Untargeted LC-HRMS-based metabolomics to identify novel biomarkers of metastatic colorectal cancer

Martín-Blázquez, Ariadna; Díaz, Caridad; González-Flores, Encarnación; Franco-Rivas, Daniel; Jiménez-Luna, Cristina; Melguizo, Consolación; Prados, José; Genilloud, Olga; Vicente, Francisca; Caba, Octavio; Palacio, José Pérez del

doi:10.1038/s41598-019-55952-8

Cited by 44 publications

(30 citation statements)

References 36 publications

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“…Bias may also be introduced because of the demographic makeup of the cohort, and a larger number of subjects with various backgrounds should be compared to ascertain the specificity of the putative biomarkers. The prediction of metastasis and prognosis using biofluids is also challenging 47 , but was beyond the scope of the present study. The comparison of polyamines measured in the present study and in our previous study (Figure S5 ) reveals clear linearity between these data.…”

Section: Discussionmentioning

confidence: 99%

Urinary charged metabolite profiling of colorectal cancer using capillary electrophoresis-mass spectrometry

Udo

Katsumata

Kuwabara

et al. 2020

Sci Rep

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Colorectal cancer (CRC) has increasing global prevalence and poor prognostic outcomes, and the development of low- or less invasive screening tests is urgently required. Urine is an ideal biofluid that can be collected non-invasively and contains various metabolite biomarkers. To understand the metabolomic profiles of different stages of CRC, we conducted metabolomic profiling of urinary samples. Capillary electrophoresis-time-of-flight mass spectrometry was used to quantify hydrophilic metabolites in 247 subjects with stage 0 to IV CRC or polyps, and healthy controls. The 154 identified and quantified metabolites included metabolites of glycolysis, TCA cycle, amino acids, urea cycle, and polyamine pathways. The concentrations of these metabolites gradually increased with the stage, and samples of CRC stage IV especially showed a large difference compared to other stages. Polyps and CRC also showed different concentration patterns. We also assessed the differentiation ability of these metabolites. A multiple logistic regression model using three metabolites was developed with a randomly designated training dataset and validated using the remaining data to differentiate CRC and polys from healthy controls based on a panel of urinary metabolites. These data highlight the changes in metabolites from early to late stage of CRC and also the differences between CRC and polyps.

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Section: Discussionmentioning

confidence: 99%

Urinary charged metabolite profiling of colorectal cancer using capillary electrophoresis-mass spectrometry

Udo

Katsumata

Kuwabara

et al. 2020

Sci Rep

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“…The column was eluted with the following gradient: 0–0.5 min 1% eluent B; 0.5–11 min 99% eluent B; 11–15.50 min 99% eluent B; 15.50–15.60 min 1% eluent B and 15.60–20 min 1% eluent B. The elution flow rate was set at 300 µL/min [ 62 ]. Then, chromatographic separation was performed using a Gemini HPLC C18 column (100 mm × 2 mm, 3 µm; Phenomenex, CA, USA) kept at 25 °C in ESI− mode.…”

Section: Methodsmentioning

confidence: 99%

Human Plasma Metabolomics for Biomarker Discovery: Targeting the Molecular Subtypes in Breast Cancer

Díaz-Beltrán

González-Olmedo

Luque-Caro

et al. 2021

Cancers

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Purpose: The aim of this study is to identify differential metabolomic signatures in plasma samples of distinct subtypes of breast cancer patients that could be used in clinical practice as diagnostic biomarkers for these molecular phenotypes and to provide a more individualized and accurate therapeutic procedure. Methods: Untargeted LC-HRMS metabolomics approach in positive and negative electrospray ionization mode was used to analyze plasma samples from LA, LB, HER2+ and TN breast cancer patients and healthy controls in order to determine specific metabolomic profiles through univariate and multivariate statistical data analysis. Results: We tentatively identified altered metabolites displaying concentration variations among the four breast cancer molecular subtypes. We found a biomarker panel of 5 candidates in LA, 7 in LB, 5 in HER2 and 3 in TN that were able to discriminate each breast cancer subtype with a false discovery range corrected p-value < 0.05 and a fold-change cutoff value > 1.3. The model clinical value was evaluated with the AUROC, providing diagnostic capacities above 0.85. Conclusion: Our study identifies metabolic profiling differences in molecular phenotypes of breast cancer. This may represent a key step towards therapy improvement in personalized medicine and prioritization of tailored therapeutic intervention strategies.

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“…Disturbances in the serum lipid profile 12 years post infection were associated with long-term systemic damage caused by high doses of methylprednisolone [ 38 ]. Metabolomics is therefore a powerful tool for identifying and monitoring the prognosis and markers at different stages of a disease, particularly for unknown infections [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

“…Both metabolome and proteome composition are dynamic and reflect genome expression under specific conditions [ 20 - 22 ]. Furthermore, these innovative combination protocols can help in understanding the metabolic profile of a given patient or group of patients [ 23 ] from urine, saliva, and blood samples [ 24 ]. The chemical profile substantially contributed to biological and medical research, leading to advancements in clinical medical practice [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Molecular and Cellular Biomarkers of COVID-19 Prognosis: Protocol for the Prospective Cohort TARGET Study

Kurizky¹,

Nóbrega²,

Soares³

et al. 2021

JMIR Res Protoc

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Background Since the beginning of the COVID-19 pandemic, the world’s attention has been focused on better understanding the relation between the human host and the SARS-CoV-2 virus, as its action has led to hundreds of thousands of deaths. Objective In this context, we decided to study certain consequences of the abundant cytokine release over the innate and adaptive immune systems, inflammation, and hemostasis, comparing mild and severe forms of COVID-19. Methods To accomplish these aims, we will analyze demographic characteristics, biochemical tests, immune biomarkers, leukocyte phenotyping, immunoglobulin profile, hormonal release (cortisol and prolactin), gene expression, thromboelastometry, neutralizing antibodies, metabolic profile, and neutrophil function (reactive oxygen species production, neutrophil extracellular trap production, phagocytosis, migration, gene expression, and proteomics). A total of 200 reverse transcription polymerase chain reaction–confirmed patients will be enrolled and divided into two groups: mild/moderate or severe/critical forms of COVID-19. Blood samples will be collected at different times: at inclusion and after 9 and 18 days, with an additional 3-day sample for severe patients. We believe that this information will provide more knowledge for future studies that will provide more robust and useful clinical information that may allow for better decisions at the front lines of health care. Results The recruitment began in June 2020 and is still in progress. It is expected to continue until February 2021. Data analysis is scheduled to start after all data have been collected. The coagulation study branch is complete and is already in the analysis phase. Conclusions This study is original in terms of the different parameters analyzed in the same sample of patients with COVID-19. The project, which is currently in the data collection phase, was approved by the Brazilian Committee of Ethics in Human Research (CAAE 30846920.7.0000.0008). Trial Registration Brazilian Registry of Clinical Trials RBR-62zdkk; https://ensaiosclinicos.gov.br/rg/RBR-62zdkk International Registered Report Identifier (IRRID) DERR1-10.2196/24211

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Untargeted LC-HRMS-based metabolomics to identify novel biomarkers of metastatic colorectal cancer

Cited by 44 publications

References 36 publications

Urinary charged metabolite profiling of colorectal cancer using capillary electrophoresis-mass spectrometry

Urinary charged metabolite profiling of colorectal cancer using capillary electrophoresis-mass spectrometry

Human Plasma Metabolomics for Biomarker Discovery: Targeting the Molecular Subtypes in Breast Cancer

Molecular and Cellular Biomarkers of COVID-19 Prognosis: Protocol for the Prospective Cohort TARGET Study

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