The Pharmacological Research of Traditional Chinese Medicine in the treatment of Melanoma
CHAPTER 03
3.Results:
3.1 Target prediction of XSLJZT:
A total of 144 active ingredients of XSLJZT were obtained in the TCMSP database according to the selected criteria. Among them, there were 4 compounds in Atractylodes macrocephala, 11 compounds in Pinellia ternata, 5 compounds in Tangerine peel, 17 compounds in Codonopsis pilosula, 6 compounds in Poria cocos, 88 compounds in Licorice, 5 compounds in Aucklandiae Radix, and 8 compounds in Villous Amomum Fruit. The ingredient information was standardized by Uniprot, and 218 ingredient targets were obtained after de-duplication and deletion of invalidation. The information of some active ingredients was collated, and the common ingredients of Chinese medicines were list. Integration of GC targets from five disease databases yielded 1765. The Venn diagram shows that there are 250 potential targets for XSLJZT.
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Figure 1: Venn diagram of XSLJZT component targets
There were 359 targets and 1727 edges in the " XSLJZT-Active Ingredient-Target” network constructed by Cytoscape. The results are shown in according to their Degree values and assigned sizes. The three chemical components that play a major role and rank in the top ten in terms of Degree value are quercetin, stigmasterol, and naringenin; the seven effective targets are PTGS2, ESR1, AR, NCOA2, NOS2, SCN5A, and PTGS1.
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Figure 2: XSLJZT-Active Ingredient-Target” visualization network
3.3:Gene Expression Analysis:
This graph displays the results of an gene expression analysis, comparing two groups of samples (e.g. controls vs disease). It features volcano plots and heat maps as plots to identify difference expressed genes (DEGs).A & B Volcano Plots show the results of the analysis of difference gene expression. Different colors show different genes Red (Up) shows Genes significantly up regulated, Blue (Down) shows Genes down regulated considerably and Gray (Not) shows non significantly statistical genes.
Panel A is the Moderate numbers of genes with strong effects. Panel B shows there are different genes that are strongly up/down regulated (broad and high plot) across greater statistical confidence, potentially due to a larger sample or better biological signal. Panel C & D -DEG Heat maps shows the patterns of gene expression across samples .Panel C shows top differentially expressed genes (dozens) Including gene names & dendrograms (clustering trees).It represents collections of topography (e.g. treatment vs control). Panel D Shows broader set of genes (or whole of relevant DEG's).
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Figure 3:Gene Expression analysis of networks
DC filtering is a process to substitute out down stream genes. In this manner, the figure shows a stepwise filtering of the protein–protein interaction (PPI) network to identify very connected and biologically important hub genes using the measure of degree centrality (DC).Components of the Picture includes Left Panel - Full PPI Network,126 nodes (proteins) and 532 interactions (edges). It is based on differentially expressed genes or genes that are differentially expressed based on databases, e.g. STRING, or Cytoscape. Yellow nodes shows nodes of possible significance or centrality.
Second Filtering: Middle Panel – DC > 6
DC (Degree Centrality): It is the degree of each of its nodes. Threshold applied: DC > 6.It includes 38 nodes and 230 edges. Removed nodes that have less connectivity .Which left us with more connected and functionally relevant genes. Many known regulators such as TP53, RELA, MYC, STAT3, EGFR. Right Panel - Core Hub Network: DC > 11.5.The final filtered network includes 16 nodes and 88 edges. These are hub genes most central and possibly most influential. Genes includes theTP53, AKT1, MAPK1, JUN, STAT3, FOS, EGFR, MYC. These are signature molecules of cell cycle, cancer, apoptosis, and immune regulation. It is here that it goes on to drug design or biomarker discovery.
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Figure 4:Degree Centrality based network reduction
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Figure 5: Protein Protein Interaction Analysis |
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Figure 6: Enrichment analysis of potential targets of XSLJZT A) GO. B) KEGG. |
3.5 KEGG Pathway Analysis:
The analysis shows the visual enumeration of gene-pathway associations taken from KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis. The results shows that
Diagram A Immunity Related Pathways:
What I want to show, Genes (left-column) have associations with immunorelated KEGG processes (right-column). There is a KEGG Immune Signalling Pathway for each the colours shown on the right. Blue shows Toll and Imd signalling pathway ,Green indicates T-cell receptor signalling ,Brown shows Toll-like receptor signalling ,Purple represents the RIG-I-like receptor signalling ,Pink shows IL-17 signalling and Yellow TNF signalling.
Genes, e.g. MAP2K1, RELA, IFIH1, TOLLIP have significance across all. The thickness of the bands are also representation of degree of association or valuation.The inner ring showing that Fold change (from light brown to yellow) indicates to what extent the quantity of gene expression had changed from what was previously observed. p-value (red-blue colour gradient) indicates the level of statistical significance, red = significant.
Diagram B Metabolism and Energy Related Pathways.
Genes (limited to the left-hand column) tend to be mitochondrial gene, or metabolism related.
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Figure 7: KEGG Pathway Enrichment Analysis
3.6 Gene Module Network Analysis:
It is an evaluation of systems biology networks of meaningful genes, signaling pathways, and transcriptional regulators that are present under a biological condition (i.e. disease, treatment response). It consists of panels A-D, each of which presents different insights by applying MCODE clustering and enrichment analysis.
Panel A – Protein-Protein Interaction Network for the MCODE Cluster.
Node = protein/gene, Edges = Known or predicted interactions. Colors shows Distinct MCODE clusters (highly correlated sets of genes). Red (MCODE1) shows largest cluster, Dark red (MCODE2) represent the second cluster and green, Blue, Purple (MCODE3-5) other .Such modules would likely represent core functional units (i.e. signaling circuits or disease modules).
Panel B – Large Scale View of Enlarged MCODE1 Cluster (Red Nodes).
Top genes includes EGFR, IL1B, VEGFA, PTEN, MMP9, CXCL8, etc. Augmented paths, hsa05200 – Pathways in,WP2431 – spinal injury and WP4754 – IL-18 signaling. Values of Log10(P) (e.g. -25.6) are indicative of extreme enrichment (smaller = stronger evidence).
Panel C – Close-up View of Enlarged MCODE2 Cluster (Blue Nodes).
Key genes includes IL2, TNF, STAT1, RELA, CCD2, CXCL10, FN1, CCND1. Pathways enriched are Signaling by interleukins (R-HSA-49147), PID AP1 pathway (M1676) and Cytokine signaling. Panel D show the Enrichment Analysis of Transcription Factor (TF). It shows the Bar chart of strongly acting transcription factors (TFs) for indicated genes of panels A–C.
Major TFs includes RELA, NF-κB1 – central in inflammation
STAT3, JUN, FOS, TP53, SP1 – regulate immunity, cancer, apoptosis
Others (e.g., ESR1, PPARG, HDAC1) regulate metabolism, stress, epigenetics
X-axis: -log10(p-value) → Greater = more statistically significant.
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Figure 8: Network and pathway analysis related to MCODEs
The Mitogen-Activated Protein Kinase (MAPK) pathway is a sequence of proteins that relay signals from cell surface receptors to nuclear DNA. It is a major regulator of cell proliferation, differentiation, apoptosis, stress response, and inflammation. There are three Major Branches in the Process:
1. Classic MAPK/ERK Pathway (Upper Left → Center).
It Stimuli EGF, PDGF, NGF growth factors. Key Steps includes Ligands are bound to Receptor Tyrosine Kinases (RTKs) (e.g. EGFR, FGFR). This activates RAS → RAF (MAPKKK) MEK1/2 (MAPKK) → and finally ERK1/2 (MAPK). ERK enters the nucleus and activates transcription factors such as c-Fos and c-Myc which result in Cell proliferation ,Differentiation and Survival.
2. JNK and p38 MAPK Pathway (Lower Left → Center).
It Stimuli Pro-inflammatory cytokines (TNF, IL-1), stress (UV, toxins), DNA damage. Key Steps includes TNF/FASL interact with their receptors (TNFR, FAS) thereby activating TRAF, ASK1, TAK1. This leads to JNK Pathway activates MKK4/7 → JNK → activates transcription factors c-Jun, ATF-2 that induce apoptosis , inflammation, or cell cycle arrest. p38 activates MKK3/6 → p38 → activates HSP27, MSK1 etc. involved in regulating inflammation, cytokine generation, and apoptosis.
3. ERK5 Pathway (Bottom Right).
It Stimuli Growth factors, oxidative stress. Key Steps includes the activates MEK5 → ERK5. ERK5 controls gene expression related to proliferation, angiogenesis, and differentiation.
Cross-Talk and Other Components
PI3K-Akt Pathway interacts with MAPK signaling, Phospholipase C (PLC) and PKC can activate MAPK components NF-κB pathway is activated by TRAF complexes and controls inflammatory genes. Different colors indicate different things ,Green boxes shows Ligands or key activating molecules (e.g., TNF, FASL),Red boxes shows the Important transcription factors or genes (e.g., c-Fos, TP53)) p Refers to phosphorylation steps and Arrows shows Activation flow ,Dashed lines to DNA Indicate transcriptional control.
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Figure 9: MPAK Signaling Pathway
3.8 Results of molecular docking:
The top nine chemical components and the top nine core targets were docked by the Autodock Vina module, the binding patterns and affinities between the active components and the core targets were visualized by molecular docking. The minimum binding energies of these nine receptors and ligands were collated to obtain the data shown in Table 4, the results were plotted as a heat map (Fig. 4A). It was found that the binding energies of molecular docking were all ≤ −5.0 kcal/mol, suggesting that the binding ability of the active ingredients to the corresponding targets was good. The lowest total binding energy of −78.3 kcal/mol was obtained by docking quercetin with the core target of XSLJZT. AKT1, MAPK14, MAPK1, and MAPK3 showed the best docking effects with the active ingredients, suggesting the synergistic effect of multiple targets.
Finally, some results were visualized for binding energies < -9.0 kcal/mol. The visualized results show that the intermolecular forces are mainly hydrogen bonds.
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Figure 10: Molecular docking analysis of target protei
Many studies have shown that SJZT is a promising formula for treatment of aging related diseases [43–45]. However, the pharmacological mechanism of action of SJZT against aging is not yet fully understood. Thus far, many SJZT network pharmacology studies have been conducted only at the prediction level, and have lacked molecular docking and experimental verification [8, 46]. Hence, in this study, we performed a comprehensive analysis of network pharmacology coupled with in vivo experiments to further identify the underlying mechanisms and therapeutic targets of SJZT in aging effect.
Through a network pharmacology approach, we identified 131 potential active components and 235 hub targets. A compound-target network of SJZT demonstrated that SJZT compounds affected multiple targets. Moreover, SJZT compounds might possess overlapping targets, resulting in synergistic effects. GO enrichment analysis showed that 235 hub targets were enriched in aging and antiapoptotic biological processes. Our data indicated that the significance of the aging terms was extremely high, supporting our previous hypothesis. KEGG pathway enrichment analysis showed that SJZT combated aging through the inhibition of the PI3K-AKT signalling pathway and MAPK signalling pathway. Similar to previous studies [8], our study involved network pharmacological analysis, and PI3K-AKT signalling pathways were identified in the KEGG enrichment analysis. However, Liang et al. [8] did not consider Caco-2 entry in compound screening, and did not carry out molecular docking and biological experimental verification. To identify the active compounds needed for molecular docking, we performed further analysis and found that kaempferol and quercetin play important roles in the effects of SJZT. We found that kaempferol and quercetin bind most closely to the AKT1, STAT3, MAPK14/P38, RELA/p65, and IL6 proteins. The main active compounds (quercetin and kaempferol) have been reported to have direct anti-aging effects. For example, quercetin can downregulate p53, P21 and IL6, which are aging-related markers, to attenuate D-galactose induced aging in rats [47]. In addition, kaempferol decreases the expression levels of STAT3, CDK1, and PI3K/AKT/mTOR and P38MAPK signalling pathway members to inhibit the expression of the SASP [48]. In terms of secondary structure, quercetin has only one more hydroxyl group than kaempferol. They are similar in structure. This explains that the binding
of quercetin and kaempferol are roughly the same on the same protein, but the binding free energy and binding site are not necessarily the same (Supplementary Tables 5, 6). These results show that the strategy of applying network pharmacology to find potential active compounds is reliable and feasible.
In conclusion, the network pharmacological analysis of SJZT identified 131 bioactive compounds and 235 target genes associated with aging. AKT1, STAT3, TP53, RELA/p65, P38MAPK and IL6 were revealed as the core targets of SJZT in anti-aging. According to the results of GO and KEGG enrichment analyses, we verified that SJZT exerts an anti-aging effect in mice by downregulating the P38 MAPK and PI3K-AKT signalling pathways. At the same time, SJZT can improve the aging-related phenotype by inhibiting aging-related markers (Figure 6). However, other signalling pathways (e.g., the Toll-like receptor signalling pathway, and HIF-1 signalling pathway) may also be involved in the anti-aging effect of SJZT. In a follow-up experiment, we will do metabonomic analysis, such as HPLC to determine the active components and potential targets of SJZT.