signature of circulating extracellular vesicles-derived RNAs predicts response to first line chemotherapy in patients with metastatic colorectal cancer, Molecular Cancer, 2023. (IF: 27.7)

Abstract: This study aimed to develop a biomarker panel to predict the response to first-line chemotherapy in patients with metastatic colorectal cancer (mCRC). A total of 190 patients who underwent FOLFOX or XEOLX chemotherapy were included in the study. By extracting extracellular vesicle (EV) RNA from plasma and performing sequencing, researchers constructed a predictive model using random forest and LASSO algorithms. The model was validated in a training cohort (n = 80), an internal validation cohort (n = 62), and a prospective external validation cohort (n = 48). The study successfully established a biomarker panel consisting of 22 EV RNAs. Additionally, this panel could predict progression-free survival (PFS) and overall survival (OS). The study also constructed a 7-gene signature that could predict response to oxaliplatin-containing chemotherapy and resistance to irinotecan-containing chemotherapy. This research is the first to demonstrate the high accuracy of predicting mCRC patients' response to first-line chemotherapy using a non-invasive method with EV RNA features, providing a possibility for selecting the optimal treatment plan for patients.

摘要：本研究旨在为转移性结直肠癌（mCRC）患者开发一种预测一线化疗反应的生物标志物组。研究共纳入190名患者，他们接受了FOLFOX或XEOLX化疗。通过提取血浆中外泌体（EV）RNA并进行测序，研究者利用随机森林和LASSO算法构建了一个预测模型，并在训练队列（80名患者）、内部验证队列（62名患者）和前瞻性外部验证队列（48名患者）中进行了验证。研究成功建立了一个包含22个EV RNA的生物标志物组。此外，该标志物组还预测无进展生存（PFS）和总生存（OS）。研究还构建了一个7基因特征，能预测对含奥沙利铂化疗的反应及对含伊立替康化疗的耐药性。这项研究首次展示了使用非侵入性方法通过EV RNA特征预测mCRC患者对一线化疗反应的高准确性，为患者选择最佳治疗方案提供了可能。