Altered microRNA expression in severe COVID‐19: Potential prognostic and pathophysiological role

  1. Nathalie Garnier
  2. Kato Pollet
  3. Marie Fourcot
  4. Morgan Caplan
  5. Guillemette Marot
  6. Julien Goutay
  7. Julien Labreuche
  8. Fabrice Soncin
  9. Rabah Boukherroub
  10. Didier Hober
  11. Lille COVID Research Network (LICORNE)
  12. Sabine Szunerits
  13. Julien Poissy
  14. Ilka Engelmann

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Abstract

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  1. Version published to 10.1002/ctm2.899
  2. ScreenIT

    SciScore for 10.1101/2022.03.21.22272480: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsIRB: The study was approved by the French Institutional Authority for Personal Data Protection (Commission Nationale de l’Informatique et des Libertés DR-2020-178, October 22nd, 2020) and the ethics committee (Comité de Protection des Personnes Nord Ouest IV, ECH20/09, September 7th, 2020)
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Recombinant DNA
    SentencesResources
    Receiver operating characteristic (ROC) analysis with area under the curve (AUC) was performed to analyze the diagnostic performance to differentiate severe and non-severe COVID-19 of promising miRNAs defined by the consensus between differential expression analysis and sPLS-DA.
    sPLS-DA
    suggested: None
    Software and Algorithms
    SentencesResources
    Only the expressed miRNAs were kept for further analyses Target prediction, GO enrichment and pathway analysis: miRWalk (http://mirwalk.umm.uni-heidelberg.de/) was used for the prediction of targets of dysregulated miRNAs and GO enrichment analysis, Kegg and Reactome pathways enrichment analyses [27].
    miRWalk
    suggested: (miRWalk, RRID:SCR_016509)
    Reactome
    suggested: (Reactome, RRID:SCR_003485)
    In parallel to these univariate analyses sparse Partial Least Squares-Discriminant Analysis (sPLS-DA) was performed with the mixOmics package [32].
    mixOmics
    suggested: (mixOmics, RRID:SCR_016889)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Study limitations: our study has a relatively small size. Therefore our results should be confirmed in a larger cohort, which could allow a multiparametric approach and the building of scores. Another limit is the heterogeneity of the control group with respect to clinical severity. Moreover, our study showed several miRNAs allowed to differentiate between severe and non-severe COVID-19. In order to be used as predictive biomarkers, the expression of these miRNAs should be studied at disease onset and during the clinical course in order to determine the kinetics of miRNA expression and their correlation with severity.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2022.03.21.22272480v1 on medRxiv