ArticleInternational journal of molecular medicine2017
Classification of cancer cell lines using matrix-assisted laser desorption/ionization time‑of‑flight mass spectrometry and statistical analysis.
Article in International journal of molecular medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 17 citations in OpenAlex.
- Discrimination of the chemotherapy resistance status of human leukemia and glioblastoma cell lines by MALDI-TOF-MS profiling.Scientific reports · 2023Article
- Advances in high-throughput mass spectrometry in drug discovery.EMBO molecular medicine · 2023Review
- Impact of Skin Tissue Collection Method on Downstream MALDI-Imaging.Metabolites · 2022Article
- Peptide barcodes in dogs affected by mitral valve disease with and without pulmonary hypertension using MALDI-TOF MS and LC-MS/MS.PloS one · 2021Article
- Ethinylestradiol and Levonorgestrel as Active Agents in Normal Skin, and Pathological Conditions Induced by UVB Exposure: In Vitro and In Ovo Assessments.International journal of molecular sciences · 2018Article
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Authors and funding
10 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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