CytokineProfile: An Integrated Web Tool for Cytokine Profiling Analysis

Background: Multiplex cytokine assays are widely used for biomarker discovery and disease profiling, but most existing web tools are optimized for metabolomics and require substantial data reformatting. A reproducible, end-to-end computational platform tailored to cytokine analysis is needed. Methods: We developed CytokineProfile, a web-based analytic tool that accepts standard tabular formats and guides users through data preview, quality control, missing-value handling, and filtering. Analytical modules include exploratory visualization (boxplots, correlation matrices, volcano plots, and dual-flashlight plots combining log fold change with strictly standardized mean difference), univariate tests (analysis of variance and normality-informed 2-sample tests), multivariate modeling (principal component analysis, sparse partial least squares discriminant analysis, and multilevel/multivariate integration variants), and machine-learning classifiers (extreme gradient boosting and random forest) with feature-importance outputs. Results: In a case study using anti-CD3/anti-CD28-stimulated peripheral blood mononuclear cell cytokine profiles from subjects with prediabetes at 20 and 72 h (33 samples per time point), CytokineProfile supported same-dataset comparison with MetaboAnalyst and CytokineExplore, generated consistent group separation in supervised analyses, and provided complementary volcano, dual-flashlight, and machine-learning-based feature results. Benchmarking on datasets up to 1,000 samples and 100 cytokines showed practical runtime and memory usage for representative workflows. Conclusion: CytokineProfile provides a reproducible, user-friendly platform integrating quality control, statistical analysis, visualization, and machine learning. The tool streamlines cytokine biomarker discovery and interpretation, supporting rigorous analysis of complex datasets.