Research Article

San Huang Decoction as an effective treatment for oral squamous cell carcinoma based on network pharmacology

Bushra Khan 1,* and M. Moshahid A. Rizvi 1,*

1    Department of Biosciences, Faculty of Life Sciences, Jamia Millia Islamia, New Delhi 110025, India.

*    Correspondence: [email protected] (B.K.) and [email protected] (MMAR)

Citation: Khan B and Rizvi MMA.    San Huang Decoction as an effective treatment for oral squamous cell carcinoma based on network pharmacology. Jour. Bas. Sci. 2025, 4(3). 1-17.

Received: January 03, 2025

Revised: January 10, 2025

Accepted: January 19, 2025

Published: January 25, 2025

doi: 10.63454/jbs20000010

ISSN: In Process

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 Abstract: Oral squamous cell carcinoma (OSCC) represents a major global health concern due to its aggressive nature, late-stage diagnosis, and resistance to conventional treatments. San Huang Decoction (SHD) is a traditional Chinese medicine formulation consisting of Astragalus membranaceus (Huangqi), Rheum officinale (Dahuang), and Curcuma longa. However, the specific pharmacological mechanisms underlying SHD’s effects on OSCC remain inadequately explored. This study aims to delineate the therapeutic potential of SHD in the treatment of OSCC. We identified 11 active compounds within SanHuang decoction, including key components such as calycosin, formononetin, curcumol, and kaempferol. By integrating these compounds with gene expression data from the GSE74530 and GSE23558 datasets, we identified 257 differentially expressed genes (DEGs) that overlap with SHD targets and OSCC-specific mutated genes. Pathway enrichment analysis revealed that these DEGs are involved in critical biological processes and pathways such as “Platinum drug resistance,” “EGFR tyrosine kinase inhibitor resistance,” and “PI3K-AKT signaling pathway.” Furthermore, protein-protein interaction (PPI) network analysis identified five key hub genes, including AKT1, MMP9, EGFR, STAT1, and ERBB2, which play pivotal roles in cancer progression, metastasis, and resistant to therapy. Our findings highlight the value of integrating TCM’s holistic principles with the development of novel strategies to enhance immune responses in OSCC patients.

Keywords: Isan Huang Decoction (SHD); Oral Squamous Cell Carcinoma (OSCC); Network Pharmacology; Molecular docking; Chinese medicine

 

1. Introduction

Oral squamous cell carcinoma (OSCC) represents a major global health concern, accounting for a significant percentage of head and neck cancers. The World Health Organization estimates around 377,713 new cases of oral cavity cancers annually, with OSCC comprising the majority of these diagnoses(1). Known for its aggressive behavior, high recurrence rates, and multifaceted etiologies, OSCC significantly impacts public health(2). OSCC is linked to environmental and lifestyle factors, including tobacco and alcohol use, alongside genetic predispositions(3). The disease is often accompanied by comorbidities such as anxiety, depression, and nutritional deficiencies, which further complicate treatment and reduce patient quality of life(4). Standard treatments—surgery, radiation, and chemotherapy—primarily address localized disease but frequently cause significant side effects and exhibit limited efficacy in advanced stages(5). Notably, resistance to conventional therapies is observed in approximately 30% of patients, underscoring the urgent need for innovative strategies(6). Among chemotherapeutics, 5-fluorouracil(5-FU) is widely employed due to its efficacy in inhibiting DNA synthesis and inducing apoptosis in cancer cells(7). Despite its benefits, 5-FU is associated with resistance mechanisms and adverse effects, necessitating the exploration of complementary therapies(8).

Sanhuang Decoction, a well-known TCM formulation, comprises Astragalus membranaceus (Huangqi), Rheum officinale (Dahuang), and Curcuma longa (Jianghuang), and is recognized for its potential anticancer properties(9). The decoction is believed to exert synergistic effects by modulating immune function, reducing inflammation, and improving blood circulation(10). Preliminary studies has demonstrated the ability of SHD to inhibit triple-negative breast cancer (TNBC) by suppressing the phosphorylation of the JAK2-STAT3 signaling pathway, which may be associated with the modulation of lipid metabolism. This effect highlights the potential of SHD as a therapeutic agent in cancer treatment(11). Furthermore, individual herbal components have demonstrated anti-inflammatory, antioxidant, and immune-enhancing properties that collectively contribute to its anticancer potential(12).

Despite its promising attributes, the pharmacological mechanisms of Sanhuang Decoction in OSCC remain underexplored. Tools like molecular docking offer powerful methodologies to study interactions between 5-FU and active herbal compounds, elucidating binding affinities and modes of action(13). Additionally, network pharmacology provides a comprehensive framework for understanding the complex multi-component and multi-target mechanisms characteristic of TCM formulations. By integrating systems biology and pharmacology, network pharmacology highlights how individual compounds within Sanhuang Decoction act synergistically to influence cancer-related pathways(14). Through the identification of active compounds, the construction of disease-related interaction networks, and molecular docking studies, we aim to delineate the mechanisms underlying Sanhuang Decoction’s effects on OSCC. By bridging traditional herbal knowledge with modern scientific approaches, these findings hold promise for improving therapeutic outcomes in OSCC management.

  1. Methods

2.1. Identification of Active Ingredients and Targets for OSCCs: To investigate the active ingredients of Sanhuang Decoction and their therapeutic targets for oral squamous cell carcinoma(OSCC), a systematic methodology was employed. Initially, a comprehensive literature review was conducted utilizing databases such as PubMed, Web of Science, and the China National Knowledge Infrastructure(CNKI). The search utilized keywords including “Sanhuang Decoction,” “active ingredients,” and “oral squamous cell carcinoma” to compile a list of chemical constituents recognized for their pharmacological effects. Following this, the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) was utilized to identify potential targets associated with these active ingredients. The identified target names were subsequently converted to gene symbols using the UniProt database for further analysis. To ensure the selection of viable therapeutic candidates, Lipinski’s rule of five was applied to filter the active compounds based on drug-likeness criteria. Ultimately, 11 active compounds were selected, comprising 10 from Huangqi and one from Dahuang, as illustrated in (Figure 1).

Figure 1. Structures of active compounds in Sanhuang Decoction (SHD).

 

2.2. Identification of Differentially Expressed Genes: To identify differentially expressed genes (DEGs) in OSCC, we employed the GEO2R tool, a user-friendly web application for analyzing gene expression data from the Gene Expression Omnibus (GEO)(15). We analyzed two datasets: GSE74530, which consists of gene expression profiles from oral squamous cell carcinoma (OSCC) tissues obtained using the Affymetrix Human Genome U133 Plus 2.0 Array, and GSE23558, which compares OSCC patients to healthy controls using Agilent microarrays. Both datasets were accessed through the GEO database. Subsequently, the Fragments per Kilobase Million (FPKM) data were normalized to transcripts per kilobase million (TPM) values. For our analysis, we set a significance threshold of adjusted p-value < 0.05 and log2 fold change > 1 to robustly identify significant gene alterations relevant to OSCC pathology (GSE74530: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74530; GSE23558: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE23558).

2.3. Identification of Common Genes Across Gene Expression Datasets and OSCC Mutated Genes: To identify common genes among the gene expression datasets GSE74530, GSE23558, the targets of SHD active compounds, and mutated genes in oral squamous cell carcinoma (OSCC), we utilized Venny 2.0 (https://bioinfogp.cnb.csic.es/tools/venny/index2.0.2.html), an interactive tool designed for visualizing relationships among multiple gene sets. This tool allows users to input lists of gene identifiers, which it processes to generate Venn diagrams that effectively display overlaps and unique elements among the datasets. The mutated genes specific to OSCC were sourced from cBioPortal, facilitating a comprehensive comparison. By employing Venny, we were able to visualize and analyze the common targets across these datasets, thus enhancing our understanding of the molecular underpinnings of OSCC mentioned in (Table 1).

2.4. Pathway Enrichment Analysis: To perform gene enrichment analysis, we utilized the ShinyGO (http://bioinformatics.sdstate.edu/go/). We input a list of common genes into the tool, selecting appropriate organism settings for analysis. The analysis included functional enrichment for Gene Ontology (GO) terms, KEGG pathways, and Reactome pathways. We set significance thresholds at a p-value of <0.05 and employed Benjamini-Hochberg correction for multiple testing. The results were visualized through charts and dot plots, highlighting enriched biological functions and pathways associated with our gene list(16).

2.5. PPI network and hub gene identification: To identify hub genes, we utilized Cytoscape in conjunction with the STRING database and the CytoHubba plugin(17). Initially, we constructed a protein-protein interaction (PPI) network by retrieving interaction data for our common gene targets list from the STRING database, setting a minimum interaction score of 0.4 to ensure reliable connections. The resulting network was imported into Cytoscape for visualization and further analysis. We employed three algorithms available in CytoHubba: EPC (Edge Percolated Component), Degree, and Bottleneck, to evaluate the importance of each node based on their connectivity and centrality measures. The top-ranked genes from these analyses were considered hub genes, representing key regulatory nodes within the network.

2.6. External validation UALCAN: To analyze the expression of hub genes, we utilized the UALCAN tool (https://ualcan.path.uab.edu/), a comprehensive online resource for cancer data analysis. Initially, we accessed the UALCAN platform and selected our cancer type of interest from the left panel. We then input the official symbols of the identified hub genes into the search area and selected the relevant TCGA dataset for analysis. UALCAN provided us with detailed expression profiles of these hub genes across various cancer subtypes and normal tissues, allowing us to visualize differences in expression levels through box plots and heat maps. Additionally, we evaluated survival data associated with these genes to assess their prognostic significance in cancer patients(18).

2.7. Molecular Docking: To perform molecular docking we used CB-Dock2.0 (https://cadd.labshare.cn/cb-dock2/index.php) and Discovery Studio 3.5 (https://discover.3ds.com/discovery-studio-visualizer-download). for analyzing the interactions between ligands and key proteins associated with oral squamous cell carcinoma (OSCC). The initial step involved preparing the protein structures of the hub genes: AKT1 (PDB ID: 6HHG), EGFR (PDB ID: 1WT5), MMP9 (PDB ID: 1L6J), ERBB2 (PDB ID: 2A91), and STAT1 (PDB ID: 8D3F) and ensuring it was free of water molecules and heteroatoms. Using CB-Dock2, we uploaded the protein structure along with the ligand files in MOL2 or SDF format. The tool automatically detected binding cavities and performed blind docking, integrating both structure-based and template-based docking methods to enhance accuracy in binding pose prediction. Subsequently, we utilized Discovery Studio for further analysis and visualization of the docking results, allowing us to assess binding affinities and interactions between the ligand and protein(19).

  1. Results

3.1. Identification of Active Ingredients and Targets for OSCCs: The investigation into the active components of Sanhuang Decoction revealed a total of 150 components from Astragalus membranaceus (Huangqi), 460 from Rheum officinale (Dahuang), and 200 from Curcuma longa (Jianghuang). From this extensive analysis, we focused on selecting 11 key active compounds based on their compliance with Lipinski’s rule of five, which assesses drug-likeness and potential bioavailability. The selected compounds include:(3R)-3-(2-hydroxy-3,4-dimethoxyphenyl)chroman-7-ol,1,7-dihydroxy-3,9 dimethoxypterocarpene, 3,9-di-O-methylnissolin, 7-O-methylisomucronulatol, calycosin, formononetin, isoflavanone, isorhamnetin, Jaranol, kaempferol, and Curcumol. These compounds were chosen for their promising pharmacological profiles and potential therapeutic effects against oral squamous cell carcinoma (OSCC), as indicated by their favorable properties in the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) its compound target network was made as in (Figure 2).

Figure 2. Compound-Target Network of Selected TCM Compounds. Yellow nodes represent Traditional Chinese Medicine (TCM) compounds, and blue nodes represent target proteins. The connections indicate interactions between these compounds and their corresponding protein targets, showcasing the potential multi-target effects of key compounds like isorhamnetin, kaempferol, and isoflavanone.

3.2. Pharmacological Insights into Active Compounds from Astragalus membranaceus and Rheum officinale: We identified a total of 11 active compounds from the herbal components of Sanhuang Decoction, specifically from Astragalus membranaceus (Huangqi) and Rheum officinale (Dahuang). These compounds include (3R)-3-(2-hydroxy-3,4-dimethoxyphenyl)chroman-7-ol, 1,7-dihydroxy-3,9-dimethoxypterocarpene, 3,9-di-O-methylnissolin, 7-O-methylisomucronulatol, calycosin, formononetin, isoflavanone, isorhamnetin, Jaranol, kaempferol, and Curcumol. Each compound exhibits unique molecular characteristics that suggest potential therapeutic benefits. For instance, (3R)-3-(2-hydroxy-3,4-dimethoxyphenyl)chroman-7-ol has a molecular weight of 302.32 g/mol and a logP of 2.57, indicating moderate lipophilicity which may enhance its bioavailability (score: 0.55). Similarly, 1,7-dihydroxy-3,9-dimethoxypterocarpene and 3,9-di-O-methylnissolin possess favourable properties with bioavailability scores of 0.55 and 0.48 respectively. Notably, compounds like isorhamnetin and kaempferol have lower logP values (1.65 and 1.58) but high TPSA values (120.36 Ų and 111.13 Ų), suggesting they may interact effectively with biological targets while maintaining solubility. The presence of multiple hydrogen bond donors and acceptors across these compounds indicates potential for strong molecular interactions within biological systems. Curcumol from Dahuang stands out with a molecular weight of 236.35 g/mol and a logP of 2.90, highlighting its potential as an effective therapeutic agent due to its favorable absorption characteristics (Table 1). Collectively, these findings emphasize the pharmacological potential of the selected compounds in Sanhuang Decoction for treating conditions such as oral squamous cell carcinoma (OSCC), warranting further investigation into their mechanisms of action and clinical efficacy in cancer therapy.

Table 1. The ADME details of the active compounds in SHD used.

S.No Name Source Molecular Weight (g/mol) LogP TPSA (Ų) Bioavailability Score H-bond Donors H-bond Acceptors OB DL
1 (3R)-3-(2-hydroxy-3,4-dimethoxyphenyl)chroman-7-ol Huangqi 302.32 g/mol 2,57 68.15 Ų 0,55 2 5 67,67 0,26
2 1,7-Dihydroxy-3,9-dimethoxypterocarpene Huangqi 314.29 g/mol 2.55 81.29 Ų 0,55 2 6 39,05 0,48
3 3,9-di-O-methylnissolin Huangqi 314.33 g/mol 2,84 46.15 Ų 0,55 0 5 53,74 0,48
4 7-O-methylisomucronulatol Huangqi 316.35 g/mol 2,98 57.15 Ų 0,55 1 5 74,69 0,3
5 calycosin Huangqi 284.26 g/mol 2,3 79.90 Ų 0,55 2 5 47,75 0,24
6 formononetin Huangqi 268.26 g/mol 2,66 59.67 Ų 0,55 1 4 69,67 0,21
7 isoflavanone Huangqi 316.31 g/mol 2,01 85.22 Ų 0,55 2 6 109,99 0,3
8 isorhamnetin Huangqi 316.26 g/mol 1,65 120.36 Ų 0,55 4 7 49,6 0,31
9 Jaranol Huangqi 314.29 g/mol 2,36 89.13 Ų 0,55 2 6 50,83 0,29
10 kaempferol Huangqi 286.24 g/mol 1,58 111.13 Ų 0,55 4 6 41,88 0,24
11 Curcumol Jianghuang 236.35 g/mol 2,9 29.46 Ų 0,55 1 2

 

3.3. Analysis of Common Genes Across OSCC Gene Expression Datasets: The analysis of differentially expressed genes (DEGs) in oral squamous cell carcinoma (OSCC) utilized two datasets, GSE74530 and GSE23558, to identify significant gene alterations associated with the disease. GSE74530 included samples from six OSCC patients and six normal adjacent tissues, revealing 885 upregulated and 315 downregulated genes based on a significance threshold of P < 0.05 and |log2 fold change| ≥ 1.0 (Figure 3A). In contrast, GSE23558, which comprised 27 OSCC patients and five healthy controls, yielded a much larger set of DEGs, with 2805 upregulated and 3863 downregulated genes (Figure 3B). The integration of these datasets using Venny 2.0 facilitated the identification of common genes across the datasets, targets of SHD active compounds, and mutated genes in OSCC (Figure 3C). A total of 257 overlapping genes were found. Notably, three genes were shared among GSE74530, SHD compound targets, and OSCC genes, while 21 genes were common to the compound targets and OSCC genes alone. Additionally, five genes overlapped between GSE74530 and OSCC genes, and ten were shared between GSE23558 and OSCC genes. The datasets also exhibited extensive overlap, with 154 common genes identified between GSE23558 and GSE74530. Among the significant overlapping genes were EPHB2, CDK4, IL18, BCL2, SLC2A1, PIK3CD, MMP7, MMP1, PLAUR, and MTOR (Table 2).

Figure 3. Differential Gene Expression and Overlap in OSCC samples. (A) The figure shows volcano plots for two datasets (GSE74530 and GSE23558), comparing gene expression in tumor versus control samples. Upregulated genes are indicated in red, downregulated genes in blue, and non-significant genes in black. (B) The Venn diagram below displays the overlap of differentially expressed genes between the two datasets, their intersection with compound targets, and OSCC genes, highlighting shared and unique genes across the categories.

Table 2. This table highlights genes identified as common across datasets and categories analyzed via Venn diagrams: GSE23558 and GSE74530: Gene expression datasets. COMPOUNDS TARGET: Genes targeted by therapeutic agents and OSCC GENES: Genes linked to oral squamous cell carcinoma. Each row shows genes shared among two or three datasets, representing significant overlaps relevant to gene function, therapy, or disease pathways.

3 common elements in “GSE74530”, “COMPOUNDS TARGET” and “OSCC GENES”: 21 common elements in “COMPOUNDS TARGET” and “OSCC GENES”: 5 common elements in “GSE74530” and “OSCC GENES”: 10 common elements in “GSE23558” and “OSCC GENES”: 2 common elements in “GSE23558”, “GSE74530” and “OSCC GENES”: 3 common elements in “GSE23558”, “COMPOUNDS TARGET” and “OSCC GENES”: 3 common elements in “GSE23558”, “GSE74530” and “COMPOUNDS TARGET”: 154 common elements in “GSE23558” and “GSE74530”: 27 common elements in “GSE23558” and “COMPOUNDS TARGET”: 30 common elements in “GSE74530” and “COMPOUNDS TARGET”:
EPHB2 CDK4 IL18 BCL2 SLC2A1 PIK3CD SLC6A4 SLITRK5 MME HPGD
MMP7 MMP1 FILIP1L ARHGAP24 PLAUR MMP9 PTK2 CRYM CD38 AR
MMP2 MTOR RECK SOX2 TERT NEK2 CYSLTR1 BCHE PTK6
PIK3CB RASGRP3 HPSE LOX PRKCB AKR1A1
PIK3CG CCL2 KLF4 ZNF254 PDE2A LRRK2
PIK3CA ZNF582 SLC25A23 VCAM1 ACVR1
CASP3 XRCC3 CD24 LNPEP VCP
EGFR PSMD9 GRHL1 MAPT CTSS
AKT1 VEGFA FAM189A2 SPHK1 INSR
MAPK1 FAT1 YOD1 PDE7A MET
CA9 CIART ADAMTS4 IDO1
ABCB1 ABHD5 CDC25B APP
PARP1 PKIB HDAC9 SLC1A3
CYP1A1 FOXE1 HSD17B1 PDE10A
KDM5B ARHGAP20 SLC22A12 EPHB2
TLR9 NFIX CDC25A ADAM10
ERBB2 TNFRSF11A CDK6 KDR
IL2 MPP7 HCRTR1 CA2
TNF CDH11 PIK3CD BMP1
PTGS2 RHOJ PRKCA CSNK1D
MDM2 CNFN AURKB PDGFRB

3.4. Visualization and Interpretation of Hub Gene Connectivity in the PPI Network: The Protein-Protein Interaction (PPI) network analysis, conducted using the STRING database, provided significant insights into the interactions among proteins associated with oral squamous cell carcinoma (OSCC). The constructed network comprised 151 nodes and 551 edges, indicating a dense interaction pattern with an average node degree of 7.3, suggesting that each protein interacts with approximately 7.3 other proteins. This high interconnectivity reflects a robust and complex network, as evidenced by an average local clustering coefficient of 0.494, which indicates a moderate level of clustering where

proteins tend to form tightly interconnected groups.

Statistical analysis revealed that the expected number of edges for a random network of this size was only 258, while the actual network exhibited 551 edges, demonstrating a significantly higher degree of connectivity than would be expected by chance. The PPI enrichment p-value was calculated to be less than 1.0e-16, confirming that the observed interactions are statistically significant and unlikely to have occurred randomly (Figure 4). These findings suggest that the proteins within this network are functionally associated and likely play coordinated roles in OSCC-related biological processes, including tumor progression, signaling pathways, and therapeutic resistance. The substantial number of interactions observed indicates the biological complexity inherent in OSCC and highlights the cooperative nature of these proteins in disease mechanisms. Consequently, these proteins may represent potential targets for therapeutic interventions aimed at improving patient outcomes in OSCC.

Figure 4.  Network Analysis of Key Genes in OSCC. This figure illustrates the identification of hub genes from gene interaction networks generated using Cytoscape. (A) EPC Network (Left): Displays the Enhanced PageRank Centrality (EPC) of genes, highlighting hub genes (e.g., AKT1, MMP9) by color gradients representing their centrality in the network. (B) Degree Network (Middle): Illustrates gene connectivity, pinpointing AKT1 as a prominent hub, indicating its significant role in the network’s structure. (C) Bottleneck Network (Right): Identifies bottleneck genes crucial for maintaining network integrity, with highlighted genes such as AKT1 and MMP9 suggesting potential therapeutic targets.

To identify key regulatory nodes within this network, we employed three algorithms from the CytoHubba plugin: EPC (Edge Percolated Component), Degree, and Bottleneck (Figure 3). These algorithms evaluate the importance of each gene based on its connectivity and centrality within the network. The results from this analysis pinpointed several hub genes, including AKT1, MMP9, EGFR, STAT1, and ERBB2, as the most influential nodes in the network. These hub genes are highly interconnected and play central roles in various cellular processes such as cell signaling, inflammation, and immune response, making them particularly valuable for further investigation. Their identification as hub genes suggests that they could be key targets for therapeutic strategies aimed at modulating these biological pathways.

Figure 5. Protein-Protein Interaction (PPI) Network. This Figure displays a protein-protein interaction (PPI) network generated using STRING. Nodes represent proteins, with node size indicating interaction strength; edges represent established interactions.

 

Figure 6. Functional evaluation. (A) and (B) belong to Gene Ontology terms enrichement analysis and (C) presents KEGG Pathway Enrichment Analysis. This Figure presents the results of gene enrichment analysis, highlighting significant Cellular Component (A), Biological Process (B), and KEGG Pathway enrichments (C). The x-axis represents signal values, while the size of the circles indicates the number of genes associated with each category. The false discovery rate (FDR) is also indicated, demonstrating the statistical significance of each enrichment.

3.5. GO gene enrichment analysis and KEGG pathway annotation: The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed significant biological insights related to oral squamous cell carcinoma (OSCC). In the GO analysis, the term “Response to oxygen-containing compound” was associated with 39 observed genes out of a background of 1547, yielding a strength of 0.52 and a false discovery rate (FDR) of 4.87E-07, with notable proteins including MAPK1, MMP2, IL2, and EGFR. The “Cellular response to organonitrogen compound” term showed 23 observed genes with an FDR of 1.22E-06, highlighting proteins such as MAPK1, MDM2, and PIK3CA. Additionally, “Intracellular signal transduction,” with 36 observed genes and an FDR of 5.71E-06, included key proteins like MAPK1 and EGFR. Other significant terms included “Cellular response to endogenous stimulus,” “Response to organic substance,” and “Regulation of programmed cell death,” each demonstrating substantial gene involvement and low FDRs. In the KEGG pathway analysis, the “Platinum drug resistance” pathway was identified with 9 observed genes and an FDR of 2.25E-06, featuring proteins such as MAPK1, MDM2, and PIK3CA. The “C-type lectin receptor signaling pathway” also emerged as significant, with 10 observed genes and an FDR of 2.25E-06, including MAPK1 and STAT1. Furthermore, the “AGE-RAGE signaling pathway in diabetic complications” showed similar significance with 10 observed genes and an FDR of 2.25E-06. Notably, the “EGFR tyrosine kinase inhibitor resistance” pathway included 8 observed genes with an FDR of 1.02E-05, reinforcing the role of EGFR in therapeutic resistance mechanisms. Overall, these analyses underscore the complex regulatory networks involved in OSCC development and highlight potential therapeutic targets that could be explored for intervention strategies aimed at improving patient outcomes. (Figure 5).

3.6. External validation UALCAN: The mRNA expression and survival analyses for AKT1, MMP9, EGFR, STAT1, and ERBB2 were performed using the UALCAN platform. This analysis revealed that elevated expression levels of these genes are significantly associated with various clinical outcomes in patients with oral squamous cell carcinoma (OSCC) (Figure 7). For AKT1, increased mRNA expression was correlated with poorer overall survival rates, suggesting its role in promoting tumor growth and progression. MMP9 also exhibited similar trends, with high expression levels linked to worse survival outcomes, indicating its involvement in extracellular matrix remodeling and metastasis. EGFR showed a strong association between high expression and reduced survival rates, highlighting its critical role in signaling pathways that drive OSCC progression. STAT1’s expression was similarly linked to unfavorable survival outcomes, suggesting its potential involvement in immune evasion mechanisms within the tumor microenvironment. Finally, ERBB2 was found to have elevated expression levels associated with poor prognosis, reinforcing its significance as a therapeutic target in OSCC (Figure 8).

Figure 7. Expression of Key Genes in Tumor vs. Normal Samples. Boxplots showing the expression levels of (A) MMP9, (B) AKT1, (C) ERBB2, (D) IGF1R, and (E) STAT1 in head and neck squamous cell carcinoma (HNSCC)) samples from TCGA. Blue boxes represent normal samples, while red boxes indicate primary tumor sample.

3.7. Molecular modelling: Docking of the active components Molecular docking studies were conducted to evaluate the binding affinities of various compounds to key proteins associated with oral squamous cell carcinoma (OSCC), specifically AKT1, EGFR, MMP9, STAT1, and ERBB2 (Table 3). The results indicated that the compound (3R)-3-(2-hydroxy-3,4-dimethoxyphenyl)chroman-7-ol exhibited the strongest binding affinity across multiple proteins, with values of -9.2 kcal/mol for AKT1, -8.5 kcal/mol for EGFR, -9.7 kcal/mol for MMP9, -6.7 kcal/mol for STAT1, and -8.2 kcal/mol for ERBB2. In comparison, the standard chemotherapeutic agent 5-fluorouracil (5FU) displayed lower binding affinities of -5.2 kcal/mol for AKT1 and -5.7 kcal/mol for EGFR, indicating that the tested compounds may offer superior binding potential to these targets than 5FU.Among the other compounds tested, Calycosin and formononetin also showed promising affinities, particularly with MMP9 (-10.1 kcal/mol and -9.9 kcal/mol respectively), suggesting their potential as effective inhibitors in OSCC treatment strategies (Figure 9). Overall, the docking results highlight the potential of these natural compounds to interact more favorably with critical proteins involved in OSCC compared to 5FU, which may lead

Figure 8. Kaplan-Meier Survival Curves for Selected Protein Expression Levels in HNSC Patients. Each plot shows the effect of different protein expression levels on BRCA patient survival. The red and blue lines represent high and low expression groups, respectively. The proteins analyzed are (A) ERBB2, (B) EGFR, (C) AKT1, (D) STAT1, and (E) MMP9. p-values are indicated for each plot, showing the statistical significance of survival differences between expression groups. Notably, ERBB2 shows a significant effect on survival with a p-value of 0.023.

to novel therapeutic avenues for enhancing treatment efficacy in this malignancy.

Table 3. It compares the docking scores of active compounds from Sanhuang Decoction with key hub genes (AKT1, EGFR, MMP9, STAT1, and ERBB2) against the chemotherapeutic agent 5-fluorouracil (5-FU).

Protein name PDB ID Compound Binding affinity (kcal/mol)
AKT1 6HHG (3R)-3-(2-hydroxy-3,4-dimethoxyphenyl)chroman-7-ol -9.2 5FU -5.2
1,7-Dihydroxy-3,9-dimethoxypterocarpene -9.1
3,9-di-O-methylnissolin -8.8
7-O-methylisomucronulatol -8.5
Calycosin -6.7
formononetin -6.8
isoflavanone -8.9
isorhamnetin -7.5
Jaranol -6.7
kaempferol -7.7
curcumol -7.7
EGFR (3R)-3-(2-hydroxy-3,4-dimethoxyphenyl)chroman-7-ol -8.5 5FU -5.7
1,7-Dihydroxy-3,9-dimethoxypterocarpene -9
3,9-di-O-methylnissolin -7.2
7-O-methylisomucronulatol -6.5
Calycosin -8.1
formononetin -7.8
isoflavanone -7.7
isorhamnetin -7.6
Jaranol -8.7
kaempferol -9.2
curcumol -6.7
MMP9 (3R)-3-(2-hydroxy-3,4-dimethoxyphenyl)chroman-7-ol -9.7 5FU -6.1
1,7-Dihydroxy-3,9-dimethoxypterocarpene -8.4
3,9-di-O-methylnissolin -9
7-O-methylisomucronulatol -6.8
Calycosin -10.1
formononetin -9.9
isoflavanone -10.3
isorhamnetin -8.8
Jaranol -8
kaempferol -9.2
curcumol -7
STAT1 (3R)-3-(2-hydroxy-3,4-dimethoxyphenyl)chroman-7-ol -6.7 5FU -4.6
1,7-Dihydroxy-3,9-dimethoxypterocarpene -7.1
3,9-di-O-methylnissolin -6.9
7-O-methylisomucronulatol -6.9
Calycosin -6.9
formononetin -7.1
isoflavanone -7.1
isorhamnetin -7.4
Jaranol -6.6
kaempferol -7.1
curcumol -5.9
ERBB2 (3R)-3-(2-hydroxy-3,4-dimethoxyphenyl)chroman-7-ol -8.2 5FU -5.2
1,7-Dihydroxy-3,9-dimethoxypterocarpene -8.3
3,9-di-O-methylnissolin -8
7-O-methylisomucronulatol -7.7
Calycosin -7.8
formononetin -7.8
isoflavanone -7.9
isorhamnetin -9.2
Jaranol -7.7
kaempferol -8.6
curcumol -7.4

 

4. Dicussion: The aggressive nature of oral squamous cell carcinoma (OSCC), its late-stage identification, and its resistance to standard treatments make it a significant health concern. Even though 5-fluorouracil (5-FU) is a commonly used chemotherapeutic drug, tumor resistance, serious side effects, and patient response variability are the reasons for its limited effectiveness(20,21,22,23,24). In an effort to overcome these constraints, recent studies have looked at the processes behind 5-FU resistance. Changes in the enzymes that are in charge of 5-FU metabolism can lead to resistance. For example, resistant cancer cells have been shown to have lower activity of orotate phosphoribosyltransferase (OPRT) and thymidine phosphorylase (TP), which results in a diminished conversion of 5-FU into its active metabolites(25,26,27.28). Resistance is linked to elevated levels of TS, an enzyme that catalyzes the transformation of deoxyuridine monophosphate into thymidine monophosphate. Intrinsic resistance in cancers can result from TS gene amplification and polymorphisms(29,30,31,32,33). In order to withstand the DNA damage caused by 5-FU, cancer cells may improve their capacity for DNA repair. This process has been associated with checkpoint kinase 1 (Chk1) activation, indicating that blocking Chk1 may make resistant cells more sensitive to 5-FU(34,35,36,37). Through the promotion of a more aggressive cancer phenotype, the EMT process can potentially lead to chemoresistance. Chemotherapy frequently results in increased survival and proliferation capacity for cells undergoing EMT(38,39,40,41,42,43). 5-FU resistance has been linked to the presence of cancer stem cells in tumors. It has been demonstrated that elements such phosphorylated c-Fos enhance resistance by promoting stemness traits(44, 45). In individuals with colorectal cancer, resistance to 5-FU and a poor prognosis have been linked to increased expression of extracellular matrix protein 1 (ECM1). Compared to patients who responded well to treatment, resistant individuals had higher ECM1 levels(46, 47). Resistance may result from mutations that impair the mismatch repair (MMR) system’s ability to identify and fix DNA damage brought on by 5-FU. For instance, the misincorporated nucleotides from 5-FU are not recognized in individuals with MLH1 gene loss(48,49,50,51). Cellular stress responses like autophagy and oxidative stress control can also act as mediators of resistance. For example, resistant cells can lessen the harmful effects of 5-FU by upregulating antioxidant enzymes(52,53). Changes that prevent apoptosis, such as elevated levels of anti-apoptotic proteins or decreased expression of pro-apoptotic proteins, are seen in many resistant cancer cells. Through a variety of mechanisms, such as hypoxia-induced alterations that modify drug delivery and boost survival pathways, the tumor microenvironment can have a substantial impact on drug resistance(54,55).

Figure 9. Protein-Ligand Interaction Analysis. Each panel displays a protein-ligand interaction, showing the 3D binding conformation (left) and the corresponding 2D interaction map (right). The binding affinities are labeled in red for each interaction. The proteins and their respective ligands are: AKT1 bound to (3R)-3-(2-hydroxy-3,4-dimethoxyphenyl)chroman-7-ol with a binding affinity of -9.2 kcal/mol, EGFR (1WT5) bound to kaempferol with a binding affinity of -9.2 kcal/mol, MMP9 (1L6J) bound to isoflavanone with a binding affinity of -10.3 kcal/mol, ERBB2 (2A91) bound to isorhamnetin with a binding affinity of -9.2 kcal/mol and STAT1 bound to isorhamnetin with a binding affinity of -7.1 kcal/mol. The 2D diagrams illustrate key molecular interactions such as hydrogen bonds, hydrophobic contacts, and the amino acid residues involved in ligand stabilization. Lower (more negative) binding energies indicate stronger interactions.

Traditional Chinese Medicine (TCM) combines herbal remedies, acupuncture, and other therapies to treat various health conditions. It emphasizes holistic treatment based on the balance of Yin and Yang and the flow of Qi. Network pharmacology is a modern approach that integrates systems biology and pharmacology, aiming to understand how drugs interact within biological networks rather than focusing on single-target interactions. In TCM, network pharmacology helps identify the molecular mechanisms of herbal formulas, uncovering complex interactions between active compounds and biological targets, which provides a more comprehensive understanding of TCM’s therapeutic effects and potential for personalized medicine(56,57,58). A traditional Chinese herbal recipe called SanHuang decoction has drawn interest recently due to its potential as a treatment for a number of illnesses. Curcuma longa, Rheum officinale, and Astragalus membranaceus make up the majority of this decoction, which is known for its detoxifying and heat-clearing qualities. Its efficacy against medication resistance in the treatment of cancer has been demonstrated by recent studies. The ingredients of San Huang Decoction (SHD), a traditional Chinese medicine formulation, include Curcuma longa (Jianghuang), Rheum officinale (Dahuang), and Astragalus membranaceus (Huangqi). Its possible therapeutic benefits for a range of malignancies have been investigated(59,60,61). Here are some conclusions from current research on its application to various cancer kinds. By altering the tumor microenvironment and enhancing patient quality of life during endocrine therapy, SHD has demonstrated potential in the treatment of breast cancer. According to a clinical investigation, patients who received SHD in addition to normal treatment had improved results in terms of inflammatory variables and chronic stress(62). Targeting Aurora Kinase A and downregulating the ERK signaling pathway may help the decoction’s anti-cancer effects by preventing tumor angiogenesis(63). According to network pharmacology research, 39 of SHD’s active components interact with 185 putative targets linked to breast cancer, pointing to a complex mode of action(64).

The precise pharmacological processes by which SHD affects OSCC are still not well understood, despite these encouraging characteristics. Strong procedures for examining interactions between 5-FU and active herbal substances are offered by molecular docking techniques, which also help to clarify binding affinities and mechanisms of action. Additionally, network pharmacology provides a thorough framework for comprehending the intricate multi-target and multi-component mechanisms found in formulations used in traditional Chinese medicine. In this study, we demonstrated how individual substances within SHD function in concert to impact cancer-related pathways by combining systems biology with pharmacology, specifically network pharmacology. In order to comprehend the therapeutic potential of Sanhuang Decoction (SHD) in the treatment of oral squamous cell carcinoma (OSCC), this study provides an integrative approach. By combining the principles of Traditional Chinese Medicine (TCM) with contemporary network pharmacology, gene expression analysis, and molecular docking, we have shed light on the multi-target mechanisms underlying the anticancer effects of SHD.

Eleven active chemicals were found in SHD by our analysis, including important constituents like kaempferol, curcumol, formononetin, and calycosin. These substances showed great promise in modifying pathways linked to OSCC and were chosen based on their drug-likeness characteristics. These chemicals were combined with gene expression data from the GSE74530 and GSE23558 datasets, and we found 257 DEGs that coincide with mutant genes specific to OSCC and SHD targets. According to pathway enrichment analysis, these DEGs are implicated in important biological pathways and processes, including “PI3K-AKT signaling pathway,” “EGFR tyrosine kinase inhibitor resistance,” and “Platinum drug resistance.” These results imply that SHD can be useful in overcoming typical chemoresistance mechanisms seen in patients with OSCC. Additionally, five important hub genes—AKT1, MMP9, EGFR, STAT1, and ERBB2—that are crucial for cancer progression, metastasis, and treatment resistance were found by our protein-protein interaction (PPI) network analysis. Strong binding affinities between the active components of SHD and these hub proteins were confirmed by molecular docking experiments, pointing to a possible mechanism through which SHD has anticancer effects. Our results demonstrate the possibility of SHD as an adjunctive treatment approach for patients with OSCC, especially those who show resistance to traditional treatments like 5-FU. Future clinical research aiming at confirming the effectiveness and safety of SHD in human populations will be based on the identification of important pathways and hub genes. Furthermore, to fully comprehend the therapeutic potential of SanHuang decoction’s active ingredients and maximize their clinical uses, further research into their pharmacokinetics and bioavailability is essential. Clarifying how these substances are absorbed, transported, metabolized, and eliminated in the body is crucial for choosing the right dosage plans and optimizing effectiveness while reducing adverse effects, therefore such research should try to accomplish just that. It is also crucial to comprehend the synergistic effects of combining SanHuang decoction with currently available chemotherapeutic agents. According to early research, the decoction may increase the cytotoxic effects of traditional therapies, possibly enabling lower dosages and fewer side effects. Furthermore, investigating the immunomodulatory effects of SanHuang decoction can provide further information about its potential therapeutic advantages. Combining immunotherapy with TCM’s holistic principles may offer new ways to boost immune responses in OSCC patients, as the immune system’s function in cancer treatment is becoming more widely acknowledged. The complex biological networks impacted by SanHuang decoction may be revealed by combining systems biology with conventional herbal therapy using cutting-edge techniques like single-cell sequencing and proteomics. The discovery of novel therapeutic targets and combination tactics that make use of both conventional and traditional methods may result from this information. In the end, it is impossible to overestimate the promise of SanHuang decoction as a comprehensive therapy approach for OSCC. We may gain a more thorough grasp of its workings and uses by combining conventional wisdom with state-of-the-art scientific study. In addition to respecting the tenets of Traditional Chinese Medicine, this integrative approach is in line with contemporary therapeutic paradigms that strive to improve patient outcomes when treating oral squamous cell carcinoma, especially when traditional therapies have failed. As the area develops, more research and cooperation between conventional and contemporary medical procedures could result in discoveries that greatly enhance the quality of life for people with OSCC and other difficult cancers.

5. Conclusions: Oral squamous cell carcinoma (OSCC) poses significant challenges in treatment due to its aggressive nature and resistance to conventional therapies. The exploration of Sanhuang Decoction as a complementary therapeutic strategy offers promising insights into overcoming these challenges, particularly through its multi-target mechanisms and potential to enhance the efficacy of existing treatments like 5-fluorouracil.

Author Contributions: Conceptualization, B.K.; methodology, B.K.; software, B.K.; validation, B.K., and M.M.A.R.; formal analysis, B.K.; investigation, B.K., and M.M.A.R.; resources, B.K., and M.M.A.R.; data curation, B.K.; writing—original draft preparation, B.K., and M.M.A.R.; writing—review and editing, B.K., and M.M.A.R.; visualization, B.K.; supervision, B.K., and M.M.A.R.; project administration, B.K., and M.M.A.R.; funding acquisition, B.K., and M.M.A.R. All authors have read and agreed to the published version of the manuscript.

Funding: Bushra Khan has received the DBT (New Delhi, India) fellowship support to carry out the designed PhD.

Acknowledgments: We are grateful to the Department of Biosciences, Jamia Millia Islamia, New Delhi, India for providing us all the facilities to carry out the entire work.

Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.

Data Availability Statement: All the related data are supplied in this work or have been referenced properly.

References

  1. Tan Y, Wang Z, Xu M, Li B, Huang Z, Qin S, et al. Oral squamous cell carcinomas: state of the field and emerging directions. Int J Oral Sci. 2023 Sep 22;15(1):44.
  2. Rezazadeh F, Andisheh-Tadbir A, Malek Mansouri Z, Khademi B, Bayat P, Sedarat H, et al. Evaluation of recurrence, mortality and treatment complications of oral squamous cell carcinoma in public health centers in Shiraz during 2010 to 2020. BMC Oral Health. 2023 May 30;23(1):341.
  3. Eloranta R, Vilén ST, Keinänen A, Salo T, Qannam A, Bello IO, et al. Oral squamous cell carcinoma: Effect of tobacco and alcohol on cancer location. Tob Induc Dis. 2024 Jun 18;22(June):1–9.
  4. Speksnijder CM, Lankhorst PJM, de Bree R, de Haan AFJ, Koole R, Merkx MAW. Depression and related factors after oral oncological treatment: a 5-year prospective cohort study. Support Care Cancer Off J Multinatl Assoc Support Care Cancer. 2021 Jun;29(6):2907–16.
  5. Day TA, Davis BK, Gillespie MB, Joe JK, Kibbey M, Martin-Harris B, et al. Oral cancer treatment. Curr Treat Options Oncol. 2003 Jan;4(1):27–41.
  6. Picon H, Guddati AK. Mechanisms of resistance in head and neck cancer. Am J Cancer Res. 2020;10(9):2742–51.
  7. Ghafouri-Fard S, Abak A, Tondro Anamag F, Shoorei H, Fattahi F, Javadinia SA, et al. 5-Fluorouracil: A Narrative Review on the Role of Regulatory Mechanisms in Driving Resistance to This Chemotherapeutic Agent. Front Oncol. 2021;11:658636.
  8. Harada K, Ferdous T, Ueyama Y. Establishment of 5-fluorouracil-resistant oral squamous cell carcinoma cell lines with epithelial to mesenchymal transition changes. Int J Oncol. 2014 Apr;44(4):1302–8.
  9. Zhang S, Wang P, Shi X, Tan H. Inhibitory properties of Chinese Herbal Formula SanHuang decoction on biofilm formation by antibiotic-resistant Staphylococcal strains. Sci Rep. 2021 Mar 30;11(1):7134.
  10. Liu Z, Wang W, Luo J, Zhang Y, Zhang Y, Gan Z, et al. Anti-Apoptotic Role of Sanhuang Xiexin Decoction and Anisodamine in Endotoxemia. Front Pharmacol. 2021 Apr 21;12:531325.
  11. Qi Y, Wu X jie, Shi J bin, Shi X wei, Zhao N, Xiong Y, et al. Sanhuang Xiexin Decoction Ameliorates TNBC By Modulating JAK2-STAT3 and Lipid Metabolism. Chin J Integr Med. 2024 Dec;30(12):1080–9.
  12. Miao K, Liu W, Xu J, Qian Z, Zhang Q. Harnessing the power of traditional Chinese medicine monomers and compound prescriptions to boost cancer immunotherapy. Front Immunol. 2023 Nov 15;14:1277243.
  13. Chao S, Liu S, Wang K, Xie M, Liu B. Network pharmacological evaluation of active ingredients and potential targets of San Huang Decoction in the treatment of breast cancer [Internet]. Bioinformatics; 2024 [cited 2024 Dec 4]. Available from: http://biorxiv.org/lookup/doi/10.1101/2024.06.20.599797
  14. Song S, Zhou J, Li Y, Liu J, Li J, Shu P. Network pharmacology and experimental verification based research into the effect and mechanism of Aucklandiae Radix–Amomi Fructus against gastric cancer. Sci Rep. 2022 Jun 7;12(1):9401.
  15. Agapito G, Milano M, Cannataro M. A statistical network pre-processing method to improve relevance and significance of gene lists in microarray gene expression studies. BMC Bioinformatics. 2022 Sep 27;23(S6):393.
  16. Ge SX, Jung D, Yao R. ShinyGO: a graphical gene-set enrichment tool for animals and plants. Valencia A, editor. Bioinformatics. 2020 Apr 15;36(8):2628–9.
  17. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019 Jan 8;47(D1):D607–13.
  18. Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi BVSK, et al. UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia. 2017 Aug;19(8):649–58.
  19. Liu Y, Yang X, Gan J, Chen S, Xiao ZX, Cao Y. CB-Dock2: improved protein–ligand blind docking by integrating cavity detection, docking and homologous template fitting. Nucleic Acids Res. 2022 Jul 5;50(W1):W159–64.
  20. Tan Y, Wang Z, Xu M, Li B, Huang Z, Qin S, et al. Oral squamous cell carcinomas: state of the field and emerging directions. Int J Oral Sci. 2023 Sep 22;15(1):44.
  21. Zhang X, Sun K, Gan R, Yan Y, Zhang C, Zheng D, et al. WNT3 promotes chemoresistance to 5-Fluorouracil in oral squamous cell carcinoma via activating the canonical β-catenin pathway. BMC Cancer. 2024 May 6;24(1):564.
  22. Feng X, Luo Q, Zhang H, Wang H, Chen W, Meng G, et al. The role of NLRP3 inflammasome in 5-fluorouracil resistance of oral squamous cell carcinoma. J Exp Clin Cancer Res. 2017 Dec;36(1):81.
  23. Pires FR, Ramos AB, Oliveira JBC de, Tavares AS, Luz PSR da, Santos TCRB dos. Oral squamous cell carcinoma: clinicopathological features from 346 cases from a single oral pathology service during an 8-year period. J Appl Oral Sci Rev FOB. 2013;21(5):460–7.
  24. Bugshan A, Farooq I. Oral squamous cell carcinoma: metastasis, potentially associated malignant disorders, etiology and recent advancements in diagnosis. F1000Research. 2020 Apr 2;9:229.
  25. Mori R, Yoshida K, Futamura M, Suetsugu T, Shizu K, Tanahashi T, et al. The inhibition of thymidine phosphorylase can reverse acquired 5FU-resistance in gastric cancer cells. Gastric Cancer Off J Int Gastric Cancer Assoc Jpn Gastric Cancer Assoc. 2019 May;22(3):497–505.
  26. Hozumi Y, Tanaka T, Nakano T, Matsui H, Nasu T, Koike S, et al. Orotate phosphoribosyltransferase localizes to the Golgi complex and its expression levels affect the sensitivity to anti-cancer drug 5-fluorouracil . Biomed Res. 2015;36(6):403–9.
  27. Isshi K, Sakuyama T, Gen T, Nakamura Y, Kuroda T, Katuyama T, et al. Predicting 5-FU sensitivity using human colorectal cancer specimens: comparison of tumor dihydropyrimidine dehydrogenase and orotate phosphoribosyl transferase activities with in vitro chemosensitivity to 5-FU. Int J Clin Oncol. 2002 Dec 1;7(6):335–42.
  28. Elamin YY, Rafee S, Osman N, O Byrne KJ, Gately K. Thymidine Phosphorylase in Cancer; Enemy or Friend? Cancer Microenviron Off J Int Cancer Microenviron Soc. 2016 Apr;9(1):33–43.
  29. Hashimoto H, Ozeki Y, Sato M, Obara K, Matsutani N, Nakagishi Y, et al. Significance of thymidylate synthase gene expression level in patients with adenocarcinoma of the lung. Cancer. 2006 Apr;106(7):1595–601.
  30. Peters GJ, Backus HHJ, Freemantle S, Van Triest B, Codacci-Pisanelli G, Van Der Wilt CL, et al. Induction of thymidylate synthase as a 5-fluorouracil resistance mechanism. Biochim Biophys Acta BBA – Mol Basis Dis. 2002 Jul;1587(2–3):194–205.
  31. Kurasaka C, Nishizawa N, Ogino Y, Sato A. Trapping of 5-Fluorodeoxyuridine Monophosphate by Thymidylate Synthase Confers Resistance to 5-Fluorouracil. ACS Omega. 2022 Feb 22;7(7):6046–52.
  32. Banerjee D, Mayer-Kuckuk P, Capiaux G, Budak-Alpdogan T, Gorlick R, Bertino JR. Novel aspects of resistance to drugs targeted to dihydrofolate reductase and thymidylate synthase. Biochim Biophys Acta BBA – Mol Basis Dis. 2002 Jul;1587(2–3):164–73.
  33. Sakamoto S, Murakami S, Sugawara M, Mishima Y, Okamoto R. Increased Activities of Thymidylate Synthetase and Thymidine Kinase in Human Thyroid Tumors. Thyroid. 1991 Jan;1(4):347–51.
  34. Zhang Y, Hunter T. Roles of Chk1 in cell biology and cancer therapy. Int J Cancer. 2014 Mar;134(5):1013–23.
  35. Akasaka T, Tsujii M, Kondo J, Hayashi Y, Ying J, Lu Y, et al. 5-FU resistance abrogates the amplified cytotoxic effects induced by inhibiting checkpoint kinase 1 in p53-mutated colon cancer cells. Int J Oncol. 2015 Jan;46(1):63–70.
  36. Martino-Echarri E, Henderson BR, Brocardo MG. Targeting the DNA replication checkpoint by pharmacologic inhibition of Chk1 kinase: a strategy to sensitize APC mutant colon cancer cells to 5-fluorouracil chemotherapy. Oncotarget. 2014 Oct 30;5(20):9889–900.
  37. Lin AB, McNeely SC, Beckmann RP. Achieving Precision Death with Cell-Cycle Inhibitors that Target DNA Replication and Repair. Clin Cancer Res. 2017 Jul 1;23(13):3232–40.
  38. Huang Y, Hong W, Wei X. The molecular mechanisms and therapeutic strategies of EMT in tumor progression and metastasis. J Hematol OncolJ Hematol Oncol. 2022 Sep 8;15(1):129.
  39. Liaghat M, Ferdousmakan S, Mortazavi SH, Yahyazadeh S, Irani A, Banihashemi S, et al. The impact of epithelial-mesenchymal transition (EMT) induced by metabolic processes and intracellular signaling pathways on chemo-resistance, metastasis, and recurrence in solid tumors. Cell Commun Signal. 2024 Dec 2;22(1):575.
  40. Jiang J, Wang K, Chen Y, Chen H, Nice EC, Huang C. Redox regulation in tumor cell epithelial–mesenchymal transition: molecular basis and therapeutic strategy. Signal Transduct Target Ther. 2017 Aug 18;2(1):17036.
  41. Harada K, Ferdous T, Ueyama Y. Establishment of 5-fluorouracil-resistant oral squamous cell carcinoma cell lines with epithelial to mesenchymal transition changes. Int J Oncol. 2014 Apr;44(4):1302–8.
  42. Zhang W, Feng M, Zheng G, Chen Y, Wang X, Pen B, et al. Chemoresistance to 5-fluorouracil induces epithelial–mesenchymal transition via up-regulation of Snail in MCF7 human breast cancer cells. Biochem Biophys Res Commun. 2012 Jan;417(2):679–85.
  43. Harada K, Ferdous T, Ueyama Y. Establishment of 5-fluorouracil-resistant oral squamous cell carcinoma cell lines with epithelial to mesenchymal transition changes. Int J Oncol. 2014 Apr;44(4):1302–8.
  44. Muhammad N, Bhattacharya S, Steele R, Phillips N, Ray RB. Involvement of c-Fos in the Promotion of Cancer Stem-like Cell Properties in Head and Neck Squamous Cell Carcinoma. Clin Cancer Res Off J Am Assoc Cancer Res. 2017 Jun 15;23(12):3120–8.
  45. Gui Y, Qian X, Ding Y, Chen Q, Fangyu Ye, Ye Y, et al. c-Fos regulated by TMPO/ERK axis promotes 5-FU resistance via inducing NANOG transcription in colon cancer. Cell Death Dis. 2024 Jan 17;15(1):61.
  46. Long S, Wang J, Weng F, Pei Z, Zhou S, Sun G, et al. ECM1 regulates the resistance of colorectal cancer to 5-FU treatment by modulating apoptotic cell death and epithelial-mesenchymal transition induction. Front Pharmacol. 2022;13:1005915.
  47. Long S, Wang J, Weng F, Xiang D, Sun G. Extracellular Matrix Protein 1 Regulates Colorectal Cancer Cell Proliferative, Migratory, Invasive and Epithelial-Mesenchymal Transition Activities Through the PI3K/AKT/GSK3β/Snail Signaling Axis. Front Oncol. 2022 Apr 27;12:889159.
  48. Carethers JM, Chauhan DP, Fink D, Nebel S, Bresalier RS, Howell SB, et al. Mismatch repair proficiency and in vitro response to 5-fluorouracil. Gastroenterology. 1999 Jul;117(1):123–31.
  49. Incorvaia L, Bazan Russo TD, Gristina V, Perez A, Brando C, Mujacic C, et al. The intersection of homologous recombination (HR) and mismatch repair (MMR) pathways in DNA repair-defective tumors. Npj Precis Oncol. 2024 Sep 5;8(1):190.
  50. Fischer F, Baerenfaller K, Jiricny J. 5-Fluorouracil Is Efficiently Removed From DNA by the Base Excision and Mismatch Repair Systems. Gastroenterology. 2007 Dec;133(6):1858–68.
  51. Manzoor S, Saber-Ayad M, Maghazachi AA, Hamid Q, Muhammad JS. MLH1 mediates cytoprotective nucleophagy to resist 5-Fluorouracil-induced cell death in colorectal carcinoma. Neoplasia N Y N. 2022 Feb;24(2):76–85.
  52. Chun KS, Joo SH. Modulation of Reactive Oxygen Species to Overcome 5-Fluorouracil Resistance. Biomol Ther. 2022 Nov 1;30(6):479–89.
  53. Emran TB, Shahriar A, Mahmud AR, Rahman T, Abir MH, Siddiquee MohdFR, et al. Multidrug Resistance in Cancer: Understanding Molecular Mechanisms, Immunoprevention and Therapeutic Approaches. Front Oncol. 2022 Jun 23;12:891652.
  54. Mohammad RM, Muqbil I, Lowe L, Yedjou C, Hsu HY, Lin LT, et al. Broad targeting of resistance to apoptosis in cancer. Semin Cancer Biol. 2015 Dec;35:S78–103.
  55. Shahar N, Larisch S. Inhibiting the inhibitors: Targeting anti-apoptotic proteins in cancer and therapy resistance. Drug Resist Updat. 2020 Sep;52:100712.
  56. Matos LC, Machado JP, Monteiro FJ, Greten HJ. Understanding Traditional Chinese Medicine Therapeutics: An Overview of the Basics and Clinical Applications. Healthc Basel Switz. 2021 Mar 1;9(3):257.
  57. Ma D, Wang S, Shi Y, Ni S, Tang M, Xu A. The development of traditional Chinese medicine. J Tradit Chin Med Sci. 2021 Nov;8:S1–9.
  58. Zhang Q, Yu H, Qi J, Tang D, Chen X, Wan J bo, et al. Natural formulas and the nature of formulas: Exploring potential therapeutic targets based on traditional Chinese herbal formulas. Csermely P, editor. PLOS ONE. 2017 Feb 9;12(2):e0171628.
  59. Xu B, Zhang J, Ye L, Yuan C. Chinese herbal compound SanHuang decoction reverses axitinib resistance in ccRCC through regulating immune cell infiltration by affecting ADAMTS18 expression. Am J Cancer Res. 2023;13(7):2841–60.
  60. Wang S, Zhou T, Zhai J peng, Wang L hua, Chen J. Effects of Modified Sanhuang Decoction (加味三黄汤) enema on serum tumor necrosis factor-α and colonic mucosa interleukin-1β, interleukin-6 levels in ulcerative colitis rats. Chin J Integr Med. 2014 Nov;20(11):865–9.
  61. Han Z, Tan X, Sun J, Wang T, Yan G, Wang C, et al. Systems pharmacology and transcriptomics reveal the mechanisms of Sanhuang decoction enema in the treatment of ulcerative colitis with additional Candida albicans infection. Chin Med. 2021 Dec;16(1):75.
  62. Feng M, Wang H, Zhu Z, Yao B, Li Y, Xue J, et al. Sanhuang Decoction Controls Tumor Microenvironment by Ameliorating Chronic Stress in Breast Cancer: A Report of Ninety Cases. Front Oncol. 2021;11:677939.
  63. Xu Y, Wang C, Chen X, Li Y, Bian W, Yao C. San Huang Decoction Targets Aurora Kinase A to Inhibit Tumor Angiogenesis in Breast Cancer. Integr Cancer Ther. 2020 Jan;19:1534735420983463.
  64. Chao S, Liu S, Wang K, Xie M, Liu B. Network pharmacological evaluation of active ingredients and potential targets of San Huang Decoction in the treatment of breast cancer [Internet]. Bioinformatics; 2024 [cited 2025 Jan 12]. Available from: http://biorxiv.org/lookup/doi/10.1101/2024.06.20.599797

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