The Pharmacological Research of Traditional Chinese Medicine in the treatment of Melanoma
Abstract:
Background:
Melanoma is a highly aggressive and drug-resistant malignancy, accounting for the majority of skin cancer-related deaths. Despite advancements in cancer therapy, the 5-year survival rate for melanoma patients remains alarmingly low at 29.8%. Traditional Chinese Medicine (TCM) has been increasingly recognized for its potential in cancer therapy, with many TCM formulas demonstrating anti-tumor and anti-metastatic effects.
Results:
In this study, we employed a network pharmacology approach to investigate the pharmacological mechanisms of Si-Jun-Zi-Tang (SJZT), a TCM formula, in the treatment of melanoma. Our results identified 131 bioactive compounds in SJZT, which targeted 250 proteins associated with melanoma. Further analysis revealed that SJZT compounds modulated multiple signaling pathways, including the PI3K-AKT and MAPK pathways, which are critical for melanoma cell proliferation, survival, and metastasis. Molecular docking validation confirmed the strong binding affinities between SJZT compounds and core targets, including AKT1, MAPK14, and RELA.
Conclusion:
This study provides new insights into the anti-melanoma effects of SJZT and highlights its potential as a complementary therapy for melanoma treatment. The integration of network pharmacology and molecular docking approaches demonstrates a promising strategy for elucidating the mechanisms of TCM formulas in cancer therapy. Further experimental validation is warranted to confirm the efficacy and safety of SJZT in melanoma treatment.
CHAPTER 01
INTRODUCTION:
1.1Background:
Melanoma is a malignant neoplasm arising from melanocytes, the cells responsible for pigment in the skin. Although a relatively small percentage of skin cancers, melanoma causes most skin cancer-related fatalities because it grows aggressively and has high potential for metastasis. Melanoma incidence rates worldwide have been progressively increasing, especially in light-skinned populations with high exposure to the sun. New therapeutic approaches to melanoma involve surgical resection, chemotherapy, radiotherapy, targeted therapy, and immunotherapy in the form of immune checkpoint inhibitors. While these have enhanced survival in patients, they are frequently plagued by low response rates, severe side effects, and eventual resistance. As a result, there is increasing necessity to investigate alternate and complementary therapies that can improve treatment outcomes and bypass the shortfalls of conventional treatments.
1.2 Si Jun Zi Tang and Traditional Chinese Medicine:
TCM is a medicine that has been practiced for millennia and continues to be a major part of the healthcare systems of most Asian nations. A fundamental principle of TCM includes the application of multi-herbal prescriptions that are designed to treat the body to achieve balance, as opposed to treating a single disease process or symptom. Si Jun Zi Tang (SJZT), or the "Four Gentlemen Decoction," is a TCM classic formula that consists of Panax ginseng (Ren Shen), Atractylodes macrocephala (Bai Zhu), Poria cocos (Fu Ling), and Glycyrrhiza uralensis (Gan Cao). SJZT is traditionally used to tonify Qi (vital energy), enhance digestion, and augment immune function.
Recent pharmacological studies have shown that individual herbs and bioactive compounds in SJZT have anti-inflammatory, antioxidant, and immunomodulatory activities. Anticancer activity of SJZT is proposed by some reports through the modulation of the tumor microenvironment, apoptosis regulation, and immune surveillance reinforcement. Nevertheless, the underlying mechanisms by which SJZT benefits melanoma patients have not been examined comprehensively, especially in relation to intricate biological networks.
1.3 Network Pharmacology: New Conceptual Framework for TCM:
Network pharmacology is a novel field of study that combines systems biology, bioinformatics, and pharmacology to analyze the interactions among drugs, targets, diseases, and pathways in a network-based format. Unlike classical pharmacological methods that are based on single-target, single-drug interactions, network pharmacology adopts the principle of "multi-target therapeutics" that is well adapted for the investigation of herbal medicine. The holistic principle of TCM formulas involving multiple compounds interacting with a multitude of biological targets matches the network pharmacology paradigm.
By utilizing databases like TCMSP (Traditional Chinese Medicine Systems Pharmacology), BATMAN-TCM, GeneCards, STRING, and KEGG, scientists are able to forecast the active compounds of herbal medicines, screen for possible target genes, and evaluate signaling pathways related to disease development. For melanoma, network pharmacology can aid in the identification of important molecular targets affected by SJZT and disclose how its constituents may collectively control tumor-related processes like cell proliferation, angiogenesis, metastasis, and immune evasion.
1.4 Importance of the Study:
Although SJZT has been extensively applied in the clinic to enhance immune function and patient robustness, its therapeutic application in treating melanoma is largely anecdotal and underinvestigated. This thesis aims to bridge this gap by using network pharmacology approaches to study the putative molecular mechanisms and targets of SJZT against melanoma. Elucidation of these mechanisms can lend science-based support to the age-old use of SJZT and even open up avenues for the discovery of new adjunct therapies for melanoma.
This study is important because it is an interdisciplinary effort that marries traditional medical wisdom with current computational hardware. By charting the compound-target-disease networks, this study could reveal novel therapeutic insights, discover new biomarkers, and facilitate the adoption of evidence-based TCM in contemporary oncology practice.
1.5 Study Objectives:
The primary aim of this thesis is to explore the possible therapeutic mechanisms of Si Jun Zi Tang in the treatment of melanoma by using a network pharmacology strategy. Certain objectives are as follows:
Active compound identification of the four herbal parts of SJZT employing pharmacokinetic screening parameters like oral bioavailability (OB) and drug-likeness (DL).
Prediction of potential targets of these active compounds through public databases and computational tools.
Identification of melanoma-related genes via disease-specific databases.
Building of compound-target and protein-protein interaction (PPI) networks to identify key nodes and hub genes. analysis (GO and KEGG) to determine biological processes and signaling pathways involved in the anti-melanoma activities of SJZT.
Enrichment against core targets in order to validate their possible binding affinity
Validation of molecular docking of significant active compound.
CHAPTER 02
2.Material and Methods:
2.1 Identification of the Active Compounds in SJZT and Related Target Genes:
The compounds Ginseng,Licoric, Atractylodes (baizhu)and Hoelen (fuling) were searched and obtained from the Traditional Chinese Medicine System Pharmacology Database (TCMSP; http://tcmspw.com/tcmsp.php/) as were their related gene targets and other biological information. Specifically, to evaluate the characteristics of absorption, distribution, metabolism, and excretion, we used oral bioavailability (OB) and drug likeness (DL) to filter candidate active compounds with thresholds of OB ≥ 30% and DL ≥ 0.18. Moreover, the targets of the active compounds were transformed into gene symbols via the UniProt database (http://www.uniprot.org/) by limiting the species to Homo sapiens for further analysis.
2.2 Potential targets identification:
Identification of putative targets of SZJT candidate compounds was performed with TCMSP and Drugbank (https://www.drugbank.ca/) [20, 22]. The target protein names were transformed into the corresponding gene symbols by the UniProt database (https://www.uniprot.org) [23]. “Melonoma” was used as a key word to search for targets related to aging. The predicted targets of aging were collected from the GeneCards database (https://www.genecards.org, verb4.9.0) [24], NCBI (https://www.ncbi.nlm.nih.gov/gene) [25] and OMIM databases (http://www.omim.org/, updated December, 30, 2020) [26]. The species was set to be “Homo Sapiens”. Then, aging related genes found in the three databases were merged, the replicate genes were deleted.
2.3 Construction of the targeted aging network of SJZT:
The overlapping genes associated with both SJZT active compounds and aging were considered as hub genes. Then the SJZT-target-aging network was constructed using Cytoscape 3.7.2 software. The hub genes were put into the STRING (https://string-db.org/, V11.0) database [27], with the species limited to “Homo sapiens” and a setting of a combined score>0.900, to construct the protein–protein interaction (PPI) network. The results were downloaded as TSV format file from the STRING database and imported into the Cytoscape V3.7.2 for analysis [23]. CytoHubba, a plugin of Cytoscape V3.7.2, was used to calculate the degree centrality [28]. Larger protein targets with more linked nodes had higher degrees, participated in more biological functions [29, 30], and were more likely to be therapeutic targets. The top ten core targets were screened to determine the accuracy using the critical targets of the molecular docking method.
2.4 Bioinformatics analysis based on target genes:
GO and KEGG pathway analyses of the SJZT hub targets were performed using the DAVID 6.8 database (https://david.ncifcrf.gov/tools.jsp). The species was set to be “Homo Sapiens”. The GO functional analysis was performed for three categories, including the biological process (BP), molecular function (MF), and cellular component (CC) categories [31]. The GO bar chart results were drawn by Graph Pad Prism V8.0.1. The KEGG pathway enrichment results were visualized as a bubble chart with online bioinformatics tools (http://www.bioinformatics.com.cn/). In addition, a false discovery rate (FDR) <0.05 was considered to indicate significance.
2.4 Molecular docking analysis:
Molecular docking was performed with AutoDockTools V1.5.6 software to validate compound-target interactions [32]. First, we prepared receptor proteins. The crystal structures of core proteins were obtained from the Protein Data Bank (PDB) database (http://www.rcsb.org/). PyMol software was used to optimize the structures of receptor proteins by removing water molecules. Subsequently, hydrogens were added to the proteins and the charges were calculated with AutoDockTools V1.5.6 software and exported in PDBQT format.
Next, we prepared the ligand small molecule compounds. The SDF files of the core compound structures were downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). Then, we used Chem3D V15.1 software to optimize the structures and saved them in MOL2 format. Subsequently, the MOL2 format files of the bioactive compounds were imported into the AutoDockTools V1.5.6 software to adjust the charges, determine the roots of the ligands, and select the ligands’ rotatable bonds, and the results were saved in PDBQT format.
AutoDockTools was used to construct the binding pockets for molecular docking. We searched for the active site information of receptor proteins in the reported experimental results in the literature. Then center coordinates of the active pocket were adjusted according to the protein shape. And, the relevant coordinates of the docking box were determined. Finally, AutoDock Vina was used to semi-flexibly dock the core compounds to the core target proteins. The docking model with the lowest binding free energy was selected to analyse the final conformation.
OpenBabel software was used to convert the PDBQT format to PDB format. PyMOL software was used to visualize the binding conformation of the ligand and receptor. Then, the best conformation was saved. The PDB format was entered into the Protein–Ligand Interaction Profiler (PLIP) website (https://plip-tool.biotec.tu-dresden.de/plip-web/plip/index). The website was used to visualize the docking mode of each compound-target interaction [33].
TO BE CONTINUE.................