miRNA-31和miRNA-365在监测乳腺癌患者术后复发转移中的价值

The Value of miRNA-31 and miRNA-365 in Monitoring Postoperative Recurrence and Metastasis in Breast Cancer Patients

摘要: 乳腺癌术后复发与转移是导致患者死亡的主要原因，亟需高灵敏度、非侵入性的生物标志物进行早期预警。微小RNA (miRNA)在肿瘤发生发展中扮演关键调控角色，其中miRNA-31与miRNA-365因其在乳腺癌转移过程中功能对立、临床价值互补而备受关注。miRNA-31作为抑癌因子，通过靶向ITGA5、RhoA、GNA13等基因抑制细胞迁移与侵袭，其高表达与患者良好预后显著相关，是“保护性标志物”。相反，miRNA-365作为促癌因子，通过抑制E-cadherin、PTEN等驱动上皮–间质转化(EMT)并重塑肿瘤微环境，其高表达与三阴性乳腺癌的早期转移和不良预后强相关，属“警示性标志物”。二者联合检测可构建“双分子预警系统”，显著提升复发转移风险预测效能(AUC = 0.87)。然而，临床转化仍面临检测标准化缺失、阈值未统一等瓶颈。未来需通过开发标准化试剂盒、开展多中心前瞻性研究、构建AI多组学模型及发展外泌体动态监测技术，推动其从科研走向临床精准应用。

Abstract: Postoperative recurrence and metastasis remain the leading causes of mortality in breast cancer patients, necessitating highly sensitive and non-invasive biomarkers for early detection. MicroRNAs (miRNAs), as critical regulators of tumorigenesis, have emerged as promising candidates. Among them, miRNA-31 and miRNA-365 attract significant attention due to their opposing biological functions and complementary clinical value in monitoring metastasis. miRNA-31 acts as a tumor suppressor by targeting genes such as ITGA5, RhoA, and GNA13 to inhibit cell migration and invasion. Its high expression correlates strongly with favorable prognosis, classifying it as a “protective biomarker”. Conversely, miRNA-365 functions as an oncogenic driver, promoting epithelial-mesenchymal transition (EMT) and remodeling the tumor microenvironment by suppressing E-cadherin and PTEN. Its upregulation is strongly associated with early metastasis and poor outcomes, particularly in triple-negative breast cancer (TNBC), marking it as a “warning biomarker”. The combined detection of both miRNAs forms a “dual-molecule early-warning system”, significantly enhancing predictive accuracy for recurrence and metastasis (AUC = 0.87). However, clinical translation is hindered by challenges such as lack of standardized detection protocols and undefined clinical thresholds. Future efforts should focus on developing standardized assay kits, conducting multicenter prospective trials, building AI-powered multi-omics risk models, and advancing exosome-based dynamic monitoring technologies to facilitate their transition from bench to bedside for precision medicine.