Chen Dongjie, Zang Longjun, Zhou Yanling, Yang Yongchao, Zhang Xianlin, Li Zheng, Shu Yufeng, Gao Wenzhe, Zhu Hongwei, Yu Xiao
Department of Hepatopancreatobiliary Surgery, Third Xiangya Hospital, Central South University, Changsha, Hunan, PR China.
Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.
Heliyon. 2024 Mar 16;10(6):e28243. doi: 10.1016/j.heliyon.2024.e28243. eCollection 2024 Mar 30.
Pancreatic cancer (PC) is a malignant digestive system tumor with a very poor prognosis. N6-methyladenosine (m6A) is mediated by a variety of readers and participates in important regulatory roles in PC. Based on TCGA_PAAD, ICGC_AU_PAAD, ICGC_CA_PAAD, GSE28735 and GSE62452 datasets, We mapped the multi-omics changes of m6A readers in PC and found that m6A readers, especially IGF2BP family genes, had specific changes and were significantly associated with poor prognosis. An unsupervised consensus clustering algorithm was used to explore the correlation between specific expression patterns of m6A readers in PC and enrichment pathways, tumor immunity and clinical molecular subtypes. Then, the principal component analysis (PCA) algorithm was used to quantify specific expression patterns and screen core genes. Machine learning algorithms such as Bootstrapping and RSF were used to quantify the expression patterns of core genes and construct a prognostic scoring model for PC patients. What's more, pharmacogenomic databases were used to screen sensitive drug targets and small molecule compounds for high-risk PC patients in an all-around and multi-angle way. Our study has not only provided new insights into personalized prognostication approaches, but also thrown light on integrating tailored risk stratification with precision therapy based on IGF2BP2-mediated m6A modification patterns.
胰腺癌（PC）是一种预后极差的恶性消化系统肿瘤。N6-甲基腺苷（m6A）由多种阅读蛋白介导，并在胰腺癌中发挥重要调控作用。基于TCGA_PAAD、ICGC_AU_PAAD、ICGC_CA_PAAD、GSE28735和GSE62452数据集，我们绘制了胰腺癌中m6A阅读蛋白的多组学变化图谱，发现m6A阅读蛋白，尤其是IGF2BP家族基因，具有特定变化且与预后不良显著相关。采用无监督一致性聚类算法探索胰腺癌中m6A阅读蛋白的特定表达模式与富集通路、肿瘤免疫及临床分子亚型之间的相关性。然后，使用主成分分析（PCA）算法量化特定表达模式并筛选核心基因。运用Bootstrapping和RSF等机器学习算法量化核心基因的表达模式，并构建胰腺癌患者的预后评分模型。此外，利用药物基因组数据库全面、多角度地为高危胰腺癌患者筛选敏感药物靶点和小分子化合物。我们的研究不仅为个性化预后方法提供了新见解，还为基于IGF2BP2介导的m6A修饰模式将定制化风险分层与精准治疗相结合提供了思路。
