ABSTRACT
In recent years, the clinical use of extracellular miRNAs as potential biomarkers of disease has increasingly emerged as a new and powerful tool. Serum, urine, saliva and stool contain miRNAs that can exert regulatory effects not only in surrounding epithelial cells but can also modulate bacterial gene expression, thus acting as a "master regulator" of many biological processes. We think that in order to have a holistic picture of the health status of an individual, we have to consider comprehensively many "omics" data, such as miRNAs profiling form different parts of the body and their interactions with cells and bacteria. Moreover, Artificial Intelligence (AI) and Machine Learning (ML) algorithms coupled to other multiomics data (i.e., big data) could help researchers to classify better the patient's molecular characteristics and drive clinicians to identify personalized therapeutic strategies. Here, we highlight how the integration of "multiomic" data (i.e., miRNAs profiling and microbiota signature) with other omics (i.e., metabolomics, exposomics) analyzed by AI algorithms could improve the diagnostic and prognostic potential of specific biomarkers of disease.