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Updated: Jan 16, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
Published on: October 3, 2025
Integrative computational and experimental validation of Piperlongumine as a therapeutic agent targeting
Rajeev Kumar Sahoo1, Zahid Alim Ansari1, Sambit Kumar Pradhan1
1Cancer Drug Resistance Laboratory, Department of Life Science, National Institute of Technology Rourkela, 769008, India.
Abstract:
Colorectal cancer (CRC) remains a major global health concern due to its high incidence and mortality. Recent studies have shown the potential of phytocompounds in CRC therapeutics. This study explores the anticancer potential of Piperlongumine (PIP), a natural phytocompound, in CRC through an integrative bioinformatics and experimental approach. Initially, integrative computational databases of GEO, CTD and GeneCards resulted in 11 common differentially expressed genes potentially targeted by PIP in CRC, showing involvement in multiple signaling pathways and apoptotic signaling. Further, protein-protein interaction analysis identified five hub genes- TP53, CCND1, AKT1, CTNNB1 and IL1B, showing significant expression alterations and correlations with poor prognosis and metastasis in CRC. The molecular docking demonstrated strong binding affinity between PIP and hub genes, alongside favorable pharmacokinetics including high gastro-intestinal absorption and minimal toxicity. Experimental validations on CRC cell lines- SW-480 and HT-29 revealed dose-dependent cytotoxicity with IC50 values of 3 μM and 4 μM, respectively. In addition, in vitro assays confirmed PIP's cytotoxic, anti-migratory, pro-apoptotic effects, and modulation of hub genes (TP53↑; CCND1, AKT1, CTNNB1, IL1B↓), supporting its mechanistic role in CRC. Together, these findings support that PIP exerts anticancer effects through modulation of hub genes, thus making it a potential therapeutic agent against CRC. This study is relevant as it bridges computational predictions with experimental validation, providing a systematic framework for natural compound evaluation in CRC. Unlike previous reports, this study uniquely combines multi-dataset transcriptomics, hub-gene prioritization, and ADMET profiling with in-vitro gene-level validation, which has not been previously reported for PIP in CRC.
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