Research Papers:

Metabolomic profile in pancreatic cancer patients: a consensus-based approach to identify highly discriminating metabolites

Iole Maria Di Gangi, Tommaso Mazza, Andrea Fontana, Massimiliano Copetti, Caterina Fusilli, Antonio Ippolito, Fulvio Mattivi, Anna Latiano, Angelo Andriulli, Urska Vrhovsek and Valerio Pazienza _

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Oncotarget. 2016; 7:5815-5829. https://doi.org/10.18632/oncotarget.6808

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Iole Maria Di Gangi1, Tommaso Mazza2, Andrea Fontana3, Massimiliano Copetti3, Caterina Fusilli2, Antonio Ippolito4, Fulvio Mattivi1, Anna Latiano4, Angelo Andriulli4, Urska Vrhovsek1,*, Valerio Pazienza4,*

1Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), San Michele all’Adige, TN, Italy

2Unit of Bioinformatics, I.R.C.C.S. “Casa Sollievo della Sofferenza” Hospital, San Giovanni Rotondo, FG, Italy

3Unit of Biostatistics I.R.C.C.S. “Casa Sollievo della Sofferenza” Hospital, San Giovanni Rotondo, FG, Italy

4Gastroenterology Unit, I.R.C.C.S. “Casa Sollievo della Sofferenza” Hospital, San Giovanni Rotondo, FG, Italy

*These authors have contributed equally to this work

Correspondence to:

Valerio Pazienza, e-mail: [email protected]

Keywords: metabolomic, pancreatic cancer

Received: July 27, 2015     Accepted: December 26, 2015     Published: January 01, 2016


Purpose: pancreatic adenocarcinoma is the fourth leading cause of cancer related deaths due to its aggressive behavior and poor clinical outcome. There is a considerable variability in the frequency of serum tumor markers in cancer’ patients. We performed a metabolomics screening in patients diagnosed with pancreatic cancer.

Experimental Design: Two targeted metabolomic assays were conducted on 40 serum samples of patients diagnosed with pancreatic cancer and 40 healthy controls. Multivariate methods and classification trees were performed.

Materials and Methods: Sparse partial least squares discriminant analysis (SPLS-DA) was used to reduce the high dimensionality of a pancreatic cancer metabolomic dataset, differentiating between pancreatic cancer (PC) patients and healthy subjects. Using Random Forest analysis palmitic acid, 1,2-dioleoyl-sn-glycero-3-phospho-rac-glycerol, lanosterol, lignoceric acid, 1-monooleoyl-rac-glycerol, cholesterol 5α,6α epoxide, erucic acid and taurolithocholic acid (T-LCA), oleoyl-L-carnitine, oleanolic acid were identified among 206 metabolites as highly discriminating between disease states. Comparison between Receiver Operating Characteristic (ROC) curves for palmitic acid and CA 19-9 showed that the area under the ROC curve (AUC) of palmitic acid (AUC=1.000; 95% confidence interval) is significantly higher than CA 19-9 (AUC=0.963; 95% confidence interval: 0.896-1.000).

Conclusion: Mass spectrometry-based metabolomic profiling of sera from pancreatic cancer patients and normal subjects showed significant alterations in the profiles of the metabolome of PC patients as compared to controls. These findings offer an information-rich matrix for discovering novel candidate biomarkers with diagnostic or prognostic potentials.

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