Issue 10, 2014

The use of EEM fluorescence data and OPLS/UPLS-DA algorithm to discriminate between normal and cancer cell lines: a feasibility study

Abstract

Excitation emission matrix (EEM) fluorescence spectroscopy combined with the OPLS method has been investigated as a promising tool to discriminate between normal and cancer cell lines in two datasets: (i) using several types of normal and cancer cells (including 3T3, ARPE, HEK, HepG2, HeLa, HT-29 and 786-0 cells); (ii) considering the expression of matrix metalloproteinase-2 and -9 (MMP-2 and MMP-9) in suspensions of HEK and 786-0 cell lines. Partial Least Squares-Discriminant Analysis (PLS-DA) using the score matrix from PARAFAC (Parallel Factor Analysis), UPLS-DA (Unfolded Partial Least Squares with Discriminant Analysis) and orthogonal projection to latent structures (OPLS) were used as the bases for the discrimination models. UPLS-DA presented relevant performance for cancer cells in both datasets, with 100% and 66.7% correct prediction for first and second cases, respectively, and poor discrimination relative to normal cells in the first dataset (25%). By using the OPLS, we achieved 75% correct prediction for normal cells and maintained 100% concordance for cancer objects. On applying OPLS to the second dataset, we obtained 100% correct prediction in both classes (normal and cancer) for calibration and prediction sets. These results suggest that EEM fluorescence spectroscopy combined with chemometrics could be used as a clinical tool for cancer cell detection based on intrinsic biomolecular signatures.

Graphical abstract: The use of EEM fluorescence data and OPLS/UPLS-DA algorithm to discriminate between normal and cancer cell lines: a feasibility study

Article information

Article type
Paper
Submitted
10 Nah 2014
Accepted
13 Cig 2014
First published
13 Cig 2014

Analyst, 2014,139, 2423-2431

The use of EEM fluorescence data and OPLS/UPLS-DA algorithm to discriminate between normal and cancer cell lines: a feasibility study

A. Carolina de Oliveira Neves, R. Fernandes de Araújo Júnior, A. Luiza Cabral de Sá Leitão Oliveira, A. Antunes de Araújo and K. M. G. de Lima, Analyst, 2014, 139, 2423 DOI: 10.1039/C4AN00296B

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