A Serum Metabolomic Signature for the Detection and Grading of Bladder Cancer

Troisi, Jacopo; Colucci, Angelo; Cavallo, Pierpaolo; Richards, Sean; Symes, Steven A.; Landolfi, Annamaria; Scala, Giovanni; Maiorino, Francesco; Califano, Alfonso; Fabiano, Marco; Silvestre, Gianmarco; Mastella, Federica; Caputo, Alessandro; D’Antonio, Antonio; Altieri, Vincenzo

doi:10.3390/app11062835

Cited by 13 publications

(16 citation statements)

References 54 publications

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“…The low levels of glutamine are consistent with the well-known "glutamine addiction" of cancer cells [34] reported in bladder cancer [35], lung cancer [36], and glioma [37]. This is due to the need for cancer cells to sustain the production of acetyl-coenzyme A to synthetize fatty acids when pyruvate is converted to lactate.…”

Section: Discussionsupporting

confidence: 86%

A Metabolomics-Based Screening Proposal for Colorectal Cancer

Troisi

Tafuro

Lombardi³

et al. 2022

Metabolites

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Colorectal cancer (CRC) is a high incidence disease, characterized by high morbidity and mortality rates. Early diagnosis remains challenging because fecal occult blood screening tests have performed sub-optimally, especially due to hemorrhoidal, inflammatory, and vascular diseases, while colonoscopy is invasive and requires a medical setting to be performed. The objective of the present study was to determine if serum metabolomic profiles could be used to develop a novel screening approach for colorectal cancer. Furthermore, the study evaluated the metabolic alterations associated with the disease. Untargeted serum metabolomic profiles were collected from 100 CRC subjects, 50 healthy controls, and 50 individuals with benign colorectal disease. Different machine learning models, as well as an ensemble model based on a voting scheme, were built to discern CRC patients from CTRLs. The ensemble model correctly classified all CRC and CTRL subjects (accuracy = 100%) using a random subset of the cohort as a test set. Relevant metabolites were examined in a metabolite-set enrichment analysis, revealing differences in patients and controls primarily associated with cell glucose metabolism. These results support a potential use of the metabolomic signature as a non-invasive screening tool for CRC. Moreover, metabolic pathway analysis can provide valuable information to enhance understanding of the pathophysiological mechanisms underlying cancer. Further studies with larger cohorts, including blind trials, could potentially validate the reported results.

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Section: Discussionsupporting

confidence: 86%

A Metabolomics-Based Screening Proposal for Colorectal Cancer

Troisi

Tafuro

Lombardi³

et al. 2022

Metabolites

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“…Metabolomic impairments of cancer cells represent a well-known cancer hallmark [ 44 ]. These impairments are widely regarded as a robust basis of cancer metabolic profiles [ 35 , 36 , 45 ].…”

Section: Discussionmentioning

confidence: 99%

“…Extraction, purification, and derivatization of serum samples were performed using the MetaboPrep GC kit (Theoreo srl, Montecorvino Pugliano, Italy) as reported in Troisi et al [ 28 , 29 , 32 , 33 , 34 , 35 , 36 ]. This sample preparation kit results in untargeted metabolomic profiles.…”

Section: Methodsmentioning

confidence: 99%

“…The purpose of this mechanism is to have an EML score = 0 for a hypothetical sample where prediction for and against EC diagnosis were equal both in terms of number and weight. In contrast, an EML score > 0 indicates an EC-“positive” patient according to our model, while an EML score < 0 indicates a non-EC (CTRL) patient [ 28 , 29 , 32 , 33 , 34 , 35 , 36 ].…”

Section: Methodsmentioning

confidence: 99%

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The Metabolomic Approach for the Screening of Endometrial Cancer: Validation from a Large Cohort of Women Scheduled for Gynecological Surgery

et al. 2022

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Endometrial cancer (EC) is the most common gynecological neoplasm in high-income countries. Five-year survival rates are related to stage at diagnosis, but currently, no validated screening tests are available in clinical practice. The metabolome offers an unprecedented overview of the molecules underlying EC. In this study, we aimed to validate a metabolomics signature as a screening test for EC on a large study population of symptomatic women. Serum samples collected from women scheduled for gynecological surgery (n = 691) were separated into training (n = 90), test (n = 38), and validation (n = 563) sets. The training set was used to train seven classification models. The best classification performance during the training phase was the PLS-DA model (96% accuracy). The subsequent screening test was based on an ensemble machine learning algorithm that summed all the voting results of the seven classification models, statistically weighted by each models’ classification accuracy and confidence. The efficiency and accuracy of these models were evaluated using serum samples taken from 871 women who underwent endometrial biopsies. The EC serum metabolomes were characterized by lower levels of serine, glutamic acid, phenylalanine, and glyceraldehyde 3-phosphate. Our results illustrate that the serum metabolome can be an inexpensive, non-invasive, and accurate EC screening test.

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“…Statistically, in case of 80% of patients tumor do not spread outside of the bladder wall. BC can also be classified by histology as low-grade (LG) tumor that rarely spread from their primary site, and high-grade ones (HG) that are more aggressive and invasive 3 .…”

Section: Introductionmentioning

confidence: 99%

Untargeted ultra-high-resolution mass spectrometry metabolomic profiling of blood serum in bladder cancer

Nizioł

Ossoliński

Płaza‐Altamer

et al. 2022

Sci Rep

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Bladder cancer (BC) is a common urological cancer of high mortality and recurrence rates. Currently, cystoscopy is performed as standard examination for the diagnosis and subsequent monitoring for recurrence of the patients. Frequent expensive and invasive procedures may deterrent patients from regular follow-up screening, therefore it is important to look for new non-invasive methods to aid in the detection of recurrent and/or primary BC. In this study, ultra-high-performance liquid chromatography coupled with ultra-high-resolution mass spectrometry was employed for non-targeted metabolomic profiling of 200 human serum samples to identify biochemical signatures that differentiate BC from non-cancer controls (NCs). Univariate and multivariate statistical analyses with external validation revealed twenty-seven metabolites that differentiate between BC patients from NCs. Abundances of these metabolites displayed statistically significant differences in two independent training and validation sets. Twenty-three serum metabolites were also found to be distinguishing between low- and high-grade of BC patients and controls. Thirty-seven serum metabolites were found to differentiate between different stages of BC. The results suggest that measurement of serum metabolites may provide more facile and less invasive diagnostic methodology for detection of bladder cancer and recurrent disease management.

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A Serum Metabolomic Signature for the Detection and Grading of Bladder Cancer

Cited by 13 publications

References 54 publications

A Metabolomics-Based Screening Proposal for Colorectal Cancer

A Metabolomics-Based Screening Proposal for Colorectal Cancer

The Metabolomic Approach for the Screening of Endometrial Cancer: Validation from a Large Cohort of Women Scheduled for Gynecological Surgery

Untargeted ultra-high-resolution mass spectrometry metabolomic profiling of blood serum in bladder cancer

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