Serum and urine analysis with gold nanoparticle-assisted laser desorption/ionization mass spectrometry for renal cell carcinoma metabolic biomarkers discovery

Bray, 2018, Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA A Cancer J Clin, 68, 394, 10.3322/caac.21492 Chow, 2010, Epidemiology and risk factors for kidney cancer, Nat Rev Urol, 7, 245, 10.1038/nrurol.2010.46 Moch, 2013, An overview of renal cell cancer: pathology and genetics, Semin Canc Biol, 23, 3, 10.1016/j.semcancer.2012.06.006 Capitanio, 2019, Epidemiology of renal cell carcinoma, Eur Urol, 75, 74, 10.1016/j.eururo.2018.08.036 Müller, 2012, Renal tumors and second primary pancreatic tumors: a relationship with clinical impact?, Patient Saf Surg, 6, 18, 10.1186/1754-9493-6-18 Yuasa, 2015, Clinical outcome of patients with pancreatic metastases from renal cell cancer, BMC Canc, 15, 46, 10.1186/s12885-015-1050-2 Ball Mark, 2015, Grade heterogeneity in small renal masses: potential implications for renal mass biopsy, J Urol, 193, 36 Falegan, 2017, Urine and serum metabolomics analyses may distinguish between stages of renal cell carcinoma, Metabolites, 7, 6, 10.3390/metabo7010006 Mytsyk, 2018, Potential clinical applications of microRNAs as biomarkers for renal cell carcinoma, Cent European J Urol, 71, 295 Ngo, 2014, Biomarkers of renal cell carcinoma, Urol Oncol Semin Orig Investig, 32, 243 Monteiro, 2014, Biomarkers in renal cell carcinoma: a metabolomics approach, Metabolomics, 10, 1210, 10.1007/s11306-014-0659-5 Pastore, 2015, Serum and urine biomarkers for human renal cell carcinoma, Dis Markers, 2015, 251403, 10.1155/2015/251403 Gupta, 2020, Role of metabolomics-derived biomarkers to identify renal cell carcinoma: a comprehensive perspective of the past ten years and advancements, Expert Rev Mol Diagn, 20, 5, 10.1080/14737159.2020.1704259 Yang, 2013, Renal cell carcinoma: translational aspects of metabolism and therapeutic consequences, Kidney Int, 84, 667, 10.1038/ki.2013.245 Rodrigues, 2017, Renal cell carcinoma: a critical analysis of metabolomic biomarkers emerging from current model systems, Transl Res, 180, 1, 10.1016/j.trsl.2016.07.018 Dill, 2010, Multivariate statistical differentiation of renal cell carcinomas based on lipidomic analysis by ambient ionization imaging mass spectrometry, Anal Bioanal Chem, 398, 2969, 10.1007/s00216-010-4259-6 Arendowski, 2018, Laser desorption/ionization MS imaging of cancer kidney tissue on silver nanoparticle-enhanced target, Bioanalysis, 10, 83, 10.4155/bio-2017-0195 Catchpole, 2011, Metabolic profiling reveals key metabolic features of renal cell carcinoma, J Cell Mol Med, 15, 109, 10.1111/j.1582-4934.2009.00939.x Wettersten, 2015, Grade-dependent metabolic reprogramming in kidney cancer revealed by combined proteomics and metabolomics analysis, Canc Res, 75, 2541, 10.1158/0008-5472.CAN-14-1703 Zira, 2010, 1H NMR metabonomic analysis in renal cell carcinoma: a possible diagnostic tool, J Proteome Res, 9, 4038, 10.1021/pr100226m Süllentrop, 2002, 31P NMR spectroscopy of blood plasma: determination and quantification of phospholipid classes in patients with renal cell carcinoma, NMR Biomed, 15, 60, 10.1002/nbm.758 Zhang, 2017, The predictive and prognostic values of serum amino acid levels for clear cell renal cell carcinoma, Urol Oncol Semin Orig Investig, 35, 392 Lin, 2010, Direct infusion mass spectrometry or liquid chromatography mass spectrometry for human metabonomics? A serum metabonomic study of kidney cancer, Analyst, 135, 2970, 10.1039/c0an00265h Zheng, 2016, Prediction and diagnosis of renal cell carcinoma using nuclear magnetic resonance-based serum metabolomics and self-organizing maps, Oncotarget, 7, 59189, 10.18632/oncotarget.10830 Nizioł, 2020, Nuclear magnetic resonance and surface-assisted laser desorption/ionization mass spectrometry-based serum metabolomics of kidney cancer, Anal Bioanal Chem, 412, 5827, 10.1007/s00216-020-02807-1 Kim, 2009, Urine metabolomics analysis for kidney cancer detection and biomarker discovery, Mol Cell Proteomics, 8, 558, 10.1074/mcp.M800165-MCP200 Ganti, 2011, Urine metabolomics for kidney cancer detection and biomarker discovery, Urol Oncol Semin Orig Investig, 29, 551 Kind, 2007, A comprehensive urinary metabolomic approach for identifying kidney cancer, Anal Biochem, 363, 185, 10.1016/j.ab.2007.01.028 Nizioł, 2018, Metabolomic study of human tissue and urine in clear cell renal carcinoma by LC-HRMS and PLS-DA, Anal Bioanal Chem, 410, 3859, 10.1007/s00216-018-1059-x Gao, 2008, Metabonomic profiling of renal cell carcinoma: high-resolution proton nuclear magnetic resonance spectroscopy of human serum with multivariate data analysis, Anal Chim Acta, 624, 269, 10.1016/j.aca.2008.06.051 Nizioł, 2021, Nuclear magnetic resonance and surface-assisted laser desorption/ionization mass spectrometry-based metabolome profiling of urine samples from kidney cancer patients, J Pharmaceut Biomed Anal, 193, 113752, 10.1016/j.jpba.2020.113752 Nizioł, 2021, Metabolomic and elemental profiling of human tissue in kidney cancer, Metabolomics, 17, 30, 10.1007/s11306-021-01779-2 Jungblut, 1997, Protein identification from 2-DE gels by MALDI mass spectrometry, Mass Spectrom Rev, 16, 145, 10.1002/(SICI)1098-2787(1997)16:3<145::AID-MAS2>3.0.CO;2-H Berkenkamp, 1998, Infrared MALDI mass spectrometry of large nucleic acids, Science, 281, 260, 10.1126/science.281.5374.260 Montaudo, 2006, Characterization of synthetic polymers by MALDI-MS, Prog Polym Sci, 31, 277, 10.1016/j.progpolymsci.2005.12.001 Gianazza, 2012, Alterations of the serum peptidome in renal cell carcinoma discriminating benign and malignant kidney tumors, J Proteomics, 76, 125, 10.1016/j.jprot.2012.07.032 Jan, 1995, Graphite surface-assisted laser desorption/ionization time-of-flight mass spectrometry of peptides and proteins from liquid solutions, Anal Chem, 67, 4335, 10.1021/ac00119a021 Abdelhamid, 2016, Gold nanoparticles assisted laser desorption/ionization mass spectrometry and applications: from simple molecules to intact cells, Anal Bioanal Chem, 408, 4485, 10.1007/s00216-016-9374-6 Liu, 2020, Influence of core size and capping ligand of gold nanoparticles on the desorption/ionization efficiency of small biomolecules in AP-SALDI-MS, Anal Sci Adv, 1, 210, 10.1002/ansa.202000002 Lai, 2017, Silver–gold alloy nanoparticles as tunable substrates for systematic control of ion-desorption efficiency and heat transfer in surface-assisted laser desorption/ionization, Phys Chem Chem Phys, 19, 20795, 10.1039/C7CP04033D Ray, 2018, Stabilisation of small mono- and bimetallic gold–silver nanoparticles using calix[8]arene derivatives, New J Chem, 42, 14128, 10.1039/C8NJ02451K Abdelhamid, 2017, One pot synthesis of gold – carbon dots nanocomposite and its application for cytosensing of metals for cancer cells, Talanta, 166, 357, 10.1016/j.talanta.2016.11.030 Sekuła, 2015, Gold nanoparticle-enhanced target for MS analysis and imaging of harmful compounds in plant, animal tissue and on fingerprint, Anal Chim Acta, 895, 45, 10.1016/j.aca.2015.09.003 Arendowski, 2018, Metabolic profiling of moulds with laser desorption/ionization mass spectrometry on gold nanoparticle enhanced target, Anal Biochem, 549, 45, 10.1016/j.ab.2018.03.016 Nizioł, 2016, Surface-Transfer mass spectrometry imaging of renal tissue on gold nanoparticle enhanced target, Anal Chem, 88, 7365, 10.1021/acs.analchem.6b01859 Arendowski, 2020, Gold nanostructures - assisted laser desorption/ionization mass spectrometry for kidney cancer blood serum biomarker screening, Int J Mass Spectrom, 456, 116396, 10.1016/j.ijms.2020.116396 Arendowski, 2020, Screening of urinary renal cancer metabolic biomarkers with gold nanoparticles - assisted laser desorption/ionization mass spectrometry, Anal Sci, 36, 1521, 10.2116/analsci.20P226 Remily-Wood, 2009, Acid hydrolysis of proteins in matrix assisted laser desorption ionization matrices, J Am Soc Mass Spectrom, 20, 2106, 10.1016/j.jasms.2009.07.007 Sekuła, 2015, Gold nanoparticle-enhanced target (AuNPET) as universal solution for laser desorption/ionization mass spectrometry analysis and imaging of low molecular weight compounds, Anal Chim Acta, 875, 61, 10.1016/j.aca.2015.01.046 Niedermeyer, 2012, mMass as a software tool for the annotation of cyclic peptide tandem mass spectra, PLoS One, 7, 10.1371/journal.pone.0044913 Wishart, 2018, HMDB 4.0: the human metabolome database for 2018, Nucleic Acids Res, 46, D608, 10.1093/nar/gkx1089 Fahy, 2007, LIPID MAPS online tools for lipid research, Nucleic Acids Res, 35, W606, 10.1093/nar/gkm324 Patiny, 2013, ChemCalc: a building block for tomorrow's chemical infrastructure, J Chem Inf Model, 53, 1223, 10.1021/ci300563h Chong, 2019, Using MetaboAnalyst 4.0 for comprehensive and integrative metabolomics data analysis, Curr Protoc Bioinf, 68, e86, 10.1002/cpbi.86 Ganti, 2012, Urinary acylcarnitines are altered in human kidney cancer, Int J Canc, 130, 2791, 10.1002/ijc.26274 Tugnoli, 2004, Phosphatidylcholine and cholesteryl esters identify the infiltrating behaviour of a clear cell renal carcinoma: 1H, 13C and 31P MRS evidence, Oncol Rep, 12, 353 Ackerman, 2018, Triglycerides promote lipid homeostasis during hypoxic stress by balancing fatty acid saturation, Cell Rep, 24, 2596, 10.1016/j.celrep.2018.08.015 Leja-Szpak, 2015, Kynuramines induce overexpression of heat shock proteins in pancreatic cancer cells via 5-hydroxytryptamine and MT1/MT2 receptors, J Physiol Pharmacol Off J Pol Physiol Soc, 66, 711 Burton, 2017, The role of urinary pteridines as disease biomarkers, Pteridines, 28, 1, 10.1515/pterid-2016-0013 Kośliński, 2016, The metabolic profiles of pterin compounds as potential biomarkers of bladder cancer—integration of analytical-based approach with biostatistical methodology, J Pharmaceut Biomed Anal, 127, 256, 10.1016/j.jpba.2016.02.038 Kim, 2011, Urine metabolomic analysis identifies potential biomarkers and pathogenic pathways in kidney cancer, OMICS J Integr Biol, 15, 293, 10.1089/omi.2010.0094 Lario, 2017, Plasma sample based analysis of gastric cancer progression using targeted metabolomics, Sci Rep, 7, 17774, 10.1038/s41598-017-17921-x