About this Research Topic
Numerous individual -omic studies on different lung disorders characterized by pulmonary fibrosis have been performed extrapolating a wide range of different molecules involved in distinct molecular pathways. Traditional transcriptomic and proteomic approaches have provided insight into disease progression by identifying discrete cellular subpopulations or microenvironmental signatures characteristic of normal or pathological tissues. These studies dramatically transformed biomedical research but there is a need for comprehensive and combined analyses to obtain a more complete scenario on PF and bioinformatics and system biology computational techniques which could be essential for data management, integration, classification, and prediction, to overcome these issues.
Moreover, traditional techniques do not examine how a given cellular state relates to its interactions with neighboring cells or its surrounding ECM with multiparametric characterization. Bulk-omic techniques mask tissue, cellular and sub-cellular heterogeneity by averaging gene expression or protein abundance across whole samples while RNA and protein subcellular localization is tightly controlled and intimately linked to the function. For this reason, it could be relevant to perform new studies of spatial-omics since the localizations of the molecules and their dynamics at the subcellular level is essential for a complete understanding of cell biology.
We invite investigators to contribute original research as well as review articles addressing recent advances on aspects regarding multi–omic and spatial-omic research on pulmonary fibrosis for a wider comprehension of this common process in different lung disorders. Research regarding the understanding of the molecular mechanisms at the basis of the diseases for personalized medicine and the discovery of new therapies.
Submissions may focus on, but are not limited to, the following:
· Analyses of the impact of multi-omics and spatial-omic approaches on pulmonary fibrosis diagnosis, prognosis, patient stratification, and response to treatments.
· Development and validation of biomarkers to predict diagnosis, prognosis and drug targets, and possible drug efficacy.
· Development and validation of multi-omics and spatial-omic molecular subtypes serving precision medicine
· Combined bioinformatics and artificial intelligence to mine new strategies and biomarkers for pulmonary fibrosis therapy
Keywords: multiomics, spatialomics, PF, Pulmonary Fibrosis, diagnosis, drug targets, bioinformatics
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