TY - JOUR

T1 - Non-invasive prostate cancer screening using chemometric processing of macro and trace element concentration profiles in urine

AU - Martynko, Ekaterina

AU - Oleneva, Ekaterina

AU - Andreev, Evgeny

AU - Savinov, Sergey

AU - Solovieva, Svetlana

AU - Protoshchak, Vladimir

AU - Karpushchenko, Evgenii

AU - Sleptsov, Aleksandr

AU - Panchuk, Vitaly

AU - Legin, Andrey

AU - Kirsanov, Dmitry

N1 - Funding Information: AL and DK acknowledge the partial financial support of this study by and «Educational Resource Center of Chemistry» for spectroscopic measurements. RFBR-NSFC project #18-53-53016 GFEN_a ( NSFC-RFBR Cooperation of China Grant No. 8171101322 ). EO, AL, and DK acknowledge the support from the Government of Russian Federation, Grant 08-08. The authors acknowledge the Research park of St. Petersburg State University's «Center for Chemical Analysis and Materials Research» Publisher Copyright: © 2020 Elsevier B.V. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/12

Y1 - 2020/12

N2 - We report on the attempt to develop a simple non-invasive screening protocol for prostate cancer (PCa). Absolute concentrations of 19 macro and trace elements (Ag, Al, B, Ba, Ca, Cd, Cu, Fe, K, Mg, Mn, Na, P, Pb, S, Si, Sr, Tl, Zn) were determined using inductively coupled plasma optical emission spectroscopy (ICP-OES) and atomic absorption spectroscopy (AAS) techniques in 34 urine samples from patients with biopsy-confirmed PCa and 32 urine samples from controls. All the possible concentration ratios were calculated as well. Various data processing methods, including Principal Component Analysis, Logistic Regression, and Decision Trees, were applied for data modeling to study if the elemental concentration profile may contribute to the development of new screening tools for prostate cancer. Several statistically significant differences between the two groups were observed both in the individual element concentrations and in their ratios. The mathematical classification models built for the prediction of the patient's status with respect to PCa based on elemental profile have shown the accuracy of up to 89%, thus exceeding the accuracy of the standard prostate-specific antigen testing.

AB - We report on the attempt to develop a simple non-invasive screening protocol for prostate cancer (PCa). Absolute concentrations of 19 macro and trace elements (Ag, Al, B, Ba, Ca, Cd, Cu, Fe, K, Mg, Mn, Na, P, Pb, S, Si, Sr, Tl, Zn) were determined using inductively coupled plasma optical emission spectroscopy (ICP-OES) and atomic absorption spectroscopy (AAS) techniques in 34 urine samples from patients with biopsy-confirmed PCa and 32 urine samples from controls. All the possible concentration ratios were calculated as well. Various data processing methods, including Principal Component Analysis, Logistic Regression, and Decision Trees, were applied for data modeling to study if the elemental concentration profile may contribute to the development of new screening tools for prostate cancer. Several statistically significant differences between the two groups were observed both in the individual element concentrations and in their ratios. The mathematical classification models built for the prediction of the patient's status with respect to PCa based on elemental profile have shown the accuracy of up to 89%, thus exceeding the accuracy of the standard prostate-specific antigen testing.

KW - Chemometrics

KW - Elemental profiling

KW - Non-invasive screening

KW - Prostate cancer

KW - Urine

UR - http://www.scopus.com/inward/record.url?scp=85090272090&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/87b9690e-d752-3096-b08f-91e317e10bba/

U2 - 10.1016/j.microc.2020.105464

DO - 10.1016/j.microc.2020.105464

M3 - Article

AN - SCOPUS:85090272090

VL - 159

JO - Microchemical Journal

JF - Microchemical Journal

SN - 0026-265X

M1 - 105464

ER -