Manuel Alejandro Rojo-Romero ¹ Nora Gutiérrez-Nájera ^2* Carlos S Cruz-Fuentes ³ Ana Luisa Romero-Pimentel ² Roberto CUAUHTEMOC Mendoza Morales ⁴ Fernando García-Dolores ⁴ Mirna Edith Morales ² Xochitl Castro ² Eli Elier Gonzalez-Saenz ⁵ Jonatan Torres-Campuzano ⁶ Tania Medina-Sánchez ³ KARLA HERNANDEZ-FONSECA ³ Humberto Nicolini ² LUIS FELIPE JIMENEZ-GARCIA ^6*

• ¹ Programa de Doctorado en Ciencias Biomédicas, Universidad Nacional Autónoma de México, Mexico City, Mexico
• ² National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico
• ³ National Institute of Psychiatry Ramon de la Fuente Muñiz (INPRFM), Mexico City, México, Mexico
• ⁴ Instituto de Ciencias Forenses (INCIFO), Ciudad de Mexico, México, Mexico
• ⁵ Instituto Nacional de Geriatría, Mexico City, México, Mexico
• ⁶ Faculty of Sciences, National Autonomous University of Mexico, Mexico City, México, Mexico

Suicide is a significant public health problem, with increased rates in low-and middleincome 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 DLPC 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. 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. Twelve 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). Bioinformatics tools were used to clarify the biological relevance of the differentially expressed proteins. Our exploratory pathway analysis highlighted oxidative stress responses and neurodevelopmental pathways. 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.