Omics and artificial intelligence integration aiming to elucidate biomarkers and molecular pathways in Meningiomas from liquid biopsy

• Grant number: 23/18424-3
• Support Opportunities: Regular Research Grants
• Start date: June 01, 2025
• End date: May 31, 2027
• Field of knowledge: Health Sciences - Medicine
• Principal Investigator: Estela de Oliveira Lima
• Grantee: Estela de Oliveira Lima
• Host Institution: Faculdade de Medicina (FMB). Universidade Estadual Paulista (UNESP). Campus de Botucatu. Botucatu , SP, Brazil
• Associated researchers: Adriana Camargo Ferrasi ; Geysson Javier Fernandez Garcia ; Pedro Tadao Hamamoto Filho

Abstract

Meningiomas (MGMs) are the most common intracranial tumors and represent around 36% of all primary tumors. They can be classified in different grades: grade 1 (benign), grade 2 (atypical) and grade 3 (anaplastic), the latter the most aggressive. Symptoms are generally non-specific and recurrence rates for grades 1, 2 and 3 vary from 7-25%, 29-52% and 50-94%, respectively. Although it is common among primary tumors, the pathogenesis of MGMs is still poorly understood, making it difficult to assertively classify the different grades and choose the best therapeutic strategy. A wide-ranging evaluation of the molecular characteristics of MGMs is fundamental, so new targets can be identified, and the prognosis can be more assertive, what contributes to the development of precision medicine for MGMs. Different molecules are involved in tumor development and progression, such as lncRNAs (long non-coding RNAs), which represent molecules involved in the control of various cellular processes, whose deregulated expression has been associated with tumorigenesis. In addition to lncRNAs, metabolites have also been promising as potential biomarkers, since they can elucidate modifications in the omics cascade reflected in the phenotype. The combination of transcriptomics (lncRNA-omics) and metabolomics analyses is fundamental for identifying new potential therapeutic targets, as well as biomarkers for grade determination, which represent a chance of improving therapeutic approaches and increasing survival. Thus, the objectives of the present study are: a) to evaluate lncRNAs and the metabolome in tumor samples of patients with MGM of different grades, and validate tissue's five overexpressed lncRNAs in plasma samples; b) to establish a predictive model of tumor aggressiveness through Artificial Intelligence and evaluate the potential of determining MGM grade through the identified biomarkers; c) to analyze interaction networks based on differentially expressed metabolites and lncRNAs. In this way, we will look for new grade biomarkers, new therapeutic targets and elucidation of the molecular mechanisms involved in the development of the disease that can be liable to pharmacological intervention. (AU)