biostudies-literatureMo JLGuangdong Medical Science and Technology Research FundSanming Project of Medicine in Shenzen MunicipalityShenzhen Science and Technology Program1435https://www.ebi.ac.uk/biostudies/studies/S-EPMC11580650biostudies-literatureUnknown24(1)<h4>Background</h4>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.<h4>Method</h4>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 validaBMC cancerA machine learning model revealed that exosome small RNAs may participate in the development of breast cancer through the chemokine signaling pathway.PMC11580650SZSM201811057B2021240JCYJ20230807120859030Mo JLLi XLei LLiu ZQLiang XSZhou HHYin JYHong WXPeng JfalseA machine learning model revealed that exosome small RNAs may participate in the development of breast cancer through the chemokine signaling pathway.<h4>Background</h4>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.<h4>Method</h4>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 valida2024-01-01T00:00:00Z2024 Nov2025-04-26T01:53:28.07Z2025-04-06T10:21:13.386ZS-EPMC115806503957405310.1186/s12885-024-13173-x