BALB/c nude mice (male, 5–6 weeks old) were purchased from Changzhou Cavens Laboratory Animal Co., Ltd. (No. SCXK (SU)-2016-0010). All mice were acclimated to a standard rearing environment (Temperature: 18–22°C, Humidity: 50–60%) for 1 week before experiments carried out. For xenograft prostate tumor model, about 1 × 10⁶ PC-3 tumor cells suspended in 0.1 ml of serum-free medium were subcutaneously injected into the flank of the mice. Then tumor-bearing nude mice with CUMS treatment were randomly divided into 4 groups by weight (with 6 nude mice/group). The equivalent dose of human clinical dose of CSS as the medium dose of CSS in mice, the mice were treated with CSS in different doses (2.4 g/kg, 4.8 g/kg and 9.6 g/kg) for 6 weeks started at the time of inoculating PC-3 cells. The negative group received 0.9% normal saline. The volume of intragastric administration was 0.1 ml/10 g to each mouse. Treatments were done by oral administration at a frequency of once a day. The animals were weighed daily to adjust the gavage volume and the maximum gavage volume was no more than 0.3 ml. Tumor volume (TV) was calculated every 3 days using the following formula: TV (mm) = D/2 ×d, where D and d are the longest and the shortest diameters, respectively. At the end of the experiment, the nude mice were sacrificed, and the tumor xenografts were removed and measured. All animal experiments complied with animal ethics and all experiments were double-blind.

CUMS was performed as described (Thaker et al., 2006; Hassan et al., 2013; Le et al., 2016). Briefly, CUMS contains several stressors including restraint (4 h), cage tilt (45°, 24 h), wet bedding (24 h), food and/or water deprivation (24 h), tail nip (1 cm from the end of the tail, 3 min), cold water swimming (4°C, 3 min), and light inversion (24 h). Stressed mice were housed singly and exposed to 3 different stressors every day. Stressed mice were restrained for 3 weeks before tumor transplantation, and continued to be restrained daily for 6 weeks.

MS-grade methanol, MS-grade acetonitrile, HPLC-grade 2-propanol were purchased from Thermo Fisher. HPLC-grade formic acid and HPLC-grade ammonium formate were purchased from Sigma. CSS is composed of seven herbs, including Citrus reticulata Blanco, Radix Bupleuri, Ligusticum chuanxiong Hort, CyperusrotundusL., Citrus aurantium L., Paeonia lactiflora Pall. and Glycyrrhiza uralensis Fisch., and the ratio is 4:4:3:3:3:3:1. The herbs were purchased from the pharmacy of Jiangsu Provincial Hospital of Traditional Chinese Medicine, Nanjing University of Chinese Medicine. All herbs were authenticated by the herbal medicine botanist Professor Yunan, Zhao, Nanjing University of Chinese Medicine.

The CSS extract was prepared using a previously reported method with some modifications (Li et al., 2019). In brief, the seven crushed herbs were mixed and macerated with water at room temperature (25°C ± 2°C) and extracted by decocting twice for 30 min. After mixing the two filtrates and concentrating, the liquid medicine was frozen dry to make lyophilized powder. The yield of the extract (CSS for short) was 27.81%. The details regarding the main marker compound identifications of CSS extracts were published previously (Li et al., 2019; Liu et al., 2020).

The mice were given access to water and 1% sucrose solution at the same time. The sucrose and water intakes of every mouse were measured, and the sucrose preference rate = sucrose intake/(sucrose intake + water intake) *100%. In the tail suspension test (TST), mice were individually suspended by the distal portion of their tails with adhesive tape for 6 min. The duration of immobility was scored for the last 4 min. In the open field test (OFT), the open-field apparatus (25 cm × 25 cm × 35 cm) with 4 squares was used to assess the locomotor activity and anxiety-like behavior. Total distances and the center distances with Top Scan for 5 min were recorded as evaluation parameters. The experiment was performed 2 days before the mice were sacrificed.

According to the TCMSP platform, there were 174 chemical components of CSS satisfying both OB ≥ 30% and DL ≥ 0.18, which were selected as candidate active ingredients. Next, we identified the potential targets in CSS using the TCMSP platform. Moreover, known Prostate Cancer-related targets were identified from five databases by searching the key word “prostate cancer”: 1) Used the DrugBank data base and obtained 25 known targets; 2) Used the GeneCards data base and obtained 1446 known targets; 3) Used the OMIM data base and obtained 169 known targets; 4) Used the PharmGkb data base and obtained 411 known targets; 5) Used the TTD data base and obtained 92 known targets. The targets of the active components of CSS and the targets related to prostate cancer disease were fitted and screened by using the R language script. After that, we deleted the repetitive target genes, calculate drug active ingredient targets and disease targets separately to draw a “Drug_Disease” Venn diagram.

Then, we built a protein-protein interaction (PPI) network diagram by submitting the “Drug and Disease Intersection Gene” file to the STRING11.0 database. The biological species was set to “Homo sapiens”, the minimum interaction threshold was set to “highest confidence” (>0.95) and the rest of the settings were default settings. Based on the degree centrality (DC), betweenness centrality (BC) and closeness centrality (CC), we analyzed the network topology parameters of CSS in the treatment of PCa.

We used the R language script to enrich the “drugs and diseases intersection gene” into the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and sum up all paths. In the GO pathway, we picked up the top 10 best recommendations for each pathway metric and in the KEGG pathway; we selected the top 30 best recommendations and maps the pathways that will be most relevant to “prostate cancer”.

Lipids were extracted according to MTBE method. Briefly, a 200 μL water, 800 μL MTBE and 240 μL methanol were added to 100 μL serum sample one by one and vortexed respectively. Then, the mixture was ultrasound 20 min at 4°C followed by sitting still for 30 min at room temperature. The solution was centrifuged at 14000 g for 15min at 10°C and the upper organic solvent layer was obtained and dried under nitrogen.

Reverse phase chromatography was selected for LC separation using CSH C18 column (1.7 μm, 2.1 mm × 100 mm, Waters). The lipid extracts were re-dissolved in 200 μL 90% isopropanol/acetonitrile, centrifuged at 14000 g for 15 min, finally 3 μL of sample was injected. Solvent A was acetonitrile–water (6:4, v/v) with 0.1% formic acid and 0.1 Mm ammonium formate and solvent B was acetonitrile–isopropanol (1:9, v/v) with 0.1% formic acid and 0.1 Mm ammonium formate. The initial mobile phase was 30% solvent B at a flow rate of 300 μL/min. It was held for 2 min, and then linearly increased to 100% solvent B in 23 min, followed by equilibrating at 30% solvent B for 10 min. Mass spectra was acquired by Q-Exactive Plus in positive and negative mode, respectively. ESI parameters were optimized and preset for all measurements as follows: Source temperature, 300°C; Capillary Temp, 350°C, the ion spray voltage was set at 3000 V, S-Lens RF Level was set at 50% and the scan range of the instruments was set at m/z 200–1800. Full mass scan mode was operated for all serum samples, followed by QC identification via data-dependent MS/MS acquisition mode. The mass charge ratio of lipid molecules and lipid fragments was collected according to the following methods: 10 fragments (MS²scan, HCD) were collected after each full scan. The resolution of MS¹ is 70,000 at m/z 200 and that of MS² is 17,500 at m/z 200. Sequently contained more than 30 lipid classes and more than 1,500,000 fragment ions in the database. Both mass tolerance for precursor and fragment were set to 5 ppm.

LipidSearch 4.0 software was used to perform peak identification, peak extraction, and lipid identification (secondary identification) on lipid molecules and internal standard lipid molecules. The main parameters are: precursor tolerance: 5 ppm, product tolerance: 5 ppm, production threshold: 5%. According to the International Lipid Classification and Nomenclature Committee, totally 38 lipid classes and 943 lipid species were exacted from samples.

Univariate and multivariate analyses Univariate analyses including Fold Change analysis (FC) and t-test. Multivariate analyses including unsupervised principal component analysis (PCA), partial Least Squares discrimination analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Differential feature parameters were as follows: p-value < 0.05, VIP > 1.0 and FC > 1.5 or <0.67. The overlap of significantly different lipid species screened in each comparison group is displayed in the form of a Venn diagram.

To analyze the generation of annotated differentiates between lipid species (VIP>1, p-value < 0.05) metabolic proximities, for a deeper comprehension of how lipids interact with biological state changes. Based on the correlation analysis method, the correlation between significantly different lipids was analyzed and visually performed in the form of correlation cluster heat maps. Further, based on the lipid-lipid correlation matrix, Chord Diagrams and Network Diagrams were displayed to show the pairs of lipid species with |r|>0.8 and p < 0.05. Then count the difference in the content of lipid spices with different carbon chain lengths and different numbers of unsaturated bonds under each subclass.

Statistical calculations were expressed as mean ± standard deviation (mean ± SD). Differences between groups were compared adopting one-way ANOVA analysis method by using Graphpad Prism (version 7) software, and Dunnett’s test was used for comparison between groups. A level of p < 0.05 was selected as the point of minimal statistical significance in every comparison.

To test the effect of depression on PCa growth, PC-3 cells, a typical human PCa cells were implanted into BALB/C nude mice. The stressed mice were exposed to CUMS. Stressed mice were restrained for 3 weeks before tumor transplantation, and continued to be restrained daily for 6 weeks (Figure 1A). And parts of them were treated with the CSS in different doses through oral administration separately (2.4 g/kg, 4.8 g/kg and 9.6 g/kg) for 6 weeks started at the time of inoculating PC-3 cells.

We found that depression significantly decrease mouse body weight (p < 0.001, Figure 1B), and significantly increased tumor volume and tumor weight (p < 0.001, Figure 1C). And in the CSS treated groups, CSS (4.8 g/kg) and CSS (9.6 g/kg) can reduce the tumor volume and tumor weight to varying degrees (p < 0.05, p < 0.001, Figure 1C), the CSS (9.6 g/kg) even can increased the body weight of the mice compared to model (p < 0.05, Figure 1B).

We evaluated potential depressive-like behaviors in these mice using SPT, TST and OFT. Stressed mice exhibited less preference to sucrose in SPT trials (p < 0.0001; Figure 1D), and such kind of phenomenon can be improved in the CSS (4.8 g/kg) and CSS (9.6 g/kg) groups (p < 0.01, p < 0.0001 respectively; Figure 1D). Stressed mice showed a significant increase in immobility duration during TST compared to non-stressed mice (p < 0.01; Figure 1D), while all the CSS groups mice show significant improvement compared to CUMS mice (p < 0.05, p < 0.01, p < 0.01 respectively; Figure 1D). Besides, in the OFT, the stressed mice exhibited reduced the total distance and center distance, shorten the center areas stay times and the times through the center areas, but the CSS (9.6 g/kg) can improve these behaviors, increase distances and times the animals moved (p < 0.05; p < 0.001 respectively; Figures 1D,E). These results manifested those mice with CSS can reduce the depressive-like phenotype of the CUMS stressed mice and the reduction may display a tendency of dose-reliance.

In this study, we observed that the levels of 6 Sphingolipids (including Cer(d18:1_24:0) +HCOO, Cer(m41:1+O) +HCOO, Hex1Cer(d18:1_24:0) +HCOO, SM(t38:4) +H, SM(d42:1) +H and SM(d42:1) +HCOO) were display the annotated differential metabolites in model vs. control and CSS vs. model. Cer is the central molecule in all sphingomyelin metabolism. Sphingomyelin is produced by ceramide and phosphatidylcholine, and the reaction is catalyzed by sphingomyelin synthase (Morigny et al., 2020). Stress response, differentiation, proliferation, apoptosis and migration of tumor cells are all regulated by sphingolipids receptor, especially Cer, sphingosine-1-phosphate (S1P) and ceramide-1-phosphate (C1P) (Pchejetski et al., 2008; Guillermet-Guibert et al., 2009; Huang et al., 2013; Gomez-Larrauri et al., 2020; Presa et al., 2020; Camacho et al., 2022). What’s more, sphingolipids are the most important elements of the lipid raft. Key signals for survival and progression of PCa cells are transmitted through the lipid raft. SM and cholesterol can regulate the dynamics of the lipid raft to affect important signaling pathways such as EFGR and Akt pathways to maintain PCa cells survival and growth (Abe and Kobayashi, 2017). Thus, SM could influence PCa growth and malignancy progression by interfering with lipid rafts.

The 40–50% proportion of the membranes in living organisms is composed of PC, which is one of the most abundant phospholipids. (van der Veen et al., 2017). PC is one of the major constituents of cell membranes and lipid signaling, and plays an important role in cell membrane signaling and enzyme activation (Blom et al., 2011). In carcinomas, it is particularly important for the maintenance of membrane biosynthetic value-added in cancer cells. Furthermore, Kanphorst et al. found that extracellular phospholipids are downregulated in cells under hypoxia due to lipid clearance (Kamphorst et al., 2015). Thus, in most human cancer cells under hypoxia, the activity of the oxygen-dependent enzyme stearoyl-CoA desaturase (SCD)1 is well reduced and fails to promote lipogenesis, which can lead to a deficiency of fatty acids in cancer cells (Luis et al., 2021). Tumors need to consume large amounts of phospholipids for cancer cell proliferation, which may be an important level of reduced phospholipid levels and thus higher PC and LPC consumption rates in cancer cells compared to normal cells (Kopecka et al., 2020). At the same time, we found that LPC may have a serious impact on the promotion of inflammation generation during PC degradation. In the lipidomic analysis, we observed a significant increase in PC and LPC concentrations in model vs. control and CSS can reverse this change. Several previous studies have reported similar results regarding phospholipid levels in multiple cancers. As a result of these findings, CSS may be able to suppress excessive lipid metabolism in PCa under CUMS.