Abstract
Oral squamous cell carcinoma (OSCC) is a tumor type with a high mortality rate. Chlorogenic acid, abundant in resources and widely utilized in cancer treatments, has seen limited studies regarding its efficacy against OSCC. This paper investigates chlorogenic acid’s mechanism in treating OSCC, aiming to guide the development of novel drugs. The study employed network pharmacology, molecular docking, and survival analysis methods. Network pharmacological analysis revealed chlorogenic acid targets 23 OSCC-related proteins, including ESR1, MMP2, MMP9, SRC, MAPK8, MAPK1, CDC42, ERBB2, ATM, and BRAF. Molecular docking simulations indicated that the primary target exhibits significant binding capacity with chlorogenic acid, with MMP9 associated with tumor migration and angiogenesis standing out. Survival analysis demonstrated that the downregulation of most primary targets correlates with improved survival rates in OSCC patients. Enrichment analysis of therapeutic targets highlighted the pivotal role of MAPK-ERK and MAPK-JNK signaling pathways in chlorogenic acid’s efficacy against OSCC. This paper predicts chlorogenic acid’s potential targets and proposes its molecular mechanism in treating OSCC, offering a theoretical foundation for its application in OSCC treatment. We used traditional Chinese medicine, a disease pharmacology-related information base, and an analysis platform to predict targets. The Cytoscape 3.9.1 and STING databases were used to address common targets for drugs and diseases, establish networks of protein interaction relationships, and screen core targets. Meastro11.5 was used for molecular docking simulation. R4.2.2 was used for survival analysis and joint target enrichment analysis. Network pharmacological analysis identified chlorogenic acid acting on 23 OSCC targets. Molecular docking simulations revealed a strong binding affinity of chlorogenic acid compounds with these targets, particularly MMP9, essential for tumor migration and angiogenesis. Survival analysis indicated that the downregulation of most core targets was correlated with improved OSCC patient survival. Enrichment analysis of therapeutic targets highlighted the critical roles of the MAPK-ERK and MAPK-JNK signaling pathways in the effectiveness of chlorogenic acid against OSCC. This study predicted the potential targets of chlorogenic acid in OSCC treatment and hypothesized its molecular mechanism, offering a theoretical foundation for its use in OSCC therapy.
Keywords: chlorogenic acid, MAPK-ERK signaling pathway, MAPK-JNK signaling pathway, network pharmacology, oral squamous cell carcinoma
1. Introduction
Oral squamous cell carcinoma (OSCC) constitutes 90% of head and neck squamous cell carcinoma cases, exhibiting the highest incidence and mortality rates in Asia compared to other regions. The prevalence of OSCC is strongly linked to lifestyle habits, including betel nut chewing, excessive alcohol consumption, and smoking, which are prevalent in South Asia.[1] It primarily affects the tonsils and tongue of middle-aged men.[2] The standard OSCC treatment involves primary tumor resection, optionally accompanied by lymph node dissection, alongside chemotherapy and radiotherapy.[3,4] However, the effectiveness of surgical treatments is compromised by resistance to chemotherapy agents such as cisplatin (CDDP) and 5-fluorouracil, leading to unsatisfactory outcomes and a high recurrence rate.[5] This situation exacerbates the survival prospects for advanced patients with tumor metastasis, reducing their survival rate to only 34%.[6] Additionally, the absence of clinical diagnostic evidence in early-stage patients delays treatment, resulting in low survival rates, significant disability, and deteriorated quality of life among survivors. Thus, identifying new drugs and therapeutic strategies is crucial.
Chlorogenic acid, a phenolic acid compound series, is found in various plants, notably in coffee and tea.[7] It is also a key active component in numerous Chinese medicinal herbs, like honeysuckle and Eucommia, where it is utilized for its heat-clearing and detoxifying properties. Chlorogenic acid exhibits a broad spectrum of pharmacological properties, including anti-cancer, anti-diabetes, anti-obesity, anti-oxidant, anti-inflammatory, anti-hypertensive, antibacterial, and immune-modulating effects, making it widely applicable in food and healthcare.[8,9] Despite its benefits, the complex extraction and purification processes, poor drug stability, debates over bioavailability versus gut microbiota interactions, and potential allergic reactions to injections necessitate further clinical research and development for medicinal applications.[10,11] Its anticancer potential may stem from inhibiting cell proliferation, reducing cell viability, preventing cell invasion, enhancing cytotoxicity, and disrupting clonogenic capacity.[12] Jiang et al[13] showed that the inhibitory effect of chlorogenic acid on human oral squamous cell carcinoma cells might be due to its oxidation-mediated cytotoxic effects. It also suppresses OSCC (KB) cell proliferation by downregulating p53 and p21.[14] Current research on chlorogenic acid’s efficacy in treating OSCC is scant, highlighting the need for further investigation.
This paper employs a network pharmacology approach in response to this context, utilizing bioinformatics tools and systems biology principles 9 to predict chlorogenic acid’s therapeutic direction for OSCC treatment.[15] It aims to holistically investigate the compound’s molecular treatment mechanisms, facilitating the rapid development of novel therapeutic options for OSCC patients through in silico experiments.
2. Materials and materials
2.1. Target gene prediction of oral squamous cell carcinoma (OSCC)
Target genes for OSCC were retrieved from the GeneCards and OMIM platforms. GeneCards, a comprehensive genetic database, amalgamates information from approximately 150 gene-centric databases. “Oral squamous cell carcinoma” served as the search keyword. The GeneCards dataset was filtered using a score > 50, with higher scores indicating greater relevance to the search term. The genes identified from GeneCards and those obtained from OMIM were designated as the final disease target genes (see Table S1, Supplemental Digital Content, http://links.lww.com/MD/N777, which explains the details of the website information).
2.2. Prediction of chlorogenic acid target gene
Initially, target genes of chlorogenic acid were sourced directly from the Therapeutic Target Database, ChEMBL Database, HERB Database, and PharmMapper Database, employing an activity threshold > 6.5 and zscore > 0.6 for target gene screening. Subsequently, the PubChem database facilitated the acquisition of the 2D structure of small drug molecules, with target genes identified via SwissTargetPrediction. Genes selected from various database sources were consolidated as target genes for chlorogenic acid, with all databases specifying “Homo sapiens” as the species criterion.
2.3. Construction and analysis of composition-target-disease network map
Critical genes for chlorogenic acid treatment of OSCC were identified by selecting common genes between drug and disease target genes. A Venn diagram, created through the Weisheng platform, visualized the expected targets, which were then utilized for further data analysis. The component-target-disease network’s relationships were directly examined against drug and disease target datasets through Cytoscape visualization.
2.4. Construction of protein–protein interaction network (PPI)
The protein–protein interaction (PPI) network was derived from the STRING database, a resource built upon public databases and literature information, providing visualizations of protein interaction networks, protein families, and subcellular localization. Common targets were input as a Multiple proteins list, with “Homo sapiens” specified as the species and a medium-level confidence score (>0.400) selected to acquire protein interaction network data for the analysis of core target data.
2.5. Screening and enrichment of core targets
Protein interaction networks were analyzed with the CytoNCA plugin in Cytoscape, arranging the core targets by descending betweenness values to identify the top 10 genes. The R packages clusterProfiler, enrichplot, ggplot2, ggnewscale, DOSE, and stringr (R × 64 4.2.2) facilitated the enrichment of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis for core target genes. GO analysis was segmented into biological process (BP), cellular component (CC), and molecular function (MF). Key pathways from KEGG analysis results were visualized using the Weisheng platform.
2.6. Kaplan–Meier analysis of core target
Due to the unavailability of OSCC-specific targets in the The Cancer Genome Atlas (TCGA) database, OSCC and core target information were compiled using R from the relevant gene target data for head and neck squamous cell carcinoma in TCGA for further analysis. The R packages “survival” and “survminer” analyzed survival and generated Kaplan–Meier curves. The study focused on Overall Survival with a Group Cutoff set at 0.5, thereby visualizing the survival analysis for core targets of chlorogenic acid treatment in OSCC.
2.7. Molecular docking
Molecular docking simulations were executed in Maestro (2018). Three-dimensional structures of protein complex crystals for the 10 core targets in chlorogenic acid treatment for OSCC were downloaded from the PDB database. The small molecule structure of chlorogenic acid was sourced from the PubChem database and subjected to energy minimization in Chem3D to select the optimal conformation. These target complex protein crystals and chlorogenic acid small molecules were then processed in Maestro for docking simulation, yielding molecular docking data.
2.8. Statistical analysis
Survival differences among groups were assessed using the log-rank test and depicted via Kaplan–Meier (KM) survival maps. All P values were derived from 2-sided statistics, with a P value < .05 deemed statistically significant. Bioinformatics data were analyzed using R × 64 4.2.2, Cytoscape 3.9.1, the GEPIA online platform, and the Microbioinformatics online mapping tool.
3. Results
3.1. Chlorogenic acid and potential targets for oral squamous cell carcinoma (OSCC)
Two hundred potential targets of chlorogenic acid were identified through the Therapeutic Target Database, ChEMBL Database, HERB database, PharmMapper database, and SwissTargetPrediction screening (see Table S2, Supplemental Digital Content, http://links.lww.com/MD/N777, which explains chlorogenic acid related targets). These targets include estrogen receptor 1 (ESR1), human epidermal growth factor receptor 2 (ErbB2), and matrix metallopeptidase 2 (MMP2), among others. The “compound - drug prediction target” network diagram (Fig. 1A) visually represents potential targets for chlorogenic acid-binding. OSCC-related genes were sourced from OMIM and GeneCards using “oral squamous cell carcinoma” as the search keyword. The OMIM search yielded 300 relevant targets, while GeneCards provided 5490 relevant targets. From these, targets with correlation scores above 50 were selected as relevant to OSCC (Fig. 1B). After combining these datasets and removing duplicates, 430 OSCC potential targets were identified as potential disease targets.
Figure 1.
Maps of potential targets for drugs and diseases.
3.2. Screening of core targets for chlorogenic acid treatment of OSCC
Twenty three intersecting genes between chlorogenic acid and OSCC were identified as potential targets for chlorogenic acid treatment of OSCC (Fig. 2A). These overlapping genes were input into the STRING database to construct the PPI network (Fig. 2B). The PPI data were analyzed in Cytoscape to identify 10 core targets for chlorogenic acid treatment of OSCC based on the network’s topological characteristics (Table 1, Fig. 2C,D).
Figure 2.
Core target screening and network construction. (A) Venn diagram of 200 targets of chlorogenic acid intersecting 430 targets of OSCC. (B) PPI network visualization. (C) Chlorogenic acid therapy OSCC prediction target network. The diagram has 23 nodes and 284 edges. Betweenness is used to screen for core targets. The lighter the color, the smaller the node indicates that the gene has less betweenness and less interaction with other targets; conversely, the darker the color, the larger the node, the more central the gene’s role. (D) Core target network, the core target with the top 10 betweenness values in the network. OSCC = oral squamous cell carcinoma, PPI = protein–protein interaction networks.
Table 1.
Core target properties.
| Gene | Name | Degree | Betweenness centrality | Closeness centrality |
|---|---|---|---|---|
| ESR1 | Estrogen receptor 1 | 42 | 67.76274 | 0.956522 |
| MMP2 | Matrix metallopeptidase 2 | 36 | 53.430447 | 0.846154 |
| ERBB2 | Human epidermal growth factor receptor 2 | 36 | 19.930996 | 0.846154 |
| MMP9 | Matrix metallopeptidase 9 | 38 | 16.996077 | 0.88 |
| SRC | SRC proto-oncogene | 36 | 13.362742 | 0.846154 |
| MAPK8 | Mitogen-activated protein kinase 8 | 30 | 9.794994 | 0.758621 |
| BRAF | B-Raf proto-oncogene, serine/threonine kinase | 28 | 8.8990345 | 0.733333 |
| ATM | Ataxia-telangiectasia mutated | 28 | 7.7367964 | 0.709677 |
| CDC42 | Cell division cycle 42 | 30 | 6.6502886 | 0.758621 |
| MAPK1 | Mitogen-activated protein kinase 1 | 28 | 5.542757 | 0.733333 |
ATM = ataxia-telangiectasia mutated, BRAF = B-Raf proto-oncogene, CDC42 = cell division cycle 42, ESR1 = estrogen receptor 1, ERBB2 = human epidermal growth factor receptor 2, MAPK1 = mitogen-activated protein kinase 1, MAPK8 = mitogen-activated protein kinase 8, MMP2 = matrix metallopeptidase 2, MMP9 = matrix metallopeptidase 9, SRC = SRC proto-oncogene.
3.3. Survival curve analysis
Core target data from TCGA were analyzed using R language for survival analysis and curve generation. The resulting figure (Fig. 3A) indicates that, except ATM and BRAF, other genes significantly influence patient survival times, including ESR1, MMP2, MMP9, SRC, MAPK8, MAPK1, CDC42, and ERBB2. The analysis demonstrates that the downregulation of these core target genes is advantageous for patient survival.
Figure 3.
Gene expression was differentiated by color, with red representing the high-expression group and blue representing the low-expression group. P < .05 is considered as a significant difference.
3.4. Molecular docking
To assess the potential of chlorogenic acid in treating OSCC, we integrated the 10 core target proteins identified previously with small drug molecules into Maestro, simulating the binding capabilities between the drugs and targets through chemical analysis. The docking score’s absolute value reflects the likelihood of interaction, with values below −5 indicating binding solid potential. The docking scores for chlorogenic acid and the target proteins were below −5 (Table 2, Fig. 4), with MMP9 demonstrating the highest docking score. The root mean square deviation indicates the reliability of the docking results. Except for MAPK1, all the docking results are <3, and the root mean square deviation values of MMP2, ATM and BRAF are <2, indicating strong reliability of the docking results (Table 3).
Table 2.
Molecular docking affinity score.
| Gene | PDB ID | Docking score |
|---|---|---|
| MMP9 | 6esm | −10.54 |
| ESR1 | 6PSJ | −7.951 |
| ERBB2 | 3pp0 | −7.587 |
| ATM | 7ni4 | −7.426 |
| BRAF | 4mnf | −7.123 |
| MAPK1 | 6g54 | −6.97 |
| MAPK8 | 2xrw | −6.929 |
| CDC42 | 2ngr | −6.88 |
| SRC | 1is0 | −5.591 |
| MMP2 | 7XGJ | −4.438 |
ATM = ataxia-telangiectasia mutated, BRAF = B-Raf proto-oncogene, CDC42 = cell division cycle 42, ESR1 = estrogen receptor 1, ERBB2 = human epidermal growth factor receptor 2, MAPK1 = mitogen-activated protein kinase 1, MAPK8 = mitogen-activated protein kinase 8, MMP2 = matrix metallopeptidase 2, MMP9 = matrix metallopeptidase 9, SRC = SRC proto-oncogene.
Figure 4.
Molecular docking overall diagram–local diagram–detail diagram. The location of the binding pocket was determined according to the position of the small molecule in the complex structure of the target protein and the small molecule crystal. The docking algorithm was Maestro’s default algorithm, and the number of cycles of the coordination capacity optimization for the docking was 100. ESR1 = estrogen receptor 1, ERBB2 = human epidermal growth factor receptor 2, MAPK1 = mitogen-activated protein kinase 1, MAPK8 = mitogen-activated protein kinase 8, MMP2 = matrix metallopeptidase 2, MMP9 = matrix metallopeptidase 9, SRC = SRC proto-oncogene.
Table 3.
KEGG top 20 channels.
| ID | Description | Gene ratio | Bg ratio | P value | P adjust | q value | Gene ID | Count |
|---|---|---|---|---|---|---|---|---|
| hsa01522 | Endocrine resistance | 13/21 | 98/8659 | 4.15E−21 | 6.85E−19 | 1.62E−19 | MMP2/ERBB2/RAF1/CDK4/IGF1R/ESR1/PTK2/MAPK1/BRAF/MAPK8/ESR2/SRC/MMP9 | 13 |
| hsa05219 | Bladder cancer | 45,556 | 41/8659 | 1.32E−16 | 1.09E−14 | 2.57E−15 | MMP2/MMP1/ERBB2/RAF1/CDK4/MAPK1/BRAF/SRC/MMP9 | 9 |
| hsa05205 | Proteoglycans in cancer | 45,647 | 205/8659 | 5.48E−15 | 3.02E−13 | 7.12E−14 | MMP2/ERBB2/RAF1/IGF1R/ESR1/PTK2/FGFR1/MAPK1/BRAF/SRC/MMP9/CDC42 | 12 |
| hsa05212 | Pancreatic cancer | 45,525 | 76/8659 | 4.48E−12 | 1.85E−10 | 4.37E−11 | ERBB2/RAF1/CDK4/MAPK1/BRAF/MAPK8/CDC42/STAT1 | 8 |
| hsa05224 | Breast cancer | 45,556 | 147/8659 | 2.26E−11 | 7.47E−10 | 1.76E−10 | ERBB2/RAF1/CDK4/IGF1R/ESR1/FGFR1/MAPK1/BRAF/ESR2 | 9 |
| hsa05215 | Prostate cancer | 45,525 | 97/8659 | 3.34E−11 | 9.19E−10 | 2.17E−10 | ERBB2/RAF1/IGF1R/FGFR1/MAPK1/BRAF/CDK2/MMP9 | 8 |
| hsa04917 | Prolactin signaling pathway | 45,494 | 70/8659 | 1.76E−10 | 4.16E−09 | 9.82E−10 | RAF1/ESR1/MAPK1/MAPK8/ESR2/SRC/STAT1 | 7 |
| hsa04510 | Focal adhesion | 45,556 | 203/8659 | 4.13E−10 | 8.52E−09 | 2.01E−09 | ERBB2/RAF1/IGF1R/PTK2/MAPK1/BRAF/MAPK8/SRC/CDC42 | 9 |
| hsa04012 | ErbB signaling pathway | 45,494 | 85/8659 | 7.11E−10 | 1.3E−08 | 3.08E−09 | ERBB2/RAF1/PTK2/MAPK1/BRAF/MAPK8/SRC | 7 |
| hsa05161 | Hepatitis B | 45,525 | 162/8659 | 2.09E−09 | 3.45E−08 | 8.14E−09 | RAF1/MAPK1/BRAF/CDK2/MAPK8/SRC/MMP9/STAT1 | 8 |
| hsa05218 | Melanoma | 45,464 | 72/8659 | 1.31E−08 | 1.86E−07 | 4.39E−08 | RAF1/CDK4/IGF1R/FGFR1/MAPK1/BRAF | 6 |
| hsa04926 | Relaxin signaling pathway | 45,494 | 129/8659 | 1.35E−08 | 1.86E−07 | 4.39E−08 | MMP2/MMP1/RAF1/MAPK1/MAPK8/SRC/MMP9 | 7 |
| hsa04068 | FoxO signaling pathway | 45,494 | 131/8659 | 1.51E−08 | 1.91E−07 | 4.51E−08 | RAF1/IGF1R/ATM/MAPK1/BRAF/CDK2/MAPK8 | 7 |
| hsa05417 | Lipid and atherosclerosis | 45,525 | 215/8659 | 1.96E−08 | 2.26E−07 | 5.34E−08 | MMP1/PPARG/PTK2/MAPK1/MAPK8/SRC/MMP9/CDC42 | 8 |
| hsa04915 | Estrogen signaling pathway | 45,494 | 137/8659 | 2.06E−08 | 2.26E−07 | 5.34E−08 | MMP2/RAF1/ESR1/MAPK1/ESR2/SRC/MMP9 | 7 |
| hsa01521 | EGFR tyrosine kinase inhibitor resistance | 45,464 | 79/8659 | 2.31E−08 | 2.39E−07 | 5.63E−08 | ERBB2/RAF1/IGF1R/MAPK1/BRAF/SRC | 6 |
| hsa04520 | Adherens junction | 45,464 | 93/8659 | 6.22E−08 | 5.7E−07 | 1.35E−07 | ERBB2/IGF1R/FGFR1/MAPK1/SRC/CDC42 | 6 |
| hsa04912 | GnRH signaling pathway | 45,464 | 93/8659 | 6.22E−08 | 5.7E−07 | 1.35E−07 | MMP2/RAF1/MAPK1/MAPK8/SRC/CDC42 | 6 |
| hsa04933 | AGE-RAGE signaling pathway in diabetic complications | 45,464 | 100/8659 | 9.62E−08 | 8.35E−07 | 1.97E−07 | MMP2/CDK4/MAPK1/MAPK8/CDC42/STAT1 | 6 |
| hsa04914 | Progesterone-mediated oocyte maturation | 45,464 | 102/8659 | 1.08E−07 | 8.94E−07 | 2.11E−07 | RAF1/IGF1R/MAPK1/BRAF/CDK2/MAPK8 | 6 |
KEGG = Kyoto Encyclopedia of Genes and Genomes.
3.5. GO analysis and KEGG analysis (Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis)
The molecular mechanisms underlying chlorogenic acid’s therapeutic effects on OSCC were explored through the computational analysis of cross-gene data using the R package “clusterProfiler.” The KEGG analysis revealed 128 pathways, with the foremost 20 and 15 presented in Table 4 and Figure 5A, respectively. Pathways pertinent to cell growth, migration, adhesion, cell cycle, and endocrine resistance were significantly implicated in OSCC. The ErbB pathway, Proteoglycans in the cancer pathway, and Endocrine resistance pathway mechanisms are depicted in Figure 5B–D. These diagrams highlight the critical roles of the ERK and JNK branches of the MAPK signaling pathway, closely tied to cell growth, migration, and adhesion. GO analysis, categorized into BP, CC, and MF sections, identified 965 BP items, 41 CC items, and 88 MF items, with the top 20 of each displayed in Table 5 and Figure 5E. BP is predominantly related to immune processes.
Table 4.
The first 20 paths of each part of GO.
| Ontology | ID | Description | Gene ratio | Bg ratio | P value | P adjust | q value | Gene ID | Count |
|---|---|---|---|---|---|---|---|---|---|
| BP | GO:0009612 | Response to mechanical stimulus | 45,527 | 211/18,903 | 8.96E−11 | 1.53E−07 | 6.98E−08 | MMP2/RAF1/IGF1R/PTK2/DDR2/MAPK8/SRC/STAT1 | 8 |
| BP | GO:0048660 | Regulation of smooth muscle cell proliferation | 45,496 | 175/18,903 | 1.12E−09 | 6.67E−07 | 3.04E−07 | MMP2/IGF1R/PPARG/DDR2/SRC/MMP9/STAT1 | 7 |
| BP | GO:0048659 | Smooth muscle cell proliferation | 45,496 | 179/18,903 | 1.31E−09 | 6.67E−07 | 3.04E−07 | MMP2/IGF1R/PPARG/DDR2/SRC/MMP9/STAT1 | 7 |
| BP | GO:0048661 | Positive regulation of smooth muscle cell proliferation | 45,466 | 98/18,903 | 1.56E−09 | 6.67E−07 | 3.04E−07 | MMP2/IGF1R/DDR2/SRC/MMP9/STAT1 | 6 |
| BP | GO:0000302 | Response to reactive oxygen species | 45,496 | 204/18,903 | 3.26E−09 | 1.11E−06 | 5.07E−07 | MMP2/MAPK1/DDR2/MAPK8/SRC/MMP9/STAT1 | 7 |
| BP | GO:0046777 | Protein autophosphorylation | 45,496 | 226/18,903 | 6.63E−09 | 1.75E−06 | 7.96E−07 | ERBB2/IGF1R/PTK2/FGFR1/ATM/DDR2/SRC | 7 |
| BP | GO:0038127 | ERBB signaling pathway | 45,466 | 126/18,903 | 7.16E−09 | 1.75E−06 | 7.96E−07 | ERBB2/PTK2/MAPK1/BRAF/SRC/MMP9 | 6 |
| BP | GO:0033002 | Muscle cell proliferation | 45,496 | 247/18,903 | 1.23E−08 | 2.62E−06 | 1.19E−06 | MMP2/IGF1R/PPARG/DDR2/SRC/MMP9/STAT1 | 7 |
| BP | GO:0034614 | Cellular response to reactive oxygen species | 45,466 | 150/18,903 | 2.04E−08 | 3.87E−06 | 1.77E−06 | MMP2/MAPK1/DDR2/MAPK8/SRC/MMP9 | 6 |
| BP | GO:0071375 | Cellular response to peptide hormone stimulus | 45,496 | 306/18,903 | 5.34E−08 | 8.38E−06 | 3.82E−06 | CDK4/IGF1R/PPARG/PTK2/DDR2/SRC/STAT1 | 7 |
| BP | GO:0050673 | Epithelial cell proliferation | 45,527 | 481/18,903 | 5.81E−08 | 8.38E−06 | 3.82E−06 | ERBB2/CDK4/ESR1/PPARG/FGFR1/MAPK1/CDC42/STAT1 | 8 |
| BP | GO:0018105 | Peptidyl-serine phosphorylation | 45,496 | 313/18,903 | 6.23E−08 | 8.38E−06 | 3.82E−06 | RAF1/ATM/MAPK1/BRAF/CDK2/MAPK8/SRC | 7 |
| BP | GO:0001667 | Ameboidal-type cell migration | 45,527 | 492/18,903 | 6.92E−08 | 8.38E−06 | 3.82E−06 | PPARG/PTK2/FGFR1/DDR2/BRAF/SRC/MMP9/CDC42 | 8 |
| BP | GO:0033674 | Positive regulation of kinase activity | 45,527 | 496/18,903 | 7.36E−08 | 8.38E−06 | 3.82E−06 | ERBB2/IGF1R/PTK2/FGFR1/DDR2/ROS1/SRC/CDC42 | 8 |
| BP | GO:0043410 | Positive regulation of MAPK cascade | 45,527 | 496/18,903 | 7.36E−08 | 8.38E−06 | 3.82E−06 | ERBB2/RAF1/IGF1R/FGFR1/DDR2/BRAF/SRC/CDC42 | 8 |
| BP | GO:1904705 | Regulation of vascular associated smooth muscle cell proliferation | 45,435 | 93/18,903 | 8.11E−08 | 8.66E−06 | 3.95E−06 | MMP2/PPARG/DDR2/SRC/MMP9 | 5 |
| BP | GO:1990874 | Vascular associated smooth muscle cell proliferation | 45,435 | 95/18,903 | 9.03E−08 | 8.84E−06 | 4.03E−06 | MMP2/PPARG/DDR2/SRC/MMP9 | 5 |
| BP | GO:0018209 | Peptidyl-serine modification | 45,496 | 332/18,903 | 9.32E−08 | 8.84E−06 | 4.03E−06 | RAF1/ATM/MAPK1/BRAF/CDK2/MAPK8/SRC | 7 |
| BP | GO:0062197 | Cellular response to chemical stress | 45,496 | 345/18,903 | 1.21E−07 | 1.09E−05 | 4.96E−06 | MMP2/ATM/MAPK1/DDR2/MAPK8/SRC/MMP9 | 7 |
| BP | GO:0071392 | Cellular response to estradiol stimulus | 45,405 | 40/18,903 | 1.48E−07 | 1.26E−05 | 5.76E−06 | MMP2/IGF1R/ESR1/ESR2 | 4 |
| CC | GO:0005925 | Focal adhesion | 45,435 | 422/19,869 | .000104 | .004315 | 0.00288 | PTK2/MAPK1/DDR2/SRC/CDC42 | 5 |
| CC | GO:0005901 | Caveola | 45,374 | 82/19,869 | .000113 | .004315 | 0.00288 | IGF1R/MAPK1/SRC | 3 |
| CC | GO:0030055 | Cell-substrate junction | 45,435 | 432/19,869 | .000116 | .004315 | 0.00288 | PTK2/MAPK1/DDR2/SRC/CDC42 | 5 |
| CC | GO:0031143 | Pseudopodium | 45,345 | 18/19,869 | .000194 | .00543 | 0.003623 | RAF1/MAPK1 | 2 |
| CC | GO:0044853 | Plasma membrane raft | 45,374 | 113/19,869 | .000292 | .00654 | 0.004364 | IGF1R/MAPK1/SRC | 3 |
| CC | GO:1902911 | Protein kinase complex | 45,374 | 136/19,869 | .000502 | .00938 | 0.006259 | CDK4/IGF1R/CDK2 | 3 |
| CC | GO:0000307 | Cyclin-dependent protein kinase holoenzyme complex | 45,345 | 50/19,869 | .001518 | .024288 | 0.016207 | CDK4/CDK2 | 2 |
| CC | GO:0061695 | Transferase complex, transferring phosphorus-containing groups | 45,374 | 295/19,869 | .004604 | .062395 | 0.041635 | CDK4/IGF1R/CDK2 | 3 |
| CC | GO:0031234 | Extrinsic component of cytoplasmic side of plasma membrane | 45,345 | 99/19,869 | .005808 | .062395 | 0.041635 | PTK2/SRC | 2 |
| CC | GO:0045121 | Membrane raft | 45,374 | 326/19,869 | .006076 | .062395 | 0.041635 | IGF1R/MAPK1/SRC | 3 |
| CC | GO:0098857 | Membrane microdomain | 45,374 | 327/19,869 | .006128 | .062395 | 0.041635 | IGF1R/MAPK1/SRC | 3 |
| CC | GO:0030175 | Filopodium | 45,345 | 108/19,869 | .006874 | .064161 | 0.042814 | SRC/CDC42 | 2 |
| CC | GO:1902554 | Serine/threonine protein kinase complex | 45,345 | 122/19,869 | .008696 | .071634 | 0.047801 | CDK4/CDK2 | 2 |
| CC | GO:1904813 | Ficolin-1-rich granule lumen | 45,345 | 124/19,869 | .008972 | .071634 | 0.047801 | MAPK1/MMP9 | 2 |
| CC | GO:0000805 | X chromosome | 45,314 | 10/19,869 | .011518 | .071634 | 0.047801 | CDK2 | 1 |
| CC | GO:0099091 | Postsynaptic specialization, intracellular component | 45,314 | 10/19,869 | .011518 | .071634 | 0.047801 | SRC | 1 |
| CC | GO:0005819 | Spindle | 45,374 | 426/19,869 | .012604 | .071634 | 0.047801 | ATM/MAPK1/CDC42 | 3 |
| CC | GO:0017119 | Golgi transport complex | 45,314 | 11/19,869 | .012663 | .071634 | 0.047801 | CDC42 | 1 |
| CC | GO:0097550 | Transcription preinitiation complex | 45,314 | 11/19,869 | .012663 | .071634 | 0.047801 | ESR1 | 1 |
| CC | GO:0045177 | Apical part of cell | 45,374 | 435/19,869 | .013332 | .071634 | 0.047801 | ERBB2/DDR2/CDC42 | 3 |
| MF | GO:0004713 | Protein tyrosine kinase activity | 45,496 | 138/18,432 | 2.51E−10 | 3.76E−08 | 1.64E−08 | ERBB2/IGF1R/PTK2/FGFR1/DDR2/ROS1/SRC | |
| MF | GO:0004714 | Transmembrane receptor protein tyrosine kinase activity | 45,435 | 60/18,432 | 9.92E−09 | 7.44E−07 | 3.24E−07 | ERBB2/IGF1R/FGFR1/DDR2/ROS1 | |
| MF | GO:0019199 | Transmembrane receptor protein kinase activity | 45,435 | 79/18,432 | 4.03E−08 | 2.01E−06 | 8.77E−07 | ERBB2/IGF1R/FGFR1/DDR2/ROS1 | |
| MF | GO:0019902 | Phosphatase binding | 45,466 | 195/18,432 | 1.13E−07 | 4.23E−06 | 1.84E−06 | ERBB2/PTK2/MAPK1/ROS1/MAPK8/STAT1 | |
| MF | GO:0106310 | Protein serine kinase activity | 45,496 | 363/18,432 | 2.03E−07 | 6.08E−06 | 2.64E−06 | RAF1/CDK4/ATM/MAPK1/BRAF/CDK2/MAPK8 | |
| MF | GO:0004674 | Protein serine/threonine kinase activity | 45,496 | 430/18,432 | 6.36E−07 | 1.59E−05 | 6.91E−06 | RAF1/CDK4/ATM/MAPK1/BRAF/CDK2/MAPK8 | |
| MF | GO:0019903 | Protein phosphatase binding | 45,435 | 149/18,432 | 9.66E−07 | 2.07E−05 | 9E−06 | ERBB2/PTK2/ROS1/MAPK8/STAT1 | |
| MF | GO:0016922 | Nuclear receptor binding | 45,405 | 139/18,432 | 2.45E−05 | .00046 | 0.0002 | ESR1/PPARG/SRC/STAT1 | |
| MF | GO:0004879 | Nuclear receptor activity | 45,374 | 52/18,432 | 3.6E−05 | .000541 | 0.000235 | ESR1/PPARG/ESR2 | |
| MF | GO:0098531 | Ligand-activated transcription factor activity | 45,374 | 52/18,432 | 3.6E−05 | .000541 | 0.000235 | ESR1/PPARG/ESR2 | |
| MF | GO:0008353 | RNA polymerase II CTD heptapeptide repeat kinase activity | 45,345 | 13/18,432 | .000115 | .001571 | 0.000684 | CDK4/MAPK1 | |
| MF | GO:0004707 | MAP kinase activity | 45,345 | 16/18,432 | .000177 | .002211 | 0.000962 | MAPK1/MAPK8 | |
| MF | GO:0004708 | MAP kinase kinase activity | 45,345 | 18/18,432 | .000225 | .002598 | 0.00113 | MAPK1/BRAF | |
| MF | GO:0035173 | Histone kinase activity | 45,345 | 20/18,432 | .000279 | .002991 | 0.001301 | ATM/CDK2 | |
| MF | GO:0001221 | Transcription coregulator binding | 45,374 | 109/18,432 | .000327 | .003123 | 0.001359 | ESR1/PPARG/STAT1 | |
| MF | GO:0005158 | Insulin receptor binding | 45,345 | 22/18,432 | .000339 | .003123 | 0.001359 | IGF1R/SRC | |
| MF | GO:0004222 | Metalloendopeptidase activity | 45,374 | 112/18,432 | .000354 | .003123 | 0.001359 | MMP2/MMP1/MMP9 | |
| MF | GO:0003707 | Nuclear steroid receptor activity | 45,345 | 24/18,432 | .000404 | .003369 | 0.001466 | ESR1/ESR2 | |
| MF | GO:0004709 | MAP kinase kinase kinase activity | 45,345 | 27/18,432 | .000513 | .004039 | 0.001758 | RAF1/BRAF | |
| MF | GO:0019838 | Growth factor binding | 45,374 | 132/18,432 | .000573 | .004039 | 0.001758 | ERBB2/IGF1R/FGFR1 | |
BP = biological process, CC = cell component, GO = Gene Ontology, MAPK = mitogen-activated protein kinase, MF = molecular function.
Figure 5.
(A) KEGG enrichment analysis of targets for chlorogenic acid treatment of diseases, with the top 15 important items shown. (B) ErbB pathway visualization with red indicating key targets for chlorogenic acid treatment of OSCC. (C) Visualization of Proteoglycans in cancer pathway, with red indicating key targets for chlorogenic acid treatment of OSCC. (D) Visualization of the Endocrine resistance pathway, with red indicating key targets for chlorogenic acid treatment of OSCC. (E) The key first 15 entries in the BP, CC, and MF sections of GO. BP = biological process, CC = cell component, GO = Gene Ontology, KEGG = Kyoto Encyclopedia of Genes and Genomes, MF = molecular function, OSCC = oral squamous cell carcinoma.
Table 5.
The first 20 paths of each part of GO.
| Ontology | ID | Description | Gene ratio | Bg ratio | P value | P adjust | q value | Gene ID | Count |
|---|---|---|---|---|---|---|---|---|---|
| BP | GO:0009612 | Response to mechanical stimulus | 45,527 | 211/18,903 | 8.96E−11 | 1.53E−07 | 6.98E−08 | MMP2/RAF1/IGF1R/PTK2/DDR2/MAPK8/SRC/STAT1 | 8 |
| BP | GO:0048660 | Regulation of smooth muscle cell proliferation | 45,496 | 175/18,903 | 1.12E−09 | 6.67E−07 | 3.04E−07 | MMP2/IGF1R/PPARG/DDR2/SRC/MMP9/STAT1 | 7 |
| BP | GO:0048659 | Smooth muscle cell proliferation | 45,496 | 179/18,903 | 1.31E−09 | 6.67E−07 | 3.04E−07 | MMP2/IGF1R/PPARG/DDR2/SRC/MMP9/STAT1 | 7 |
| BP | GO:0048661 | Positive regulation of smooth muscle cell proliferation | 45,466 | 98/18,903 | 1.56E−09 | 6.67E−07 | 3.04E−07 | MMP2/IGF1R/DDR2/SRC/MMP9/STAT1 | 6 |
| BP | GO:0000302 | Response to reactive oxygen species | 45,496 | 204/18,903 | 3.26E−09 | 1.11E−06 | 5.07E−07 | MMP2/MAPK1/DDR2/MAPK8/SRC/MMP9/STAT1 | 7 |
| BP | GO:0046777 | Protein autophosphorylation | 45,496 | 226/18,903 | 6.63E−09 | 1.75E−06 | 7.96E−07 | ERBB2/IGF1R/PTK2/FGFR1/ATM/DDR2/SRC | 7 |
| BP | GO:0038127 | ERBB signaling pathway | 45,466 | 126/18,903 | 7.16E−09 | 1.75E−06 | 7.96E−07 | ERBB2/PTK2/MAPK1/BRAF/SRC/MMP9 | 6 |
| BP | GO:0033002 | Muscle cell proliferation | 45,496 | 247/18,903 | 1.23E−08 | 2.62E−06 | 1.19E−06 | MMP2/IGF1R/PPARG/DDR2/SRC/MMP9/STAT1 | 7 |
| BP | GO:0034614 | Cellular response to reactive oxygen species | 45,466 | 150/18,903 | 2.04E−08 | 3.87E−06 | 1.77E−06 | MMP2/MAPK1/DDR2/MAPK8/SRC/MMP9 | 6 |
| BP | GO:0071375 | Cellular response to peptide hormone stimulus | 45,496 | 306/18,903 | 5.34E−08 | 8.38E−06 | 3.82E−06 | CDK4/IGF1R/PPARG/PTK2/DDR2/SRC/STAT1 | 7 |
| BP | GO:0050673 | Epithelial cell proliferation | 45,527 | 481/18,903 | 5.81E−08 | 8.38E−06 | 3.82E−06 | ERBB2/CDK4/ESR1/PPARG/FGFR1/MAPK1/CDC42/STAT1 | 8 |
| BP | GO:0018105 | Peptidyl-serine phosphorylation | 45,496 | 313/18,903 | 6.23E−08 | 8.38E−06 | 3.82E−06 | RAF1/ATM/MAPK1/BRAF/CDK2/MAPK8/SRC | 7 |
| BP | GO:0001667 | Ameboidal-type cell migration | 45,527 | 492/18,903 | 6.92E−08 | 8.38E−06 | 3.82E−06 | PPARG/PTK2/FGFR1/DDR2/BRAF/SRC/MMP9/CDC42 | 8 |
| BP | GO:0033674 | Positive regulation of kinase activity | 45,527 | 496/18,903 | 7.36E−08 | 8.38E−06 | 3.82E−06 | ERBB2/IGF1R/PTK2/FGFR1/DDR2/ROS1/SRC/CDC42 | 8 |
| BP | GO:0043410 | Positive regulation of MAPK cascade | 45,527 | 496/18,903 | 7.36E−08 | 8.38E−06 | 3.82E−06 | ERBB2/RAF1/IGF1R/FGFR1/DDR2/BRAF/SRC/CDC42 | 8 |
| BP | GO:1904705 | Regulation of vascular associated smooth muscle cell proliferation | 45,435 | 93/18,903 | 8.11E−08 | 8.66E−06 | 3.95E−06 | MMP2/PPARG/DDR2/SRC/MMP9 | 5 |
| BP | GO:1990874 | Vascular associated smooth muscle cell proliferation | 45,435 | 95/18,903 | 9.03E−08 | 8.84E−06 | 4.03E−06 | MMP2/PPARG/DDR2/SRC/MMP9 | 5 |
| BP | GO:0018209 | Peptidyl-serine modification | 45,496 | 332/18,903 | 9.32E−08 | 8.84E−06 | 4.03E−06 | RAF1/ATM/MAPK1/BRAF/CDK2/MAPK8/SRC | 7 |
| BP | GO:0062197 | Cellular response to chemical stress | 45,496 | 345/18,903 | 1.21E−07 | 1.09E−05 | 4.96E−06 | MMP2/ATM/MAPK1/DDR2/MAPK8/SRC/MMP9 | 7 |
| BP | GO:0071392 | Cellular response to estradiol stimulus | 45,405 | 40/18,903 | 1.48E−07 | 1.26E−05 | 5.76E−06 | MMP2/IGF1R/ESR1/ESR2 | 4 |
| CC | GO:0005925 | Focal adhesion | 45,435 | 422/19,869 | .000104 | .004315 | 0.00288 | PTK2/MAPK1/DDR2/SRC/CDC42 | 5 |
| CC | GO:0005901 | Caveola | 45,374 | 82/19,869 | .000113 | .004315 | 0.00288 | IGF1R/MAPK1/SRC | 3 |
| CC | GO:0030055 | Cell-substrate junction | 45,435 | 432/19,869 | .000116 | .004315 | 0.00288 | PTK2/MAPK1/DDR2/SRC/CDC42 | 5 |
| CC | GO:0031143 | Pseudopodium | 45,345 | 18/19,869 | .000194 | .00543 | 0.003623 | RAF1/MAPK1 | 2 |
| CC | GO:0044853 | Plasma membrane raft | 45,374 | 113/19,869 | .000292 | .00654 | 0.004364 | IGF1R/MAPK1/SRC | 3 |
| CC | GO:1902911 | Protein kinase complex | 45,374 | 136/19,869 | .000502 | .00938 | 0.006259 | CDK4/IGF1R/CDK2 | 3 |
| CC | GO:0000307 | Cyclin-dependent protein kinase holoenzyme complex | 45,345 | 50/19,869 | .001518 | .024288 | 0.016207 | CDK4/CDK2 | 2 |
| CC | GO:0061695 | Transferase complex, transferring phosphorus-containing groups | 45,374 | 295/19,869 | .004604 | .062395 | 0.041635 | CDK4/IGF1R/CDK2 | 3 |
| CC | GO:0031234 | Extrinsic component of cytoplasmic side of plasma membrane | 45,345 | 99/19,869 | .005808 | .062395 | 0.041635 | PTK2/SRC | 2 |
| CC | GO:0045121 | Membrane raft | 45,374 | 326/19,869 | .006076 | .062395 | 0.041635 | IGF1R/MAPK1/SRC | 3 |
| CC | GO:0098857 | Membrane microdomain | 45,374 | 327/19869 | .006128 | .062395 | 0.041635 | IGF1R/MAPK1/SRC | 3 |
| CC | GO:0030175 | Filopodium | 45,345 | 108/19,869 | .006874 | .064161 | 0.042814 | SRC/CDC42 | 2 |
| CC | GO:1902554 | Serine/threonine protein kinase complex | 45,345 | 122/19,869 | .008696 | .071634 | 0.047801 | CDK4/CDK2 | 2 |
| CC | GO:1904813 | Ficolin-1-rich granule lumen | 45,345 | 124/19,869 | .008972 | .071634 | 0.047801 | MAPK1/MMP9 | 2 |
| CC | GO:0000805 | X chromosome | 45,314 | 10/19,869 | .011518 | .071634 | 0.047801 | CDK2 | 1 |
| CC | GO:0099091 | Postsynaptic specialization, intracellular component | 45,314 | 10/19,869 | .011518 | .071634 | 0.047801 | SRC | 1 |
| CC | GO:0005819 | Spindle | 45,374 | 426/19,869 | .012604 | .071634 | 0.047801 | ATM/MAPK1/CDC42 | 3 |
| CC | GO:0017119 | Golgi transport complex | 45,314 | 11/19,869 | .012663 | .071634 | 0.047801 | CDC42 | 1 |
| CC | GO:0097550 | Transcription preinitiation complex | 45,314 | 11/19,869 | .012663 | .071634 | 0.047801 | ESR1 | 1 |
| CC | GO:0045177 | Apical part of cell | 45,374 | 435/19,869 | .013332 | .071634 | 0.047801 | ERBB2/DDR2/CDC42 | 3 |
| MF | GO:0004713 | Protein tyrosine kinase activity | 45,496 | 138/18,432 | 2.51E−10 | 3.76E−08 | 1.64E−08 | ERBB2/IGF1R/PTK2/FGFR1/DDR2/ROS1/SRC | |
| MF | GO:0004714 | Transmembrane receptor protein tyrosine kinase activity | 45,435 | 60/18,432 | 9.92E−09 | 7.44E−07 | 3.24E−07 | ERBB2/IGF1R/FGFR1/DDR2/ROS1 | |
| MF | GO:0019199 | Transmembrane receptor protein kinase activity | 45,435 | 79/18,432 | 4.03E−08 | 2.01E−06 | 8.77E−07 | ERBB2/IGF1R/FGFR1/DDR2/ROS1 | |
| MF | GO:0019902 | Phosphatase binding | 45,466 | 195/18,432 | 1.13E−07 | 4.23E−06 | 1.84E−06 | ERBB2/PTK2/MAPK1/ROS1/MAPK8/STAT1 | |
| MF | GO:0106310 | Protein serine kinase activity | 45,496 | 363/18,432 | 2.03E−07 | 6.08E−06 | 2.64E−06 | RAF1/CDK4/ATM/MAPK1/BRAF/CDK2/MAPK8 | |
| MF | GO:0004674 | Protein serine/threonine kinase activity | 45,496 | 430/18,432 | 6.36E−07 | 1.59E−05 | 6.91E−06 | RAF1/CDK4/ATM/MAPK1/BRAF/CDK2/MAPK8 | |
| MF | GO:0019903 | Protein phosphatase binding | 45,435 | 149/18,432 | 9.66E−07 | 2.07E−05 | 9E−06 | ERBB2/PTK2/ROS1/MAPK8/STAT1 | |
| MF | GO:0016922 | Nuclear receptor binding | 45,405 | 139/18,432 | 2.45E−05 | .00046 | 0.0002 | ESR1/PPARG/SRC/STAT1 | |
| MF | GO:0004879 | Nuclear receptor activity | 45,374 | 52/18,432 | 3.6E−05 | .000541 | 0.000235 | ESR1/PPARG/ESR2 | |
| MF | GO:0098531 | Ligand-activated transcription factor activity | 45,374 | 52/18,432 | 3.6E−05 | .000541 | 0.000235 | ESR1/PPARG/ESR2 | |
| MF | GO:0008353 | RNA polymerase II CTD heptapeptide repeat kinase activity | 45,345 | 13/18,432 | .000115 | .001571 | 0.000684 | CDK4/MAPK1 | |
| MF | GO:0004707 | MAP kinase activity | 45,345 | 16/18,432 | .000177 | .002211 | 0.000962 | MAPK1/MAPK8 | |
| MF | GO:0004708 | MAP kinase kinase activity | 45,345 | 18/18,432 | .000225 | .002598 | 0.00113 | MAPK1/BRAF | |
| MF | GO:0035173 | Histone kinase activity | 45,345 | 20/18,432 | .000279 | .002991 | 0.001301 | ATM/CDK2 | |
| MF | GO:0001221 | Transcription coregulator binding | 45,374 | 109/18,432 | .000327 | .003123 | 0.001359 | ESR1/PPARG/STAT1 | |
| MF | GO:0005158 | Insulin receptor binding | 45,345 | 22/18,432 | .000339 | .003123 | 0.001359 | IGF1R/SRC | |
| MF | GO:0004222 | Metalloendopeptidase activity | 45,374 | 112/18,432 | .000354 | .003123 | 0.001359 | MMP2/MMP1/MMP9 | |
| MF | GO:0003707 | Nuclear steroid receptor activity | 45,345 | 24/18,432 | .000404 | .003369 | 0.001466 | ESR1/ESR2 | |
| MF | GO:0004709 | MAP kinase kinase kinase activity | 45,345 | 27/18,432 | .000513 | .004039 | 0.001758 | RAF1/BRAF | |
| MF | GO:0019838 | Growth factor binding | 45,374 | 132/18,432 | .000573 | .004039 | 0.001758 | ERBB2/IGF1R/FGFR1 | |
BP = biological process, CC = cell component, GO = Gene Ontology, MAPK = mitogen-activated protein kinase, MF = molecular function.
4. Discussion
OSCC is among the most prevalent malignancies in the head and neck region and is characterized by high invasiveness and heterogeneity.[16] Despite continuous advancements in treatment methods, the complexity of its pathogenesis and ambiguous diagnostic criteria limits the efficacy of therapeutic drugs.[17] Chlorogenic acid, which is extensively used in cancer therapy, has seen limited research in the context of OSCC.[18]
In this study, we identified 200 potential targets of chlorogenic acid and 430 gene targets related to OSCC for further analysis. Through Venn diagram analysis of these datasets, we pinpointed crucial drug treatment targets and conducted KEGG and GO analyses to determine vital molecular mechanisms. Network analysis of essential genes facilitated the calculation of core targets, which were then validated via molecular docking and Kaplan–Meier (KM) curve analysis (Fig. 6). The core targets identified include ESR1, MMP2, MMP9, SRC, MAPK8 (JNK1), MAPK1, CDC42, ERBB2, ATM, and BRAF, totaling ten data points. The experimental process is shown in Figure 6.
Figure 6.
Flow chart of experimental research on drug treatment of disease. GO = Gene Ontology, KEGG = Kyoto Encyclopedia of Genes and Genomes, OMIM = Online Mendelian Inheritance in Man CC cell component ESR1 estrogen receptor, OSCC = oral squamous cell carcinoma.
KEGG analysis indicated that treatment with chlorogenic acid may affect cell growth, adhesion, migration, and the cell cycle, with most targets involved in the ERK and JNK pathways within MAPK. Abnormal activation of proteins in the MAPK pathway is a critical factor in many cancers, and targeting this pathway may be a viable tumor treatment strategy.[19,20] ERK primarily influences cell growth, migration, and the cell cycle and receives upstream signals from Ras/Raf proteins.[21,22] The JNK pathway is also associated with inflammation, apoptosis, and cell growth. Core targets such as ERBB2, SRC, MAPK1 (ERK2), BRAF, and CDC42 in the ErbB and proteoglycan pathways in cancer are implicated in cell growth, migration, and adhesion via the ERK pathway. The ERK pathway has gained significant attention in OSCC research. The overexpression of growth differentiation factor 15 enhances ErbB2 phosphorylation, triggering downstream AKT and ERK signaling pathways promoting OSCC cell proliferation.[23] The combined action of p63 and the MEK/ERK-MAPK pathway synergistically induces ARL4C expression, thereby promoting the growth and proliferation of OSCC tumor cells.[24] Semilicoisoflavone B downregulates MAPK and Ras/Raf/MEK signaling and concurrently induces reactive oxygen species production to trigger OSCC cell apoptosis.[25] Machilin D impedes the FAK/Src and MAPK pathways, reducing the adhesion, migration, and invasion of OSCC cells, leading to apoptotic cell death.[26] 7-Epitaxol reduces ERK1/2 phosphorylation and elevates the expression of OSCC cell apoptosis and autophagy marker proteins (cleaved-poly ADP-ribose polymerase and microtubule-associated protein 1 light chain 3-I/II).[27] The expression of BRAF-activated long noncoding RNA is positively correlated with MAPK signaling pathway-associated proteins (p-erk, p-akt, and p-38).[28] Its suppression notably hinders OSCC cell proliferation, migration, and invasion, whereas overexpression has the opposite effect. The galectin-1 inhibitor OTX008 decreased OSCC cell viability in a dose-dependent manner through the MAPK/ERK pathway.[29] Isorhamnetin induces OSCC cell death via the ERK/MAPK pathway by curtailing cell viability, inhibiting cyclin CDC2, and disrupting cell migration.[30] The JNK pathway is equally pivotal in OSCC, and JNK phosphorylation levels are closely linked to higher differentiation phenotypes.[31] JNK1, a subtype of the JNK family, is particularly noteworthy.[32] Coronarin D affects the JNK1/2 signaling pathway, promoting apoptosis and cell cycle arrest in 5-fluorouracil-resistant OSCC cell lines.[33] Hispolon exerts anti-OSCC effects by activating the JNK pathway through HO-1 upregulation, thereby inducing caspase-dependent apoptosis.[34] GO-Y078 activates the p38/JNK1/2 pathway, enhancing AP-1 DNA binding activity, leading to upregulating HO-1 gene transcription and inhibiting OSCC cell growth.[35] In summary, the ERK and JNK branches of the MAPK pathway are crucial in OSCC.
Matrix metalloproteinases (MMPs) are crucial for degrading extracellular matrix proteins, a fundamental step in tumor migration and invasion, and are closely linked to angiogenesis.[36] Our study identified MMP2 and MMP9 as core target proteins for chlorogenic acid treatment of OSCC. In patients with OSCC, MMP2 and MMP9 activities were positively correlated with dipeptidyl peptidase IV mRNA levels, potentially regulating tumor metastasis.[37] In vitro studies have demonstrated that MicroRNA-29a upregulates MMP2, enhancing cancer invasion and anti-apoptosis capabilities.[38] Similarly, miR-31-5p elevates the ERK-MMP9 cascade and targets ACOX1, positively affecting the cell motility linked to OSCC metastasis.[39] Treating OSCC cell lines with the MMP2 inhibitor ARP101 markedly reduced cell proliferation and mobility.[40] Cordycepin significantly lowered MMP2 and MMP9 activities and FAK and Akt phosphorylation in a concentration-dependent manner, curbing migration and invasion of HSC-4 cells and inducing autophagy.[41] In mouse orthotopic tongue cancer models, halofuginone targeted cancer associated fibroblast inhibition, reduced MMP2 expression and collagen deposition, and impeded OSCC migration and invasion.[42] PAIP1 knockdown reduced MMP9 activity and SRC phosphorylation, hindering OSCC cell formation and invasion.[43]
These findings suggest chlorogenic acid significantly influences the MAPK-ERK and MAPK-JNK signaling pathways during OSCC treatment. Apart from ATM and BRAF, the downregulation of core targets appears to be beneficial for OSCC patient survival. However, it is essential to note that network pharmacology primarily guides future research, and further experiments are required to validate these results.
5. Conclusion
In conclusion, chlorogenic acid’s therapeutic strategy for OSCC might influence the MAPK-ERK and MAPK-JNK pathways by targeting ESR1, MMP2, MMP9, SRC, MAPK8, MAPK1, CDC42, ERBB2, ATM, and BRAF. This intervention could inhibit cell migration, invasion, growth, and apoptosis. This study introduces a novel perspective and strategy for researching targeted therapy mechanisms.
Acknowledgments
We would like to thank all the participants for their outstanding contributions, as well as the Therapeutic Target Database, ChEMBL Database, HERB Database, PharmMapper Database, SwissTarget prediction screening medium, OMIM database, and GeneCards, which supported our experimental research.
Author contributions
Conceptualization: Yutao Yang.
Data curation: Xulong Xve.
Formal analysis: Zhanqin Feng.
Funding acquisition: Zhanqin Feng.
Investigation: Zhanqin Feng.
Methodology: Zhanqin Feng, Puyu Hao.
Project administration: Jun Zhang.
Resources: Jun Zhang.
Software: Yutao Yang.
Validation: Yutao Yang.
Visualization: Xulong Xve.
Writing – original draft: Zhanqin Feng, Puyu Hao.
Writing – review & editing: Jun Zhang.
Supplementary Material
Abbreviations:
- ACOX1
- acyl-CoA oxidase 1
- AKT
- protein kinase B
- ARL4C
- ADP ribosylation factor like GTPase 4C
- ATM
- ataxia-telangiectasia mutated
- BP
- biological process
- BRAF
- B-Raf proto-oncogene, serine/threonine kinase
- CC
- cell component
- CDC42
- cell division cycle 42
- ERBB2
- human epidermal growth factor receptor 2
- ESR1
- estrogen receptor 1
- FAK
- focal adhesion kinase pathways
- GO
- Gene Ontology
- HO-1
- heme oxygenase 1
- KEGG
- Kyoto Encyclopedia of Genes and Genomes
- MAPK1
- mitogen-activated protein kinase 1
- MAPK8
- mitogen-activated protein kinase 8
- MAPK-ERK
- pathways mitogen-activated protein kinases-regulated kinase pathways
- MAPK-JNK
- pathways mitogen-activated protein kinases -cJun NH (2)-terminal kinase pathways
- MF
- molecular function
- MMP2
- matrix metallopeptidase 2
- MMP9
- matrix metallopeptidase 9
- OMIM
- Online Mendelian Inheritance in Man CC cell component ESR1 estrogen receptor
- OSCC
- oral squamous cell carcinoma
- PAIP1
- poly(A) binding protein interacting protein 1
- PPI
- protein–protein interaction networks
- SRC
- SRC proto-oncogene
- TCGA
- The Cancer Genome Atlas.
Supported by the Weifang Municipal Health Commission project, “Research on the effect of predicting postpartum VTE based on Improved QLD and Caprini Evaluation Models.”
The authors have no conflicts of interest to disclose.
All data generated or analyzed during this study are included in this published article [and its supplementary information files].
Supplemental Digital Content is available for this article.
How to cite this article: Feng Z, Hao P, Yang Y, Xve X, Zhang J. Network pharmacology and molecular docking to explore the potential molecular mechanism of chlorogenic acid treatment of oral squamous cell carcinoma. Medicine. 2024;103:45(e40218).
ZF and PH contributed to this article equally.
Contributor Information
Zhanqin Feng, Email: fzhanqin@163.com.
Puyu Hao, Email: 1311771491@qq.com.
Yutao Yang, Email: 1571167297@qq.com.
Xulong Xve, Email: xuexl0521@163.com.
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