Objective: This study aims to investigate the potential mechanism of cucurbitacin E in the treatment of Pancreatic adenocarcinoma (PAAD) using network pharmacology and molecular docking technology.

Methods: We collect the action targets of cucurbitacin E from Swiss Target Prediction, Pharmmapper and TargetNet databases. Additionally, we obtained PAAD-related targets from GeneCard, OMIM, PharmGKB, DrugBank and TTD databases. The intersection targets of cucurbitacin E and PAAD was regarded as potential therapeutic targets against PAAD, the protein-protein interaction network was constructed by using String database and Cytoscape software, and the David database was used for GO and KEGG enrichment analysis. Then performed molecular docking of cucurbitacin E with the core target proteins using LeDock software. Analyze the expression differences of core genes in PAAD using the GEPIA database; explore the relationship between core gene expression and the survival time of PAAD patients as well as tumor immune infiltration using the TIMER database, and conduct SMART database analysis on the methylation of core genes.

Results: 97 potential targets of Cucurbitacin E against PAAD were identified, among which the top ten core targets were SRC, HRAS, HSP90AA1, Alb, ANXA5, MMP9, IGF1, CASP3, MAPK1 and EGFR. GO enrichment analysis shows 62 biological processes, 19 cellular components, and 17 molecular functions. KEGG enrichment analysis identifies 90 signaling pathways. Molecular docking results indicate that Cucurbitacin E shows strong affinity with most core target proteins. In addition, gene differential expression, survival analysis, immune infiltration and methylation analysis suggest that the core targets MAPK1 and CASP3 may serve as biomarkers for the prognosis of PAAD treatment.

Conclusion: This study theoretically elucidates the potential molecular mechanisms of Cucurbitacin E in the treatment of PAAD through methods such as network pharmacology and bioinformatics, providing theoretical support and reference for subsequent basic experiments and clinical research.

Shen B, Zhang HL, Li1 XD, Wu JM, Teng M, et al. (2025) Explore the Potential Mechanism of Cucurbitacin E in the Treatment of Pancreatic Adenocarcinoma Based on Network Pharmacology and Molecular Docking. J Materials Applied Sci 6(1): 1016.

• Network pharmacology
• Molecular docking
• Cucurbitacin E
• Pancreatic adenocarcinoma
• Immune infiltration

Pancreatic adenocarcinoma (PAAD) is a prevalent malignant tumor of the digestive tract, currently lacks effective methods for early diagnosis and treatment, resulting in a very poor prognosis, with a five-year survival rate of less than 10% [1]. Therefore, there is an urgent need to develop new therapeutic drugs. Natural products, characterized by their complex structures and multi-target capabilities, are an important source for new drug development, particularly for anti-cancer drugs [2]. Cucurbitacin E (CuE) is an oxygen-containing tetracycli triterpenoid compound extracted from cucurbitaceous plants, which has potential anti-cancer properties. As a promising anti-cancer natural molecule, it exerts anti- tumor effects by affecting the cytoskeleton and mitosis, among other pathways [3]. However, there are few reports on the research of Cucurbitacin E against PAAD, and its potential mechanisms remain unclear. Therefore, this study utilizes network pharmacology and bioinformatics methods to investigate the potential effects and molecular mechanisms of Cucurbitacin E in treating PAAD [4]. The goal is to provide a theoretical basis for its application in PAAD treatment.

Acquisition of Targets for Cucurbitacin E

Using “Cucurbitacin E” as the search term in the PubChem database (https://pubchem.ncbi.nlm.nih.gov/), the 3D structure was searched and the SMILES structure was downloaded. The targets of Cucurbitacin E were obtained through Swiss Target Prediction (http://www. swiss target prediction.ch/), Pharmmapper (http:// www.lilab-ecust.cn/pharmmapper/), and the TargetNet database (http://targetnet.scbdd.com/). After merging and removing duplicates, a total of 401 drug action targets were identified.

Acquisition of PAAD-Related Targets

Using the search term “Pancreatic adenocarcinoma” in databases such as GeneCards (https://www.genecards. org/), OMIM (https://omim.org/), PharmGKB (https:// www.pharmgkb.org/), DisGeNET (https://www.disgenet. org/), DrugBank (https://go.drugbank.com/), and TTD (https://db.idrblab.net/ttd/), PAAD-related targets were retrieved, merged, and deduplicated, resulting in 1,149 disease targets.

Construction of Protein-Protein Interaction Network and Core Gene Screening

We performed an intersection analysis between drug targets and disease target genes using the website (http:// bioinformatics.Psb.Ugent.be/webtools/Venn/), which yielded 97 potential anti-PAAD targets. Subsequently, these potential targets were imported into the STRING database (https://www.string-db.org/), selecting the species “Homo sapiens” to analyze protein-protein interactions (PPI) among the targets. The data was imported into Cytoscape_v3.10.0 software to construct the PPI network, and the top 10 core genes were screened using the CytoHubba plugin.

Core Gene GO and KEGG Enrichment Analysis

Using the DAVID database (https://david.ncifcrf.gov/ summary.jsp), GO and KEGG enrichment analyses were performed on core genes. The GO enrichment analysis includes Biological Process (BP), Cellular Component (CC), and Molecular Function (MF). Finally, the top 10 enriched BP, CC, MF, and the top 20 signaling pathways were visualized.

Construction of “Core Target-Pathway” Network

The “Core Target-Pathway” network was constructed using Cytoscape_v3.10.0 software to analyze the relationship between core targets and signaling pathways.

Differential Expression Analysis of Core Genes in PAAD

Differential expression analysis of core genes in PAAD was conducted using the GEPIA website (http://gepia. cancer-pku.cn/) to understand the expression differences of core genes between normal tissues and PAAD tissues.

Analysis of the Impact of Core Genes on Survival in PAAD Patients

Survival analysis of core genes was performed using the “Survival” module of the TIMER website (https:// cistrome.shinyapps.io/timer/) to explore the impact of core genes on the survival of PAAD patients.

Core Gene Methylation Analysis

Using the SMART database (http://www.bioinfo-zs. com/smartapp/), to analyze the methylation of core genes, studying the differences in methylation levels of core genes between normal tissues and PAAD tissues.

Correlation Analysis of Core Gene Expression and Tumor Immune Infiltration

Analyzing the relationship between core gene expression and tumor immune infiltration through the TIMER website, to understand the potential role of core genes in PAAD immunotherapy and their association with prognosis.

Compound-Core Target Protein Molecular Docking

Molecular docking of Cucurbitacin E with core target proteins, preliminarily analyzing and investigate its mechanism of action against PAAD. Downloading the PDB Format of the corresponding proteins of core genes from the PDB database (https://www.rcsb.org/), using LeDock software for molecular docking of Cucurbitacin E with core targets, the binding energy is less than -5.00kcal/mol, indicating that the two can effectively combine [5], and then using PyMOL for visualization analysis of the docking results.

Acquisition of the 3D Structure and Targets of Cucurbitacin E

The 3D structure of Cucurbitacin E was downloaded from the Pubchem database. A total of 401 targets of Cucurbitacin E were obtained through the Swiss Target Prediction, Pharmmapper, and TargetNet databases.

Acquisition of PAAD-Related Targets and Potential Targets

By screening the GeneCards, OMIM, Pharmgkb, Disgenet, Drugbank, and TTD databases, 1149 PAAD- related targets were identified. From these the intersection with the targets of Cucurbitacin E yielded 97 potential target genes (Figure 1A,Figure 1B).

Figure 1: A. The 3D structure of Cucurbitacin E; B. Potential action targets of Cucurbitacin E in the treatment of PAAD.

Construction of Protein Interaction Network and Core Gene Screening

The 97 intersecting genes mentioned above were imported into the STRING database to construct the interaction network among the targets, resulting in 1081 interconnected lines, with an average node count of 22.3 and an average local clustering coefficient of 0.64. The enrichment P-value for protein interactions was <1.0×10-

16. The protein interaction network between Cucurbitacin E and PAAD was constructed using Cytoscape_v3.10.0Figure 2A). The top 10 core genes were obtained using the CytoHubba plugin (Figure 2B), which are SRC, HRAS, HSP90AA1, CASP3, ALB, ANXA5, MMP9, IGF1, EGFR, and MAPK1.

Figure 2: A. Protein interaction network diagram; B. Network diagram of the top 10 core genes.

GO and KEGG Enrichment Analysis

Using the DAVID database, GO functional and KEGG pathway enrichment analyses were performed on the aforementioned 10 core genes, with all parameters set to default. A total of 62 biological processes, 19 cellular components, and 17 molecular functions were obtained from the GO enrichment. A total of 90 pathways were obtained from the KEGG enrichment. The top 20 results of the GO enrichment (Figure 3A); the top 20 results of 

the KEGG enrichment (Figure 3B). From Figure 3A, it can be seen that the biological processes in the GO enrichment include peptide tyrosine autophosphorylation, positive regulation of Ras protein signal transduction, and negative regulation of endogenous apoptosis signaling pathways, among others. Cellular components include focal adhesions, extracellular regions, cytoplasm, and membrane rafts. Molecular functions include phospholipase activator activity, protein phosphatase binding, protein tyrosine kinase activity, and protein serine/threonine/tyrosine kinase activity, among others. In Figure 3B, the KEGG pathway enrichment mainly affects pathways related to PAAD, including the estrogen signaling pathway, Rap1 signaling pathway, ErbB signaling pathway, PI3K-Akt signaling pathway, MAPK signaling pathway, Ras signaling pathway, mTOR signaling pathway, GnRH signaling pathway, apoptosis, and VEGF signaling pathway, among others.

Core Target-Signaling Pathway Network Construction

The relevant data of core targets and signaling pathways were imported into Cytoscape_v3.10.0 to construct the “target-signaling pathway” network (Figure 3C). The results indicate that Cucurbitacin E can act on PAAD through multiple targets and multiple pathways.

Figure 3: A. Core gene GO enrichment analysis; B. Core gene KEGG enrichment analysis; C. Core gene-signaling pathway diagram.

Differential Expression Analysis of Core Genes in PAAD

Using the GAPIA database, we compared the differential expression of core genes in PAAD tissues versus normal tissues. It was found that SRC, HRAS, HSP90AA1, ANXA5, MMP9, CASP3, and MAPK1 were significantly upregulated 

in PAAD tissues (|Log2FC| < 1, P < 0.01), while ALB was significantly downregulated. There was no significant difference in IGF1 and EGFR (P > 0.05) (Figure 4A).

Figure 4: A. Differential expression analysis of core genes in PAAD

Correlation Analysis of Differential Expression of Core Genes and Survival Time in PAAD Patients

Using the TIMER database, we analyzed the correlation between the expression of core genes and the survival time of PAAD patients (Figure 4B). It was found that PAAD patients with high expression of CASP3, MAPK1, and EGFR had a lower survival rate compared to those with low expression (Log-rank P < 0.05), suggesting that the high expression of core genes CASP3, MAPK1, and EGFR affects the survival time of PAAD patients.

FIGURE:B. Survival time analysis of core gene expression in PAAD patients

Analysis of Core Gene Promoter Methylation

To explore the differences in methylation levels of core genes between normal tissues and PAAD tissues, we used the SMART database to analyze the differences in methylation levels of core genes between tumor tissues and normal tissues. Compared to the corresponding normal tissues, the methylation levels of SRC, HSP90AA1, ANXA5, CASP3, MAPK1, and EGFR were significantly reduced in PAAD tissues (P < 0.01) (Figure 4C).

FIGURE:C. Promoter methylation analysis of core genes.

Correlation  between  Core  Gene  Expression  and

Immune Infiltration of PAAD

Using the TIMER website, we analyzed the correlation of core gene expression with the infiltration of six types of immune cells in PAAD tissues: B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells. We explored the prognostic significance of differential expression of core genes in PAAD immunotherapy. The results showed that the expression of 10 core genes had a certain correlation with the six common immune cells,

among which HSP90AA1, ANXA5, IGF1, CASP3, MAPK1,

and EGFR were significantly positively correlated with four types of immune infiltrating cells: CD8+ T cells, neutrophils, macrophages, and dendritic cells (P<0.01) (Figure 5).

Figure 5: Analysis of the correlation between the expression of core genes and immune infiltration of PAAD.

Compounds - Molecular Docking of Core Target Proteins

Download the PDB format files of the corresponding proteins for the above core genes from the PDB database, and download the SDF format file of Cucurbitacin E from the PubChem database. Convert it to mol2 format using Openbabel software, import it into LeDock software for molecular docking, and visualize it using PyMOL (Figure 6).

Figure 6: Molecular docking model of Cucurbitacin E core target protein.

Except for ANXA5 and EGFR, the binding energies of the other 8 core target proteins are all less than -5.00kcal/ mol. Among them, HSP90AA1(5FWK): -7.61kcal/mol; HRAS(6Q21):-7.45kcal/mol; ALB(4IK9):-7.18kcal/mol; CASP3 (3DEJ):-6.42kcal/mol; MMP9(1GKC):-6.06kcal/ mol;    IGF1(1K3A):-6.06kcal/mol;MAPK1(4FUY):-6.04kcal/mol; SRC(2H8H):-5.39kcal/mol. The molecular docking results indicate that Cucurbitacin E has good binding activity with most of its core target proteins for treating PAAD.

Through research methods such as network pharmacology, 97 potential targets of Cucurbitacin E for the treatment of PAAD were screened. Analysis using the CytoHubba plugin identified the top 10 core genes, including SRC, HRAS, HSP90AA1, CASP3, ALB, ANXA5,MMP9, IGF1, EGFR, and MAPK1. The non-receptor tyrosine kinase SRC is believed to regulate various fundamental cellular activities that promote malignant phenotypes in various human tumors [6]. In the progression of PAAD, SRC is often activated, with the mechanism involving the downregulation of E-cadherin and the induction of epithelial-mesenchymal transition to promote disease progression [7]. HRAS, a member of the RAS small GTPase family, can activate the RAS-RAF-MEK-ERK pathway. Mutations in RAS family members lead to aberrant activation of the RAS signaling pathway. HRAS is a highly mutation-prone oncogene, particularly in head and neck 

cancer, bladder cancer, and lung cancer [8]. The active form of HRAS binds to downstream effectors, promoting cell growth and proliferation. Enhanced HRAS signaling contributes to the occurrence, invasion, and metastasis of various tumors [9]. HSP90AA1, a member of the heat shock protein family with molecular chaperone functions, plays an important role in the occurrence and development of cancer [10]. Studies have shown that low expression of HSP90AA1 in lung cancer tissues can inhibit the AKT1 and ERK pathways, thereby suppressing cell proliferation and inducing apoptosis [11]. ALB is an important antioxidant in the body, capable of neutralizing potential toxins and removing reactive oxygen species (ROS) [12], which can regulate various cell signaling pathways related to inflammation, transformation, tumor proliferation, angiogenesis, invasion, and cancer metastasis [13]. Research has found that downregulation of ROS is beneficial for the development of precancerous tumors, while increasing ROS levels in pancreatic ductal adenocarcinoma (PDAC) enhances the metastasis of cancer cells [14]. ANXA5, a member of the annexin family of calcium and phospholipid-binding proteins, is primarily expressed in the cytoplasm and can bind to calcium and phospholipids, acting as an endogenous regulatory factor involved in cell signaling, tumor cell differentiation, and metastasis [15,16]. Studies suggest that ANXA5 may participate in the carcinogenesis process by inhibiting protein kinase C activity in the RTK-Ras/Raf/MEK/ERK signaling pathway [17]. MMP9 is an important metalloproteinase that plays A key role in many biological processes [18]. MMP9 has been widely associated with tumor invasion, metastasis, and angiogenesis [19]. Recent studies indicate that MMP9 induces epithelial-mesenchymal transition in PAAD cells by activating PAR1 [20]. IGF-1, a peptide hormone of the insulin protein family, exerts almost all its biological effects through binding to the tyrosine kinase receptor (IGF1R), a transmembrane receptor of the insulin receptor family [21]. IGF-1 and IGF1R are highly expressed on the surface of PAAD cell lines, thereby initiating intracellular signaling related to proliferation, invasion, and angiogenesis [22]. EGFR is a transmembrane growth factor receptor with tyrosine kinase activity, and studies have shown its high expression in various tumor cells [23]. Its activation affects signaling pathways related to cell growth, differentiation, and proliferation [24]. MAPK1 (ERK2) is an effector kinase in the ERK signaling pathway that regulates biological activities such as cell proliferation, differentiation, apoptosis, or migration, and ERK is associated with various cancers, including breast cancer, hepatocellular carcinoma, and lung cancer [25].

Molecular docking results show that, except for ANXA5 and EGFR, the proteins corresponding to the other core genes have good binding activity with Cucurbitacin E. Gene differential expression analysis from the GEPIA database indicates that core genes SRC, HRAS, HSP90AA1, ANXA5, MMP9, CASP3, and MAPK1 are significantly upregulated in PAAD. It is speculated that Cucurbitacin E may act on PAAD through these targets. Survival analysis results from the TIMER database show that CASP3, MAPK1, and EGFR have statistically significant Log-rank P values (<0.05), affecting patient survival time. Immune infiltration analysis from the TIMER database shows that HSP90AA1, ANXA5, IGF1, CASP3, MAPK1, and EGFR are significantly positively correlated with four types of immune infiltrating cells: CD8+ T cells, neutrophils, macrophages, and dendritic cells (P<0.01), suggesting that core differential genes may influence the final survival rate by altering the tumor microenvironment [26]. Analysis of the methylation levels of core gene promoters between PAAD and adjacent normal tissues from the SMART database indicates that, compared to the corresponding normal tissues, the methylation levels of SRC, HSP90AA1, ANXA5, CASP3, MAPK1, and EGFR promoters are significantly reduced in PAAD tissues, suggesting that the methylation levels of these core genes may be related to the occurrence and development of PAAD [26]. GO and KEGG enrichment analysis results indicate that the functions of genes related to Cucurbitacin E treatment of PAAD mainly focus on peptide tyrosine autophosphorylation and protein serine/ threonine/tyrosine kinase activity. In KEGG enrichment, the effects of Cucurbitacin E on PAAD are mainly realized through pathways such as the VEGF signaling pathway, PI3K-Akt signaling pathway, mTOR signaling pathway, and MAPK signaling pathway. VEGF mediates angiogenesis, promoting the occurrence of tumor blood vessels in PAAD [27]. Existing studies have shown that the PI3K/AKT signaling pathway is closely related to the occurrence and development of various tumors and plays an important role in regulating PAAD cell proliferation, apoptosis, cell cycle, and angiogenesis [28]. The mTOR signaling pathway is abnormally activated in PAAD, promoting the proliferation and metastasis of PAAD cells and is associated with drug resistance [29]. The MAPK signaling pathway also plays an important role in the occurrence and development of PAAD, promoting its invasion and metastasis [30].

Although this study explored the potential mechanisms of Cucurbitacin E on PAAD through various methods, there are still some limitations. For example, while online databases and RNA-seq datasets provide rich data resources, the treatment of PAAD with Cucurbitacin E may involve multiple targets and pathways, and other unrecorded targets and pathways also need further attention and research. Finally, more accurate experiments, such as surface plasmon resonance (SPR), drug affinity responsive target stability (DARTS), and cellular thermal shift assays (CETSA), should be conducted to verify whether Cucurbitacin E indeed treats PAAD through the aforementioned targets. Additionally, combining animal models or clinical trials will help demonstrate the efficacy and safety of Cucurbitacin E in treating PAAD. Therefore, this will be a key area of focus in our next phase of research.

In summary, the potential molecular mechanisms of Cucurbitacin E in treating PAAD may involve the regulation of core genes such as SRC, HRAS, HSP90AA1, and CASP3, and affect pathways such as the PI3K-Akt pathway, MAPK pathway, and mTOR pathway to exert anti-cancer effects. Furthermore, data analysis suggests that CASP3 and MAPK1 may serve as candidate genes for predicting the prognosis of PAADr treatment. This study theoretically elucidates the potential molecular mechanisms of Cucurbitacin E in the treatment of PAAD through methods such as network pharmacology and bioinformatics, providing theoretical support and reference for subsequent basic experiments and clinical research

BS, HLZ, XDL, JMW, MT, and BX contributed to the conception design of the study. BS collected the original data, finished the analysis, and wrote the first draft of the manuscript. XDL, HLZ, and JMW helped to revise the manuscript and offered constructive comments on experimental studies. BX provided the funding, made many constructive comments for the final version, and supervised the study. All the authors approved the final version of the manuscript. 

This research was supported financially by the Science and Technology Program of Anshun City (No. ASKC-2024–17) and the Science and Technology Fund Project of the Guizhou Provincial Health Commission (No. gzwkj2024-523).

We thank Dr. Lunmin Bao and Xiaopin Hu for their assistance in our research, the PubChem database,and GeneCards database for providing analytical data. We also thank STRING and LeDock for providing the platforms used in our data analysis

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