Plasma Polyamine Biomarker Panels: Agmatine in Support of Prostate Cancer Diagnosis

Prostate cancer is the most frequent malignant tumour among males (19%), often clinically silent and of difficult prognosis. Although several studies have highlighted the diagnostic and prognostic role of circulating biomarkers, such as PSA, their measurement does not necessarily allow the detection of the disease. Within this context, many authors suggest that the evaluation of circulating polyamines could represent a valuable tool, although several analytical problems still counteract their clinical practice. In particular, agmatine seems particularly intriguing, being a potential inhibitor of polyamines commonly derived from arginine. The aim of the present work was to evaluate the potential role of agmatine as a suitable biomarker for the identification of different classes of patients with prostate cancer (PC). For this reason, three groups of human patients—benign prostatic hyperplasia (BPH), precancerous lesion (PL), and prostate cancer (PC)—were recruited from a cohort of patients with suspected prostate cancer (n = 170), and obtained plasma was tested using the LC-HRMS method. Statistics on the receiver operating characteristics curve (ROC), and multivariate analysis were used to examine the predictive value of markers for discrimination among the three patient groups. Statistical analysis models revealed good discrimination using polyamine levels to distinguish the three classes of patients. AUC above 0.8, sensitivity ranging from 67% to 89%, specificity ranging from 74% to 89% and accuracy from 73% to 86%, considering the validation set, were achieved. Agmatine plasma levels were measured in PC (39.9 ± 12.06 ng/mL), BPH (77.62 ± 15.05 ng/mL), and PL (53.31 ± 15.27 ng/mL) patients. ROC analysis of the agmatine panel showed an AUC of 0.959 and p ≤ 0.001. These results could represent a future tool able to discriminate patients belonging to the three different clinical groups.


Introduction
Prostate cancer is the most frequent neoplasm among men, and is the fifth leading cause of death worldwide, with the risk increasing with age [1]. Most cancers arise in the periphery of the prostate gland, and cause symptoms only when they have grown to compress the urethra or invade the sphincter or neurovascular bundle [2]. Early prostate cancer usually does not cause pain, and most affected men exhibit no noticeable symptoms. Within this context, clinical assessment of the onset of this disease is complicated by its clinically silent and biologically non-aggressive form. Severities and outcomes of prostate cancer is differ widely. Early-stage prostate cancer can usually be treated successfully, of which is not always predictable. Benign prostatic hyperplasia (BPH)-also called prostate gland enlargement-is a common condition as men get older. Having BPH does not increase a patient's risk for prostate cancer. An enlarged prostate gland can cause uncomfortable urinary symptoms, such as blocking the flow of urine out of the bladder. The PL category includes patients with a histopathological diagnosis of atypical small acinar proliferation (ASAP) and premalignant proliferation of atypical ductal and acinar cells. Prostatic intraepithelial neoplasia (PIN), particularly high-grade PIN (HGPIN), and atypical small acinar proliferation (ASAP) have been identified as being precancerous lesions of the prostate, that is, they function as precursor lesions to prostatic carcinoma. These are categories with increased risk of developing prostate adenocarcinoma. The chance that a patient diagnosed with ASAP will develop cancer is about 40% [38]. The morphological appearance of HGPIN (i.e., tufting, micropapillary, cribriform, flat) is not always correlated with the consequent development of the neoplasm, meaning that clinical follow-up is highly recommended [39][40][41][42][43][44][45][46][47][48]. PIN refers to the precancerous end of a morphologic spectrum involving cellular proliferation within prostatic ducts, ductules, and acini. HGPIN possesses high predictive value for the future development of adenocarcinoma [49], and ASAP has potential significance for the development of synchronic malignant disease close to the source of origin of the biopsy [50]. Both are of great clinical importance for the early diagnosis of PCa. Whether or not the extent of high-grade PIN in biopsies is a predictor of prostate cancer is still controversial [51].
The aim of the present study was to investigate the levels of circulating polyamines in a population of subjects with suspected prostate cancer in order to understand whether they are capable of differentiation among different patient groups and could thus be used for clinical decision support.
To achieve this goal, high-resolution mass spectrometry (HRMS) was used in combination with high-performance liquid chromatography (HPLC), both of which are highly convenient due to their extraction of target ions from the total ion chromatogram, meaning that even minor polyamines can be detected and quantitatively analysed in complex matrices [52].
Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) analysis was performed using an UPLC Ultimate 3000 (Thermo Fisher-Dionex San Jose, CA, USA) system equipped with a HESI-II electrospray source to a Q-Exactive-Orbitrap™-based mass spectrometer (all from Thermo Scientific, San Jose, CA, USA). Chromatographic separation was carried out on the C18 column of the Gemini C18 (Phenomenex, Torrance, CA, USA), 100 mm × 2 mm, particle size 3 µm, the column was held at 37 • C. Chromatographic separation was achieved with gradient elution using a mobile phase composed of 0.05% heptafluorobutyric acid (HFBA) in water (A) and 0.05% HFBA in methanol (B).

Plasma Prostate Cancer Sample Preparation
A total of 170 male patients were recruited at the Urology Department of the University Hospital of Sassari between September 2018 and September 2019. The studies were conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from each subject before the study. Following the clinical diagnosis criteria (TRUS, PB, and biopsy analysis), three subsets of patients were ordered; 92 patients had prostate cancer diagnosed (PC), 50 had benign prostatic hypertrophy or no evidence of malignancy (BPH), and the remaining 28 had precancerous lesion (PL). Table 1 shows detailed information on demographic, pathological and treatment patients list of the clinical patient's characteristics. All plasma samples were stored at −80 • C for one month between collection and measurement. An aliquot of 250 µL of plasma was transferred into an Eppendorf microtube and mixed with 150 µL of methanol (containing 0.05% HFBA) and 100 µL of water for 50 s. After precipitation, samples were centrifuged for 9 min at 15,000 rpm, and frozen overnight at −20 • C. The supernatant was evaporated to dryness at 36 • C under a stream of nitrogen. The residue was reconstituted into 500 µL of mobile phases and 50 µL of IS (internal standard, deuterated histamine). An aliquot of 5 µL of the solution was injected into the LC-HMRS system for analysis. Plasma sample preparation was performed in the same manner as the quality control (QC) samples. The supernatants obtained from these solutions were also used as the QC samples. The QC sample was a mixture of all samples, containing all information in the plasma samples, and it was used to optimise and supervise the injection process. QC samples were injected occasionally to test the stability of both the samples and the system during acquisition. Prior to sample analysis, the QC samples were injected six times to monitor the stability of the instrument. The six QC samples were then processed in parallel and injected to assess the repeatability of the method.

Arginine Decarboxylase (ADC) Quantification
The concentration of human arginine decarboxylase was evaluated using Human ADC (Arginine decarboxylase) ELISA Kit (Nordic Bioscience AB, Propellervägen, Sweden). Plate was washed twice before starting experiments according to the manufacturer's instructions. After washing, 100 µL of each sample was incubated in a pre-treated plate at 37 • C for 90 min. After two washing steps in washing buffer, 100 µL Biotin-labelled antibody working solution was added in each well and incubated incubate at 37 • C for 60 min. Antibody was then removed and replaced by HRP-Streptavidin Conjugate solution for 30 min at

Statistical Analysis
Results are expressed as an average value (mean ± DS). The distribution of variables was evaluated using the Kruskal-Wallis test and applied in order to compare the groups. The Kruskal-Wallis rank sum was employed to evaluate the distributions of each variance in the three groups under observation, assuming the value p < 0.05 as statistically significant. Statistical comparisons among the groups of parametric variables were evaluated using the unpaired Student t-test. The non-parametric continuous variables were compared with the case of normally distributed samples and with the median ± median absolute deviation (MAD) in the case of non-normal sample distribution. Correlations among variables were estimated using Pearson correlation. To verify the presence of associations among variables potentially involved in the development of the disease, a logistic regression analysis was performed. The analysis of the receiver operational characteristics curve (ROC) was used to test the ability of polyamines to predict prostate cancer. ROC curves were obtained by calculating the area under the curve (AUC). A supervised analysis was carried out by applying the orthogonal partial discriminant analysis of the minimum square (OPLS-DA), representing a rotation of the corresponding PLS-DA models and simplifying the information in a only one predictive component while maintaining the same predictive capacity [53]. To avoid model overfitting, the corresponding PLS-DA models were validated by 300-fold permutation tests. The prediction strength of the model was evaluated by "Leave out" analysis. Variable Importance Parameter (VIP) values were used to assess the overall contribution of each X variable to the model, summed over all components and weighted according to the Y variation accounted for by each component. The number of terms in the sum depends on the number of PLS-DA components found to be significant in distinguishing the classes. The Y-axis indicates the VIP scores corresponding to each variable on the X-axis. Bars indicate the factors with the highest VIP scores, and thus are the most contributory variables in class discrimination in the PLS-DA model. Comparison of univariate peaks was performed for the integrals of distinct metabolites using the non-parametric Mann-Whitney U analysis. The statistical analysis was carried out using Statgraphics Centurion XVII (v.17.2) software, MedCalc for Windows, version 15.4 64 bit (MedCalc Software, Ostend, Belgium), and SIMCA-P version 13.0, (Umetrics AB, Umea, Sweden) [54]. Table 1 shows the most important clinical information of the patients, including age, body mass index, lifestyle habits (smoking, alcohol), family history of prostate cancer, total serum PSA, serum PSA index, blood cell counts (WBC, RBC, HGB, PLT), leukocyte counts (Lymphocytes, Neutrophils, Monocytes), plasma inflammatory indices (MRL, NLR, PLR), and combined plasma inflammatory indices (SIRI, AISI), Charlson comorbidities, G6PD, IPSS (international prostatic symptoms score), IIEF (international index of erectile function), and TRUS (trans rectal ultrasound).

Clinical Data
Average age of PC patients was 70 ± 7.86 years, PL patients 68 ± 7.87 and BPH patients 65 ± 8.17, showing that patients were aged from 57 to 77.
In the total sample, the most common pathological change in the prostate was prostate cancer in 55.10% of cases, followed by BPH in 29.34% of cases, precancerous conditions (atypical small acinar proliferation-ASAP-and high-grade prostatic intraepithelial neoplasia-HGPIN) in 15.57% of cases, and atrophic and inflammatory changes in the prostate in 14.1 % of cases. Based on the diagnosis, patients were divided into three groups. The age distribution of pathological changes in the prostate demonstrates that patients with prostate cancer were the oldest, with an average age of 70 years, followed by patients with precancerous conditions (HGPIN and ASAP) with 68 years, while the youngest were patients with BPH, with an average age of 65 years. Total PSA values were significantly different between PC patients and BPH. The analysis by Kruskal-Wallis test indicates that there is a significant difference, with increasing age, in Charlson and a decrease in TRUSS between prostate cancer and benign prostatic hyperplasia.
The correlation between PC patients and PL patients for PSA exhibits an increasing trend, while those for Charlson and IIEF exhibit a decreasing trend. Table 2 shows the levels of plasma polyamines, and of the related amino acids (arginine, lysine) and metabolites (GABA) in the three groups. Patients with prostate cancer exhibited a significant increase in GABA, spermine, spermidine, putrescine, cadaverine and lysine levels as compared to Pl patients. An opposite trend could be observed for agmatine and acetyl-putrescine levels.

Polyamine Analysis
Values of agmatine and acetyl putrescine were significantly decreased, while GABA, spermine, spermidine, putrescine, cadaverine, lysine and ornithine levels were increased, in PC patients as compared to BPH. The analysis by Kruskal-Wallis test indicated that there was a significant difference in the observed changes ( Figure 1).

Mono and Multivariate Analysis
In Figure 1, the multivariate analysis using the PLS-DA method (partial least squares discriminant analysis) shows good discrimination between the three groups of samples; in Figure 2, the OPLS-DA-orthogonal partial least squares discriminant analysis-reports the score plots derived from the LC-HRMS spectra of the plasma and the corresponding loading plots of the coefficients obtained from the three groups. The orthogonal matrix makes it possible to explore the 'orthogonal' components more easily and to fully understand data set. The characteristics of the OPLS method have been investigated for the purpose Biomolecules 2022, 12, 514 7 of 17 of discriminant analysis (OPLS-DA), demonstrating that class-orthogonal variation can be exploited to increase classification performance in those cases in which the individual classes exhibit divergence with respect to within-class variation [52].

Mono and Multivariate Analysis
In Figure 1, the multivariate analysis using the PLS-DA method (partial least squares discriminant analysis) shows good discrimination between the three groups of samples; in Figure 2, the OPLS-DA-orthogonal partial least squares discriminant analysis-  The supervised analysis was performed by applying orthogonal partial least squares discriminant analysis (OPLS-DA), which implies a rotation of the corresponding PLS-DA models and simplifies the concentration of information into one predictive component while maintaining the same predictive ability [53]. We found that patients in the different groups were distributed in different areas, which indicated the significant difference in the metabolic mode between the group with PC (GREEN), the PL group (RED), and the BPH group (BLUE). Figures 1 and 2 show the corresponding 2D predictive scoring plot against the first orthogonal component; the three groups tended to cluster naturally. Compared to the unsupervised principal component analysis, the supervised OPLS-DA makes it possible to obtain the variables' influence on projection (VIP), highlighting the differences between the groups (Figures 3 and 4).
VIP is a parameter used to calculate the cumulative measure of the influence of individual X-variables on the model. Variable influence on projection is applied in multivariate clinical data analysis to achieve improved diagnosis of process dynamics. Variable influence on projection (VIP) is commonly used to summarise the importance of the X-variables in multivariate models based on projections to latent structures, e.g., the PLS and OPLS methods. VIP values as great as or greater than 1 point to the most relevant variables, and generally, VIP values below 0.5 are considered to be irrelevant variables. Thus, VIP for OPLS with positive contribution scores corresponds to the metabolites contributing to class discrimination in the OPLS-DA model. The number of terms in the sum depends on the number of OPLS-DA components that are significant for distinguishing classes. The Y-axis shows the VIP scores corresponding to each variable on the X-axis. To avoid model overfitting, the corresponding OPLS-DA models were validated by 300-fold permutation tests, shown in Figure 5. The resulting regression lines showed an intercept of R2 at 0.0249 and an intercept of Q2 at −0.246, indicating the validity of the model. This difference was maintained when the paired analysis of the samples from three groups was split. Based on the OPLS-DA results, there was a statistically significant difference between the three classes of patients. The scoring plot of the predictive component showed no overlap. avoid model overfitting, the corresponding OPLS-DA models were validated by 300-fold permutation tests, shown in Figure 5. The resulting regression lines showed an intercept of R2 at 0.0249 and an intercept of Q2 at −0.246, indicating the validity of the model. This difference was maintained when the paired analysis of the samples from three groups was split. Based on the OPLS-DA results, there was a statistically significant difference between the three classes of patients. The scoring plot of the predictive component showed no overlap.   avoid model overfitting, the corresponding OPLS-DA models were validated by 300-fold permutation tests, shown in Figure 5. The resulting regression lines showed an intercept of R2 at 0.0249 and an intercept of Q2 at −0.246, indicating the validity of the model. This difference was maintained when the paired analysis of the samples from three groups was split. Based on the OPLS-DA results, there was a statistically significant difference between the three classes of patients. The scoring plot of the predictive component showed no overlap.   The accuracy for the optimal cut-off of biomarkers predicting disease progression (combined with pathological variables) was determined using ROC curves ( Figure 6) and by univariate analysis (Supplementary Materials). ROC curves were used to quantify the predictive accuracy of the metabolites. We observed no significant differences between the three groups using the ROC curves calculated for ornithine, acetyl-spermidine, spermine and acetyl-spermine; increased levels of cadaverine were observed in PC patients, while there was no significant difference between BPH patients and PL. ROC curves of putrescine and spermidine are indeed able to discriminate between PC and PL, while acetyl-putrescine significantly decreases in PC groups (Supplementary Materials). The predictive ability of PSA was analysed using the ROC curves. The area under the ROC curve and its 95% confidence intervals for PSA are shown in Figure S10 in the Supplementary Materials. The area under the curve for PSA is 0.685. Comparison of PSA among the three groups revealed no statistically significant differences. Analysis of the agmatine panel shows an AUC of 0.959 and p ≤ 0.001 when differentiating between PC and BPH patients ( Figure 6). The accuracy for the optimal cut-off of biomarkers predicting disease progression (combined with pathological variables) was determined using ROC curves ( Figure 6) and by univariate analysis (Supplementary Materials). ROC curves were used to quantify the predictive accuracy of the metabolites. We observed no significant differences between the three groups using the ROC curves calculated for ornithine, acetyl-spermidine, spermine and acetyl-spermine; increased levels of cadaverine were observed in PC patients, while there was no significant difference between BPH patients and PL. ROC curves of putrescine and spermidine are indeed able to discriminate between PC and PL, while acetyl-putrescine significantly decreases in PC groups (Supplementary Materials). The predictive ability of PSA was analysed using the ROC curves. The area under the ROC curve and its 95% confidence intervals for PSA are shown in Figure S10 in the Supplementary Materials. The area under the curve for PSA is 0.685. Comparison of PSA among the three groups revealed no statistically significant differences. Analysis of the agmatine panel shows an AUC of 0.959 and p ≤ 0.001 when differentiating between PC and BPH patients ( Figure 6).

Arginine Decarboxylase (ADC) Quantification
The concentration of ADC, an enzyme responsible for catalysing the conversion of L-arginine to agmatine and carbon dioxide, was quantified in the plasma of the PC, PL and BPH patient groups. Figure 7 shows an increase in ADC concentration in PL and BPH patients compared to PC patients, confirming the trend in plasma agmatine levels.

Arginine Decarboxylase (ADC) Quantification
The concentration of ADC, an enzyme responsible for catalysing the conversion of Larginine to agmatine and carbon dioxide, was quantified in the plasma of the PC, PL and BPH patient groups. Figure 7 shows an increase in ADC concentration in PL and BPH patients compared to PC patients, confirming the trend in plasma agmatine levels.

Arginine Decarboxylase (ADC) Quantification
The concentration of ADC, an enzyme responsible for catalysing the conversion of Larginine to agmatine and carbon dioxide, was quantified in the plasma of the PC, PL and BPH patient groups. Figure 7 shows an increase in ADC concentration in PL and BPH patients compared to PC patients, confirming the trend in plasma agmatine levels.

Discussion
Among the metabolites tested, agmatine, in terms of sensitivity and specificity, was significantly different among the three groups. This increase was further confirmed by the observed increase in arginine decarboxylase activity levels in PL and BPH compared to PC (Figure 7) [55,56]. Different authors have demonstrated that agmatine is able to inhibit polyamine synthesis by increasing the expression of antizyme mediating the degradation of ornithine de-carboxylase and increasing the spermidine-spermine acetyl transferase activity [55,[57][58][59][60]. On the other hand, decreased polyamine levels, observed by us in PL and BPH patients, could stabilise the transcripts (p53, TGF-β, JunD) of the genes inhibiting growth [59][60][61][62][63][64]. The observed agmatine level trend in plasma fit perfectly with previous results described by other authors in different kinds of tumour [65].
Agmatine is derived from arginine by the action of arginine decarboxylase. Many studies have shown that arginine is an important player in a variety of different biological processes, such as cell growth, and becomes a limiting factor in states of rapid turnover (e.g., neoplasms). Arginine deprivation therapy is being investigated as an adjuvant therapy for cancer; however, arginine is also required for the immune destruction of malignant cells [66,67]. Agmatine, with is a derivative of arginine that is irregularly distributed in organs and tissues, has a dual behaviour. On one hand, it interferes with polyamine synthesis by reducing the activity of ornithine decarboxylase (ODC), while on the other hand, it inhibits the uptake of polyamines [68,69]; this interference in turn leads to the suppression of tumour cell proliferation in vitro [57,70]. In this context, other authors have also shown that agmatine inhibits the proliferation of cancer cell lines in vivo by interfering with the polyamine pathway [71]. Our results support evidence of a promising plasma biomarker for prostate cancer patients that could be used in current medical practice to reduce unnecessary biopsies. Analysis of ROC curves showed that variations in agmatine concentrations, in terms of sensitivity and specificity, may be promising discriminators among the three groups of patients examined.

Conclusions
The data collected in this study and their statistical analysis suggest effective correlations between variations in indices of systemic inflammation and polyamine levels, al-lowing classification of the three categories of patients examined. The data obtained encourage us to further explore the use of promising biomarkers of disease progression in future clinical practice to differentiate patients into diagnostic classes and identify highrisk patients at an early stage. The results not only contribute to the understanding of prostate cancer, but also increase the knowledge of the complex interrelationships between polyamine metabolism and tumour cell proliferation, also in relation to the possible role played by the microenvironment surrounding the tumour. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.

Conflicts of Interest:
The authors declare no conflict of interest. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.