Integrative Network Pharmacology and Molecular Docking Analyses on the Mechanisms of San-Zhong-Kui-Jian-Tang in Treating Oral Squamous Cell Carcinoma
Chun Hoe Tan , Haresh Sivakumar , Da-gui Luo , Yu-xin Cen
Current Medical Science ›› : 1 -20.
Integrative Network Pharmacology and Molecular Docking Analyses on the Mechanisms of San-Zhong-Kui-Jian-Tang in Treating Oral Squamous Cell Carcinoma
Oral squamous cell carcinoma (OSCC) is an aggressive cancer with a high mortality rate. San-Zhong-Kui-Jian-Tang (SZKJT), a Chinese herbal formula, has long been used as an adjuvant therapy in cancer clinical practice. Although its therapeutic effects and molecular mechanisms in OSCC have been previously elucidated, the potential interactions and mechanisms between the active phytochemicals and their therapeutic targets are still lacking.
The present study employed network pharmacology and topology approaches to establish a “herbal ingredients–active phytochemicals–target interaction” network to explore the potential therapeutic targets of SZKJT-active phytochemicals in the treatment of OSCC. The role of the target proteins in oncogenesis was assessed via GO and KEGG enrichment analyses, and their interactions with the active phytochemicals of SZKJT were calculated via molecular docking and dynamic simulations. The pharmacokinetic properties and toxicity of the active phytochemicals were also predicted.
A total of 171 active phytochemicals of SZKJT fulfilled the bioavailability and drug-likeness screening criteria, with the flavonoids quercetin, kaempferol, and naringenin having the greatest potential. The 4 crucial targets of these active phytochemicals are PTGS2, TNF, BCL2, and CASP3, which encode cyclooxygenase-2, tumor necrosis factor (TNF), BCL-2 apoptosis regulator, and caspase-3, respectively. The interactions between phytochemicals and target proteins were predicted to be thermodynamically feasible and stable via molecular docking and dynamics simulations. Finally, the results revealed that the IL-6/JAK/STAT3 pathway and TNF signaling via NF-κB are the two prominent pathways targeted by SZKJT.
In summary, this study provides computational data for in-depth exploration of the mechanism by which SZKJT activates phytochemicals to treat OSCC.
Binding interactions / Hub genes / In silico / Protein‒protein interaction network / Traditional Chinese medicine
| [1] |
|
| [2] |
|
| [3] |
World Health Organization. Oral health. 2025. Available from: https://www.who.int/news-room/fact-sheets/detail/oral-health. |
| [4] |
World Health Organization. Oral Health Malaysia 2022 country profile. 2022. Available from: https://www.who.int/publications/m/item/oral-health-mys-2022-country-profile. |
| [5] |
|
| [6] |
|
| [7] |
|
| [8] |
|
| [9] |
|
| [10] |
|
| [11] |
|
| [12] |
|
| [13] |
|
| [14] |
|
| [15] |
|
| [16] |
|
| [17] |
|
| [18] |
|
| [19] |
|
| [20] |
|
| [21] |
|
| [22] |
|
| [23] |
|
| [24] |
|
| [25] |
|
| [26] |
|
| [27] |
Tan CH, Lim SH, Sim KS. Computational Elucidation of Hub Genes and Pathways Correlated with the Development of 5-Fluorouracil Resistance in HCT 116 Colorectal Carcinoma Cell Line. Biochem Genet. 2025. |
| [28] |
|
| [29] |
|
| [30] |
|
| [31] |
Tan C-H, Yeap JS-Y, Lim S-H, et al. The Bisindole Alkaloids Angustilongines M and A from Alstonia penangiana Induce Mitochondrial Apoptosis and G0/G1 Cell Cycle Arrest in HT-29 Cells through Promotion of Tubulin Polymerization. J Nat Prod. 2021;84(5):1524–1533. |
| [32] |
Yap LX, Ramle AQ, Sim KS, et al. In Vitro and Computational Studies of Indoleninyl-Pyrimido[1,2-b]Indazoles as DNA Binding Agents. Natl Acad Sci Lett 2024. |
| [33] |
|
| [34] |
Funmi AZ, Lim CY, Ng MP, et al. Investigation of cytotoxic indoleninyl-thiobarbiturate zwitterions as DNA targeting agents. J Mol Struct. 2024:140278. |
| [35] |
|
| [36] |
Hassan HA, Ramle AQ, Tan CH, et al. 2-hydroxylbenzoyl-pyrazolo[1,5-a]pyrimidines as inhibitors against bacterial pathogens and MCF7 human breast cancer cells. J Mol Struct. 2025:142278. |
| [37] |
|
| [38] |
|
| [39] |
|
| [40] |
Çevik UA, Işik A, Karakaya A. ADMET and Physicochemical Assessments in Drug Design. In: Computational Methods for Rational Drug Design. Hoboken: John Wiley & Sons, Inc.; 2025. p:123–151. |
| [41] |
|
| [42] |
|
| [43] |
Sganzerla Martinez G, Dutt M, Kumar A, et al. Multiple Protein Profiler 1.0 (MPP): A Webserver for Predicting and Visualizing Physiochemical Properties of Proteins at the Proteome Level. Protein J. 2024;43(4):711–717. |
| [44] |
|
| [45] |
|
| [46] |
|
| [47] |
|
| [48] |
|
| [49] |
|
| [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] |
|
| [88] |
|
| [89] |
|
| [90] |
|
| [91] |
|
| [92] |
|
| [93] |
|
| [94] |
|
| [95] |
|
| [96] |
|
| [97] |
|
| [98] |
|
| [99] |
|
| [100] |
|
| [101] |
|
| [102] |
|
The Author(s), under exclusive licence to Huazhong University of Science and Technology
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