A machine learning model revealed that exosome small RNAs may participate in the development of breast cancer through the chemokine signaling pathway.

IF 3.4 2区 医学 Q2 ONCOLOGY
Jun-Luan Mo, Xi Li, Lin Lei, Ji Peng, Xiong-Shun Liang, Hong-Hao Zhou, Zhao-Qian Liu, Wen-Xu Hong, Ji-Ye Yin
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Abstract

Background: Exosome small RNAs are believed to be involved in the pathogenesis of cancer, but their role in breast cancer is still unclear. This study utilized machine learning models to screen for key exosome small RNAs and analyzed and validated them.

Method: Peripheral blood samples from breast cancer screening positive and negative people were used for small RNA sequencing of plasma exosomes. The differences in the expression of small RNAs between the two groups were compared. We used machine learning algorithms to analyze small RNAs with significant differences between the two groups, fit the model through training sets, and optimize the model through testing sets. We recruited new research subjects as validation samples and used PCR-based quantitative detection to validate the key small RNAs screened by the machine learning model. Finally, target gene prediction and functional enrichment analysis were performed on these key RNAs.

Results: The machine learning model incorporates six small RNAs: piR-36,340, piR-33,161, miR-484, miR-548ah-5p, miR-4282, and miR-6853-3p. The area under the ROC curve (AUC) of the machine learning model in the training set was 0.985 (95% CI = 0.948-1), while the AUC in the test set was 0.972 (95% CI = 0.882-0.995). RT-qPCR was used to detect the expression levels of these key small RNAs in the validation samples, and the results revealed that their expression levels were significantly different between the two groups (P < 0.05). Through target gene prediction and functional enrichment analysis, it was found that the functions of the target genes were enriched mainly in the chemokine signaling pathway.

Conclusion: The combination of six plasma exosome small RNAs has good prognostic value for women with positive breast cancer by imaging screening. The chemokine signaling pathway may be involved in the early stage of breast cancer. It is worth further exploring whether small RNAs mediate chemokine signaling pathways in the pathogenesis of breast cancer through the delivery of exosomes.

机器学习模型显示,外泌体小 RNA 可能通过趋化因子信号通路参与乳腺癌的发展。
背景:外泌体小RNA被认为参与了癌症的发病机制,但它们在乳腺癌中的作用仍不明确。本研究利用机器学习模型筛选关键的外泌体小RNA,并对其进行分析和验证:方法:采用乳腺癌筛查阳性和阴性人群的外周血样本,对血浆外泌体进行小RNA测序。比较两组间小 RNA 表达的差异。我们使用机器学习算法分析两组之间存在显著差异的小 RNA,通过训练集拟合模型,并通过测试集优化模型。我们招募了新的研究对象作为验证样本,并使用基于 PCR 的定量检测来验证机器学习模型筛选出的关键小 RNA。最后,对这些关键RNA进行了靶基因预测和功能富集分析:结果:机器学习模型纳入了六个小 RNA:piR-36,340、piR-33,161、miR-484、miR-548ah-5p、miR-4282 和 miR-6853-3p。机器学习模型在训练集中的 ROC 曲线下面积(AUC)为 0.985(95% CI = 0.948-1),而在测试集中的 AUC 为 0.972(95% CI = 0.882-0.995)。采用 RT-qPCR 技术检测了验证样本中这些关键小 RNA 的表达水平,结果显示,两组样本中这些关键小 RNA 的表达水平存在显著差异(P 结论:血浆外泌体小 RNA 的六种组合的表达水平在两组样本中存在显著差异:六种血浆外泌体小 RNA 的组合对影像筛查阳性乳腺癌妇女具有良好的预后价值。趋化因子信号通路可能涉及乳腺癌的早期阶段。小 RNA 是否通过外泌体的传递介导趋化因子信号通路在乳腺癌发病机制中的作用值得进一步探讨。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Cancer
BMC Cancer 医学-肿瘤学
CiteScore
6.00
自引率
2.60%
发文量
1204
审稿时长
6.8 months
期刊介绍: BMC Cancer is an open access, peer-reviewed journal that considers articles on all aspects of cancer research, including the pathophysiology, prevention, diagnosis and treatment of cancers. The journal welcomes submissions concerning molecular and cellular biology, genetics, epidemiology, and clinical trials.
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