Machine Learning Ensemble Investigates Age in the Transcriptomic Response to Spaceflight in Murine Mammary Tissue: Observational Study

Abstract Background Spaceflight presents unique environmental stressors, such as microgravity and radiation, that significantly affect biological systems at the molecular, cellular, and organismal levels. Astronauts face an increased risk of developing cancer due to exposure to ionizing radiation and other spaceflight-related factors. Age plays a crucial role in the body’s response to the cellular stresses that lead to cancer, with younger organisms generally exhibiting more efficient response mechanisms than older ones. The vast majority of research investigating breast cancer risk from spaceflight uses cell lines exposed to simulated radiation and microgravity, but cell lines cannot capture the combinatorial response expressed across tissues, organs, and systems to real radiation and microgravity in space. Objective The primary objective of this in silico observational study is to characterize the molecular response to spaceflight of in vivo murine mammary tissue. We use an ensemble of linear binary classifiers to identify the molecular biomarkers enriched in this response using mice flown on the International Space Station. The secondary objective is to determine if age plays a role in this response. Methods The National Aeronautics and Space Administration (NASA) Open Science Data Repository has curated transcriptomic data obtained from 10 BALB/cAnNTac female mice flown on the International Space Station and 33 control mice kept on earth (OSD-511). In this observational study focused on two age groups (old/young), we used an ensemble of 4 machine learning binary classifiers with linear decision boundaries (logistic regression, support vector machine, stochastic gradient descent, and single-layer perceptron) to analyze gene expression profiles to predict age (old vs young) and condition (spaceflight vs ground control). Using the genes our ensemble identified as most predictive, we performed pathway enrichment analysis to investigate the molecular pathways involved in spaceflight-related health risks, particularly in the context of breast cancer. Results The pathway enrichment analyses revealed age-differentiated responses to spaceflight (false discovery rate–adjusted q values