Serum Colorectal Cancer Biomarkers Unraveled by NMR Metabolomics: Past, Present, and Future

Salmerón, Ana M.; Tristán, Ana Isabel; Abreu, Ana Cristina; Fernández, Ignacio

doi:10.1021/acs.analchem.1c04360

Cited by 11 publications

(17 citation statements)

References 108 publications

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“…Acetate, glycine, lactate, phenylalanine, cholesterol, 3-hydroxybutyrate, histidine, isoleucine, leucine, serine, sphinganine and glucose were found to be upregulated in RS-CRC model compared to RS-Colon model and metabolites such as citrate, creatinine and glycocholic acid were found to be downregulated [29, 30]. Novel findings from this analysis on RS-CRC model are some of the upregulated metabolites that belong to RS metabolism like 4-chlorocatechol OH-adduct, guanine OH-adduct, 6-methyluracil-5-OH-adduct, purine OH-adduct, radicals from arabinose, nitrous acid and nitrogen trioxide.…”

Section: Resultsmentioning

confidence: 99%

“…PGE2 contributes to CRC by inducing fattyacid oxidation associated genes [40] and its level is upregulated in the RS-CRC model compared to RS-Colon model. There are few disagreements between different studies on CRC metabolomics [29, 30] as there is no standard approach of metabolomics studies which result in multiple conclusions. On that note, there are few discripancies with the downregulated metabolites between the literature and our analysis.…”

Section: Discussionmentioning

confidence: 99%

“…The significant metabolites with log fold change of ≥ 1.5 or ≤ −0.65 were considered to be upregulated and downregulated respectively [28] in the RS-CRC model. The results were compared with various serum metabolomic studies on colorectal cancer to validate the findings [29, 30].…”

Section: Methodsmentioning

confidence: 99%

“…The results were compared with various serum metabolomic studies on colorectal cancer to validate the findings [29,30].…”

Section: Production Rates Of Metabolitesmentioning

confidence: 96%

See 3 more Smart Citations

Modeling Reactive Species Metabolism in Colorectal Cancer for Identifying Metabolic Targets and Devising Therapeutics

Bhalla

Sridhar

Kullu

et al. 2022

Preprint

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Reactive species (RS) are known to play significant roles in cancer development as well as in treating or managing cancer. On the other hand, genome scale metabolic models are being used to understand cell metabolism in disease contexts including cancer, and also in planning strategies to handle diseases. Despite their crucial roles in cancers, the reactive species have not been adequately modeled in the genome scale metabolic models (GSMMs) when probing disease models for their metabolism or detection of drug targets. In this work, we have developed a module of reactive species reactions, which is scalable - it can be integrated with any human metabolic model as it is, or with any metabolic model with fine-tuning. When integrated with a cancer (colorectal cancer in this case) metabolic model, the RS module highlighted the deregulation occurring in important CRC pathways such as fatty acid metabolism, cholesterol metabolism, arachidonic acid and eicosanoid metabolism. We show that the RS module helps in better deciphering crucial metabolic targets for devising better therapeutics such as FDFT1, FADS2 and GUK1 by taking into account the effects mediated by reactive species during colorectal cancer progression. The results from this reactive species integrated CRC metabolic model reinforces ferroptosis as a potential target for colorectal cancer therapy.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…The results were compared with various serum metabolomic studies on colorectal cancer to validate the findings [29,30].…”

Section: Production Rates Of Metabolitesmentioning

confidence: 96%

See 2 more Smart Citations

Modeling Reactive Species Metabolism in Colorectal Cancer for Identifying Metabolic Targets and Devising Therapeutics

Bhalla

Sridhar

Kullu

et al. 2022

Preprint

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show abstract

“…[3] for a more details). For instance, cryoprobes [132][133][134] and magic angle techniques [17,135,136] are today commonly used for improving the signal-to-noise ratio, while AI methods are used both for signal pre-processing, such as baseline optimization [137][138][139], and for data analysis, as discussed in the main text of this review.…”

Section: Appendix a Technical Aspectsmentioning

confidence: 99%

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

et al. 2022

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NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy.

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Metabolomic landscape of overall and common cancers in the UK Biobank: A prospective cohort study

Hu,

Fan,

Lin

et al. 2024

Intl Journal of Cancer

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Information about the NMR metabolomics landscape of overall, and common cancers is still limited. Based on a cohort of 83,290 participants from the UK Biobank, we used multivariate Cox regression to assess the associations between each of the 168 metabolites with the risks of overall cancer and 20 specific types of cancer. Then, we applied LASSO to identify important metabolites for overall cancer risk and obtained their associations using multivariate cox regression. We further conducted mediation analysis to evaluate the mediated role of metabolites in the effects of traditional factors on overall cancer risk. Finally, we included the 13 identified metabolites as predictors in prediction models, and compared the accuracies of our traditional models. We found that there were commonalities among the metabolic profiles of overall and specific types of cancer: the top 20 frequently identified metabolites for 20 specific types of cancer were all associated with overall cancer; most of the specific types of cancer had common identified metabolites. Meanwhile, the associations between the same metabolite with different types of cancer can vary based on the site of origin. We identified 13 metabolic biomarkers associated with overall cancer, and found that they mediated the effects of traditional factors. The accuracies of prediction models improved when we added 13 identified metabolites in models. This study is helpful to understand the metabolic mechanisms of overall and a wide range of cancers, and our results also indicate that NMR metabolites are potential biomarkers in cancer diagnosis and prevention.

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Serum Colorectal Cancer Biomarkers Unraveled by NMR Metabolomics: Past, Present, and Future

Cited by 11 publications

References 108 publications

Modeling Reactive Species Metabolism in Colorectal Cancer for Identifying Metabolic Targets and Devising Therapeutics

Modeling Reactive Species Metabolism in Colorectal Cancer for Identifying Metabolic Targets and Devising Therapeutics

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

Metabolomic landscape of overall and common cancers in the UK Biobank: A prospective cohort study

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