
==== Front
Transl Oncol
Transl Oncol
Translational Oncology
1936-5233
Neoplasia Press

S1936-5233(24)00050-0
10.1016/j.tranon.2024.101925
101925
Original Research
Single-cell sequencing revealed metabolic reprogramming and its transcription factor regulatory network in prostate cancer
Wei Guojiang dctwey@gmail.com
bc1⁎
Zhu Hongcai d1
Zhou Yupeng bc1
Pan Yang e
Yi Bocun bc
Bai Yangkai byk19912022@163.com
ac⁎
a Department of Urology, Hanzhong Central Hospital, Hanzhong, Shaanxi 723000, PR China
b Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, PR China
c Department of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, PR China
d Department of Medical Oncology, Hanzhong Central Hospital, Hanzhong, Shaanxi 723000, PR China
e Department of Urology, Tianjin Medical University General Hospital, Tianjin 300052, PR China
⁎ Corresponding authors: dctwey@gmail.combyk19912022@163.com
1 Equal contribution and first authorship: These authors contributed equally to this work and share first authorship.

06 3 2024
6 2024
06 3 2024
44 1019252 10 2023
19 12 2023
28 2 2024
© 2024 The Authors. Published by Elsevier Inc.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Highlight

• Unveiling Metabolic Reprogramming in Prostate Cancer and the Significance of SREBPs: Insights from Single-Cell Sequencing.

• Unlocking Metabolic Variations and Transcription Factor Dynamics Across Distinct Prostate Cancer Subtypes via Single-Cell Sequencing.

• The SREBP Inhibitor Betulin Elicits Remarkable Efficacy in Prostate Cancer Suppression.

Background/Aims

Prostate cancer is the most frequently diagnosed cancer among men in the United States and is the second leading cause of cancer-related deaths in men. The incidence of prostate cancer is gradually rising due to factors such as aging demographics and changes in dietary habits. The objective of this study is to investigate the metabolic reprogramming changes occurring in prostate cancer and identify potential therapeutic targets.

Methods

In this study, we utilized single-cell sequencing to comprehensively characterize the alterations in metabolism and the regulatory role of transcription factors in various subtypes of prostate cancer.

Results

In comparison to benign prostate tissue, prostate cancer displayed substantial metabolic variations, notably exhibiting heightened activity in fatty acid metabolism and cholesterol metabolism. This metabolic reprogramming not only influenced cellular energy utilization but also potentially impacted the activity of the androgen receptor (AR) pathway through the synthesis of endogenous steroid hormones. Through our analysis of transcription factor activity, we identified the crucial role of SREBPs, which are transcription factors associated with lipid metabolism, in prostate cancer. Encouragingly, the inhibitor Betulin effectively suppresses prostate cancer growth, highlighting its potential as a therapeutic agent for prostate cancer treatment.

Graphical abstract

Image, graphical abstract

Keywords

Prostate cancer
Metabolic reprogramming
SREBPs
Betulin
Fatty acid metabolism
Cholesterol metabolism
==== Body
pmcIntroduction

Prostate cancer (PCa) ranks as the second most prevalent solid tumor among men and stands as the fifth leading cause of cancer-related mortality [1]. The progression of prostate cancer has been linked to various factors, including overexpression of androgen receptor (AR) and MYC, downregulation of NKX3–1, PTEN, TP53, among others [2]. AR serves as a vital transcription factor involved in male sexual development and the maintenance of accessory sex organs [3,4]. AR plays a pivotal role in all stages of prostate cancer, including the castration-resistant prostate cancer (CRPC) stage, where the AR pathway remains activated [5], [6], [7]. Consequently, androgen deprivation therapy (ADT) that targets the AR signaling pathway continues to be the primary treatment for prostate cancer [8,9]. Initially, prostate cancer cells exhibit a favorable response to castration during the initial treatment phase. However, almost all patients, eventually develop resistance to castration therapy after a certain duration of treatment, leading to the emergence of metabolic symptoms, cardiovascular disease, osteoporosis, and severe complications such as cognitive decline [10]. Therefore, there is a pressing need to explore alternative mechanisms and identify novel therapeutic targets.

Metabolic reprogramming represents a crucial feature in the development of cancer [11]. In order to meet the energy requirements for rapid proliferation, cancer cells frequently undergo alterations in their metabolic processes. Dysregulation of lipid metabolism has emerged as a hallmark of the malignant phenotype [12]. The normal prostate exhibits a distinct intermediary profile, functioning as a source of secreted citrate and zinc. The citric acid cycle can be regarded as a dual-purpose metabolic pathway, serving as an amphibolic pathway. Consequently, significant quantities of citrate that would typically be secreted can now be utilized as a substrate for the de novo synthesis of fatty acids and cholesterol [13,14]. Prostate cancer cells display heightened expression of numerous lipogenic enzymes. Notably, ACLY, a pivotal enzyme involved in the metabolism of acetyl-CoA necessary for fatty acid synthesis from citrate, is significantly upregulated in prostate cancer [15]. Additionally, enzymes such as ACACA, FASN, SCD1, among others, are also markedly elevated in prostate cancer [16]. Furthermore, cholesterol metabolism is substantially enhanced in prostate cancer [17]. Cholesterol serves as a crucial substrate for the synthesis of steroid hormones. In prostate cancer cells, androgens synthesized from cholesterol within the cells may contribute to the progression of prostate cancer [18]. While the reprogramming of lipid metabolism in prostate cancer is widely acknowledged as a driving force behind its development, our understanding of the specific mechanisms involved in prostate cancer remains limited.

Emerging technologies, such as single-cell RNA sequencing (scRNA-seq), offer a powerful approach to unravel the metabolic distinctions among different cell populations within prostate cancer. Additionally, scRNA-seq enables the investigation of the regulatory relationships between transcription factors, providing valuable insights into the intricate metabolic landscape of prostate cancer. To comprehend the molecular mechanisms underlying lipid metabolism in prostate cancer and identify novel targets, we employed single-cell sequencing to investigate the alterations and evolution of metabolism in distinct prostate cancer subtypes. Additionally, we explored the involvement of transcription factors in regulating these metabolic changes. This comprehensive approach allows us to gain deeper insights into the molecular underpinnings of prostate cancer and potentially identify new targets for therapeutic intervention in lipid metabolism.

Results

Single-cell analysis reveals composition of prostate benign and cancer cells

We first investigated single-cell differences between benign and radical prostatectomy (RP) prostate cancer tissues. A total of 25,030 high-quality cells passed QC, of which 12,463 cells were derived from benign tissues and 12,567 cells were derived from RP tissues. We identified 25 clusters by clustering analysis which were visualized by t-SNE (Fig. 1A), and the top five most differential genes per cluster were shown by heatmap (Fig. 1B). The difference in cell distribution between benign and cancerous tissues displayed by t-SNE was shown in Fig. 1C. A total of eight major cell types have been identified, including T cells, B cells, Macrophages, Endothelial cells, Fibroblasts, Mast cells, Monocytes and Epithelial cells (Fig. 1D, E, Table S1). Heatmap revealed the most differential genes in each cell types (Fig. 1F). Notably, in epithelial cells, KLK3 was the most differential gene, which also known as prostate-specific antigen (PSA) was a glycoprotein enzyme [19]. We then analyzed the proportion of different cells between normal and RP samples. In macrophages (RP samples accounting for 55.47 %) and T cells (RP samples accounting for 58.42 %), RP samples contribute more, while in B cells (benign samples accounting for 65.96 %), fibroblasts (benign samples accounting for 70.26 %), and endothelial cells (benign samples accounting for 67.98 %), benign samples accounted for more. Similarly, we counted the proportions of different cell types in benign and RP samples (Fig. 1G). Epithelial cells, T cells and macrophages accounted for the most in RP samples, accounting for 37.34 %, 35.20 % and 7.42 % respectively. However, in benign samples, epithelial cells, T cells and endothelial cells accounted for the largest proportion, accounting for 34.09 %, 25.26 % and 14.11 % respectively (Fig. 1H).Fig. 1 Identification of major cell types in normal and radical prostatectomy (RP) samples of prostate cancer. (A) t-SNE plot of 25 cell clusters including normal and RP samples. (B) Heatmap of the top five marker genes in each cell cluster. (C) t-SNE plot of all cells between normal and tumor samples. (D) t-SNE plot of main cell type annotations. (E) t-SNE plot of cell type annotations in normal and tumor samples, respectively. (F) Heatmap of the top five marker genes in each main cell types. (G) Left panel: horizontal bar graphs indicating the relative cell composition in tumor and normal samples. Right panel: horizontal bar graphs indicating the relative composition across each patient. (Ends with the letter n meaning normal sample; ends with the letter t meaning tumor sample) (H) bar graphs of cell type composition by sample.

Fig. 1

Characteristics and metabolic differences of epithelial cells in benign and rp samples

We next analyzed the characteristics of epithelial cells in benign and RP samples separately. First, we marked epithelial cells into luminal cells, basal cells, and intermediate cells based on epithelial cell morphology and PAM50 gene signature [20]. The clusters predominantly highly expressing cell markers such as KLK3, KLK2, NKX3.1 and ACPP were identified as luminal cells. Conversely, clusters predominantly expressing cell markers such as KRT5, KRT15, KRT7, and DST were annotated as basal cells. Additionally, clusters expressing cell markers like RARRES1, PIGR, SCGB3A1, and OLFM4 were classified as intermediate cells. Finally, a distinct cluster was identified solely in the RP sample, which did not possess the aforementioned characteristics associated with the other clusters. The cluster predominantly exhibited the expression of cell markers such as NPY, PCA3, ERG, and PCAT14. PCA3 (also known as DD3) is a non-coding RNA gene that is exclusively expressed in human prostate tissue and is a prostate cancer specific marker [21,22]. Consequently, the cluster has been designated as PCA3+ cells (Fig. 2A, B, C, Table S2). We used InferCNV to analyze the potential CNV mutations in different subgroups and to infer the proportion of malignant cells in different subgroups (Figure S2A). The composition of different epithelial cells was analyzed in the study. Among the luminal and basal cells, the RP samples constituted the majority, comprising 66.51 % and 57.86 %, respectively (Fig. 2D). In the RP samples, two major cell populations were found to be the most abundant. PCA3+ cells accounted for 39.23 % of the RP samples, while luminal cells constituted 35.09 % of the RP samples. In the benign samples, the intermediate cells were found to be the predominant cell population, comprising a significant majority of 67.32 % of the benign samples (Fig. 2E).Fig. 2 Identification of epithelial cell types and metabolic difference. (A) t-SNE plot of epithelial cells. (B) Cell distribution in tumor and normal samples. (C) Heatmap of the top ten marker genes in each epithelial cell types. (D) Left panel: horizontal bar graphs indicating the relative cell composition in tumor and normal samples. Right panel: horizontal bar graphs indicating the relative composition across each patient. (Ends with the letter n meaning normal sample; ends with the letter t meaning tumor sample) (E) bar graphs of epithelial cell type composition by sample. (F) Volcano plot of the differentially expressed genes (DEGs) between tumor epithelial cells and epithelial cells. Some of the most significant genes are indicated. (G) GSVA enrichment analysis of differentially expressed genes between tumor and normal samples. (H) Single cell metabolism analysis of epithelial cells between tumor and normal samples. (I) Dot plot of main metabolic pathway in each epithelial cell types. Top: normal; Bottom: tumor. (J) t-SNE plot of partial metabolic pathway quantification.

Fig. 2

Subsequently, a differential analysis of epithelial cells was conducted to compare the gene expression patterns between benign and RP samples (Adjusted P value 〈 0.05, Log2FoldChange 〉 1) (Fig. 2F). We performed GSVA enrichment analysis on differential genes. In RP samples, we observed that epithelial cells were mainly up regulated in androgen response, fatty acid metabolism, PI3K pathway and bile acid metabolism (Fig. 2G). Building upon these insights, we further explored the disparities in epithelial cell metabolism between the benign and RP samples. The goal was to gain a better understanding of the metabolic alterations that occur in the transition from benign to RP tissue. The analysis indicated that the majority of metabolic processes were upregulated in the RP samples compared to the benign samples (Fig. 2H). Based on the further subdivision of the differences between different types of epithelial cells, it was observed that the upregulation of metabolic processes was primarily concentrated in luminal cells and PCA3+ cells within the RP samples (Fig. 2I, J). This finding suggests that these specific cell populations undergo significant metabolic alterations, which may contribute to the metabolic reprogramming associated with prostate cancer progression.

Characterizing prostate cancer subtypes

We pooled RP samples and castration-resistant prostate cancer (CRPC) samples. The RP samples were presumed to be derived from androgen-dependent prostate cancer (ADPC). It has been confirmed that some neuroendocrine prostate cancer (NEPC) cells are present within the CRPC samples [23]. Similarly, we initially annotated all cells into major cell types (Fig. 3A, B). Epithelial cells exhibited a higher abundance in samples derived from CRPC (Fig. 3C). Genes such as KLK3, KLK2, ACPP, and MSMB remained as primary cell markers for epithelial cells, even in the context of CRPC samples (Fig. 3D, Table S3). Among epithelial/cancer cells, CRPC samples accounted for a significantly larger proportion, reaching 76.46 % of all epithelial cells.Fig. 3 Comparison of ADPC and CRPC cells revealed evolution of Prostate Cancer (PCa). (A) t-SNE plot of 25 cell clusters including normal and RP samples. (B) t-SNE plot of main cell type annotations including ADPC and CRPC cells. (C) t-SNE plot of cell type annotations in ADPC and CRPC samples, respectively. (D) Heatmap of the top ten marker genes in each main cell types. (E) Left panel: vertical bar graphs of cell type composition by sample. Right panel: horizontal bar graphs indicating the relative cell composition in ADPC and CRPC samples. (F) GSVA enrichment analysis for all major cell types. (G) Volcano plot for differential analysis of epithelial/cancer cells between CRPC and ADPC samples. (H) GSVA enrichment analysis of differentially expressed genes between CRPC and ADPC epithelial/cancer cells.

Fig. 3

In contrast to the previous comparison of benign samples, the proportion of T cells in CRPC samples exhibited a significant reduction, accounting for only 9.66 % of all CRPC samples, whereas T cells accounted for 37.30 % of ADPC samples (Fig. 3E). These findings suggest a lower degree of T cell infiltration in CRPC samples. The diminished infiltration of T cells in CRPC samples might indicate an immunosuppressive microenvironment or an evasion of the immune system by the tumor cells. We performed GSVA enrichment analysis to assess functional differences across cell types including ADPC and CRPC samples. Our analysis revealed that endothelial cells and fibroblasts exhibit closer associations with epithelial cell-enriched pathways, indicating potential functional similarities between these cell types. On the other hand, T cells, macrophages, and monocytes demonstrated similar expression patterns, suggesting potential shared functional characteristics among these immune cell populations (Fig. 3F). We conducted a differential analysis on epithelial/cancer cells derived from samples of both ADPC and CRPC (Fig. 3G). Subsequently, we performed GSVA enrichment analysis on the identified differentially expressed genes. The enrichment analysis of epithelial/cancer cells from CRPC samples revealed a pronounced enrichment of the MYC target pathway. Conversely, epithelial/cancer cells derived from androgen-ADPC samples exhibited significant enrichments in pathways related to androgen response, bile acid metabolism, and fatty acid metabolism (Fig. 3H).

Macrophages were associated with immunosuppression in the tumor microenvironment of prostate cancer

In order to investigate cell interactions within the prostate cancer microenvironment, we utilized Cellphonedb, a tool specifically designed for the analysis of cell communication networks. By analyzing receptor-ligand pairs between different cell types, we gained insights into the potential signaling interactions occurring within the prostate cancer microenvironment. Cellphonedb allows for the identification and characterization of specific receptor-ligand interactions, shedding light on the cellular communication networks that contribute to tumor development, progression, and immune responses. Our analysis using Cellphonedb revealed that among immune cells, macrophages and monocytes exhibited the most abundant communication with epithelial cells within the prostate cancer microenvironment (Fig. 4A, B). The communication between macrophages, monocytes, and epithelial cells may play a crucial role in modulating immune responses, tumor progression, and potentially influencing the microenvironment's overall immunological landscape. The differential analysis of macrophages in prostate benign and prostate cancer samples revealed similar expression patterns between macrophages in tumors and epithelial cells in RP samples (Figure S1).Fig. 4 Cellular interactions in the prostate cancer microenvironment. (A) Heatmap of interactions between different cell types. The numbers represent the number of receptor-ligand pairs. (B) Network diagram of the interaction strength between cell types. (C) Dot plot of receptor-ligand pairs between epithelial/cancer cells and others. (D) Chord plot of interactions between different cell types. Chords represent different receptor-ligand pairs. (E) Chord plot of interactions between epithelial/cancer cells and macrophage cells.

Fig. 4

Mounting evidence supported the association between macrophages and prostate cancer progression. The analysis of receptor-ligand pairs has provided evidence suggesting that macrophages play a role in promoting prostate cancer progression. The interaction between macrophages and prostate cancer cells through specific receptor-ligand pairs appears to contribute to various aspects of tumor development and progression (Fig. 4C, D, E). Activation of CEACAM1 and SEMA4D in prostate cancer epithelial cells has been associated with their interaction with macrophages. CEACAM1, a member of the carcinoembryonic antigen (CEA) gene family, exhibits a diverse range of functions in various cellular processes. It is a cell surface glycoprotein that is involved in numerous biological activities, contributing to tissue differentiation, angiogenesis, apoptosis, metastasis, and the regulation of innate and adaptive immune responses [24]. SEMA4D (Semaphorin 4D), also known as CD100, is a protein that has been found to be highly expressed in many human tumor tissues. It plays a significant role in various aspects of tumor biology, including tumor angiogenesis, invasion, and metastasis [25]. As an important pro-angiogenic factor, it indeed plays a crucial role in promoting tumor growth by stimulating angiogenesis. Similarly, it has been observed that within tumors, epithelial cells can exert a suppressive effect on macrophages, contributing to the development of an immunosuppressive microenvironment (Fig. 4C, D, E). The presence of CD47 on epithelial cells hinders the phagocytosis of cancer cells by macrophages through the signaling mechanism commonly referred to as the "don't eat me" signal [26]. These findings suggested that targeting the CD47-SIRPα interaction between prostate cancer epithelial cells and macrophages could serve as an effective strategy for anti-tumor interventions.

Characteristics and differences of epithelial cells in prostate cancer subtypes

We isolated epithelial cells from both ADPC and CRPC samples. To classify these cells, we initially divided them into three categories based on their morphological characteristics, aligning with the luminal cells, basal cells, and intermediate cells mentioned previously. Among the extracted epithelial cells, we observed the presence of a distinct cluster that was exclusively present in the CRPC samples. This particular cluster exhibited typical characteristics associated with neuroendocrine prostate cancer (NEPC). Notably, the cells within this cluster expressed neuroendocrine prostate cancer markers such as ASCL1, CHGB, SYP, and potentially other markers specific to NEPC [27,28]. Among the remaining clusters of epithelial cells, we found that they did not exhibit above characteristics. However, we noticed a distinct pattern where these clusters were predominantly enriched in either ADPC or CRPC samples. Accordingly, we annotated these clusters as ADPC-enrichment or CRPC-enrichment clusters (Fig. 5A, D, Table S4). We used InferCNV to analyze the potential CNV mutations in different subgroups for Epithelial cells and to infer the proportion of malignant cells in different subgroups (Figure S2B). Within the ADPC-enrichment cluster, 99.21 % of the cells were derived from ADPC samples. Similarly, within the CRPC-enrichment cluster, 99.43 % of the cells originated from CRPC samples. As for the NE cluster, all the cells (100 %) were derived exclusively from CRPC samples (Fig. 5B, C). In the ADPC samples, the two clusters with the largest proportions were the ADPC-enrichment cluster, accounting for 37.43 % of the cells, and the Intermediate cells cluster, which constitutes 34.25 % of the cells. In contrast, the Luminal cells cluster represented a smaller proportion, accounting for only 9.82 % of the cells within the ADPC samples. In the context of CRPC samples, the CRPC-enrichment cluster comprised the largest proportion, accounting for 35.51 % of the cells. Following closely behind was the Luminal cells cluster, representing 31.08 % of the cells. In contrast, the NE cluster constituted a smaller proportion, accounting for only 9.04 % of the cells within the CRPC samples (Fig. 5C).Fig. 5 Epithelial cell characteristics in ADPC and CRPC samples. (A) t-SNE plot of epithelial cell type annotations including ADPC and CRPC samples. (B) t-SNE plot of epithelial cell type annotations in ADPC and CRPC samples, respectively. (C) Left panel: vertical bar graphs of epithelial cell type composition by sample. Right panel: horizontal bar graphs indicating the relative cell composition in ADPC and CRPC samples. (D) Heatmap of the top ten marker genes in each epithelial cell types. (E) GSVA enrichment analysis for all epithelial cell types. (F) Volcano plot for differential analysis between CRPC-enrichment cells and ADPC-enrichment cells. (G)(H)(I) GSVA enrichment analysis for differential analysis of ADPC-enrichment and other cells, CRPC-enrichment and other cells, and NE and other cells, respectively.

Fig. 5

The results of the GSVA enrichment analysis revealed distinct functional characteristics associated with different subtypes of epithelial cells in prostate cancer. The intermediate cells and basal cells exhibited similar expression patterns, suggesting potential functional similarities between these two subtypes. The ADPC-enrichment cluster exhibited enrichment in pathways related to androgen response, fatty acid metabolism, and bile acid metabolism. These findings suggested that this cluster may be associated with the activity of androgen signaling, lipid metabolism, and the regulation of bile acid-related processes. Dysregulation of androgen signaling is a well-known feature of prostate cancer, and the enrichment of pathways related to fatty acid and bile acid metabolism may reflect the metabolic adaptations of prostate cancer cells in response to disease progression. In contrast, the NE cluster showed enrichment in pathways related to DNA repair and cell cycle regulation (Fig. 5E). The volcano plot illustrating the differential gene expression analysis results between the ADPC-enrichment and CRPC-enrichment clusters was shown in Fig. 5F.

The comparison of gene expression differences between ADPC-enrichment, CRPC-enrichment, and NE clusters with other cell types revealed distinct enrichment patterns in various pathways. For the ADPC-enrichment cluster, GSVA enrichment analysis showed enrichment in pathways related to androgen response, protein secretion, bile acid metabolism, and fatty acid metabolism (Fig. 5G). In contrast, the CRPC-enrichment cluster exhibited enrichment specifically in pathways related to epithelial-mesenchymal transition (EMT) and MYC. EMT is a cellular process associated with cancer metastasis and invasiveness, and MYC is a well-known oncogene involved in various aspects of cancer development and progression (Fig. 5H). The NE cluster displayed enrichment primarily in pathways related to cell cycle regulation and DNA repair (Fig. 5I).

Metabolic differences in prostate cancer subtypes

Altered energy metabolism has been found to be intricately linked to the development of prostate cancer [29]. Prostate cancer cells exhibit distinct metabolic adaptations to meet their energy demands and support their rapid growth and proliferation [30]. Indeed, increased fatty acid synthesis in prostate cancer cells serves multiple purposes in supporting their energy requirements and promoting cancer progression. Firstly, fatty acids generated through de novo lipogenesis can be utilized as a fuel source for energy production through beta-oxidation [31]. This allows cancer cells to meet their high energy demands for growth and proliferation. Moreover, the excess fatty acids produced can contribute to membrane biogenesis and the synthesis of various lipids necessary for cellular processes, including signaling, cell division, and membrane remodeling [32]. These lipids play crucial roles in supporting cancer cell survival, migration, and invasion. Additionally, the enhanced fatty acid synthesis in prostate cancer cells has been associated with the generation of cholesterol, a precursor molecule for androgen synthesis. Prostate cancer is typically dependent on androgen signaling for its growth and survival [33]. By synthesizing cholesterol, cancer cells can support the intratumoral production of androgens, which can then activate androgen receptors and drive the development and progression of prostate cancer. Metabolic analysis of different prostate cancer subtypes has revealed distinct metabolic characteristics, with ADPC-enrichment showing notable metabolic alterations (Fig. 6A). In particular, ADPC-enrichment demonstrated significant upregulation in several metabolic pathways, indicating its involvement in metabolic changes related to fatty acid metabolism, pentose glucuronide interconversion, sterol biosynthesis, and sterol hormone biosynthesis. Within the fatty acid metabolism pathway, ADPC-enrichment exhibited increased activity in both fatty acid synthesis and degradation processes (Fig. 6B).Fig. 6 Changes in metabolism and steroid hormone synthesis in different types of prostate cancer. (A) Dot plot of main metabolic pathway in each epithelial cell types including ADPC and CRPC samples. (B) Box plot of main metabolic pathway in each epithelial cell types including ADPC and CRPC samples. (C) A schematic diagram of steroid hormone synthesis. (D) Box plot of expression of genes related to steroid hormone synthesis in each epithelial cell types.

Fig. 6

Cholesterol plays a crucial role as a precursor for androgen synthesis in prostate cancer. Androgens, such as testosterone and dihydrotestosterone (DHT), are essential for the growth and progression of prostate cancer (Fig. 6C). Abiraterone is a medication that has been developed to target and inhibit the activity of the key enzyme CYP17A, which is involved in the biosynthesis of androgens [34]. The analysis of steroid hormone biosynthesis genes in different prostate cancer subtypes provided valuable insights into the molecular characteristics and potential hormone-related mechanisms underlying the disease. 17-β-Hydroxysteroid dehydrogenase (HSD17B) is the last key enzyme for the synthesis of testosterone from cholesterol, several isoforms of which were significantly upregulated in ADPC-enrichment, including HSD17B4, HSD17B6, HSD17B10 and HSD17B11 (Fig. 6D). 5-alpha reductase (SRD5A) is a crucial enzyme involved in the conversion of testosterone to dihydrotestosterone (DHT). In the NE subtype, the isoform SRD5A1 of 5-alpha reductase was upregulated. On the other hand, in the ADPC-enrichment subtype, the isoform SRD5A3 of 5-alpha reductase was upregulated (Fig. 6D).

Regulatory network of transcription factors in prostate cancer

Transcription factors are key cellular components that control gene expression. They are responsible for controlling when and to what extent specific genes are turned on or off in response to various internal and external signals. The use of PySCENIC in our study allowed us to analyze the regulation of transcription factors in prostate cancer cells. In our analysis, we identified a total of 174 transcription factors that exhibited differential activity across the different cell types. We generated an expression heatmap based on the binary regulon area under the curve (AUC) values, encompassing all cells including ADPC samples and CRPC samples. By clustering the columns in the heatmap, we observed that the resulting clusters aligned well with the previous clustering based on cell marker (Fig. 7A). This indicated that cells within the same cluster exhibit similar transcription factor activities.Fig. 7 Regulatory signatures of transcription factors in different types of prostate cancer or prostate epithelial cells. (A) Heatmap of binary regulon AUC values of all epithelial cells analyzed by Pyscenic. (B) Heatmap of average regulon AUC values in each epithelial cell types. (C) t-SNE plot of different regulon AUC values in epithelial cells. (D) Dot plot of Regulon specificity score (RSS) in each epithelial cell types. (E) Regulatory network of transcription factors SREBF1 and SREBF2 and their target genes.

Fig. 7

Next, we calculated the average regulon AUC value for each cluster. In NE cells, PAX6, LHX2, EBF3, EZH2, and E2F1 exhibited the highest average activities among the transcription factors. Furthermore, NE cells also demonstrated high activity levels of other E2F family transcription factors (Fig. 7B, C, Table S5). PAX6 plays a crucial role in the development of various nervous systems, including the eyes, brain, and olfactory bulbs [35]. PAX6 has been implicated in regulating neuroendocrine marker expression and promoting neuroendocrine differentiation in various cancers. E2F1, as a transcription factor, assumes a critical role in the regulation of the cell cycle. It plays a key role in governing the transition from G1 to S phase and is involved in DNA repair mechanisms during the cell cycle [36]. Studies have shown that Pax6 is highly correlated with E2F1 function [37]. The Lhx2 gene serves as a pivotal transcription factor involved in the regulation of brain development. It is closely associated with olfactory signal transduction and the development of the nervous system [38]. Collectively, the high average activities of these transcription factors in NE cells highlighted their potential roles in driving the neuroendocrine phenotype, cellular plasticity, and proliferation-related processes associated with NEPC. Within the ADPC-enrichment cluster, we observed elevated activity of FOXA1, which aligns with previous reports and findings. FOXA1 is a pioneer transcription factor that plays a crucial role in the regulation of androgen receptor (AR) signaling and the development of the prostate gland. It is known to bind to enhancer regions and facilitate the recruitment of AR to its target genes, thereby promoting androgen-dependent gene expression and prostate cell differentiation [39]. Significantly, we noted a prominent activation of SREBPs (including SREBF1 and SREBF2), transcription factors closely associated with fatty acid metabolism and cholesterol metabolism, within the ADPC-enrichment cluster. SREBPs are key regulators of lipid synthesis and metabolism. Likewise, we observed heightened activity of PPARD (peroxisome proliferator-activated receptor delta), a transcription factor associated with fatty acid utilization, within the CRPC-enrichment cluster (Fig. 7B, C). PPARD is a nuclear receptor that regulates the expression of genes involved in fatty acid oxidation and energy metabolism. Activation of PPARD promotes the uptake, transport, and utilization of fatty acids as an energy source [40]. These results not only contribute to a deeper understanding of the molecular characteristics of prostate cancer subtypes but also provide potential therapeutic targets for intervention.

We then calculated the Regulon specificity score (RSS) for each cluster, allowing us to identify the specific transcription factors associated with each cluster. Interestingly, SREBF1 indeed showed higher RSS values in both the CRPC-enrichment and ADPC-enrichment clusters, with RSS scores of 0.25 and 0.21, respectively (corresponding to Z scores of 0.40 and 1.83). On the other hand, SREBF2 had RSS scores of 0.26 and 0.18 in the CRPC-enrichment and ADPC-enrichment clusters, respectively, with corresponding Z scores of 0.58 and 1.34 (Fig. 7D, Table S6). This hinted that SREBPs may also have higher specificity in CRPC-enrichment. We constructed a regulatory network mapping the interactions between SREBPs and their target genes (Fig. 7E). LDLR (Low-Density Lipoprotein Receptor), FASN (Fatty Acid Synthase), and ACLY (ATP Citrate Lyase) were identified as common target genes of both SREBPs. ACLY plays a pivotal role in transferring citric acid to lipid metabolism, while LDLR is associated with cholesterol uptake, and FASN is a crucial enzyme involved in fatty acid synthesis. HMGCR (3‑hydroxy-3-methylglutaryl-coenzyme A reductase) was a target gene of SREBF2 and is a key enzyme involved in cholesterol synthesis.

The SREBPs inhibitor Betulin holed potential for the treatment of prostate cancer

Given the significant involvement of SREBPs in lipid metabolism in prostate cancer, the findings suggested that targeting SREBPs could be an effective approach for the treatment of prostate cancer. Betulin, a naturally occurring compound derived from Betula pendula and Betula pubescens, has been identified as an inhibitor of SREBPs [41]. Its inhibitory effect on SREBPs suggests that Betulin may serve as a promising drug candidate for the treatment of prostate cancer. To evaluate the therapeutic potential of Betulin in prostate cancer treatment, we selected two cell lines, LNCaP and PC3, representing androgen-sensitive prostate cancer and androgen-independent prostate cancer, respectively. These cell lines were utilized to investigate the impact of Betulin on prostate cancer cells. In our study, we aimed to evaluate the inhibitory effect of Betulin on LNCaP and PC3 cell lines using the CCK-8 assay. We performed experiments at two time points, specifically 24 h and 48 h after treatment with Betulin. To determine the inhibitory effect, we prepared different concentration gradients of Betulin ranging from 1.67 μg/mL to 25.80 μg/mL. These concentrations were selected based on previous studies and preliminary experiments. From the dose-response curves, the half-maximal inhibitory concentration (IC50) of Betulin for LNCaP and PC3 cell lines at 24 h and 48 h was determined. At 24 h, the IC50 of Betulin for LNCaP cells was determined to be 4.921 μg/mL. For PC3 cells, the IC50 of Betulin at 24 h was found to be 2.936 μg/mL. At 48 h, the IC50 of Betulin for LNCaP cells was determined to be 7.347 μg/mL, while for PC3 cells, the IC50 was found to be 3.035 μg/mL (Fig. 8A). These findings suggest that both LNCaP and PC3 cells exhibit sensitivity to Betulin treatment, with PC3 cells showing slightly higher sensitivity at both 24 h and 48 h.Fig. 8 SREBPs inhibitor Betulin effectively inhibited Prostate Cancer in vitro. (A) CCK8 detected the inhibitory effect of different concentrations of Betulin on prostate cancer cell lines, acting for 24 h and 48 h respectively. (B) qRT-PCR of fatty acid and cholesterol metabolism relative genes and AR downstream genes of LNCaP cells after Betulin treatment (6 μg/mL, 48 h). (C) qRT-PCR of fatty acid and cholesterol metabolism relative genes of PC3 cells after Betulin treatment (6 μg/mL, 48 h). (D) Oil red O staining of cells: cells in the experimental group were treated with 6 μg/mL Betulin for 48 h, and counterstained with hematoxylin. (E) EdU proliferation assay to detect changes in cell proliferation after Betulin treatment (6 μg/mL, 48 h). (F) Effect of Betulin on Survival of Prostate Cancer Cells by Clonogenic Assay (6 μg/mL, 48 h). (G) Transwell migration assay to detect the effect of Betulin on the migration ability of prostate cancer cells (6 μg/mL, 48 h). Data was presented as Mean+SD. (t-test; ns, p ≥ 0.05; *, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001.).

Fig. 8

Subsequently, we performed qRT-PCR to validate the changes in the expression of SREBPs target genes, including genes related with fatty acid metabolism and cholesterol metabolism, and downstream genes regulated by AR, following the treatment of Betulin. In LNCaP cells, the expression of SREBF-1 and SREBF-2, which are their own direct targets [42], was inhibited by approximately 50 % and 40 %, respectively, following the treatment with Betulin. Furthermore, among the remaining nine genes associated with fatty acid metabolism and cholesterol metabolism, various degrees of down-regulation were observed. Notably, HMGCR, LDLR, ACLY, and FASN exhibited significant down-regulation in response to Betulin treatment. Simultaneously, we also assessed the expression changes of downstream genes regulated by AR, such as KLK3, TMPRSS2, NKX3–1, and KLK2. Remarkably, all these genes exhibited significant down-regulation. This observation suggests that the inhibition of SREBPs might lead to a reduction in the synthesis of endogenous steroid hormones (Fig. 8B). In the PC3 cell line, SREBF-2 displayed a more pronounced down-regulation compared to SREBF-1. Additionally, genes associated with cholesterol metabolism demonstrated a more substantial downregulation, including LDLR, HMGCR, HMGCS1, LSS, FDFT1, and DHCR77(the expression of AR downstream-related genes was not detected in PC3 cells) (Fig. 8C).

Given the notable downregulation of SREBP target genes by Betulin in prostate cancer cell lines, our next objective was to assess the impact of Betulin on lipid droplet formation in the cells. This was accomplished through Oil Red O staining, which enables the visualization of lipid droplets. In LNCaP cells, we observed a substantial accumulation of lipid droplets, indicating active lipid metabolism in these cells. However, after treatment with Betulin, we observed a significant reduction in the number and size of lipid droplets. This suggests that Betulin treatment effectively inhibits lipid droplet formation in LNCaP cells. On the other hand, PC3 cells naturally exhibited fewer lipid droplets compared to LNCaP cells. Nevertheless, similar to the observations in LNCaP cells, Betulin treatment significantly inhibited the formation of lipid droplets in PC3 cells (Fig. 8D, Figure S3A). In our study, we investigated the effect of Betulin on cell proliferation using the EdU (5-ethynyl-2′-deoxyuridine) assay. The results revealed that in both LNCaP and PC3 cells, treatment with Betulin led to a significant inhibition of cell proliferation. This observation suggested that Betulin exerts an anti-proliferative effect on prostate cancer cells (Fig. 8E, Figure S3B). In order to evaluate the effect of Betulin on the survival and clonogenic potential of prostate cancer cells, we performed a colony formation assay. Upon analysis, we observed that in both LNCaP and PC3 cells, the formation of single colonies was significantly impaired after Betulin treatment. This indicates that Betulin effectively suppresses the clonogenic potential of prostate cancer cells (Fig. 8F, Figure S3C). To investigate the effect of Betulin on the migration ability of prostate cancer cells, we conducted a transwell migration assay. In our study, we observed that both LNCaP and PC3 cells exhibited significantly reduced migration ability after Betulin treatment. The impaired migration ability suggests a potential role for Betulin in suppressing the metastatic potential of prostate cancer cells (Fig. 8G, Figure S3D). The results obtained from our study strongly suggest that SREBPs, as key regulators of lipid metabolism, hold promise as potential therapeutic targets in the treatment of prostate cancer. The significant downregulation of SREBP target genes by Betulin, along with the inhibition of lipid droplet accumulation, cell proliferation, and migration, provides compelling evidence for the therapeutic potential of targeting SREBPs in prostate cancer. However, further research is needed to elucidate the precise mechanisms of action underlying the effects of Betulin on SREBPs and to explore its therapeutic potential in preclinical and clinical settings.

Discussion

Prostate cancer is indeed a highly heterogeneous tumor, characterized by distinct subtypes with diverse biological behaviors [43]. Understanding the specific characteristics and behaviors of these subtypes is crucial for a comprehensive understanding of prostate cancer and for tailoring appropriate treatment strategies.

In our study, our primary objective was to characterize the differences between prostate benign tissue and prostate cancer, with a specific focus on various cell types present in these tissues. Among the different cell types, we recognized the significance of epithelial cells as the primary component of prostate cancer. Hence, we conducted an in-depth analysis to compare the characteristics of benign prostate epithelial cells with epithelial cells derived from samples obtained through radical prostatectomy (RP). In addition to cell morphology and the PAM50 signature, we further identified a distinct cluster of cells that lacked the aforementioned characteristics but exhibited high expression of prostate cancer markers such as PCA3, ERG and PCAT14. To gain insights into the differences between benign prostate tissue and prostate cancer, we conducted a differential analysis, primarily focusing on identifying changes that occur during the transition from benign to malignant states in the prostate. This analysis aimed to elucidate the molecular alterations associated with the progression of prostate cancer from a benign state. When comparing epithelial cells derived from benign samples to those obtained from radical prostatectomy (RP) samples, several notable differences were observed. Epithelial cells from RP samples displayed enhanced androgen response, activation of the MTORC1 pathway, increased fatty acid metabolism, heightened DNA repair mechanisms, upregulated PI3K-AKT pathway, and alterations in bile acid metabolism. Firstly, there was an augmented androgen response in the epithelial cells from RP samples, indicating increased sensitivity to androgen signaling, which is known to play a critical role in prostate cancer development and progression [5]. Additionally, we observed heightened activity in the MTORC1 (mammalian target of rapamycin complex 1) pathway, which regulates cellular growth, metabolism, and proliferation [44]. Furthermore, the epithelial cells from RP samples demonstrated enhanced fatty acid metabolism, highlighting the importance of lipid metabolism alterations in prostate cancer. DNA repair mechanisms were also found to be more active in the epithelial cells from RP samples, and the PI3K-AKT pathway, known for its role in promoting cell survival and growth [45], was also found to be more active in the epithelial cells from RP samples.

It is worth noting that the up regulation of pathways such as fatty acid metabolism and bile acid metabolism suggests changes in prostate cancer metabolism, which may be closely related to the occurrence and development of prostate cancer. Building upon this insight, we conducted further comparisons to explore metabolic changes between the two sample groups. As anticipated, luminal cells and PCA3+ cells demonstrated varying degrees of metabolic enhancement in the RP samples. Our observations indicated that luminal cells exhibited enhanced glycolysis/gluconeogenesis, whereas PCA3+ cells displayed increased activity in glycolysis/gluconeogenesis, fatty acid elongation, fatty acid synthesis, and sterol hormone anabolism. These findings suggested the possibility of targeting early metabolic alterations as a potential therapeutic strategy for prostate cancer.

In order to provide a more comprehensive characterization of the transition in prostate cancer, we incorporated CRPC samples that contained neuroendocrine (NE) cells into our study. Intriguingly, our findings suggest that metabolic changes in epithelial cells can potentially influence immune cells. Through GSVA enrichment analysis, we discovered that endothelial cells and fibroblasts showed a closer association with pathways enriched in epithelial cells, indicating a potential functional similarity between these cell types. Furthermore, T cells, macrophages, and monocytes exhibited similar expression patterns, suggesting that these immune cells may share common behavioral characteristics. Analysis of intercellular communication revealed that macrophages, being the most active class of immune cells in communication with epithelial cells, promoted prostate cancer development through receptor-ligand pairs. These findings are consistent with previous studies by Ming O Li et al., who investigated the mechanism of energy competition between tumor-associated macrophages (TAMs) and tumor cells in MYC-PyMT mice, highlighting the impact of energy competition on tumor immunity [46]. Additionally, Dali Han et al. demonstrated that macrophages, upon entering the tumor microenvironment, can be induced by the tumor to adopt a pro-tumor growth state and suppress T-cell function, which may contribute to the significant decrease in the proportion of T cells observed in CRPC samples [47]. In addition, previous studies have reported that macrophages can exert influence on the insulin receptor activity and glucose utilization of other cells through the release of exosomes [48], [49], [50]. Additionally, macrophages have been implicated in providing cholesterol and promoting castration resistance in prostate cancer cells through the activation of the LXR receptor [51]. In conclusion, the energetic competition between macrophages and epithelial cells may play a key role in the regulation of immune responses and tumor progression and may influence the overall immune landscape of the microenvironment.

We further delved into the activity of transcription factors in different subtypes of prostate cancer. Notably, SREBPs, a transcription factor closely associated with fatty acid metabolism and cholesterol metabolism [52], exhibited the highest average activity in the ADPC-enrichment cluster. Furthermore, RSS analysis revealed that SREBPs were highly specific to both the ADPC-enrichment and CRPC-enrichment clusters. These findings indicate that SREBPs hold significant potential as a target for prostate cancer treatment. The involvement of SREBPs in regulating metabolic pathways related to fatty acid and cholesterol metabolism highlights their importance in prostate cancer progression and suggests that targeting SREBPs could be a promising therapeutic approach for prostate cancer.

Research by Audet-Walsh's group revealed the AR control over multiple isocitrate dehydrogenase (IDH) isoforms, particularly upregulating IDH1 [53]. Blocking IDH1 genetically significantly impaired prostate cancer cell proliferation. Moreover, AR reprogramed prostate cancer cell metabolism by selectively inducing extramitochondrial IDH activity. They also revealed the specific mechanism of reprogramming of the truncated tricarboxylic acid (TCA) cycle in prostate cancer [54]. Giguère and Audet-Walsh et al. demonstrated that the AR and mTOR bind to the regulatory region of SREBF1, controlling its expression [55,56]. The dual activation of these signaling pathways also promoted SREBF1 cleavage and its translocation to the nucleus, providing new insights into metabolic reprogramming in prostate cancer via the AR/mTOR-SREBF1 axis. Additionally, Pandolfi et al. showed that PML is frequently co-deleted with PTEN in metastatic human prostate cancer, leading to MAPK reactivation, subsequent overactivation of the aberrant SREBP metastatic adipogenic program, and a unique lipidomic signature [57]. Targeting SREBP in vivo via Fatostatin effectively blocked tumor growth and distant metastasis.

We used the SREBPs inhibitor Betulin to verify the effect of SREBPs on the development of prostate cancer and its effect as a therapeutic drug. The results showed that Betulin has certain effects on inhibiting SREBPs, reducing lipid synthesis in prostate cancer, and affecting the downstream of AR. These results suggest that Betulin may be a very effective drug in the treatment of prostate cancer.

In this study, we have uncovered the metabolic alterations occurring during the progression of prostate cancer. Additionally, we have identified a natural small molecule inhibitor of SREBPs that exhibits a remarkable inhibitory effect on prostate cancer. Our findings suggest that targeting SREBPs could serve as a promising therapeutic strategy for prostate cancer treatment, with the potential for synergistic effects in combination with immunotherapy. Moving forward, our future research endeavors will delve deeper into the role of SREBPs in prostate cancer, encompassing both tumor development and immune response. Furthermore, we aim to comprehensively evaluate the impact of SREBPs and their targets on prostate cancer to gain a more comprehensive understanding of their significance and therapeutic potential in this disease.

Materials and methods

Data collection and preparation

Single-cell sequencing data from normal prostate samples and prostate cancer samples that underwent RP were obtained from the GEO database (GSE193337) [58]. Castration-resistant prostate cancer (CRPC) single-cell sequencing data including neuroendocrine prostate cancer (NEPC) were obtained from GSE137829 [23]. In the GSE193337 dataset we only selected the single-cell data part.

Single-cell analysis

To begin, we merged the benign samples and radical prostatectomy (RP) prostate cancer samples from the GSE193337 dataset. Subsequently, we conducted quality control measures to ensure the reliability and validity of the merged dataset. Quality control was performed using the Seurat package with the following criteria: (i) genes expressed in less than three cells were excluded; (ii) cells with gene expression counts below 201 (considered as low-quality cells) or exceeding 8000 genes (indicative of potential doublets). Additionally, cells with more than 20 % of unique molecular identifiers (UMIs) derived from the mitochondrial genome were also excluded. In addition to the previous quality control steps, we further excluded specific gene categories from the analysis, including mitochondrial genes, ribosomal genes, hemoglobin genes, and the MALAT1 gene. When merging prostate cancer samples from the GSE193337 dataset (those that underwent radical prostatectomy) with castration-resistant prostate cancer (CRPC) samples from the GSE137829 dataset, we utilized the Harmony package for data integration. This approach helped to harmonize the data and mitigate batch effects between the two datasets. The remaining steps of the analysis, including quality control and any subsequent analyses, were performed in a similar manner as previously described. Data normalization was carried out using the NormalizeData function in the Seurat package. The normalized data were subsequently pooled together through principal component analysis (PCA). The top 25 principal components (PCs) were visualized using t-distributed random neighborhood embedding (t-SNE) to provide a comprehensive representation of the data structure.

Feature selection and cell annotation

To annotate cell groups and identify conserved differentially expressed genes, we employed the FindConservedMarkers function in the Seurat package. We performed cluster annotation by leveraging marker genes derived from the literature. Following cluster annotation, we employed the FindAllMarkers function to identify differentially expressed genes between the various annotated cell groups.

Single-cell transcription factor analysis

PySCENIC was used to identify transcription factor status in epithelial/cancer cells [59]. In addition to PySCENIC, we utilized the SCENIC R package for visualizing selected results. Co-expression modules between transcription factors and candidate target genes were inferred based on co-expression using GRNBoost (Gradient Boosting). Each co-expression module was analyzed for cis-regulatory motifs using RcisTarget. To identify direct targets of transcription factors (TFs), we performed TF-motif enrichment analysis. Additionally, we scored the activity of each regulon in each cell using the AUCell algorithm. The binary regulon activity matrix provided information about the "on" or "off" status of each regulon in individual cells. Cytoscape was used for the visualization of transcription factors and target genes.

Cell–cell communication

To investigate cell-to-cell interactions, we utilized the cellphone DB tool for cell communication analysis [60]. Receptor-ligand showed the interaction between epithelial cells and other cells in the costimulatory gene family through dot plots. Chord diagrams were used to show the interactions between all cell types, epithelial/cancer cells, and macrophages.

Single-cell metabolism

To quantify single-cell metabolism, we utilized the scMetabolism Package [61]. The VISION algorithm was used to score each cell, and finally the activity score of the cell in each metabolic pathway was obtained. The gene set used was a preset KEGG pathway.

Enrichment analysis

We used gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) to perform functional enrichment analysis on different clusters, and the gene set was the Hallmark pathway obtained from MSigDB. The differential genes were obtained by the FindMarkers function (the parameters were set to logFC.threshold = 0, min.pct = 0 to obtain the differential results of all genes). Dot plots and box plots were used to show the activity of different metabolic pathways.

InferCNV

We used InferCNV to infer CNV variations in different subgroups and infer the proportion of potentially malignant cells. Analysis was performed using the infercnv R package, including all Epithelial cells (T cells as normal cell controls). When analyzing, cluster_by_groups=T and HMM=T. (inferCNV of the Trinity CTAT Project. https://github.com/broadinstitute/inferCNV).

Cell culture

All cells were purchased from Procell Life Science&Technology Co., Ltd. Cell lines were cultured in a humidified incubator at 37 °C and 5 % CO2 condition. The medium is RPMI-1640 medium (Procell, PM150110) supplemented with 10 % fetal bovine serum (Procell, 164,210–50).

Quantitative RT-PCR analysis

The expression of mRNA was detected with RT-PCR. RNA was isolated from cells using Trizol reagent (Invitrogen) and reverse transcribed using the Reverse Transcription System (Roche) according to manufacturer's instructions. After cDNA extraction, PCR was performed using the Applied Biosystems 7900 Real Time PCR System (Thermo Scientific) and SYBR Green PCR Master Mix (Roche) according to the manufacturer's instructions. β-actin was used as an internal control. All primers used are listed in Supplementary Table S7.

Oil red O staining

Sterile glass slides were placed in a six-well plate for cell culture. Following the designated treatment and culture period, the cells were fixed with 4 % paraformaldehyde for 15 min. Subsequently, the fixative was discarded, and the slides were washed twice with distilled water. To rinse the slides, 60 % isopropanol was added for a period of 10–20 s. The isopropanol was then discarded, and the slides were immersed in an Oil Red O dye solution for 30 min. The Oil Red O dyeing solution was prepared as follows: 0.3 g of Oil Red O powder was dissolved in 50 ml of isopropanol to create the Oil Red O preservation solution. The Oil Red O preservation solution was then mixed with distilled water at a 3:2 ratio to form the Oil Red O working solution. Prior to use, the working solution was filtered using a 0.22-micron microporous membrane. After the dye solution was discarded, the slides were soaked in 60 % isopropanol until the background was clear. The slides were washed 2–5 times to remove any excess dye solution. Finally, the slides were stained with hematoxylin for 2 min to visualize the nuclei and subsequently differentiated into an anti-blue color.

IC50 determination

A total of 5000 cells were seeded per well in a 96-well plate using a seeding volume of 100 microliters. After culturing for 24 h, drugs were added, and the medium was replaced when the cells adhered to the well walls and exhibited growth. Six replicate wells were set up for each drug concentration. Following a 24-hour and 48-hour incubation period after drug addition, 10 microliters of CCK8 working solution were added to each well and incubated for one hour. The reaction was terminated by adding 0.1 M HCL solution, and the optical density (OD) at 450 nm was measured using a microplate reader to obtain the data.

Edu proliferation assay

Sterile glass slides were placed in a six-well plate for cell culture. After the cells adhered to the well walls, drugs were added, or the medium was replaced accordingly. Following a 24-hour treatment, the medium was replaced with a medium containing 10uM Edu concentration for 1 hour. After the treatment period, the cells were washed with PBS and fixed with 4 % paraformaldehyde for 15 min. Following fixation, the cells were permeabilized with a 0.5 % Triton-100 PBS solution for 15 min. According to the reagent instructions, 1 mL of Click reaction solution was added to each well and incubated in the dark for 30 min. The dye solution was then discarded, and the cells were washed with PBS. DAPI was used to stain the cell nuclei for 2 min. After staining, the staining solution was discarded, and the cells were washed with PBS.

Clonogenic experiment

Cells in the logarithmic growth phase were evenly inoculated with 500 cells per well in a six-well plate. Once the cells adhered to the well walls, drugs were added, or the medium was replaced accordingly. The cells were then cultured until most single clone colonies reached a size larger than 50. After cloning, the cells were fixed with 4 % paraformaldehyde for 15 min. Following fixation, the cells were washed with PBS and stained with crystal violet staining solution for 15 min. The cells were then washed several times with PBS, allowed to dry, and photographed for documentation.

Transwell migration experiment

A total of 10,000 cells were added to each well of a 24-well Transwell culture chamber with an 8-micron pore size. The cells were resuspended in 100 microliters of serum-free medium. In the lower chamber, 500 microliters of complete medium were added. For the experimental group, treatment drugs were added to the complete medium. The appropriate time point for treatment varied based on the specific cell line used. After the designated treatment period, the Transwell chamber was fixed in 4 % paraformaldehyde for 15 min. Following fixation, the chamber was rinsed with PBS and stained with crystal violet for 15 min. After staining, excess crystal violet was rinsed off with PBS, and the upper layer of non-migrated cells was gently wiped off using a cotton swab.

Statistical analysis

Analyses between two groups were performed utilizing the Wilcoxon test. One-way ANOVA was used to compare three or more groups. Generally, statistical analyses were conducted by R studio (version 4.1.1) and GraphPad Software (version 8.0). The significance level was set at P < 0.05. When measuring cell viability by CCK8, each dose contained 5 replicates. Each set of qRT-PCR experiments contained at least 3 replicates. Oil Red O staining, EdU proliferation experiment, cell migration experiment, and cell colony formation experiment all contained 3 biological replicates.

CRediT authorship contribution statement

Guojiang Wei: Conceptualization, Data curation, Formal analysis, Investigation, Software, Validation, Writing – original draft, Writing – review & editing. Hongcai Zhu: Conceptualization, Investigation, Resources, Supervision. Yupeng Zhou: Investigation, Methodology, Supervision, Writing – original draft. Yang Pan: Project administration, Resources, Visualization. Bocun Yi: Methodology, Software. Yangkai Bai: Conceptualization, Data curation, Methodology, Resources, Supervision, Writing – review & editing.

Declaration of competing interest

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.

Appendix Supplementary materials

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Funding

This work has funded by the Key R&D Program of 10.13039/501100015401 Shaanxi Province (No. 2021SF-268 ).

Ethics approval and consent to participate

The data in this study were sourced from public databases and obtained local ethics approval.

Availability of data and material

All data are mentioned in the methods.

Acknowledgements

Not applicable.

Consent for publication

Not applicable.

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.tranon.2024.101925.
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