Lipid profiling of PPAT
We performed untargeted lipidomic profiling of 40 PPAT samples using LC–MS/MS and GC–MS/MS platforms. Patients were stratified according to ISUP GG into two categories: low-risk (ISUP groups I and II, n = 20 tissues) and high-risk (ISUP groups III, IV, and V, n = 20 tissues). Patients’ characteristics are summarized in Additional file 1. Of 264 individual lipid metabolites detected, 67 were significantly differentially expressed between low- and high-risk PPAT samples (Additional file 3).
Analysis of total FA profile obtained after FAME analysis (reveals all FA cellular composition, including acyl chains and free fatty acids) showed that the percentage of total saturated FA (SFA) was not different between the low- and high-risk groups whereas a higher percentage of monounsaturated FA (MUFA) was observed in high-risk PPAT tissue when compared with low-risk counterpart’s (27.59% vs 28.53%, respectively; p = 0.001). Analysis of polyunsaturated FA (PUFA) revealed a significant difference for ω-6 PUFA, which were lower in abundance in high-risk PPAT samples compared with low-risk (17.05% vs 18.98% respectively, p = 0.001) (Fig. 1A, Additional file 4).
No changes were observed between the two studied groups for the following metabolites obtained by LIP-II analysis: DG, p = 0.565; SM, p = 0.565; lysophosphatidylethanolamine (LPE), p > 0.9; LPC, p = 0.873; PC, p = 0.155; and PC-O, p = 0.337. By contrast, a significant difference (p < 0.05) was detected in the total levels of lysophosphatidylinositol (LPI), which were higher in high-risk PPAT samples. Also, a trend for lower levels of TG (p = 0.063) was found in high-risk PPAT samples (Fig. 1B).
Analysis of PPAT lipid signatures for prognosis of PCa
To identify the most relevant lipid features that would allow us to correctly stratify low-risk versus high-risk patients based on the PPAT lipid profile, we performed PLS-DA on a data set of 70 variables (including the 67 significantly deregulated lipid metabolites shown in Additional file 3 and we also included the variables total SFA, total MUFA, and total PUFA). The score plot of the PLS-DA model showed a separation between patients regarding PCa aggressiveness (Additional file 5: Fig. S1A). We then performed VIP analysis to examine the contribution of the 70 variables in determining the degree of PCa aggressiveness, finding that 33/70 variables had a VIP score ≥ 1 and were therefore considered as important in the model for determining PCa aggressiveness (Additional file 5: Fig. S1B).
The selected 33 variables were then back evaluated with PLS-DA to test the strength of the model, and again the patients were segregated into two differentiated groups (Fig. 2A). The PLS-DA model over fitting, measured as the Q2/R2 ratio (R2—how well the model predicts the calibration of variables, and Q2—how well the model predicts PCa aggressivity) was 0.59, indicating that the model fitted well (Fig. 2B). A model is considered predictive when the Q2/R2 ratio is greater than 0.5 [13]. To obtain the minimum number of significant lipidomic PPAT signatures that could separate the low- and high-risk PCa groups, we performed a second VIP analysis and the model showed that only 16 of the 33 variables had a VIP score ≥ 1 (Fig. 2C). The heat map in Fig. 3 shows that based on these 16 features the PPAT samples, categorized by ISUP grade group, naturally clustered into 2 separate groups corresponding to low- and high-risk PCa.
Multivariate stepwise backward regression analysis with the 16 signatures from VIP panel allowed us to evaluate the independent lipid metabolite predictors associated with PCa aggressiveness. Results showed that 12,13-EpOME (B = 0.008, p = 0.039, 95% CI = 0–0.779) and MG (18:0) (B = 0.942, p = 0.005, 95% CI = 0.9–0.982) were independently associated with PCa aggressiveness.
Lipid derangements in PPAT from patients with PCa are related to metabolic alterations
The PPAT lipid signatures revealed an apparent disorder in lipid metabolism according to PCa pathogenesis. To obtain a global overview of the altered metabolic pathways, we performed metabolite set enrichment analysis using MetaboAnalyst 5.0 and SMPDB metabolite set library with the all-lipid metabolites outlined in Fig. 3. These functional approaches revealed that alterations in linoleic acid metabolism, biosynthesis of FA, and β-oxidation of very long-chain fatty acid had the highest impact in the PPAT lipidome (Fig. 4A) (p < 0.05). Then, several SFA metabolites’ profiles obtained by FAME (Additional file 4) were mapped onto de novo lipid synthesis pathways, and we observed that the amount of palmitic acid and the total amount of its intermediate products, which may be further elongated to form other FA, showed a gradually decreasing trend when patients were stratified by ISUP group. When samples were grouped into low- and high-risk PPAT, significant differences were observed in the amounts of palmitic acid, stearic acid, arachidic acid, and behenic acid (Fig. 4B).
When mapping metabolites related to the linoleic acid pathway, we observed that in general, metabolites showed a gradual decrease when PPAT samples were stratified by ISUP group (Fig. 4C). Significant lower amounts of linoleic acid (LA) only appeared in high-risk PPAT samples when grouped by lower and high-risk PPAT (Fig. 4C). Linoleic acid can be oxygenated by 15-lipoxygenase 1 (LOX-1) in humans, primarily to 13-oxoODE or 9-oxoODE, which were also found to be significantly lower in high-risk than in low-risk PPAT.
Linoleic acid can also be converted by cytochrome P450 to epoxy-octadecenoic acids (EpOMEs) in the form of either 9(10)-EpOME (leukotoxin, coronaric acid) or its regioisomer 12(13)-EpOME (isoleukotoxin, vernolic acid); both metabolites were also lower in abundance in high-risk PPAT than in low-risk PPAT (Fig. 4C).
PPAT from aggressive PCa tumors exhibits an altered gene expression profile related to lipid metabolism and inflammation
We next aimed to determine the specific contribution of selected genes in relation to the altered metabolic pathways. We evaluated the expression of sterol regulatory element binding transcription factor 1 (SREBP1), fatty acid synthase (FASN), and acetyl-CoA carboxylase (ACACA) genes, which are involved in de novo FA synthesis. Levels of FASN and ACACA were significantly lower in high-risk PPAT (Fig. 5A), whereas levels of SREBP1 were reduced in high-risk PPAT when compared with low-risk PPAT (Fig. 5B) but did not reach significance. All this data indicates a diminished de novo FA synthesis, in good agreement with the levels of these metabolite pathway products in PPAT (Fig. 4).
Linoleic acid can be converted to 13-OxoODE, which is an endogenous ligand for peroxisome proliferator-activated receptor gamma (PPARG) [14]. When we measured PPARG expression levels, we observed a downregulating trend in high-risk PPAT when compared with low-risk PPAT samples (Fig. 5B). This finding can be related to the lower levels of 13-oxoODE in high-risk PPAT (Fig. 4C). A significant reduction in ADIPOQ gene expression, a gene directly regulated by PPARG, was observed in high-risk PPAT samples when compared with their low-risk counterparts (Fig. 5C).
We also observed that the expression of hormone-sensitive lipase (LIPE) was significantly lower in high-risk PPAT samples than in low-risk samples (Fig. 5D), which has also been described in breast cancer-associated adipocytes [4].
Interestingly, we also revealed an altered inflammatory state in high-risk PPAT samples, demonstrated predominantly by the significantly higher expression of the proinflammatory cytokine interleukin 6 (IL-6) in addition to a trend for higher levels of interleukin 1 (IL-1B) and tumor necrosis factor alpha (TNFα) (Fig. 5E).
Ex vivo co-culture of PPAT explants with PCa cell lines triggers changes in the expression of lipid-, inflammatory-, and tumor-related genes
The observed reduction in metabolites from pathways associated with de novo FA synthesis and the increased inflammatory profile led us to question if these gene expression alterations were due to direct contact with tumor cells.
To address this, we co-cultured PPAT explants with 2 different PCa cell lines (PC-3 and LNCaP) and then performed gene expression analysis. Results showed that regardless of the cell line used in the co-culture experiment, the expression of membrane lipid transporter CD36 and the expression of genes implicated in de novo lipogenesis such as FASN and PPARG, a transcription factor implicated in lipid metabolism and inflammation, were all significantly downregulated in PPAT explants (Fig. 6A). Of note, the key gene regulator factor SREBP1 was slightly reduced but did not reach significance (Fig. 6A). The expression of carnitine palmitoyltransferase 1A (CPT1A), responsible for the translocation of FA from the cytosol to the mitochondrial matrix, was also reduced. By contrast, the inflammatory profile in PPAT co-cultured explants was elevated, as shown by an upregulation of cytokines such as IL-6 and IL-1B. Interestingly, fatty acid binding protein 4 (FAPB4) expression was higher after co-culture, indicating an active cytoplasmatic lipid mobilization process between PPAT and PCa cell lines (Fig. 6A). The expression of the lipase PNPLA2 was reduced in PPAT explants after co-culture, indicating that lipolysis was likely not upregulated under this culturing condition (Fig. 6A). Of note, no differences in co-cultured explants between high-risk and low-risk tumors were observed following co-culture with PCa cell lines in the lipid and inflammatory genes here analyzed (Additional file 6: Fig. S2A-S2B).
Conversely, co-cultured PCa cell lines (both PC-3 and LNCaP) showed a significant increase in the expression of lipid metabolism-related genes such as CD36, FATP5, CPT1A, and FABP4 and of the inflammation-related genes IL-6 and IL-1B (Fig. 6B, C). Noteworthy, while FASN, SREBP1, PPARG, and PNPLA2 were upregulated in PC-3 after PPAT explant co-culture (Fig. 6B), no changes were observed in these genes in LNCaP cells after co-culture (Fig. 6C).
Alteration in PCa cell aggressiveness was also observed in both cell lines after PPAT explant co-culture, demonstrated by a significant increase in expression levels of genes implicated in tumor cell proliferation (ESRRA) and in tumor invasive and metastatic potential (MMP-9, TWIST1) (Fig. 6B, C).
An increase in fatty acid uptake is shown in both cell lines (Fig. 6D) after explant co-culture. Also, both PCa cell lines exhibited enhanced lipid accumulation after explant co-culture (Fig. 6E). Of note, LNCaP co-culture cells showed a significantly higher uptake when compared with co-culture PC-3 cells. No differences in accumulation or in uptake rates were observed in the PCa cell lines studied (PC-3 and LNCaP) regardless of PPAT explant aggressiveness (Additional file 6: Fig. S2C-S2D).
