Abstract

摘要

Metabolic reprogramming in prostate cancer has been widely recognized as a promoter of tumor progression and treatment resistance. This study investigated its association with ferroptosis resistance in prostate cancer and explored its therapeutic potential. In this study, we identified differences in the epithelial characteristics between normal prostate tissue and tissues of various types of prostate cancer using single-cell sequencing.

前列腺癌中的代谢重编程已被广泛认为是肿瘤进展和治疗抵抗的促进因素。本研究调查了其与前列腺癌中铁死亡抵抗的关联，并探讨了其治疗潜力。在本研究中，我们使用单细胞测序鉴定了正常前列腺组织与各种类型前列腺癌组织之间的上皮特征差异。

Through transcription factor regulatory network analysis, we focused on the candidate transcription factor, SREBF1. We identified the differences in SREBF1 transcriptional activity and its association with ferroptosis, and further verified this association using hdWGCNA. We constructed a risk score based on SREBF1 target genes associated with the biochemical recurrence of prostate cancer by combining bulk RNA analysis.

通过转录因子调控网络分析，我们聚焦于候选转录因子SREBF1。我们确定了SREBF1转录活性的差异及其与铁死亡的关联，并使用hdWGCNA进一步验证了这一关联。我们通过结合bulk RNA分析，基于与前列腺癌生化复发相关的SREBF1靶基因构建了一个风险评分。

Finally, we verified the effects of the SREBPs inhibitor Betulin on the treatment of prostate cancer and its chemosensitization effect. We observed characteristic differences in fatty acid and cholesterol metabolism between normal prostate tissue and prostate cancer tissue, identifying high transcriptional activity of SREBF1 in prostate cancer tissue.

最后，我们验证了SREBPs抑制剂白桦脂醇对前列腺癌治疗的效果及其化疗增敏作用。我们观察到正常前列腺组织和前列腺癌组织在脂肪酸和胆固醇代谢方面存在特征性差异，并确定了前列腺癌组织中SREBF1的高转录活性。

This indicates that SREBF1 is crucial for the metabolic reprogramming of prostate cancer, and that its mediated metabolic changes promoted ferroptosis resistance in prostate cancer in multiple ways. SREBF1 target genes are associated with biochemical recurrence of prostate cancer. Finally, our experiments verified that SREBF1 inhibitors can significantly promote an increase in ROS, the decrease in GSH, and the decrease in mitochondrial membrane potential in prostate cancer cells and confirmed their chemosensitization effect in vivo.

这表明SREBF1对前列腺癌的代谢重编程至关重要，其介导的代谢变化通过多种方式促进了前列腺癌的铁死亡抵抗。SREBF1靶基因与前列腺癌的生化复发相关。最后，我们的实验验证了SREBF1抑制剂可以显著促进前列腺癌细胞中ROS的增加、GSH的减少以及线粒体膜电位的降低，并证实了它们在体内的化学增敏作用。

Our findings highlighted a close associati.

我们的研究结果强调了一个密切的关联。

Introduction

介绍

Prostate cancer is the most prevalent malignant tumor among men in Europe and the United States, ranking as the second leading cause of male cancer-related deaths [

前列腺癌是欧美男性中最普遍的恶性肿瘤，位列男性癌症相关死亡原因的第二位 [

1

1

]. This disease is highly heterogeneous, with its progression linked to multiple gene deletions or mutations, including FOXA1, ZNF292, CHD1, PTEN, and TP53 [

]. 这种疾病具有高度异质性，其进展与多个基因的缺失或突变有关，包括FOXA1、ZNF292、CHD1、PTEN和TP53 [

2

2

,

，

3

3

]. Prostate growth and development are dependent on androgens, and androgen deprivation therapy (ADT) remains the cornerstone of current prostate cancer treatment [

]. 前列腺的生长和发育依赖于雄激素，而雄激素剥夺疗法（ADT）仍然是当前前列腺癌治疗的基石 [

4

4

]. However, due to the complex heterogeneity of prostate cancer, the therapeutic efficacy of ADT can vary significantly, thereby affecting patient prognosis. Furthermore, nearly all patients eventually develop resistance to castration therapy after a certain treatment period [

]. 但是，由于前列腺癌的复杂异质性，ADT 的治疗效果可能会有很大差异，从而影响患者的预后。此外，几乎所有患者在经过一段时间的治疗后最终都会对去势治疗产生耐药性 [

5

5

]. The mechanisms underlying castration resistance are extremely complex and ultimately result in the failure of treatments targeting the androgen receptor (AR) signaling pathway. Therefore, there is an urgent need to identify novel therapeutic targets for prostate cancer.

]. 去势抵抗的机制非常复杂，最终导致针对雄激素受体（AR）信号通路的治疗失败。因此，迫切需要为前列腺癌确定新的治疗靶点。

Metabolic reprogramming is a distinct characteristic of prostate cancer. Normal prostate tissue exhibits unique metabolic features, notably the secretion of large amounts of citric acid, a semen component that is abundantly synthesized in prostate cells [

代谢重编程是前列腺癌的一个显著特征。正常前列腺组织表现出独特的代谢特性，特别是大量分泌柠檬酸，这是在前列腺细胞中大量合成的精液成分。

6

6

,

，

7

7

]. Citric acid serves as a crucial hub linking cell metabolism, particularly by mediating lipid metabolism through the amphibolic pathway. Consequently, the substantial secretion of citric acid from normal prostate tissues can be utilized to synthesize fatty acids and cholesterol during cancer development [.

]. 柠檬酸作为连接细胞代谢的关键枢纽，特别是通过两栖代谢途径介导脂质代谢。因此，在癌症发展过程中，正常前列腺组织分泌的大量柠檬酸可用于合成脂肪酸和胆固醇 [。

8

8

]. On one hand, the metabolic shift in prostate cancer enables cancer cells to adapt to their energy requirements, while simultaneously, the utilization of cholesterol for the synthesis of endogenous steroid hormones promotes the proliferation of prostate cancer cells [

]. 一方面，前列腺癌的代谢转变使癌细胞能够适应其能量需求，同时，利用胆固醇合成内源性类固醇激素促进了前列腺癌细胞的增殖 [

9

9

]. Consequently, exploring the metabolic vulnerabilities in prostate cancer may provide a direction for novel treatment strategies.

]. 因此，探索前列腺癌的代谢脆弱性可能为新的治疗策略提供方向。

Ferroptosis is a unique form of iron-dependent cell death characterized by the accumulation of excessive lipid peroxidation in the cell membrane [

铁死亡是一种独特的铁依赖性细胞死亡形式，其特征是细胞膜中过度的脂质过氧化积累 [

10

10

]. Resistance to ferroptosis not only facilitates tumor development but also contributes to tumor resistance to treatment [

]. 对铁死亡的抵抗不仅促进了肿瘤的发展，还导致了肿瘤对治疗的抵抗 [

11

11

]. Multiple mechanisms are involved in ferroptosis resistance in prostate cancer. Liang et al. first reported that AR induce ferroptosis resistance in prostate cancer by regulating the activity of MBOAT2 [

]. 前列腺癌中铁死亡抗性涉及多种机制。梁等人首次报道了AR通过调控MBOAT2的活性来诱导前列腺癌的铁死亡抗性 [

12

12

]. Yi et al. demonstrated that SREBPs activation mediated by the PI3K-AKT-mTOR pathway can promote ferroptosis resistance in prostate cancer [

]. 易等人证明了由PI3K-AKT-mTOR通路介导的SREBPs激活可以促进前列腺癌对铁死亡的抵抗性 [

13

13

]. However, the relationship between SREBPs-mediated metabolic reprogramming and ferroptosis resistance in prostate cancer remains unclear.

]. 然而，SREBPs介导的代谢重编程与前列腺癌中铁死亡抵抗之间的关系仍不清楚。

We confirmed the association between SREBF1-mediated metabolic reprogramming in prostate cancer and ferroptosis in human samples, using a combination of single-cell sequencing and Bulk-RNA analysis. Additionally, we validated SREBF1 as a potential therapeutic vulnerability and an effective target for prostate cancer by employing SREBF1 inhibitors..

我们通过结合单细胞测序和Bulk-RNA分析，在人类样本中证实了SREBF1介导的前列腺癌代谢重编程与铁死亡之间的关联。此外，我们通过使用SREBF1抑制剂验证了SREBF1作为前列腺癌潜在治疗脆弱性和有效靶点的作用。

Results

结果

Overview of single-cell sequencing characteristics of normal prostate tissue and different types of prostate cancers

正常前列腺组织和不同类型前列腺癌的单细胞测序特征概述

Our study included normal prostate tissue cells, prostate cancer cells from radical prostatectomy (RP) samples representing primary cancer, and prostate cancer cells from castration-resistant prostate cancer (CRPC) samples. Following quality control (QC), a total of 51,092 cells were included in the study.

我们的研究包括正常前列腺组织细胞、来自根治性前列腺切除术（RP）样本的原发癌细胞以及来自去势抵抗性前列腺癌（CRPC）样本的前列腺癌细胞。经过质量控制（QC），研究共纳入了51,092个细胞。

Subsequently, all cells were divided into 33 clusters by setting the resolution to 0.6. These clusters were annotated based on characteristic gene expression differences, identifying T cells, B cells, Macrophages, Endothelial cells, Fibroblasts, Mast cells, Monocytes, and Epithelial cells (Fig. .

随后，通过将分辨率设置为0.6，所有细胞被分为33个簇。这些簇基于特征基因表达差异进行注释，识别出T细胞、B细胞、巨噬细胞、内皮细胞、成纤维细胞、肥大细胞、单核细胞和上皮细胞（图。

1A

1A

). It is noteworthy that regardless of the sample origin, epithelial cells constituted the predominant cell type, accounting for 32.54%, 37.56%, and 59.37% of normal, primary cancer, and CRPC samples, respectively. Interestingly, in CRPC-derived samples, the proportion of T cells significantly decreased, accounting for only 10.39% of all cells, compared to 29.55% and 37.22% in normal and primary cancer samples, respectively (Fig.

）。值得注意的是，无论样本来源如何，上皮细胞都是主要的细胞类型，分别占正常样本、原发癌样本和去势抵抗性前列腺癌（CRPC）样本的32.54%、37.56%和59.37%。有趣的是，在CRPC来源的样本中，T细胞的比例显著下降，仅占所有细胞的10.39%，相比之下，正常样本和原发癌样本中这一比例分别为29.55%和37.22%（图。

.

。

1B, C

1B，C

). Differential gene expression analysis identified several highly expressed genes in epithelial cells, including KLK3, PRAC1, and TSPAN1. KLK3, which encodes the prostate-specific antigen (PSA), is specifically expressed in prostate tissues, and is widely used in prostate cancer screening. Prostate Cancer Susceptibility Candidate Protein 1(PRAC1) is highly expressed in the prostate [.

差异基因表达分析鉴定出上皮细胞中几个高表达的基因，包括KLK3、PRAC1和TSPAN1。KLK3编码前列腺特异性抗原（PSA），特异性表达于前列腺组织，并广泛用于前列腺癌筛查。前列腺癌易感候选蛋白1（PRAC1）在前列腺中高度表达[。

14

14

]. TSPAN1 is upregulated in various cancers and is regulated by androgens, promoting the proliferation and migration of prostate cancer [

]. TSPAN1 在多种癌症中上调，并受雄激素调控，促进前列腺癌的增殖和迁移 [

15

15

] (Fig.

]（图。

1D

1天

). Furthermore, we identified characteristic differences between different clusters through Gene Set Variation Analysis (GSVA) of the average expression levels. Epithelial cells exhibited higher activity in pathways, such as the PI3K-AKT pathway, P53 pathway, MYC, androgen response, glycolysis, fatty acid metabolism, and bile acid metabolism.

）。此外，我们通过平均表达水平的基因集变异分析（GSVA）识别了不同簇之间的特征差异。上皮细胞在诸如PI3K-AKT通路、P53通路、MYC、雄激素反应、糖酵解、脂肪酸代谢和胆汁酸代谢等通路中表现出更高的活性。

T cells showed high activity in the interferon, IL-2, and IL-6 signaling pathways, whereas fibroblasts exhibited uniquely high activity in the epithelial-mesenchymal transition pathway (Fig. .

T细胞在干扰素、IL-2和IL-6信号通路中表现出高活性，而纤维母细胞在上皮-间质转化通路中表现出独特的高活性（图。

1E

1E

).

）。

Fig. 1: Overview of single-cell RNA sequencing characteristics of prostate cancer.

图1：前列腺癌单细胞RNA测序特征概述。

A

A

tSNE plot showing cell clusters after dimensionality reduction and cell type annotation following quality control.

tSNE图显示了在降维和质量控制后的细胞簇及细胞类型注释。

B

B

tSNE plot displaying facet diagrams from Normal, Primary cancer, and CRPC samples.

tSNE 图显示了正常样本、原发癌样本和去势抵抗性前列腺癌（CRPC）样本的分面图。

C

C

Distribution of different cell types in Normal, primary cancer, and CRPC samples.

正常样本、原发癌和去势抵抗性前列腺癌（CRPC）样本中不同细胞类型的分布。

D

D

Heatmap of the top 10 differentially expressed genes for each cell type.

每种细胞类型中排名前十的差异表达基因的热图。

E

E

Heatmap of Gene Set Variation Analysis (GSVA) based on the average gene expression for each cell type. Gene set: Hallmark from MSigDB.

基于每种细胞类型平均基因表达的基因集变异分析 (GSVA) 热图。基因集：来自 MSigDB 的 Hallmark。

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Characteristics of different types of epithelial cells

不同类型的上皮细胞的特征

Given the lipid-related metabolic signatures observed in epithelial cells, we further analyzed the epithelial cells (Fig.

鉴于在上皮细胞中观察到的脂质相关代谢特征，我们进一步分析了上皮细胞（图。

2B

2B

). Citrate, as a hub linking glycolysis and lipid metabolism, including fatty acid and cholesterol synthesis, plays an important role in prostate cancer (Fig.

). 柠檬酸作为连接糖酵解和脂质代谢（包括脂肪酸和胆固醇合成）的枢纽，在前列腺癌中发挥重要作用（图。

2A

2A

). Through gene differential analysis, we identified significant gene expression differences in epithelial cells from normal, primary cancer, and CRPC samples. In the primary cancer samples, the expression of PCA3, ERG, NPY, and AMACR was significantly upregulated, making them the characteristic genes with the largest expression differences.

通过基因差异分析，我们发现正常组织、原发性癌症和去势抵抗性前列腺癌（CRPC）样本中的上皮细胞存在显著的基因表达差异。在原发性癌症样本中，PCA3、ERG、NPY 和 AMACR 的表达显著上调，成为表达差异最大的特征基因。

PCA3, a long non-coding RNA (lncRNA), is highly expressed specifically in prostate cancer and has been used for urine detection of prostate cancer [.

PCA3是一种长链非编码RNA（lncRNA），在前列腺癌中特异性高表达，已被用于前列腺癌的尿液检测。

16

16

]. The ERG gene serves as a prostate cancer marker, and the ERG-TMPRSS gene fusion is one of the most common gene rearrangements in prostate cancer [

]. ERG基因可作为前列腺癌的标志物，而ERG-TMPRSS基因融合是前列腺癌中最常见的基因重排之一 [

17

17

]. AMACR holds significant value in the pathological diagnosis of prostate cancer, serving as a characteristic marker, and its expression is closely related to the fatty acid metabolism in prostate cancer [

]. AMACR在前列腺癌的病理诊断中具有重要价值，是特征性标志物，其表达与前列腺癌的脂肪酸代谢密切相关 [

18

18

]. ATP-related genes were significantly upregulated in the CRPC cells (Fig.

与ATP相关的基因在CRPC细胞中显著上调（图。

2C

2C

). Subsequently, we observed significant differences in the characteristics of the epithelial cells derived from these three sources using GSVA. In normal samples, some inflammation-related pathways showed higher activity, whereas epithelial cells derived from primary cancer samples mainly exhibited higher activity in androgen response, fatty acid metabolism, cholesterol metabolism, and other pathways.

随后，我们使用GSVA观察到来源于这三种组织的上皮细胞特性存在显著差异。在正常样本中，一些炎症相关通路表现出较高的活性，而来源于原发癌样本的上皮细胞主要在雄激素反应、脂肪酸代谢、胆固醇代谢等通路表现出较高的活性。

Epithelial cells derived from the CRPC samples were primarily concentrated in the E2F, MYC, and DNA repair pathways. Epithelial cells derived from primary cancer and CRPC samples showed higher activity in the glycolysis and MTORC1 pathways (Fig. .

来自CRPC样本的上皮细胞主要集中在E2F、MYC和DNA修复通路。来自原发癌和CRPC样本的上皮细胞在糖酵解和MTORC1通路中表现出更高的活性（图。

2D

二维

). Overexpression of ACLY, a key gene in the flow of citric acid to lipid metabolism, was observed in epithelial cells derived from tumor samples. The average expression levels in primary cancer samples were 0.6657, while in CRPC samples, it was 0.3280, and in normal samples, it was only 0.1954. FASN, a key gene in mediating fatty acid metabolism, also exhibited high expression levels in primary cancer samples and CRPC samples, with values of 0.6295 and 0.6141, respectively, compared to 0.1640 in normal samples.

在从肿瘤样本中提取的上皮细胞中观察到，ACLY（柠檬酸流向脂质代谢的关键基因）过表达。原发癌症样本中的平均表达水平为0.6657，而CRPC样本中为0.3280，正常样本中仅为0.1954。FASN（介导脂肪酸代谢的关键基因）在原发癌症样本和CRPC样本中也表现出高表达水平，分别为0.6295和0.6141，而正常样本中为0.1640。

SCD, which mediates the formation of monounsaturated fatty acids (MUFA) and is related to ferroptosis resistance, showed expression levels of 0.7122, 0.2897, and 0.1811 in primary cancer, CRPC, and normal samples, respectively (Fig. .

SCD介导单不饱和脂肪酸（MUFA）的形成，并与铁死亡抵抗性相关，在原发癌、CRPC和正常样本中的表达水平分别为0.7122、0.2897和0.1811（图。

2E

2E

).

）。

Fig. 2: Characteristic differences in epithelial cells of prostate tissue and different types of prostate cancer.

图2：前列腺组织上皮细胞及不同类型前列腺癌的特征差异。

A

A

Schematic diagram of intracellular energy metabolism, with highlighted products and enzymes.

细胞内能量代谢的示意图，重点标注了产物和酶。

B

B

tSNE plot of epithelial cells, colored by sample types: Normal, Primary cancer, and CRPC.

上皮细胞的tSNE图，按样本类型着色：正常、原发癌和去势抵抗性前列腺癌（CRPC）。

C

C

Differential gene analysis in epithelial cells from Normal, Primary cancer, and CRPC samples, showing genes with the largest average log fold change (LogFC).

对来自正常样本、原发癌和去势抵抗性前列腺癌（CRPC）样本的上皮细胞进行差异基因分析，显示平均对数倍变化（LogFC）最大的基因。

D

D

Heatmap displaying the average gene set variation analysis (GSVA) scores in epithelial cells from Normal, Primary cancer, and CRPC samples. The gene set used is Hallmark from MSigDB.

热图显示了正常、原发癌和去势抵抗性前列腺癌（CRPC）样本中上皮细胞的平均基因集变异分析（GSVA）得分。使用的基因集是来自MSigDB的Hallmark。

E

E

Violin plot showing the expression levels of ACLY, FASN, and SCD genes in epithelial cells from Normal, Primary cancer, and CRPC samples.

小提琴图显示了正常样本、原发癌和去势抵抗性前列腺癌（CRPC）样本中上皮细胞内ACLY、FASN和SCD基因的表达水平。

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Transcription factor regulatory network of prostate epithelial cells

前列腺上皮细胞的转录因子调控网络

We analyzed the transcription factor activity in epithelial cells using SCENIC, resulting in the identification of 219 transcription factors. Among these, 30 transcription factors exhibited a higher activity based on an RSS value greater than 0.2 and a Z value greater than 1.4. These included SREBF1, SREBF2, and FOXA1 (Table .

我们使用SCENIC分析了上皮细胞中的转录因子活性，结果鉴定出219个转录因子。其中，30个转录因子基于RSS值大于0.2和Z值大于1.4表现出较高的活性，这些因子包括SREBF1、SREBF2和FOXA1（表。

S1

S1

). The average area under the curve (AUC) of the regulons in each group was calculated. The activities of SREBF1 and FOXA1 were higher in the primary cancer samples, while the activities of FOXC1 and TP73 were higher in normal samples, and the activities of transcription factors such as FOXA3 were higher in the CRPC samples (Fig.

）。计算每组中调节子的平均曲线下面积（AUC）。SREBF1和FOXA1在原发癌样本中的活性较高，而FOXC1和TP73在正常样本中的活性较高，而FOXA3等转录因子的活性在CRPC样本中较高（图。

.

。

3A

3A

). The RSS values of SREBF1 were higher in primary cancer and CRPC samples, with values of 0.32 and 0.40, respectively, while the value was only 0.21 in the Normal sample (Fig.

）。SREBF1 的 RSS 值在原发性癌症和去势抵抗性前列腺癌（CRPC）样本中较高，分别为 0.32 和 0.40，而在正常样本中该值仅为 0.21（图。

3B

3B

). We visualized the target genes of SREBF1, including HMGCS1, DHCR7, SC5D, SCD1, ACLY, FASN, and LDLR. (Fig.

)。我们可视化了SREBF1的目标基因，包括HMGCS1、DHCR7、SC5D、SCD1、ACLY、FASN和LDLR。（图。

3C

3C

and Table

和表格

S2

S2

). Furthermore, the transcriptional activity of SREBF1 was higher in primary cancer samples (Fig.

). 此外，SREBF1 在原发癌样本中的转录活性较高（图。

3

3

D, E).

D，E）。

Fig. 3: Analysis of transcription factors in prostate cancer epithelial cells.

图3：前列腺癌上皮细胞中转录因子的分析。

A

A

Heatmap showing the average Area Under the Curve (AUC) values of 30 transcription factors.

显示30个转录因子的平均曲线下面积（AUC）值的热图。

B

B

RSS plot of 30 transcription factors. Color represents the Z value, and the size of the circle represents the RSS value.

30个转录因子的RSS图。颜色代表Z值，圆圈的大小代表RSS值。

C

C

Visualization of SREBF1 and its target genes.

SREBF1及其靶基因的可视化。

D

D

Violin plot showing the AUC values of SREBF1 in epithelial cells from Normal, Primary cancer, and CRPC samples.

小提琴图展示了SREBF1在正常、原发癌和去势抵抗性前列腺癌（CRPC）样本的上皮细胞中的AUC值。

E

E

tSNE plot displaying the expression of SREBF1 in epithelial cells from Normal, Primary cancer, and CRPC samples. Color represents the AUC value.

tSNE图显示了正常样本、原发癌和去势抵抗性前列腺癌（CRPC）样本中上皮细胞SREBF1的表达。颜色代表AUC值。

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Characteristic differences based on SREBF1 transcriptional activity grouping

基于SREBF1转录活性分组的特征差异

We divided all cells into SREBF1-positive and SREBF1-negative groups based on the binary regulon AUC matrix in SCENIC, and analyzed the differences between the two groups (Fig.

我们根据SCENIC中的二元调控子AUC矩阵，将所有细胞分为SREBF1阳性和SREBF1阴性两组，并分析了这两组之间的差异（图。

4A

4A

). Notably, “positive” and “negative” mentioned above do not represent the presence or absence of SREBF1 activity, but only the classification of the binary results of regulon AUC, representing the level of SREBF1 activity in cells. We conducted GSVA on the two groups and found significant differences in the pathways related to cholesterol and fatty acid metabolism.

）。值得注意的是，上述“阳性”和“阴性”并不代表SREBF1活性的有无，仅代表调控子AUC二元结果的分类，反映细胞中SREBF1活性的水平。我们对这两组进行了GSVA分析，发现胆固醇和脂肪酸代谢相关通路存在显著差异。

Among them, the “Cholesterol Metabolism with Bloch and Kandutsch-Russell Pathways” showed the most obvious enrichment difference (t Value: 60.78), while in fatty acid metabolism, “Omega9 Fatty Acid Synthesis” ranked second (t Value: 55.68). Both the Bloch and Kandutsch-Russell pathways are involved in cholesterol synthesis.

其中，“Bloch 和 Kandutsch-Russell 途径的胆固醇代谢”显示出最显著的富集差异（t 值：60.78），而在脂肪酸代谢中，“Omega9 脂肪酸合成”排名第二（t 值：55.68）。Bloch 和 Kandutsch-Russell 途径均参与胆固醇的合成。

Omega 9 Fatty Acid (MUFA) Synthesis is associated with anti-ferroptosis and is mainly regulated by SCD. Other pathways, such as the “Mevalonate Arm of Cholesterol Biosynthesis Pathway” (t value: 30.43) and “Mevalonate Pathway” (t Value: 27.68), also showed significant differences. Cholesterol can synthesize endogenous androgens to promote prostate cancer cell proliferation, and high activity of the Mevalonate (MVA) pathway is associated with resistance to ferroptosis (Fig.

Omega-9脂肪酸（MUFA）的合成与抗铁死亡有关，主要由SCD调控。其他通路，如“胆固醇生物合成途径的甲羟戊酸分支”（t值：30.43）和“甲羟戊酸途径”（t值：27.68），也显示出显著差异。胆固醇可以合成内源性雄激素以促进前列腺癌细胞增殖，并且甲羟戊酸（MVA）途径的高活性与对铁死亡的抵抗性相关（图）。

.

。

4

4

B, D). We compared differences in the expression of some SREBF1 targeted genes related to ferroptosis between the two groups. SCD, FASN, and ACLY were significantly upregulated in SREBF1-positive cells, while cholesterol-related LDLR and DHCR7 were also significantly up-regulated in SREBF1-positive cells.

B、D）。我们比较了两组之间一些与铁死亡相关的SREBF1靶向基因表达的差异。SCD、FASN和ACLY在SREBF1阳性细胞中显著上调，而与胆固醇相关的LDLR和DHCR7也在SREBF1阳性细胞中显著上调。

Additionally, we observed the up-regulation of AR and MBOAT2, which are involved in the anti-ferroptosis mechanism of MBOAT2, in SREBF1-positive cells (Fig. .

此外，我们观察到AR和MBOAT2在SREBF1阳性细胞中上调，这两种基因参与了MBOAT2的抗铁死亡机制（图。

4C

4C

).

）。

Fig. 4: Differences between the two groups SREBF1_POS and SREBF1_NEG.

图4：SREBF1_POS组和SREBF1_NEG组之间的差异。

A

A

Volcano plot showing the differential gene expression analysis between SREBF1_POS and SREBF1_NEG groups.

火山图展示了SREBF1_POS和SREBF1_NEG组之间的差异基因表达分析。

B

B

Gene Set Variation Analysis (GSVA) comparing the SREBF1_POS and SREBF1_NEG groups. The gene set used is CP:WIKIPATHWAYS from MSigDB.

使用MSigDB中的CP:WIKIPATHWAYS基因集进行SREBF1_POS和SREBF1_NEG组之间的基因集变异分析（GSVA）。

C

C

Violin plot displaying the expression levels of selected genes between SREBF1_POS and SREBF1_NEG groups.

小提琴图显示了SREBF1_POS和SREBF1_NEG组之间选定基因的表达水平。

D

D

Pathway map of ferroptosis and lipid metabolism.

铁死亡和脂质代谢的通路图。

E

E

Schematic diagram of cholesterol synthesis, from acetyl-CoA to cholesterol.

胆固醇合成的示意图，从乙酰辅酶A到胆固醇。

F

F

Dot plot showing the expression levels of cholesterol synthesis-related genes between SREBF1_POS and SREBF1_NEG groups.

显示SREBF1_POS和SREBF1_NEG组之间胆固醇合成相关基因表达水平的点图。

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Furthermore, we focused on the gene expression differences in the cholesterol synthesis pathway, from acetyl-CoA to cholesterol (Fig.

此外，我们重点关注了从乙酰辅酶A到胆固醇的胆固醇合成途径中的基因表达差异（图。

4E

4E

). Almost all genes were highly expressed in the SREBF1-positive group, with a higher expression ratio. Only PMVK and FDPS showed higher average expression levels in the SREBF1-negative cells, but their expression percentages were lower. This indicated that the cholesterol synthesis pathway was highly activated in the SREBF1-positive group, with an average expression percentage of genes in 7-dehydrocholesterol (7-DHC) synthesis, including EBP (81.05%), SC5D (95.72%), and MSMO1 (73.30%), which was significantly higher than that of DHCR7 (54.94%) (Fig.

）。几乎所有基因在SREBF1阳性组中均高表达，且表达比例较高。仅PMVK和FDPS在SREBF1阴性细胞中显示出较高的平均表达水平，但其表达百分比较低。这表明胆固醇合成通路在SREBF1阳性组中被高度激活，7-脱氢胆固醇（7-DHC）合成相关基因的平均表达百分比显著升高，包括EBP（81.05%）、SC5D（95.72%）和MSMO1（73.30%），明显高于DHCR7（54.94%）（图。

.

。

4F

4楼

).

）。

Single-cell sequencing hdWGCNA identifies modules associated with SREBF1 and their characteristics

单细胞测序hdWGCNA识别与SREBF1相关的模块及其特征

We performed hdWGCNA analysis of epithelial cell subsets. First, the topological overlap matrix (TOM) was calculated by selecting an optimal soft power of 10 (Figs.

我们对上皮细胞亚群进行了hdWGCNA分析。首先，通过选择最佳的软阈值10来计算拓扑重叠矩阵（TOM）（图。

5A

5A

and

和

S1A

S1A

). The eigenvalues between each module were then calculated for correlation analyses between modules (Fig.

). 然后计算每个模块之间的特征值，用于模块间的相关性分析（图。

5B

5B

) and between modules and traits. Highly connected genes within each module were determined by calculating the eigengene-based connectivity, kME (Fig.

）以及模块与性状之间。每个模块内高度连接的基因通过计算基于特征基因的连通性 kME 来确定（图。

5E

5E

). We performed a correlation analysis between the modules and single-cell sequencing data features. Single-cell data features were the activity scores (AUCell scores) of the transcription factors identified by SCENIC in each cell (Fig.

我们对模块与单细胞测序数据特征进行了相关性分析。单细胞数据特征是每个细胞中由SCENIC识别的转录因子的活性评分（AUCell评分）（图。

5C

5C

). Among them, the module with the strongest correlation with SREBF1 activity was epithelial cells-M1, with correlation coefficients of 0.91, 0.84, and 0.39 in the Normal, Primary cancer, and CRPC groups, respectively (Figs.

）。其中，与SREBF1活性相关性最强的模块是上皮细胞-M1，在正常组、原发癌组和CRPC组中的相关系数分别为0.91、0.84和0.39（图。

5C

5C

and

和

S1B, C, D

S1B，C，D

). The hub genes of Epithelial cells-M1 are shown in Fig.

）。上皮细胞-M1的枢纽基因如图所示。

5D

5D

. KEGG pathway enrichment analysis of the hub genes in Epithelial cells-M1 showed significant enrichment in ferroptosis and fatty acid metabolism-related pathways (Fig.

Epithelial cells-M1中枢纽基因的KEGG通路富集分析显示在铁死亡和脂肪酸代谢相关通路中有显著富集（图。

5F

5楼

).

）。

Fig. 5: The relationship between SREBF1 and ferroptosis revealed by hdWGCNA.

图5：通过hdWGCNA揭示的SREBF1与铁死亡之间的关系。

A

A

Dendrogram of hdWGCNA in prostate cancer epithelial cells.

前列腺癌上皮细胞中hdWGCNA的树状图。

B

B

Correlation diagram between modules identified by hdWGCNA.

通过hdWGCNA识别的模块之间的相关性图。

C

C

Correlation analysis between the modules identified by hdWGCNA and the transcription factor activities identified by SCENIC.

hdWGCNA识别的模块与SCENIC识别的转录因子活性之间的相关性分析。

D

D

Top 40 hub genes of Epithelial cells-M1 module.

上皮细胞-M1模块的前40个枢纽基因。

E

E

Hubgenes of all modules identified by hdWGCNA, ranked by kME.

通过hdWGCNA识别的所有模块的中心基因，按kME排名。

F

F

KEGG pathway enrichment analysis of hub genes in the Epithelial cells-M1 module.

上皮细胞-M1模块中枢纽基因的KEGG通路富集分析。

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Construction of a biochemical recurrence risk score for prostate cancer based on SREBF1 target genes combined with bulk RNA seq analysis

基于SREBF1靶基因结合bulk RNA seq分析构建前列腺癌生化复发风险评分

We performed a correlation analysis between all genes in the TCGA prostate cancer samples and SREBF1, and the results are shown in Fig.

我们对TCGA前列腺癌样本中的所有基因与SREBF1进行了相关性分析，结果如图所示。

6A

6A

. Among these, SCD1 showed the strongest correlation with SREBF1, with a correlation coefficient of 0.77. SCD-mediated MUFA synthesis is associated with anti-ferroptosis. Additionally, some genes directly related to cholesterol synthesis also have a high correlation, such as IDI1, LSS, and DHCR24. SREBF2 and SREBF1, both parts of the SREBPs family related to cholesterol metabolism, are often discussed together.

其中，SCD1与SREBF1的相关性最强，相关系数为0.77。SCD介导的MUFA合成与抗铁死亡有关。此外，一些直接参与胆固醇合成的基因也有较高的相关性，例如IDI1、LSS和DHCR24。SREBF2和SREBF1同属于与胆固醇代谢相关的SREBPs家族，常被共同讨论。

ACAT2, HMGCS1, and HMGCR, key genes related to the MVK pathway in cholesterol synthesis, were highly correlated with SREBF1 (Fig. .

ACAT2、HMGCS1 和 HMGCR 是胆固醇合成中与 MVK 途径相关的关键基因，与 SREBF1 高度相关（图 。

6A

6A

and Table

和表格

S3

S3

). Additionally, we used GSVA to analyze the activity of related gene sets in prostate cancer samples. Prostate cancer samples were divided into two groups based on the expression level of SREBF1. The GSVA score results showed that in the SREBF1 high expression group, the omega-9 fatty acid and the cholesterol synthesis pathways had higher activity (Fig.

此外，我们使用GSVA分析了前列腺癌样本中相关基因集的活性。根据SREBF1的表达水平将前列腺癌样本分为两组。GSVA得分结果显示，在SREBF1高表达组中，ω-9脂肪酸和胆固醇合成途径的活性较高（图。

.

。

6B

6B

).

）。

Fig. 6: Bulk-RNA seq analysis based on SREBF1 and its target genes and risk scoring model associated with biochemical recurrence of prostate cancer.

图6：基于SREBF1及其靶基因的Bulk-RNA seq分析和与前列腺癌生化复发相关的风险评分模型。

A

A

Histogram of correlation analysis between all genes and SREBF1 expression in TCGA-PRAD.

TCGA-PRAD中所有基因与SREBF1表达相关性分析的直方图。

B

B

Violin plot of gene sets GSVA scores.

基因集GSVA得分的小提琴图。

C

C

Risk forest plot of genes associated with biochemical recurrence of prostate cancer obtained by univariate Cox regression analysis.

通过单变量Cox回归分析获得的与前列腺癌生化复发相关的基因的风险森林图。

D

D

Coefficient path diagram of genes in Lasso regression analysis.

Lasso回归分析中基因的系数路径图。

E

E

Cross-validation curve in Lasso regression analysis, nfolds = 10.

Lasso回归分析中的交叉验证曲线，nfolds = 10。

F

F

Distribution of risk scores and biochemical recurrence characteristics of samples in TCGA cohort.

TCGA队列中样本的风险评分和生化复发特征的分布。

G

G

Distribution of risk scores and biochemical recurrence characteristics of samples in GSE116918 cohort.

GSE116918队列中样本的风险评分分布和生化复发特征。

H

H

KM curve of biochemical recurrence based on risk score grouping in the TCGA cohort.

基于TCGA队列中风险评分分组的生化复发KM曲线。

I

我

KM curve of biochemical recurrence based on risk score grouping in the GSE116918 cohort.

基于GSE116918队列中风险评分分组的生化复发KM曲线。

J

J

ROC curve of the risk score group-based prediction model for biochemical recurrence in the TCGA cohort.

TCGA队列中基于风险评分组的生化复发预测模型的ROC曲线。

K

K

Multivariate Cox regression analysis of risk scores and clinical data associated with biochemical recurrence.

多变量Cox回归分析与生化复发相关的风险评分和临床数据。

L

L

Nomogram based on risk score and clinical data associated with biochemical recurrence. BCR biochemical recurrence, p_T pathological T stage, ROC Curve receiver operating characteristic curve, AUC area under curve, KM Curve Kaplan–Meier curve.

基于风险评分和临床数据与生化复发相关的列线图。BCR生化复发，p_T病理T分期，ROC曲线受试者工作特征曲线，AUC曲线下面积，KM曲线Kaplan-Meier曲线。

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全尺寸图像

Subsequently, we used the SREBF1 target gene to construct a risk score for the biochemical recurrence (BCR) of prostate cancer. First, a total of 48 genes associated with the biochemical recurrence of prostate cancer were screened using univariate Cox regression (Fig.

随后，我们使用SREBF1靶基因构建了前列腺癌生化复发（BCR）的风险评分。首先，通过单变量Cox回归筛选出与前列腺癌生化复发相关的共48个基因（图。

6C

6C

). Sixteen target genes associated with biochemical recurrence of prostate cancer were further screened out by Lasso regression, including MAN1C1, CTBS, LAMC1, DEGS1, TRNT1, DHX30, FSTL1, EIF4G1, FAM50B, GRB10, SPTBN2, NADSYN1, SYTL2, NAGLU, SERPINB5, and PRDM15 (Fig.

). 通过Lasso回归进一步筛选出与前列腺癌生化复发相关的16个目标基因，包括MAN1C1、CTBS、LAMC1、DEGS1、TRNT1、DHX30、FSTL1、EIF4G1、FAM50B、GRB10、SPTBN2、NADSYN1、SYTL2、NAGLU、SERPINB5和PRDM15（图。

6D, E

6D，E

). In the univariate Cox analysis of these 16 genes associated with biochemical recurrence of prostate cancer, only three genes had a risk-reducing effect (Fig.

）。在与前列腺癌生化复发相关的这16个基因的单变量Cox分析中，只有三个基因具有降低风险的作用（图。

S2

S2

). Subsequently, a multivariate Cox regression analysis based on these 16 genes was used to construct a risk score (Fig.

)。随后，基于这16个基因的多变量Cox回归分析被用来构建风险评分（图。

6F

6楼

). The KM survival curve showed that the high-risk and low-risk groups, based on the median risk score of the TCGA cohort (0.9230274), had significant differences in BCR survival analysis, with the high-risk group having a higher risk of biochemical recurrence (Fig.

). KM生存曲线显示，基于TCGA队列的中位风险评分（0.9230274）划分的高风险组和低风险组在BCR生存分析中存在显著差异，高风险组的生化复发风险更高（图。

6H

6小时

). We also validated the model using an external cohort (GSE116918), which showed that the high-risk group had a higher risk of biochemical recurrence (Fig.

）。我们还使用外部队列（GSE116918）验证了该模型，结果显示高风险组的生化复发风险更高（图。

6G, I

6G，我

). The ROC curve demonstrated the model efficiency of the risk score grouping based on TCGA cohort associated with BCR risk, with AUC values of 0.7843, 0.8153, and 0.8643 at one year, three years, and five years, respectively (Fig.

）。ROC曲线展示了基于TCGA队列的风险评分分组与BCR风险相关的模型效率，其AUC值在一年、三年和五年分别为0.7843、0.8153和0.8643（图。

6J

6J

). We included clinical indicators, such as age, Gleason grouping (≤7 points for Gleason low-risk group), and pathological T stage, to compare their relationship with BCR risk with the risk grouping we constructed in multivariate Cox analysis. The T3 stage in pathological T stage was associated with increased BCR risk (HR = 3.65, .

我们将临床指标（如年龄、格里森评分组（格里森低风险组≤7分）和病理T分期）纳入考量，以比较它们与生化复发（BCR）风险的关系，并与我们在多变量Cox分析中构建的风险分组进行对比。病理T分期中的T3期与较高的BCR风险相关（HR=3.65，）。

P

P

= 0.0186), and the high-risk group of the risk score was also associated with increased BCR risk (HR = 3.59,

= 0.0186），风险评分的高风险组也与增加的BCR风险相关（HR = 3.59，

P

P

= 0.0033) (Fig.

= 0.0033)（图。

6K

6K

). Based on these features, a nomogram was constructed (Fig.

）。基于这些特征，构建了一个列线图（图。

6L

6升

).

）。

The SREBF1 inhibitor Betulin significantly promotes ferroptosis in prostate cancer

SREBF1抑制剂白桦脂醇显著促进前列腺癌中的铁死亡

Considering the regulation of metabolic reprogramming in prostate cancer by SREBF1 and its relationship with ferroptosis, we investigated the effect of the SREBF1 inhibitor Betulin on promoting ferroptosis in prostate cancer. RSL3, an inhibitor of the classical ferroptosis-resistant pathway, was used as a positive control to promote ferroptosis.

考虑到SREBF1对前列腺癌代谢重编程的调控及其与铁死亡的关系，我们研究了SREBF1抑制剂白桦脂醇对促进前列腺癌铁死亡的作用。RSL3是经典抗铁死亡途径的抑制剂，作为促进铁死亡的阳性对照。

Our experiments involved the androgen-sensitive prostate cancer cell line LNCaP and the castration-resistant prostate cancer cell line PC3..

我们的实验涉及雄激素敏感的前列腺癌细胞系LNCaP和去势抵抗性前列腺癌细胞系PC3。

We verified the expression of SREBF1 target genes and ferroptosis-related genes in drug-treated cell lines using quantitative real-time polymerase chain reaction (qRT-PCR). In the LNCaP cell line, the expression of target genes such as SCD1, DHCR7, MSMO1, CYP51A1, and EBP decreased significantly, and GPX4 decreased to a certain extent (Fig.

我们使用定量实时聚合酶链反应 (qRT-PCR) 验证了药物处理的细胞系中 SREBF1 靶基因和铁死亡相关基因的表达。在 LNCaP 细胞系中，SCD1、DHCR7、MSMO1、CYP51A1 和 EBP 等靶基因的表达显著下降，GPX4 也在一定程度上下降（图。

.

。

7A

7A

). However, in the PC3 cell line, ferroptosis-related genes such as SCD5, GPX4, and SLC7A11 were not affected much in the Betulin group (Fig.

）。然而，在PC3细胞系中，与铁死亡相关的基因如SCD5、GPX4和SLC7A11在白桦脂醇组中并未受到太大影响（图。

7B

7B

). Next, we detected intracellular reactive oxygen species (ROS) to determine the degree of ferroptosis in prostate cancer cells after Betulin treatment. Fluorescence microscopy revealed a significant increase in ROS levels in both LNCaP and PC3 cells following Betulin treatment (Fig.

）。接下来，我们检测了细胞内活性氧（ROS）以确定Betulin处理后前列腺癌细胞中铁死亡的程度。荧光显微镜显示，Betulin处理后，LNCaP和PC3细胞中的ROS水平显著增加（图。

7C, D

7C，D

). The results of intracellular ROS detection using flow cytometry were consistent. In LNCaP cells, the Mean Fluorescence Intensity (MFI) of ROS detection in the Betulin group was significantly increased (126), compared with 89 in the RSL3 group and 64.73 in the control group (Fig.

）。使用流式细胞术检测细胞内ROS的结果一致。在LNCaP细胞中，Betulin组的ROS检测平均荧光强度（MFI）显著增加（126），相比之下，RSL3组为89，对照组为64.73（图。

7E

7E

). Similarly, in the PC3 cell line, the average MFI of ROS detection in the Betulin and RSL3 group were 85.07 and 67, respectively, which were significantly higher than the 30.87 in the control group (Fig.

）。同样，在PC3细胞系中，Betulin组和RSL3组的ROS检测平均MFI分别为85.07和67，显著高于对照组的30.87（图。

7F

7F

). Intracellular glutathione (GSH) levels were measured to observe intracellular oxidative stress. In LNCaP cells, GSH decreased significantly in the Betulin group and RSL3 group, with levels of 13.94 μg/10

）。通过测量细胞内谷胱甘肽（GSH）水平来观察细胞内氧化应激情况。在LNCaP细胞中，Betulin组和RSL3组的GSH显著下降，水平为13.94 μg/10。

6

6

cells and 11.82 μg/10

细胞和11.82微克/10

6

6

cells, respectively, compared to 19.33 μg/10

细胞，分别与19.33 μg/10相比，

6

6

cells in the control group. Similarly, in PC3 cells, GSH also decreased in the Betulin group and RSL3 group, with levels of 16.16 μg/10

对照组细胞。同样，在PC3细胞中，GSH在白桦脂醇组和RSL3组也有所下降，含量为16.16 μg/10。

6

6

cells and 12.73 μg/10

细胞和12.73微克/10

6

6

cells, respectively, compared to 17.28 μg/10

细胞，分别与17.28 μg/10相比，

6

6

cells in the control group (Fig.

对照组中的细胞（图。

7G

7G

). Finally, we examined whether castration and Betulin treatment had a synergistic effect on hormone-sensitive prostate cancer cell lines. We observed that the Q value was greater than 1 at Betulin concentrations ranging from 0.625 μg/mL to 40 μg/mL, indicating a synergistic effect between the two treatments.

最后，我们研究了去势和白桦脂醇处理是否对激素敏感型前列腺癌细胞系具有协同作用。我们观察到，在白桦脂醇浓度范围为0.625 μg/mL至40 μg/mL时，Q值大于1，表明两种处理之间存在协同作用。

The Q value was highest around 10 μg/mL, indicating the most significant synergistic effect (Fig. .

Q值在10 μg/mL左右最高，表明协同作用最显著（图 。

7H

7小时

).

）。

Fig. 7: Validation of the SREBF1 inhibitor Betulin’s ability to promote ferroptosis in prostate cancer cells.

图7：验证SREBF1抑制剂白桦脂醇在前列腺癌细胞中促进铁死亡的能力。

qRT-PCR analysis of the expression of some SREBF1 target genes and ferroptosis-related genes in LNCaP (

qRT-PCR分析LNCaP中一些SREBF1靶基因和铁死亡相关基因的表达(

A

A

) and PC3 (

`) 和 PC3 (`

B

B

) cell lines (

`) 细胞系 (`

n

n

= 3). Fluorescence microscopy observation of ROS content in LNCaP (

= 3）。荧光显微镜观察LNCaP中ROS含量（

C

C语言

) and PC3 (

) 和 PC3 (

D

D

) cells. Hoechst was used to stain the nuclei, and ROS was visualized using the DCFH-DA probe (

）细胞。使用 Hoechst 染色标记细胞核，并使用 DCFH-DA 探针可视化 ROS （

n

n

= 3). Cell flow cytometry analysis of ROS content in LNCaP (

= 3）。LNCaP中ROS含量的细胞流式细胞术分析（

E

E

) and PC3 (

) 和 PC3 (

F

F

) cells. Quantitative comparison by Mean Fluorescence Intensity (MFI) (

）细胞。通过平均荧光强度（MFI）进行定量比较（

n

n

= 3).

= 3).

G

G

Measurement of GSH content in LNCaP and PC3 cells (

测量LNCaP和PC3细胞中的GSH含量 (

n

n

= 6).

= 6)。

H

H

Cell viability assay in LNCaP cells after androgen deprivation culture and Betulin treatment. The left y-axis represents cell viability, and the right

在雄激素剥夺培养和白桦脂醇处理后，LNCaP细胞的细胞活力测定。左纵轴代表细胞活力，右纵轴代表

y

y

-axis represents the

轴代表了

Q

问题

value calculated for each concentration group (

为每个浓度组计算的值 (

n

n

= 3). CS charcoal Stripped. Data were presented as mean ± SD. (ns,

= 3). CS 活性炭去除。数据以平均值±标准差表示。(ns,

P

P

≥ 0.05; *

≥ 0.05；*

P

P

< 0.05; **

< 0.05；**

P

P

< 0.01; ***

< 0.01; ***

P

P

< 0.001; ****

< 0.001；****

P

P

< 0.0001).

< 0.0001)。

Full size image

全尺寸图像

Betulin decreased mitochondrial membrane potential in prostate cancer cells and exhibited synergistic therapeutic effects with docetaxel

白桦脂醇降低了前列腺癌细胞的线粒体膜电位，并与多西他赛表现出协同治疗效果

Previous research has shown that the SREBF1 inhibitor Betulin can significantly increase ROS levels and decrease GSH levels in prostate cancer cells. Detection of mRNA levels revealed a decrease in the expression of genes closely related to lipid metabolism and ferroptosis resistance, including SCD1, DHCR7, and MSMO1.

以往的研究表明，SREBF1抑制剂白桦脂醇可以显著提高前列腺癌细胞中的活性氧水平并降低谷胱甘肽水平。mRNA水平的检测结果显示，与脂质代谢和铁死亡抵抗密切相关的基因表达下降，包括SCD1、DHCR7和MSMO1。

Since ferroptosis is closely related to the intracellular ferrous ion content, we examined whether Betulin affects intracellular ferrous ion levels using intracellular ferrous ion staining. The results indicated that the intracellular ferrous ion content did not change significantly in either the PC3 or LNCaP cell lines (Fig.

由于铁死亡与细胞内亚铁离子含量密切相关，我们通过细胞内亚铁离子染色检测了白桦脂醇是否影响细胞内亚铁离子水平。结果表明，在PC3或LNCaP细胞系中，细胞内亚铁离子含量均未发生显著变化（图。

.

。

8A, B

8A，B

), suggesting that Betulin-mediated ferroptosis is not related to cellular ferrous ion content. Subsequently, we observed changes in the mitochondrial membrane potential, which decreased in both PC3 and LNCaP prostate cancer cell lines (Fig.

），这表明白桦脂醇介导的铁死亡与细胞内的亚铁离子含量无关。随后，我们观察了线粒体膜电位的变化，在PC3和LNCaP前列腺癌细胞系中均发现其下降（图。

8C, D

8C，D

). Currently, ADT is the primary treatment for prostate cancer; however, almost all patients develop castration resistance after a period of treatment, making chemotherapy drugs crucial for advanced prostate cancer. Finally, we investigated the potential synergistic effects of Betulin and docetaxel, which are chemotherapeutic drugs commonly used in advanced prostate cancer.

）。目前，ADT是前列腺癌的主要治疗方法；然而，几乎所有患者在治疗一段时间后都会产生去势抵抗性，这使得化疗药物对晚期前列腺癌至关重要。最后，我们研究了Betulin和多西他赛这两种常用于晚期前列腺癌的化疗药物的潜在协同作用。

Different concentrations of Betulin and docetaxel were combined to treat PC3 cell lines (Fig. .

不同浓度的白桦脂醇和多西他赛联合处理PC3细胞系（图。

8E

8E

). Synergy analysis revealed a synergistic therapeutic effect, with a ZIP synergy score of 5.54 (Fig.

协同作用分析显示了协同治疗效果，ZIP协同评分达到5.54（图。

8F, G

8楼，G

).

）。

Fig. 8: Changes in ferroptosis-related characteristics of prostate cancer cells treated with Betulin and the synergistic therapeutic effect of Betulin and docetaxel.

图8：白桦脂醇处理前列腺癌细胞后铁死亡相关特征的变化以及白桦脂醇与多西他赛的协同治疗效果。

A

A

Intracellular ferrous ion staining was performed using the ferrous ion probe FerroOrange, and cell nuclei were stained using DAPI (

使用亚铁离子探针FerroOrange进行细胞内亚铁离子染色，并使用DAPI对细胞核进行染色（

n

n

= 3).

= 3).

B

B

Quantification of ferrous iron staining.

亚铁染色的定量分析。

C

C

Intracellular mitochondrial membrane potential staining, using JC-1 staining, aggregates are in red, and monomers are in green (

细胞内线粒体膜电位染色，使用JC-1染色，聚集体为红色，单体为绿色 (

n

n

= 3).

= 3)。

D

D

Quantification of JC-1 staining, results were shown as the ratio of aggregates to monomers.

JC-1染色的量化结果表示为聚集体与单体的比值。

E

E

Heat map of cell activity, with the horizontal axis representing the betulin treatment concentration and the vertical axis representing the docetaxel treatment concentration (

细胞活性热图，横轴表示白桦脂醇处理浓度，纵轴表示多西他赛处理浓度 (

n

n

= 5).

= 5).

F

F

Heat map of drug combination synergistic effects, presented as the analysis results of the zero interaction potency (ZIP) model, completed by SynergyFinder.

药物组合协同效应的热图，以零相互作用效力（ZIP）模型的分析结果呈现，由SynergyFinder完成。

G

G

3D heat map of drug combination synergy, presented as the analysis result of zero interaction potency (ZIP) model, completed by SynergyFinder. ZIP zero interaction potency (ZIP). Data were presented as mean ± SD. (ns,

药物组合协同作用的3D热图，作为零相互作用效力（ZIP）模型的分析结果，由SynergyFinder完成。ZIP零相互作用效力（ZIP）。数据以平均值±标准差表示。（ns，

P

P

≥ 0.05; *

≥ 0.05；*

P

P

< 0.05; **

小于0.05；**

P

P

< 0.01; ***

< 0.01; ***

P

P

< 0.001; ****

< 0.001; ****

P

P

< 0.0001).

< 0.0001)。

Full size image

全尺寸图像

Verification of Betulin’s therapeutic effect and its chemosensitizing effect on docetaxel in vivo

验证白桦脂醇的治疗效果及其对多西他赛的体内化疗增敏作用

We constructed a PC3 prostate cancer subcutaneous xenograft tumor model and used Betulin, docetaxel alone, or a combination of Betulin and docetaxel to observe their therapeutic effects (Fig.

我们构建了PC3前列腺癌皮下异种移植肿瘤模型，并使用白桦脂醇、多西他赛单独或联合使用来观察它们的治疗效果（图。

9A

9A

). Betulin and docetaxel moderately inhibited tumor growth when used alone, whereas the tumor growth inhibitory effect was more pronounced when used in combination (Fig.

). 单独使用时，白桦脂醇和多西他赛对肿瘤生长有中等程度的抑制作用，而联合使用时，对肿瘤生长的抑制作用更为显著（图。

9B, D, E

9B、D、E

). No significant effect was noted on the body weight of the mice, whether they were used alone or in combination (Fig.

）。无论单独使用还是联合使用，均未对小鼠体重产生显著影响（图。

9C

九C

). The analysis of variance results of the factorial design showed that Betulin treatment (

）。因子设计的方差分析结果显示，白桦脂醇处理（

F

F

= 24.800,

= 24.800,

P

P

< 0.001, partial eta squared = 0.608) and docetaxel treatment (

< 0.001，部分η平方 = 0.608）和多西他赛治疗（

F

F

= 55.272,

= 55.272，

P

P

< 0.001, partial eta squared = 0.776) had significant main effects. Notably, the interaction effect of Betulin and docetaxel treatment was significant (

<0.001，部分η²=0.776）具有显著的主效应。值得注意的是，Betulin和多西他赛治疗的交互作用显著（

F

F

= 6.922,

= 6.922,

P

P

= 0.018, eta squared = 0.302) (Fig.

= 0.018，eta平方 = 0.302）（图。

9F

九楼

). Oil Red O staining showed that Betulin treatment significantly reduced the level of tissue lipid droplets (Fig.

）。油红O染色显示，白桦脂醇处理显著降低了组织脂滴的水平（图。

9G, H

9G，H

), and Ki-67 staining indicated that Betulin and docetaxel treatment alone inhibited tumor proliferation, with the combination of Betulin and docetaxel showing a stronger inhibitory effect (Fig.

），Ki-67 染色表明，单独使用桦木醇和多西他赛治疗可抑制肿瘤增殖，而桦木醇与多西他赛联合使用显示出更强的抑制作用（图。

9I, J

9我，J

).

）。

Fig. 9: In vivo experiment of betulin combined with docetaxel in the treatment of PC3 prostate cancer subcutaneous xenograft tumor model.

图9：桦木醇联合多西他赛治疗PC3前列腺癌皮下异种移植瘤模型的体内实验。

A

A

Schematic of experimental design and procedure (

实验设计和程序示意图 (

n

n

= 5).

= 5)。

B

B

Image of the tumor after treatment.

治疗后肿瘤的图像。

C

C

Body weight change curve of mice during the treatment process.

治疗过程中小鼠的体重变化曲线。

D

D

Tumor size change curve during treatment.

治疗期间肿瘤大小变化曲线。

E

E

Tumor weight after treatment completion.

治疗完成后肿瘤重量。

F

F

Results of the variance analysis of the factorial design for treatment effects.

因子设计的治疗效果方差分析结果。

G

G

Oil red O staining of tumor tissue after treatment.

治疗后肿瘤组织的油红O染色。

H

H

Quantification of Oil Red O staining.

油红O染色的定量分析。

I

我

Ki-67 staining of tumor tissue after treatment.

治疗后肿瘤组织的Ki-67染色。

J

J

Quantification of Ki-67 staining. i.p. intraperitoneal Injections, SC subcutaneous. Data were presented as mean ± SD. (ns,

Ki-67染色的定量。i.p. 腹腔注射，SC 皮下注射。数据以均值±标准差表示。（ns，

P

P

≥ 0.05; *

≥ 0.05；*

P

P

< 0.05; **

< 0.05；**

P

P

< 0.01; ***

< 0.01；***

P

P

< 0.001; ****

< 0.001；****

P

P

< 0.0001).

< 0.0001)。

Full size image

全尺寸图像

Discussion

讨论

Metabolic reprogramming is a hallmark of cancer that encompasses alterations in glucose, lipid, and amino acid metabolism [

代谢重编程是癌症的一个标志，涵盖了葡萄糖、脂质和氨基酸代谢的改变 [

19

19

]. Cancer cells adapt their energy metabolism to fulfill their high energy demands, often altering their metabolic pathways to suit their needs. Warburg first discovered that cancer cells prefer glycolysis for their energy supply, a phenomenon known as the “Warburg effect” [

]. 癌细胞调整其能量代谢以满足其高能量需求，常常改变其代谢途径以适应自身需求。瓦伯格首先发现癌细胞偏好通过糖酵解获取能量，这一现象被称为“瓦伯格效应”[

20

20

]. Despite being less efficient in ATP production, glycolysis offers several advantages to tumor cells, marking the earliest chapter in the understanding of tumor metabolic changes.

]. 尽管糖酵解在ATP生产方面效率较低，但它为肿瘤细胞提供了多个优势，标志着对肿瘤代谢变化理解的最早篇章。

Metabolic reprogramming in cancer affects various biological behaviors of cancer cells, promoting proliferation, metastasis, and drug resistance. The reprogramming of lipid metabolism is particularly important in cancers [

癌症中的代谢重编程影响癌细胞的各种生物学行为，促进增殖、转移和耐药性。脂质代谢的重编程在癌症中尤为重要 [

21

21

,

，

22

22

]. Lipids, including fatty acids and cholesterol, play critical roles in energy storage, metabolism, and various biological functions within cells, such as serving as the main components of cell membranes and acting as signaling molecules [

]. 脂质，包括脂肪酸和胆固醇，在能量储存、代谢以及细胞内的各种生物功能中发挥着关键作用，例如作为细胞膜的主要成分和信号分子发挥作用 [

23

二十三

]. In prostate cancer, the unique metabolic features of high zinc concentrations and citrate accumulation and production further emphasize the importance of lipid metabolism reprogramming. However, the vulnerability of prostate cancer to metabolic changes presents new therapeutic opportunities and directions for cancer treatment..

]. 在前列腺癌中，高锌浓度和柠檬酸积累与生产的独特代谢特征进一步强调了脂质代谢重编程的重要性。然而，前列腺癌对代谢变化的脆弱性为癌症治疗提供了新的治疗机会和方向。

Ferroptosis is an iron-dependent form of cell death characterized by lipid peroxidation and is closely linked to lipid metabolism [

铁死亡是一种依赖铁的细胞死亡形式，其特征是脂质过氧化，并且与脂质代谢密切相关。

24

24

]. Resistance to ferroptosis is a common feature of tumor cells. Elevated levels of ROS associated with ferroptosis resistance can induce genetic mutations and enhance the malignancy of tumor cells. Additionally, tumor cells can significantly bolster their defence against oxidative stress through ferroptosis resistance, enabling their survival and resistance to drug treatments [.

]. 抗铁死亡是肿瘤细胞的共同特征。与抗铁死亡相关的活性氧 (ROS) 水平升高可诱导基因突变并增强肿瘤细胞的恶性程度。此外，肿瘤细胞可通过抗铁死亡显著增强其对氧化应激的防御能力，从而存活并对药物治疗产生抵抗 [。

25

25

].

].

In this study, we investigated the relationship between metabolic reprogramming and ferroptosis in prostate cancer regulated by SREBF1. First, we analyzed the characteristics of different cell types in prostate cancer samples using single-cell sequencing and identified high levels of activation of pathways such as the PI3K-AKT pathway, P53 pathway, MYC pathways, androgen response, glycolysis, fatty acid metabolism, and bile acid metabolism in prostate cancer epithelial cells.

在本研究中，我们调查了由SREBF1调控的前列腺癌中代谢重编程与铁死亡之间的关系。首先，我们使用单细胞测序分析了前列腺癌样本中不同细胞类型的特点，并确定了前列腺癌上皮细胞中诸如PI3K-AKT通路、P53通路、MYC通路、雄激素反应、糖酵解、脂肪酸代谢和胆汁酸代谢等通路的高度激活。

Of particular interest were lipid-related metabolic pathways such as androgen response, glycolysis, fatty acid metabolism, and bile acid metabolism. Next, we observed characteristic differences in epithelial cells among Normal, Primary cancer, and CRPC samples and found that epithelial cells derived from primary cancer samples mainly exhibited higher activity in the androgen response, fatty acid metabolism, cholesterol metabolism, and other pathways.

特别令人感兴趣的是脂质相关的代谢途径，如雄激素反应、糖酵解、脂肪酸代谢和胆汁酸代谢。接下来，我们观察到正常样本、原发癌样本和去势抵抗性前列腺癌（CRPC）样本中上皮细胞的特征差异，并发现来源于原发癌样本的上皮细胞主要在雄激素反应、脂肪酸代谢、胆固醇代谢及其他途径中表现出较高的活性。

Additionally, epithelial cells derived from both primary cancer and CRPC samples showed higher activity in glycolysis and the MTORC1 pathway..

此外，来自原发癌和去势抵抗性前列腺癌样本的上皮细胞显示出更高的糖酵解和MTORC1通路活性。

We observed a significant upregulation of ACLY, FASN, and SCD1 in primary cancer and CRPC samples. ACLY is the first enzyme in the citric acid pathway, which leads to lipid metabolism. The significantly upregulated expression of ACLY in epithelial cells from primary cancer and CRPC samples suggests increased utilization of citric acid in the lipid synthesis pathway during tumorigenesis.

我们观察到在原发性癌症和去势抵抗性前列腺癌（CRPC）样本中，ACLY、FASN 和 SCD1 显著上调。ACLY 是柠檬酸循环途径中的第一个酶，该途径参与脂质代谢。在原发性癌症和 CRPC 样本的上皮细胞中，ACLY 的显著上调表达表明在肿瘤发生过程中柠檬酸在脂质合成途径中的利用增加。

Furthermore, FASN and SCD1, both associated with fatty acid metabolism, were significantly upregulated in primary cancer and CRPC samples. FASN is a key enzyme that mediates fatty acid synthesis, whereas SCD1 plays a crucial role in monounsaturated fatty acid (MUFA) synthesis. SCD1, a key regulator of the fatty acid metabolic pathway, regulates ferroptosis resistance [.

此外，与脂肪酸代谢相关的 FASN 和 SCD1 在原发性癌症和去势抵抗性前列腺癌（CRPC）样本中显著上调。FASN 是介导脂肪酸合成的关键酶，而 SCD1 在单不饱和脂肪酸（MUFA）合成中起关键作用。SCD1 是脂肪酸代谢途径的关键调节因子，调控铁死亡抗性。

26

26

].

].

We further investigated the relationship between transcription factors and metabolic changes by analyzing the transcription factor regulatory network. Among the 30 transcription factors with high activity in the epithelial cells of single-cell samples, SREBF1 caught our attention.

我们通过分析转录因子调控网络，进一步研究了转录因子与代谢变化之间的关系。在单细胞样本的上皮细胞中高活性的30个转录因子中，SREBF1引起了我们的注意。

SREBPs are a family of basic helix-loop-helix leucine zipper transcription factors that regulate de novo synthesis of fatty acids and cholesterol, as well as cholesterol uptake, making them closely associated with lipid metabolism [

SREBPs 是一个基本螺旋-环-螺旋亮氨酸拉链转录因子家族，它们调控脂肪酸和胆固醇的从头合成以及胆固醇的摄取，因此与脂质代谢密切相关。

27

27

,

，

28

28

,

，

29

29

]. Our analysis revealed that the target genes of SREBF1 included HMGCS1, DHCR7, SC5D, SCD1, ACLY, FASN, and LDLR.

我们的分析显示，SREBF1 的靶基因包括 HMGCS1、DHCR7、SC5D、SCD1、ACLY、FASN 和 LDLR。

We observed that SREBF1 activity was highest in epithelial cells from primary cancer samples and accordingly divided cells into SREBF1_POS and SREBF1_NEG groups based on the transcriptional activity of epithelial cells to observe the differences between them. Significant differences were evident in several fatty acid- and cholesterol-related pathways that also play crucial roles in ferroptosis resistance.

我们观察到SREBF1活性在原发癌症样本的上皮细胞中最高，并据此根据上皮细胞的转录活性将细胞分为SREBF1_POS和SREBF1_NEG组，以观察它们之间的差异。在多个与脂肪酸和胆固醇相关的通路中显示出显著差异，这些通路在铁死亡抗性中也起着关键作用。

One of the most significant differences was observed in the Cholesterol Metabolism pathway, specifically in the steps from Lanosterol to cholesterol synthesis. The exact reaction sequence between lanosterol and cholesterol is not yet fully understood. Based on the reaction sequence of DHCR24, the synthesis pathway from lanosterol to cholesterol can be divided into two pathways: the Bloch and the Kandutsch–Russell pathways [.

胆固醇代谢途径中观察到最显著的差异之一，特别是在从羊毛固醇到胆固醇合成的步骤中。羊毛固醇和胆固醇之间的准确反应序列尚未完全了解。基于DHCR24的反应序列，从羊毛固醇到胆固醇的合成途径可以分为两条途径：布洛赫途径和坎杜特奇-拉塞尔途径。

30

30

]. The upregulation of cholesterol synthesis is conducive to the synthesis of endogenous androgens, which activate AR independently of exogenous androgens, thereby promoting the proliferation of prostate cancer. AR activation may further upregulate the activity of SREBF1, forming a positive feedback loop [.

]. 胆固醇合成的上调有利于内源性雄激素的合成，这些雄激素在不依赖外源性雄激素的情况下激活雄激素受体 (AR)，从而促进前列腺癌的增殖。AR 的激活可能进一步上调 SREBF1 的活性，形成正反馈循环 [。

31

31

,

，

32

32

].

].

Furthermore, Omega9 Fatty Acid Synthesis, which produces MUFA, has been shown to confer ferroptosis resistance. Interestingly, there were significant differences in pathways related to the Mevalonate (MVA) pathway, including the Mevalonate Arm of Cholesterol Biosynthesis Pathway and the Mevalonate Pathway.

此外，产生MUFA的Omega9脂肪酸合成已被证明可赋予对铁死亡的抵抗力。有趣的是，与甲羟戊酸（MVA）途径相关的通路存在显著差异，包括胆固醇生物合成途径的甲羟戊酸分支和甲羟戊酸途径。

The MVA pathway is the first stage in cholesterol synthesis, generating the structural precursors of the steroid substances, isopentenyl pyrophosphate (IPP), and dimethylallyl pyrophosphate (DMAPP) from acetyl-CoA..

甲瓦龙酸途径是胆固醇合成的第一阶段，从乙酰辅酶A生成类固醇物质的结构前体——异戊烯基焦磷酸（IPP）和二甲基丙烯基焦磷酸（DMAPP）。

The MVA pathway is an important contributor to selenoprotein synthesis, and GPX4 is a selenoprotein with selenocysteine in its active center. Because the genetic code for selenocysteine is UGA, which is the same as the stop codon, a specific transporter is required to insert selenocysteine into GPX4 [.

甲羟戊酸途径是硒蛋白合成的重要贡献者，GPX4 是一种含有硒代半胱氨酸的硒蛋白。由于硒代半胱氨酸的遗传密码是 UGA，与终止密码子相同，因此需要特定的转运蛋白将硒代半胱氨酸插入 GPX4 中。

33

33

]. This transporter is a selenocysteine tRNA that contains isopentenyladenosine and can decode the genetic code for selenocysteine by precisely inserting selenocysteine into the corresponding protein. However, the maturation of selenocysteine tRNA requires tRNA-isopentenyl transferase to catalyze the transfer of the isopentenyl group from isopentenyl pyrophosphate (IPP) to specific adenine sites on selenocysteine tRNA precursors [.

].该转运RNA是一种含异戊烯基腺苷的硒代半胱氨酸tRNA，能够通过将硒代半胱氨酸精确插入相应蛋白质来解码硒代半胱氨酸的遗传密码。但是，硒代半胱氨酸tRNA的成熟需要tRNA-异戊烯基转移酶催化异戊烯基从异戊烯基焦磷酸（IPP）转移到硒代半胱氨酸tRNA前体上的特定腺嘌呤位点[。

34

34

]. Therefore, upregulation of this pathway can promote resistance to ferroptosis. We also observed that the cholesterol synthesis pathways, including the MVA pathway, were highly activated in the SREBF1_POS group.

]. 因此，该通路的上调可以促进对铁死亡的抵抗。我们还观察到，包括甲羟戊酸途径在内的胆固醇合成通路在SREBF1_POS组中被高度激活。

In the combined Bulk-RNA seq analysis, we observed that genes closely related to SREBF1 were mainly involved in pathways related to fatty acid synthesis and cholesterol synthesis, with some of these genes acting as key regulators in these pathways. For instance, SCD1, which showed the highest correlation with SREBF1, is crucial for regulating MUFA synthesis, whereas ACLY and FASN are key genes involved in fatty acid synthesis.

在联合的Bulk-RNA测序分析中，我们观察到与SREBF1密切相关的基因主要参与脂肪酸合成和胆固醇合成相关通路，其中一些基因在这些通路中充当关键调控因子。例如，与SREBF1相关性最高的SCD1在调节MUFA合成中起重要作用，而ACLY和FASN是参与脂肪酸合成的关键基因。

These findings were consistent with those of our single-cell sequencing analyses. Survival analysis further indicated that high expression of SCD1, ACLY, DHCR7, and SREBF1 was associated with poor prognosis..

这些发现与我们的单细胞测序分析结果一致。生存分析进一步表明，SCD1、ACLY、DHCR7 和 SREBF1 的高表达与不良预后相关。

Interestingly, studies by Li et al. and Freitas et al. showed that the cholesterol synthesis precursor 7-dehydrocholesterol (7-DHC) can confer resistance to ferroptosis [

有趣的是，Li等人和Freitas等人的研究表明，胆固醇合成前体7-脱氢胆固醇（7-DHC）可以赋予对铁死亡的抵抗性[

35

35

,

，

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36

]. The average expression levels of the genes involved in 7-DHC synthesis, including EBP and SC5D, were higher than those in DHCR7. Li et al. suggested that the high expression of DHCR7 promotes ferroptosis. However, we believe that this should be discussed in this context. Firstly, when the activity of genes involved in 7-DHC synthesis is significantly higher than that of DHCR7, excess of 7-DHC is available to resist ferroptosis.

]. 参与7-DHC合成的基因（包括EBP和SC5D）的平均表达水平高于DHCR7。李等人认为，DHCR7的高表达促进铁死亡。然而，我们认为这在当前背景下值得商榷。首先，当参与7-DHC合成的基因活性显著高于DHCR7时，过多的7-DHC可能会抵抗铁死亡。

Secondly, the continuous and strong activation of the cholesterol synthesis pathway leads to sufficient 7-DHC production, regardless of the level of DHCR7 expression level. Moreover, cholesterol synthesis in prostate cancer is conducive to the production of endogenous steroid hormones, thereby promoting tumor progression..

其次，胆固醇合成途径的持续强烈激活使得7-DHC的产量充足，而与DHCR7表达水平无关。此外，前列腺癌中的胆固醇合成有助于内源性类固醇激素的产生，从而促进肿瘤进展。

It is noteworthy that our results also revealed significant activity and enrichment of SREBF2. Furthermore, correlation analysis demonstrated a strong association between the expression levels of SREBF2 and SREBF1, suggesting that these two factors may interact rather than function independently. SREBF1 primarily participates in the synthesis of fatty acids and cholesterol as well as cholesterol uptake, while SREBF2 predominantly regulates cholesterol metabolism and absorption [.

值得注意的是，我们的结果还揭示了SREBF2的显著活性和富集。此外，相关性分析显示SREBF2和SREBF1的表达水平之间存在很强的关联，表明这两个因子可能会相互作用，而不是独立发挥作用。SREBF1主要参与脂肪酸和胆固醇的合成以及胆固醇的摄取，而SREBF2则主要调控胆固醇的代谢和吸收。

22

22

]. Elevated activation of cholesterol synthesis pathways driven by SREBPs, including the mevalonate pathway, not only enhances ferroptosis resistance in prostate cancer but also increases cholesterol flux towards steroid hormone synthesis [

]. SREBPs驱动的胆固醇合成途径的升高激活，包括甲羟戊酸途径，不仅增强了前列腺癌对铁死亡的抵抗性，还增加了胆固醇向类固醇激素合成的流动 [

37

37

]. Steroid hormones, such as testosterone and dihydrotestosterone (DHT), further contribute to the progression of prostate cancer.

]. 类固醇激素，如睾酮和二氢睾酮（DHT），进一步促进了前列腺癌的进展。

A biochemical recurrence risk scoring model based on the SREBF1 target gene identified several genes closely related to the biochemical recurrence of prostate cancer and demonstrated a good predictive effect. However, the mechanisms underlying the biochemical recurrence of prostate cancer require further investigation..

基于SREBF1靶基因的生化复发风险评分模型筛选出多个与前列腺癌生化复发密切相关的基因，并具有良好的预测效果，但前列腺癌生化复发的机制仍需进一步研究。

Finally, we investigated the effect of the SREBF1 inhibitor Betulin on promoting ferroptosis in prostate cancer. Previous studies have confirmed that Betulin exhibits significant anti-prostate cancer activity [

最后，我们研究了SREBF1抑制剂白桦脂醇对促进前列腺癌铁死亡的影响。先前的研究已证实，白桦脂醇表现出显著的抗前列腺癌活性[

38

38

]. Here, we explored its effect on ferroptosis in tumor cells. Our results demonstrated that treatment with Betulin led to a significant down-regulation of SREBF1 target genes associated with ferroptosis resistance at the genetic level. Additionally, intracellular ROS levels increased significantly, whereas GSH content decreased.

]. 在这里，我们探讨了它对肿瘤细胞中铁死亡的影响。我们的研究结果表明，用白桦脂醇处理会导致与铁死亡抵抗相关的SREBF1靶基因在遗传水平上显著下调。此外，细胞内ROS水平显著增加，而GSH含量下降。

Furthermore, the combination of castration treatment and Betulin treatment exhibited a synergistic therapeutic effect. In addition, our experiments showed no significant changes in the intracellular ferrous ion content, whereas the mitochondrial membrane potential decreased significantly after Betulin treatment.

此外，去势治疗和白桦脂醇治疗的结合表现出协同治疗效果。另外，我们的实验显示细胞内亚铁离子含量没有显著变化，而线粒体膜电位在白桦脂醇处理后显著下降。

Ferrous ions play a critical role in ferroptosis [.

铁离子在铁死亡中起着关键作用。

10

10

]. Notably, this study observed no changes in intracellular ferrous ion levels following the application of the SREBF1 inhibitor. This finding suggests that ferroptosis resulting from SREBF1 inhibition is not independent of the regulation of ferrous ion levels. Betulin and docetaxel exert synergistic therapeutic effects.

]。值得注意的是，本研究观察到应用SREBF1抑制剂后，细胞内亚铁离子水平没有变化。这一发现表明，SREBF1抑制引起的铁死亡并不独立于亚铁离子水平的调节。白桦脂醇和多西他赛发挥协同治疗作用。

In vivo experiments verified the excellent therapeutic effect of Betulin and its chemosensitizing effect on docetaxel, suggesting that SREBF1 inhibitors may have a promising therapeutic potential in prostate cancer..

体内实验验证了白桦脂醇的优秀治疗效果及其对多西他赛的化学增敏作用，表明SREBF1抑制剂在前列腺癌治疗中可能具有良好的潜力。

Some limitations of this study should be noted. Although the Harmony method was used to correct for the batch effect, the batch effect between different datasets cannot be ignored. Secondly, scRNA-seq studies with more patients and more cell numbers are conducive to eliminating individual differences between patients.

本研究的一些局限性应该被注意。虽然使用了Harmony方法来校正批次效应，但不同数据集之间的批次效应不可忽视。其次，包含更多患者和更多细胞数量的scRNA-seq研究有助于消除患者之间的个体差异。

Therefore, other mechanisms of ferroptosis resistance based on SREBPs still need to be explored and biologically verified. Our study provided directions and potential therapeutic targets for prostate cancer for future research..

因此，仍需探索和生物学验证基于SREBPs的其他铁死亡抵抗机制。我们的研究为前列腺癌的未来研究提供了方向和潜在治疗靶点。

In conclusion, our study revealed the role of SREBF1-mediated metabolic reprogramming in prostate cancer and its association with ferroptosis resistance. By combining single-cell sequencing and Bulk-RNA analysis, we demonstrated that the metabolic changes regulated by SREBF1 promote ferroptosis resistance in prostate cancer.

总之，我们的研究揭示了SREBF1介导的代谢重编程在前列腺癌中的作用及其与铁死亡抵抗的关联。通过结合单细胞测序和Bulk-RNA分析，我们证明了SREBF1调控的代谢变化促进前列腺癌的铁死亡抵抗。

Moreover, we found that SREBF1 inhibitors have excellent chemosensitizing effects in prostate cancer therapy, highlighting their potential for therapeutic applications in prostate cancer..

此外，我们发现SREBF1抑制剂在前列腺癌治疗中具有出色的化学增敏效果，突显了它们在前列腺癌治疗中的潜在应用价值。

Materials and methods

材料与方法

Data acquisition and processing

数据采集与处理

Prostate cancer single-cell RNA sequencing data were obtained from the GEO datasets GSE193337 and GSE137829. GSE193337 includes normal prostate tissue, prostate cancer RP tissue, and GSE137829 includes CRPC-derived tumor tissue. Bulk RNA sequencing data were obtained from the TCGA database. Data analysis and processing were conducted using the R language (version 4.2.1) and Python.

前列腺癌单细胞RNA测序数据来源于GEO数据库的GSE193337和GSE137829数据集。GSE193337包含正常前列腺组织、前列腺癌根治术（RP）组织，而GSE137829包含去势抵抗性前列腺癌（CRPC）来源的肿瘤组织。批量RNA测序数据来源于TCGA数据库。数据分析与处理使用R语言（版本4.2.1）和Python进行。

The Seurat package (v5.0.3) was utilized for single-cell sequencing analysis and visualization, while PySCENIC was used for the prediction of transcriptional regulatory factors from single-cell sequencing data..

Seurat包（v5.0.3）用于单细胞测序分析和可视化，而PySCENIC用于从单细胞测序数据中预测转录调控因子。

Single-cell RNA sequencing analysis

单细胞RNA测序分析

We initially merged single-cell sequencing data from the GSE193337 and GSE137829 datasets. Quality control was performed on the merged data using the following criteria: (1) Cells with fewer than 200 measured genes (min.features = 200) and genes covered by fewer than 3 cells (min.cells = 3) were filtered out.

我们最初合并了来自GSE193337和GSE137829数据集的单细胞测序数据。使用以下标准对合并后的数据进行了质量控制：(1) 过滤掉测量基因少于200个（min.features = 200）且覆盖细胞少于3个（min.cells = 3）的细胞。

(2) Cells with gene expression counts below 201 (considered low-quality cells) or more than 8000 genes (indicating potential doublets) were excluded. (3) Cells with more than 20% of unique molecular identifiers (UMIs) derived from the mitochondrial genome were also excluded. (4) Mitochondrial genes, ribosomal genes, hemoglobin genes, and MALAT1 were removed.

(2) 基因表达计数低于201（被认为是低质量细胞）或超过8000个基因（可能表示双细胞）的细胞被排除。(3) 独特分子标识符（UMIs）中有超过20%来源于线粒体基因组的细胞也被排除。(4) 线粒体基因、核糖体基因、血红蛋白基因和MALAT1被移除。

The Harmony package was used to remove batch effects between single-cell datasets from different sources. Data dimensionality reduction was performed using the RunPCA method, and 25 principal components were used for subsequent analyses. Nonlinear dimensionality reduction and result presentation were achieved using UMAP and tSNE.

Harmony包用于去除来自不同来源的单细胞数据集之间的批次效应。使用RunPCA方法进行数据降维，并使用25个主成分进行后续分析。非线性降维和结果展示通过UMAP和tSNE实现。

Cell clusters were manually annotated based on knowledge and relevant literature..

基于知识和相关文献，手动注释了细胞簇。

Single-cell RNA sequencing transcription factor analysis

单细胞RNA测序转录因子分析

We employed PySCENIC to analyze and identify transcriptional regulators based on single-cell sequencing data [

我们使用 PySCENIC 分析并鉴定了基于单细胞测序数据的转录调控因子 [

39

39

]. The analysis and result visualization were performed in R using the SCENIC package. The GRNboost algorithm was used to construct a co-expression network between transcription factors and candidate target genes. TF-motif enrichment analysis was performed to identify direct targets of the transcription factors.

]. 使用SCENIC包在R中进行分析和结果可视化。GRNboost算法用于构建转录因子与候选靶基因之间的共表达网络。进行了TF-motif富集分析以识别转录因子的直接靶标。

Each processed TF and its potential direct target genes were used as regulons for subsequent analyses. AUCell was used to score all genes in each regulon. The resulting score is the Area Under Curve (AUC), which represents the “activity” of regulons in each cell..

每个处理过的转录因子及其潜在的直接目标基因被用作后续分析的调控子。AUCell用于对每个调控子中的所有基因进行评分。所得分数是曲线下面积（AUC），代表每个细胞中调控子的“活性”。

hdWGCNA

hdWGCNA

hdWGCNA (v0.3.03) analysis was performed on all epithelial cell subpopulations. The optimal SoftPower was determined using the TestSoftPowers() function. Correlation analysis between modules and features was conducted using the ModuleTraitCorrelation() function. Transcription factors were identified and screened by SCENIC.

对所有上皮细胞亚群进行了 hdWGCNA (v0.3.03) 分析。使用 TestSoftPowers() 函数确定了最佳的 SoftPower。模块与特征之间的相关性分析通过 ModuleTraitCorrelation() 函数进行。转录因子通过 SCENIC 进行了鉴定和筛选。

The analysis involved adding the transcription factor AUCell activity scores to the metadata in the Seurat object, with module features labeled as “hMEs”. Finally, KEGG pathway enrichment analysis was performed on the Hubgenes of specific module..

分析涉及将转录因子AUCell活性得分添加到Seurat对象的元数据中，模块特征标记为“hMEs”。最后，对特定模块的Hubgenes进行了KEGG通路富集分析。

Bulk-RNA Seq analysis

Bulk-RNA测序分析

RNA-seq data and clinical data were obtained from the XENA and TCGA databases. Gene set expression activity scores in different samples were analyzed using GSVA. To construct the risk score, the target genes of SREBF1 identified by SCENIC were first analyzed using univariate Cox regression analysis to identify genes significant with biochemical recurrence of prostate cancer.

RNA-seq数据和临床数据从XENA和TCGA数据库中获取。使用GSVA分析不同样本中的基因集表达活性得分。为了构建风险评分，首先使用单变量Cox回归分析SCENIC识别的SREBF1靶基因，以确定与前列腺癌生化复发显著相关的基因。

LASSO regression was then used to further filter these genes for risk score construction. The coefficients used to calculate the risk score were derived from the multivariate Cox analysis. External validation was performed using the GEO dataset (GSE116918). Risk grouping was based on the median risk score calculated from the TCGA cohort, which distinguished between high-risk and low-risk groups.

然后使用LASSO回归进一步筛选这些基因以构建风险评分。计算风险评分的系数来源于多变量Cox分析。使用GEO数据集（GSE116918）进行外部验证。风险分组基于从TCGA队列计算的中位风险评分，区分高风险和低风险组。

A nomogram was constructed by performing multivariate Cox regression analysis on both the risk grouping and clinical data. Samples with missing data were excluded from the cohort..

通过多变量Cox回归分析风险分组和临床数据，构建了列线图。数据缺失的样本已从队列中排除。

Cell culture and drug treatment

细胞培养和药物处理

Cell lines were cultured in a humidified incubator at 37 °C with 5% CO

细胞系在37°C、5% CO₂的湿润培养箱中培养。

2

2

. They were obtained from Procell Life Science & Technology Co., Ltd and authenticated by STR cell sequencing to confirm their identity and ensure that they were free of mycoplasma contamination. RPMI-1640 medium (Procell, PM150110) supplemented with 10% fetal calf serum (Procell, 164210-50) was used for cell culture, and charcoal-stripped serum was used to simulate androgen-deprived conditions.

它们从普罗塞尔生命科学与技术有限公司获得，并通过STR细胞测序进行鉴定，以确认其身份并确保无支原体污染。使用补充了10%胎牛血清（Procell，164210-50）的RPMI-1640培养基（Procell，PM150110）进行细胞培养，并使用活性炭剥离血清来模拟雄激素剥夺条件。

Betulin was added to the culture medium at a concentration of 5 μg/mL [.

白桦脂醇以5 μg/mL的浓度添加到培养基中。

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38

]. RSL3 was used as the positive control for ferroptosis induction at a concentration of 0.1 μM.

]. RSL3 以 0.1 μM 的浓度用作铁死亡诱导的阳性对照。

Quantitative real-time polymerase chain reaction (qRT-PCR)

定量实时聚合酶链反应 (qRT-PCR)

RNA was extracted using TRIzol reagent (Invitrogen). Subsequently, cDNA was synthesized following the manufacturer’s protocol (Evo M-MLV RT Mix Kit with gDNA Clean for qPCR Ver.2, AG11728). Real-time PCR was performed using the Applied Biosystems 7900 Real-Time PCR System (Thermo Scientific) and SYBR Green PCR Master Mix (Roche).

使用TRIzol试剂（Invitrogen）提取RNA。随后，按照制造商的协议（Evo M-MLV RT Mix Kit with gDNA Clean for qPCR Ver.2，AG11728）合成cDNA。使用Applied Biosystems 7900实时PCR系统（Thermo Scientific）和SYBR Green PCR Master Mix（Roche）进行实时PCR。

β-actin was used as the internal control, and the primer sequences are listed in Table .

β-actin被用作内参，引物序列列于表中。

S4

S4

.

。

Detection of intracellular ROS

检测细胞内活性氧

The ROS fluorescent probe 2′,7′-Dichlorodihydrofluorescein diacetate (DCFH-DA) (Aladdin, H131224) was used for intracellular ROS detection. DCFH-DA was added to the cells to achieve a final concentration of 20 μM. During cell flow cytometry analysis, the cells were incubated with DCFH-DA for 30 min, and detection was performed at an excitation wavelength of 488 nm and an emission wavelength of 525 nm.

用于检测细胞内活性氧（ROS）的荧光探针2′,7′-二氯二氢荧光素二乙酸酯（DCFH-DA）（Aladdin，H131224）被使用。将DCFH-DA加入细胞中，使其最终浓度达到20 μM。在进行细胞流式分析时，细胞与DCFH-DA孵育30分钟，并在激发波长488 nm和发射波长525 nm下进行检测。

For fluorescence microscopy, the cells were incubated with DCFH-DA for 30 min, and the nuclei were stained with Hoechst stain..

对于荧光显微镜观察，细胞与DCFH-DA孵育30分钟，并用Hoechst染料对细胞核进行染色。

Detection of intracellular glutathione

细胞内谷胱甘肽的检测

After experimental treatment, the cells were collected, counted, and subjected to GSH detection. GSH reacts with 5,5’-dithiobis-2-nitrobenoic acid (DTNB) to produce GSSG, which was detected at a wavelength of 412 nm. The detection procedure was performed according to the manufacturer’s instructions (Solarbio, BC1170)..

实验处理后，收集细胞、计数，并进行GSH检测。GSH与5,5'-二硫代双-2-硝基苯甲酸（DTNB）反应生成GSSG，该物质在412 nm波长处被检测。检测程序按照制造商的说明书进行（Solarbio，BC1170）。

Cell counting kit-8 (CCK-8) and drug synergistic analysis

细胞计数试剂盒-8 (CCK-8) 和药物协同分析

The CCK-8 test kit was purchased from ApexBio Technology (K1018). This procedure was carried out according to the manufacturer’s instructions. To explore the effects of Betulin and androgen deprivation on cells, the charcoal-adsorbed serum group was used as the androgen deprivation group, and Betulin medication group was set with a concentration gradient of 0, 0.625, 1.25, 2.5, 5, 10, 20, and 40 µg/mL.

CCK-8测试试剂盒购自ApexBio Technology（K1018）。此程序按照制造商的说明进行。为了探讨白桦脂醇和雄激素剥夺对细胞的影响，使用活性炭吸附血清组作为雄激素剥夺组，并设置白桦脂醇给药组，浓度梯度为0、0.625、1.25、2.5、5、10、20和40 µg/mL。

The test was performed after 48 hours of culture. The Jin Zhengjun method was used to determine whether there is a synergistic inhibition of cell proliferation between androgen-deprived culture and Betulin treatment [.

培养48小时后进行检测。采用金正俊方法来确定雄激素剥夺培养和白桦脂醇处理之间是否存在对细胞增殖的协同抑制作用。

40

40

,

，

41

41

,

，

42

42

]. The specific formula:

]. 具体公式：

Q

Q

=

=

E

E

(A+B)

(A+B)

/(

/(

E

E

A

A

+

加号

E

E

B

B

–

–

E

E

A

A

·

·

E

E

B

B

),

),

E

E

A

A

is the effect of A treatment,

是A治疗的效果，

E

E

B

B

is the effect of B treatment, and

是B处理的效果，而且

E

E

(A+B)

(A+B)

is the joint treatment effect. A Q value greater than 1 indicates a synergistic effect. When investigating the effects of Betulin and docetaxel on PC3 cells, a Betulin treatment group (with a concentration gradient of 0.625, 1.25, 2.5, 5, and 10 µg/mL), a docetaxel treatment group (with a concentration gradient of 0.375, 0.75, 1.5, 3, 6, and 12 nmol/L), and a combined treatment group were set up.

是联合治疗效果。Q值大于1表示有协同作用。在研究白桦脂醇和多西他赛对PC3细胞的影响时，设置了白桦脂醇治疗组（浓度梯度为0.625、1.25、2.5、5和10 µg/mL）、多西他赛治疗组（浓度梯度为0.375、0.75、1.5、3、6和12 nmol/L）以及联合治疗组。

The concentration of Betulin was converted to a molar concentration. SynergyFinder was used to analyze the synergistic effects of the two treatments when used in combination [.

将白桦脂醇的浓度转换为摩尔浓度。使用SynergyFinder分析两种治疗方法联合使用时的协同效应[。

43

43

].

].

Ferrous ions and mitochondrial membrane potential staining

铁离子和线粒体膜电位染色

FerroOrange was used as a fluorescent probe for ferrous ions to perform fluorescence imaging of ferrous ions in cells. DoJinDo (F374) was used as the product, and the procedure was performed according to the manufacturer’s instructions. JC-1 was used as a fluorescent probe to detect changes in the cell membrane potential.

FerroOrange 作为一种铁离子荧光探针，用于细胞内铁离子的荧光成像。使用了 DoJinDo (F374) 产品，并按照制造商的说明进行操作。JC-1 作为一种荧光探针，用于检测细胞膜电位的变化。

Beyotime (C2006) was used for detection according to the manufacturer’s instructions..

根据制造商的说明，使用碧云天试剂盒（C2006）进行检测。

Animal experiment

动物实验

All mice were housed under specific pathogen-free (SPF) conditions. Mice had ad libitum access to water and standard rodent chow. To establish a PC3 prostate cancer subcutaneous tumor mouse model, the PC3 cell line was subcutaneously inoculated into the dorsal flank of 5–6 week-old male nude mice, with 3 × 10.

所有小鼠均在特定无病原体（SPF）条件下饲养，小鼠可自由饮水和摄取标准啮齿类饲料。为建立PC3前列腺癌皮下肿瘤小鼠模型，将PC3细胞系接种到5-6周龄雄性裸鼠的背部侧翼，接种量为3 × 10。

6

6

cells inoculated per mouse. The tumor size (including length and width) was measured daily, and the tumor volume was estimated using the formula

每只小鼠接种的细胞数量。每天测量肿瘤大小（包括长度和宽度），并使用以下公式估算肿瘤体积：

V

V

= (width

= （宽度

2

2

× length)/2. When solid tumors became palpable, the mice were randomly divided into four groups: (1) Control group (vehicle group); (2) Betulin group: intraperitoneal injection of 2 mg/kg Betulin once every three days; (3) Docetaxel group: intraperitoneal injection of 2 mg/kg docetaxel once a week; (4) Betulin and Docetaxel combination group: the dose and frequency of administration were the same as those of the single drug groups.

×长度)/2。当实体瘤可以触摸到时，小鼠被随机分为四组：（1）对照组（溶剂组）；（2）白桦脂醇组：每三天腹腔注射2毫克/千克白桦脂醇一次；（3）多西他赛组：每周腹腔注射2毫克/千克多西他赛一次；（4）白桦脂醇和多西他赛联合组：剂量和给药频率与单一药物组相同。

The operations, and vehicles of the injected drugs were consistent across all four groups. After 14 days of treatment, the mice were sacrificed, and the tumors were dissected for subsequent experiments. Experimenters were blinded to treatment allocation. Data collection and analysis were performed on anonymized datasets to ensure objectivity.

注射药物的操作和载体在所有四组中都是一致的。治疗14天后，处死小鼠并解剖肿瘤用于后续实验。实验人员对治疗分配保持盲态。数据收集和分析是在匿名数据集上进行的，以确保客观性。

For Oil Red O and Ki-67 immunohistochemical staining, fresh tumor tissues were dehydrated, and frozen sections were prepared. An Oil Red O stock solution was prepared by dissolving 0.3 g of Oil Red O powder in 50 ml of isopropanol. The Oil Red O stock solution was then mixed with distilled water at a ratio of 3:2 to obtain the Oil Red O working solution.

对于油红O和Ki-67免疫组织化学染色，将新鲜肿瘤组织脱水并制备冷冻切片。通过将0.3克油红O粉末溶解于50毫升异丙醇中制备油红O储备液。然后将油红O储备液与蒸馏水按3:2的比例混合，得到油红O工作液。

Before use, the working solution was filtered with a 0.22-micron microporous membrane. The sections were rinsed with 60% isopropanol, stained with the Oil Red O working solution for 15 min, washed with 60% isopropanol until the background is colorless, and rinsed with distilled water to remove the excess stain.

使用前，用0.22微米的微孔滤膜过滤工作液。将切片用60%异丙醇冲洗，用油红O工作液染色15分钟，用60%异丙醇洗涤直至背景无色，并用蒸馏水冲洗以去除多余的染料。

Finally, hematoxylin was used to stain the nucleus..

最后，用苏木精染色细胞核。

Graphing and statistical analysis

绘图与统计分析

Graphing and statistical analyses were primarily conducted using R language, RStudio, SPSS, and Prism 9. The figures were formatted using Affinity Designer and Affinity Photo. Fluorescence, immunohistochemistry, and Oil Red O staining were quantified using Image J and Fiji software. All experiments were performed independently at least three times, and the number of independent experiments is reported in the figure legend.

图形绘制和统计分析主要使用R语言、RStudio、SPSS和Prism 9进行。图表使用Affinity Designer和Affinity Photo进行格式化。荧光、免疫组化和油红O染色使用Image J和Fiji软件进行量化。所有实验均独立重复至少三次，图例中报告了独立实验的次数。

Statistical analyses included unpaired two-tailed .

统计分析包括非配对双尾。

t

t

-tests to compare differences between two groups and one-way ANOVA to compare differences between multiple groups. Unless otherwise stated,

- 使用检验来比较两组之间的差异，使用单向方差分析（ANOVA）来比较多组之间的差异。除非另有说明，

P

P

< 0.05 was considered statistically significant. No statistical methods were used to predetermine sample size.

< 0.05 被认为具有统计学意义。未使用统计方法预先确定样本量。

Data availability

数据可用性

All data are mentioned in the methods.

所有数据都在方法中提到。

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Funding

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This work was supported by the Scientific and Technological Research Program of Tianjin Municipal Education Commission (No. 2019ZD025).

这项工作得到了天津市教委科学技术研究计划（No. 2019ZD025）的支持。

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作者笔记

These authors contributed equally: Guojiang Wei, Ying Huang, Wenya Li.

这些作者贡献相同：魏国江、黄英、李文亚。

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Department of Radiology, The Second Hospital of Tianjin Medical University, Tianjin, People’s Republic of China

天津市人民医院第二医院放射科，中华人民共和国天津市

Guojiang Wei, Ying Huang, Wenya Li, Yuxin Xie, Deyi Zhang, Yuanjie Niu & Yang Zhao

魏国江，黄颖，李文雅，谢雨欣，张德义，牛元杰，赵阳

Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, People’s Republic of China

天津市泌尿外科研究所，天津医科大学第二医院，中国天津市

Guojiang Wei, Ying Huang, Wenya Li, Yuxin Xie, Deyi Zhang, Yuanjie Niu & Yang Zhao

魏国江，黄英，李文雅，谢雨欣，张德义，牛元杰，赵阳

Department of Urology, The Second Hospital of Tianjin Medical University, Tianjin, People’s Republic of China

天津市第二人民医院泌尿外科，中国天津市

Guojiang Wei, Yuxin Xie, Deyi Zhang, Yuanjie Niu & Yang Zhao

魏国江，谢雨欣，张德义，牛元杰，赵阳

Department of Urology, Tianjin Medical University General Hospital, Tianjin, People’s Republic of China

中国天津市天津医科大学总医院泌尿外科

Yuanjie Niu

牛元杰

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Guojiang Wei

国际江 Wei

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Ying Huang

黄莺

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李文雅

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谢雨欣

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杨昭

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Contributions

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Guojiang Wei analyzed the data, designed the experiments, and wrote the manuscript. Ying Huang conducted experiments and collected data. Wenya Li organized the data and conducted experiments. Yuxin Xie collected and organized the data. Deyi Zhang reviewed and edited the manuscript. Yuanjie Niu and Yang Zhao provided funding, supervision, and revised the manuscript..

魏国江分析了数据、设计了实验并撰写了手稿。黄英进行了实验并收集了数据。李文雅整理了数据并进行了实验。谢雨欣收集并整理了数据。张德义审阅并编辑了手稿。牛元杰和赵阳提供了资金支持、监督并修订了手稿。

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牛元杰

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Yang Zhao

杨昭

.

。

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Competing interests

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The authors declare no conflict of interest.

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Ethics approval and consent to participate

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The data used in this study were sourced from public databases and local ethics approval was obtained. The animal experimental protocol was approved by the Experimental Animal Ethics Committee of Tianjin University of Traditional Chinese Medicine (TCM-LAEC2024098w1629).

本研究数据来源于公共数据库，并获得当地伦理批准。动物实验方案经天津中医药大学实验动物伦理委员会批准（TCM-LAEC2024098w1629）。

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Wei, G., Huang, Y., Li, W.

魏, G., 黄, Y., 李, W.

et al.

等人

SREBF1-based metabolic reprogramming in prostate cancer promotes tumor ferroptosis resistance.

基于SREBF1的前列腺癌代谢重编程促进肿瘤对铁死亡的抵抗。

Cell Death Discov.

细胞死亡发现。

11

11

, 75 (2025). https://doi.org/10.1038/s41420-025-02354-7

，75（2025）。https://doi.org/10.1038/s41420-025-02354-7

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https://doi.org/10.1038/s41420-025-02354-7

https://doi.org/10.1038/s41420-025-02354-7

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Subjects

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Fatty acids

脂肪酸

Gene regulation

基因调控

Next-generation sequencing

下一代测序

Prostate cancer

前列腺癌