Uncovering the therapeutic potential of Commiphora wightii phytochemicals in colorectal cancer through network pharmacology, survival, immune infiltration, and molecular docking
Anupam Sharma , Abhinav Sharma , Milan Kumar Bera , Vandana Sharma , Anil Kumar Sharma
Genome Instability & Disease ›› 2026, Vol. 7 ›› Issue (3) : 15
Colorectal cancer (CRC) continues to represent one of the leading causes of cancer-related morbidity and mortality, globally. Natural phytochemicals from Commiphora wightii (C. wightii) has recently garnered attention as possible anticancer agents due to their multi-targeting mechanisms. The aim of this study was to investigate the molecular mechanisms of inhibition for these phytochemicals against CRC, via an integrated in silico approach.
Potential targets of C. wightii phytochemicals and CRC-associated genes were retrieved from publicly available databases. Overlapping targets were identified using Venn analysis, which were subjected to protein-protein interaction (PPI) network construction and hub gene selection using Cytoscape and CytoHubba. Functional enrichment was performed through Gene Ontology (GO) and KEGG pathway analyses. The expression and prognostic significance of hub genes were evaluated using GEPIA2 and Kaplan–Meier survival analysis. Immune infiltration correlations were assessed using TIMER 3.0, and molecular docking was performed using CB-Dock2 to explore binding affinities of phytochemicals with hub proteins.
A total of 85 overlapping targets were identified between C. wightii phytochemicals and CRC-associated genes. Network analysis revealed ten hub genes, among which MET, CDK1, MMP9, PLAU, and CCND1 were significantly upregulated in CRC tissues. Immune infiltration analysis demonstrated significant association of MMP9 and PLAU with macrophage infiltration in both COAD and READ, suggesting their potential role in modulating the tumor immune microenvironment. Molecular docking revealed strong binding affinities of E-Guggulsterol toward MMP9 (-10.6 kcal/mol) and MET (-9.4 kcal/mol), while Quercetin showed favorable interactions with PLAU (-8.5 kcal/mol), MMP9 (-8.7 kcal/mol), and MET (-8.3 kcal/mol). These findings highlight E-Guggulsterol-MMP9, E-Guggulsterol-MET, Quercetin-PLAU, and Quercetin-MET as promising compound-target pairs for further investigations.
This study identified key CRC-associated targets potentially modulated by C. wightii phytochemicals. Integrated network pharmacology, immune infiltration, and molecular docking analyses highlighted MMP9, PLAU, and MET as biologically relevant targets. E-Guggulsterol and Quercetin demonstrated favorable interactions with these proteins, particularly E-Guggulsterol-MMP9, E-Guggulsterol-MET, Quercetin-PLAU, and Quercetin-MET, suggesting their potential as lead compounds for CRC therapy. Since this work relies on in silico analysis, we call for further in vitro and in vivo studies to support our findings and check if these effects could have therapeutic implications.
Commiphora wightii / Colorectal cancer (CRC) / Network pharmacology / Hub genes / Gene Ontology (GO) / KEGG pathways / Immune infiltration / Molecular docking / Quercetin / Guggulsterols / Ellagic acid / Vanillic acid.
| [1] |
|
| [2] |
|
| [3] |
American Cancer Society (2025). Key statistics for colorectal cancer. https://www.cancer.org/cancer/colon-rectal-cancer/about/key-statistics.html |
| [4] |
|
| [5] |
|
| [6] |
|
| [7] |
BIOVIA, D. S. (2017). Discovery Studio Modeling Environment (Release 4.5). Dassault Systèmes. https://www.3ds.com/products-services/biovia/ |
| [8] |
|
| [9] |
|
| [10] |
|
| [11] |
|
| [12] |
|
| [13] |
|
| [14] |
|
| [15] |
Colorectal (2023). cancer. In Oncology for nurses (pp. 106–111). Routledge. https://doi.org/10.4324/9781003306689-16 |
| [16] |
|
| [17] |
|
| [18] |
|
| [19] |
|
| [20] |
|
| [21] |
|
| [22] |
|
| [23] |
|
| [24] |
|
| [25] |
|
| [26] |
International Agency for Research on Cancer (2025). IARC marks colorectal cancer awareness month 2025. https://www.iarc.who.int/ |
| [27] |
|
| [28] |
|
| [29] |
|
| [30] |
|
| [31] |
|
| [32] |
|
| [33] |
|
| [34] |
|
| [35] |
|
| [36] |
|
| [37] |
|
| [38] |
|
| [39] |
|
| [40] |
|
| [41] |
|
| [42] |
|
| [43] |
|
| [44] |
|
| [45] |
Nan, X. (2025). TargetNet: A web server for predicting potential drug targets. https://nanx.app/targetnet/ |
| [46] |
|
| [47] |
|
| [48] |
|
| [49] |
Oliveros, J. C. (2007). Venny: An interactive tool for comparing lists with Venn’s diagrams. https://bioinfogp.cnb.csic.es/tools/venny/index.html |
| [50] |
|
| [51] |
|
| [52] |
|
| [53] |
|
| [54] |
|
| [55] |
|
| [56] |
|
| [57] |
|
| [58] |
|
| [59] |
|
| [60] |
|
| [61] |
|
| [62] |
|
| [63] |
|
| [64] |
|
| [65] |
|
| [66] |
|
| [67] |
|
| [68] |
|
| [69] |
|
| [70] |
|
| [71] |
|
| [72] |
|
| [73] |
|
| [74] |
|
| [75] |
|
| [76] |
|
| [77] |
|
| [78] |
|
| [79] |
|
| [80] |
|
| [81] |
|
| [82] |
|
| [83] |
|
| [84] |
|
| [85] |
|
| [86] |
|
| [87] |
|
Shenzhen University School of Medicine; Fondazione Istituto FIRC di Oncologia Molecolare
/
| 〈 |
|
〉 |