Classification of cancer cell lines using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and statistical analysis

Serafim, Vlad, Shah, Ajit J. ORCID: https://orcid.org/0000-0002-2350-6384, Puiu, Maria, Andreescu, Nicoleta, Coricovac, Dorina, Nosyrev, Alexander, Spandidos, Demetrios A., Tsatsakis, Aristides M., Dehelean, Cristina and Pinzaru, Iulia (2017) Classification of cancer cell lines using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and statistical analysis. International Journal of Molecular Medicine, 40 (4) . pp. 1096-1104. ISSN 1107-3756 [Article] (doi:10.3892/ijmm.2017.3083)

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Abstract

Over the past decade, matrix-assisted laser desorption/ionization time‑of‑flight mass spectrometry (MALDI‑TOF MS) has been established as a valuable platform for microbial identification, and it is also frequently applied in biology and clinical studies to identify new markers expressed in pathological conditions. The aim of the present study was to assess the potential of using this approach for the classification of cancer cell lines as a quantifiable method for the proteomic profiling of cellular organelles. Intact protein extracts isolated from different tumor cell lines (human and murine) were analyzed using MALDI‑TOF MS and the obtained mass lists were processed using principle component analysis (PCA) within Bruker Biotyper® software. Furthermore, reference spectra were created for each cell line and were used for classification. Based on the intact protein profiles, we were able to differentiate and classify six cancer cell lines: two murine melanoma (B16‑F0 and B164A5), one human melanoma (A375), two human breast carcinoma (MCF7 and MDA‑MB‑231) and one human liver carcinoma (HepG2). The cell lines were classified according to cancer type and the species they originated from, as well as by their metastatic potential, offering the possibility to differentiate non‑invasive from invasive cells. The obtained results pave the way for developing a broad‑based strategy for the identification and classification of cancer cells

Item Type: Article
Research Areas: A. > School of Science and Technology > Natural Sciences
Item ID: 22506
Notes on copyright: Copyright: © Serafim et al. This is an open access article distributed under the terms of Creative Commons Attribution License (https://creativecommons.org/licenses/by-nc-nd/4.0/)
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Depositing User: Ajit Shah
Date Deposited: 14 Sep 2017 15:17
Last Modified: 19 Jun 2021 18:12
URI: https://eprints.mdx.ac.uk/id/eprint/22506

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