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Identification of key gene expression associated with quality of life after recovery from COVID-19

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Abstract

Post-acute sequelae of COVID-19 (PASC) is a persistent complication of severe acute respiratory syndrome coronavirus 2 infection that includes symptoms, such as fatigue, cognitive impairment, and respiratory distress. These symptoms severely affect the quality of life of patients after their recovery from COVID-19. In this study, a group of machine learning algorithms analyzed the whole blood RNA-seq data from patients with different PASC levels. The purpose of this analysis was to identify the gene markers associated with PASC and the special expression patterns for different PASC levels. By comparing the quality of life of patients after the acute phase of COVID-19 and before the disease, samples in the dataset were divided into three groups, namely, “Better,” “The Same,” and “Worse.” Each patient was represented by the expression levels of 58,929 genes. The machine learning-based workflow included six feature-ranking algorithms, incremental feature selection (IFS), and four classification algorithms. The feature ranking algorithms were in charge of assessing feature importance, whereas IFS with classification algorithms were used to extract essential genes and to construct efficient classifiers and classification rules. The expression of top genes in the results was associated with the immune response to viral infection, which is supported by the published literature. For example, patients with low CCDC18 expression and high CPED1 expression had good quality of life, whereas those with low CDC16 expression had poor quality of life.

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Funding

This work was supported by the National Key R&D Program of China (2022YFF1203202), Strategic Priority Research Program of Chinese Academy of Sciences (XDA26040304, XDB38050200), the Fund of the Key Laboratory of Tissue Microenvironment and Tumor of Chinese Academy of Sciences (202002), and Shandong Provincial Natural Science Foundation (ZR2022MC072).

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Author notes

  1. JingXin Ren, Qian Gao and XianChao Zhou contributed equally to this work.

Authors and Affiliations

  1. School of Life Sciences, Shanghai University, Shanghai, 200444, China

    JingXin Ren & Yu-Dong Cai

  2. Department of Pharmacy, Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China

    Qian Gao

  3. Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China

    XianChao Zhou

  4. College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China

    Lei Chen

  5. Key Laboratory of Stem Cell Biology, Shanghai Jiao Tong University School of Medicine (SJTUSM) & Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai, 200030, China

    Wei Guo

  6. Department of Computer Science, Guangdong AIB Polytechnic College, Guangzhou, 510507, China

    KaiYan Feng

  7. Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China

    Tao Huang

  8. CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China

    Tao Huang

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  1. JingXin Ren
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Contributions

All authors contributed to the study conception and design. Conceptualization: Tao Huang, Yu-Dong Cai; methodology: JingXin Ren, Qian Gao, Lei Chen, KaiYan Feng; formal analysis and investigation: JingXin Ren, XianChao Zhou, Wei Guo; writing — original draft preparation: JingXin Ren, Qian Gao, XianChao Zhou; writing — review and editing: Tao Huang; funding acquisition: Tao Huang, Yu-Dong Cai; supervision: Yu-Dong Cai.

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Correspondence to Tao Huang or Yu-Dong Cai.

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Ren, J., Gao, Q., Zhou, X. et al. Identification of key gene expression associated with quality of life after recovery from COVID-19. Med Biol Eng Comput 62, 1031–1048 (2024). https://doi.org/10.1007/s11517-023-02988-8

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