GEOapplication/xmlftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE244nnn/GSE244427/primaryOK200TranscriptomicsHomo sapiensExpression profiling by high throughput sequencinghttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE244427GEOGSEfalsemRNA expression profiling of A549 cells treated with TSA at different time pointsCellular senescence (CS) refers to a stable arrest of the cell cycle characterized by an altered profiles of gene expression networks. However, a reliable strategy to quantify senescent levels is lacking. Here we developed a machine learning-based tool (Predictive Cellular Senescence Model, PreCSenM) that addresses this issue by defining senescent levels as CS score through incorporating senescent features from multiplatform data. While applying this model to predict clinical outcomes in patients with lung adenocarcinoma (LUAD), PreCSenMo identified that HDAC inhibitors (HDACi) can trigger LUAD cellular senescence. To reveal the transcriptional profiling dynamics of HDACi-induced senescence in lung cancer cells, we performed RNA-seq of A549 treated with TSA at different time points.2026/04/26GSE244427GSM7815990GSM7815991GSM7815983GSM7815984GSM7815982GSM7815987GSM7815988GSM7815985GSM7815986GSM781598924676244427Homo sapiens