GEOapplication/xmlftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE245nnn/GSE245231/primaryOK200GenomicsHomo sapiensGenome binding/occupancy profiling by high throughput sequencinghttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE245231GEOGSEfalseEpigenetic profiling of A549 cells treated with TSA [ATAC-seq]Cellular 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 epigenetic profiling dynamics of HDACi-induced senescence in lung cancer cells, we performed ATAC-seq of A549 treated with TSA.2026/04/26GSE245231GSM7839299GSM783930024676245231Homo sapiens