Circulating miRNA Expression Profiles and Machine Learning Models in Association with Response to Irinotecan-Based Treatment in Metastatic Colorectal Cancer.

International journal of molecular sciences(2022)

引用 2|浏览22
暂无评分
摘要
Colorectal cancer represents a leading cause of cancer-related morbidity and mortality. Despite improvements, chemotherapy remains the backbone of colorectal cancer treatment. The aim of this study is to investigate the variation of circulating microRNA expression profiles and the response to irinotecan-based treatment in metastatic colorectal cancer and to identify relevant target genes and molecular functions. Serum samples from 95 metastatic colorectal cancer patients were analyzed. The microRNA expression was tested with a NucleoSpin miRNA kit (Machnery-Nagel, Germany), and a machine learning approach was subsequently applied for microRNA profiling. The top 10 upregulated microRNAs in the non-responders group were hsa-miR-181b-5p, hsa-miR-10b-5p, hsa-let-7f-5p, hsa-miR-181a-5p, hsa-miR-181d-5p, hsa-miR-301a-3p, hsa-miR-92a-3p, hsa-miR-155-5p, hsa-miR-30c-5p, and hsa-let-7i-5p. Similarly, the top 10 downregulated microRNAs were hsa-let-7d-5p, hsa-let-7c-5p, hsa-miR-215-5p, hsa-miR-143-3p, hsa-let-7a-5p, hsa-miR-10a-5p, hsa-miR-142-5p, hsa-miR-148a-3p, hsa-miR-122-5p, and hsa-miR-17-5p. The upregulation of microRNAs in the miR-181 family and the downregulation of those in the let-7 family appear to be mostly involved with non-responsiveness to irinotecan-based treatment.
更多
查看译文
关键词
artificial intelligence,colorectal cancer,irinotecan,machine learning,microRNAs,resistance
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要