## 3 Results and Discussion  
Elemental distribution maps (Figure 1) are inhomogeneous. Areas with higher fat content (brighter in the photo) are characterized by lower water content and, consequently, high signal intensity from \({}^{13}\)C (used as an internal standard) and lower intensities from typical electrolytes. Several similarities between elements can be observed: electrolytes such as Na, K, Mg, and Ca are correlated, whereas other elements demonstrate diversity--S and Fe are quite uniformly distributed in low-fat areas and in case of Cl and Zn unique enrichment areas are visible. It can be noted that \({}^{13}\)C and \({}^{23}\)Na signal intensities are negatively correlated, and two regions of interest (ROIs) were distinguished based on this observation: (1) rich in organic matter/fat and poor in water (F); and (2) rich in water and poor in organic matter (W). As it can be seen from Figure 1, the comparison of whole ablated areas would be unreliable, due to the presence of two substantially different tissues, covering variant, irregular surface of each sample. This is why the numerical distinguishing of two ROIs was proposed. The signals were ordered as \({}^{13}\)C descending (Figure 2A,B), as described in the previous paper by [PERSON] and [PERSON] [71]. Such ordering allowed for relatively precise selection of the signal integration range. Numerical distinction of two ROIs was established for each sample based on signal intensity versus the number of data point plots ordered by the descending \({}^{13}\)C signal. A clear cut-off point could be seen, corresponding to a decrease in the signal intensity for \({}^{13}\)C and a simultaneous increase in the signal intensity for \({}^{23}\)Na (Figure 2). All registered data (Figure 2A) were ordered as C descending (Figure 2B). A signal integration range was selected to omit the problem with variable heterogeneity of the samples and to extract the areas of the condensed chemical information from each ROI separately [71]. Signal intensities (normalized to \({}^{13}\)C as IS) from each ROI of each sample were averaged separately. Through principal component analysis, it was shown that even after excluding C from calculations, the difference between F-ROI and W-ROI was clearly visible (Figure 3). Moreover, two groups of coexisting elements can be distinguished: (1) Na, K and Cl (with similar tendency for Fe and Al); and (2) Ca, Cu, Mg, P, S, and Zn. Average relative signal intensities from both ROIs (\"W\" ROI and \"F\" ROI) of each sample were included in further calculations. Values from the same condition group (for example: Zn-supplemented with cancer cells) were treated as biological replicates and all samples (n = 70) were subjected to the statistical analysis. Analysis of variance (ANOVA) tests were performed, preceded by checking normal distribution by Shapiro-Wilk test and homoscedasticity by [PERSON]'s test. In the case of statistically significant differences (\(p\)-value below 0.05), [PERSON]'s post hoc test was performed in order to find which means were significantly different from each other. The condition group's results were compared for each element in order to check if cancer cell implantation and/or diet supplementation influenced the elemental content in specific regions of the prostate gland. Figure 1: Elemental distribution maps of eight selected samples of the prostate gland, fed with two different diets and with (experimental) or without (control) implanted cancer cells. Each column represents the sample taken from the other animal (biological replicate). Relative signal intensities are normalized to the maximal signal for each sample and each element (100%). Figure 3: Biplot of averaged isotopic signal intensities in F1/F2 dimension constructed from the data of all prostate glands (n = 70), divided into rich in fat (F) and rich in water (W) parts.