Proteome analysis of the prefrontal cortex and the application of machine learning models for the identification of potential biomarkers related to suicide - Rojo-Romero, Manuel Alejandro; Gutiérrez-Nájera, Nora Andrea; Cruz-Fuentes, Carlos Sabás; Romero-Pimentel, Ana Luisa; Mendoza-Morales, Roberto; García-Dolores, Fernando; Morales-Marín, Mirna Edith; Castro-Martínez, Xóchitl; González-Sáenz, Elier; Torres-Campuzano, Jonatan; Medina-Sánchez, Tania; Hernández-Fonseca, Karla; et al. (Frontiers in Psychiatry, 2025)
IntroductionSuicide 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.MethodsBrain 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.ResultsTwelve differentially expressed proteins were also identified (t14 ≤ 0.5). Using Western blotting, we validated the decrease in expression of peroxiredoxin 2 and alpha-internexin in the suicide cases. ML models were trained using densitometry data from the 2D gel images of each selected protein and the models could differentiate between both groups (control and suicide cases).DiscussionOur 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.