Gas chromatography-mass spectrometry untargeted profiling of non-Hodgkin’s lymphoma urinary metabolite markers

Duarte, Gustavo Henrique Bueno; Fernandes, Anna Maria Alves de Piloto; Silva, Alex; Zamora-Obando, Hans Rolando; Amaral, Alan Gonçalves; Mesquita, Alessandra de Sousa; Schmidt-Filho, Jayr; Lima, Vladmir Cláudio Cordeiro de; Costa, Felipe D’Almeida; Andrade, Victor Piana de; Porcari, Andréia M.; Eberlin, Marcos N.; Simionato, Ana Valéria Colnaghi

doi:10.1007/s00216-020-02881-5

Cited by 10 publications

(4 citation statements)

References 60 publications

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“…Other machine learning (ML) algorithms like Decision Trees, PCA, t-SNE and PLS have also shown promising results in identifying and classifying various cancers based on metabolomic data such as ovarian [23][24][25], lung [26][27][28], endometrial [29], skin and kidney carcinomas [30][31][32], glioma and meningioma brain tumors [33], and non-Hodgkin's lymphoma [34]. For breast cancer particularly, Henneges et al [35] achieved sensitivity and specificity of 83.5% and 90.6% respectively with an SVM-based metabolomic approach.…”

Section: Discussionmentioning

confidence: 99%

Identifying Robust Biomarker Panels for Breast Cancer Screening

Vaida,

Arumalla,

Tatikonda

et al. 2024

Preprint

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Breast cancer remains a major public health concern, and early detection can result in more treatment options, which are crucial for improving survival rates. Metabolomics offers the potential to develop blood-based screening and diagnostics tools that are less invasive and more cost-effective. However, the inherent complexity of metabolomic datasets makes identifying the most diagnostically relevant biomarkers a difficult task, with multiple studies demonstrating lim-ited agreement on the specific metabolites and pathways involved. This study aims to identify a set of biomarkers for early diagnosis of breast cancer using metabolomics data. Plasma samples from 185 breast cancer patients and 53 controls (CHTN) were analyzed. We utilized univariate Naïve Bayes, L2-regularized Support Vector Machines, and Principal Component Analysis (PCA), along with feature engineering techniques, to select the most informative features. Multiple ma-chine learning models, including Support Vector Machines, Multidimensional Scaling, Logistic Regression, and Ensemble Learning were utilized for classification. The best-performing feature set comprised 4 biomarkers and 2 demographic variables, achieving an accuracy of 98%, demon-strating the potential for a robust, cost-effective, non-invasive breast cancer screening and diagnostic tool.

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Section: Discussionmentioning

confidence: 99%

Identifying Robust Biomarker Panels for Breast Cancer Screening

Vaida,

Arumalla,

Tatikonda

et al. 2024

Preprint

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“…According to a recent urine metabolomics study of non-Hodgkin's lymphoma (NHL) patients (15 samples), 18 metabolites were detected by GC-MS and gave a satisfying diagnostic value with an area under the receiver operating characteristic (ROC) > 0.998 (52). Similarly, there were significant metabolic differences between MM patients and healthy controls in bone marrow supernatant and peripheral plasma.…”

Section: Potential Clinical Application Of Metabolic Reprogramming 1 ...mentioning

confidence: 99%

“…In addition, serum metabolomic analysis has been used to identify metabolites associated with the outcome of children with AML receiving chemotherapy ( 50, 51 ). According to a recent urine metabolomics study of patients with non–Hodgkin lymphoma (NHL; 15 samples), 18 metabolites were detected by gas chromatography–mass spectroscopy (GC-MS) and gave a satisfying diagnostic value with an area under the ROC > 0.998 ( 52 ). Similarly, there were significant metabolic differences between patients with multiple myeloma and healthy controls in bone marrow supernatant and peripheral plasma.…”

Section: Potential Clinical Application Of Metabolic Reprogrammingmentioning

confidence: 99%

Metabolic Reprogramming in Hematologic Malignancies: Advances and Clinical Perspectives

Wang

Zhou

2022

Cancer Research

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Metabolic reprogramming is a hallmark of cancer progression. Metabolic activity supports tumorigenesis and tumor progression, allowing cells to uptake essential nutrients from the environment and use the nutrients to maintain viability and support proliferation. The metabolic pathways of malignant cells are altered to accommodate increased demand for energy, reducing equivalents, and biosynthetic precursors. Activated oncogenes coordinate with altered metabolism to control cell-autonomous pathways, which can lead to tumorigenesis when abnormalities accumulate. Clinical and preclinical studies have shown that targeting metabolic features of hematological malignancies is an appealing therapeutic approach. This review provides a comprehensive overview of the mechanisms of metabolic reprogramming in hematologic malignancies and potential therapeutic strategies to target cancer metabolism.

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“…8,9 VOMs are promising candidates for metabolome-based diagnostics as they can be performed by safe, non-invasive, and specific tests for the early detection of different cancer types. [10][11][12] One primary origin of the volatile biomarkers for cancer may be linked to the reactive oxygen species (ROS) associated with cancer growth and progression. 13,14 These oxidative species promote damage in the DNA of cells, endoplasmic reticulum stress, and the oxidation of lipids and proteins, culminating in small metabolites being released into body fluids.…”

Section: Introductionmentioning

confidence: 99%