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Catala-Senent, J. F.

Publications and source records attributed to Catala-Senent, J. F..

3 recordsLinked to original sources

Transcriptomic meta-analysis in plaque psoriasis: an integrative bioinformatic approach to deciphering the genetic landscape and molecular pathways

Psoriasis is a chronic inflammatory skin disease influenced by both genetic and environmental factors. Despite extensive research, its precise etiology remains unclear, posing significant challenges to understanding and treatment. The disease pathogenesis involves self-reactive T cells and immune-related cytokines. Genome-wide association studies have identified various susceptibility loci for immune-related diseases, but the underlying mechanisms remain only partially understood. Recent discoveries of critical signaling pathways, biological processes, and immune cell involvement have expanded our knowledge and offer hope for improved therapeutic strategies. This study aimed to enhance our understanding of psoriasis and proposes novel therapeutic approaches by employing integrated bioinformatics to identify signaling pathways and biological processes as potential disease markers. Presenting a systematic review and taking a meta-analytical approach to transcriptomic profiles, this investigation examined differential gene expression patterns across 44 studies involving 975 samples comparing lesional psoriasis, non-lesional psoriasis, and healthy controls. Consensus transcriptome signatures revealed a significant association between immune-related genes and psoriasis pathogenesis. Functional enrichment analysis identified several enriched pathways related to immunity and immune system processes. Comparison of these findings with the existing literature indicated that some immune-related genes were already known, while others are novel in the context of psoriasis. Additionally, novel gene analysis demonstrated psoriasis involvement in pathways such as gluconeogenesis, the FoxO signaling pathway, and mitophagy. This integrative approach confirmed classic genetic associations while uncovering novel gene expression patterns and pathways relevant to psoriasis. Notably, the disruption of the gluconeogenesis pathway emerged as a critical finding. These insights enhance our understanding of psoriasis pathophysiology and pave the way for targeted therapies, offering improved management options for affected individuals.

molecular biology↗

Unveiling common transcriptomic features between melanoma brain metastases and neurodegenerative diseases

Melanoma represents a critical clinical challenge due to its high incidence rates and unfavorable clinical outcomes. This type of skin cancer presents unique adaptability to the brain microenvironment, but its underlying molecular mechanisms are poorly understood. To further characterize its tumor neurobiology, we explore the relation between the transcriptional profiles of melanoma brain metastasis (MBM) and the neurodegenerative diseases Alzheimers disease, Parkinsons disease, and multiple sclerosis. Through an in silico approach, we unveiled the neurodegenerative signature of MBM when compared to melanoma non-brain metastasis (53 dysregulated genes enriched in 11 functional terms) and to non tumor-bearing brain controls (195 dysregulated genes, mostly involved in development and cell differentiation, chromatin remodeling and nucleosome organization, and translation). Two genes, ITGA10 and DNAJC6, emerged as key potential markers, as they are dysregulated in both scenarios. Lastly, we developed a user-friendly web tool (https://bioinfo.cipf.es/metafun-mbm/) as an open source, so that any user can interactively delve into the results.

cancer biology↗

MetaFun: Unveiling sex differences in multiple omics studies through comprehensive functional meta-analysis

While sex-based differences in various health scenarios have been thoroughly acknowledged in the literature, we lack a deep analysis of sex as a variable in this context. To fill this knowledge gap, we created MetaFun as an easy-to-use web-based tool to meta-analyze multiple transcriptomic datasets with a sex-based perspective to gain major statistical power and biological soundness. Furthermore, MetaFun can be used to perform case-control meta-analyses, allowing researchers with basic programming skills to access this methodology. Availability and implementationMetaFun is freely available at http://bioinfo.cipf.es/metafun The back end was implemented in R and Java, and the front end was developed using Angular Contactfgarcia@cipf.es, mhidalgo@cipf.es Supplementary informationR code available at https://gitlab.com/ubb-cipf/metafunr

bioinformatics↗