Establishment of the prediction model and biological mechanism exploration for secondary imatinib-resistant in gastrointestinal stromal tumor. (2nd November 2022)

• Record Type: Journal Article
• Authors: Wang, Chao; Shen, Zhanlong; Jiang, Kewei; Gao, Zhidong; Ye, Yingjiang
• Is Part Of: Scandinavian journal of gastroenterology. Volume 57:Number 11(2022)
• Page Start: 1334
• Page End: 1343
• Publication Date: 2022-11-02
• Subjects: Gastrointestinal stromal tumor -- imatinib -- secondary resistance -- bioinformatic mining -- prediction model; Gastroenterology -- Periodicals; Digestive organs -- Diseases -- Periodicals; 616.33
• DOI: 10.1080/00365521.2022.2087475
• Languages: English
• ISSNs: 0036-5521

Abstract

Abstract: A gastrointestinal stromal tumor (GIST) is mostly driven by the auto-activated, mutant KIT receptor tyrosine kinase gene or by the platelet-derived growth factor receptor alpha. Inhibition of KIT -signaling is the primary molecular target therapy for GIST, which is performed by the drug imatinib clinically. However, more than half of advanced or metastatic GIST develop secondary resistance to imatinib within 2 years after initiation of treatment, and the mechanism of acquired imatinib-resistant in GIST remains unclear. Therefore, we designed the present study, and firstly analyzed the gene expression profile of imatinib-resistant and sensitive GIST from GEO DataSet and identified 44 differential expressed genes. Then, a model including nine genes with their expressed coefficients was identified as a risk score to predict imatinib-resistant GIST. Internal and external validation of the prediction model was performed through the ROC curve, and the area under the curve was 0.967 (95%CI 0.901–1.000) and 0.917 (95%CI 0.753–1.000), separately. Lastly, the effect of immune, m 6 A, pyroptosis, and ferroptosis-related genes on imatinib-resistant GIST was also assessed because DNA replication was the most enriched biological function of DEGs after functional annotation, pathway enrichment, and protein-protein interaction network analyses. In conclusion, the present study established a novel model to predict secondary imatinib-resistant GIST. Meanwhile, the bioinformatic mining results provided potential and promising targets for imatinib-resistant therapy.