Direct quantitative profiling of amino acids in tissues for the assessment of lung cancer
Direct molecular analysis of tissue samples is a promising approach to increase the accuracy, speed and molecular specificity of cancer diagnosis. Herein, alterations of amino acids between human lung cancer tissues and matched adjacent normal tissues were profiled by internal extraction electrospra...
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Veröffentlicht in: | Talanta (Oxford) 2021-10, Vol.233, p.122544-122544, Article 122544 |
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Zusammenfassung: | Direct molecular analysis of tissue samples is a promising approach to increase the accuracy, speed and molecular specificity of cancer diagnosis. Herein, alterations of amino acids between human lung cancer tissues and matched adjacent normal tissues were profiled by internal extraction electrospray ionization mass spectrometry (iEESI-MS). The results indicated that the levels of 11 detected amino acids (including serine, proline, valine, threonine, asparagine, aspartic acid, methionine, histidine, phenylalanine, arginine and tyrosine) in the cancerous tissues were lower than that in the adjacent normal tissues. Based on the orthogonal partial least squares discriminant analysis (OPLS-DA) model, cancerous and adjacent normal tissues were clearly discriminated, and the amino acids that played the major role in the differentiation between cancerous and adjacent normal tissues were identified. Moreover, metabolic pathway analysis revealed alterations of differential amino acids in several metabolic pathways upon lung cancer. The current study extends the power of iEESI-MS as a promising tool for quantitative characterization of amino acids in tissues, and allows the study of alterations in amino acids metabolism associated with the development of lung cancer.
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•Direct quantitative and qualitative analysis of amino acids in lung cancer tissues was achieved by iEESI-MS.•The levels of determined 11 amino acids in the cancerous tissues were lower than that in the adjacent normal tissues.•Alterations of differential amino acids in several metabolic pathways upon lung cancer were revealed. |
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ISSN: | 0039-9140 1873-3573 |
DOI: | 10.1016/j.talanta.2021.122544 |