses, tumor cells might experience increased dysregulation in two key aspects: the positive control of epithelial-mesenchymal transition and the negative regulation of antigen presentation, which may lead to further exacerbation of the disease. Observations from the NMF clustering results indicate that the heterogeneity among samples within the TAECs3 subtypes is significantly higher compared to the other two subtypes. Anyway, the TAECs subtype 3 may play a crucial role in the progression of tumors in the future. Before that, we believe that in-depth research on the existence and function of subtype 3 in normal tissues is essential.

In further pan-cancer analysis, we conducted a detailed study of several transcription factors that exhibited aberrant transcriptional activity in prostate cancer to understand their expression and prognosis in different cancer types. We observed that YY1 and NKX3-1 were significantly overexpressed in prostate cancer tumor tissues, while ATF3 showed significantly low expression relative to adjacent tissues, which was consistent with the results of previous single-cell analyses. In particular, the overall survival hazard ratio of YY1 in prostate cancer was 11.9 (95% confidence interval 1.76−81.2, P = 0.0112). This result highlights that high expression of YY1 in prostate cancer patients is associated with poorer overall survival. In addition, the hazard ratio for disease-free survival of ATF3 in prostate cancer was 0.791 (95% confidence interval 0.631−0.991, P = 0.0411) and the hazard ratio for progression-free survival was 0.88 (95% confidence interval 0.778−0.995, P = 0.0413). This suggests that high expression of ATF3 in prostate cancer patients might be associated with better survival.

We further compared the expression and prognosis of these transcription factors in a variety of cancers. Interestingly, YY1, EHF, and NKX3-1 showed consistent poor prognosis in pancreatic cancer, with high expression of YY1 associated with poorer survival (DFI HR 8.11, 95% CI 2.18−30.1, P = 0.00177; OS HR 3.17, 95% CI 1.68−5.97, P = 0.000357; PFI HR 2.63, 95% CI 1.41−4.89, P = 0.00232).

For renal papillary cell carcinoma, significantly higher expression of YY1 was associated with an increased risk of poor prognosis (DFI HR 3.71, 95% confidence interval 1.27−10.9, P = 0.0166; OS HR 9.1, 95% confidence interval 3.78−21.9, P = 8.32*10^-8, PFI HR 4.19, 95% confidence interval 2−8.78, P = 0.000149). These pan-cancer analyses further reinforce the importance of YY1 and ATF3 in prostate cancer and suggest that they may have a broad range of roles in multiple cancer types. However, we should note that these results are observational only and further validation and in-depth functional studies are needed to confirm the exact mechanism of their action in cancer. In addition, more comprehensive studies are needed to understand the biological significance of the different roles of these transcription factors in other cancer types. In future studies, the use of laboratory models and clinical samples could be considered to further validate these findings and explore potential therapeutic targets. Such research efforts will provide new insights into cancer prognosis assessment and treatment.

Conclusions

Through a detailed analysis of prostate TAECs subsets, especially those that match the differentiation status of normal epithelial cells, we successfully identified unique or shared tumor characteristics among them. Enrichment analysis in epithelial cells at different stages of differentiation revealed key pathways associated with three TAECs subsets, providing valuable clues for insight into the development of prostate cancer. This study expands our understanding of the prostate cancer microenvironment at the microscopic level and lays the groundwork for the discovery of new targets for prostate cancer treatment. The results of immune invasion and pan-cancer analysis highlight the critical role of YY1 and ATF3 in prostate cancer and correlate their aberrant expression with patient survival. In-depth studies of the expression and prognosis of these transcription factors in other cancers have revealed their potential roles in different cancer types, providing new perspectives for conducting cross-cancer research. Taken together, our study provides insights into the abnormal expression characteristics of TAECs in prostate cancer across different differentiation backgrounds.

Limitations

1. Limited sample source: The small number of samples may not fully represent the tumor heterogeneity of the entire patient population. The research results may not be generalizable to a broader patient population, limiting the clinical application value of the findings.

2. The conclusions of the study have not been sufficiently validated through pathological sections, in vivo and in vitro experiments. Further research is needed to enhance the credibility of the research conclusions, providing a more solid scientific basis for the diagnosis, treatment, and prognosis assessment of prostate cancer.

Supplementary Information

Additional file 1.

Additional file 2.

Additional file 3.

Additional file 4.

Additional file 5.

The authors gratefully thank the GEO and UCSC for providing data for this work

Author contributions

XZ proposed the topic. JY and XM contributed to the design and interpretation of the study. JY, XM, JL, HY, RT and LL conducted the calculation and figures in R. CW contributed to writing, review and editing. All authors contributed to the article and approved the submitted version.

Funding

This research was supported by the Projects of Science and Technology Department of Jilin Province (YDZJ202301ZYTS127).

Data availability

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material. The single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data that support the findings of this study have been deposited in the GEO public database (GSE193337) and UCSC public database.

Declarations

Ethics approval and consent to participate

The study utilized relevant data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) public databases. In accordance with the usage agreements and applicable laws and regulations of these databases, it is not necessary to obtain informed consent from the subjects again for the use of this data.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Siegel RL Miller KD Wagle NS Jemal A Cancer statistics, 2023 CA Cancer J Clin 2023 73 17 48 10.3322/caac.21763 36633525
Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73:17–48. 10.3322/caac.21763.36633525 10.3322/caac.21763
2. Lowrance W Dreicer R Jarrard DF Scarpato KR Kim SK Kirkby E Buckley DI Griffin JC Cookson MS Updates to advanced prostate cancer: AUA/SUO guideline (2023) J Urol 2023 209 1082 1090 10.1097/JU.0000000000003452 37096583
Lowrance W, Dreicer R, Jarrard DF, Scarpato KR, Kim SK, Kirkby E, Buckley DI, Griffin JC, Cookson MS. Updates to advanced prostate cancer: AUA/SUO guideline (2023). J Urol. 2023;209:1082–90. 10.1097/JU.0000000000003452.37096583 10.1097/JU.0000000000003452
3. Lawson DA Kessenbrock K Davis RT Pervolarakis N Werb Z Tumour heterogeneity and metastasis at single-cell resolution Nat Cell Biol 2018 20 1349 1360 10.1038/s41556-018-0236-7 30482943
Lawson DA, Kessenbrock K, Davis RT, Pervolarakis N, Werb Z. Tumour heterogeneity and metastasis at single-cell resolution. Nat Cell Biol. 2018;20:1349–60. 10.1038/s41556-018-0236-7.30482943 10.1038/s41556-018-0236-7
4. Hausser J Alon U Tumour heterogeneity and the evolutionary trade-offs of cancer Nat Rev Cancer 2020 20 247 257 10.1038/s41568-020-0241-6 32094544
Hausser J, Alon U. Tumour heterogeneity and the evolutionary trade-offs of cancer. Nat Rev Cancer. 2020;20:247–57. 10.1038/s41568-020-0241-6.32094544 10.1038/s41568-020-0241-6
5. del Flores-Téllez TNJ Baena E Experimental challenges to modeling prostate cancer heterogeneity Cancer Lett 2022 524 194 205 10.1016/j.canlet.2021.10.012 34688843
del Flores-Téllez TNJ, Baena E. Experimental challenges to modeling prostate cancer heterogeneity. Cancer Lett. 2022;524:194–205. 10.1016/j.canlet.2021.10.012.34688843 10.1016/j.canlet.2021.10.012
6. Wang S Sun S-T Zhang X-Y Ding H-R Yuan Y He J-J Wang M-S Yang B Li Y-B The evolution of single-cell RNA sequencing technology and application: progress and perspectives Int J Mol Sci 2023 24 2943 10.3390/ijms24032943 36769267
Wang S, Sun S-T, Zhang X-Y, Ding H-R, Yuan Y, He J-J, Wang M-S, Yang B, Li Y-B. The evolution of single-cell RNA sequencing technology and application: progress and perspectives. Int J Mol Sci. 2023;24:2943. 10.3390/ijms24032943.36769267 10.3390/ijms24032943
7. Jovic D Liang X Zeng H Lin L Xu F Luo Y Single-cell RNA sequencing technologies and applications: a brief overview Clin Transl Med 2022 12 e694 10.1002/ctm2.694 35352511
Jovic D, Liang X, Zeng H, Lin L, Xu F, Luo Y. Single-cell RNA sequencing technologies and applications: a brief overview. Clin Transl Med. 2022;12:e694. 10.1002/ctm2.694.35352511 10.1002/ctm2.694
8. Kulkarni A Anderson AG Merullo DP Konopka G Beyond bulk: a review of single cell transcriptomics methodologies and applications Curr Opin Biotechnol 2019 58 129 136 10.1016/j.copbio.2019.03.001 30978643
Kulkarni A, Anderson AG, Merullo DP, Konopka G. Beyond bulk: a review of single cell transcriptomics methodologies and applications. Curr Opin Biotechnol. 2019;58:129–36. 10.1016/j.copbio.2019.03.001.30978643 10.1016/j.copbio.2019.03.001
9. Li Y Hu X Lin R Zhou G Zhao L Zhao D Zhang Y Li W Zhang Y Ma P Single-cell landscape reveals active cell subtypes and their interaction in the tumor microenvironment of gastric cancer Theranostics 2022 12 3818 3833 10.7150/thno.71833 35664061
Li Y, Hu X, Lin R, Zhou G, Zhao L, Zhao D, Zhang Y, Li W, Zhang Y, Ma P, et al. Single-cell landscape reveals active cell subtypes and their interaction in the tumor microenvironment of gastric cancer. Theranostics. 2022;12:3818–33. 10.7150/thno.71833.35664061 10.7150/thno.71833
10. Saviano A Henderson NC Baumert TF Single-cell genomics and spatial transcriptomics: discovery of novel cell states and cellular interactions in liver physiology and disease biology J Hepatol 2020 73 1219 1230 10.1016/j.jhep.2020.06.004 32534107
Saviano A, Henderson NC, Baumert TF. Single-cell genomics and spatial transcriptomics: discovery of novel cell states and cellular interactions in liver physiology and disease biology. J Hepatol. 2020;73:1219–30. 10.1016/j.jhep.2020.06.004.32534107 10.1016/j.jhep.2020.06.004
11. Wang W Xu Y Wang L Zhu Z Aodeng S Chen H Cai M Huang Z Han J Wang L Single-cell profiling identifies mechanisms of inflammatory heterogeneity in chronic rhinosinusitis Nat Immunol 2022 23 1484 1494 10.1038/s41590-022-01312-0 36138182
Wang W, Xu Y, Wang L, Zhu Z, Aodeng S, Chen H, Cai M, Huang Z, Han J, Wang L, et al. Single-cell profiling identifies mechanisms of inflammatory heterogeneity in chronic rhinosinusitis. Nat Immunol. 2022;23:1484–94. 10.1038/s41590-022-01312-0.36138182 10.1038/s41590-022-01312-0
12. Fan X-X Wu Q Decoding lung cancer at single-cell level Front Immunol 2022 13 883758 10.3389/fimmu.2022.883758 35677034
Fan X-X, Wu Q. Decoding lung cancer at single-cell level. Front Immunol. 2022;13:883758. 10.3389/fimmu.2022.883758.35677034 10.3389/fimmu.2022.883758
13. Xu J Zhou H Cheng Y Xiang G Identifying potential signatures for atherosclerosis in the context of predictive, preventive, and personalized medicine using integrative bioinformatics approaches and machine-learning strategies EPMA J 2022 13 433 449 10.1007/s13167-022-00289-y 36061826
Xu J, Zhou H, Cheng Y, Xiang G. Identifying potential signatures for atherosclerosis in the context of predictive, preventive, and personalized medicine using integrative bioinformatics approaches and machine-learning strategies. EPMA J. 2022;13:433–49. 10.1007/s13167-022-00289-y.36061826 10.1007/s13167-022-00289-y
14. Heidegger I Fotakis G Offermann A Goveia J Daum S Salcher S Noureen A Timmer-Bosscha H Schäfer G Walenkamp A Comprehensive characterization of the prostate tumor microenvironment identifies CXCR4/CXCL12 crosstalk as a novel antiangiogenic therapeutic target in prostate cancer Mol Cancer 2022 21 132 10.1186/s12943-022-01597-7 35717322
Heidegger I, Fotakis G, Offermann A, Goveia J, Daum S, Salcher S, Noureen A, Timmer-Bosscha H, Schäfer G, Walenkamp A, et al. Comprehensive characterization of the prostate tumor microenvironment identifies CXCR4/CXCL12 crosstalk as a novel antiangiogenic therapeutic target in prostate cancer. Mol Cancer. 2022;21:132. 10.1186/s12943-022-01597-7.35717322 10.1186/s12943-022-01597-7
15. Butler A Hoffman P Smibert P Papalexi E Satija R Integrating single-cell transcriptomic data across different conditions, technologies, and species Nat Biotechnol 2018 36 411 420 10.1038/nbt.4096 29608179
Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36:411–20. 10.1038/nbt.4096.29608179 10.1038/nbt.4096
16. Huang Q Liu Y Du Y Garmire LX Evaluation of cell type annotation R packages on single-cell RNA-seq data Genom Proteom Bioinform 2021 19 267 281 10.1016/j.gpb.2020.07.004
Huang Q, Liu Y, Du Y, Garmire LX. Evaluation of cell type annotation R packages on single-cell RNA-seq data. Genom Proteom Bioinform. 2021;19:267–81. 10.1016/j.gpb.2020.07.004.10.1016/j.gpb.2020.07.004
17. Devarajan K Nonnegative matrix factorization: an analytical and interpretive tool in computational biology PLoS Comput Biol 2008 4 e1000029 10.1371/journal.pcbi.1000029 18654623
Devarajan K. Nonnegative matrix factorization: an analytical and interpretive tool in computational biology. PLoS Comput Biol. 2008;4:e1000029. 10.1371/journal.pcbi.1000029.18654623 10.1371/journal.pcbi.1000029
18. Qiu X Mao Q Tang Y Wang L Chawla R Pliner HA Trapnell C Reversed graph embedding resolves complex single-cell trajectories Nat Methods 2017 14 979 982 10.1038/nmeth.4402 28825705
Qiu X, Mao Q, Tang Y, Wang L, Chawla R, Pliner HA, Trapnell C. Reversed graph embedding resolves complex single-cell trajectories. Nat Methods. 2017;14:979–82. 10.1038/nmeth.4402.28825705 10.1038/nmeth.4402
19. Subramanian A Tamayo P Mootha VK Mukherjee S Ebert BL Gillette MA Paulovich A Pomeroy SL Golub TR Lander ES Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles Proc Natl Acad Sci U S A 2005 102 15545 15550 10.1073/pnas.0506580102 16199517
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102:15545–50. 10.1073/pnas.0506580102.16199517 10.1073/pnas.0506580102
20. Jin S Guerrero-Juarez CF Zhang L Chang I Ramos R Kuan C-H Myung P Plikus MV Nie Q Inference and analysis of cell-cell communication using cell chat Nat Commun 2021 12 1088 10.1038/s41467-021-21246-9 33597522
Jin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, Kuan C-H, Myung P, Plikus MV, Nie Q. Inference and analysis of cell-cell communication using cell chat. Nat Commun. 2021;12:1088. 10.1038/s41467-021-21246-9.33597522 10.1038/s41467-021-21246-9
21. Aibar S González-Blas CB Moerman T Huynh-Thu VA Imrichova H Hulselmans G Rambow F Marine J-C Geurts P Aerts J SCENIC: single-cell regulatory network inference and clustering Nat Methods 2017 14 1083 1086 10.1038/nmeth.4463 28991892
Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, Rambow F, Marine J-C, Geurts P, Aerts J, et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017;14:1083–6. 10.1038/nmeth.4463.28991892 10.1038/nmeth.4463
22. Van De Sande B Flerin C Davie K De Waegeneer M Hulselmans G Aibar S Seurinck R Saelens W Cannoodt R Rouchon Q A scalable SCENIC workflow for single-cell gene regulatory network analysis Nat Protoc 2020 15 2247 2276 10.1038/s41596-020-0336-2 32561888
Van De Sande B, Flerin C, Davie K, De Waegeneer M, Hulselmans G, Aibar S, Seurinck R, Saelens W, Cannoodt R, Rouchon Q, et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat Protoc. 2020;15:2247–76. 10.1038/s41596-020-0336-2.32561888 10.1038/s41596-020-0336-2
23. Goldman MJ Craft B Hastie M Repečka K McDade F Kamath A Banerjee A Luo Y Rogers D Brooks AN Visualizing and interpreting cancer genomics data via the Xena platform Nat Biotechnol 2020 38 675 678 10.1038/s41587-020-0546-8 32444850
Goldman MJ, Craft B, Hastie M, Repečka K, McDade F, Kamath A, Banerjee A, Luo Y, Rogers D, Brooks AN, et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol. 2020;38:675–8. 10.1038/s41587-020-0546-8.32444850 10.1038/s41587-020-0546-8
24. Li D Ding J Bar-Joseph Z Unsupervised cell functional annotation for single-cell RNA-seq Genome Res 2022 32 1765 1775 10.1101/gr.276609.122 35764397
Li D, Ding J, Bar-Joseph Z. Unsupervised cell functional annotation for single-cell RNA-seq. Genome Res. 2022;32:1765–75. 10.1101/gr.276609.122.35764397 10.1101/gr.276609.122
25. Clarke ZA Andrews TS Atif J Pouyabahar D Innes BT MacParland SA Bader GD Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods Nat Protoc 2021 16 2749 2764 10.1038/s41596-021-00534-0 34031612
Clarke ZA, Andrews TS, Atif J, Pouyabahar D, Innes BT, MacParland SA, Bader GD. Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods. Nat Protoc. 2021;16:2749–64. 10.1038/s41596-021-00534-0.34031612 10.1038/s41596-021-00534-0
26. Locati M Curtale G Mantovani A Diversity, mechanisms and significance of macrophage plasticity Annu Rev Pathol 2020 15 123 147 10.1146/annurev-pathmechdis-012418-012718 31530089
Locati M, Curtale G, Mantovani A. Diversity, mechanisms and significance of macrophage plasticity. Annu Rev Pathol. 2020;15:123–47. 10.1146/annurev-pathmechdis-012418-012718.31530089 10.1146/annurev-pathmechdis-012418-012718
27. Mantovani A Allavena P Marchesi F Garlanda C Macrophages as tools and targets in cancer therapy Nat Rev Drug Discov 2022 21 799 820 10.1038/s41573-022-00520-5 35974096
Mantovani A, Allavena P, Marchesi F, Garlanda C. Macrophages as tools and targets in cancer therapy. Nat Rev Drug Discov. 2022;21:799–820. 10.1038/s41573-022-00520-5.35974096 10.1038/s41573-022-00520-5
28. Mantovani A Sozzani S Locati M Allavena P Sica A Macrophage polarization: tumor-associated macrophages as a paradigm for polarized M2 mononuclear phagocytes Trends Immunol 2002 23 549 555 10.1016/S1471-4906(02)02302-5 12401408
Mantovani A, Sozzani S, Locati M, Allavena P, Sica A. Macrophage polarization: tumor-associated macrophages as a paradigm for polarized M2 mononuclear phagocytes. Trends Immunol. 2002;23:549–55. 10.1016/S1471-4906(02)02302-5.12401408 10.1016/S1471-4906(02)02302-5
29. Tran HTN Ang KS Chevrier M Zhang X Lee NYS Goh M Chen J A benchmark of batch-effect correction methods for single-cell RNA sequencing data Genome Biol 2020 21 12 10.1186/s13059-019-1850-9 31948481
Tran HTN, Ang KS, Chevrier M, Zhang X, Lee NYS, Goh M, Chen J. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 2020;21:12. 10.1186/s13059-019-1850-9.31948481 10.1186/s13059-019-1850-9
30 Leek JT Scharpf RB Bravo HC Simcha D Langmead B Johnson WE Geman D Baggerly K Irizarry RA Tackling the widespread and critical impact of batch effects in high-throughput data Nat Rev Genet 2010 10.1038/nrg2825 20838408
Leek JT, Scharpf RB, Bravo HC, Simcha D, Langmead B, Johnson WE, Geman D, Baggerly K, Irizarry RA. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet. 2010. 10.1038/nrg2825.20838408 10.1038/nrg2825
31. Knudsen BS Miranti CK The impact of cell adhesion changes on proliferation and survival during prostate cancer development and progression J Cell Biochem 2006 99 345 361 10.1002/jcb.20934 16676354
Knudsen BS, Miranti CK. The impact of cell adhesion changes on proliferation and survival during prostate cancer development and progression. J Cell Biochem. 2006;99:345–61. 10.1002/jcb.20934.16676354 10.1002/jcb.20934
32. Pelekanou V Castanas E Androgen control in prostate cancer J Cell Biochem 2016 117 2224 2234 10.1002/jcb.25572 27104784
Pelekanou V, Castanas E. Androgen control in prostate cancer. J Cell Biochem. 2016;117:2224–34. 10.1002/jcb.25572.27104784 10.1002/jcb.25572
33. Coutinho I Day TK Tilley WD Selth LA Androgen receptor signaling in castration-resistant prostate cancer: a lesson in persistence Endocr Relat Cancer 2016 23 T179 T197 10.1530/ERC-16-0422 27799360
Coutinho I, Day TK, Tilley WD, Selth LA. Androgen receptor signaling in castration-resistant prostate cancer: a lesson in persistence. Endocr Relat Cancer. 2016;23:T179–97. 10.1530/ERC-16-0422.27799360 10.1530/ERC-16-0422
34. Chrisofos M Papatsoris AG Lazaris A Deliveliotis C Precursor lesions of prostate cancer Crit Rev Clin Lab Sci 2007 44 243 270 10.1080/10408360601177236 17453919
Chrisofos M, Papatsoris AG, Lazaris A, Deliveliotis C. Precursor lesions of prostate cancer. Crit Rev Clin Lab Sci. 2007;44:243–70. 10.1080/10408360601177236.17453919 10.1080/10408360601177236
35. Chen S Zhu G Yang Y Wang F Xiao Y-T Zhang N Bian X Zhu Y Yu Y Liu F Single-cell analysis reveals transcriptomic remodellings in distinct cell types that contribute to human prostate cancer progression Nat Cell Biol 2021 23 87 98 10.1038/s41556-020-00613-6 33420488
Chen S, Zhu G, Yang Y, Wang F, Xiao Y-T, Zhang N, Bian X, Zhu Y, Yu Y, Liu F, et al. Single-cell analysis reveals transcriptomic remodellings in distinct cell types that contribute to human prostate cancer progression. Nat Cell Biol. 2021;23:87–98. 10.1038/s41556-020-00613-6.33420488 10.1038/s41556-020-00613-6
36. Berglund E Maaskola J Schultz N Friedrich S Marklund M Bergenstråhle J Tarish F Tanoglidi A Vickovic S Larsson L Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity Nat Commun 2018 9 2419 10.1038/s41467-018-04724-5 29925878
Berglund E, Maaskola J, Schultz N, Friedrich S, Marklund M, Bergenstråhle J, Tarish F, Tanoglidi A, Vickovic S, Larsson L, et al. Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity. Nat Commun. 2018;9:2419. 10.1038/s41467-018-04724-5.29925878 10.1038/s41467-018-04724-5
37. Pujol-Borrell R Soldevila G Vives M Badenas J Marakiant R Franc G Inappropriate expression of HLA class II molecules in endocrine epithelial cells: the phenomenon, the new experimental data and comparison with animal models J Autoimmun 1989 10.1016/0896-8411(89)90127-3 2673270
Pujol-Borrell R, Soldevila G, Vives M, Badenas J, Marakiant R, Franc G. Inappropriate expression of HLA class II molecules in endocrine epithelial cells: the phenomenon, the new experimental data and comparison with animal models. J Autoimmun. 1989. 10.1016/0896-8411(89)90127-3.2673270 10.1016/0896-8411(89)90127-3
38. Jasinski-Bergner S Eckstein M Taubert H Wach S Fiebig C Strick R Hartmann A Seliger B The human leukocyte antigen G as an immune escape mechanism and novel therapeutic target in urological tumors Front Immunol 2022 13 811200 10.3389/fimmu.2022.811200 35185904
Jasinski-Bergner S, Eckstein M, Taubert H, Wach S, Fiebig C, Strick R, Hartmann A, Seliger B. The human leukocyte antigen G as an immune escape mechanism and novel therapeutic target in urological tumors. Front Immunol. 2022;13:811200. 10.3389/fimmu.2022.811200.35185904 10.3389/fimmu.2022.811200
