bioRxiv2025
Cancer research has been revolutionized by mass spectrometry (MS)-based proteomics, enabling large-scale profiling of proteins and post-translational modifications (PTMs) to identify critical alterations in cancer signaling pathways. However, the lack of comprehensive, userfriendly platforms for integrative analysis limits efficient data exploration, biomarker identification, and translational insights. To address this gap, we developed OncoProExp, a Shiny-based interactive web application designed for in-depth exploration of cancer proteomes and phosphoproteomes. OncoProExp offers robust workflows for data preprocessing, including missing value imputation and statistical filtering. The platform features interactive visualizations such as principal component analysis (PCA), hierarchical clustering heatmaps, and gene set enrichment analysis (GSEA), enabling detailed functional annotation. Differential expression analysis to identify differentially expressed proteins (DEPs) and phosphoproteins (DEPPs) facilitating the discovery of potential biomarkers and therapeutic targets. The application supports survival analysis and pan-cancer exploration using clinical and proteome/phosphoproteomic datasets. OncoProExp incorporates state-of-the-art predictive modeling using machine learning algorithms, including Support Vector Machines (SVMs), Random Forests, and Artificial Neural Networks (ANNs) for cancer risk stratification, achieving near-perfect accuracy in multi-cancer and single-cancer classification. These models are enhanced by SHapley Additive exPlanations (SHAP) for interpretability. To enhance its translational utility, the platform supports user-uploaded data and enables protein-protein interaction analysis, pathway enrichment analysis, cancer drug relevance evaluation, and clinical annotation using curated cancer-specific datasets. OncoProExp is deployable via Docker containers, ensuring flexible and scalable integration into individual servers. Its utility has been demonstrated using Clinical Proteomic Tumor Analysis Consortium (CPTAC) datasets, showcasing its potential to advance cancer biomarker discovery, risk stratification, therapeutic target identification, and personalized treatment strategies. OncoProExp is freely accessible at https://oncopro.cs.ut.ee/ without login requirements, offering a comprehensive resource for translational cancer research.