[Levels of T-Lymphocyte Subsets, IL-17, IL-35 and IFN-γ in Peripheral Blood and Their Clinical Significance in

Zhao-Juan Xu1, Dong Zhao2, Fu-Ping Li1

  • 1School of Nursing, Hebei College of Traditional Chinese Medicine, Shijiazhuang 050200, Hebei Province, China.

Abstract

Insights

Immune system changes, including T-lymphocyte subsets and cytokines like IL-17, IL-35, and IFN-γ, are linked to multiple myeloma progression. These immune markers may indicate disease advancement and patient prognosis.

Area of Science:

  • Immunology
  • Hematology
  • Oncology

Background:

  • Multiple myeloma (MM) is a hematologic malignancy characterized by uncontrolled proliferation of plasma cells.
  • Immune dysregulation plays a crucial role in the pathogenesis and progression of MM.

Purpose of the Study:

  • To investigate the levels of T-lymphocyte subsets (CD4+, CD8+, Treg) and key cytokines (IL-17, IL-35, IFN-γ) in the peripheral blood of MM patients.
  • To determine the clinical significance of these immune markers in relation to MM staging and disease status.

Main Methods:

  • Flow cytometry was used to quantify CD4+/CD8+ T cell ratios and regulatory T cell (Treg) levels in 86 MM patients and 30 healthy controls.
  • Enzyme-linked immunosorbent assay (ELISA) measured serum levels of IL-17, IL-35, and IFN-γ.
  • Statistical comparisons were made between different MM stages and disease statuses.

Main Results:

  • MM patients exhibited decreased CD4+/CD8+ T cell ratios and increased CD8+ T cells and Tregs compared to controls.
  • Treg levels and IL-17 showed a significant increasing trend with advancing MM clinical stages (Stage III) and disease progression.
  • Conversely, IL-35 and IFN-γ levels decreased with disease progression, being highest in controls and lowest in Stage III MM.

Conclusions:

  • Abnormal levels of T-lymphocyte subsets, Tregs, IL-17, IL-35, and IFN-γ are associated with the progression and prognosis of multiple myeloma.
  • These immune markers hold potential as indicators for monitoring MM disease status and predicting patient outcomes.