Combining serum microRNAs and machine learning algorithms for diagnosing infectious fever after HSCT

Infection post-hematopoietic stem cell transplantation (HSCT) is one of the main causes of patient mortality. Fever is the most crucial clinical symptom indicating infection. However, current microbial detection methods are limited. Therefore, timely diagnosis of infectious fever and administration of antimicrobial drugs can effectively reduce patient mortality. In this study, serum samples were collected from 181 patients with HSCT with or without infection, as well as the clinical information. And more than 80 infectious-related microRNAs in the serum were selected according to the bulk RNA-seq result and detected in the 345 time-pointed serum samples by Q-PCR. Unsupervised clustering result indicates a close association between these microRNAs expression and infection occurrence. Compared to the uninfected cohort, more than 10 serum microRNAs were identified as the combined diagnostic markers in one formula constructed by the Random Forest (RF) algorithms, with a diagnostic accuracy more than 0.90. Furthermore, correlations of serum microRNAs to immune cells, inflammatory factors, pathgens, infection tissue, and prognosis were analyzed in the infection cohort. Overall, this study demonstrates that the combination of serum microRNAs detection and machine learning algorithms holds promising potential in diagnosing infectious fever after HSCT.