Data Sheet 1_Identification and prognostic prediction of high-risk multiple myeloma by exosomal microRNA.docx

IntroductionCirculating exosomal miRNAs have emerged as important tools for liquid biopsy in cancer diagnosis and prognosis prediction. This project establishes a plasma exosomal miRNA-based system for diagnosing multiple myeloma(MM) and evaluating patient prognosis, aiming to provide novel strategies for diagnosis and early warning in high-risk MM patients.
MethodsThe study prospectively collected plasma samples and clinical data of newly diagnosed multiple myeloma (NDMM) patients and healthy controls (HCs). Plasma samples were obtained in MM patients before the administration of any chemotherapy. The study comprises three stages to identify plasma exosomal miRNAs associated with the diagnosis and prognosis of MM. In the screening stage, next-generation sequencing of plasma circulating exosomes was performed in 17 NDMM patients and 8 HCs to screen the candidate differentially expressed miRNAs. We further investigated a testing stage of 80 individuals (including 60 NDMM patients and 20 HCs) and a verification stage of 130 NDMM through qPT-PCR.
ResultsUtilizing a testing cohort of 60 newly diagnosed MM cases, we developed a diagnostic model based on six miRNAs (hsa-miR-192-5p, hsa-miR-10a-5p, hsa-miR-10b-3p, hsa-miR-148a-3p, hsa-miR-193b-5p, hsa-miR-483-3p) achieving an AUC of 0.94, sensitivity of 0.88, and specificity of 0.94. In a validation cohort of 130 MM patients, we developed a prognostic nomogram that amalgamated the expression levels of three key exosomal miRNAs (hsa-miR-193b-5p, miR-483-3p, and let-7b-5p) with critical clinical variables, which exhibits superior performance compared to the ISS staging system. This integrative model effectively predicted 1-, 3-, and 5-year survival probabilities, thereby stratifying patients into distinct risk categories for enhanced clinical decision-making and personalized follow-up strategies.
DiscussionThis research validates the diagnostic and prognostic utility of exosomal miRNA models in MM, emphasizing their discriminative and predictive capabilities.