# Skin Cancer Metabolic Profile Assessed by Different Analytical Platforms
## 7 Skin Cancer Metabolomics Using Different Analytical Platforms
### NMR Based Skin Cancer Metabolomics  
In addition to GC-MS and LC-MS, NMR spectroscopy is another technique widely recognized for revealing the structure of molecules. The quantitative link between intra-molecular and inter-molecular resonances is revealed by the \({}^{1}\)H (proton) NMR spectrum with chemical shift and coupling constants [55]. For the collection of metabolomics data, NMR has many benefits. It may often be used in a non-invasive way, requiring little to no sample preparation, and is non-destructive. It is suitable for research on biofluids, cell extracts, and in vitro or in vivo cell cultures and tissues. The NMR's multinuclear capabilities offer a variety of ways to study diverse compounds [56]. Table 5 compares the three different analytical platforms. Being the deadliest form of skin cancer, melanoma causes metabolic rewiring and intensifies metastatic transformation. Preclinical models, particularly cell lines, are a major part of efforts to enhance its early and accurate diagnosis. To map the metabolic changes presumably regulating metastasis, a study set out to describe a combinational NMR and ultra-high-performance liquid chromatography-high resolution tandem mass spectrometry (UHPLC-HRMS/MS)-mediated untargeted metabolomic profiling of melanoma cells, by looking at two patient cell lines, WM115 and WM2664. WM115 was obtained from a primary, pre-metastatic tumor, while WM2664 was clonally amplified from lymph node metastases. NMR and UHPLC-HRMS were used to examine metabolite sample data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Analytical Platform** & **Advantages** & **Disadvantages** & **Detectable Molecules** \\ \hline \multirow{8}{*}{GC-MS [57, 58]} & \(\bullet\) & Sensitive platform to detect a & & \\  & wide range of metabolites & & & \\  & \(\bullet\) & Reproducible technique. & \(\bullet\) & Destructive technique & \\  & \(\bullet\) & Excellent separation & \(\bullet\) & Limited to volatile and & \(\bullet\) & Organic acids \\  & \(\bullet\) & Quantitative technique & & thermally stable compounds & \(\bullet\) & Amino acids \\  & \(\bullet\) & Compatible for gases and & \(\bullet\) & Cannot analyze solids & \(\bullet\) & Fatty acids \\  & & liquids & \(\bullet\) & Derivatization required & \(\bullet\) & Glucose \\  & \(\bullet\) & Able to detect most organic and & \(\bullet\) & Separation required & \(\bullet\) & Amines \\  & \(\bullet\) & some inorganic compounds & \(\bullet\) & Long analysis time (20–40 & \(\bullet\) & Polyols \\  & \(\bullet\) & Suitable for analysis of mixtures & & min/sample) & & \\  & \(\bullet\) & Generally cheaper than LC-MS & & & \\  & & because it uses a less & & & \\  & sophisticated detector. & & & & \\ \hline \multirow{8}{*}{LC-MS [58, 59]} & \(\bullet\) & Highly sensitive technique & \(\bullet\) & Destructive technique & \(\bullet\) & Organic acids \\  & \(\bullet\) & Derivatization is not required & \(\bullet\) & Expensive instrument and needs & \(\bullet\) & Nucleotides \\  & \(\bullet\) & Can analyze thermally unstable & \(\bullet\) & skilled personnel & \(\bullet\) & Active peptides \\  & \(\bullet\) & compounds & \(\bullet\) & Ionization suppression resulting & \(\bullet\) & Vitamins \\  & & & from co-elution of molecules & \(\bullet\) & Bile acids \\ \cline{1-1}  & \(\bullet\) & Compatible with liquids and & & with dramatic variances in & \(\bullet\) & Flavonoids \\ \cline{1-1}  & \(\bullet\) & solids & & & \(\bullet\) & Polyphenols \\ \cline{1-1}  & \(\bullet\) & Small sample volumes are needed & \(\bullet\) & Unwanted solvent matrix effects & \(\bullet\) & Lipids \\ \cline{1-1}  & & needed & \(\bullet\) & Long analysis time (15–40 & \(\bullet\) & Carbohydrates \\ \cline{1-1}  & & & min/sample) & \(\bullet\) & Amino acids \\ \cline{1-1}  & & & \(\bullet\) & Not suitable for gases & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between different analytical platforms. The metabolites were identified by multivariate (S-plot, Variable Importance in Projection (VIPs)) and univariate statistical analysis.