Ontology highlight
ABSTRACT: Background
The long-term consequences of chemotherapy and radiotherapy result in a high prevalence and early onset of age-related chronic diseases in survivors. We aimed to examine whether childhood and adolescent cancer survivors (CS) demonstrate biomarkers of accelerated aging.Methods
We evaluated 50 young adult CS at 11 [8-15] years after cancer diagnosis, and 30 healthy, age and sex-matched controls, who were unexposed to cancer therapy. Using a machine-learning approach, we assessed factors discriminating CS from controls and compared selected biomarkers and lymphocyte subpopulations with data from the Framingham Heart Study (FHS) cohort and the Genotype Tissue Expression (GTEx) project.Results
Survivors compared with controls had higher levels of C-reactive protein and fibrinogen. The surface expression of CD38 on T cells was increased, and there was an increase in the percentage of memory T cells in survivors, compared with the unexposed group. The relationships between above cell subpopulations and age were consistent in CS, FHS, and GTEx cohorts, but not in controls.Conclusions
Young pediatric cancer survivors differ from age-related controls in terms of activation of the adaptive immune system and chronic, low-grade inflammation. These changes resemble aging phenotype observed in older population. Further research in biomarkers of aging in young, adult childhood cancer survivors is warranted, as it may facilitate screening and prevention of comorbidities in this population.
SUBMITTER: Sulicka-Grodzicka J
PROVIDER: S-EPMC7940211 | biostudies-literature | 2021 Mar
REPOSITORIES: biostudies-literature

Cancer medicine 20210219 5
<h4>Background</h4>The long-term consequences of chemotherapy and radiotherapy result in a high prevalence and early onset of age-related chronic diseases in survivors. We aimed to examine whether childhood and adolescent cancer survivors (CS) demonstrate biomarkers of accelerated aging.<h4>Methods</h4>We evaluated 50 young adult CS at 11 [8-15] years after cancer diagnosis, and 30 healthy, age and sex-matched controls, who were unexposed to cancer therapy. Using a machine-learning approach, we ...[more]