Xie Weiwei, Li Xuehui, Chen Hangyu, Chu Jinlin, Zhang Lei, Tang Bo, Huang Wenrong, Li Linlin, Lin Jian, Dong Yujun
Department of Hematology, Peking University First Hospital, Beijing, People's Republic of China.
Department of Pharmacology, Xinjiang Medical University, Urumqi, China.
Cancer Med. 2024 Dec;13(24):e70477. doi: 10.1002/cam4.70477.
An effective urine-based method for the diagnosis, differential diagnosis and prognosis of multiple myeloma (MM) has not yet been developed. Urine cell-free DNA (cfDNA) carrying cancer-specific genetic and epigenetic aberrations may enable a noninvasive "liquid biopsy" for diagnosis and monitoring of cancer.
We first identified MM-specific hydroxymethylcytosine signatures by comparing 64 MM patients, 23 amyloidosis (AM) patients and 59 healthy cohort. Then, we applied a machine learning algorithm to develop diagnostic and differential diagnosis model. Finally, the prognosis of MM patients was predicted based on their survival time at the last follow-up.
We identified 11 5hmC markers using logistic regression algorithm could effectively diagnosis MM (AUC = 0.902), and achieved 85.00% specificity and 85.71% sensitivity. These 11 markers could also effectively differential diagnosis MM (AUC = 0.805) with 88.89% specificity and 73.08% sensitivity. In addition, the prognostic prediction model also effectively predicted the prognosis of patients with MM (p < 0.01), of which 4 differential markers (RAPGEF2, BRD1, TET2, TRAF3IP2) could independently predict the prognosis of MM.
Together, our findings showed the value of urine cfDNA hydroxymethylcytosine markers in the diagnosis, differential diagnosis and prognosis of MM. Meantime, our study provides a promising and completely non-invasive method for the diagnosis, differential diagnosis and prognosis prediction of MM.
尚未开发出一种有效的基于尿液的多发性骨髓瘤（MM）诊断、鉴别诊断和预后评估方法。携带癌症特异性遗传和表观遗传异常的尿液游离DNA（cfDNA）可能实现用于癌症诊断和监测的非侵入性“液体活检”。
我们首先通过比较64例MM患者、23例淀粉样变性（AM）患者和59例健康对照，确定MM特异性羟甲基胞嘧啶特征。然后，我们应用机器学习算法建立诊断和鉴别诊断模型。最后，根据MM患者最后一次随访时的生存时间预测其预后。
我们使用逻辑回归算法确定了11个5hmC标记物，可有效诊断MM（AUC = 0.902），特异性达85.00%，敏感性达85.71%。这11个标记物也能有效鉴别诊断MM（AUC = 0.805），特异性为88.89%，敏感性为73.08%。此外，预后预测模型也能有效预测MM患者的预后（p < 0.01），其中4个差异标记物（RAPGEF2、BRD1、TET2、TRAF3IP2）可独立预测MM的预后。
总之，我们的研究结果显示了尿液cfDNA羟甲基胞嘧啶标记物在MM诊断、鉴别诊断和预后评估中的价值。同时，我们的研究为MM的诊断、鉴别诊断和预后预测提供了一种有前景的完全非侵入性方法。
