0000000000587713
AUTHOR
Antonio Gomez
showing 3 related works from this author
DNA methylomes reveal biological networks involved in human eye development, functions and associated disorders
2017
This work provides a comprehensive CpG methylation landscape of the different layers of the human eye that unveils the gene networks associated with their biological functions and how these are disrupted in common visual disorders. Herein, we firstly determined the role of CpG methylation in the regulation of ocular tissue-specification and described hypermethylation of retinal transcription factors (i.e., PAX6, RAX, SIX6) in a tissue-dependent manner. Second, we have characterized the DNA methylome of visual disorders linked to internal and external environmental factors. Main conclusions allow certifying that crucial pathways related to Wnt-MAPK signaling pathways or neuroinflammation are…
Genomic Profiling of Patient-Derived Xenografts for Lung Cancer Identifies B2M Inactivation Impairing Immunorecognition
2017
Abstract Purpose: We aimed to maximize the performance of detecting genetic alterations in lung cancer using high-throughput sequencing for patient-derived xenografts (PDXs). Experimental Design: We undertook an integrated RNA and whole-exome sequencing of 14 PDXs. We focused on the genetic and functional analysis of β2-microglobulin (B2M), a component of the HLA class-I complex. Results: We identified alterations in genes involved in various functions, such as B2M involved in immunosurveillance. We extended the mutational analysis of B2M to about 230 lung cancers. Five percent of the lung cancers carried somatic mutations, most of which impaired the correct formation of the HLA-I complex. …
Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective …
2022
Item does not contain fulltext BACKGROUND: Two acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS. METHODS: In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifie…