Studies of Secondary Melanoma on C57BL/6J Mouse Liver Using 1H NMR Metabolomics

Accumulation of Linoleic Acid by Altered Peroxisome Proliferator-Activated Receptor-α Signaling Is Associated with Age-Dependent Hepatocarcinogenesis in Ppara Transgenic Mice

Long-term ligand activation of PPARα in mice causes hepatocarcinogenesis through a mechanism that requires functional PPARα. However, hepatocarcinogenesis is diminished in both Ppara-null and PPARA-humanized mice, yet both lines develop age-related liver cancer independently of treatment with a PPARα agonist. Since PPARα is a master regulator of liver lipid metabolism in the liver, lipidomic analyses were carried out in wild-type, Ppara-null, and PPARA-humanized mice treated with and without the potent agonist GW7647. The levels of hepatic linoleic acid in Ppara-null and PPARA-humanized mice were markedly higher compared to wild-type controls, along with overall fatty liver. The number of liver CD4+ T cells was also lower in Ppara-null and PPARA-humanized mice and was negatively correlated with the elevated linoleic acid. Moreover, more senescent hepatocytes and lower serum TNFα and IFNγ levels were observed in Ppara-null and PPARA-humanized mice with age. These studies suggest a new role for PPARα in age-associated hepatocarcinogenesis due to altered lipid metabolism in Ppara-null and PPARA-humanized mice and the accumulation of linoleic acid as part of an overall fatty liver that is associated with loss of CD4+ T cells in the liver in both transgenic models. Since fatty liver is a known causal risk factor for liver cancer, Ppara-null and PPARA-humanized mice are valuable models for examining the mechanisms of PPARα and age-dependent hepatocarcinogenesis.

Skin Cancer Diagnosis Based on Neutrosophic Features with a Deep Neural Network

Recent years evidenced an increase in the total number of skin cancer cases, and it is projected to grow exponentially. This paper proposes a computer-aided diagnosis system for the classification of a malignant lesion, where the acquired image is primarily pre-processed using novel methods. Digital artifacts such as hair follicles and blood vessels are removed, and thereafter, the image is enhanced using a novel method of histogram equalization. Henceforth, the pre-processed image undergoes the segmentation phase, where the suspected lesion is segmented using the Neutrosophic technique. The segmentation method employs a thresholding-based method along with a pentagonal neutrosophic structure to form a segmentation mask of the suspected skin lesion. The paper proposes a deep neural network base on Inception and residual blocks with softmax block after each residual block which makes the layer wider and easier to learn the key features more quickly. The proposed classifier was trained, tested, and validated over PH2, ISIC 2017, ISIC 2018, and ISIC 2019 datasets. The proposed segmentation model yields an accuracy mark of 99.50%, 99.33%, 98.56% and 98.04% for these datasets, respectively. These datasets are augmented to form a total of 103,554 images for training, which make the classifier produce enhanced classification results. Our experimental results confirm that the proposed classifier yields an accuracy score of 99.50%, 99.33%, 98.56%, and 98.04% for PH2, ISIC 2017, 2018, and 2019, respectively, which is better than most of the pre-existing classifiers.

Deep Learning-Based Fully Automated Diagnosis of Melanocytic Lesions by Using Whole Slide Images

Background Erroneous diagnoses of melanocytic lesions (benign, atypical, and malignant types) result in inappropriate surgical treatment plans.Objective To propose a deep learning (DL)-based fully automated diagnostic method using whole slide images (WSIs) for melanocytic lesions.Methods The method consisted of patch prediction using a DL model and patient diagnosis using an aggregation module. The method was developed with 745 WSIs, and evaluated using internal and external testing sets comprising 182 WSIs and 54 WSIs, respectively. The results were compared with those of the classification by one junior and two senior pathologists. Furthermore, we compared the performance of the three pathologists in the classification of melanocytic lesions with and without the assistance of our method.Results The method achieved an accuracy of 0.963 and 0.930 on the internal and external testing sets, respectively, which was significantly higher than that of the junior pathologist (0.419 and 0.535). With assistance from the method, all three pathologists achieved higher accuracy on the internal and external testing sets; the accuracy of the junior pathologist increased by 39.0% and 30.2%, respectively (p < 0.05).Conclusion This generalizable method can accurately classify melanocytic lesions and effectively improve the diagnostic accuracy of pathologists.

Polydextrose with and without Bifidobacterium animalis ssp. lactis 420 drives the prevalence of Akkermansia and improves liver health in a multi-compartmental obesogenic mice study

The past two decades of research have raised gut microbiota composition as a contributing factor to the development of obesity, and higher abundance of certain bacterial species has been linked to the lean phenotype, such as Akkermansia muciniphila. The ability of pre- and probiotics to affect metabolic health could be via microbial community alterations and subsequently changes in metabolite profiles, modulating for example host energy balance via complex signaling pathways. The aim of this mice study was to determine how administration of a prebiotic fiber, polydextrose (PDX) and a probiotic Bifidobacterium animalis ssp. lactis 420 (B420), during high fat diet (HFD; 60 kcal% fat) affects microbiota composition in the gastrointestinal tract and adipose tissue, and metabolite levels in gut and liver. In this study C57Bl/6J mice (N = 200) were split in five treatments and daily gavaged: 1) Normal control (NC); 2) HFD; 3) HFD + PDX; 4) HFD + B420 or 5) HFD + PDX + B420 (HFD+S). At six weeks of treatment intraperitoneal glucose-tolerance test (IPGTT) was performed, and feces were collected at weeks 0, 3, 6 and 9. At end of the intervention, ileum and colon mucosa, adipose tissue and liver samples were collected. The microbiota composition in fecal, ileum, colon and adipose tissue was analyzed using 16S rDNA sequencing, fecal and liver metabolomics were performed by nuclear magnetic resonance (NMR) spectroscopy. It was found that HFD+PDX intervention reduced body weight gain and hepatic fat compared to HFD. Sequencing the mice adipose tissue (MAT) identified Akkermansia and its prevalence was increased in HFD+S group. Furthermore, by the inclusion of PDX, fecal, lleum and colon levels of Akkermansia were increased and liver health was improved as the detoxification capacity and levels of methyl-donors were increased. These new results demonstrate how PDX and B420 can affect the interactions between gut, liver and adipose tissue.

One extraction tool for in vitro-in vivo extrapolation? SPME-based metabolomics of in vitro 2D, 3D, and in vivo mouse melanoma models

Solid phase microextraction (SPME) in combination with high-resolution mass spectrometry was employed for the determination of metabolomic profile of mouse melanoma growth within in vitro 2D, in vitro 3D, and in vivo models. Such multi-model approach had never been investigated before. Due to the low-invasiveness of SPME, it was possible to perform time-course analysis, which allowed building time profile of biochemical reactions in the studied material. Such approach does not require the multiplication of samples as subsequent analyses are performed from the very same cell culture or from the same individual. SPME already reduces the number of animals required for experiment; therefore, it is with good concordance with the 3Rs rule (replacement, reduction, and refinement). Among tested models, the largest number of compounds was found within the in vitro 2D cell culture model, while in vivo and in vitro 3D models had the lowest number of detected compounds. These results may be connected with a higher metabolic rate, as well as lower integrity of the in vitro 2D model compared to the in vitro 3D model resulting in a lower number of compounds released into medium in the latter model. In terms of in vitro-in vivo extrapolation, the in vitro 2D model performed more similar to in vivo model compared to in vitro 3D model; however, it might have been due to the fact that only compounds secreted to medium were investigated. Thus, in further experiments to obtain full metabolome information, the intraspheroidal assessment or spheroid dissociation would be necessary.

Targeted Metabolomics Identifies Plasma Biomarkers in Mice with Metabolically Heterogeneous Melanoma Xenografts

Melanomas are genetically and metabolically heterogeneous, which influences therapeutic efficacy and contributes to the development of treatment resistance in patients with metastatic disease. Metabolite phenotyping helps to better understand complex metabolic diseases, such as melanoma, and facilitates the development of novel therapies. Our aim was to characterize the tumor and plasma metabolomes of mice bearing genetically different melanoma xenografts. We engrafted the human melanoma cell lines A375 (BRAF mutant), WM47 (BRAF mutant), WM3000 (NRAS mutant), and WM3311 (BRAF, NRAS, NF1 triple-wildtype) and performed a broad-spectrum targeted metabolomics analysis of tumor and plasma samples obtained from melanoma-bearing mice as well as plasma samples from healthy control mice. Differences in ceramide and phosphatidylcholine species were observed between melanoma subtypes irrespective of the genetic driver mutation. Furthermore, beta-alanine metabolism differed between melanoma subtypes and was significantly enriched in plasma from melanoma-bearing mice compared to healthy mice. Moreover, we identified beta-alanine, p-cresol sulfate, sarcosine, tiglylcarnitine, two dihexosylceramides, and one phosphatidylcholine as potential melanoma biomarkers in plasma. The present data reflect the metabolic heterogeneity of melanomas but also suggest a diagnostic biomarker signature for melanoma screening.

The Universal Soldier: Enzymatic and Non-Enzymatic Antioxidant Functions of Serum Albumin

As a carrier of many biologically active compounds, blood is exposed to oxidants to a greater extent than the intracellular environment. Serum albumin plays a key role in antioxidant defence under both normal and oxidative stress conditions. This review evaluates data published in the literature and from our own research on the mechanisms of the enzymatic and non-enzymatic activities of albumin that determine its participation in redox modulation of plasma and intercellular fluid. For the first time, the results of numerous clinical, biochemical, spectroscopic and computational experiments devoted to the study of allosteric modulation of the functional properties of the protein associated with its participation in antioxidant defence are analysed. It has been concluded that it is fundamentally possible to regulate the antioxidant properties of albumin with various ligands, and the binding and/or enzymatic features of the protein by changing its redox status. The perspectives for using the antioxidant properties of albumin in practice are discussed.

Melanoma Diagnosis Using Deep Learning and Fuzzy Logic

Melanoma or malignant melanoma is a type of skin cancer that develops when melanocyte cells, damaged by excessive exposure to harmful UV radiations, start to grow out of control. Though less common than some other kinds of skin cancers, it is more dangerous because it rapidly metastasizes if not diagnosed and treated at an early stage. The distinction between benign and melanocytic lesions could at times be perplexing, but the manifestations of the disease could fairly be distinguished by a skilled study of its histopathological and clinical features. In recent years, deep convolutional neural networks (DCNNs) have succeeded in achieving more encouraging results yet faster and computationally effective systems for detection of the fatal disease are the need of the hour. This paper presents a deep learning-based 'You Only Look Once (YOLO)' algorithm, which is based on the application of DCNNs to detect melanoma from dermoscopic and digital images and offer faster and more precise output as compared to conventional CNNs. In terms with the location of the identified object in the cell, this network predicts the bounding box of the detected object and the class confidence score. The highlight of the paper, however, lies in its infusion of certain resourceful concepts like two phase segmentation done by a combination of the graph theory using minimal spanning tree concept and L-type fuzzy number based approximations and mathematical extraction of the actual affected area of the lesion region during feature extraction process. Experimented on a total of 20250 images from three publicly accessible datasets-PH2, International Symposium on Biomedical Imaging (ISBI) 2017 and The International Skin Imaging Collaboration (ISIC) 2019, encouraging results have been obtained. It achieved a Jac score of 79.84% on ISIC 2019 dataset and 86.99% and 88.64% on ISBI 2017 and PH2 datasets, respectively. Upon comparison of the pre-defined parameters with recent works in this area yielded comparatively superior output in most cases.

Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm

Skin lesion segmentation has a critical role in the early and accurate diagnosis of skin cancer by computerized systems. However, automatic segmentation of skin lesions in dermoscopic images is a challenging task owing to difficulties including artifacts (hairs, gel bubbles, ruler markers), indistinct boundaries, low contrast and varying sizes and shapes of the lesion images. This paper proposes a novel and effective pipeline for skin lesion segmentation in dermoscopic images combining a deep convolutional neural network named as You Only Look Once (YOLO) and the GrabCut algorithm. This method performs lesion segmentation using a dermoscopic image in four steps: 1. Removal of hairs on the lesion, 2. Detection of the lesion location, 3. Segmentation of the lesion area from the background, 4. Post-processing with morphological operators. The method was evaluated on two publicly well-known datasets, that is the PH2 and the ISBI 2017 (Skin Lesion Analysis Towards Melanoma Detection Challenge Dataset). The proposed pipeline model has achieved a 90% sensitivity rate on the ISBI 2017 dataset, outperforming other deep learning-based methods. The method also obtained close results according to the results obtained from other methods in the literature in terms of metrics of accuracy, specificity, Dice coefficient, and Jaccard index.

Impact of phytosterols on liver and distal colon metabolome in experimental murine colitis model: an explorative study

Phytosterols are known to reduce plasma cholesterol levels and thereby reduce cardiovascular risk. Studies conducted on human and animal models have demonstrated that these compounds have also anti-inflammatory effects. Recently, an experimental colitis model (dextran sulphate sodium-induced) has shown that pre-treatment with phytosterols decreases infiltration of inflammatory cells and accelerates mucosal healing. This study aims to understand the mechanism underlying the colitis by analysing the end-products of the metabolism in distal colon and liver excised from the same mice used in the previous work. In particular, an unsupervised gas chromatography-mass spectrometry (GC-MS) and NMR based metabolomics approach was employed to identify the metabolic pathways perturbed by the dextran sodium sulphate (DSS) insult (i.e. Krebs cycle, carbohydrate, amino acids, and nucleotide metabolism). Interestingly, phytosterols were able to restore the homeostatic equilibrium of the hepatic and colonic metabolome.

Assessment of MR-based R2* and quantitative susceptibility mapping for the quantification of liver iron concentration in a mouse model at 7T

PURPOSE

To assess the feasibility of quantifying liver iron concentration (LIC) using R2* and quantitative susceptibility mapping (QSM) at a high field strength of 7 Tesla (T).

METHODS

Five different concentrations of Fe-dextran were injected into 12 mice to produce various degrees of liver iron overload. After mice were sacrificed, blood and liver samples were harvested. Ferritin enzyme-linked immunosorbent assay (ELISA) and inductively coupled plasma mass spectrometry were performed to quantify serum ferritin concentration and LIC. Multiecho gradient echo MRI was conducted to estimate R2* and the magnetic susceptibility of each liver sample through complex nonlinear least squares fitting and a morphology enabled dipole inversion method, respectively.

RESULTS

Average estimates of serum ferritin concentration, LIC, R2*, and susceptibility all show good linear correlations with injected Fe-dextran concentration; however, the standard deviations in the estimates of R2* and susceptibility increase with injected Fe-dextran concentration. Both R2* and susceptibility measurements also show good linear correlations with LIC (R2  = 0.78 and R2  = 0.91, respectively), and a susceptibility-to-LIC conversion factor of 0.829 ppm/(mg/g wet) is derived.

CONCLUSION

The feasibility of quantifying LIC using MR-based  R2* and QSM at a high field strength of 7T is demonstrated. Susceptibility quantification, which is an intrinsic property of tissues and benefits from being field-strength independent, is more robust than R2* quantification in this ex vivo study. A susceptibility-to-LIC conversion factor is presented that agrees relatively well with previously published QSM derived results obtained at 1.5T and 3T.

Discovery of potential biomarkers in human melanoma cells with different metastatic potential by metabolic and lipidomic profiling

Malignant melanoma, characterized by its ability to metastasize to other organs, is responsible for 90% of skin cancer mortality. To investigate alterations in the cellular metabolome and lipidome related to melanoma metastasis, gas chromatography-mass spectrometry (GC-MS) and direct infusion-mass spectrometry (DI-MS)-based metabolic and lipidomic profiling were performed on extracts of normal human melanocyte (HEMn-LP), low metastatic melanoma (A375, G361), and highly metastatic melanoma (A2058, SK-MEL-28) cell lines. In this study, metabolomic analysis identified aminomalonic acid as a novel potential biomarker to discriminate between different stages of melanoma metastasis. Uptake and release of major metabolites as hallmarks of cancer were also measured between high and low metastatic melanoma cells. Lipid analysis showed a progressive increase in phosphatidylinositol (PI) species with saturated and monounsaturated fatty acyl chains, including 16:0/18:0, 16:0/18:1, 18:0/18:0, and 18:0/18:1, with increasing metastatic potential of melanoma cells, defining these lipids as possible biomarkers. In addition, a partial-least-squares projection to latent structure regression (PLSR) model for the prediction of metastatic properties of melanoma was established, and central metabolic and lipidomic pathways involved in the increased motility and metastatic potential of melanoma cells were identified as therapeutic targets. These results could be used to diagnose and control of melanoma metastasis.

Proton NMR characterization of intact primary and metastatic melanoma cells in 2D & 3D cultures

Objective

To characterize the differences between the primary and metastatic melanoma cell lines grown in 2D cultures and 3D cultures.

Methods

Primary melanoma cells (WM115) and metastatic melanoma cells (WM266) extracted from a single donor was cultured in 2D as well as 3D cultures. These cells were characterized using proton NMR spectrometry, and the qualitative chemical shifts markers were identified and discussed.

Results

In monolayer culture (2D), we observed one qualitative chemical shift marker for primary melanoma cells. In spheroid cultures (3D), we observed nine significant chemical shifts, of which eight markers were specific for primary melanoma spheroids, whereas the other one marker was specific to metastatic melanoma spheroids. This study suggests that the glucose accumulation and phospholipid composition vary significantly between the primary and metastatic cells lines that are obtained from a single donor and also with the cell culturing methods. 14 qualitative chemical shift markers were obtained in the comparison between monolayer culture and spheroids cultures irrespective of the differences in the cell lines. Among which 4 were unique to monolayer cultures whereas 10 chemical shifts were unique to the spheroid cultures. This study also shows that the method of cell culture would drastically affect the phospholipid composition of the cells and also depicts that the cells in spheroid culture closely resembles the cells in vivo.

Conclusion

This study shows the high specificity of proton NMR spectrometry in characterizing cancer cell lines and also shows the variations in the glucose accumulation and phospholipid composition between the primary and metastatic melanoma cell lines from the same donor. Differences in the cell culture method does plays an important role in phospholipid composition of the cells.

Electronic supplementary material

The online version of this article (doi:10.1186/s40659-017-0117-8) contains supplementary material, which is available to authorized users.

NMR metabolic fingerprints of murine melanocyte and melanoma cell lines: application to biomarker discovery

Melanoma is the most aggressive type of skin cancer and efforts to improve the diagnosis of this neoplasia are largely based on the use of cell lines. Metabolomics is currently undergoing great advancements towards its use to screening for disease biomarkers. Although NMR metabolomics includes both 1D and 2D methodologies, there is a lack of data in the literature regarding heteronuclear 2D NMR assignments of the metabolome from eukaryotic cell lines. The present study applied NMR-based metabolomics strategies to characterize aqueous and lipid extracts from murine melanocytes and melanoma cell lines with distinct tumorigenic potential, successfully obtaining fingerprints of the metabolites from the extracts of the cell lines by means of 2D NMR HSQC correlation maps. Relative amounts of the identified metabolites were compared between the 4 cell lines. Multivariate analysis of ¹H NMR data was able not only to differentiate the melanocyte cell line from the tumorigenic ones but also distinguish among the 3 tumorigenic cell lines. We also investigated the effects of mitogenic agents, and found that they can markedly influence the metabolome of the melanocyte cell line, resembling the pattern of most proliferative cell lines.

Characterization of hepatic fatty acids in mice with reduced liver fat by ultra-short echo time (1)H-MRS at 14.1 T in vivo

Alterations in the hepatic lipid content (HLC) and fatty acid composition are associated with disruptions in whole body metabolism, both in humans and in rodent models, and can be non-invasively assessed by (1)H-MRS in vivo. We used (1)H-MRS to characterize the hepatic fatty-acyl chains of healthy mice and to follow changes caused by streptozotocin (STZ) injection. Using STEAM at 14.1 T with an ultra-short TE of 2.8 ms, confounding effects from T2 relaxation and J-coupling were avoided, allowing for accurate estimations of the contribution of unsaturated (UFA), saturated (SFA), mono-unsaturated (MUFA) and poly-unsaturated (PUFA) fatty-acyl chains, number of double bonds, PU bonds and mean chain length. Compared with in vivo (1) H-MRS, high resolution NMR performed in vitro in hepatic lipid extracts reported longer fatty-acyl chains (18 versus 15 carbons) with a lower contribution from UFA (61 ± 1% versus 80 ± 5%) but a higher number of PU bonds per UFA (1.39 ± 0.03 versus 0.58 ± 0.08), driven by the presence of membrane species in the extracts. STZ injection caused a decrease of HLC (from 1.7 ± 0.3% to 0.7 ± 0.1%), an increase in the contribution of SFA (from 21 ± 2% to 45 ± 6%) and a reduction of the mean length (from 15 to 13 carbons) of cytosolic fatty-acyl chains. In addition, SFAs were also likely to have increased in membrane lipids of STZ-induced diabetic mice, along with a decrease of the mean chain length. These studies show the applicability of (1)H-MRS in vivo to monitor changes in the composition of the hepatic fatty-acyl chains in mice even when they exhibit reduced HLC, pointing to the value of this methodology to evaluate lipid-lowering interventions in the scope of metabolic disorders.

Assessing Susceptibility to Epilepsy in Three Rat Strains Using Brain Metabolic Profiling Based on HRMAS NMR Spectroscopy and Chemometrics

The possibility that a metabolomic approach can inform about the pathophysiology of a given form of epilepsy was addressed. Using chemometric analyses of HRMAS NMR data, we compared several brain structures in three rat strains with different susceptibilities to absence epilepsy: Genetic Absence Epilepsy Rats from Strasbourg (GAERS), Non Epileptic Control rats (NEC), and Wistar rats. Two ages were investigated: 14 days postnatal (P14) before the onset of seizures and 5 month old adults with fully developed seizures (Adults). The relative concentrations of 19 metabolites were assessed using (1)H HRMAS NMR experiments. Univariate and multivariate analyses including multiblock models were used to identify the most discriminant metabolites. A strain-dependent evolution of glutamate, glutamine, scyllo-inositol, alanine, and glutathione was highlighted during cerebral maturation. In Adults, data from somatosensory and motor cortices allowed discrimination between GAERS and NEC rats with higher levels of scyllo-inositol, taurine, and phosphoethanolamine in NEC. This epileptic metabolic phenotype was in accordance with current pathophysiological hypothesis of absence epilepsy (i.e., seizure-generating and control networks) and putative resistance of NEC rats and was observed before seizure onset. This methodology could be very efficient in a clinical context.

Emerging field of metabolomics: big promise for cancer biomarker identification and drug discovery

Most cancers are lethal and metabolic alterations are considered a hallmark of this deadly disease. Genomics and proteomics have contributed vastly to understand cancer biology. Still there are missing links as downstream to them molecular divergence occurs. Metabolomics, the omic science that furnishes a dynamic portrait of metabolic profile is expected to bridge these gaps and boost cancer research. Metabolites being the end products are more stable than mRNAs or proteins. Previous studies have shown the efficacy of metabolomics in identifying biomarkers associated with diagnosis, prognosis and treatment of cancer. Metabolites are highly informative about the functional status of the biological system, owing to their proximity to organismal phenotypes. Scores of publications have reported about high-throughput data generation by cutting-edge analytic platforms (mass spectrometry and nuclear magnetic resonance). Further sophisticated statistical softwares (chemometrics) have enabled meaningful information extraction from the metabolomic data. Metabolomics studies have demonstrated the perturbation in glycolysis, tricarboxylic acid cycle, choline and fatty acid metabolism as traits of cancer cells. This review discusses the latest progress in this field, the future trends and the deficiencies to be surmounted for optimally implementation in oncology. The authors scoured through the most recent, high-impact papers archived in Pubmed, ScienceDirect, Wiley and Springer databases to compile this review to pique the interest of researchers towards cancer metabolomics.

1H NMR Metabolomics Study of Metastatic Melanoma in C57BL/6J Mouse Spleen

Melanoma is a malignant tumor of melanocytes. Although extensive investigations have been done to study metabolic changes in primary melanoma in vivo and in vitro, little effort has been devoted to metabolic profiling of metastatic tumors in organs other than lymph nodes. In this work, NMR-based metabolomics combined with multivariate data analysis is used to study metastatic B16-F10 melanoma in C57BL/6J mouse spleen. Principal Component Analysis (PCA), an unsupervised multivariate data analysis method, is used to detect possible outliers, while Orthogonal Projection to Latent Structure (OPLS), a supervised multivariate data analysis method, is employed to find important metabolites responsible for discriminating the control and the melanoma groups. Two different strategies, i.e. spectral binning and spectral deconvolution, are used to reduce the original spectral data before statistical analysis. Spectral deconvolution is found to be superior for identifying a set of discriminatory metabolites between the control and the melanoma groups, especially when the sample size is small. OPLS results show that the melanoma group can be well separated from its control group. It is found that taurine, glutamate, aspartate, O-Phosphoethanolamine, niacinamide,ATP, lipids and glycerol derivatives are decreased statistically and significantly while alanine, malate, xanthine, histamine, dCTP, GTP, thymidine, 2'-Deoxyguanosine are statistically and significantly elevated. These significantly changed metabolites are associated with multiple biological pathways and may be potential biomarkers for metastatic melanoma in spleen.

Metastatic Melanoma Induced Metabolic Changes in C57BL/6J Mouse Stomach Measured by 1H NMR Spectroscopy

Melanoma is a malignant tumor of melanocytes with high capability of invasion and rapid metastasis to other organs. Malignant melanoma is the most common metastatic malignancy found in Gastrointestinal Tract (GI). In this work, the 1H NMR-based metabolomics approach is used to investigate the metabolite profile differences of stomach tissue extracts of metastatic B16-F10 melanoma and control groups in C57BL/6J mouse and to search for specific metabolite biomarker candidates. Principal Component Analysis (PCA), an unsupervised multivariate data analysis method, is used to detect possible outliers, while Orthogonal Projection to Latent Structure (OPLS), a supervised multivariate data analysis method, is employed to evaluate important metabolites responsible for discriminating the control and the melanoma groups. Both PCA and OPLS results reveal that the melanoma group can be well separated from its control group. Among the 50 identified metabolites, it is found that the concentrations of 19 metabolites are significantly changed with the levels of O-phosphocholine and hypoxanthine down-regulated while the levels of isoleucine, leucine, valine, isobutyrate, threonine, cadaverine, alanine, glutamate, glutamine, methionine, citrate, asparagine, tryptophan, glycine, serine, uracil, and formate up-regulated in the melanoma group. These significantly changed metabolites are associated with multiple biological pathways and may be potential biomarkers for metastatic melanoma in stomach.