RNA-seq–Based Mechanistic Evaluation of Phytochemicals and Small Molecules in Experimental Cancer Models

Abstract

High-throughput RNA sequencing (RNA-seq) has emerged as a powerful tool for deciphering the molecular mechanisms underlying phytochemical and small molecule-mediated cancer suppression. This comprehensive review synthesizes current knowledge on integrating RNA-seq-derived transcriptomic analysis with mechanistic evaluation of phytochemical and synthetic compound effects on apoptosis, inflammation, oxidative stress, and epithelial-mesenchymal transition (EMT) in experimental cancer models. We examine how transcriptomic profiling links gene expression changes to known biochemical and cellular mechanisms of action, with particular emphasis on major phytochemicals including curcumin, resveratrol, and sulforaphane. Integration of pathway enrichment analyses using KEGG (Kyoto Encyclopedia of Genes and Genomes) and Gene Ontology (GO) databases enables systematic identification of affected molecular pathways and biological processes. We discuss methodological approaches for connecting RNA-seq signatures to functional phenotypes, validation strategies for mechanistic hypotheses, and challenges in translating transcriptomic findings to therapeutic development. The review emphasizes how multi-level transcriptomic analysis encompassing individual gene expression, pathway enrichment, gene co-expression networks, and temporal dynamics provides comprehensive mechanistic insights superior to single-methodology approaches. Finally, we address future directions including integration of multi-omics data, spatial transcriptomics for tumor microenvironment characterization, and machine learning approaches for signature discovery and mechanism prediction.