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

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Abstract

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.

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Data availability

The raw Illumina read data, qPCR raw data, and the group information data for all samples, as well as the bioinformatic analysis code, can be obtained upon request from the authors.

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Acknowledgements

No applicable.

Funding

This research was funded by the National Key Research and Development Program of China (2023YFF1204100), Tianjin Natural Science Foundation (23JCZXJC00040), Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2021-12 M-C&T-B-080 to S.F.), PUMC Youth Fund and supported by the Fundamental Research Funds for the Central Universities of China (3332021055 to X.Z) and the National Natural Science Foundation of China (81670171 to E.J., 81601369 to X.P., 81870090 to S.F., 82101853 to W.S.), and the National Basic Research Program of China (2015CB964402 to E.J.).

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Author notes

  1. Wenwei Shao and Yixuan Wang contributed equally to this work.

Authors and Affiliations

  1. Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, China

    Wenwei Shao, Yixuan Wang & Shuangjie Liu

  2. State Key Laboratory of Experimental Hematology, Hematopoietic Stem Cell Transplantation Center, Haihe Laboratory of Cell Ecosystem, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China

    Li Liu, Yiran Ren, Jieru Wang, Yuqing Cui, Jia Liu, Xiaoyu Zhang, Sudong Zhang, Erlie Jiang, Sizhou Feng & Xiaolei Pei

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  1. Wenwei Shao
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Contributions

Conceptualization, X.P. and S.F.; methodology, L.L.,J.W., and Y.C.; software, Y.R.; validation, X.P., S.F. and W.S.; formal analysis, W.S.; investigation, J.L. and X.Z.; resources, S.Z.; data curation, E.J.; writing—original draft preparation, W.S.; writing—review and editing, Y.W. and W.S.; visualization, X.P.; supervision, X.P.; project administration, S.F.; funding acquisition, S.F. All authors have read and agreed to the published version of the manuscript.

Corresponding authors

Correspondence to Sizhou Feng or Xiaolei Pei.

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Informed consent statement

Informed consent was obtained from all subjects involved in the study.

Competing interests

The authors declare no competing interests.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Blood Diseases Hospital, Chinese Academy of Medical Sciences (Approval No. CAMSCRF2021013-EC-2, 2021-03-01) and the Ethics Committee of Tianjin First Central Hospital (Approval No. 2019N134KY, 2019-01-04).

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Shao, W., Wang, Y., Liu, L. et al. Combining serum microRNAs and machine learning algorithms for diagnosing infectious fever after HSCT. Ann Hematol 103, 2089–2102 (2024). https://doi.org/10.1007/s00277-024-05755-3

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