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

Arendowski, Adrian; Ossoliński, Krzysztof; Ossolińska, Anna; Ossoliński, Tadeusz; Nizioł, Joanna; Ruman, Tomasz

doi:10.1016/j.advms.2021.07.003

Cited by 16 publications

(11 citation statements)

References 55 publications

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“…Several research groups have developed in vitro RCC detection nanoplatforms by examining bodily liquids. Adrian Arendowski et al developed gold nanoparticle enhanced target (AuNPET) surface assisted-laser desorption/ionization (SALDI) MS for in vitro assessment of RCC, and successfully differentiated serum/urine samples from healthy volunteers and patients with advanced RCC [ 37 , 38 ]. In another study, Joanna Nizioł et al further improved the detection accuracy and constructed a prediction model construction by comprehensive high resolution proton nuclear magnetic resonance spectroscopy ( 1 H NMR) and silver-109 nanoparticle-enhanced steel target laser desorption/ionization mass spectrometry ( 109 AgNPET LDI MS) approaches [ 26 , 39 ].…”

Section: Diagnosis Of Renal Cell Carcinoma Using Nanomedicinementioning

confidence: 99%

Nanomedicine for renal cell carcinoma: imaging, treatment and beyond

Wang

Gai

et al. 2023

J Nanobiotechnol

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The kidney is a vital organ responsible for maintaining homeostasis in the human body. However, renal cell carcinoma (RCC) is a common malignancy of the urinary system and represents a serious threat to human health. Although the overall survival of RCC has improved substantially with the development of cancer diagnosis and management, there are various reasons for treatment failure. Firstly, without any readily available biomarkers, timely diagnosis has been greatly hampered. Secondly, the imaging appearance also varies greatly, and its early detection often remains difficult. Thirdly, chemotherapy has been validated as unavailable for treating renal cancer in the clinic due to its intrinsic drug resistance. Concomitant with the progress of nanotechnological methods in pharmaceuticals, the management of kidney cancer has undergone a transformation in the recent decade. Nanotechnology has shown many advantages over widely used traditional methods, leading to broad biomedical applications ranging from drug delivery, prevention, diagnosis to treatment. This review focuses on nanotechnologies in RCC management and further discusses their biomedical translation with the aim of identifying the most promising nanomedicines for clinical needs. As our understanding of nanotechnologies continues to grow, more opportunities to improve the management of renal cancer are expected to emerge.

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Section: Diagnosis Of Renal Cell Carcinoma Using Nanomedicinementioning

confidence: 99%

Nanomedicine for renal cell carcinoma: imaging, treatment and beyond

Wang

Gai

et al. 2023

J Nanobiotechnol

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“…Coupling data on molecular profiles to advanced data processing algorithms facilitates the development of platforms for various goals. Analysis and comparison of the levels of small biomolecules in the organism can be useful in studies of metabolomics [6], the search for cancer biomarkers [7], disease pathogenesis [8], differentiation between stages [9], reasonable prognosis and the careful selection of the treatment strategy [10].…”

Section: Introductionmentioning

confidence: 99%

Nanostructured Layer of Silver for Detection of Small Biomolecules in Surface-Assisted Laser Desorption Ionization Mass Spectrometry

et al. 2022

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A facile approach for the synthesis of a silver nanostructured layer for application in surface-assisted laser desorption/ionization mass spectrometry of low-molecular-weight biomolecules was developed using electrochemical deposition. The deposition was carried out using the following silver salts: trifluoroacetate, acetate and nitrate, varying the voltage and time. The plate based on trifluoroacetate at 10 V for 15 min showed intense SALDI-MS responses for standards of various classes of compounds: fatty acids, cyclitols, saccharides and lipids at a concentration of 1 nmol/spot, with values of the signal-to-noise ratio ≥50. The values of the limit of detection were 0.71 µM for adonitol, 2.08 µM for glucose and 0.39 µM for palmitic acid per spot. SEM analysis of the plate showed anisotropic flower-like microstructures with nanostructures on their surface. The reduced chemical background in the low-mass region can probably be explained by the absence of stabilizers and reducing agents during the synthesis. The plate synthesized with the developed approach showed potential for future use in the analysis of low-molecular-weight compounds of biological relevance. The absence of the need for the utilization of sophisticated equipment and the synthesis time (10 min) may benefit large-scale applications of the layer for the detection of various types of small biomolecules.

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“…Furthermore, Monge and co-workers recently reported a 26-lipid panel that discriminates early from late-stage clear cell RCC in human serum samples [ 15 ]. Arendowski et al identified indole-3-acetylglycine, urothion, and myo-inositol 1,4-bisphosphate in urine samples as potential markers to discriminate low stages (pT1 and pT2) from high stages (pT1 and pT2) RCC [ 16 ]. Furthermore, Niziol and co-workers, in their urine metabolomics study, identified increasing abundances of isoleucine, N-dimethylglycine, sucrose, and glycolate as RCC tumor grade increases, while urea, 2-fluoroglycine, trigonelline, and 4-hydroxyphenylacetate has the opposite trend [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Urine-Based Metabolomics and Machine Learning Reveals Metabolites Associated with Renal Cell Carcinoma Stage

Bifarin

Gaul

Sah

et al. 2021

Cancers

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Urine metabolomics profiling has potential for non-invasive RCC staging, in addition to providing metabolic insights into disease progression. In this study, we utilized liquid chromatography-mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and machine learning (ML) for the discovery of urine metabolites associated with RCC progression. Two machine learning questions were posed in the study: Binary classification into early RCC (stage I and II) and advanced RCC stages (stage III and IV), and RCC tumor size estimation through regression analysis. A total of 82 RCC patients with known tumor size and metabolomic measurements were used for the regression task, and 70 RCC patients with complete tumor-nodes-metastasis (TNM) staging information were used for the classification tasks under ten-fold cross-validation conditions. A voting ensemble regression model consisting of elastic net, ridge, and support vector regressor predicted RCC tumor size with a R2 value of 0.58. A voting classifier model consisting of random forest, support vector machines, logistic regression, and adaptive boosting yielded an AUC of 0.96 and an accuracy of 87%. Some identified metabolites associated with renal cell carcinoma progression included 4-guanidinobutanoic acid, 7-aminomethyl-7-carbaguanine, 3-hydroxyanthranilic acid, lysyl-glycine, glycine, citrate, and pyruvate. Overall, we identified a urine metabolic phenotype associated with renal cell carcinoma stage, exploring the promise of a urine-based metabolomic assay for staging this disease.

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

Cited by 16 publications

References 55 publications

Nanomedicine for renal cell carcinoma: imaging, treatment and beyond

Nanomedicine for renal cell carcinoma: imaging, treatment and beyond

Nanostructured Layer of Silver for Detection of Small Biomolecules in Surface-Assisted Laser Desorption Ionization Mass Spectrometry

Urine-Based Metabolomics and Machine Learning Reveals Metabolites Associated with Renal Cell Carcinoma Stage

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