ArticleOncotarget2016
Development of a metabolites risk score for one-year mortality risk prediction in pancreatic adenocarcinoma patients.
Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 18 citations in OpenAlex.
- Development and Validation of a Survival Prediction Model for Patients With Pancreatic Cancer.Clinical and translational gastroenterology · 2025Article
- Perioperative changes in the plasma metabolome of patients receiving general anesthesia for pancreatic cancer surgery.Oncotarget · 2021Article
- Current State of "Omics" Biomarkers in Pancreatic Cancer.Journal of personalized medicine · 2021Review
- A prognostic score based on long-term survivor unique transcriptomic signatures predicts patient survival in pancreatic ductal adenocarcinoma.American journal of cancer research · 2021Article
- Developing a preoperative serum metabolome-based recurrence-predicting nomogram for patients with resected pancreatic ductal adenocarcinoma.Scientific reports · 2019Article
- A PD-L2-based immune marker signature helps to predict survival in resected pancreatic ductal adenocarcinoma.Journal for immunotherapy of cancer · 2019Article
- Review and Comparison of Cancer Biomarker Trends in Urine as a Basis for New Diagnostic Pathways.Cancers · 2019Review
- Integration of metabolomic and transcriptomic data reveals metabolic pathway alteration in breast cancer and impact of related signature on survival.Journal of cellular physiology · 2019Article
- Identification of ENTPD8 and cytidine in pancreatic cancer by metabolomic and transcriptomic conjoint analysis.Cancer science · 2018Article
- Development and validation of a multivariate predictive model for rheumatoid arthritis mortality using a machine learning approach.Scientific reports · 2017Article
- Metabolomics approaches in pancreatic adenocarcinoma: tumor metabolism profiling predicts clinical outcome of patients.BMC medicine · 2017Article
- Computational Model Predicts the Effects of Targeting Cellular Metabolism in Pancreatic Cancer.Frontiers in physiology · 2017Article
- Metabolomic profile in pancreatic cancer patients: a consensus-based approach to identify highly discriminating metabolites.Oncotarget · 2016Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeSurvival among patients with adenocarcinoma pancreatic cancer (PDCA) is highly variable, which ranges from 0% to 20% at 5 years. Such a wide range is due to tumor size and stage, as well other patients' characteristics. We analyzed alterations in the metabolomic profile, of PDCA patients, which are potentially predictive of patient's one-year mortality. EXPERIMENTAL
designA targeted metabolomic assay was conducted on serum samples of patients diagnosed with pancreatic cancer. Statistical analyses were performed only for those 27 patients with information on vital status at follow-up and baseline clinical features. Random Forest analysis was performed to identify all metabolites and clinical variables with the best capability to predict patient's mortality risk at one year. Regression coefficients were estimated from multivariable Weibull survival model, which included the most associated metabolites. Such coefficients were used as weights to build a metabolite risk score (MRS) which ranged from 0 (lowest mortality risk) to 1 (highest mortality risk). The stability of these weights were evaluated performing 10,000 bootstrap resamplings.
resultsMRS was built as a weighted linear combination of the following five metabolites: Valine (HR = 0.62, 95%CI: 0.11-1.71 for each standard deviation (SD) of 98.57), Sphingomyeline C24:1 (HR = 2.66, 95%CI: 1.30-21.09, for each SD of 20.67), Lysine (HR = 0.36, 95%CI: 0.03-0.77, for each SD of 51.73), Tripentadecanoate TG15 (HR = 0.25, 95%CI: 0.01-0.82, for each SD of 2.88) and Symmetric dimethylarginine (HR = 2.24, 95%CI: 1.28-103.08, for each SD of 0.62), achieving a very high discrimination ability (survival c-statistic of 0.855, 95%CI: 0.816-0.894). Such association was still present even after adjusting for the most associated clinical variables (confounders).
conclusionsThe mass spectrometry-based metabolomic profiling of serum represents a valid tool for discovering novel candidate biomarkers with prognostic ability to predict one-year mortality risk in patients with pancreatic adenocarcinoma.
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