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28 August 2026

Network Pharmacology-Based Elucidation of Piperlongumine Action on Oncogenic Survival and Immune-Evasive Pathways in Pancreatic Cancer †

,
,
and
1
Department of Biology, Faculty of Natural Life Science, Earth and Universe Sciences, Mohamed Khider University, P.O. Box 145 RP, Biskra 07000, Algeria
2
Department of Oral and Maxillofacial Surgery, University of California, 707 Parnassus Ave Suite D-1201, San Francisco, CA 94143, USA
3
Laboratory of Biotechnology, National Higher School of Biotechnology, Constantine 25000, Algeria
*
Authors to whom correspondence should be addressed.
Med. Sci. Forum2026, 48(1), 3;https://doi.org/10.3390/msf2026048003
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Abstract

Pancreatic cancer (PC) remains one of the most aggressive malignancies, largely due to late diagnosis, rapid metastatic progression, and the limited efficacy of current therapeutics. Piperlongumine, a bioactive alkaloid isolated from Piper longum, has attracted attention for its selective cytotoxicity toward cancer cells and its ability to modulate multiple oncogenic pathways. However, its molecular mechanisms in PC remain insufficiently understood. In this computational systems pharmacology study, a network pharmacology strategy combined with molecular docking was used to investigate the molecular targets and pathways associated with piperlongumine anticancer activity. Targets of piperlongumine and PC-related genes were retrieved from public databases and intersected to identify shared targets. Overlapping targets were used to construct a protein–protein interaction network using the STRING (v.12.0) platform. Hub genes were identified using the CytoHubba plugin using six topological algorithms. Functional enrichment analyses (Gene ontology and KEGG pathway) were performed using the ShinyGO 0.85.1 platform. Prognostic relevance of the hub targets was evaluated using Kaplan–Meier Plotter platform, while immune cell infiltration patterns were explored using the TIMER-2 database. Molecular docking was conducted using AutoDock Vina (1.5.7) to validate ligand-target interactions. A total of 237 intersecting genes were identified. Network analysis revealed several central hub genes, including TP53, Akt-1, STAT-3, CTNNB-1, IL-6, TNF-α, and Bcl-2. KEGG pathway analysis highlighted PC as the most enriched pathway, together with chronic myeloid leukemia, the AGE-RAGE signaling pathway in diabetic complications, prostate cancer, and C-type lectin receptor signaling. Survival analysis revealed significant prognostic associations for several hub genes, while TIMER analysis suggested correlations with tumor-associated immune cell infiltration. Molecular docking confirmed favorable binding affinities of piperlongumine toward key oncogenic targets, particularly Bcl-2 (−7.2 kcal/mol), TP53 (−6.2 kcal/mol), CTNNB-1 (−6.2 kcal/mol), and Akt-1 (−5.8 kcal/mol). Collectively, this study uncovers piperlongumine anticancer potential in PC, mediated through coordinated regulation of apoptosis, inflammatory, and immune pathways, highlighting its promise for future experimental and translational investigations.

1. Introduction

Pancreatic cancer (PC), of which pancreatic ductal adenocarcinoma (PDAC) accounts for approximately 90% of cases, remains one of the deadliest human malignancies and represents a major global health challenge [1]. Despite advances in diagnosis and treatment, PDAC is characterized by a poor prognosis, with a 5-year survival rate below 15%, largely because of late diagnosis, rapid metastatic progression, and resistance to therapy [1,2]. Its aggressive behavior is further exacerbated by substantial molecular heterogeneity and complex interactions between malignant cells and the tumor microenvironment, making durable therapeutic responses difficult to achieve [1,2]. Activating mutations in KRAS, detected in more than 90% of patients, represent the initiating oncogenic event, while the subsequent inactivation of tumor suppressor genes, including TP53, CDKN2A, and SMAD4 promotes genomic instability, metastatic progression, and therapeutic resistance [1,2,3]. These alterations converge on multiple dysregulated signaling pathways, including PI3K/AKT, MAPK/ERK, NF-κB, JAK/STAT, TGF-β, and Wnt/β-catenin signaling, which collectively regulate tumor cell proliferation, apoptosis evasion, invasion, angiogenesis, immune escape, and metabolic adaptation [1,2,3]. Beyond tumor-intrinsic alterations, PDAC is distinguished by a dense desmoplastic stroma and an immunosuppressive tumor microenvironment enriched with cancer-associated fibroblasts, pancreatic stellate cells, tumor-associated macrophages, myeloid-derived suppressor cells, and regulatory T-cells [2,3]. This highly organized stromal network not only restricts drug delivery but also promotes chronic inflammation, suppresses antitumor immunity, and facilitates metabolic reprogramming, thereby contributing substantially to disease progression and resistance to systemic therapies.
Surgical resection remains the only potentially curative treatment; however, fewer than 20% of patients present with resectable disease, and recurrence remains frequent even after surgery [1,4]. Consequently, systemic chemotherapy, primarily FOLFIRINOX or gemcitabine-based combinations, remains the cornerstone of treatment for advanced PDAC [1,3]. Although these approaches can improve survival, their efficacy is frequently compromised by systemic toxicity and acquired chemoresistance. Recent advances in precision oncology have enabled the development of targeted therapies against selected genomic alterations, including BRCA1/2, PALB2, NTRK, NRG1, mismatch repair deficiency, and KRAS^G12C^ mutations; however, these molecular alterations occur in only a limited subset of patients [5,6,7]. Likewise, immune checkpoint blockade has demonstrated limited efficacy in most PDAC patients, partly because of the highly immunosuppressive tumor microenvironment [8]. Collectively, these limitations emphasize the need to explore therapeutic strategies capable of modulating multiple molecular processes involved in PDAC progression.
Natural products continue to represent an important source of bioactive molecules for anticancer drug discovery because of their structural diversity and capacity to interact with multiple biological targets. Among these, piperlongumine (PL), a naturally occurring alkaloid amide isolated from Piper longum L., has emerged as a particularly promising anticancer candidate [9,10]. Unlike conventional cytotoxic drugs, whose therapeutic efficacy is often limited by indiscriminate toxicity toward normal proliferating tissues, piperlongumine exhibits remarkable selectivity toward malignant cells. Its biological effects have been associated with disruption of cellular redox homeostasis, particularly through interference with antioxidant defense mechanisms and subsequent accumulation of reactive oxygen species (ROS) in cancer cells. Because malignant cells rely heavily on antioxidant defense systems to maintain redox homeostasis, piperlongumine selectively disrupts these protective mechanisms, leading to excessive ROS accumulation, that in turn can trigger mitochondrial dysfunction, DNA damage, cell-cycle arrest, and apoptotic cell death while largely sparing normal cells with lower basal oxidative stress [9,10]. This unique mechanism offers an attractive therapeutic strategy to overcome one of the major limitations of conventional chemotherapy. The antitumor activity of PL was documented across numerous experimental cancer models, including pancreatic, breast, colorectal, lung, gastric, liver, ovarian, prostate, and hematological malignancies [11,12]. Indeed, experimental studies have implicated PL in the modulation of several signaling pathways and cellular processes, including PI3K/AKT/mTOR, NF-κB, JAK/STAT3, Wnt/β-catenin, p53, Bcl-2-associated pathways, MAPK, and Nrf2-mediated oxidative stress responses [11,12,13]. PL has also been investigated in the context of cell migration, invasion, epithelial–mesenchymal transition, angiogenesis, cancer stemness, autophagy, and therapeutic resistance [11,12,13]. Additionally, accumulating evidence suggests that PL enhances the efficacy of chemotherapy and radiotherapy by sensitizing resistant tumor cells through ROS-dependent mechanisms [11], highlighting its potential as both a therapeutic and chemo-sensitizing agent.
Encouragingly, several experimental studies have demonstrated the therapeutic potential of PL against pancreatic cancer. PL has been reported to inhibit the proliferation of pancreatic cancer cells, induce cell-cycle arrest and apoptosis, suppress tumor growth in xenograft models, and enhance the antitumor efficacy of gemcitabine. Mechanistically, these effects have largely been attributed to the disruption of intracellular redox homeostasis, resulting in excessive reactive oxygen species accumulation, together with inhibition of key survival pathways such as NF-κB and its downstream effectors, including cyclin D1, c-Myc, Bcl-2, VEGF, and MMP-9 [14]. Other investigations have further suggested that piperlongumine may promote ferroptosis, regulate autophagy, and overcome therapeutic resistance through oxidative stress-dependent mechanisms [13,15].
Despite these encouraging advances, the current understanding of piperlongumine in pancreatic cancer remains incomplete. Most previous studies have focused on validating specific molecular pathways or specific biological mechanisms, which may not fully capture the complex, interconnected nature of PDAC biology. Considering the remarkable molecular heterogeneity of pancreatic cancer and the extensive crosstalk among oncogenic signaling pathways, it is unlikely that the antitumor activity of PL can be fully explained by modulation of a limited number of signaling cascades. To date, a comprehensive systems-level characterization integrating target prediction, protein–protein interaction networks, functional enrichment analysis, prognostic relevance, tumor immune microenvironment, and ligand–target interactions has not been performed.
To address these knowledge gaps, the present study employed an integrative network pharmacology strategy combined with protein–protein interaction analysis, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses, survival analysis, immune infiltration assessment, and molecular docking to systematically elucidate the molecular mechanisms underlying the anticancer activity of piperlongumine in pancreatic cancer. Rather than re-examining previously reported mechanisms, this approach aims to expand the current understanding of piperlongumine by identifying novel therapeutic targets, uncovering interconnected signaling networks, and evaluating their clinical relevance. By integrating computational pharmacology with clinically relevant bioinformatics analyses, this study provides a comprehensive systems-level framework that may facilitate the development of piperlongumine as a multitarget therapeutic candidate for pancreatic cancer.

2. Materials and Methods

2.1. Identification of Piperlongumine-Associated Targets

To comprehensively characterize the molecular targets of piperlongumine, an integrative target-mining strategy was employed by combining multiple complementary databases encompassing experimentally supported interactions, pharmacological annotations, and ligand-based target prediction algorithms. Putative protein targets were collected from SwissTargetPrediction platform (top 100 targets, accessed on 4 January 2025), SuperPred database (probability ≥ 50%, accessed on 4 January 2025), Similarity Ensemble Approach database (SEA, accessed on 4 January 2025, score ≥ 0.45), STITCH database (score ≥ 0.8, accessed on 4 January 2025), Comparative Toxicogenomics Database (CTD, accessed on 5 January 2025), and the Therapeutic Target Database (TTD, accessed on 5 January 2025) [16,17]. The inclusion criteria for SwissTargetPrediction, SuperPred, SEA, and STITCH databases were adopted from the previously reported target-mining methodology [17] and were applied consistently to reduce the inclusion of low-confidence predicted associations. The canonical SMILES of piperlongumine was retrieved from the PubChem database and used as the query input for target prediction. The target lists obtained from all databases were merged, duplicate entries were removed, and protein identifiers were standardized to official gene symbols using the UniProt KB database, restricting the search to reviewed (Swiss-Prot) human entries [16,17].

2.2. Collection of Pancreatic Cancer-Related Genes

Genes associated with pancreatic cancer were compiled from the GeneCards (score ≥ 3, accessed on 7 February 2026) [18] and Online Mandelian Inheritance in Man (OMIM, accessed on 7 February 2026) databases using “pancreatic cancer (PC)” as the search keyword. The retrieved gene lists were merged to maximize disease-target coverage and duplicate entries were eliminated [17].

2.3. Intersecting Genes

The standardized target set of piperlongumine and pancreatic cancer were subsequently compared to define their molecular overlap. Intersection analysis was performed using the online Venn diagram platform (https://jvenn.toulouse.inrae.fr/app/index.html, accessed on 8 February 2026), enabling determination and extraction of the shared genes. The intersecting genes were considered possible therapeutic candidates through which piperlongumine may influence pancreatic cancer progression.

2.4. Protein–Protein Interaction (PPI) Network and Topology Analysis

Protein–protein interaction (PPI) data were obtained from the STRING database (version 12.0, accessed on 8 February 2026) using Homo sapiens as the reference organism, with the minimum required interaction confidence score set to 0.7 (high confidence). The resulting interaction network was imported into Cytoscape (version 3.10.2) for visualization and topological analysis. To identify the most influential nodes within the network, the CytoHubba plugin was employed [19]. Network centrality was evaluated using six complementary algorithms, namely Degree, Maximum Clique Centrality (MCC), Maximum Neighborhood Component (MNC), Edge Percolated Component (EPC), Closeness, and Radiality [19]. The top 10 ranked genes generated by each algorithm were extracted, and genes consistently ranked across multiple algorithms were considered hub genes.

2.5. Functional Enrichment Analysis

Gene ontology (GO) enrichment and Kayoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using the ShinyGO (version 0.85.1, accessed on 9 February 2026) platform to investigate the biological significance and signaling pathways associated with the overlapping targets [20]. GO analysis included the categories of Biological Process (BP), Molecular Function (MF), and Cellular Component (CC) terms with a false discovery rate (FDR)-adjusted p-value < 0.01 were considered significantly enriched.

2.6. Prognostic Significance of the Hub Genes

Clinical relevance of the identified hub genes was investigated through survival analyses using the Kaplan–Meier Plotter database (https://kmplot.com/analysis/index.php?p=home, accessed on 10 March 2026) [21]. Overall survival was evaluated in patients with pancreatic cancer by stratifying samples into high and low expression groups according to the median transcript expression levels generated by the platform. Hazard ratios (HRs), confidence intervals (CI), and log-rank p-values were used to estimate the prognostic impact of each gene, with statistical significance defined as p < 0.05.

2.7. Immune Cell Infiltration Analysis

The association between the expression of the hub genes and the immune microenvironment in pancreatic adenocarcinoma (PAAD) was investigated using the TIMER 2.0 web server (https://compbio.cn/timer2/, accessed on 10 March 2026) [22]. Correlation analyses were conducted between hub gene expression and the abundance of major infiltrating immune cell populations in pancreatic adenocarcinoma (PAAD), including B lymphocytes, CD4+ and CD8+ T lymphocytes, macrophages, neutrophils, and dendritic cells. Correlation coefficients (r) and corresponding p values were used to evaluate the significance of these associations.

2.8. Molecular Docking

To evaluate the binding behavior of piperlongumine toward the identified hub genes, molecular docking calculations were conducted. The 3D crystal structures of the selected proteins were retrieved from the Protein Data Bank (PDB) and prepared using Autodock Tools (1.5.7). Protein preparation included the removal of crystallographic water molecules and co-crystallized ligands, followed by the addition of polar hydrogen atoms, assignment of Kollman charges, and conversion of the structures into the PDBQT format. In parallel, the 3D structure of piperlongumine was downloaded from the PubChem database and subjected to geometry optimization using Avogadro software (version 2.0) with the MMFF94 force field. Following this, hydrogen atoms were added, Gasteiger partial charges were assigned, rotatable bonds were defined, and the optimized ligand structure was converted into the PDBQT format using AutoDock Tools. Docking simulations were carried out using AutoDock Vina (version 1.2.5). Individual configuration files were prepared for each protein to define the docking search space, including the grid box center coordinates and dimensions that encompass the corresponding ligand binding site. The center coordinates and dimensions used for each target are provided in Supplementary Table S1. The exhaustiveness parameter was set to 8 [23]. For each target, the highest binding pose was selected. Molecular interactions were visualized using Discovery Studio Visualizer (version 2025).

2.9. In Silico ADME, Drug-likeness, and Toxicity Prediction

The physicochemical properties, drug-likeness, and selected pharmacokinetic parameters were assessed using the SwissADME web tool. The potential toxicity profile of piperlongumine was predicted using the ProTox-3.0 web platform, including acute oral toxicity and the available toxicity endpoints.

3. Results

3.1. Identification of Candidate Targets

For piperlongumine, a total of 246 human protein targets were identified, while for pancreatic cancer, a number of 19,414 and 959 associated genes were retrieved from GeneCards and OMIM, respectively. Following dataset integration and removal of redundant entries, 19,425 genes related to pancreatic cancer were retained. Intersection analysis revealed a total of 236 shared genes between piperlongumine and pancreatic cancer. The overlap result is presented in Figure 1.
Figure 1. Overlapping genes between piperlongumine targets and pancreatic cancer genes; PL: piperlongumine, PC: pancreatic cancer.

3.2. Protein–Protein Interaction (PPI) Network and Hub Genes

The PPI network constructed using the 236 overlapping genes is presented in Figure S1. After excluding disconnected nodes, the resulting network consisted of 235 nodes connected by 1281 edges, with an average node degree of 10.9 and an average local clustering coefficient of 0.506.
The topological ranking of the network using six CytoHubba algorithms is summarized in Figure S2. Consensus analysis of the top 10 genes generated by each algorithm identified seven common genes, namely TP53, AKT1, STAT3, CTNNB1, IL6, TNF-α, and BCL2, which were selected for subsequent analysis (Figure S2). Notably, these hub genes also exhibited high GeneCards relevance scores, ranging from 46.24 for BCL2 to 257.00 for TP53 (Table S2), further supporting their strong association with pancreatic cancer.

3.3. Gene Ontology (GO) and KEGG Enrichment Analysis

Functional enrichment analysis of the 236 overlapping targets identified 985 significantly enriched biological process (BP) terms, 339 cellular component (CC) terms, 703 molecular function (MF) terms, and 239 KEGG pathways (FDR < 0.01). For clarity, the top 15 enriched terms from each GO category and the top 15 KEGG pathways are presented in Figure 2 and Figure 3, respectively.
Figure 2. GO enrichment analysis of the intersecting genes; (A) top 15 biological process terms; (B) top 15 molecular function terms; (C) top 15 cellular component terms.
Figure 3. Bar-plot representation of the top 15 KEGG enrichment pathways.
Within the BP category, the overlapping targets were primarily enriched in response to nitrogen- and oxygen-containing compounds, protein phosphorylation, cellular responses to chemical and endogenous stimuli, intracellular signal transduction, and the positive regulation of cell communication and signaling (Figure 2A). The MF category revealed significant enrichment for protein serine/threonine kinase activity, protein kinase activity, phosphotransferase activity, nucleotide binding, enzyme binding, catalytic activity acting on proteins, and anion binding (Figure 2B). The CC analysis indicated that the enriched targets were predominantly associated with protein kinase complexes, membrane rafts, membrane microdomains, receptor complexes, transferase complexes transferring phosphorus-containing groups, as well as the mitochondrion, nucleoplasm, cell junction, dendrite, neuronal cell body, and synapse (Figure 2C).
KEGG pathway enrichment analysis identified pancreatic cancer as the most significantly enriched pathway, with a fold enrichment of 28.97 involving 27 genes. In contrast, pathways in cancer contained the largest number of mapped genes, encompassing 60 target genes. Other highly enriched pathways included chronic myeloid leukemia, the AGE-RAGE signaling pathway in diabetic complications, prostate cancer, C-type lectin receptor signaling, yersinia infection, and FoxO signaling (Figure 3).

3.4. Prognostic Significance of the Hub Genes in Pancreatic Cancer

To evaluate the clinical relevance of the identified hub genes, Kaplan–Meier survival analysis was performed to examine the association between gene expression and overall survival (OS) in pancreatic cancer samples. The prognostic value of all genes was assessed based on hazard ration (HR), 95% confidence interval (CI), and the log-rank test.
Among the analyzed genes, six showed significant associations with overall survival (Figure 4). High expression of AKT1 (HR = 0.83, CI: 0.71–0.97, p = 0.0167), BCL2 (HR = 0.85, CI: 0.74–0.98, p = 0.0216), IL6 (HR = 0.84, CI: 0.72–0.98, p = 0.0299), and TNF-α (HR = 0.86, CI: 0.75–0.99, p = 0.0407) was significantly associated with prolonged overall survival. In contrast, elevated expression of STAT3 (HR = 1.26, CI: 1.08–1.47, p = 0.00259) and CTNNB1 (HR = 1.19, CI: 1.03–1.37, p = 0.0208) was significantly associated with poorer overall survival. Regarding TP53, although its expression showed a trend toward poorer overall survival (HR = 1.13, CI: 0.98–1.30), this did not reach statistical significance (p = 0.0844), suggesting that TP53 expression was not significantly associated with patient prognosis in the analyzed data.
Figure 4. Kaplan–Meier overall survival curves for the identified hub genes in pancreatic cancer. (A) AKT1; (B) BCL2; (C) IL6; (D) TNF; (E) STAT3; (F) CTNNB1; (G) TP53.

3.5. Association Between Hub Genes Expression and Immune Cell Infiltration in Pancreatic Cancer

Immune infiltration analysis was performed to investigate the relation between hub gene expression and the abundance of six major immune cell populations in pancreatic adenocarcinoma (PAAD). Results generated by the TIMER 2.0 database are summarized in Table 1. Overall, the analysis revealed that STAT3, IL6, BCL2, and CTNNB1 were significantly correlated with all examined immune cell populations. In particular, they exhibited the strongest correlations with CD8+ T-cell, macrophages, neutrophils, and dendritic cells. In contrast, TP53 displayed the weakest immune associations, showing significant correlations with only two immune cell types (Table 1).
Table 1. Correlation between expression of the hub genes and immune cell infiltration in pancreatic cancer based on TIMER analysis.
Specifically, expression of TP53 was selectively correlated with CD4+ T-cells (r = 0.248, p = 0.001) and neutrophils (r = 0.160, p = 0.036), whereas significant associations were observed with B-cells, CD8+ T-cells, macrophages, or dendritic cells (p > 0.05). Similarly, AKT1 expression was significantly and positively correlated with CD8+ T-cells, macrophages, neutrophils (r = 0.259, p = 0.0006), and dendritic cells, while no significant correlations were detected with B-cells or CD4+ T-cells (Table 1). Conversely, IL6 and BCL2 demonstrated the broadest immune infiltration profiles. IL6 expression was significantly and positively correlated with all examined immune cell populations, with the strongest association observed for neutrophils (r = 0.457, p = 3.08 × 10−10), followed by macrophages (r = 0.400, p = 5.94 × 10−8), dendritic cells (r = 0.388, p = 1.49 × 10−7), CD8+ T-cells (r = 0.350, p = 2.69 × 10−6), CD4+ T-cells (r = 0.180, p = 0.0191), and B-cells (r = 0.175, p = 0.0218). Similarly, BCL2 expression exhibited significant positive correlations with all six immune cell populations, with the strongest correlation observed for macrophages (r = 0.658, p = 1.29 × 10−22), followed by dendritic cells (r = 0.640, p = 4.037 × 10−21), neutrophils (r = 0.593, p = 1.18 × 10−17), CD8+ T-cells (r = 0.561, p = 1.36 × 10−15), B-cells (r = 0.497, p = 4.43 × 10−12), and CD4+ T-cells (r = 0.419, p = 1.42 × 10−8).
STAT3 and CTNNB1 expression demonstrated also significant and positive correlation with all analyzed immune cell populations, except with CD4+ T-cells (Table 1). For STAT3, the strongest positive correlations were observed with macrophages (r = 0.685, p = 4.258 × 10−25), followed by CD8+ T-cells (r = 0.641, p = 3.133 × 10−21), neutrophils (r = 0.559, p = 1.886 × 10−15), dendritic cells (r = 0.548, p = 7.729 × 10−15), and B-cells (r = 0.377, p = 3.468 × 10−7). Regarding CTNNB1 expression, the strongest positive correlations were observed with CD8+ T-cells (r = 0.619, p = 1.727 × 10−19), followed by macrophage (r = 0.536, p = 4.052 × 10−14), dendritic cells (r = 0.519, p = 3.528 × 10−13), neutrophils (r = 0.503, p = 2.152 × 10−12), and B-cells (r = 0.305, p = 4.775 × 10−5). Finally, TNF-α expression was associated with a statistically significant positive correlation with CD4+ T-cells (r = 0.515, p = 7.31 × 10−13), neutrophils (r = 0.461, p = 2.06 × 10−10), dendritic cells (r = 0.353, p = 2.05 × 10−6), B-cells (r = 0.215, p = 4.73 × 10−3), and macrophages (r = 0.203, p = 7.65 × 10−3), whereas no significant association was detected with CD8+ T-cells.

3.6. Molecular Docking Analysis

Molecular docking was performed to investigate the binding behavior of piperlongumine with the identified hub proteins. The docking protocol was validated by re-docking the corresponding co-crystallized native ligands into their respective binding sites (Table S3). RMSD values below 2.0 Å were obtained for the tested targets, supporting the reproducibility of the docking protocol (Figures S3–S8). IL6 was not subjected to redocking validation because no suitable co-crystallized ligand was available.
The results revealed differential binding affinities, with docking scores ranged from −7.2 to −4.2 kcal/mol (Table 2). The highest predicted score was observed with BCL2 (−7.2 kcal/mol), followed by TP53 and CTNNB1 (−6.2 kcal/mol). AKT1 exhibited an intermediate docking score (−5.8 kcal/mol), followed by TNF-α (−5.6 kcal/mol) and STAT3 (−5.2 kcal/mol). The lowest predicted affinity was obtained for IL6 (−4.2 kcal/mol). For AKT1, docking was performed using the structure 1UNQ, corresponding to the isolated pleckstrin homology (PH) domain; therefore, the predicted interaction represents binding to the PH domain rather than to the ATP-binding catalytic domain.
Table 2. Docking scores of piperlongumine toward the selected hub genes.
Detailed analysis of the docking poses revealed that piperlongumine established multiple non-covalent interactions, including conventional hydrogen bonds, carbon hydrogen bonds, π-donor hydrogen bonds, alkyl interactions, π-alkyl interactions, and π-π stacked interactions, which collectively contributed to ligand stabilization within the binding pockets (Figure 5 and Figure S9). In the BCL2 complex, the ligand formed a conventional hydrogen bond with Arg143 (3.05 Å), an additional carbon hydrogen bond with Leu134, and alkyl/π-alkyl interactions with Phe101 and Met112 (Figure 5A). In the TP53 complex, piperlongumine formed two conventional hydrogen bonds with Thr150 (2.71 Å) and Thr230 (2.99 Å), a carbon hydrogen bond with Asp228, and multiple alkyl interactions involving Leu145, Trp146, Val147, Pro151, Pro222, Pro223, Cys220, and Cys229 (Figure 5B). With CTNNB1 the compound established a conventional hydrogen bond with Ser246 (3.03 Å), three carbon hydrogen bonds with Gly245, Pro247, and Gln203, together with alkyl/π-alkyl interactions involving Lys242, Ala211, Val208, and Val248 (Figure 5C).
Figure 5. Three-dimensional molecular interaction analysis of piperlongumine with the hug proteins; (A) BCL2-piperlongumine complex; (B) TP53-piperlongumine complex; (C) CTNNB1-piperlongumine complex; (D) AKT1-piperlongumine complex; (E) TNF-α-piperlongumine complex; (F) STAT3-piperlongumine complex; (G) IL6-piperlongumine complex.
Binding to the PH domain of AKT1 was dominated by polar interactions, comprising three conventional hydrogen bonds with Lys14 (3.05 Å), Arg23 (3.06 Å), and Arg86 (3.06 Å), carbon hydrogen bonds with Arg15, Gly16, Gly17, and Asn53 (Figure 5D).
The TNF-α-piperlongumine complex was characterized by one conventional hydrogen bonds with Tyr151 (2.89 Å), π-donor hydrogen bond with Tyr59, together with π-alkyl interactions with His15, Leu36, and Ile155 (Figure 5E). Binding to STAT3 was stabilized by three conventional hydrogen bonds involving Tyr657 (2.98 Å), Lys658 (3.26 Å), and Met660 (3.18 Å), a carbon hydrogen bond with Tyr657, and additional alkyl and π-alkyl interactions with Leu666, Tyr640, Ile659, and Ile653 (Figure 5F). The weakest binding was observed for IL6, where the ligand established a conventional hydrogen bond with Gln281 (2.98 Å), a π-π stacked interaction with Phe279, and π-alkyl interaction with Phe229 (Figure 5G).

3.7. Predicted Physicochemical and Drug-likeness Properties

SwissADME analysis showed that piperlongumine had a molecular weight of 317.34 g/mol, with 0 hydrogen-bond donors and 5 hydrogen-bond acceptors. The molecule contained 4 rotatable bonds and exhibited a topological polar surface area (TPSA) of 65.07 Å. Its predicted lipophilicity (Consensus Log P) was 1.83, while the predicted aqueous solubility (ESOL Log S) was −2.91 (Table 3). Piperlongumine complied with both the Lipinski and Veber drug-likeness criteria and showed a predicted bioavailability score of 0.55. No PAINS alerts were predicted. The predicted synthetic accessibility score was 3.18, indicating relatively favorable synthetic accessibility (Table 3).
Table 3. Physicochemical and drug-likeness properties of piperlongumine predicted by SwissADME server.

3.8. Pharmacokinetic and Toxicity Prediction

The predicted pharmacokinetic and toxicity profiles of piperlongumine are summarized in Table 4. Piperlongumine was predicted to have high gastrointestinal absorption and to be permeable across the blood–brain barrier (BBB), while it was predicted to be neither a P-glycoprotein (P-gp) substrate nor an inhibitor of CYP2C19, CYP2C9, CYP2D6, or CYP3A4. In contrast, CYP1A2 inhibition was predicted. Regarding toxicity, piperlongumine showed a predicted LD50 of 1180 mg/kg, corresponding to toxicity class 4. It was predicted to be inactive for hepatotoxicity, nephrotoxicity, cardiotoxicity, carcinogenicity, mutagenicity, and cytotoxicity.
Table 4. Predicted pharmacokinetic and toxicity profile of piperlongumine.

4. Discussion

In this study, we sought to expand the current understanding of the anticancer mechanisms of piperlongumine in pancreatic cancer using a network pharmacology approach. Rather than providing definitive mechanistic evidence, the present study establishes a preliminary molecular framework that generates biologically possible hypotheses and prioritizes key targets and pathways for future experimental validation. Unlike conventional approaches that focus on individual targets, network pharmacology integrates compound–target interactions with disease-associated genes to reveal the interconnected molecular networks underlying therapeutic responses. This systems-level strategy is particularly relevant to pancreatic cancer, where multiple dysregulated signaling pathways collectively drive tumor initiation, progression, metastasis, immune evasion, and therapeutic resistance.
Using this integrative approach, we identified 236 overlapping targets between piperlongumine and pancreatic cancer, suggesting that its antitumor activity is mediated through the coordinated modulation of multiple molecular pathways rather than a single target. Protein–protein interaction network analysis further prioritized seven hub genes (TP53, AKT1, STAT3, CTNNB1, IL6, TNF, and BCL2) based on their consistent ranking across six CytoHubba algorithms. As highly interconnected nodes within the interaction network, these hub genes are likely to play central roles in the pharmacological activity of piperlongumine and provide a rational basis for the mechanistic interpretation of the subsequent findings. Among the identified hub genes, TP53, AKT1, STAT3, and CTNNB1 represent key regulators of the major oncogenic signaling networks that govern pancreatic adenocarcinoma development and progression. TP53 is one of the most frequently altered genes in PC, with mutations reported in approximately 50–75% of cases [24,25]. Although TP53 normally preserves genomic integrity by regulating DNA repair, cell-cycle progression, and apoptosis, accumulating evidence indicates that mutant TP53 proteins frequently acquire gain-of-function properties that actively promote pancreatic tumorigenesis. These alterations remodel transcriptional programs, enhance tumor cell proliferation and survival, facilitate invasion and metastasis, promote metabolic reprogramming, and contribute to therapeutic resistance through extensive interactions with multiple oncogenic signaling pathways [24,25,26]. The central role of TP53 in coordinating these biological processes is consistent with its identification as a major hub within the present interaction network.
In parallel, AKT1, STAT3, and CTNNB1 further emphasize the importance of survival and proliferative signaling in the molecular landscape predicted for piperlongumine. AKT1 is a core component of the PI3K/AKT pathway, which is frequently activated in PC and cooperates with the oncogenic KRAS to promote tumor growth, survival, metastasis, and resistance to apoptosis [27,28]. Likewise, constitutive activation of STAT3 integrates inflammatory and oncogenic signals to stimulate proliferation, epithelial–mesenchymal transition, cancer stemness, immune evasion, and metastatic dissemination, with the IL6/STAT3 axis representing one of the principal drivers of PC progression [29,30,31]. The growing success of therapeutic strategies targeting STAT3 further highlights its clinical relevance in pancreatic cancer [32,33,34]. Although mutations in CTNNB1 are relatively uncommon, aberrant activation of Wnt/β-catenin signaling is frequently observed in PC through ligand-dependent mechanisms and crosstalk with other oncogenic pathways. This signaling cascade promotes proliferation, invasion, epithelial–mesenchymal transition, and therapeutic resistance, and has been associated with more aggressive disease and poorer clinical outcomes [35,36]. Collectively, the identification of these signaling molecules suggests that piperlongumine may influence several interconnected oncogenic pathways rather than a single molecular axis.
Another notable hub gene identified in the present study was BCL2, a key regulator of the intrinsic apoptotic pathway. By preventing mitochondrial apoptosis, BCL2 enhances tumor cell survival and facilitates adaptation to cellular stress. Increasing evidence indicates that BCL2 also contributes to chemoresistance in PC. Ref. [37] demonstrated that gemcitabine-resistant PC cells exhibit elevated expression of anti-apoptotic BCL2 family proteins, while pharmacological inhibition of BCL2 restored the sensitivity of resistant cells to gemcitabine, highlighting BCL2 as a promising therapeutic target for overcoming chemoresistance. In addition, Ref. [38] reported that pancreatic cancer-derived exosomes promote tumor cell proliferation, invasion, and metastasis through activation of the TFAP2A/PTEN/AKT signaling pathway, which is accompanied by increased BCL2 expression and suppression of apoptosis.
Inflammatory signaling also emerged as a prominent feature of the identified hub network through the presence of IL6 and TNF, further reinforcing the intimate relationship between chronic inflammation and pancreatic carcinogenesis. IL6 has emerged as one of the principal drivers of pancreatic carcinogenesis. Increasing evidence indicates that IL6 participates throughout the entire course of PC, from the earliest pancreatic intraepithelial neoplasia (PanIN) lesions to invasive disease, metastasis, and cancer-associated cachexia [39,40]. By activating oncogenic signaling pathways, particularly the IL6/JAK/STAT3 axis, IL6 promotes tumor cell proliferation, survival, angiogenesis, and immune escape, thereby accelerating disease progression [39,41]. Moreover, experimental studies have demonstrated that IL6 cooperates with oncogenic KRAS to sustain pancreatic tumor development by regulating the inflammatory tumor microenvironment, whereas genetic ablation of IL6 markedly impairs disease progression, underscoring its indispensable role in pancreatic carcinogenesis [40]. Owing to these multifaceted functions, IL6 has been recognized as a promising diagnostic and prognostic biomarker, while therapeutic strategies targeting IL6 signaling, including monoclonal antibodies, are currently under active investigation [39]. TNF similarly contributes to the establishment of a pro-tumorigenic inflammatory microenvironment. Although TNF can induce apoptosis under certain conditions, chronic TNF signaling predominantly activates NF-κB and related inflammatory pathways, thereby promoting tumor cell survival, proliferation, angiogenesis, invasion, immune modulation, and resistance to therapy [40,42]. The simultaneous identification of IL6, TNF, and STAT3 is particularly noteworthy, as these molecules constitute a tightly interconnected inflammatory signaling network that plays a central role in PC progression. Their enrichment among the hub genes therefore suggests that modulation of inflammation-associated pathways may represent an important component of the predicted anticancer mechanism of piperlongumine.
The biological relevance of the identified hub genes was further supported by functional enrichment analyses, which demonstrated that these targets converge on key biological processes and signaling pathways implicated in pancreatic ductal adenocarcinoma (PDAC). Gene Ontology enrichment revealed a significant overrepresentation of processes related to responses to nitrogen- and oxygen-containing compounds, cellular responses to endogenous and chemical stimuli, intracellular signal transduction, protein phosphorylation, and cell communication. Collectively, these findings suggest that piperlongumine may interfere with the adaptive signaling networks that enable pancreatic cancer cells to survive under oxidative stress, chronic inflammation, hypoxia, and metabolic reprogramming, all of which are recognized drivers of PDAC progression. These observations were further reinforced by molecular function enrichment, which highlighted protein serine/threonine kinase activity, protein kinase activity, phosphotransferase activity, and enzyme binding. Given that aberrant kinase signaling is a defining feature of PDAC, the enrichment of these functions supports the hypothesis that piperlongumine acts through the coordinated modulation of phosphorylation-dependent signaling networks rather than through a single molecular target. This interpretation is consistent with the identification of AKT1, STAT3, and other signaling regulators as central hub genes within the interaction network.
KEGG pathway analysis provided additional support for this proposed mechanism. The enrichment of the pancreatic cancer pathway, together with the broad “pathways in cancer” category, confirms that the predicted targets are closely associated with the molecular circuitry underlying pancreatic tumorigenesis. Furthermore, the enrichment of pathways such as the AGE-RAGE, FoxO, C-type lectin receptor, and chronic myeloid leukemia pathways highlight the extensive crosstalk among signaling networks controlling proliferation, apoptosis, inflammation, oxidative stress, and immune regulation. Notably, several hub genes identified in the present study, including TP53, AKT1, STAT3, IL6, TNF, and BCL2, were mapped to multiple enriched pathways, underscoring their central role within the predicted pharmacological network of piperlongumine. Overall, these enrichment analyses complement the hub gene findings by demonstrating that the predicted therapeutic effects of piperlongumine are mediated through interconnected biological processes and signaling pathways rather than isolated molecular events.
To further evaluate the clinical relevance of the identified hub genes, their prognostic significance was investigated using overall survival analysis in patients with pancreatic adenocarcinoma. Elevated expressions of STAT3 and CTNNB1 were significantly associated with poorer overall survival, whereas higher expression of AKT1, BCL2, IL6, and TNF correlated with prolonged survival. In contrast, TP53 expression showed no significant association with patient prognosis. The adverse prognostic impact of STAT3 and CTNNB1 is consistent with their well-established oncogenic roles in PC. Supporting this concept, Ref. [43] demonstrated that pharmacological inhibition of STAT3 remodels the tumor microenvironment, enhances intratumoral drug delivery, and improves the therapeutic efficacy of gemcitabine, further highlighting STAT3 as an attractive therapeutic target in PC. Likewise, elevated CTNNB1 expression is in agreement with accumulating evidence where increased expression was associated with unfavorable clinical outcomes [44].
In contrast, the favorable prognostic associations observed for AKT1, BCL2, IL6, and TNF should be interpreted with caution. Although these molecules are well-established contributors to pancreatic tumor progression and therapeutic resistance, their biological functions do not necessarily translate into adverse overall survival. Prognostic associations derived from transcriptomic datasets reflect the combined influence of tumor heterogeneity, treatment response, the cellular composition of the tumor microenvironment, and post-transcriptional regulatory mechanisms. Consequently, genes that actively participate in PDAC pathogenesis may not invariably predict poor clinical outcome when evaluated at the mRNA level. In other words, being a driver of tumor biology does not automatically make a gene a negative prognostic biomarker.
Interestingly, the favorable prognostic association observed for AKT1 is consistent with previous reports demonstrating that both AKT and phosphorylated AKT1 expression correlate with prolonged survival in patients with PC, with phosphorylated AKT1 identified as an independent favorable prognostic factor [27]. Similarly, the prognostic value of BCL2 remains controversial. While BCL2 contributes to apoptosis resistance and chemoresistance in PC, conflicting clinical findings likely reflect differences in patient cohorts, tumor stage, molecular subtype, and the dynamic interplay among members of the BCL2 family [45].
A similar explanation may account for the unexpected prognostic associations observed for IL6 and TNF. Because both cytokines are produced not only by tumor cells but also by stromal fibroblasts and infiltrating immune cells, transcriptomic data generated from tumor tissues do not distinguish their cellular source. Consequently, elevated transcript levels may, in certain biological contexts, reflect an active immune microenvironment rather than enhanced tumor aggressiveness. This interpretation is supported by our subsequent immune infiltration analysis, which demonstrated significant positive correlations between IL6 expression and multiple immune cell populations. Likewise, the prognostic significance of TNF remains controversial. Although our analysis associated higher TNF expression with prolonged survival, previous studies have reported elevated TNF-α expression as an independent predictor of poor prognosis [46]. Such discrepancies likely reflect differences in patient populations, analytical platforms, and the pleiotropic nature of TNF, which exerts both tumor-promoting and antitumor functions depending on the cellular source, disease stage, and tumor microenvironment. Collectively, these findings emphasize the importance of interpreting inflammatory mediators within their biological context rather than solely on the basis of transcript abundance.
It is important to note that these findings should be interpreted in the context of the available experimental and clinical evidence rather than as evidence that AKT1, BCL2, IL6, or TNF exert intrinsically protective effects in pancreatic cancer. The concordance of the AKT1 finding with previous clinical observations provides external support for its prognostic association, whereas the conflicting evidence reported for BCL2, IL6, and TNF highlights the context-dependent nature of their prognostic roles. Nevertheless, the present survival analysis evaluates statistical associations and does not establish causality or demonstrate that these genes independently determine patient outcome. In addition, bulk transcriptomic data cannot resolve the cellular origin of cytokine expression. Therefore, the observed associations and their proposed biological explanations should be regarded as hypothesis-generating and warrant further investigation using multivariable clinical models and cell-type-resolved or experimental approaches.
Unlike the other hub genes, TP53 expression was not significantly associated with overall survival. This finding is not unexpected, as the prognostic relevance of TP53 in PC is primarily determined by mutation status and the functional consequences of mutant p53 proteins rather than transcript abundance. Indeed, TP53 mutations and p53 protein accumulation have consistently been shown to provide greater prognostic value than TP53 mRNA expression alone [47,48]. Therefore, the absence of a significant association in the present analysis does not diminish the biological importance of TP53 but rather highlights that expression-based analyses may not fully capture the complexity of p53 dysregulation in pancreatic cancer.
Complementing these findings, immune cell infiltration analysis revealed that most hub genes were significantly associated with multiple immune cell populations. Notably, STAT3, IL6, BCL2, and CTNNB1 exhibited the broadest immune infiltration profiles, whereas TP53 displayed a more selective pattern, showing significant associations only with CD4+ T-cells and neutrophils. The extensive immune correlations observed for STAT3 and CTNNB1 are consistent with their established roles in shaping the tumor immune microenvironment and further support their association with poor overall survival. Conversely, the broad immune infiltration profiles of IL6 and TNF provide a plausible explanation for their favorable prognostic associations observed in the present study. Because both cytokines are expressed by tumor cells as well as stromal and infiltrating immune cells, their transcript levels in bulk tumor tissues may reflect increased immune cell infiltration rather than exclusively tumor cell activity. Likewise, the strong immune associations identified for BCL2 may partly explain its heterogeneous prognostic significance in PC. In contrast, the relatively restricted immune infiltration profile of AKT1 and the selective associations observed for TP53 suggest that their transcript abundance may be less influenced by variations in immune cell composition than those of the inflammatory mediators.
Overall, these findings complement the survival analysis by demonstrating that the prognostic significance of several hub genes is closely linked to their interactions with the tumor immune microenvironment. They further emphasize that transcriptome-based biomarkers should be interpreted within their cellular context rather than solely on the basis of gene expression levels, providing additional support for the multifaceted mechanisms through which piperlongumine may exert its predicted therapeutic effects in PC.
Complementing the predictions generated by network pharmacology, molecular docking analysis provided structural evidence supporting the potential interaction of piperlongumine with the identified hub proteins. Piperlongumine exhibited favorable predicted binding affinities toward all evaluated targets, with the strongest interaction predicted for BCL2, followed by TP53 and CTNNB1, while AKT1, STAT3, and TNF showed moderate binding affinities. Importantly, these computational predictions are supported by accumulating experimental evidence demonstrating that piperlongumine modulates several of the signaling pathways identified in the present study. In pancreatic cancer models, piperlongumine has been shown to suppress tumor growth and sensitize cancer cells to gemcitabine through inhibition of NF-κB signaling accompanied by downregulation of multiple survival-associated proteins, including BCL2 [14]. Similarly, previous mechanistic studies have identified STAT3 among the molecular target of piperlongumine. Piperlongumine has been reported to interact with STAT3, inhibit its phosphorylation and nuclear translocation, and consequently suppress STAT3-dependent transcriptional activity, resulting in reduced proliferation and enhanced apoptosis in cancer cells [49]. Moreover, although inhibition of the PI3K/AKT signaling pathway has been recognized among the molecular mechanisms underlying the anticancer activity of piperlongumine in various cancer models [50,51,52], evidence supporting this mechanism in pancreatic cancer remain relatively limited.
Conversely, although TP53, CTNNB1, IL6, and TNF also demonstrated favorable docking scores and occupied central positions within the predicted interaction network, direct experimental evidence establishing these proteins as primary molecular targets of piperlongumine in pancreatic cancer remains limited. However, Ref. [53] have demonstrated that piperlongumine suppresses cell migration in osteosarcoma through downregulation of CTNNB1, together with other epithelial–mesenchymal transition-related genes. Similarly, piperlongumine has been reported to modulate inflammatory mediators, including IL6, TNF-α, and COX-2, largely through inhibition of NF-κB and STAT-dependent signaling rather than direct targeting of these cytokines [54]. Thus, rather than indicating direct inhibition of each individual protein, these findings likely reflect the capacity of piperlongumine to modulate interconnected oncogenic, inflammatory, and stress-response signaling networks. This interpretation is consistent with the multitarget pharmacological profile of natural products and with the network pharmacology framework adopted in the present study, in which therapeutic efficacy arises from the coordinated regulation of multiple molecular pathways rather than a single dominant target.
An important finding of the present study is the identification of several potential molecular targets that have not been extensively investigated as mediators of piperlongumine activity in pancreatic cancer. While previous experimental studies have primarily focused on pathways involving STAT3, NF-κB, PI3K/AKT, and apoptosis, our integrative network pharmacology and molecular docking analyses suggest that additional proteins, including CTNNB1, TP53, IL6, and TNF, may also contribute to the pharmacological effects of piperlongumine in PDAC. Although these computational predictions do not establish direct molecular interactions, they provide biologically possible hypotheses that merit further biochemical and functional validation.
From a translational perspective, the pharmacological potential of piperlongumine should also be considered in relation to its pharmaceutical and pharmacokinetic properties. Although the present SwissADME analysis predicted high gastrointestinal absorption and generally favorable drug-likeness characteristics, computational predictions cannot establish the extent of systemic exposure, metabolic disposition, or tumor-site distribution. Experimental evidence indicates that piperlongumine has limited aqueous solubility, with an experimentally determined intrinsic solubility of approximately 26 ± 2.9 µg/mL, together with pH-dependent stability [55]. Importantly, the same study demonstrated that this limitation can be addressed through appropriate solubilization approaches. For example, 10% Tween 80 increased piperlongumine solubility approximately 27-fold, while cyclodextrin-based formulations increased solubility to approximately 1 mg/mL [55]. Pharmacokinetic investigations have nevertheless demonstrated measurable systemic exposure following administration. In mice, plasma concentrations of approximately 1511.9, 418.2, and 41.9 ng/mL were reported at 30 min, 3 h, and 24 h, respectively, following piperlongumine administration [56]. Thus, the available evidence indicates that piperlongumine can achieve systemic exposure, while its limited aqueous solubility and chemical stability may influence the magnitude and consistency of this exposure. Human liver microsome studies further identified four oxidative metabolites and demonstrated the involvement of CYP1A2 and CYP3A4 in its metabolism [56].
Importantly, these pharmaceutical limitations do not necessarily preclude oral administration but highlight opportunities for formulation and delivery optimization. Piperlongumine-loaded nano-emulsions have been reported to increase its solubility to >2 mg/mL and oral bioavailability by approximately 1.5-fold compared with free piperlongumine [57]. Other delivery systems, including polymeric micelles and chitosan- or chitosan–fucoidan-based formulations, have likewise been reported to improve solubility, bioavailability, cellular uptake, or tumor delivery [56]. Notably, these improvements in pharmaceutical properties did not necessarily compromise biological activity; polymeric micelles and nano-emulsions have been reported to maintain or enhance piperlongumine-induced cytotoxicity and antitumor activity in preclinical models [56]. These findings suggest that appropriate delivery strategies may simultaneously address pharmaceutical limitations while preserving or enhancing the biological activity of piperlongumine.
Nevertheless, the available experimental evidence remains limited and is largely derived from preclinical models, different formulations, administration conditions, and tumor systems. Consequently, these findings cannot yet establish the optimal formulation, route of administration, systemic exposure, metabolic stability, biodistribution, or tumor penetration of piperlongumine, particularly in pancreatic cancer. More comprehensive studies integrating formulation development with pharmacokinetic, biodistribution, tumor-exposure, safety, and in vivo efficacy assessments are therefore required. Accordingly, the present ADME predictions should be considered supportive preliminary evidence rather than a definitive assessment of pharmaceutical suitability.
Finally, we acknowledge that this study has several limitations. The computational target-prediction and network pharmacology analyses may include false-positive predictions and do not establish tissue-specific or causal relationships. Similarly, survival, immune-infiltration, and molecular-docking analyses provide hypothesis-generating evidence rather than experimental confirmation of target engagement or therapeutic activity. In particular, the survival and immune-infiltration analyses were based on uncorrected p-values; therefore, associations with marginal statistical significance should be interpreted with caution and require confirmation in independent studies. In addition, the ADME and available pharmacokinetic evidence remain largely computational or preclinical, and further experimental studies are required to establish the pharmacokinetic profile, tumor exposure, and therapeutic relevance of piperlongumine. Therefore, the findings should be considered a framework for further experimental validation rather than definitive evidence of clinical efficacy.

5. Conclusions

In conclusion, the present study provides a systems-level computational characterization of potential molecular mechanisms underlying piperlongumine activity in pancreatic ductal adenocarcinoma. Integration of network pharmacology, protein–protein interaction analysis, functional enrichment, survival analysis, immune-infiltration profiling, and molecular docking identified candidate targets and signaling pathways potentially involved in its anticancer activity. Collectively, these analyses suggest a potential multitarget mechanism involving apoptosis, inflammatory signaling, oncogenic pathways, and the tumor immune microenvironment. While the findings are consistent with previously reported mechanisms involving STAT3, PI3K/AKT, and apoptosis, they also prioritize additional candidate targets, including CTNNB1, TP53, IL6, and TNF, for further investigation. However, these computational findings should be considered hypothesis-generating and do not establish direct target engagement, pharmacological efficacy, or clinical suitability. Given the limited solubility and pharmacokinetic properties of piperlongumine, formulation and drug-delivery strategies, including nano- and polymer-based systems, may be promising approaches to improve its solubility, stability, bioavailability, and tumor exposure. Further pharmacokinetic, formulation, and in vivo studies are therefore warranted to validate the predicted mechanisms and determine whether optimized delivery can translate the pharmacological potential of piperlongumine into therapeutic benefit.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/msf2026048003/s1, Figure S1: STRING-derived protein-protein interaction (PPI) network of the overlapping genes; Figure S2: Candidate hub genes ranked by CytoHubba-based algorithms; (A) Degree; (B) MCC; (C) MNC; (D) EPC; (E) Closeness; (F) radiality; Figure S3: Superposition of docked and co-crystallized conformations of the native ligand within the BCL2 active site. The red color represents the native co-crystallized ligand and the green color is the docked ligand; Figure S4: Superposition of docked and co-crystallized conformations of the native ligand within the TP53 binding site. The green color represents the native co-crystallized ligand and the grey color is the docked ligand; Figure S5: Superposition of docked and co-crystallized conformations of the native ligand within the CTNNB1 binding site. The green color represents the native co-crystallized ligand and the yellow color is the docked ligand; Figure S6: Superposition of docked and co-crystallized conformations of the native ligand within the AKT PH domain. The red green represents the native co-crystallized ligand and the black color is the docked ligand; Figure S7: Superposition of docked and co-crystallized conformations of the native ligand within the TNF-α binding site. The green color represents the native co-crystallized ligand and the blue color is the docked ligand; Figure S8: Superposition of docked and co-crystallized conformations of the native ligand within the STAT3 binding site. The green color represents the native co-crystallized ligand and the purple color is the docked ligand; Figure S9: Two-dimensional molecular interaction analysis of piperlongumine with the hug proteins; (A) BCL2-piperlongumine complex; (B) TP53-piperlongumine complex; (C) CTNNB1-piperlongumine complex; (D) AKT1-piperlongumine complex; (E) STAT3-piperlongumine complex; (F) TNF-α-piperlongumine complex; (G) IL6-piperlongumine complex; Table S1: Docking grid-box parameters for the investigated target proteins; Table S2: GeneCards relevance scores of the identified hub genes; Table S3: Co-crystallized ligands and RMSD values obtained from redocking validation for each target protein.

Author Contributions

Conceptualization, R.R. and I.D.; methodology, I.D.; software, R.R. and I.D.; validation, R.R., L.J. and A.B.; formal analysis, R.R., L.J. and A.B.; resources, R.R., L.J. and A.B.; data curation, R.R.; writing—original draft preparation, I.D. and R.R.; writing—review and editing, R.R.; visualization, L.J. and A.B.; supervision, R.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

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