In this case–control pilot study a recent method based on HPLC hyphenated to ICP-MS was employed for the quantification of serum glutathione peroxidase type 3 (GPx3), seleno-protein P (SelP) and seleno-albumin (SeAlb) in 42 patients with colorectal cancer (CRC) and 20 controls. Patients with early cancer stage (TNM I) showed a significantly higher level of SeAlb (19±3ng/mL) in respect to both metastatic CRC patients (TNM IV, 16±4 ng/mL) and healthy controls (16±3 ng/mL). Classification models based on logistic regression analysis, classification trees and artificial neural networks were constructed using seleno-proteins concentra- tions as predictors. Neural networks lead to the best performances, up to 95% of corrected predictions in TNM I vs. controls discrimination. These results suggest a potential association between individual seleno- proteins and CRC progression. Age and radiochemoteraphy were assessed as confounding factors, showing no significant effects. Still, SeAlb level tended to reduce with the age in healthy persons, but did not in CRC patients. Seleno-proteins concentration was also compared with a number of clinical parameters considered as prognostic factors in CRC. Significant Spearman's correlations were revealed between SelP and SeAlb, and presence of peri- tumoural lymphocytic infiltration (ρ = − 0.57 and ρ = − 0.37, respectively); and SeAlb and degree of cellular dif- ferentiation (grading, ρ=−0.37). This study marks the importance to systematically introduce speciation analysis and multidisciplinary approaches in the investigation of the role of seleno-proteins as a potential com- bined biomarker for CRC.

In this case-control pilot study a recent method based on HPLC hyphenated to ICP-MS was employed for the quantification of serum glutathione peroxidase type 3 (GPx3), seleno-protein P (SelP) and seleno-albumin (SeAlb) in 42 patients with colorectal cancer (CRC) and 20 controls. Patients with early cancer stage (TNM I) showed a significantly higher level of SeAlb (19 ± 3. ng/mL) in respect to both metastatic CRC patients (TNM IV, 16 ± 4. ng/mL) and healthy controls (16 ± 3. ng/mL). Classification models based on logistic regression analysis, classification trees and artificial neural networks were constructed using seleno-proteins concentrations as predictors. Neural networks lead to the best performances, up to 95% of corrected predictions in TNM I vs. controls discrimination. These results suggest a potential association between individual seleno-proteins and CRC progression. Age and radiochemoteraphy were assessed as confounding factors, showing no significant effects. Still, SeAlb level tended to reduce with the age in healthy persons, but did not in CRC patients. Seleno-proteins concentration was also compared with a number of clinical parameters considered as prognostic factors in CRC. Significant Spearman's correlations were revealed between SelP and SeAlb, and presence of peritumoural lymphocytic infiltration (ρ = - 0.57 and ρ = - 0.37, respectively); and SeAlb and degree of cellular differentiation (grading, ρ = - 0.37). This study marks the importance to systematically introduce speciation analysis and multidisciplinary approaches in the investigation of the role of seleno-proteins as a potential combined biomarker for CRC. © 2012 Elsevier B.V.

Serum seleno-proteins status for colorectal cancer screening explored by data mining techniques - a multidisciplinary pilot study

ROMAN, Marco
;
BARBANTE, Carlo
2012-01-01

Abstract

In this case-control pilot study a recent method based on HPLC hyphenated to ICP-MS was employed for the quantification of serum glutathione peroxidase type 3 (GPx3), seleno-protein P (SelP) and seleno-albumin (SeAlb) in 42 patients with colorectal cancer (CRC) and 20 controls. Patients with early cancer stage (TNM I) showed a significantly higher level of SeAlb (19 ± 3. ng/mL) in respect to both metastatic CRC patients (TNM IV, 16 ± 4. ng/mL) and healthy controls (16 ± 3. ng/mL). Classification models based on logistic regression analysis, classification trees and artificial neural networks were constructed using seleno-proteins concentrations as predictors. Neural networks lead to the best performances, up to 95% of corrected predictions in TNM I vs. controls discrimination. These results suggest a potential association between individual seleno-proteins and CRC progression. Age and radiochemoteraphy were assessed as confounding factors, showing no significant effects. Still, SeAlb level tended to reduce with the age in healthy persons, but did not in CRC patients. Seleno-proteins concentration was also compared with a number of clinical parameters considered as prognostic factors in CRC. Significant Spearman's correlations were revealed between SelP and SeAlb, and presence of peritumoural lymphocytic infiltration (ρ = - 0.57 and ρ = - 0.37, respectively); and SeAlb and degree of cellular differentiation (grading, ρ = - 0.37). This study marks the importance to systematically introduce speciation analysis and multidisciplinary approaches in the investigation of the role of seleno-proteins as a potential combined biomarker for CRC. © 2012 Elsevier B.V.
2012
105
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/39324
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