School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, China.
Institute of Medical Research, Northwestern Polytechnical University, Xi'an 710072, China.
Biomolecules. 2023 Apr 20;13(4):701. doi: 10.3390/biom13040701.
Gastrointestinal (GI) cancer accounts for one in four cancer cases and one in three cancer-related deaths globally. A deeper understanding of cancer development mechanisms can be applied to cancer medicine. Comprehensive sequencing applications have revealed the genomic landscapes of the common types of human cancer, and proteomics technology has identified protein targets and signalling pathways related to cancer growth and progression. This study aimed to explore the functional proteomic profiles of four major types of GI tract cancer based on The Cancer Proteome Atlas (TCPA). We provided an overview of functional proteomic heterogeneity by performing several approaches, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), t-stochastic neighbour embedding (t-SNE) analysis, and hierarchical clustering analysis in oesophageal carcinoma (ESCA), stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), and rectum adenocarcinoma (READ) tumours, to gain a system-wide understanding of the four types of GI cancer. The feature selection approach, mutual information feature selection (MIFS) method, was conducted to screen candidate protein signature subsets to better distinguish different cancer types. The potential clinical implications of candidate proteins in terms of tumour progression and prognosis were also evaluated based on TCPA and The Cancer Genome Atlas (TCGA) databases. The results suggested that functional proteomic profiling can identify different patterns among the four types of GI cancers and provide candidate proteins for clinical diagnosis and prognosis evaluation. We also highlighted the application of feature selection approaches in high-dimensional biological data analysis. Overall, this study could improve the understanding of the complexity of cancer phenotypes and genotypes and thus be applied to cancer medicine.
胃肠道（GI）癌症占全球癌症病例的四分之一和癌症相关死亡的三分之一。对癌症发展机制的更深入了解可应用于癌症医学。全面测序应用揭示了常见人类癌症的基因组景观，蛋白质组学技术确定了与癌症生长和进展相关的蛋白质靶标和信号通路。本研究旨在基于癌症蛋白质组图谱（TCPA）探索四种主要类型的胃肠道癌症的功能蛋白质组学特征。我们通过执行几种方法，包括主成分分析（PCA）、偏最小二乘判别分析（PLS-DA）、t-随机邻域嵌入（t-SNE）分析和层次聚类分析，提供了功能蛋白质组异质性的概述，在食管癌（ESCA）、胃腺癌（STAD）、结肠癌（COAD）和直肠腺癌（READ）肿瘤中，以全面了解四种类型的胃肠道癌症。特征选择方法，互信息特征选择（MIFS）方法，用于筛选候选蛋白质特征子集，以更好地区分不同的癌症类型。还根据 TCPA 和癌症基因组图谱（TCGA）数据库评估候选蛋白质在肿瘤进展和预后方面的潜在临床意义。结果表明，功能蛋白质组学分析可以识别四种胃肠道癌症之间的不同模式，并提供候选蛋白质用于临床诊断和预后评估。我们还强调了特征选择方法在高维生物学数据分析中的应用。总体而言，这项研究可以提高对癌症表型和基因型复杂性的理解，并应用于癌症医学。
