AUTHOR=Xiao Jun , Li Huimin , Li Xiaowei , Lei Huifen , Li Zhicai , Li Cuiying 

TITLE=Establishment of a nomogram-based prognostic model (LASSO-Cox regression) for predicting platelet storage lesions under different storage conditions

JOURNAL=Frontiers in Molecular Biosciences

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2025.1561114

DOI=10.3389/fmolb.2025.1561114

ISSN=2296-889X

ABSTRACT=IntroductionPlatelet concentrates (PCs) are critical blood products used for transfusion, but stored platelets often experience quality deterioration, resulting in reduced efficacy post-transfusion. Currently, the lack of effective prediction models hinders the assessment of platelet storage quality. To address this, we developed a miRNA-based prognosis prediction model that comprehensively evaluates platelet quality under diverse storage conditions, offering valuable insights into platelet shelf life.MethodsWe enrolled 249 eligible PC samples, divided into a training dataset and internal validation dataset (7:3). Through microRNA sequencing, we identified 13 differentially expressed miRNAs with platelets storage lesions (PSLs). Leveraging the LASSO-Cox regression model, we constructed a nomogram-based classifier based on the association between miRNA expression and the duration of PSLs-free survival. Performance evaluation using measures like concordance index, area under the curve, calibration curves, and decision curve analyses to confirm the model’s robustness.ResultsThe nomogram classifier, incorporating miRNAs (miR-4485-3p, miR-12136, miR-25-5p, miR-148b-5p) and storage method, effectively categorized PCs into high-risk and low-risk groups. Notably, significant differences in PSLs-free survival were observed across all datasets, underscoring the precision and accuracy of our nomogram-based model.DiscussionThis innovative classifier provides clinicians with a reliable tool to predict PSLs occurrence in PCs stored under different methods, facilitating improved clinical decision-making.