Recently, non-coding RNAs (ncRNAs) have emerged as crucial regulators of gene
expression. To uncover common lncRNA signatures, we retrieved and analyzed seven
publicly available RNA-seq datasets. These datasets included different in vitro models
of senescence (replicative senescence, drug-induced senescence, and H2O2-induced
senescence) in four human cell types: fibroblasts, mesenchymal stem cells,
endothelial cells, and smooth muscle cells.
The computational analysis consisted of several phases. The first phase involved
querying and selecting RNA-seq datasets from open repositories such as the Gene
Expression Omnibus (GEO). In the second phase, we performed RNA-seq data analysis,
including data quality assessment of pre-processed read counts, filtering and
normalization annotation, and calculation of differentially expressed genes (DEGs) and
differentially expressed long non-coding RNAs (DELs). To enhance the biological
relevance of the modulated genes derived from the different senescence datasets, we
performed enrichment analysis using the Gprofiler R package, focusing on statistically
modulated KEGGs terms. All analyses were performed in the R environment (version
2023.12.1.402), using open-source packages tailored to the specific features of each
dataset. Finally, to identify common signatures, we compared the lists of up- and downregulated long non-coding genes and genes, with a particular focus on the replicative
senescence model, which was the most represented senescence model across the
dataset
