Search

As the aging population grows progressively around the globe, the need to research and develop strategies to healthy aging is ever more critical and takes on new urgency1. Primary hallmarks of aging include cell autonomous changes linked to epigenetic alterations, genomic instability, telomere attrition and loss of proteostasis (protein homeostasis), which are followed by antagonistic responses such as deregulated nutrient sensing, altered mitochondrial function and cellular senescence. In addition, many functions of the immune system show a progressive decline with age, referred as immunosenescence, leading to a higher risk of infection, cancer, and autoimmune diseases2. Although chronological age is the most powerful risk factor for most chronic diseases, the underlying molecular mechanisms that lead to generalized disease susceptibility are largely unknown. In recent years, rapidly developing high-throughput omics have provided a broader insight, with the identification of a number of longevity-relevant loci based on genome-wide association studies (GWAS) and epigenome analyses. Despite this success, APOE, FOXO3 and 5q33.3 are the only identified loci consistently associated with longevity3. Hence, the complexity of the aging phenomenon, influenced by genetic and epigenetic regulation, post-translational regulation, metabolic regulation, host–microbiome interactions, lifestyle, and many other elements, primarily explains the poor understanding of many of the molecular and cellular processes that underlie the progressive loss of healthy physiology.

(This article belongs to the Proceedings of 3rd XoveTIC Conference) Stomach cancer is a complex disease and one of the leading causes of cancer mortality in the world. With the view to improve patient diagnosis and prognosis, it has been stratified into four molecular subtypes. In this work, we compare the results of multiple machine learning algorithms for the prediction of stomach cancer molecular subtypes from gene expression data. Moreover, we show the importance of decorrelating clinical and technical covariates. This work was supported by the FCT (Fundação para a Ciência e a Tecnologia) research grant Ph.D. Studentship SFRH/BD/145707/2019 and the research grant IF/01127/2014, funded in the scope of the FCT Investigator Exploratory Project: “Understanding the impact of acquired and germline genetic variants in the complexity of gastric cancer”; GenomePT project (reference 22184): “National Laboratory for Genome Sequencing and Analysis”; QREN L3 project (reference NORTE-01-0145-FEDER-000029): “Mapping genetic and phenotypic heterogeneity in HER2 positive cancers to anticipate and counteract resistance phenotypes”.