Time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS) is an imaging‐based analytical technique that can characterize the surfaces of biomaterials. We used TOF‐SIMS to identify important metabolites and oncogenic KRAS mutation expressed in human colorectal cancer (CRC). We obtained 540 TOF‐SIMS spectra from 180 tissue samples by scanning cryo‐sections and selected discriminatory molecules using the support vector machine (SVM) algorithm. Each TOF‐SIMS spectrum contained nearly 860,000 ion profiles and hundreds of spectra were analyzed; therefore, reducing the dimensionality of the original data was necessary. We performed principal component analysis after preprocessing the spectral data, and the principal components (20) of each spectrum were used as the inputs of the SVM algorithm using the R package. The performance of the algorithm was evaluated using the receiver operating characteristic (ROC) area under the curve (AUC) (0.9297). Spectral peaks (m/z) corresponding to discriminatory molecules used to classify normal and tumor samples were selected according to p‐value and were assigned to arginine, α‐tocopherol, and fragments of glycerophosphocholine. Pathway analysis using these discriminatory molecules showed that they were involved in gastrointestinal disease and organismal abnormalities. In addition, spectra were classified according to the expression of KRAS somatic mutation, with 0.9921 AUC. Taken together, TOF‐SIMS efficiently and simultaneously screened metabolite biomarkers and performed KRAS genotyping. In addition, a machine learning algorithm was provided as a diagnostic tool applied to spectral data acquired from clinical samples prepared as frozen tissue slides, which are commonly used in a variety of biomedical tests.

中文翻译：

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利用二次离子质谱法筛选结肠癌中的重要代谢物和 KRAS 基因型

飞行时间二次离子质谱（TOF-SIMS）是一种基于成像的分析技术，可以表征生物材料的表面。我们使用 TOF-SIMS 来鉴定人类结直肠癌 (CRC) 中表达的重要代谢物和致癌 KRAS 突变。我们通过扫描冷冻切片从 180 个组织样本中获得了 540 个 TOF-SIMS 光谱，并使用支持向量机 (SVM) 算法选择了有区别的分子。每个 TOF-SIMS 谱包含近 860,000 个离子剖面，并分析了数百个谱；因此，有必要对原始数据进行降维。我们在预处理光谱数据后进行主成分分析，并将每个光谱的主成分（20）用作使用R包的SVM算法的输入。使用受试者工作特征 (ROC) 曲线下面积 (AUC) (0.9297) 评估算法的性能。根据p值选择与用于分类正常和肿瘤样品的区分分子相对应的光谱峰 ( m/z ) ，并将其分配给精氨酸、α-生育酚和甘油磷酸胆碱片段。使用这些区别性分子进行的通路分析表明它们与胃肠道疾病和机体异常有关。此外，根据KRAS体细胞突变的表达情况对谱图进行分类，AUC为0.9921。总而言之，TOF-SIMS 高效、同步地筛选代谢物生物标志物并进行 KRAS 基因分型。此外，还提供了机器学习算法作为诊断工具，应用于从制备为冷冻组织载玻片的临床样本中获取的光谱数据，这些样本通常用于各种生物医学测试。