High-throughput transcriptome-based techniques have revolutionised the identification of molecular markers for neuropathic. While existing tools offer in-depth analysis and interactive visualizations for individual datasets, they limit the ability to draw generalised conclusions and inferences. There is a need to develop tools that combine multiple datasets for integrative data visualisation and exploration, turning individual data into collective knowledge. To address this issue, we developed Pain RNAseq Hub (PRH), a Shiny App for hosting and visualising bulk/spatial RNA sequencing data in neuropathic pain-related studies. The app includes useful features such as exploration of transcriptomic changes in the context of gene expression and protein-protein interaction networks, downloadable plots and tables, and reproducible code tracking for customization and reproducibility. By providing a flexible, reproducible, and easy-to-understand code template for hosting high-throughput sequencing data, we invite researchers with varying computational expertise to build Shiny apps for sharing data with the community to improve accessibility and transparency.  Together, this can lead to better data sharing and utilisation, resulting in effective use of sequencing data.