IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0305920.html
   My bibliography  Save this article

Transcriptional markers classifying Escherichia coli and Staphylococcus aureus induced sepsis in adults: A data-driven approach

Author

Listed:
  • Mahnaz Irani Shemirani

Abstract

Sepsis is a life-threatening condition mainly caused by gram-negative and gram-positive bacteria. Understanding the type of causative agent in the early stages is essential for precise antibiotic therapy. This study sought to identify a host gene set capable of distinguishing between sepsis induced by gram-negative bacteria; Escherichia coli and gram-positive bacteria; Staphylococcus aureus in community-onset adult patients. In the present study, microarray expression information was used to apply the Least Absolute Shrinkage and Selection Operator (Lasso) technique to select the predictive gene set for classifying sepsis induced by E. coli or S. aureus pathogens. We identified 25 predictive genes, including LILRA5 and TNFAIP6, which had previously been associated with sepsis in other research. Using these genes, we trained a logistic regression classifier to distinguish whether a sample contains an E. coli or S. aureus infection or belongs to a healthy control group, and subsequently assessed its performance. The classifier achieved an Area Under the Curve (AUC) of 0.96 for E. coli and 0.98 for S. aureus-induced sepsis, and perfect discrimination (AUC of 1) for healthy controls from the other conditions in a 10-fold cross-validation. The genes demonstrated an AUC of 0.75 in distinguishing between sepsis patients with E. coli and S. aureus pathogens. These findings were further confirmed in two distinct independent validation datasets which gave high prediction AUC ranging from 0.72–0.87 and 0.62 in distinguishing three groups of participants and two groups of patients respectively. These genes were significantly enriched in the immune system, cytokine signaling in immune system, innate immune system, and interferon signaling. Transcriptional patterns in blood can differentiate patients with E. coli-induced sepsis from those with S. aureus-induced sepsis. These diagnostic markers, upon validation in larger trials, may serve as a foundation for a reliable differential diagnostics assay.

Suggested Citation

  • Mahnaz Irani Shemirani, 2024. "Transcriptional markers classifying Escherichia coli and Staphylococcus aureus induced sepsis in adults: A data-driven approach," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-19, July.
  • Handle: RePEc:plo:pone00:0305920
    DOI: 10.1371/journal.pone.0305920
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0305920
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0305920&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0305920?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Claire L. Smith & Paul Dickinson & Thorsten Forster & Marie Craigon & Alan Ross & Mizanur R. Khondoker & Rebecca France & Alasdair Ivens & David J. Lynn & Judith Orme & Allan Jackson & Paul Lacaze & K, 2014. "Identification of a human neonatal immune-metabolic network associated with bacterial infection," Nature Communications, Nature, vol. 5(1), pages 1-15, December.
    2. S. le Cessie & J. C. van Houwelingen, 1992. "Ridge Estimators in Logistic Regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(1), pages 191-201, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    2. André Altmann & Michal Rosen-Zvi & Mattia Prosperi & Ehud Aharoni & Hani Neuvirth & Eugen Schülter & Joachim Büch & Daniel Struck & Yardena Peres & Francesca Incardona & Anders Sönnerborg & Rolf Kaise, 2008. "Comparison of Classifier Fusion Methods for Predicting Response to Anti HIV-1 Therapy," PLOS ONE, Public Library of Science, vol. 3(10), pages 1-9, October.
    3. Janns Alvaro Patiño-Saucedo & Paola Patricia Ariza-Colpas & Shariq Butt-Aziz & Marlon Alberto Piñeres-Melo & José Luis López-Ruiz & Roberto Cesar Morales-Ortega & Emiro De-la-hoz-Franco, 2022. "Predictive Model for Human Activity Recognition Based on Machine Learning and Feature Selection Techniques," IJERPH, MDPI, vol. 19(19), pages 1-21, September.
    4. František Dařena & Jan Přichystal, 2018. "Analysis of the Association between Topics in Online Documents and Stock Price Movements," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 66(6), pages 1431-1439.
    5. repec:wyi:journl:002122 is not listed on IDEAS
    6. Wayne DeSarbo & Heungsun Hwang & Ashley Stadler Blank & Eelco Kappe, 2015. "Constrained Stochastic Extended Redundancy Analysis," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 516-534, June.
    7. Li Shaoyu & Lu Qing & Fu Wenjiang & Romero Roberto & Cui Yuehua, 2009. "A Regularized Regression Approach for Dissecting Genetic Conflicts that Increase Disease Risk in Pregnancy," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-30, October.
    8. Meisam Moghimbeygi & Anahita Nodehi, 2022. "Multinomial Principal Component Logistic Regression on Shape Data," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 578-599, November.
    9. G Johnes, 2005. "Nations will fall? Revisiting the economic determinants of attitudes to European integration," Working Papers 566772, Lancaster University Management School, Economics Department.
    10. Butaru, Florentin & Chen, Qingqing & Clark, Brian & Das, Sanmay & Lo, Andrew W. & Siddique, Akhtar, 2016. "Risk and risk management in the credit card industry," Journal of Banking & Finance, Elsevier, vol. 72(C), pages 218-239.
    11. repec:lan:wpaper:4385 is not listed on IDEAS
    12. Matthew Herland & Richard A. Bauder & Taghi M. Khoshgoftaar, 2020. "Approaches for identifying U.S. medicare fraud in provider claims data," Health Care Management Science, Springer, vol. 23(1), pages 2-19, March.
    13. Paolo Cimbali & Marco De Leonardis & Alessio Fiume & Barbara La Ganga & Luciana Meoli & Marco Orlandi, 2023. "A decision-making rule to detect insufficient data quality - an application of statistical learning techniques to the non-performing loans banking data," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Post-pandemic landscape for central bank statistics, volume 58, Bank for International Settlements.
    14. Zanin, Luca, 2020. "Combining multiple probability predictions in the presence of class imbalance to discriminate between potential bad and good borrowers in the peer-to-peer lending market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 25(C).
    15. Franz Ratzinger & Harald Bruckschwaiger & Martin Wischenbart & Bernhard Parschalk & Delmiro Fernandez-Reyes & Heimo Lagler & Alexandra Indra & Wolfgang Graninger & Stefan Winkler & Sanjeev Krishna & M, 2012. "Rapid Diagnostic Algorithms as a Screening Tool for Tuberculosis: An Assessor Blinded Cross-Sectional Study," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-6, November.
    16. repec:plo:pone00:0110331 is not listed on IDEAS
    17. Arvanitakis, K. & Avlonitis, M. & Papadimitriou, E., 2018. "Introducing stochastic recurrence interval to classification algorithms for identifying asperity patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 566-577.
    18. Sunil Kumar & Ilyoung Chong, 2018. "Correlation Analysis to Identify the Effective Data in Machine Learning: Prediction of Depressive Disorder and Emotion States," IJERPH, MDPI, vol. 15(12), pages 1-24, December.
    19. Aykut Ekinci & Safa Sen, 2024. "Forecasting Bank Failure in the U.S.: A Cost-Sensitive Approach," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3161-3179, December.
    20. Wenfa Li & Hongzhe Liu & Peng Yang & Wei Xie, 2016. "Supporting Regularized Logistic Regression Privately and Efficiently," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-19, June.
    21. Luca Insolia & Ana Kenney & Martina Calovi & Francesca Chiaromonte, 2021. "Robust Variable Selection with Optimality Guarantees for High-Dimensional Logistic Regression," Stats, MDPI, vol. 4(3), pages 1-17, August.
    22. M. Revan Özkale & Atif Abbasi, 2022. "Iterative restricted OK estimator in generalized linear models and the selection of tuning parameters via MSE and genetic algorithm," Statistical Papers, Springer, vol. 63(6), pages 1979-2040, December.
    23. Kadri Ulas Akay, 2014. "A graphical evaluation of logistic ridge estimator in mixture experiments," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(6), pages 1217-1232, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0305920. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.