Characterizing Hormone Secretion Patterns in PitNETs with Metabolomics: Implications for Understanding Tumor Biology

Characterizing Hormone Secretion Patterns in PitNETs with Metabolomics: Implications for Understanding Tumor Biology | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Characterizing Hormone Secretion Patterns in PitNETs with Metabolomics: Implications for Understanding Tumor Biology Fatmanur Köktaşoğlu, Metin Demirel, Halime Dulun Ağaç, Mehtap Alim, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4761839/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Pituitary neuroendocrine tumors (PitNETs) are heterogeneous neoplasms originating from the pituitary gland. Metabolomics, a comprehensive analysis of small molecules, has emerged as a valuable tool for studying pituitary tumors. In the presen investigation, a metabolomic methodology was employed to facilitate a more comprehensive understanding of tumor pathogenesis. Methods Nuclear Magnetic Resonance (NMR) Spectroscopy was utilized to investigate the metabolic profiles of hypophyseal tissue samples obtained from 22 patients with PitNETs, who underwent excisional surgery and exhibited varying hormone secretion statuses. Results Using NMR analysis, we identified 10 metabolites with significant changes, including O-Phosphoethanolamine (PEA), myo-Inositol (I), choline, and several amino acids in tissue samples. In the non-functioning (NF) group, elevated levels of PEA, myo-I, Glycine, and Choline were observed, whereas Glutamate, Phenylalanine, Valine, Isoleucine, Tyrosine, and Methionine exhibited decreased levels in the same group. Phospholipid metabolism, inositol phosphate metabolism, and amino acid metabolism are proposed as potential mechanisms underlying the secretory characteristics of tumor tissue. Conclusions Functioning and nonfunctioning PitNETs display distinct metabolic characteristics. Elevated PEA levels observed in the nonfunctioning group might have inhibited hormone synthesis by suppressing mitochondrial activity, which could potentially contribute to the development of tumors. Further research is warranted to validate these findings and explore their potential clinical applications, such as biomarker discovery and therapeutic targeting Pituitary neuroendocrine tumors (PitNETs) Metabolomics Hormone secretion O-Phosphoethanolamine (PEA) Figures Figure 1 Figure 2 Figure 3 Figure 4 BACKROUND The pituitary gland possesses a highly intricate structure due to its composition of both glandular and glial cells, as well as its stromal framework, and its connection to the hypothalamus, which serves as a critical link between the central nervous system and the endocrine system. The tumors that originate from the pituitary gland represent a heterogeneous group of neoplasms, typically displaying benign behavior with indolent growth characteristics; however, they can also exhibit invasive tendencies ( 1 ). Until 2022, neoplasms originating from the pituitary gland were historically presumed to be adenomas in the absence of any metastatic behavior ( 2 ). In 2022, WHO published a new classification system for the pituitary tumors. Based on the established guidelines, pituitary adenomas are now referred to as Pituitary Neuroendocrine Tumors (PitNET). The adoption of the term "PitNET" in lieu of "pituitary adenoma" is intended to alleviate confusion surrounding the notion of a "metastasized adenoma" and related concepts ( 3 ). According to the classification of their capacity to synthesize and secrete bioactive hormones, there are two principal types of PitNETs: nonfunctioning and functioning. The latter is further categorized into various subtypes, including prolactinomas, growth hormone tumors, adrenocorticotrophic hormone tumors, thyroid hormone tumors, gonadotropic hormone tumors, and multi-hormone adenomas ( 4 ). PitNETs can have variable and unfavorable effects on morbidity and mortality depending on the cell type, hormone secretion activity, and growth behavior ( 5 ). Numerous studies have been conducted to elucidate the underlying processes and mechanisms that define and clarify the pathogenesis of (PiNETs) in this context. Genetic mutations, variation in miRNA molecules, alterations in signaling pathway, derangements in stem cell differentiation and several angiogenic factors have been proposed as potential contributors to the pathogenesis of PitNET, yet no precise estimates have been made to date ( 6 – 10 ). Metabolomics is an omics discipline that entails the comprehensive analysis of low molecular weight molecules, typically less than 1500 Da, present in bio-fluids including serum, plasma, cerebrospinal fluid, urine, and tissue samples. This scientific field aims to identify and potentially quantify a diverse array of molecules, including but not limited to carbohydrates, amino acids, nucleic acids, organic acids, and lipids. The application of metabolomics is often employed to elucidate the underlying pathophysiological mechanisms associated with diseases and variations observed in diverse clinical contexts. Furthermore, metabolomics is utilized to evaluate the efficacy of diagnostic and treatment regimens, as well as in the discovery of potential biomarkers ( 11 ). Nuclear Magnetic Resonance Spectroscopy (NMR) represents one of the primary analytical platforms utilized in metabolomics investigations. With the advancement of technology and the corresponding expansion of measurement capabilities, metabolomics has become increasingly applicable to the study of pituitary tumors over recent decades ( 12 – 14 ). Indeed, compelling evidence has emerged attesting to the utility of metabolomics in evaluating diagnosis, the therapeutic approach, identifying biomarkers and pathogenic mechanism ( 15 ). The aim of our investigation was to clarify the mechanisms involved in hormone secretion within the framework of tumorigenesis at the tissue level. To achieve this objective, we employed Nuclear Magnetic Resonance (NMR) spectroscopy to investigate tissue samples obtained from tumors classified according to functionality. Specifically, we aimed to detect potential metabolic alterations and appraise metabolic profiles related to PitNETs. Our study contributes to the growing body of research utilizing a metabolomics approach with the NMR platform to evaluate hormone secretion status in PitNETs. METHODS Chemicals and Reagents : Disodium hydrogen phosphate (Na 2 HPO 4 ), sodium phosphate monobasic (NaH 2 PO 4 ) sodium 2-(trimethylsilyl)-1-propanesulfonate-d 6 (DSS), were purchased from Sigma-Aldrich (Dorset, UK). Study design, sample, and clinical data collection: I n this investigation, specimens were procured from volunteers who visited the Department of Brain Surgery Clinic and were recommended to undergo surgery at the Bezmialem Vakif University Health Application and Research Center. The study adhered to the principles set forth in the 1964 Helsinki Declaration. Approval for the study was obtained from the clinical research ethics committee of Bezmialem Vakif University on November 17, 2021 (Number: E.40190). A comprehensive verbal and written explanation of the study particulars was provided to all participants, and their informed consent was obtained. The study involved the utilization of a total of 22 tissue samples which were collected from a cohort of patients. 22 post-op tissue samples of patients with PitNET were included in which 12 of them classified as nonfunctioning cause of the absence of hormonal disturbance in serum. 10 hormone secreting PiTNETs tissue samples were consist of 6 growth hormone secreting tumors, 2 adrenocorticotropic releasing hormone and 1 patient for each of prolactin and luteinizing hormone secreting group. Exclusion criteria included pregnancy, smoking, malnutrition, any other tumor, and any chronic disease presence. Samples obtained during surgery were separated for pathological evaluation and metabolomic analysis. For metabolomic study samples were put into microcentrifuge tubes and stored − 86°C until experiment. Sample preparation for NMR : We utilized a metabolite extraction method with a phosphate buffer, making minor modifications to a previously described procedure, in order to extract hydrophilic metabolites ( 16 ). Briefly, 100 milligrams of tissue samples, previously stored at -80°C, were weighed and subsequently mixed with 1 mL 20 mM pH 7.2 phosphate buffer. The mixture was homogenized using a beed homogenizer with cooling stage and centrifuged at 10000 g for 20 minutes at 4°C. Following centrifugation, 600 µL of the supernatant was transferred to the NMR tube, followed by the addition of 100 µL of 1.25 mM Sodium 2-(trimethylsilyl)-1-propanesulfonate-d6 (DSS) prepared in deuterium oxide. The complete process was conducted on ice. A pooled sample was prepared by using supernatant of all samples of about 100 µL. This sample named quality control (Qc) sample and was then used to confirmation of determination of metabolites. IV. Extractions of datasets: In this study The NMR study was conducted using a 500 MHz Bruker Avance (Germany, Karlsruhe) located at the ILMER center of Bezmialem Vakif University. The 1D NOESY method was employed with the following parameters: acquisition time of 4 s, spectral width of 12 ppm, time domain of 2 s, delay time of 5 s, mixing time of 0.2 s, dummy scans of 8, scan number of 128, center of the spectrum at 4.7 ppm, and receiver gain set to 101. After completing the 1D NMR experiments on the tissue samples, 100 µL was taken from each sample constituting the experimental groups to prepare quality control (QC) samples separately for each group. The prepared group QC samples were analyzed using a 700 MHz Bruker Neo (Germany, Karlsruhe) NMR spectrometer with cryogenic probe capabilities, located at Gebze TÜBİTAK MAM in Türkiye. The 2D spectra were then recorded. The two-dimensional NMR analyses take an average of 8–12 hours per sample. Therefore, 2D NMR analyses were performed at least twice only on QC samples. Detailed NMR spectrometry method was given SI. The resulting spectra were finally transferred to COLMARm 13C-1H HSQC, HSQC-TOCSY and TOCSY Query and Verification database ( 17 ). Unified and isomer-specific NMR metabolomics database for the accurate analysis of 13C–1H HSQC spectra in Topspin ASCII format. For this purpose, the following settings were used in the COLMARm database program: 1H chemical shift cutoff (ppm) 0.04, 13C chemical shift cutoff (ppm) 0.3, matching ratio cutoff 0.90, peak picking method Deep Picker model 2, peak line shape in peak fitting Gaussian, spectral referencing solvent: water, and compounds as references: DSS. Metabolite identification was performed using the COLMARm database. Subsequently, only the metabolites identified here were included in the Chenomx NMR suite V10.1 program (Chenomx, Inc., Edmonton, AB, Canada) for quantitation in the 1D NOESY spectrum. The quantitative analysis of metabolites were performed using the Chenomx NMR Suite V10.1 program. The quantification was based on the results of 1D NOESY experiments, where specific proton signals corresponding to compounds and the internal standard DSS were manually integrated. Phase correction, line broadening (0.3 Hz), and zero-filling factors were automatically adjusted. The chemical shift values of the analytes were calibrated using DSS (0.0 ppm). The relative concentrations of the metabolites were calculated using the 1H NMR spectrum signal intensity method, with DSS serving as the internal standard. V. Statistical analysis: Univariate analysis and determination of mean and median values of metabolites were conducted using SPSS version 26. Statistical significance was determined using the Student's t-test and the Mann-Whitney U test, depending on the homogeneity of variances. Spearman correlation analysis was performed on the means of metabolites. The list of identified metabolites obtained through NMR analysis was transferred to MetabolAnalyst 6.0 software for data reduction and visualization. Prior to chemometric analysis, the data were processed through sample normalization by median, cube root transformation, and range-scaling. Metabolites that displayed significant differentiation between groups were subjected to individual testing using the exact Wilcoxon rank-sum test to determine the significance of differences in their levels. Statistically significant differential metabolites were visualized by generating a Volcano Plot, which displays large magnitude fold-changes (x-axis) and high statistical significance (-log10 of p-value, y-axis). Linear discriminant models were constructed using quantified ratios of metabolites as input features. On concentration-based data, visualization was conducted using unsupervised analysis methods, including Partial Least Squares Discriminant Analysis (PLS-DA) followed by the generation of Variable Importance in Projection (VIP) plots. Pathway analyses were performed with MetaboAnalyst 6.0 to assess a set of biologically meaningful metabolites for specific pathways. RESULTS Clinical characteristics of participants were shown in Table 1 . The socio-demographic data comparing the two groups showed no difference in age and gender. To facilitate analysis, the datasets were separated based on the presence of samples. Table 1 Clinical characteristics of participants. Functioning group (n = 10; 7 female, 3 male) Mean (± SD) Nonfunctioning group (n = 12; 5 female, 7 male)Mean (± SD) Age (years) 43.9 (± 16.9) 55.6 (± 13.7) Serum Glucose (mg/dL) 117.7 (± 51) 96.16 (± 18.02) Volume of Tumor (mm 3 ) 5.5 (± 4.2) 8.99 (± 9.2) TSH (uIU/mL) 1.86 (± 1.3) 3.32 (± 4) Cortisol (ug/dL) 9.38 (± 5.85) 8.84 (± 5) FSH (mIU/mL) 12.5 (± 5.5) Male 26.27 (± 33.8) Female 12.54 (± 9.1) Male 9.4 (± 12.09) Female LH (mIU/mL) 4.76 (± 2.23) Male 10.36 (± 12.9) Female 2.89 (± 1.2) Male 2.53 (± 2.1) Female PRL (ng/mL) 30.39 (± 14.5) Male 139.02 (± 315.2) Female 26.66 (± 29.1) Male 23.38 (± 7.2) Female GH (ng/mL) 8.45 (± 10.2) 0.25(± 0.13) IGF-1 (ng/mL) 418.9 (± 305.7) 78.5 (± 29.7) ACTH (pg/mL) 45.27 (± 26.3) 22.78 (± 12.44) Outcomes of NMR Spectroscopy : Based on the 1D NOESY 1H and 2D NMR spectroscopy assays, we were able to identify and quantify the following thirty-seven 37 metabolites: acetate, acetoacetate, alanine, arginine, ascorbate, aspartate, choline, creatine, creatine phosphate, ethanolamine, fumarate, glucose, glutamate, glutamine, glycine, histidine, hypoxanthine, isocitrate, isoleucine, lactate, leucine, lysine, methionine, n-acetylaspartate, n-acetylglutamine, o-phosphocholine, PEA, phenylalanine, proline, s-adenosylhomocysteine, taurin, threonine, tyrosine, uracil, valine, myo-I, sn-glycero-3-phosphocholine. The median and mean values of the compounds which has p value of < 0.05, classified based on their respective groups, are presented in Table 2 . For all metabolites are included in Table 1 in Supplementary Information (SI). Table 2 Absolute concentrations of important metabolites selected by fold-change (FC) analysis and t-test results. METABOLITE Functioning group (µM) Nonfunctioning Group (µM) Median Mean Level Median Mean Level P value - FDR Myo-I 479.85 631.39 ↓ 2622.8 2622.7 ↑ 3.3883E-5 6.2684E-4 PEA* 1165.35 1766.6 ↓ 3924.1 4027.45 ↑ 1.5569E-5 5.7605E-4 Isoleucine 239 252.64 ↑ 135.1 134.07 ↓ 0.0010839 0.010026 Valine 452.25 455.93 ↑ 341.05 302.74 ↓ 0.0063019 0.037499 Glutamate 2172.15 2855.28 ↑ 1319.05 1486.76 ↓ 0.008213 0.037985 Tyrosine 326.9 336.01 ↑ 197.15 199.88 ↓ 0.013319 0.049282 Choline 210 272.01 ↓ 391.65 451.6 ↑ 3.1506E-4 0.0038857 Phenylalanine* 282.6 394.79 ↑ 169.75 192.15 ↓ 0.010125 0.041624 Glycine 1233.5 1593.18 ↓ 1958.73 1640.85 ↑ 0.0070944 0.037499 Methionine 177.3 208.85 ↑ 111 132.6 ↓ 0,012718 0,047058 FC values log scaled, p values transformed by -log10, p-value was calculated by the Wilcoxon Mann Whitney test, p value of < 0.05). *Indicate that have non-normal distribution. PEA: phosphoethanolamine. Arrow marks show the change in level relative to the other group. Seven out of the top 10 metabolites that were statistically significant according to MetaboAnalyst 6.0 were also found to be significant in univariate analysis conducted with SPSS. Table 2 presents the raw p-values of the metabolites that showed statistically significant differences in functioning and nonfunctioning PitNETs. The nonfunctioning group demonstrated higher concentrations of PEA, myo-I, choline, and glycine, along with lower levels of isoleucine, phenylalanine, valine, glutamate, tyrosine, and methionine in comparison to the functioning group as visualized in Fig. 1 and NMR spectral vision in Fig. 1 in SI. To evaluate visually the importance of each metabolite to the separation of two groups heatmap graph was conducted as shown in Fig. 2 . In the realm of unsupervised multivariate statistical analysis, PCA was employed to evaluate tissue samples through 1D 1 H NMR spectroscopy. The PCA score plots elucidated the differentiation and clustering of data. Specifically, the first two principal components, PC1 and PC2, accounted for 44.5% of the variance (28.2% by PC1 and 16.3% by PC2), as depicted in Fig. 3 a. This separation was indicative of distinct groupings within the dataset. Furthermore, a supervised analysis technique, PLS-DA, was applied to the same dataset, comprising 37 identified metabolites from the tissue samples. This analysis revealed a pronounced segregation between the functioning and non-functioning groups, as illustrated in Fig. 3 b. The efficacy of this method was further substantiated by the performance metrics obtained from a 5-fold cross-validation procedure. Optimal outcomes were demonstrated with two components, manifesting an R 2 value of 0.93 and a Q 2 value of 0.81, as illustrated in Fig. 3 c. In the analysis of VIP plot within the constructed model, the metabolites myo-I, PEA, choline, isoleucine, and tyrosine emerged as the five principal contributors exhibiting significant influence. This is visually represented in Fig. 3 d. ROC Analysis To evaluate potential biomarker features of metabolites ROC Analysis was conducted that shows predictive accuracies with different features. Linear support vector machine (SVM) and were used for classification method and SVM built-in for feature ranking method. Based on the importance scores, the metabolites were ranked in descending order of significance. These include myo-I, glutamate, isoleucine, ascorbate, phenylalanine, PEA, N-acetylglutamate, and creatine phosphate. It was determined that models incorporating these metabolites achieved an accuracy exceeding 94%. Figures 2 and 3 in SI show that models with 2, 3, 5 and 10 feature have a great value of accuracy as 98.3, 97.1, 94.9, and 96, respectively. Pathway Analysis : Fig. 4 illustrates the pathways influenced by the functioning of Pituitary Neuroendocrine Tumors (PitNETs). The y-axis of the figure represents the p-values, obtained through pathway enrichment analysis, which indicate the statistical significance of pathway involvement. On the other hand, the x-axis represents the pathway impact values, obtained through pathway topology analysis, which assess the overall impact or relevance of each pathway in PitNETs. This integrated approach combining pathway enrichment and topology analysis provides a comprehensive understanding of the key pathways implicated in PitNETs and their significance in the context of tumor function. The colour and size of each circle are based on p-values and pathway impact values. Statistically significant small p-values and larger pathway impact circles signify that the respective pathway has been significantly altered. The selected pathway enrichment analysis method was Globaltest. Notably, pathways with p-values less than 0.05 and impact values greater than 0.1, which are considered statistically significant, are enumerated as follows: Glycine, serine and threonine metabolism; Glycerophospholipid metabolism; Inositol phosphate metabolism; Phenylalanine, tyrosine and tryptophan biosynthesis; Phenylalanine metabolism; Tyrosine metabolism; Histidine metabolism; Cysteine and methionine metabolism. Detailed information, such as exact impact and p-values, and the number of hits, were given in Table 2 in Supplementary data. DISCUSSION Pituitary gland tumors, which are a type of endocrine tumor, are more prevalent than initially estimated due to the possibility of being asymptomatic ( 18 ). These symptoms may be attributed to the effects of overproduction of the secreted hormone or mass effect, and may include changes in vision, headaches, and signs of increased intracranial pressure. Particularly in cell types that secrete ACTH, there is a possibility of a clinically silent, nonfunctioning tumor transitioning into a functioning one ( 19 ). This transformation, which indicates the aggressiveness of the tumor, requires a deeper understanding of the pathogenesis of tumor behavior and progression. The current study investigated metabolic perturbations of patients with PitNETs from tissue samples grouped as status of hormone secreting. In 1 H-NMR analysis, 10 out of 37 measured metabolites were found to have significant changes. PEA, myo-I, choline, and glycine showed higher concentrations in the nonfunctioning group, whereas isoleucine, phenylalanine, valine, glutamate, tyrosine, and methionine demonstrated higher levels in the functioning group. Based on their VIP scores obtained from PLS-DA the top 5 metabolites were myo-I, PEA, isoleucine, valine and glutamate. PEA and myo-I were identified as outstanding metabolites for differentiating between hormone-secreting and non-secreting pituitary gland tumors. Pathway analysis was conducted to better understand the alterations in these metabolic pathways in tumor biology, including glycine, serine and threonine metabolism; glycerophospholipid metabolism; inositol phosphate metabolism; phenylalanine, tyrosine and tryptophan biosynthesis; phenylalanine metabolism; tyrosine metabolism; histidine metabolism; cysteine and methionine metabolism. Metabolites in phospholipid metabolism : O-Phosphoethanolamine, also known as PEA, belongs to the class of phosphoethanolamines that contain a phosphate linked to the second carbon of an ethanolamine. It plays a crucial role in the biosynthesis of both glycerophospholipids and sphingolipids. The levels of this metabolite have been reported to change in various biological materials, such as plasma or tissue samples, in many clinical and pre-clinical metabolomic studies. These studies include experimentally induced diabetic mice, patients with acne vulgaris, major depressive disorder, and endometrial cancer ( 20 – 23 ). The Kennedy pathways are the primary routes used by mammalian cells for synthesizing phosphatidylcholine (PC) and phosphatidylethanolamine (PE), making them fundamentally important in the biosynthesis of phospholipids. These two phospholipids are the most abundant in mammalian cells ( 24 ). PEA is a precursor of PE, which is an indicator of brain phospholipid turnover. Given that the PEA levels in tissue samples of hypophysis adenoma were 4–5 times higher than in other brain tissues, increased levels of PEA may also be an indicator of pituitary cell membrane synthesis and signal transduction ( 25 , 26 ). Numerous studies have demonstrated a noteworthy decrease of PEA levels in tumoral pituitary tissue comparing non-tumoral one ( 15 ). The decrease in PEA levels may lead to tumorigenesis by eliminating its inhibitory effect, as previously suggested by in-vivo and in-vitro experiments ( 27 ). The presence of PEA hinders the normal function of the mitochondrial electron transport chain (ETC) and inhibits mitochondrial activity, thus contributing to tumorigenesis ( 28 ). Based on the results of our study, it could be hypothesized that the observed increase in PEA levels in the nonfunctioning group may have reduced hormone synthesis via the suppression of mitochondrial activity. Another way of synthesizing PE in mammalian cells is through the phosphatidylserine (PS) decarboxylation pathway, which only occurs in mitochondria and uses phosphatidylserine decarboxylase to decarboxylate PS to PE ( 29 ). Increasing the serine amino acid due to the decarboxylation of PS could contribute to the transformation of serine to glycine and lead to increased glycine levels, which correlated with PEA levels in the nonfunctioning group (r: 0.923). The functioning group also showed a correlation between glycine levels and PEA levels, but with a weaker correlation coefficient (r: 0.806). Choline : Being a significant supplier of methyl groups in metabolism, choline is an indispensable nutrient for humans ( 30 ). It serves as a precursor to three important compounds: the neurotransmitter acetylcholine, and the membrane lipids phosphatidylcholine and sphingomyelin ( 31 ). Several studies have demonstrated alterations in choline metabolism during the process of malignant cellular transformation, as well as in major depressive disorder in brain tissue ( 32 , 33 ). Nevertheless, increased choline levels could be an indicator of tumor proliferation in suprasellar tumors ( 34 ). In our study, choline levels were statistically higher in the nonfunctioning group compared to the functioning group (451.6 and 272, respectively). Contrarily, Ijare et al. found decreased levels of choline-containing phospholipids in the nonfunctioning group compared to the LH/FSH-secreting group in ex vivo hypophysis tissue samples ( 35 ). Another study found high choline levels in pituitary adenomas compared to normal brain tissue ( 36 ). Given that phosphatidylethanolamine (PE) is synthesized from PEA and that choline is essential for the synthesis of phosphatidylcholine (PC), the observed increase in PEA and choline levels in the nonfunctioning group suggests that these molecules play a prominent role in the synthesis and secretion of hormones in the pituitary gland ( 37 ). The observed difference in levels of these metabolites may be attributed to synthesis and subsequently exocytosis of hormones. The functioning group may have had lower levels of choline and PEA as a result of increased turnover and consumption of both membrane and intracellular phospholipids occurring during the synthesis and exocytosis of these hormones. Myo-Inositol The sugar alcohol known as myo-I significantly participates in signal transduction, protein phosphorylation, and numerous pathways related to genetic metabolism. Furthermore, inositol/myo-I represents a crucial constituent of the lipids denominated as phosphatidylinositol (PI) and phosphatidylinositol phosphate (PIP), which are involved in a multitude of biological processes such as signal transduction, cell proliferation, differentiation, and apoptosis ( 38 ). Our pathway analysis results (Fig. 4 ) reveal that myo-inositol participates in inositol phosphate, galactose, ascorbate, and aldarate metabolism, as well as in the phosphatidylinositol signaling system as an intermediate metabolite. Therefore, changes in these pathways may distinguish between functioning and nonfunctioning endocrine tumors. Myo-I is considered one of the most abundant metabolites in the normal brain. Studies have demonstrated a reduction in myo-I levels in pituitary samples that secrete PRL in comparison to other types of hormone-secreting pituitary adenomas, as well as nonfunctioning adenomas ( 15 ). In the current study, higher levels of myo-I were observed in the nonfunctioning group compared to the functioning group (2622.7 and 631.3, respectively). The complexity observed in our results may be attributed to the small number of patients (n = 1) with PRL secretion in our functioning tumor patient group. Myo-I is a molecule that helps with cell osmoregulation in metabolism and has been found to have reduced levels in cancerous cells of the lung, breast, colon, and thyroid ( 39 – 42 ). In particular, Deja et al have shown that myo-I levels decrease in malignant thyroid tumors, which are neuroendocrine tumors, but not in benign lesions such as non-neoplastic nodules and follicular adenomas ( 40 ). Amino Acids Nearly every metabolic alteration and pathological condition may entail modifications in amino acid metabolism ( 43 ). For a peptide hormone-secreting tumor, it is expected that the levels of amino acids would decrease in the functioning group. An increase in these amino acids in the hormone-secreting cells, rather than a reduction, could be due to the induced flow through the bloodstream to the gland, increased de novo amino acid synthesis, and turnover of amino acids. The fact that almost all of the amino acids with increased levels are glucogenic confirms that there is an increase in energy metabolism in tissues with increased hormone secretion. In a study, it was shown that glutamate levels, which also serve as a neurotransmitter in the central nervous system, were elevated in pituitary gland tumors that secrete PRL, while they were decreased in nonfunctioning and other subtypes ( 35 ). There have been also several studies on cancer metabolism, including endocrine tumors, that have revealed fluctuations in glutamate levels ( 44 , 45 ). A study concluded that the serum valine, another amino acid, levels in hypopituitary males were higher than those in healthy controls, consistent with our findings of increased levels in the functioning group. This suggests that the amino acid valine plays an active role in hormone synthesis. Furthermore, the lower levels observed in individuals with congenital hypopituitarism compared to those with acquired hypopituitarism may indicate that this deficiency is responsible for the inability to synthesize hormones ( 46 ). Contrary to our findings, a study conducted by Ljare et al. in patients with PitNET revealed higher levels of phenylalanine and tyrosine in nonfunctioning PitNETs. However, the changes in tyrosine levels were reported as nonsignificant ( 47 ). The inconsistency in these results highlights the need for further research in this area. In our study, it was observed that, unlike all the other amino acids, glycine concentrations were lower in the functioning group compared to those in the nonfunctioning group. It is known that glycine has anti-inflammatory and anti-cancer effects by providing methionine clearance ( 48 , 49 ). Such a mechanism might offer an explanation for the reduced glycine and increased methionine levels observed in the functioning group of our study. Another study has demonstrated that GH is capable of modulating circulating glycine levels in plasma ( 50 ). The observed decrease in glycine levels in the functioning group may be attributed to the suppressive effect of GH on glycine, especially since the functioning group has the highest number of patients with the GH-secreting subtype. In subjects with nonfunctioning PitNETs, detection typically occurs due to the compression of surrounding tissues by the tumor or incidentally. In our study, the mean tumor volume in nonfunctioning PitNETs was higher than in the other group (55.6 mm³ vs. 43.9 mm³, respectively). The ability to synthesize hormones may have enabled earlier diagnosis. The measurement of blood hormone levels in patients with PitNETs is not always reliable for diagnostic or screening purposes. Early-stage tumors, intermittent hormone secretion, hormonal subunits, or localized hormone effects can result in blood hormone levels falling within the normal reference range, despite underlying abnormalities. Histopathological evaluation, including staining techniques, is therefore critical in the diagnosis of pituitary adenomas. In our study, we observed significant variability in preoperative blood hormone levels among patients, even within the same group and subtype, leading to high standard deviations ( 51 ). CONCLUSIONS Cancer biology and related research areas have gained notable attention, and metabolomics has emerged as a comprehensive method for analyzing and understanding metabolism. In this study, post-operative tissue samples of PitNETs were evaluated using 1 H-NMR to identify metabolic differences between functioning and nonfunctioning tumors. The results showed that PEA, myo-I, choline, and glycine levels were higher in the nonfunctioning group, while isoleucine, methionine, valine, glutamate, phenylalanine, and tyrosine levels were higher in the functioning group. These differences could lead to variations in metabolic processes and signaling pathways. Consequently, functioning and nonfunctioning PitNETs exhibit divergent metabolic properties. Increased levels of PEA in the nonfunctioning group may have reduced hormone synthesis via the suppression of mitochondrial activity, potentially contributing to tumorigenesis. The exploration of the underlying mechanisms driving hormone secretion necessitates the utilization of sophisticated and distinct methodologies. However, the small sample size and the lack of a control group using normal tissue due to ethical constraints were significant limitations of this investigation. Studies including a larger number of patients with different subtypes, as well as comparisons with normal pituitary tissue, are necessary to obtain more reliable results. Additionally, further research is needed to confirm these findings, enhance our understanding of advanced biological processes, and apply this knowledge in clinical applications such as biomarker research, drug development, and treatment processes. Abbreviations PitNET: Pituitary Neuroendocrine Tumor NMR: Nuclear Magnetic Resonance PEA: O-Phosphoethanolamine Myo-I: Myo-Inositol DSS: Sodium 2-(trimethylsilyl)-1-propanesulfonate-d6 NOESY: Nuclear Overhauser Effect Spectroscopy HSQC: Heteronuclear Single Quantum Coherence Spectra TOCSY: Total Correlation Spectroscopy PLS-DA: Partial Least Squares Regression Analysis VIP: Variable Importance Projection TSH: Thyroid Stimulating Hormone FSH: Follicle Stimulating Hormone LH: Luteinizing Hormone PRL: Prolactin ACTH: Adrenocorticotropic hormone GH: Growth Hormone IGF-1: Insulin-like growth factor 1 FC : Fold-Change PCA: Principal Components Analysis PE: Phosphatidylethanolamine PC: Phosphatidylcholine PS: Phosphatidylserine Declarations Compliance with Ethical Standards All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Approval for the study was obtained from the clinical research ethics committee of Bezmialem Vakif University on November 17, 2021 (Number: E.40190). A comprehensive verbal and written explanation of the study particulars was provided to all participants, and their informed consent was obtained. Availability of Data and Materials The raw data obtained from the study are available at Metabolomics www.Workbench.org under Data Track ID 4665 and study number ST003122. Author Conflict of Interest Statement: The authors have no competing interests to declare that are relevant to the content of this article. Fatmanur Koktasoglu declares that she has no conflict of interest. Metin Demirel declares that he has no conflict of interest. Halime Dulun Agac declares that she has no conflict of interest. Mehtap Alim declares that she has no conflict of interest. Ufuk Sarikaya declares that he has no conflict of interest. Öykü Dağdeviren declares that she has no conflict of interest. Merve Çavuşoğlu declares that she has no conflict of interest. Kerime Akdur declares that she has no conflict of interest. Büşra Karacam declares that she has no conflict of interest. Somer Bekiroglu declares that he has no conflict of interest. Sahabettin Selek declares that he has no conflict of interest. Mustafa Aziz Hatiboğlu declares that he has no conflict of interest. Funding: The funding for this study was provided by the Department of Scientific Research Projects at Bezmialem Vakif University with the project number 20211210. The study design, data collection, analysis, interpretation, report writing, and decision to submit the article for publication were carried out independently without any support or involvement from the funder company. Authors' Contributions F.K. and M.D. wrote the main manuscript and conducted the metabolomic analysis. H.D.A. and M.A. prepared graphs and figures. US, Ö.D. and B.K. conducted laboratory preparations for metabolomics research. K.A. and M.Ç. carried out clinical organizations and obtained ethical approval from patients. S.B. conducted 2D NMR analysis at TUBITAK MAM Research Center. M.A.H. and Ş.S. supervised all stages of the study and critically analyzed the reports. ACKNOWLEDGEMENTS The authors wish to express their sincere gratitude to Ahmet Balcı and Şule Yalçın from the Bezmialem Vakıf University Drug Application and Research Center for their invaluable contributions to the Nuclear Magnetic Resonance (NMR) analysis. References Melmed S. Pathogenesis of pituitary tumors. Nat Reviews Endocrinol. 2011;7(5):257–66. Asa SL, Mete O, Cusimano MD, McCutcheon IE, Perry A, Yamada S, et al. Pituitary neuroendocrine tumors: a model for neuroendocrine tumor classification. Mod Pathol. 2021;34(9):1634–50. Asa SL, Mete O, Perry A, Osamura RY. Overview of the 2022 WHO classification of pituitary tumors. Endocr Pathol. 2022;33(1):6–26. Qin J, Li K, Wang X, Bao Y. 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Peverelli E, Giardino E, Treppiedi D, Meregalli M, Belicchi M, Vaira V, et al. Dopamine receptor type 2 (DRD2) and somatostatin receptor type 2 (SSTR2) agonists are effective in inhibiting proliferation of progenitor/stem-like cells isolated from nonfunctioning pituitary tumors. Int J Cancer. 2017;140(8):1870–80. Zatelli MC. Pathogenesis of non-functioning pituitary adenomas. Pituitary. 2018;21(2):130–7. Zhang A, Sun H, Wang P, Han Y, Wang X. Modern analytical techniques in metabolomics analysis. Analyst. 2012;137(2):293–300. Calligaris D, Feldman DR, Norton I, Olubiyi O, Changelian AN, Machaidze R et al. MALDI mass spectrometry imaging analysis of pituitary adenomas for near-real-time tumor delineation. Proceedings of the National Academy of Sciences. 2015;112(32):9978-83. Oklu R, Deipolyi AR, Wicky S, Ergul E, Deik AA, Chen JW, et al. Identification of small compound biomarkers of pituitary adenoma: a bilateral inferior petrosal sinus sampling study. 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Zheng G, Lu L, Zhu H, You H, Feng M, Liu X, et al. Clinical, laboratory, and treatment profiles of silent corticotroph adenomas that have transformed to the functional type: a case series with a literature review. Front Endocrinol. 2020;11:558593. Mora-Ortiz M, Nunez Ramos P, Oregioni A, Claus SP. NMR metabolomics identifies over 60 biomarkers associated with Type II Diabetes impairment in db/db mice. Metabolomics. 2019;15:1–16. Arda Düz S, Mumcu A, Doğan B, Yılmaz E, İnci Çoşkun E, Sarıdogan E, et al. Metabolomic analysis of endometrial cancer by high-resolution magic angle spinning NMR spectroscopy. Arch Gynecol Obstet. 2022;306(6):2155–66. Yu S, Xiao Z, Yang XO, Wang X, Zhang D, Li C. Untargeted metabolomics analysis of the plasma metabolic signature of moderate-to-severe acne. Clin Chim Acta. 2022;533:79–84. Kawamura N, Shinoda K, Sato H, Sasaki K, Suzuki M, Yamaki K, et al. Plasma metabolome analysis of patients with major depressive disorder. 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Ethanolamine and phosphoethanolamine inhibit mitochondrial function in vitro: implications for mitochondrial dysfunction hypothesis in depression and bipolar disorder. Biol Psychiatry. 2004;55(3):273–7. Vance JE. Phosphatidylserine and phosphatidylethanolamine in mammalian cells: two metabolically related aminophospholipids. J Lipid Res. 2008;49(7):1377–87. Zeisel SH. What choline metabolism can tell us about the underlying mechanisms of fetal alcohol spectrum disorders. Mol Neurobiol. 2011;44:185–91. Zeisel SH, Mar M-H, Zhou Z, Da Costa K-A. Pregnancy and lactation are associated with diminished concentrations of choline and its metabolites in rat liver. J Nutr. 1995;125(12):3049–54. Glunde K, Bhujwalla ZM, Ronen SM. Choline metabolism in malignant transformation. Nat Rev Cancer. 2011;11(12):835–48. Riley CA, Renshaw PF. Brain choline in major depression: A review of the literature. Psychiatry Research: Neuroimaging. 2018;271:142–53. Einstien A, Virani RA. Clinical relevance of single-voxel 1H Mrs metabolites in discriminating suprasellar tumors. J Clin Diagn research: JCDR. 2016;10(7):TC01. Ijare OB, Baskin DS, Pichumani K. Ex Vivo 1H NMR study of pituitary adenomas to differentiate various immunohistochemical subtypes. Sci Rep. 2019;9(1):1–8. Usenius J, Kauppinen RA, Vainio PA, Hernesniemi JA, Vapalahti MP, Paljärvi LA, et al. Quantitative metabolite patterns of human brain tumors: detection by 1H NMR spectroscopy in vivo and in vitro. J Comput Assist Tomogr. 1994;18(5):705–13. Canonico PL, Cronin MJ, Sortino MA, Speciale C, Scapagnini U, MacLeod RM. Phospholipid metabolism and prolactin secretion in vitro. Hormone Res Paediatrics. 1985;22(3):164–71. Chhetri DR. Myo-inositol and its derivatives: their emerging role in the treatment of human diseases. Front Pharmacol. 2019;10:1172. Bathen TF, Jensen LR, Sitter B, Fjösne HE, Halgunset J, Axelson DE, et al. MR-determined metabolic phenotype of breast cancer in prediction of lymphatic spread, grade, and hormone status. Breast Cancer Res Treat. 2007;104:181–9. Deja S, Dawiskiba T, Balcerzak W, Orczyk-Pawiłowicz M, Głód M, Pawełka D, et al. Follicular adenomas exhibit a unique metabolic profile. 1H NMR studies of thyroid lesions. PLoS ONE. 2013;8(12):e84637. Rocha CM, Barros AS, Gil AM, Goodfellow BJ, Humpfer E, Spraul M, et al. Metabolic profiling of human lung cancer tissue by 1H high resolution magic angle spinning (HRMAS) NMR spectroscopy. J Proteome Res. 2010;9(1):319–32. Tessem M-B, Selnæs KM, Sjursen W, Tranø G, Giskeødegård GF, Bathen TF, et al. Discrimination of patients with microsatellite instability colon cancer using 1H HR MAS MR spectroscopy and chemometric analysis. J Proteome Res. 2010;9(7):3664–70. Simińska E, Koba M. Amino acid profiling as a method of discovering biomarkers for early diagnosis of cancer. Amino Acids. 2016;48:1339–45. Gu Y, Chen T, Fu S, Sun X, Wang L, Wang J, et al. Perioperative dynamics and significance of amino acid profiles in patients with cancer. J translational Med. 2015;13(1):1–14. Xu F, Shi J, Qin X, Zheng Z, Chen M, Lin Z, et al. Hormone-Glutamine Metabolism: A Critical Regulatory Axis in Endocrine-Related Cancers. Int J Mol Sci. 2022;23(17):10086. Zhang Y, Sun S, Wang M, Yu W, Chen P, Yuan F, et al. Untargeted LC/MS-based metabolic phenotyping of hypopituitarism in young males. Front Pharmacol. 2021;12:684869. Ijare OB, Holan C, Hebert J, Sharpe MA, Baskin DS, Pichumani K. Elevated levels of circulating betahydroxybutyrate in pituitary tumor patients may differentiate prolactinomas from other immunohistochemical subtypes. Sci Rep. 2020;10(1):1334. Zhong Z, Wheeler MD, Li X, Froh M, Schemmer P, Yin M, et al. L-Glycine: a novel antiinflammatory, immunomodulatory, and cytoprotective agent. Curr Opin Clin Nutr Metabolic Care. 2003;6(2):229–40. Miller RA, Harrison DE, Astle CM, Bogue MA, Brind J, Fernandez E, et al. Glycine supplementation extends lifespan of male and female mice. Aging Cell. 2019;18(3):e12953. Young JA, Duran-Ortiz S, Bell S, Funk K, Tian Y, Liu Q, et al. Growth Hormone Alters Circulating Levels of Glycine and Hydroxyproline in Mice. Metabolites. 2023;13(2):191. Trouillas J, Jaffrain-Rea M-L, Vasiljevic A, Raverot G, Roncaroli F, Villa C. How to Classify Pituitary Neuroendocrine Tumors (PitNET)s in 2020. Cancers. 2020;12(2):514. Additional Declarations No competing interests reported. Supplementary Files Suppfile.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4761839","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":338456342,"identity":"9882ed77-9e00-4ca4-8d5b-ebdd9b7af7ab","order_by":0,"name":"Fatmanur 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Medicine","correspondingAuthor":false,"prefix":"","firstName":"Sahabettin","middleName":"","lastName":"Selek","suffix":""},{"id":338456357,"identity":"61e114c0-a76d-42c5-9538-4556f987c3e6","order_by":11,"name":"Mustafa Aziz Hatiboğlu","email":"","orcid":"","institution":"Bezmialem Vakif University Faculty of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mustafa","middleName":"Aziz","lastName":"Hatiboğlu","suffix":""}],"badges":[],"createdAt":"2024-07-18 10:25:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4761839/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4761839/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62661187,"identity":"487afac7-cd1d-4e49-8f2e-00520376f9e9","added_by":"auto","created_at":"2024-08-17 02:38:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":762284,"visible":true,"origin":"","legend":"\u003cp\u003eThe box-plot graphs were utilized to illustrate the metabolites that exhibited statistical significance. In these graphs, black hues represent the metabolite levels in the functioning group (F), whereas red hues denote levels in the nonfunctioning group (NF). The boxplots positioned on the left depict the original concentrations of the metabolites, while those on the right display the concentrations after normalization\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4761839/v1/b1336453789def97dfdf073c.png"},{"id":62661190,"identity":"19633ca7-360e-4c5a-8df1-7a0dff38acab","added_by":"auto","created_at":"2024-08-17 02:38:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":167214,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolite variations detected through metabolomics are displayed in this heatmap, which visualizes alterations linked to patterns of hormone release. The plot is constructed using unit variance scaling. In this visualization, a decrease in metabolite levels is denoted by blue, whereas an increase is represented by red \u003cbr\u003e\nF: Functioning group\u003cbr\u003e\nNF: Nonfunctioning group\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4761839/v1/d261efceddd170d771e689e9.jpg"},{"id":62661741,"identity":"7e45872f-7ce0-4852-ad04-7745882ea203","added_by":"auto","created_at":"2024-08-17 02:46:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":847843,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3a, located in the upper left, presents the visualization of the PCA. This analysis elucidates that the first principal component (PC1) is responsible for 28.2% of the total variance, whereas the second principal component (PC2) accounts for 16.3%. Cumulatively, PC1 and PC2 contribute to 44.5% of the overall variance. In the upper right, Figure b displays the results of the \u0026nbsp;PLS-DA. This model demonstrates optimal performance with two components, as evidenced by the R² value of 0.938, a Q² value of 0.81, for 2 components (best case) as shown in Figure c. Furthermore, the VIP, depicted in Figure d, highlights the metabolites exerting significant influence on the model\u003cbr\u003e\nF: Functioning group\u003cbr\u003e\nNF: Nonfunctioning group\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4761839/v1/febc8ee217f8f69b2c0d14ac.png"},{"id":62661189,"identity":"edb96145-4032-4702-b665-eddfb3dbde85","added_by":"auto","created_at":"2024-08-17 02:38:46","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":111751,"visible":true,"origin":"","legend":"\u003cp\u003eInfluenced pathways according to function of PitNETs. Pathways were considered significant if they had p-values less than 0.05 and impact values greater than 0.1. The most significantly affected pathways included Glycine, Serine, and Threonine Metabolism; Glycerophospholipid Metabolism; Inositol Phosphate Metabolism; Biosynthesis of Phenylalanine, Tyrosine, and Tryptophan; Metabolism of Phenylalanine and Tyrosine; Histidine Metabolism; and Cysteine and Methionine Metabolism\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4761839/v1/5c27ec6435beb5a85c66929a.jpg"},{"id":71729875,"identity":"688cde20-878b-4224-a9d1-9f0b2cf09527","added_by":"auto","created_at":"2024-12-18 06:39:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2414102,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4761839/v1/07c0c9c1-40f8-4677-8b04-21ba1266d519.pdf"},{"id":62661191,"identity":"2cf7105c-a16e-49ea-8759-789ca3166d73","added_by":"auto","created_at":"2024-08-17 02:38:47","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":701066,"visible":true,"origin":"","legend":"","description":"","filename":"Suppfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-4761839/v1/e883ab913e1698b9a4fead57.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characterizing Hormone Secretion Patterns in PitNETs with Metabolomics: Implications for Understanding Tumor Biology","fulltext":[{"header":"BACKROUND","content":"\u003cp\u003eThe pituitary gland possesses a highly intricate structure due to its composition of both glandular and glial cells, as well as its stromal framework, and its connection to the hypothalamus, which serves as a critical link between the central nervous system and the endocrine system. The tumors that originate from the pituitary gland represent a heterogeneous group of neoplasms, typically displaying benign behavior with indolent growth characteristics; however, they can also exhibit invasive tendencies (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Until 2022, neoplasms originating from the pituitary gland were historically presumed to be adenomas in the absence of any metastatic behavior (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In 2022, WHO published a new classification system for the pituitary tumors. Based on the established guidelines, pituitary adenomas are now referred to as Pituitary Neuroendocrine Tumors (PitNET). The adoption of the term \"PitNET\" in lieu of \"pituitary adenoma\" is intended to alleviate confusion surrounding the notion of a \"metastasized adenoma\" and related concepts (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). According to the classification of their capacity to synthesize and secrete bioactive hormones, there are two principal types of PitNETs: nonfunctioning and functioning. The latter is further categorized into various subtypes, including prolactinomas, growth hormone tumors, adrenocorticotrophic hormone tumors, thyroid hormone tumors, gonadotropic hormone tumors, and multi-hormone adenomas (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePitNETs can have variable and unfavorable effects on morbidity and mortality depending on the cell type, hormone secretion activity, and growth behavior (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Numerous studies have been conducted to elucidate the underlying processes and mechanisms that define and clarify the pathogenesis of (PiNETs) in this context. Genetic mutations, variation in miRNA molecules, alterations in signaling pathway, derangements in stem cell differentiation and several angiogenic factors have been proposed as potential contributors to the pathogenesis of PitNET, yet no precise estimates have been made to date (\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMetabolomics is an omics discipline that entails the comprehensive analysis of low molecular weight molecules, typically less than 1500 Da, present in bio-fluids including serum, plasma, cerebrospinal fluid, urine, and tissue samples. This scientific field aims to identify and potentially quantify a diverse array of molecules, including but not limited to carbohydrates, amino acids, nucleic acids, organic acids, and lipids. The application of metabolomics is often employed to elucidate the underlying pathophysiological mechanisms associated with diseases and variations observed in diverse clinical contexts. Furthermore, metabolomics is utilized to evaluate the efficacy of diagnostic and treatment regimens, as well as in the discovery of potential biomarkers (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Nuclear Magnetic Resonance Spectroscopy (NMR) represents one of the primary analytical platforms utilized in metabolomics investigations. With the advancement of technology and the corresponding expansion of measurement capabilities, metabolomics has become increasingly applicable to the study of pituitary tumors over recent decades (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Indeed, compelling evidence has emerged attesting to the utility of metabolomics in evaluating diagnosis, the therapeutic approach, identifying biomarkers and pathogenic mechanism (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe aim of our investigation was to clarify the mechanisms involved in hormone secretion within the framework of tumorigenesis at the tissue level. To achieve this objective, we employed Nuclear Magnetic Resonance (NMR) spectroscopy to investigate tissue samples obtained from tumors classified according to functionality. Specifically, we aimed to detect potential metabolic alterations and appraise metabolic profiles related to PitNETs. Our study contributes to the growing body of research utilizing a metabolomics approach with the NMR platform to evaluate hormone secretion status in PitNETs.\u003c/p\u003e"},{"header":"METHODS","content":" \u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eChemicals and Reagents\u003c/span\u003e: Disodium hydrogen phosphate (Na\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e), sodium phosphate monobasic (NaH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e) sodium 2-(trimethylsilyl)-1-propanesulfonate-d\u003csub\u003e6\u003c/sub\u003e (DSS), were purchased from Sigma-Aldrich (Dorset, UK).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStudy design, sample, and clinical data collection: I\u003c/span\u003en this investigation, specimens were procured from volunteers who visited the Department of Brain Surgery Clinic and were recommended to undergo surgery at the Bezmialem Vakif University Health Application and Research Center. The study adhered to the principles set forth in the 1964 Helsinki Declaration. Approval for the study was obtained from the clinical research ethics committee of Bezmialem Vakif University on November 17, 2021 (Number: E.40190). A comprehensive verbal and written explanation of the study particulars was provided to all participants, and their informed consent was obtained. The study involved the utilization of a total of 22 tissue samples which were collected from a cohort of patients. 22 post-op tissue samples of patients with PitNET were included in which 12 of them classified as nonfunctioning cause of the absence of hormonal disturbance in serum. 10 hormone secreting PiTNETs tissue samples were consist of 6 growth hormone secreting tumors, 2 adrenocorticotropic releasing hormone and 1 patient for each of prolactin and luteinizing hormone secreting group. Exclusion criteria included pregnancy, smoking, malnutrition, any other tumor, and any chronic disease presence. Samples obtained during surgery were separated for pathological evaluation and metabolomic analysis. For metabolomic study samples were put into microcentrifuge tubes and stored \u0026minus;\u0026thinsp;86\u0026deg;C until experiment.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSample preparation for NMR\u003c/span\u003e: We utilized a metabolite extraction method with a phosphate buffer, making minor modifications to a previously described procedure, in order to extract hydrophilic metabolites (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Briefly, 100 milligrams of tissue samples, previously stored at -80\u0026deg;C, were weighed and subsequently mixed with 1 mL 20 mM pH 7.2 phosphate buffer. The mixture was homogenized using a beed homogenizer with cooling stage and centrifuged at 10000 g for 20 minutes at 4\u0026deg;C. Following centrifugation, 600 \u0026micro;L of the supernatant was transferred to the NMR tube, followed by the addition of 100 \u0026micro;L of 1.25 mM Sodium 2-(trimethylsilyl)-1-propanesulfonate-d6 (DSS) prepared in deuterium oxide. The complete process was conducted on ice. A pooled sample was prepared by using supernatant of all samples of about 100 \u0026micro;L. This sample named quality control (Qc) sample and was then used to confirmation of determination of metabolites.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\n\u003ch3\u003eIV. Extractions of datasets:\u003c/h3\u003e\n\u003cp\u003eIn this study The NMR study was conducted using a 500 MHz Bruker Avance (Germany, Karlsruhe) located at the ILMER center of Bezmialem Vakif University. The 1D NOESY method was employed with the following parameters: acquisition time of 4 s, spectral width of 12 ppm, time domain of 2 s, delay time of 5 s, mixing time of 0.2 s, dummy scans of 8, scan number of 128, center of the spectrum at 4.7 ppm, and receiver gain set to 101.\u003c/p\u003e \u003cp\u003eAfter completing the 1D NMR experiments on the tissue samples, 100 \u0026micro;L was taken from each sample constituting the experimental groups to prepare quality control (QC) samples separately for each group. The prepared group QC samples were analyzed using a 700 MHz Bruker Neo (Germany, Karlsruhe) NMR spectrometer with cryogenic probe capabilities, located at Gebze T\u0026Uuml;BİTAK MAM in T\u0026uuml;rkiye. The 2D spectra were then recorded. The two-dimensional NMR analyses take an average of 8\u0026ndash;12 hours per sample. Therefore, 2D NMR analyses were performed at least twice only on QC samples. Detailed NMR spectrometry method was given SI. The resulting spectra were finally transferred to COLMARm 13C-1H HSQC, HSQC-TOCSY and TOCSY Query and Verification database (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Unified and isomer-specific NMR metabolomics database for the accurate analysis of 13C\u0026ndash;1H HSQC spectra in Topspin ASCII format. For this purpose, the following settings were used in the COLMARm database program: 1H chemical shift cutoff (ppm) 0.04, 13C chemical shift cutoff (ppm) 0.3, matching ratio cutoff 0.90, peak picking method Deep Picker model 2, peak line shape in peak fitting Gaussian, spectral referencing solvent: water, and compounds as references: DSS. Metabolite identification was performed using the COLMARm database. Subsequently, only the metabolites identified here were included in the Chenomx NMR suite V10.1 program (Chenomx, Inc., Edmonton, AB, Canada) for quantitation in the 1D NOESY spectrum.\u003c/p\u003e \u003cp\u003eThe quantitative analysis of metabolites were performed using the Chenomx NMR Suite V10.1 program. The quantification was based on the results of 1D NOESY experiments, where specific proton signals corresponding to compounds and the internal standard DSS were manually integrated. Phase correction, line broadening (0.3 Hz), and zero-filling factors were automatically adjusted. The chemical shift values of the analytes were calibrated using DSS (0.0 ppm). The relative concentrations of the metabolites were calculated using the 1H NMR spectrum signal intensity method, with DSS serving as the internal standard.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eV. Statistical analysis:\u003c/h2\u003e \u003cp\u003eUnivariate analysis and determination of mean and median values of metabolites were conducted using SPSS version 26. Statistical significance was determined using the Student's t-test and the Mann-Whitney U test, depending on the homogeneity of variances. Spearman correlation analysis was performed on the means of metabolites. The list of identified metabolites obtained through NMR analysis was transferred to MetabolAnalyst 6.0 software for data reduction and visualization. Prior to chemometric analysis, the data were processed through sample normalization by median, cube root transformation, and range-scaling. Metabolites that displayed significant differentiation between groups were subjected to individual testing using the exact Wilcoxon rank-sum test to determine the significance of differences in their levels. Statistically significant differential metabolites were visualized by generating a Volcano Plot, which displays large magnitude fold-changes (x-axis) and high statistical significance (-log10 of p-value, y-axis). Linear discriminant models were constructed using quantified ratios of metabolites as input features. On concentration-based data, visualization was conducted using unsupervised analysis methods, including Partial Least Squares Discriminant Analysis (PLS-DA) followed by the generation of Variable Importance in Projection (VIP) plots. Pathway analyses were performed with MetaboAnalyst 6.0 to assess a set of biologically meaningful metabolites for specific pathways.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eClinical characteristics of participants were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The socio-demographic data comparing the two groups showed no difference in age and gender. To facilitate analysis, the datasets were separated based on the presence of samples.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFunctioning group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10; 7 female, 3 male) Mean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNonfunctioning group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;12; 5 female, 7 male)Mean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.9 (\u0026plusmn;\u0026thinsp;16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.6 (\u0026plusmn;\u0026thinsp;13.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum Glucose (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117.7 (\u0026plusmn;\u0026thinsp;51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.16 (\u0026plusmn;\u0026thinsp;18.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume of Tumor (mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.5 (\u0026plusmn;\u0026thinsp;4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.99 (\u0026plusmn;\u0026thinsp;9.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH (uIU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.86 (\u0026plusmn;\u0026thinsp;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.32 (\u0026plusmn;\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCortisol (ug/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.38 (\u0026plusmn;\u0026thinsp;5.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.84 (\u0026plusmn;\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFSH (mIU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.5 (\u0026plusmn;\u0026thinsp;5.5) Male\u003c/p\u003e \u003cp\u003e26.27 (\u0026plusmn;\u0026thinsp;33.8) Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.54 (\u0026plusmn;\u0026thinsp;9.1) Male\u003c/p\u003e \u003cp\u003e9.4 (\u0026plusmn;\u0026thinsp;12.09) Female\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLH (mIU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.76 (\u0026plusmn;\u0026thinsp;2.23) Male\u003c/p\u003e \u003cp\u003e10.36 (\u0026plusmn;\u0026thinsp;12.9) Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.89 (\u0026plusmn;\u0026thinsp;1.2) Male\u003c/p\u003e \u003cp\u003e2.53 (\u0026plusmn;\u0026thinsp;2.1) Female\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRL (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.39 (\u0026plusmn;\u0026thinsp;14.5) Male\u003c/p\u003e \u003cp\u003e139.02 (\u0026plusmn;\u0026thinsp;315.2) Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.66 (\u0026plusmn;\u0026thinsp;29.1) Male\u003c/p\u003e \u003cp\u003e23.38 (\u0026plusmn;\u0026thinsp;7.2) Female\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGH (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.45 (\u0026plusmn;\u0026thinsp;10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25(\u0026plusmn;\u0026thinsp;0.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIGF-1 (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e418.9 (\u0026plusmn;\u0026thinsp;305.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.5 (\u0026plusmn;\u0026thinsp;29.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACTH (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.27 (\u0026plusmn;\u0026thinsp;26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.78 (\u0026plusmn;\u0026thinsp;12.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eOutcomes of NMR Spectroscopy\u003c/b\u003e: Based on the 1D NOESY 1H and 2D NMR spectroscopy assays, we were able to identify and quantify the following thirty-seven 37 metabolites: acetate, acetoacetate, alanine, arginine, ascorbate, aspartate, choline, creatine, creatine phosphate, ethanolamine, fumarate, glucose, glutamate, glutamine, glycine, histidine, hypoxanthine, isocitrate, isoleucine, lactate, leucine, lysine, methionine, n-acetylaspartate, n-acetylglutamine, o-phosphocholine, PEA, phenylalanine, proline, s-adenosylhomocysteine, taurin, threonine, tyrosine, uracil, valine, myo-I, sn-glycero-3-phosphocholine. The median and mean values of the compounds which has p value of \u0026lt;\u0026thinsp;0.05, classified based on their respective groups, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. For all metabolites are included in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e in Supplementary Information (SI).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAbsolute concentrations of important metabolites selected by fold-change (FC) analysis and t-test results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMETABOLITE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eFunctioning group (\u0026micro;M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eNonfunctioning Group (\u0026micro;M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e- FDR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyo-I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e479.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e631.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2622.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2622.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e3.3883E-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.2684E-4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEA*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1165.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1766.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3924.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4027.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.5569E-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.7605E-4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsoleucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e252.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e135.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e134.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0010839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.010026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e452.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e455.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e341.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e302.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0063019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.037499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutamate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2172.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2855.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1319.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1486.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.008213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.037985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyrosine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e326.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e336.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e197.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e199.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.013319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.049282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e272.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e391.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e451.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e3.1506E-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0038857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenylalanine*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e282.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e394.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e169.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e192.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.010125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.041624\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1233.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1593.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1958.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1640.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0070944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.037499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethionine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e177.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e208.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e132.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0,012718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,047058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eFC values log scaled, p values transformed by -log10, p-value was calculated by the Wilcoxon Mann Whitney test, p value of \u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e*Indicate that have non-normal distribution. PEA: phosphoethanolamine. Arrow marks show the change in level relative to the other group.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSeven out of the top 10 metabolites that were statistically significant according to MetaboAnalyst 6.0 were also found to be significant in univariate analysis conducted with SPSS. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the raw p-values of the metabolites that showed statistically significant differences in functioning and nonfunctioning PitNETs. The nonfunctioning group demonstrated higher concentrations of PEA, myo-I, choline, and glycine, along with lower levels of isoleucine, phenylalanine, valine, glutamate, tyrosine, and methionine in comparison to the functioning group as visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and NMR spectral vision in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e in SI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo evaluate visually the importance of each metabolite to the separation of two groups heatmap graph was conducted as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In the realm of unsupervised multivariate statistical analysis, PCA was employed to evaluate tissue samples through 1D \u003csup\u003e1\u003c/sup\u003eH NMR spectroscopy. The PCA score plots elucidated the differentiation and clustering of data. Specifically, the first two principal components, PC1 and PC2, accounted for 44.5% of the variance (28.2% by PC1 and 16.3% by PC2), as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea. This separation was indicative of distinct groupings within the dataset. Furthermore, a supervised analysis technique, PLS-DA, was applied to the same dataset, comprising 37 identified metabolites from the tissue samples. This analysis revealed a pronounced segregation between the functioning and non-functioning groups, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eb. The efficacy of this method was further substantiated by the performance metrics obtained from a 5-fold cross-validation procedure. Optimal outcomes were demonstrated with two components, manifesting an R\u003csup\u003e2\u003c/sup\u003e value of 0.93 and a Q\u003csup\u003e2\u003c/sup\u003e value of 0.81, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ec. In the analysis of VIP plot within the constructed model, the metabolites myo-I, PEA, choline, isoleucine, and tyrosine emerged as the five principal contributors exhibiting significant influence. This is visually represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ed.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eROC Analysis\u003c/strong\u003e \u003cp\u003eTo evaluate potential biomarker features of metabolites ROC Analysis was conducted that shows predictive accuracies with different features. Linear support vector machine (SVM) and were used for classification method and SVM built-in for feature ranking method. Based on the importance scores, the metabolites were ranked in descending order of significance. These include myo-I, glutamate, isoleucine, ascorbate, phenylalanine, PEA, N-acetylglutamate, and creatine phosphate. It was determined that models incorporating these metabolites achieved an accuracy exceeding 94%. Figures\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e in SI show that models with 2, 3, 5 and 10 feature have a great value of accuracy as 98.3, 97.1, 94.9, and 96, respectively.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePathway Analysis\u003c/b\u003e: Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the pathways influenced by the functioning of Pituitary Neuroendocrine Tumors (PitNETs). The y-axis of the figure represents the p-values, obtained through pathway enrichment analysis, which indicate the statistical significance of pathway involvement. On the other hand, the x-axis represents the pathway impact values, obtained through pathway topology analysis, which assess the overall impact or relevance of each pathway in PitNETs. This integrated approach combining pathway enrichment and topology analysis provides a comprehensive understanding of the key pathways implicated in PitNETs and their significance in the context of tumor function. The colour and size of each circle are based on p-values and pathway impact values. Statistically significant small p-values and larger pathway impact circles signify that the respective pathway has been significantly altered. The selected pathway enrichment analysis method was Globaltest. Notably, pathways with p-values less than 0.05 and impact values greater than 0.1, which are considered statistically significant, are enumerated as follows: Glycine, serine and threonine metabolism; Glycerophospholipid metabolism; Inositol phosphate metabolism; Phenylalanine, tyrosine and tryptophan biosynthesis; Phenylalanine metabolism; Tyrosine metabolism; Histidine metabolism; Cysteine and methionine metabolism. Detailed information, such as exact impact and p-values, and the number of hits, were given in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e in Supplementary data.\u003c/p\u003e "},{"header":"DISCUSSION","content":"\u003cp\u003ePituitary gland tumors, which are a type of endocrine tumor, are more prevalent than initially estimated due to the possibility of being asymptomatic (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). These symptoms may be attributed to the effects of overproduction of the secreted hormone or mass effect, and may include changes in vision, headaches, and signs of increased intracranial pressure. Particularly in cell types that secrete ACTH, there is a possibility of a clinically silent, nonfunctioning tumor transitioning into a functioning one (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This transformation, which indicates the aggressiveness of the tumor, requires a deeper understanding of the pathogenesis of tumor behavior and progression. The current study investigated metabolic perturbations of patients with PitNETs from tissue samples grouped as status of hormone secreting.\u003c/p\u003e \u003cp\u003eIn \u003csup\u003e1\u003c/sup\u003eH-NMR analysis, 10 out of 37 measured metabolites were found to have significant changes. PEA, myo-I, choline, and glycine showed higher concentrations in the nonfunctioning group, whereas isoleucine, phenylalanine, valine, glutamate, tyrosine, and methionine demonstrated higher levels in the functioning group. Based on their VIP scores obtained from PLS-DA the top 5 metabolites were myo-I, PEA, isoleucine, valine and glutamate. PEA and myo-I were identified as outstanding metabolites for differentiating between hormone-secreting and non-secreting pituitary gland tumors. Pathway analysis was conducted to better understand the alterations in these metabolic pathways in tumor biology, including glycine, serine and threonine metabolism; glycerophospholipid metabolism; inositol phosphate metabolism; phenylalanine, tyrosine and tryptophan biosynthesis; phenylalanine metabolism; tyrosine metabolism; histidine metabolism; cysteine and methionine metabolism.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMetabolites in phospholipid metabolism\u003c/b\u003e: O-Phosphoethanolamine, also known as PEA, belongs to the class of phosphoethanolamines that contain a phosphate linked to the second carbon of an ethanolamine. It plays a crucial role in the biosynthesis of both glycerophospholipids and sphingolipids. The levels of this metabolite have been reported to change in various biological materials, such as plasma or tissue samples, in many clinical and pre-clinical metabolomic studies. These studies include experimentally induced diabetic mice, patients with acne vulgaris, major depressive disorder, and endometrial cancer (\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The Kennedy pathways are the primary routes used by mammalian cells for synthesizing phosphatidylcholine (PC) and phosphatidylethanolamine (PE), making them fundamentally important in the biosynthesis of phospholipids. These two phospholipids are the most abundant in mammalian cells (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). PEA is a precursor of PE, which is an indicator of brain phospholipid turnover. Given that the PEA levels in tissue samples of hypophysis adenoma were 4\u0026ndash;5 times higher than in other brain tissues, increased levels of PEA may also be an indicator of pituitary cell membrane synthesis and signal transduction (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Numerous studies have demonstrated a noteworthy decrease of PEA levels in tumoral pituitary tissue comparing non-tumoral one (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The decrease in PEA levels may lead to tumorigenesis by eliminating its inhibitory effect, as previously suggested by \u003cem\u003ein-vivo\u003c/em\u003e and \u003cem\u003ein-vitro\u003c/em\u003e experiments (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The presence of PEA hinders the normal function of the mitochondrial electron transport chain (ETC) and inhibits mitochondrial activity, thus contributing to tumorigenesis (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Based on the results of our study, it could be hypothesized that the observed increase in PEA levels in the nonfunctioning group may have reduced hormone synthesis via the suppression of mitochondrial activity. Another way of synthesizing PE in mammalian cells is through the phosphatidylserine (PS) decarboxylation pathway, which only occurs in mitochondria and uses phosphatidylserine decarboxylase to decarboxylate PS to PE (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Increasing the serine amino acid due to the decarboxylation of PS could contribute to the transformation of serine to glycine and lead to increased glycine levels, which correlated with PEA levels in the nonfunctioning group (r: 0.923). The functioning group also showed a correlation between glycine levels and PEA levels, but with a weaker correlation coefficient (r: 0.806).\u003c/p\u003e \u003cp\u003e \u003cb\u003eCholine\u003c/b\u003e: Being a significant supplier of methyl groups in metabolism, choline is an indispensable nutrient for humans (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). It serves as a precursor to three important compounds: the neurotransmitter acetylcholine, and the membrane lipids phosphatidylcholine and sphingomyelin (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Several studies have demonstrated alterations in choline metabolism during the process of malignant cellular transformation, as well as in major depressive disorder in brain tissue (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Nevertheless, increased choline levels could be an indicator of tumor proliferation in suprasellar tumors (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). In our study, choline levels were statistically higher in the nonfunctioning group compared to the functioning group (451.6 and 272, respectively). Contrarily, Ijare et al. found decreased levels of choline-containing phospholipids in the nonfunctioning group compared to the LH/FSH-secreting group in ex vivo hypophysis tissue samples (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Another study found high choline levels in pituitary adenomas compared to normal brain tissue (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Given that phosphatidylethanolamine (PE) is synthesized from PEA and that choline is essential for the synthesis of phosphatidylcholine (PC), the observed increase in PEA and choline levels in the nonfunctioning group suggests that these molecules play a prominent role in the synthesis and secretion of hormones in the pituitary gland (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The observed difference in levels of these metabolites may be attributed to synthesis and subsequently exocytosis of hormones. The functioning group may have had lower levels of choline and PEA as a result of increased turnover and consumption of both membrane and intracellular phospholipids occurring during the synthesis and exocytosis of these hormones.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMyo-Inositol\u003c/strong\u003e \u003cp\u003eThe sugar alcohol known as myo-I significantly participates in signal transduction, protein phosphorylation, and numerous pathways related to genetic metabolism. Furthermore, inositol/myo-I represents a crucial constituent of the lipids denominated as phosphatidylinositol (PI) and phosphatidylinositol phosphate (PIP), which are involved in a multitude of biological processes such as signal transduction, cell proliferation, differentiation, and apoptosis (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Our pathway analysis results (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e) reveal that myo-inositol participates in inositol phosphate, galactose, ascorbate, and aldarate metabolism, as well as in the phosphatidylinositol signaling system as an intermediate metabolite. Therefore, changes in these pathways may distinguish between functioning and nonfunctioning endocrine tumors. Myo-I is considered one of the most abundant metabolites in the normal brain. Studies have demonstrated a reduction in myo-I levels in pituitary samples that secrete PRL in comparison to other types of hormone-secreting pituitary adenomas, as well as nonfunctioning adenomas (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In the current study, higher levels of myo-I were observed in the nonfunctioning group compared to the functioning group (2622.7 and 631.3, respectively). The complexity observed in our results may be attributed to the small number of patients (n\u0026thinsp;=\u0026thinsp;1) with PRL secretion in our functioning tumor patient group. Myo-I is a molecule that helps with cell osmoregulation in metabolism and has been found to have reduced levels in cancerous cells of the lung, breast, colon, and thyroid (\u003cspan additionalcitationids=\"CR40 CR41\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). In particular, Deja et al have shown that myo-I levels decrease in malignant thyroid tumors, which are neuroendocrine tumors, but not in benign lesions such as non-neoplastic nodules and follicular adenomas (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAmino Acids\u003c/strong\u003e \u003cp\u003eNearly every metabolic alteration and pathological condition may entail modifications in amino acid metabolism (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). For a peptide hormone-secreting tumor, it is expected that the levels of amino acids would decrease in the functioning group. An increase in these amino acids in the hormone-secreting cells, rather than a reduction, could be due to the induced flow through the bloodstream to the gland, increased de novo amino acid synthesis, and turnover of amino acids. The fact that almost all of the amino acids with increased levels are glucogenic confirms that there is an increase in energy metabolism in tissues with increased hormone secretion. In a study, it was shown that glutamate levels, which also serve as a neurotransmitter in the central nervous system, were elevated in pituitary gland tumors that secrete PRL, while they were decreased in nonfunctioning and other subtypes (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). There have been also several studies on cancer metabolism, including endocrine tumors, that have revealed fluctuations in glutamate levels (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). A study concluded that the serum valine, another amino acid, levels in hypopituitary males were higher than those in healthy controls, consistent with our findings of increased levels in the functioning group. This suggests that the amino acid valine plays an active role in hormone synthesis. Furthermore, the lower levels observed in individuals with congenital hypopituitarism compared to those with acquired hypopituitarism may indicate that this deficiency is responsible for the inability to synthesize hormones (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Contrary to our findings, a study conducted by Ljare et al. in patients with PitNET revealed higher levels of phenylalanine and tyrosine in nonfunctioning PitNETs. However, the changes in tyrosine levels were reported as nonsignificant (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). The inconsistency in these results highlights the need for further research in this area. In our study, it was observed that, unlike all the other amino acids, glycine concentrations were lower in the functioning group compared to those in the nonfunctioning group. It is known that glycine has anti-inflammatory and anti-cancer effects by providing methionine clearance (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Such a mechanism might offer an explanation for the reduced glycine and increased methionine levels observed in the functioning group of our study. Another study has demonstrated that GH is capable of modulating circulating glycine levels in plasma (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). The observed decrease in glycine levels in the functioning group may be attributed to the suppressive effect of GH on glycine, especially since the functioning group has the highest number of patients with the GH-secreting subtype.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eIn subjects with nonfunctioning PitNETs, detection typically occurs due to the compression of surrounding tissues by the tumor or incidentally. In our study, the mean tumor volume in nonfunctioning PitNETs was higher than in the other group (55.6 mm\u0026sup3; vs. 43.9 mm\u0026sup3;, respectively). The ability to synthesize hormones may have enabled earlier diagnosis.\u003c/p\u003e \u003cp\u003eThe measurement of blood hormone levels in patients with PitNETs is not always reliable for diagnostic or screening purposes. Early-stage tumors, intermittent hormone secretion, hormonal subunits, or localized hormone effects can result in blood hormone levels falling within the normal reference range, despite underlying abnormalities. Histopathological evaluation, including staining techniques, is therefore critical in the diagnosis of pituitary adenomas. In our study, we observed significant variability in preoperative blood hormone levels among patients, even within the same group and subtype, leading to high standard deviations (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e).\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eCancer biology and related research areas have gained notable attention, and metabolomics has emerged as a comprehensive method for analyzing and understanding metabolism. In this study, post-operative tissue samples of PitNETs were evaluated using \u003csup\u003e1\u003c/sup\u003eH-NMR to identify metabolic differences between functioning and nonfunctioning tumors. The results showed that PEA, myo-I, choline, and glycine levels were higher in the nonfunctioning group, while isoleucine, methionine, valine, glutamate, phenylalanine, and tyrosine levels were higher in the functioning group. These differences could lead to variations in metabolic processes and signaling pathways. Consequently, functioning and nonfunctioning PitNETs exhibit divergent metabolic properties. Increased levels of PEA in the nonfunctioning group may have reduced hormone synthesis via the suppression of mitochondrial activity, potentially contributing to tumorigenesis. The exploration of the underlying mechanisms driving hormone secretion necessitates the utilization of sophisticated and distinct methodologies. However, the small sample size and the lack of a control group using normal tissue due to ethical constraints were significant limitations of this investigation. Studies including a larger number of patients with different subtypes, as well as comparisons with normal pituitary tissue, are necessary to obtain more reliable results. Additionally, further research is needed to confirm these findings, enhance our understanding of advanced biological processes, and apply this knowledge in clinical applications such as biomarker research, drug development, and treatment processes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003ePitNET: \u0026nbsp;\u003c/strong\u003ePituitary Neuroendocrine Tumor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNMR:\u0026nbsp;\u003c/strong\u003eNuclear Magnetic Resonance\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePEA:\u0026nbsp;\u003c/strong\u003eO-Phosphoethanolamine\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMyo-I:\u0026nbsp;\u003c/strong\u003eMyo-Inositol\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDSS:\u0026nbsp;\u003c/strong\u003eSodium 2-(trimethylsilyl)-1-propanesulfonate-d6\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOESY:\u0026nbsp;\u003c/strong\u003eNuclear Overhauser Effect Spectroscopy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHSQC:\u0026nbsp;\u003c/strong\u003eHeteronuclear Single Quantum Coherence Spectra\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTOCSY:\u0026nbsp;\u003c/strong\u003eTotal Correlation Spectroscopy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePLS-DA:\u0026nbsp;\u003c/strong\u003ePartial Least Squares Regression Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVIP:\u0026nbsp;\u003c/strong\u003eVariable Importance Projection\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTSH:\u0026nbsp;\u003c/strong\u003eThyroid Stimulating Hormone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFSH:\u0026nbsp;\u003c/strong\u003eFollicle Stimulating Hormone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLH:\u0026nbsp;\u003c/strong\u003eLuteinizing Hormone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePRL:\u0026nbsp;\u003c/strong\u003eProlactin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACTH:\u0026nbsp;\u003c/strong\u003eAdrenocorticotropic hormone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGH:\u0026nbsp;\u003c/strong\u003eGrowth Hormone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIGF-1: \u0026nbsp;\u003c/strong\u003eInsulin-like growth factor 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFC :\u0026nbsp;\u003c/strong\u003eFold-Change\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCA:\u0026nbsp;\u003c/strong\u003ePrincipal Components Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePE:\u0026nbsp;\u003c/strong\u003ePhosphatidylethanolamine\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePC:\u0026nbsp;\u003c/strong\u003ePhosphatidylcholine\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePS:\u003c/strong\u003e Phosphatidylserine\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Approval for the study was obtained from the clinical research ethics committee of Bezmialem Vakif University on November 17, 2021 (Number: E.40190). A comprehensive verbal and written explanation of the study particulars was provided to all participants, and their informed consent was obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data obtained from the study are available at Metabolomics www.Workbench.org under Data Track ID 4665 and study number ST003122.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Conflict of Interest Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003eFatmanur Koktasoglu declares that she has no conflict of interest.\u003c/p\u003e\n\u003cp\u003eMetin Demirel declares that he has no conflict of interest.\u003c/p\u003e\n\u003cp\u003eHalime Dulun Agac declares that she has no conflict of interest.\u003c/p\u003e\n\u003cp\u003eMehtap Alim declares that she has no conflict of interest.\u003c/p\u003e\n\u003cp\u003eUfuk Sarikaya declares that he has no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u0026Ouml;yk\u0026uuml; Dağdeviren\u0026nbsp;declares that she has no conflict of interest.\u003c/p\u003e\n\u003cp\u003eMerve \u0026Ccedil;avuşoğlu\u0026nbsp;declares that she has no conflict of interest.\u003c/p\u003e\n\u003cp\u003eKerime Akdur\u0026nbsp;declares that she has no conflict of interest.\u003c/p\u003e\n\u003cp\u003eB\u0026uuml;şra Karacam\u0026nbsp;declares that she has no conflict of interest.\u003c/p\u003e\n\u003cp\u003eSomer Bekiroglu\u0026nbsp;declares that he has no conflict of interest.\u003c/p\u003e\n\u003cp\u003eSahabettin Selek declares that he has no conflict of interest.\u003c/p\u003e\n\u003cp\u003eMustafa Aziz Hatiboğlu declares that he has no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe funding for this study was provided by the Department of Scientific Research Projects at Bezmialem Vakif University with the project number 20211210. The study design, data collection, analysis, interpretation, report writing, and decision to submit the article for publication were carried out independently without any support or involvement from the funder company.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eF.K. and M.D. wrote the main manuscript and conducted the metabolomic analysis.\u003cbr\u003e\u0026nbsp;H.D.A. and M.A. prepared graphs and figures.\u003cbr\u003e\u0026nbsp;US, \u0026Ouml;.D. and B.K. conducted laboratory preparations for metabolomics research.\u003cbr\u003e\u0026nbsp;K.A. and M.\u0026Ccedil;. carried out clinical organizations and obtained ethical approval from patients.\u003cbr\u003e\u0026nbsp;S.B. conducted 2D NMR analysis at TUBITAK MAM Research Center.\u003cbr\u003e\u0026nbsp;M.A.H. and Ş.S. supervised all stages of the study and critically analyzed the reports.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to express their sincere gratitude to Ahmet Balcı and Şule Yal\u0026ccedil;ın from the Bezmialem Vakıf University Drug Application and Research Center for their invaluable contributions to the Nuclear Magnetic Resonance (NMR) analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMelmed S. Pathogenesis of pituitary tumors. Nat Reviews Endocrinol. 2011;7(5):257\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsa SL, Mete O, Cusimano MD, McCutcheon IE, Perry A, Yamada S, et al. Pituitary neuroendocrine tumors: a model for neuroendocrine tumor classification. Mod Pathol. 2021;34(9):1634\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsa SL, Mete O, Perry A, Osamura RY. Overview of the 2022 WHO classification of pituitary tumors. Endocr Pathol. 2022;33(1):6\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQin J, Li K, Wang X, Bao Y. A comparative study of functioning and non-functioning pituitary adenomas. Medicine. 2021;100(14).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMelmed S, Kaiser UB, Lopes MB, Bertherat J, Syro LV, Raverot G, et al. Clinical biology of the pituitary adenoma. 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Metabolites. 2023;13(2):191.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrouillas J, Jaffrain-Rea M-L, Vasiljevic A, Raverot G, Roncaroli F, Villa C. How to Classify Pituitary Neuroendocrine Tumors (PitNET)s in 2020. Cancers. 2020;12(2):514.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pituitary neuroendocrine tumors (PitNETs), Metabolomics, Hormone secretion, O-Phosphoethanolamine (PEA)","lastPublishedDoi":"10.21203/rs.3.rs-4761839/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4761839/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePituitary neuroendocrine tumors (PitNETs) are heterogeneous neoplasms originating from the pituitary gland. Metabolomics, a comprehensive analysis of small molecules, has emerged as a valuable tool for studying pituitary tumors. In the presen investigation, a metabolomic methodology was employed to facilitate a more comprehensive understanding of tumor pathogenesis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eNuclear Magnetic Resonance (NMR) Spectroscopy was utilized to investigate the metabolic profiles of hypophyseal tissue samples obtained from 22 patients with PitNETs, who underwent excisional surgery and exhibited varying hormone secretion statuses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUsing NMR analysis, we identified 10 metabolites with significant changes, including O-Phosphoethanolamine (PEA), myo-Inositol (I), choline, and several amino acids in tissue samples. In the non-functioning (NF) group, elevated levels of PEA, myo-I, Glycine, and Choline were observed, whereas Glutamate, Phenylalanine, Valine, Isoleucine, Tyrosine, and Methionine exhibited decreased levels in the same group. Phospholipid metabolism, inositol phosphate metabolism, and amino acid metabolism are proposed as potential mechanisms underlying the secretory characteristics of tumor tissue.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eFunctioning and nonfunctioning PitNETs display distinct metabolic characteristics. Elevated PEA levels observed in the nonfunctioning group might have inhibited hormone synthesis by suppressing mitochondrial activity, which could potentially contribute to the development of tumors. Further research is warranted to validate these findings and explore their potential clinical applications, such as biomarker discovery and therapeutic targeting\u003c/p\u003e","manuscriptTitle":"Characterizing Hormone Secretion Patterns in PitNETs with Metabolomics: Implications for Understanding Tumor Biology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-17 02:38:41","doi":"10.21203/rs.3.rs-4761839/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d52232a8-119d-4fc7-a3f4-3ac138242075","owner":[],"postedDate":"August 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-18T06:39:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-17 02:38:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4761839","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4761839","identity":"rs-4761839","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}