Faculty of Medicine and Health Technology, Tampere University, 33520 Tampere, Finland.
TAYS Cancer Centre, Tampere University Hospital, Wellbeing Services County of Pirkanmaa, 33521 Tampere, Finland.
Int J Mol Sci. 2024 Oct 13;25(20):11002. doi: 10.3390/ijms252011002.
Phospholipids are the main building components of cell membranes and are also used for cell signaling and as energy storages. Cancer cells alter their lipid metabolism, which ultimately leads to an increase in phospholipids in cancer tissue. Surgical energy instruments use electrical or vibrational energy to heat tissues, which causes intra- and extracellular water to expand rapidly and degrade cell structures, bursting the cells, which causes the formation of a tissue aerosol or smoke depending on the amount of energy used. This gas phase analyte can then be analyzed via gas analysis methods. Differential mobility spectrometry (DMS) is a method that can be used to differentiate malignant tissue from benign tissues in real time via the analysis of surgical smoke produced by energy instruments. Previously, the DMS identification of cancer tissue was based on a 'black box method' by differentiating the 2D dispersion plots of samples. This study sets out to find datapoints from the DMS dispersion plots that represent relevant target molecules. We studied the ability of DMS to differentiate three subclasses of phospholipids (phosphatidylcholine, phosphatidylinositol, and phosphatidylethanolamine) from a control sample using a bovine skeletal muscle matrix with a 5 mg addition of each phospholipid subclass to the sample matrix. We trained binary classifiers using linear discriminant analysis (LDA) and support vector machines (SVM) for sample classification. We were able to identify phosphatidylcholine, -inositol, and -ethanolamine with SVM binary classification accuracies of 91%, 73%, and 66% and with LDA binary classification accuracies of 82%, 74%, and 72%, respectively. Phosphatidylcholine was detected with a reliable classification accuracy, but ion separation setups should be adjusted in future studies to reliably detect other relevant phospholipids such as phosphatidylinositol and phosphatidylethanolamine and improve DMS as a microanalysis method and identify other phospholipids relevant to cancer tissue.
磷脂是细胞膜的主要结构成分,也用于细胞信号转导和能量储存。癌细胞改变其脂质代谢,最终导致癌细胞组织中磷脂含量增加。外科能量仪器使用电能或振动能加热组织,这会导致细胞内外的水迅速膨胀并降解细胞结构,使细胞破裂,从而根据使用的能量量形成组织气溶胶或烟雾。然后可以通过气体分析方法分析这种气相分析物。差分迁移谱(DMS)是一种可通过分析能量仪器产生的手术烟雾来实时区分恶性组织和良性组织的方法。以前,DMS 对癌症组织的识别是基于通过区分样品的 2D 分散图来区分恶性组织和良性组织的“黑盒方法”。本研究旨在从 DMS 分散图中找到表示相关靶分子的数据集点。我们研究了 DMS 使用牛骨骼肌基质(在样品基质中每种磷脂亚类添加 5mg)从对照样品中区分三种亚类磷脂(磷脂酰胆碱、磷脂酰肌醇和磷脂酰乙醇胺)的能力。我们使用线性判别分析(LDA)和支持向量机(SVM)为样品分类训练了二进制分类器。我们能够使用 SVM 二进制分类准确率为 91%、73%和 66%,以及 LDA 二进制分类准确率为 82%、74%和 72%分别识别磷脂酰胆碱、-肌醇和-乙醇胺。检测到磷脂酰胆碱具有可靠的分类准确率,但在未来的研究中应调整离子分离装置,以可靠地检测其他相关磷脂,如磷脂酰肌醇和磷脂酰乙醇胺,并改进 DMS 作为微分析方法并识别与癌症组织相关的其他磷脂。