Bulk RNA sequencing data analysis aims to capture global transcriptomic changes across different conditions while maintaining biological variability and enabling the exploration of gene expression dynamics. In this work, we focus on integrating and analyzing bulk RNA-Seq datasets derived from various experimental conditions to identify key transcriptional signatures and regulatory mechanisms of cancer related disease states. The raw sequencing data generate large, high-dimensional count matrices, making downstream processing, including differential expression analysis and pathway enrichment, challenging. In addition, we aim to integrate the processed transcriptomics data with other omics layers such as spatial proteomics, whole exome and single-cell transcriptomics datasets. With this proposal, we request computational power (including extra CPUs and memory for the read alignments and GPU support for optimized data processing and deep learning applications) to efficiently execute comprehensive in silico pipelines.