Metabolomic and elemental profiling of human tissue in kidney cancer

Fiber Laser-Generated Silver-109 Nanoparticles for Laser Desorption/Ionization Mass Spectrometry of Illicit Drugs

Cannabinoids and opioids are the most prominently used drugs in the world, with fentanyl being the main cause of drug overdose-related deaths. Monitoring drug use in groups as well as in individuals is an important forensic concern. Analytical methods, such as mass spectrometry (MS), have been found most useful for the identification of drug abuse on a small and large scale. Pulsed fiber laser 2D galvoscanner laser-generated nanomaterial (PFL 2D GS LGN) was obtained from monoisotopic silver-109. Nanomaterial was used for laser desorption/ionization mass spectrometry of selected illicit drug standards with standard high-resolution reflectron-based time-of-flight MALDI apparatus. Δ9-THC, 11-OH-THC, 11-COOH-THC, fentanyl, codeine, 6-monoacetylmorphine (6-MAM), heroin, tramadol, and methadone were chosen as test compounds. Illicit drugs were tested in a concentration range from 100 μg/mL to 10 pg/mL, equating to 50 μg to 50 fg per measurement spot. For all analyzed compounds, identification and quantification by silver-109-assisted laser desorption/ionization (LDI) MS was possible, with uncommon [M + 109Ag3]+ and [M - H]+ ions present for certain structures. The results of the quantitative analysis of drugs using silver-109 PFL 2D GS LGN for LDI MS are presented. Laser-generated NPs are proven to be useful for the analysis of selected drugs, with exceptionally good results for fentanyl monitoring in a broad range of concentrations.

Heavy metal exposure and metabolomics analysis: an emerging frontier in environmental health

Exposure to heavy metals in various populations can lead to extensive damage to different organs, as these metals infiltrate and bioaccumulate in the human body, causing metabolic disruptions in various organs. To comprehensively understand the metal homeostasis, inter-organ "traffic," and extensive metabolic alterations resulting from heavy metal exposure, employing complementary analytical methods is crucial. Metabolomics is pivotal in unraveling the intricacies of disease vulnerability by furnishing thorough understandings of metabolic changes linked to different metabolic diseases. This field offers exciting prospects for enhancing the disease prevention, early detection, and tailoring treatment approaches to individual needs. This article consolidates the existing knowledge on disease-linked metabolic pathways affected by the exposure of diverse heavy metals providing concise overview of the underlying impact mechanisms. The main aim is to investigate the connection between the altered metabolic pathways and long-term complex health conditions induced by heavy metals such as diabetes mellitus, cardiovascular diseases, renal disorders, inflammation, neurodegenerative diseases, reproductive risks, and organ damage. Further exploration of common pathways may unveil the shared targets for treating associated pathological conditions. In this article, the role of metabolomics in disease susceptibility is emphasized that metabolomics is expected to be routinely utilized for the diagnosis and monitoring of diseases and practical value of biomarkers derived from metabolomics, as well as determining their appropriate integration into extensive clinical settings.

Development of metabolome extraction strategy for metabolite profiling of skin tissue

INTRODUCTION

Changes in skin phenotypic characteristics are based on skin tissue. The study of the metabolic changes in skin tissue can help understand the causes of skin diseases and identify effective therapeutic interventions.

OBJECTIVES

We aimed to establish and optimize a non-targeted skin metabolome extraction system for skin tissue metabolomics with high metabolite coverage, recovery, and reproducibility using gas chromatography/mass spectrometry.

METHODS

The metabolites in skin tissues were extracted using eleven different extraction systems, which were designed using reagents with different polarities based on sequential solid-liquid extraction employing a two-step strategy and analyzed using gas chromatograph/mass spectrometry. The extraction efficiency of diverse solvents was evaluated by coefficient of variation (CV), multivariate analysis, metabolites coverage, and relative peak area analysis.

RESULTS

We identified 119 metabolites and the metabolite profiles differed significantly between the eleven extraction systems. Metabolites with high abundances in the organic extraction systems, followed by aqueous extraction, were involved in the biosynthesis of unsaturated fatty acids, while metabolites with high abundances in the aqueous extraction systems, followed by organic extraction, were involved in amino sugar and nucleotide sugar metabolism, and glycerolipid metabolism. MeOH/chloroform-H2O and MeOH/H2O-chloroform were the extraction systems that yielded the highest number of metabolites, while MeOH/acetonitrile (ACN)-H2O and ACN/H2O-IPA exhibited superior metabolite recoveries.

CONCLUSION

Our results demonstrated that our research facilitates the selection of an appropriate metabolite extraction approach based on the experimental purpose for the metabolomics study of skin tissue.

Untargeted metabolomics of bladder tissue using liquid chromatography and quadrupole time-of-flight mass spectrometry for cancer biomarker detection

Bladder cancer (BC) ranks among the most common cancers globally, with an increasing occurrence, particularly in developed nations. Utilizing tissue metabolomics presents a promising strategy for identifying potential biomarkers for cancer detection. In this study, we utilized ultra-high-performance liquid chromatography coupled with ultra-high-resolution mass spectrometry (UHPLC-UHRMS), incorporating both C18-silica and HILIC columns, to comprehensively analyze both polar and non-polar metabolite profiles in tissue samples from 99 patients with bladder cancer. By utilizing an untargeted approach with external validation, we identified twenty-five tissue metabolites that hold promise as potential indicators of BC. Furthermore, twenty-five characteristic tissue metabolites that exhibit discriminatory potential across bladder cancer tumor grades, as well as thirty-nine metabolites that display correlations with tumor stages were presented. Receiver operating characteristics analysis demonstrated high predictive power for all types of metabolomics data, with area under the curve (AUC) values exceeding 0.966. Notably, this study represents the first report in which human bladder normal tissues adjacent to cancerous tissues were analyzed using UHPLC-UHRMS. These findings suggest that the metabolite markers identified in this investigation could serve as valuable tools for the detection and monitoring of bladder cancer stages and grades.

Metabolomic profiling of human bladder tissue extracts

INTRODUCTION

Bladder cancer is a common malignancy affecting the urinary tract and effective biomarkers and for which monitoring therapeutic interventions have yet to be identified.

OBJECTIVES

Major aim of this work was to perform metabolomic profiling of human bladder cancer and adjacent normal tissue and to evaluate cancer biomarkers.

METHODS

This study utilized nuclear magnetic resonance (NMR) and high-resolution nanoparticle-based laser desorption/ionization mass spectrometry (LDI-MS) methods to investigate polar metabolite profiles in tissue samples from 99 bladder cancer patients.

RESULTS

Through NMR spectroscopy, six tissue metabolites were identified and quantified as potential indicators of bladder cancer, while LDI-MS allowed detection of 34 compounds which distinguished cancer tissue samples from adjacent normal tissue. Thirteen characteristic tissue metabolites were also found to differentiate bladder cancer tumor grades and thirteen metabolites were correlated with tumor stages. Receiver-operating characteristics analysis showed high predictive power for all three types of metabolomics data, with area under the curve (AUC) values greater than 0.853.

CONCLUSION

To date, this is the first study in which bladder human normal tissues adjacent to cancerous tissues are analyzed using both NMR and MS method. These findings suggest that the metabolite markers identified in this study may be useful for the detection and monitoring of bladder cancer stages and grades.

Metabolomic profiles of intact tissues reflect clinically relevant prostate cancer subtypes

BACKGROUND

Prostate cancer (PC) is a heterogenous multifocal disease ranging from indolent to lethal states. For improved treatment-stratification, reliable approaches are needed to faithfully differentiate between high- and low-risk tumors and to predict therapy response at diagnosis.

METHODS

A metabolomic approach based on high resolution magic angle spinning nuclear magnetic resonance (HR MAS NMR) analysis was applied on intact biopsies samples (n = 111) obtained from patients (n = 31) treated by prostatectomy, and combined with advanced multi- and univariate statistical analysis methods to identify metabolomic profiles reflecting tumor differentiation (Gleason scores and the International Society of Urological Pathology (ISUP) grade) and subtypes based on tumor immunoreactivity for Ki67 (cell proliferation) and prostate specific antigen (PSA, marker for androgen receptor activity).

RESULTS

Validated metabolic profiles were obtained that clearly distinguished cancer tissues from benign prostate tissues. Subsequently, metabolic signatures were identified that further divided cancer tissues into two clinically relevant groups, namely ISUP Grade 2 (n = 29) and ISUP Grade 3 (n = 17) tumors. Furthermore, metabolic profiles associated with different tumor subtypes were identified. Tumors with low Ki67 and high PSA (subtype A, n = 21) displayed metabolite patterns significantly different from tumors with high Ki67 and low PSA (subtype B, n = 28). In total, seven metabolites; choline, peak for combined phosphocholine/glycerophosphocholine metabolites (PC + GPC), glycine, creatine, combined signal of glutamate/glutamine (Glx), taurine and lactate, showed significant alterations between PC subtypes A and B.

CONCLUSIONS

The metabolic profiles of intact biopsies obtained by our non-invasive HR MAS NMR approach together with advanced chemometric tools reliably identified PC and specifically differentiated highly aggressive tumors from less aggressive ones. Thus, this approach has proven the potential of exploiting cancer-specific metabolites in clinical settings for obtaining personalized treatment strategies in PC.

Targeted and untargeted urinary metabolic profiling of bladder cancer

Bladder cancer (BC) is frequent cancer affecting the urinary tract and is one of the most prevalent malignancies worldwide. No biomarkers that can be used for effective monitoring of therapeutic interventions for this cancer have been identified to date. This study investigated polar metabolite profiles in urine samples from 100 BC patients and 100 normal controls (NCs) using nuclear magnetic resonance (NMR) and two methods of high-resolution nanoparticle-based laser desorption/ionization mass spectrometry (LDI-MS). Five urine metabolites were identified and quantified using NMR spectroscopy to be potential indicators of bladder cancer. Twenty-five LDI-MS-detected compounds, predominantly peptides and lipids, distinguished urine samples from BC and NCs individuals. Level changes of three characteristic urine metabolites enabled BC tumor grades to be distinguished, and ten metabolites were reported to correlate with tumor stages. Receiver-Operating Characteristics analysis showed high predictive power for all three types of metabolomics data, with the area under the curve (AUC) values greater than 0.87. These findings suggest that metabolite markers identified in this study may be useful for the non-invasive detection and monitoring of bladder cancer stages and grades.

Nanomedicine for renal cell carcinoma: imaging, treatment and beyond

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.

Metabolomics and NMR

The purpose of this manuscript will be to convince the reader to dive deeper into NMR spectroscopy and prevent the technique from being just another "black-box" in the lab. We will try to concisely highlight interesting topics and supply additional references for further exploration at each stage. The advantages of delving into the technique will be shown. The secondary objective, i.e., avoiding common problems before starting, will hopefully then become clear. Lastly, we will emphasize the spectrometer information needed for manuscript reporting to allow reproduction of results and confirm findings.

Metabolomic and elemental profiling of blood serum in bladder cancer

Bladder cancer (BC) is one of the most frequently diagnosed types of urinary cancer. Despite advances in treatment methods, no specific biomarkers are currently in use. Targeted and untargeted profiling of metabolites and elements of human blood serum from 100 BC patients and the same number of normal controls (NCs), with external validation, was attempted using three analytical methods, i.e., nuclear magnetic resonance, gold and silver-109 nanoparticle-based laser desorption/ionization mass spectrometry (LDI-MS), and inductively coupled plasma optical emission spectrometry (ICP-OES). All results were subjected to multivariate statistical analysis. Four potential serum biomarkers of BC, namely, isobutyrate, pyroglutamate, choline, and acetate, were quantified with proton nuclear magnetic resonance, which had excellent predictive ability as judged by the area under the curve (AUC) value of 0.999. Two elements, Li and Fe, were also found to distinguish between cancer and control samples, as judged from ICP-OES data and AUC of 0.807 (in validation set). Twenty-five putatively identified compounds, mostly related to glycans and lipids, differentiated BC from NCs, as detected using LDI-MS. Five serum metabolites were found to discriminate between tumor grades and nine metabolites between tumor stages. The results from three different analytical platforms demonstrate that the identified distinct serum metabolites and metal elements have potential to be used for noninvasive detection, staging, and grading of BC.

CBX Family Members in Two Major Subtypes of Renal Cell Carcinoma: A Comparative Bioinformatic Analysis

The biological function and clinical values of Chromobox (CBX) family proteins in renal cell carcinoma (RCC) are still poorly investigated. This study aimed to compare the expression profiles and clinical relevance of CBXs between the two most frequent subtypes of RCC, clear cell renal cell carcinomas (ccRCC) and papillary renal cell carcinomas (pRCC), and to investigate whether CBXs would play a more or less similar role in the pathogenesis and progression of these RCC subtypes. Considering these two RCC populations in the TCGA database, we built a bioinformatics framework by integrating a computational pipeline with several online tools. CBXs showed a similar trend in ccRCC and pRCC tissues but with some features specific for each subtype. Specifically, the relative expressions of CBX3 and CBX2 were, respectively, the highest and lowest among all CBXs in both RCC subtypes. These data also found confirmation in cellular validation. Except for CBX4 and CBX8, all others were deregulated in the ccRCC subtype. CBX1, CBX6, and CBX7 were also significantly associated with the tumor stage. Further, low expression levels of CBX1, CBX5, CBX6, CBX7, and high expression of CBX8 were associated with poor prognosis. Otherwise, in the pRCC subtype, CBX2, CBX3, CBX7, and CBX8 were deregulated, and CBX2, CBX6, and CBX7 were associated with the tumor stage. In addition, in pRCC patients, low expression levels of CBX2, CBX4, and CBX7 were associated with an unfavorable prognosis. Similarly, CBX3, CBX6, and CBX7 presented the highest alteration rate in both subtypes and were found to be functionally related to histone binding, nuclear chromosomes, and heterochromatin. Furthermore, CBX gene expression levels correlated with immune cell infiltration, suggesting that CBXs might reflect the immune status of RCC subtypes. Our results highlight similarities and differences of CBXs within the two major RCC subtypes, providing new insights for future eligible biomarkers or possible molecular therapeutic targets for these diseases.

GC-TOF/MS-Based Metabolomics for Comparison of Volar and Non-Volar Skin Types

Skin has heterogenous identities on different body sites despite similar cellular compositions. There are two types of skin, volar (palmoplantar) and non-volar (dorsal), which are characterized by epidermal thickness, pigmentation, and presence of hair follicles. However, the mechanisms underlying the development of these different skin types remain unclear. To investigate these, we profiled the cellular metabolites of volar and non-volar skin in mice using gas chromatography-time-of-flight/mass spectrometry (GC-TOF/MS), and further assessed the metabolic differences between them. In total, 96 metabolites from both volar and non-volar skin of mice were identified using the BinBase database system. Metabolomics analysis revealed important differences associated with amino acid metabolism (phenylalanine, tyrosine, and tryptophan biosynthesis; aspartate and glutamate metabolism), sugar metabolism (pentose phosphate pathway), and nucleotide metabolism (pyrimidine metabolism) in volar skin. Fifty metabolites were identified as potential biomarkers differentiating the physiological characteristics of these skin types. Of these, nine were highly increased whereas 41 were significantly decreased in volar skin compared with those in non-volar skin. Overall, these results provide valuable information for understanding the metabolic differences between volar and non-volar skin.

Identification of HGD and GSTZ1 as Biomarkers Involved Metabolic Reprogramming in Kidney Renal Clear Cell Carcinoma

Kidney renal clear cell carcinoma (KIRC) with poor prognosis is the main histological subtype of renal cell carcinoma, accounting for more than 80% of patients. Most patients are diagnosed at an advanced stage due to being asymptomatic early on. Advanced KIRC has an extremely poor prognosis due to its inherent resistance to radiotherapy and chemotherapy. Therefore, a comprehensive understanding of the molecular mechanisms of KIRC and the development of effective early diagnostic and therapeutic strategies is urgently needed. In this study, we aimed to identify the prognosis-related biomarker and analyzed its relationship with tumor progression. Metabolic changes are an important feature of kidney cancer, where the reduction of fumarate allows us to target the tyrosine metabolic pathway. The homogentisate 1,2-dioxygenase (HGD) and glutathione S-transferase zeta 1 (GSTZ1) related with prognosis of KIRC was identified through bioinformatics analysis based on The Cancer Genome Atlas (TCGA) databases. Mechanistically, we found that decreased HGD and GSTZ1 promote aerobic glycolysis in KIRC, coordinate the balance of amino acid metabolism and energy metabolism in tumor cells, and ultimately activate the tumor cell cycle and tumor progression. In summary, we identified the tyrosine metabolizing enzymes HGD and GSTZ1 as biomarkers of KIRC, which will further the understanding of the tumor metabolism profile, provide novel strategies and theoretical support for diagnosing and treating KIRC and as referential for future clinical research.

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

PURPOSE

Renal cell carcinoma (RCC) is a very aggressive and often fatal heterogeneous disease that is usually asymptomatic until late in the disease. There is an urgent need for RCC specific biomarkers that may be exploited clinically for diagnostic and prognostic purposes.

MATERIALS/METHODS

Serum and urine samples were collected from patients with diagnosed kidney cancer and assessed with gold nanoparticle enhanced target (AuNPET) surface assisted-laser desorption/ionization mass spectrometry (SALDI MS) based metabolomics and statistical analysis.

RESULTS

A database search allowed providing assignment of signals for the most promising features with a satisfactory value of the area under the curve and accuracy. Four potential biomarkers were found in urine and serum samples to distinguish clear cell renal cell carcinoma (ccRCC) from controls, 4 for the ccRCC with and without metastases, and 6 metabolites to distinguish low and high stages or grades.

CONCLUSIONS

This pilot study suggests that serum and urine metabolomics based on AuNPET-LDI MS may be useful in distinguishing types, grades and stages of human RCC.