Prognosis and personalized treatment prediction of IGF2BP2- mediated m6A modification patterns in pancreatic cancer

Abstract

Abstract Background: 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. Therefore, it is necessary to screen out its specific modification mode from the m6A readers, and deeply explore its mechanism and its pharmacogenomic characteristics, so as to provide a new direction for the precision treatment of PC. Methods: Based on TCGA-PAAD, PACA-AU, PACA-CA, GSE28735 and GSE62452 datasets, we explored the specific variations of m6A readers at the multi-omics level. 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 gene. Machine learning algorithms such as Bootstrapping and RSF were used to quantify the expression patterns of core gene 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. Results: 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. Otherwise, two specific expression patterns of the m6A readers were constructed and IGF2BP2 was identified as the core gene. We confirmed that abnormally high expression of IGF2BP2 was associated with enrichment and activation of cell cycle and tumor-related pathways in PC patients. Then, a poor prognostic signature (PPS) including 13 genes (FNDC3B, L1CAM, PLXNA1, HMGA2, FAM110B, FAM83A, COX7A1, PMAIP1, KIF20B, SPDL1, SNCG, TGM2 and MUC16) was constructed. Finally, we identified seven therapeutic targets (FOXM1, PRC1, CCNB1, SLC16A3, CCNA2, GGCX, and AURKA) and two agents (Tipifarnib and Vemurafenib) for high-PPS score patients. Conclusion: 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.