Proteome analysis of the prefrontal cortex and the application of machine learning models for the identification of potential biomarkers related to suicide.

Introduction: Suicide is a significant public health problem, with increased rates in low- and middle-income countries such as Mexico; therefore, suicide prevention is important. Suicide is a complex and multifactorial phenomenon in which biological and social factors are involved. Several studies on the biological mechanisms of suicide have analyzed the proteome of the dorsolateral prefrontal cortex (DLPFC) in people who have died by suicide. The aim of this work was to analyze the protein expression profile in the DLPFC of individuals who died by suicide in comparison to age-matched controls in order to gain information on the molecular basis in the brain of these individuals and the selection of potential biomarkers for the identification of individuals at risk of suicide. In addition, this information was analyzed using machine learning (ML) algorithms to propose a model for predicting suicide. Methods: Brain tissue (Brodmann area 9) was sampled from male cases (n=9) and age-matched controls (n=7). We analyzed the proteomic differences between the groups using two-dimensional polyacrylamide gel electrophoresis and mass spectrometry. Bioinformatics tools were used to clarify the biological relevance of the differentially expressed proteins. In addition, this information was analyzed using machine learning (ML) algorithms to propose a model for predicting suicide. Results: Twelve differentially expressed proteins were also identified ( Discussion: Our exploratory pathway analysis highlighted oxidative stress responses and neurodevelopmental pathways as key processes perturbed in the DLPFC of suicides. Regarding ML models, KNeighborsClassifier was the best predicting conditions. Here we show that these proteins of the DLPFC may help to identify brain processes associated with suicide and they could be validated as potential biomarkers of this outcome.