DataSheet2_A Novel Quantification System Combining iTRAQ Technology and Multi-Omics Assessment to Predict Prognosis and Immunotherapy Efficacy in Colon Cancer.docx

Background: Colon cancer is one of the most common cancer types, although it has certain unique genetic features. This study aimed to develop a unique score for assessing prognosis and immunotherapy efficacy using integrated multi-omics analysis.
Methods: Isobaric tagging for relative and absolute quantification (iTRAQ) based proteomic analysis was used to screen differentially expressed proteins (DEP) between tumor and normal samples. DEP mRNA obtained from TCGA were clustered into different categories to show landscape-related prognosis and function. Following that, DEG was extracted from DEP mRNA, and the DEP-related score (DEPRS) was constructed to investigate the difference in immunotherapy prognosis and sensitivity. Finally, WCGNA, random forest, and artificial neural networks were used to screen for key genes. The prognostic value and protein level of these genes were validated.
Results: A total of 243 DEPs were identified through iTRAQ analysis, and the corresponding DEP mRNA was clustered into three. Following a series of tests, 1,577 DEGs were identified from overlapped DEP mRNA clusters and were classified into three gene clusters. The two types of clusters described above shared comparable characteristics in terms of prognosis and function. Then, it was established that a high DEPRS indicated a poor prognosis and DEPRS had significant associations with TMB, MSI status, and immunotherapeutic response. Finally, the key genes HART3 and FBLN2 were identified and were found to be implicated in immunotherapy and prognosis.
Conclusion: The development of a DEPRS based on multi-omics analysis will aid in improving our understanding of colon cancer and guiding a more effective immunotherapy strategy. DEPRS and key genes are used as biomarkers in the clinical evaluation of patients.

背景：结肠癌是最为常见的恶性肿瘤类型之一，尽管其具备独特的遗传学特征。本研究旨在通过整合多组学分析，构建一种用于评估患者预后与免疫治疗疗效的专属评分体系。
方法：本研究采用基于同重同位素相对与绝对定量（isobaric tagging for relative and absolute quantification, iTRAQ）的蛋白质组学分析，筛选肿瘤组织与正常组织样本间的差异表达蛋白（differentially expressed proteins, DEP）。从癌症基因组图谱（The Cancer Genome Atlas, TCGA）中获取DEP对应的mRNA数据，并将其聚类为不同类别，以展现与预后及功能相关的表达谱全貌。随后，从DEP对应的mRNA中提取差异表达基因（differentially expressed genes, DEG），并构建DEP相关评分（DEP-related score, DEPRS），以探究免疫治疗预后与敏感性的差异。最后，采用WCGNA、随机森林以及人工神经网络筛选关键基因，并对这些基因的预后价值与蛋白表达水平进行验证。
结果：通过iTRAQ分析共鉴定出243个差异表达蛋白，对应的DEP mRNA被聚类为3个类别。经过一系列验证实验后，从重叠的DEP mRNA聚类结果中鉴定出1577个差异表达基因，并将其划分为3个基因聚类簇。上述两类聚类簇在预后与功能层面具有相似的特征。随后研究证实，高DEP相关评分（DEPRS）提示不良预后，且DEPRS与肿瘤突变负荷（tumor mutational burden, TMB）、微卫星不稳定（microsatellite instability, MSI）状态以及免疫治疗响应均存在显著关联。最后，筛选得到关键基因HART3与FBLN2，研究发现二者均与免疫治疗及预后密切相关。
结论：基于多组学分析构建的DEPRS评分体系，有助于加深我们对结肠癌的认知，并指导制定更为高效的免疫治疗策略。DEPRS与关键基因可作为临床评估患者病情的生物标志物。