ArticleInternational journal of immunopathology and pharmacology
Flow cytometry-based evaluation of CD4/CD25 and IL-17 expression in sepsis of cancer surgical patients.
Article in International journal of immunopathology and pharmacology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Interventional Effects of Edible Bird's Nest or Sialic Acids on Sepsis-Induced Immunosuppression in Mice.Food science & nutrition · 2026Article
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7 authors.
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Abstract
INTRODUCTION AND
objectivesSepsis is a critical, life-threatening condition marked by organ dysfunction. Patients undergoing surgery for cancer are especially susceptible, facing a significantly increased risk compared to the general population. Prompt diagnosis is essential for optimal treatment outcomes. This study investigates the expression levels of IL-17 and CD4/CD25 in postoperative surgical oncology patients diagnosed with sepsis and examines their association with various clinicopathological parameters, including mortality.
methodsPeripheral blood samples were collected 48 h following admission to the surgical ICU. Flow cytometry was employed to measure the expression of IL-17 and CD4/CD25 in both septic patients and non-septic controls.
resultsThe study included 30 septic patients and an equal number of controls. The expression of IL-17 and CD4/CD25 was significantly higher in septic patients compared to non-septic patients, demonstrating high diagnostic performance. Additionally, elevated IL-17 expression was strongly associated with an increased risk of mortality.
conclusionOur study provides insight into immune dysregulation in postoperative cancer patients with sepsis, highlighting the dual arms of active inflammation and immunoparalysis. Elevated IL-17 levels, coupled with Treg activation, were linked to poorer outcomes. Therapeutic strategies that target IL-17 signaling and modulate Treg activity may help restore immune balance and improve prognosis in immunocompromised individuals. Furthermore, developing predictive models using machine learning to classify sepsis severity based on immune markers could enhance diagnosis and prognosis.
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