Abstract Background Breast cancer is the most diagnosed cancer in women and the second leading cause of cancer-related deaths worldwide. Chemotherapy faces challenges like drug resistance, side effects, and recurrence, underscoring the need for innovative therapies. This study explores cryptolepine, a natural compound, for its therapeutic potential against heterogeneous BC by targeting specific molecular mechanisms. Methods we conducted an ADMET analysis to assess cryptolepine’s pharmacokinetic properties and drug-likeness. Target prediction was performed using SWISS-TARGET-PREDICTION and Integrative Pharmacology for BC. Identified targets were cross-referenced with BC-related genes from Gene Atlas, TCGA, and OMIM. Protein–protein interactions were analyzed using STRING, and pathway enrichment was assessed using KEGG and ShinyGO. Molecular docking and dynamics simulations evaluated cryptolepine’s binding efficacy while in-vitro assays, including proliferation studies and mRNA expression analysis, validated these findings. Results Cryptolepine demonstrated favorable drug-likeness and multi-target activity, interacting with key cancer pathways such as p53, STAT3, and PI3K-Akt. Network pharmacology revealed its potential to reduce drug resistance. Cryptolepine regulated important genes (PTGS2, STAT3, CCND1) across critical pathways (cAMP, PI3K/AKT, P53, IL6/JAK2/STAT3). Molecular docking confirmed strong binding (ΔG − 8.2 kcal/mol), and in-vitro assays showed IC50 values of 4.6 μM for MDA-MB-231 and 3.1 μM for Mcf-7. mRNA expression analysis indicated increased cytochrome C and BAX, while pro-caspase levels decreased. Conclusion Cryptolepine shows promise as a therapeutic candidate for BC. Future research should optimize its pharmacological profile for specificity and reduced toxicity. Graphical Abstract [28]graphic file with name 12672_2025_3158_Figa_HTML.jpg Supplementary Information The online version contains supplementary material available at 10.1007/s12672-025-03158-y. Keywords: BC, STAT3, P53, Signaling pathways, Cryptolepine Highlights * Breast cancer is highly prevalent; resistance, relapse, and toxicity limit chemotherapy, demanding targeted therapies. * This study investigates cryptolepine’s anti-cancer role through molecular mechanism and pathway interaction analysis. * Cryptolepine shows drug-like properties and modulates key pathways like p53, STAT3, PI3K-Akt, and tumor progression axes. * It may counteract resistance and cancer stemness, offering promise for aggressive, refractory breast cancer forms. Supplementary Information The online version contains supplementary material available at 10.1007/s12672-025-03158-y. Introduction Among women, breast carcinoma (BC) is the most frequently identified type of malignancy. According to estimates, BC cases account for an even larger percentage (36%) of all cancer cases among women aged 30 to 49 [[29]3]. This is more than the combined number of the next four most common malignancies in this age group: thyroid (14%), colorectal, melanoma, and cervix (6% each). In 2020, BC accounted for 25% of all female cancer diagnoses, making it the most widespread cancer among women. Its incidence has been gradually increasing worldwide, particularly in underdeveloped countries [[30]34]. By 2040, there will likely be more than 40.0% more new instances of BC diagnosed, with an estimated yearly incidence of 3 million cases. Additionally, it is anticipated that mortality rates would increase by almost 50% attaining one million cases by 2040 [[31]89]. The primary and invasive types of breast carcinoma are classified as Luminal A, HER2 overexpression, Luminal B and TNBC according to genetic alterations, cellular molecular markers, and hormone receptor status [[32]44, [33]93]. However, it comes with serious issues such as the progression of resistance and recurrent breast carcinoma, particularly in the hostile Triple negative BC subtype, which responds poorly to the available hormonal and targeted therapies [[34]2]. Prognoses often vary considerably due to the heterogeneous nature of the disease [[35]88]. The primary treatment for BC remains surgery often complemented by radiation therapy, chemotherapy, endocrine therapy, and targeted treatments. Notably, molecular targeted therapy has revolutionized BC management by specifically targeting signaling pathways that drive tumor initiation and metastasis [[36]56]. This approach allows for the selective elimination of cancer cells while preserving surrounding healthy tissue by targeting the disease at both the molecular and cellular levels [[37]97]. These advances have marked the beginning of a new era in the biological treatment of breast cancer, significantly improving patient prognosis. Targeted therapies for BC focus on molecules that are critical for the growth, survival, and progression of cancer cells [[38]90]. Because of their diverse range of origins, biological activity, and structural variety, natural products hold great potential for contemporary drug development [[39]21]. In recent decades, natural products made from biodiversity have greatly benefited humans, especially as sources for targeted medicines. Islam, M. R., and colleagues [[40]39] evaluated a range of natural compounds and demonstrated that several of them exhibited greater efficacy than conventional breast cancer (BC) therapies. Their findings suggest that these natural agents could enhance treatment outcomes and reduce mortality rates, potentially contributing to a cure. These compounds exert potent anticancer effects by modulating various cellular processes, including angiogenesis, differentiation, proliferation, migration, invasion, and metastasis [[41]29]. Through the use of structural transformation and optimization approaches, scientists have created novel compounds from natural products that are less poisonous and have improved anti-resistance qualities. Combining computer-aided drug design and medicinal chemistry has allowed for this breakthrough [[42]68]. Over 64% of novel medicines may trace their roots back to natural compounds and their derivatives, making them crucial in the design and development of new drugs [[43]52]. The primary indoloquinoline that is isolated from Cryptolepis sanguinolenta, a well-known anti-malarial plant from West Africa, is Cryptolepine. Among its many pharmacological effects is its strong anti-malarial effectiveness against strains that are susceptible to and resistant to chloroquine (CQ) [[44]27]. Cryptolepine is said to have a variety of biological properties, including antiplasmodial [[45]15], antifungal [[46]81], antihypertensive [[47]65], antibacterial [[48]9] and anti-inflammatory properties [[49]64]. In West and Central African nations, cryptolepine is used as an anti-malarial drug. In traditional medicine, the root decoction of C. sanguinolenta is used to treat both infectious and non-infectious disorders, including malaria. Furthermore research has demonstrated that cryptolepine exhibits cytotoxic effects against several mammalian cancers [[50]66, [51]67]. Several in-vitro and in-vivo studies evaluated the anti-tumor effects of CRP and reported it is promising therapeutic agent for the treatment of melanoma [[52]67], hepatocellular carcinoma [[53]22], colorectal carcinoma [[54]76] and breast adenocarcinomas [[55]59, [56]109]. To identify new treatment options for BC, we investigated the natural compound cryptolepine (CRP), known for its medicinal properties and potential as a repurposed anti-cancer agent. Previously studied for its efficacy against triple-negative BC (TNBC), CRP has shown promising anti-cancer activity and is already used as an anti-malarial drug. Building on this foundation, we evaluated CRP's broader therapeutic potential and mechanisms of action across BC subtypes, emphasizing its ability to target key oncogenic pathways. Using network pharmacology techniques and a variety of computational approaches, we examined the mechanisms of action of cryptolepine against breast tumor. Finding important and cutting-edge treatments is urgently needed, since the number of instances of BC among all carcinomas has significantly increased. Our research indicates that cryptolepine is a new and successful therapeutic, which has been confirmed by the network pharmacology method. Network pharmacology is a collaborative research methodology that can be utilized to explore the complex underlying factors of diseases and to build up valuable therapeutic strategies from a comprehensive schematic perspective [[57]33, [58]47, [59]48, [60]107]. Network pharmacology is an innovative interdisciplinary approach that integrates pharmacology, pharmacodynamics, systems biology, and bioinformatics to systematically explore the interactions between drugs, targets, and disease networks. Unlike traditional single-target approaches, it enables the analysis of multi-component therapeutics—such as natural products—by revealing their synergistic effects and complex mechanisms of action across multiple biological pathways and molecular targets. This holistic perspective is particularly valuable for addressing multifactorial diseases like cancer, where effective treatment often requires modulation of several interconnected signaling pathways rather than isolated targets [[61]37, [62]74, [63]110]. It offers transformative potential in personalized medicine and toxicogenomics by enabling advanced analysis of complex patient data. Its powerful algorithms can process and integrate diverse datasets—such as genomic information, clinical records, and patient histories—to identify patterns and build predictive models [[64]58, [65]86]. These models can forecast treatment outcomes, assess toxicity risks, and support personalized therapeutic strategies by expediting data interpretation and enhancing decision-making [[66]35, [67]36, [68]38, [69]85]. This multidisciplinary approach integrates various scientific fields, including network biology, computer science, systems biology, and pharmacology. Network pharmacology, in particular, serves as an effective strategy to unravel the complex multicompound, multitarget, multipathway, and multipharmacological properties of herbal medicines. Consequently, it is widely employed to identify therapeutic targets and active constituents responsible for the pharmacological effects of these medicines. Network pharmacology examines the interactions between key targets and constituents, shedding light on their collective impact on interconnected systemic processes [[70]33, [71]47, [72]48, [73]72, [74]107]. Materials and methods Cell culture reagents BC cell lines MDA-MB-231 and Mcf-7 were obtained from NCCS Pune, India. They were cultured in RPMI-1640 (Roswell Park Memorial Institute Medium-1640) culture media supplemented with 10% Fetal Bovine Serum (FBS) and 1% penicillin–streptomycin that were obtained from Himedia, Gibco Thermofischer Scientific USA. TrypLE™ Express enzyme was procured from Thermofischer scientific. Cryptolepine was obtained from Sigma-Aldrich (Cat no. C7124, CAS: 480-25-2-10MG). Compound: cryptolepine After gathering information on the compound “Cryptolepine” from the literature and our previous study, the candidate compound was evaluated for drug-likeness [[75]22, [76]73, [77]95]. Subsequently, the compound was analyzed using the SwissADME database (Antoine [[78]18, [79]19]), which facilitates the calculation of physicochemical descriptors and prediction of pharmacokinetic properties, drug-likeness, and pharmacological characteristics—particularly regarding ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) parameters—thereby streamlining the drug development process [[80]16, [81]18, [82]19, [83]98]. Prospective target gene prediction for cryptolepine and BC Chemical components' target genes were predicted using SWISSPROT-TAREGT-PREDICTION [[84]20], and the Integrative Pharmacology-based Research Platform for BC predicts targets by comparing structural similarity evaluations with a known molecule [[85]30, [86]104]. By calculating the primary potential macromolecular targets of a small molecule, the Swiss Target Prediction approach provides information about chemical compounds and their biological function [[87]20]. The target was predicted by analyzing its structural resemblance to a known chemical. Targets from the Swiss Target Prediction database were filtered using a probability score threshold of less than 0.1, which made it possible to choose pertinent targets associated with the chemical of interest. The phrase "BC" was also used to access the Gene Cards website [[88]87] which offers thorough and easily navigable information on all interpreted and anticipated genes linked to human illnesses in a investigated, integrated database [[89]31, [90]87]. The database was searched using the phrase "BC" in addition to other important terms like "triple negative BC". Discovery of common target genes and creation of a unified network for cryptolepine and BC BC samples were evaluated to determine the top 10% of mRNA genes with the greatest levels of over- and under-expression, as documented in the TCGA database. Data from the TCGA database [[91]100], OMIM [[92]32], and the Gene Atlas of BC [[93]17] were then analyzed to determine the common target genes between cryptolepine and BC. We created a Venn diagram to show the overlapped genes by contrasting the genes unique to BC found in the TCGA data (with the sample size of 201) with the putative gene targets of cryptolepine. A graphic depiction emphasizing the common targets between cryptolepine and BC was made possible by this investigation. This method gave insights into the potential of cryptolepine by elucidating the precise genes and pathways that it may affect in the setting of BC. Constructing a mutually beneficial network for BC and cryptolepine Protein–protein interactions (PPIs) are valuable for identifying critical regulatory genes within disease pathways. The STRING database [[94]91], which compiles extensive information on known and predicted PPIs across species [[95]92] was utilized in this study to examine PPIs related to BC. For this analysis, a minimum confidence threshold of 0.4 was applied, with high-confidence interactions set at a score of 0.7 or higher before submitting confirmed targets to STRING. Only interactions involving Homo sapiens were considered. Following these parameters, PPI data were obtained, focusing on interactions associated with the most relevant proteins in BC. Our analysis specifically targeted the top 30 proteins most highly implicated in BC, as these are hypothesized to be primary targets of cryptolepine. This approach provided a refined view of cryptolepine's potential mechanisms in modulating protein interactions that play a significant role in BC development and progression. Gene Ontology (GO) evaluation and enrichment analysis of pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) The shared target genes identified were further analyzed using KEGG pathway enrichment [[96]43] and Gene Ontology (GO) analysis via ShinyGO [[97]4], with Homo sapiens as the exclusive species parameter [[98]103]. The results were visualized through a combination of bar charts and bubble charts. Enrichment results for Gene Ontology terms were grouped into three main categories: cellular component (CC), biological process (BP), and molecular function (MF), each represented in bar chart format. Additionally, a KEGG pathway enrichment bubble chart was used to display pathways significantly associated with the target genes. These visualizations provided insights into the biological roles, functional interactions, and pathways that cryptolepine may impact within BC cells, highlighting potential mechanisms of action and therapeutic implications. Investigation of the interaction network between cryptolepine, target genes, and pathways To illustrate the relationships among chemical prescriptions, active components, diseases, target genes, and pathways, Cytoscape 3.8.0 [[99]82] was used to construct a network model. In this network, each element—drug, disease, target gene, and pathway—serves as a "node," while the connections between them are termed "edges", Nodes with a degree (a measure of connections) higher than the network's average were selected for further screening, focusing on elements with high centrality, which indicates their significance within the network. Degree centrality, or the number of direct connections a node has, is a fundamental measure of a node’s importance in network analysis. Cytoscape, an open-source tool, enables researchers to visualize and analyze complex networks of molecular interactions and biological pathways, making it an essential resource for understanding multifaceted biological relationships [[100]28, [101]106]. In addition, Cytoscape can analyze these networks after integrating them with gene expression profiles, annotations, and other complex datasets. To assess each node’s importance, three key topological parameters—degree, betweenness, and clustering coefficient—can be calculated. The "degree" of a node indicates the number of direct connections it has within the network, while "betweenness" reflects a node's role in connecting different parts of the network. Following quality assessment, all active components with a degree above the network's average were selected for further screening, as degree serves as a straightforward measure of node centrality. Cytoscape’s ability to incorporate various biological data types and compute these topological metrics makes it a valuable tool for interpreting the interactions and centrality of components within complex molecular and biological networks [[102]77]. Molecular docking This study focuses on elucidating the binding properties of the p53 protein by employing geometrically optimized ligands, specifically 82,143 (cryptolepine) and 52,918,385 (APR-246). Before conducting molecular interaction experiments, the protein was analyzed for any missing side-chain residues using the Open Molecular Mechanics (MM) simulation program [[103]24, [104]25]. For molecular docking investigations, Autodock version 4.2.6 was utilized [[105]61]. The binding cavity was defined based on the co-crystallized X-ray structure of the p53 protein obtained from the RCSB Protein Data Bank (PDB) [[106]8], and the positions of the residues were calculated using the three-dimensional coordinates of the co-crystallized ligand. Energy minimization was performed following cavity selection, employing both the conjugate gradient and steepest descent methods. The receptor and target molecules were subsequently converted to pdbqt format after incorporating non-polar hydrogens [[107]57]. The spacing between the grid boxes was set at 0.3 Ångström. To determine the lowest free energy of binding (ΔG), docking experiments for the protein–ligand complex were carried out using the Lamarckian Genetic Algorithm (LGA). Three iterations of molecular modeling were performed with default parameters, including a maximum of 2700 generations, 2,500,000 evaluations, and 50 solutions for each iteration. After the docking process, Root Mean Square Deviation (RMSD) clustering maps were created by re-clustering the results with clustering tolerances of 0.25, 0.5, and 1. This approach was employed to identify the best cluster with the lowest energy score and the highest population, ultimately providing insights into the binding characteristics of the ligands to the p53 protein. Molecular dynamics simulation (MDS) Docking complexes of the p53 protein with ligands 82,143 (cryptolepine) and 52,918,385 (APR-246) underwent molecular dynamics (MDS) simulations using Desmond 2020.1 from Schrödinger, LLC [[108]10]. The simulations employed the OPLS-2005 force field along with an explicit solvent model utilizing SPC water molecules [[109]10, [110]13, [111]42, [112]83]. To neutralize the system's 0.15 M charge, sodium ions (Na +) were added, and physiological conditions were simulated by including NaCl solutions. Initially, an NVT ensemble was utilized to equilibrate the system for 15 ns, allowing for retraining across the protein–ligand complexes. This was followed by a brief minimization and equilibration phase using an NPT ensemble for 20 ns. The NPT ensemble was configured using the Nose–Hoover chain coupling method [[113]54], maintaining a temperature of 37 °C with a relaxation time of 5.0 ps and constant pressure at 1 bar throughout the simulations. The time step used for the simulations was two femtoseconds. Pressure control was implemented using the Martyna-Tuckerman-Klein chain coupling method and a barostat with a relaxation length of 2 ps [[114]53]. Long-range electrostatic interactions were calculated using the particle mesh Ewald method, with a cutoff radius of 9 Å for Coulomb interactions [[115]94]. The RESPA integrator was employed to compute the bonded forces for each trajectory with a time step of two femtoseconds. To assess the stability of the molecular dynamics simulations (MDS), several metrics were calculated, including solvent accessible surface area (SASA), root mean square fluctuation (RMSF), radius of gyration (Rg), and the number of hydrogen bonds (H-bonds). These parameters provided insights into the structural dynamics and interactions of the p53-ligand complexes during the simulations. Proliferation assay Following the determination of mechanisms followed by cryptolepine in BC we looked at the drug's time- and dose-dependent effects on cell growth (MDA-MB-231 and Mcf-7). In a 96-well plate, 3 × 10^3 cells were cultured in each well. After 24 h, the culture was disposed of, and the cells were exposed to CRP at respective doses of 2.5 μM, 5 μM, 10 μM, 20 μM, 40 μM, and 60 μM, as well as DMSO (negative control) in each well with four replicates. Following a 24- to 72-h incubation period, the cells were measured for proliferation using the Vybrant Proliferation Kit (Cat. No. V-13154, Thermofischer Scientific, USA) [[116]69, [117]73]. DAPI Six-well plates were seeded with 8 × 10^4 MDA-MB-231 cells per well. Twenty-four hours were spent exposing the cells to CRP with two replicates. Following that, they were fixed for 20 min in 4% paraformaldehyde and then stained for a further of 15 min using DAPI. PBS was used to wash the cells, and a fluorescence microscope/Floid (Thermofischer) was used to analyse them [[118]5]. qRT-PCR MDA-MB-231 cells were cultured in 100 mm Moxcare culture plates until approximately 70% confluency was reached. After 24 h, the culture medium was replaced with fresh medium containing the compound CRP, and the cells were incubated in a CO₂ incubator at 37 °C for an additional 24 h. Total RNA was then extracted following the standard protocol [[119]12] using two replicates. Subsequently, 500 ng of cDNA was synthesized and used in quantitative real-time PCR (qRT-PCR) with SYBR Green PCR Master Mix (Promega), in accordance with the manufacturer's instructions [[120]55]. GAPDH served as the internal control to ensure equal sample loading. Relative mRNA expression levels of target genes were calculated using the ΔCT method and normalized to GAPDH expression. Each reaction was independently repeated twice [[121]26]. The primer sequences used are listed in Table [122]1. Table 1. Primers used and their sequence Oligo name Sequence (5’-3’) Tm GC% μl for 100 μM W. Stock (10 μM) BAX FORWARD TGCTTCAGGGTTTCATCC 61.3 50 387 10 BAX REVERSE CCACTCGGAAAAAGACCT 59.3 50 551 10 Cyt. C FORWARD TGGGTGATGTTGAGAAAG 56.9 44.4 428 10 Cyt. C REVERSE CTCCATCAGTGTATCCTC 52.2 50 600 10 CAS 3 FORWARD CTGCCGTGGTACAGAACT 58.8 55.5 611 10 CAS 3 REVERSE TGTCGGCATACTGTTTCA 58.7 44.4 483 10 GAPDH FORWARD TGGAAGGACTCATGACCACA 64.4 50 632 10 GAPDH REVERSE CCAGTAGAGGCAGGGATGAT 63.0 55 271 10 [123]Open in a new tab Statistics The IC₅₀ values of the compounds were determined using non-linear regression analysis in GraphPad Prism version 8.43. Statistical significance was assessed through one-way ANOVA, followed by Tukey’s multiple comparisons test. A p-value of less than 0.05 was considered statistically significant. Results Network pharmacology analysis Cryptolepine was selected as the study's focus based on the current literature as well as our earlier research [[124]71]. The features of cryptolepine are shown in Table [125]2, emphasizing both its structural and functional attributes. An evaluation of several drug-like qualities was part of the inquiry, especially pharmacokinetic traits and ADMET parameters (absorption, distribution, metabolism, excretion, and toxicity). The results, which are compiled in Table [126]3, show that cryptolepine satisfies every necessary ADMET requirement, confirming its potential as a promising medicinal compound. To further elucidate the potential of cryptolepine, a multifaceted approach was employed that included network pharmacology to identify its biological targets and pathways, molecular docking studies to assess binding interactions with target proteins, and molecular dynamics (MD) modeling to evaluate the stability of the protein–ligand complexes over time. Additionally, a series of in-vitro experiments were conducted to confirm the biological activity and therapeutic efficacy of cryptolepine against breast carcinoma cells. The results from these comprehensive analyses provide a robust foundation for understanding the mechanisms by which cryptolepine may exert its pharmacological effects, paving the way for future studies aimed at its clinical application. This integrative methodology not only highlights the drug's potential but also aligns with contemporary trends in drug discovery, emphasizing the importance of systematic evaluation in identifying promising candidates for therapeutic development. Table 2. Information regarding the compound's quantitative analysis Compound name IUPAC name PubChem ID Molecular formula Molecular weight Cryptolepine 5-Methylindolo[3,2-b]quinoline 82,143 C[16]H[12]N[2] 232.28 g/mol [127]Open in a new tab Table 3. ADMET analysis results Compound name Mol. Wt TPSA (A^2) GI absorption Lipinski’s rule Ghose rule Veber rule Egan rule Muegge rule Solubility ADMET screening Cryptolepine 232.28 g/mol 17.8Å^2 High Yes Yes Yes Yes Yes Moderately soluble Yes [128]Open in a new tab Target gene selection and building of interaction networks As shown in Fig. [129]1A, a comprehensive analysis identified 114 putative target genes associated with the single active compound (Cryptolepine). Concurrently, a search of the GeneCards database revealed 12,063 breast cancer-related target genes. Notably, 100 of the cryptolepine-associated target genes overlapped with those linked to breast cancer, highlighting potential key targets for therapeutic intervention Supplementary File 1. Furthermore, a complex network with 407 edges and 100 nodes was discovered by the following protein–protein interaction (PPI) analysis of the shared target genes, as shown in Fig. [130]1B. Fig. 1. [131]Fig. 1 [132]Open in a new tab A Venn diagram showing shared target genes between the compound and disease; red and blue circles represent BC-specific and LC–MS-derived targets, respectively, with the overlap indicating potential therapeutic targets B Protein–protein interaction (PPI) network of common targets, where nodes depict target genes with 3D structures and edges indicate known (cyan, purple) or predicted (green, red, blue-purple) interactions; chartreuse, black, and light blue represent other interaction types. C Frequency analysis of the top 30 common targets highlights key genes potentially involved in disease-relevant biological processes The top 30 target genes' frequency of occurrence is shown in Fig. [133]1C, which also highlights important proteins with high interaction rates, including STAT3, PTGS2, CCND1 (cyclin D1), PPARG, GRM5, GRIN2B, P53, and DRD2. Given their probable function as key node proteins in the network, these proteins appear to play a critical part in the molecular processes underlying BC. These high-frequency target proteins may also be promising therapeutic targets for the treatment of BC, as evidenced by the strong binding activity that has been seen between them and Cryptolepine. This study emphasizes how crucial it is to combine quantitative elements with gene-target analysis in order to identify new therapeutic approaches, deepening our knowledge of the biology of BC and opening the door for the creation of tailored treatments that might enhance patient outcomes. The functional significance of these connections and the ways in which cryptolepine affects BC pathways will be investigated in more detail in future research. Identification of essential pathways in BC The biological activity was mostly linked to cell–cell signalling and reactions to oxygen-containing compounds, as shown by the Gene Ontology (GO) analysis of the common target genes Fig. [134]2 Supplementary File 2, 3 and 4. Molecular transducer activity and signalling receptor activity were among the key functions found. Furthermore, the study of the cellular components showed that the plasma membrane included a sizable number of essential components. Figure [135]3 shows the common target genes' KEGG pathway enrichment analysis Supplementary File 5. Following the exclusion of more general pathways, the top 13 signalling pathways were listed in Table [136]4. These results imply that there may be several routes and intricate interactions between them in the mechanism by which cryptolepine treats BC stemness. Fig. 2. [137]Fig. 2 [138]Open in a new tab A–C Gene Ontology (GO) enrichment analysis of target genes in three categories: molecular function, cellular component, and biological process. Bubble size reflects the number of genes associated with each GO term D Overview of key enriched GO terms with corresponding gene counts, indicating major functional roles relevant to disease mechanisms and therapeutic strategies Fig. 3. [139]Fig. 3 [140]Open in a new tab Bubble chart of the top 20 enriched KEGG pathways, with bubble size indicating gene count and color representing enrichment significance Table 4. Lists of top 13 signaling pathways after broader pathways were excluded from consideration Pathway involved No. of genes Fold enrichment Small cell lung cancer 7 37.70605 EGFR tyrosine kinase inhibitor resistance 6 37.63786 Acute myeloid leukemia 5 36.98248 Non-small cell lung cancer 5 34.41425 P53 signaling pathway 5 33.94282 Pancreatic cancer 5 32.60297 Antifolate resistance 2 31.97195 AGE-RAGE signaling pathway in diabetic complications 6 29.73391 Calcium signaling pathway 14 28.90797 Prolactin signaling pathway 4 28.31801 Gap junction 5 28.15711 PD-L1 expression and PD-1 checkpoint pathway in cancer 5 27.84074 MicroRNAs in cancer 9 27.7024 [141]Open in a new tab This comprehensive approach may enhance our understanding of how cryptolepine influences BC biology and could lead to the development of novel therapeutic strategies that leverage these pathways to improve treatment outcomes. To prepare for its possible clinical use, more research is required to clarify the precise connections and regulatory networks underlying cryptolepine's anti-BC effects. Interaction network of cryptolepine and BC targets The interactions between cryptolepine, BC, target genes, and pathways were visualized in the network shown in Fig. [142]4. This network comprises a total of 115 nodes, which include 114 target genes and 1 active component (Cryptolepine). The results of the interaction network for this active compound are detailed in Fig. [143]4. Notably, cryptolepine demonstrated a degree of 100, indicating its significant influence within the network Table [144]5. These findings imply that cryptolepine may affect the entire biological network system rather than being limited to a single target gene Supplementary File 6. A protein–protein interaction (PPI) network based on common targets was constructed by importing PPI data from the STRING database into Cytoscape software, as depicted in Fig. [145]4A. In this network, the protein nodes are arranged according to their degree, highlighting the most connected proteins. Figure [146]4B presents the major pathway network derived from the KEGG enrichment analysis, illustrating the biological pathways relevant to the identified targets. Figure [147]4B further emphasizes the relationships by clearly showing the edges connecting cryptolepine, represented by a yellow hexagon node, to the 100 common target genes, depicted as blue oval nodes. This visualization underscores the interconnectedness of cryptolepine within the network and its potential role in modulating multiple pathways especially involved in BC stemness and drug resistance and target interactions, suggesting that it could serve as a multi-target therapeutic agent in the treatment of BC. Further exploration of these interactions could provide deeper insights into the mechanisms by which cryptolepine exerts its effects and highlight the potential areas for future research in targeted cancer therapies. Fig. 4. [148]Fig. 4 [149]Open in a new tab A Protein–protein interaction (PPI) network of common targets based on KEGG analysis; nodes represent proteins, and edges show their interactions, revealing functional connections in the context of BC. B Cryptolepine is marked as a yellow hexagon, with 100 common targets shown as blue ovals, highlighting their interaction network and shared involvement in cryptolepine-mediated BC pathways Table 5. Interaction network details of cryptolepine Component Degree Target gene Cryptolepine 100 GABRG2, GRIN1, AURKA, CA12, CA2, CA9, CCR1, CHEK1, CNR1, CYP19A1, EPHX2, ESR2, GRM5, HSD11B1, JAK2, KDR, NCSTN, PDE10A, PIM1, PIM2, PSEN1, PSEN2, PSENEN, PTGS2, S1PR1, ABCC1, ACHE, ADAM17, ALDH2, ALPL, AR, BCHE, BCL2A1, CCND1, CCNE1, CCNE2, CCR4, CCR5, CDK2, CDK4, CEL, CHRM3, CHUK, CSNK2A1, CTSB, CTSC, CTSK, CTSL, CTSS, CYP11B1, CYP11B2, DRD2, DRD3, DRD4, FAAH, GABRA2, GABRA3, GABRA5, GRIA4, GRIN2A, GRIN2B, HRH2, HSD11B2, HTR2A, HTR2C, IDO1, JAK1, LIMK1, MAOA, MAOB, P53, MGLL, MMP1, MMP13, MMP3, MMP8, MPO, MYLK, NQO2, OXTR, P2RX7, P2RY1, PIP4K2C, PLA2G6, PPARG, PRKCA, PTAFR, PTGS1, PTPN1, SLC6A3, SRD5A1, STAT3, TAAR1, TBXAS1, TERT, TNNI3, TNNT2, TOP2A, TRPA1, TXN [150]Open in a new tab Molecular docking As shown in Table [151]4, the p53 signaling pathway exhibited the highest fold enrichment among all targeted pathways. This prompted further investigation, where we performed molecular docking studies to explore the interaction between cryptolepine (ligand 82,143) and p53. The optimal docking cluster, representing 95% of the data and characterized by the lowest root mean square deviation (RMSD) of 0.25 Å, was selected for binding energy calculations. The results revealed that cryptolepine displayed a strong binding affinity for p53, with the lowest recorded binding energy of ΔG = − 8.2 kcal/mol and an inhibitory concentration (Ki) of 14 µM Fig. [152]5A. Notably, Arg1490 and Glu1567 in the p53 binding cavity were involved in a pi-cation interaction with the ligand, while van der Waals interactions with surrounding amino acids further stabilized the binding (Fig. [153]5A, right panel). In comparison, the positive control ligand, APR-246 (ligand 52,918,385), also demonstrated significant affinity for p53, with a Ki of 40 µM and a binding energy of ΔG = − 4.8 kcal/mol Fig. [154]5B. The binding mode of APR-246 was characterized by the formation of standard hydrogen bonds between the p53 residues Arg1490 and Arg1583 (Fig. [155]5B, right panel). These findings suggest that both cryptolepine (82,143) and APR-246 (52,918,385) effectively bind to the p53 protein, but cryptolepine exhibits a stronger binding affinity. Understanding these molecular interactions is critical for optimizing ligand design and enhancing therapeutic efficacy, particularly in targeting p53-related pathways for cancer treatment. The distinct binding modes and affinities observed for these ligands could provide valuable insights for further drug development efforts aimed at harnessing the therapeutic potential of p53 modulation. Fig. 5. [156]Fig. 5 [157]Open in a new tab A Binding pose of p53 with ligand 82143: left panel shows p53 ribbon structure with 82143 in stick model; right panel highlights 2D interactions in wireframe format B Binding pose of p53 with ligand 52918385: left panel displays p53 ribbon structure with 52918385 in stick model; right panel presents 2D interaction details in wireframe format C RMSD plot showing molecular vibrations of p53 complexed with ligands 82143 and 52918385 over a 100 ns simulation, indicating complex stability D RMSF plots illustrating amino acid fluctuations in p53 during the 100 ns simulation, identifying flexible and rigid regions E Hydrogen bond graph showing interaction stability over 100 ns for both p53-ligand complexes F Radius of gyration plots assessing the compactness of p53 with ligands 82143 and 52918385 G Energy plot for p53 with ligand 82143, revealing binding stability H Energy plot for p53 with ligand 52918385, showing binding stability I SASA analysis of the p53 + 82143 complex, comparing ligand-bound (black) and unbound (red) regions at the binding pocket J SASA analysis for p53 + 52918385, highlighting the effect of ligand binding on protein exposure to the solvent Molecular dynamics simulation To assess the stability and convergence of the p53-ligand complexes with 82,143 (cryptolepine) and 52918385 (APR-246) molecular dynamics (MDS) simulations were conducted over a duration of 100 ns. The results, shown in Fig. [158]5C, indicate that both complexes maintained stable conformations throughout the simulation. The root mean square deviation (RMSD) for the p53-82143 complex exhibited a minor deviation of 0.2 Å, while the p53-52918385 complex showed an even lower deviation of 0.1 Å. These low RMSD values suggest that both ligand-bound proteins retained stable structural conformations with minimal fluctuations, highlighting strong and effective binding interactions. Further analysis through root mean square fluctuations (RMSF) provided additional insights into the stability of the complexes. In the p53-82143 complex, the RMSF plot revealed minimal fluctuations at residues 16 (1.8 Å), 80 (2.2 Å), and 90 (2.0 Å), suggesting that these amino acids maintained consistent conformations throughout the simulation Fig. [159]5D. In contrast, the p53-52918385 complex demonstrated more pronounced fluctuations, particularly at residues 70 (1.6 Å) and 80 (1.8 Å). This suggests localized dynamic changes in the protein structure, potentially related to the binding of this specific ligand. The hydrogen bond analysis further highlighted the stability and importance of ligand–protein interactions. Throughout the 100 ns simulation, a substantial number of hydrogen bonds were observed between p53 and both ligands Fig. [160]5E. The average hydrogen bond counts for both the p53-82143 and p53-52918385 complexes were consistent, underscoring the crucial role of these interactions in maintaining complex stability. The compactness of the protein was assessed using the radius of gyration (Rg), as shown in Fig. [161]5F. The p53-82143 complex exhibited a slight decrease in Rg, stabilizing between 14.2 and 14.1 Å, while the p53-52918385 complex maintained stable values between 14.2 and 14.3 Å. These observations, along with the decreasing RMSD values, suggest that both complexes retain compact structures. Energy profiles of the p53-ligand complexes provided further evidence of overall system stability. The p53-82143 complex displayed a stable average total energy of − 50 kcal/mol Fig. [162]5G, with coulombic interaction energy contributing significantly (− 48 kcal/mol), indicating the importance of electrostatic interactions. The van der Waals (vdW) energy made a smaller contribution (− 10 kcal/mol), suggesting a less pronounced effect on overall stability. In comparison, the p53-52918385 complex exhibited an average total energy of − 40 kcal/mol Fig. [163]5H with vdW energy contributing − 21 kcal/mol and coulombic interactions at − 15 kcal/mol. The substantial negative potential energy values for both complexes indicate that they are progressing toward their global minima, suggesting favorable binding interactions. Finally, solvent-accessible surface area (SASA) analysis provided insight into the binding effects of the ligands. As shown in Fig. [164]5I and J, the SASA values for the ligand-bound complexes were lower than those of the unbound p53, confirming that ligand binding results in a more compact protein conformation with reduced solvent exposure. The unbound p53 state exhibited a higher solvent-accessible surface area, further emphasizing the stabilizing influence of ligand binding on the p53 structure. In conclusion, these findings underscore the robustness and stability of the p53-cryptolepine interactions, highlighting their potential therapeutic value in targeting p53-related pathways for cancer treatment. The analysis emphasizes the critical role of binding dynamics in understanding how cryptolepine affects p53 function. By elucidating these interactions, the study provides valuable insights that could guide the development of novel therapeutic strategies aimed at modulating p53 function. Such strategies may hold promise for targeted cancer therapies, offering new avenues for combating cancer and improving patient outcomes. Experimental validation of cryptolepine inhibits BC proliferation and induces its anti-cancer effects Our study's findings show that cryptolepine targets a number of important proteins involved in crucial signalling pathways, such as P53, STAT3, and PI3K-Akt, all of which are linked to the development of cancer, stemness, and resistance to treatment. This emphasizes how crucial it is to assess cryptolepine's anti-cancer effectiveness in BC cell lines. Thus, we carried out a thorough evaluation of cryptolepine's anticancer effects in a methodical way on both MDA-MB-231 (containing mtP53) and Mcf-7 (Wt P53) in a both time and dose-dependent manner. Cell viability was assessed at 24, 48, and 72 h using the Vybrant cell proliferation kit (Invitrogen, Thermo Fisher, USA) and different dosages of cryptolepine 2.5 μM, 5 μM, 10 μM, 20 μM, 40 μM, and 60 μM with (0.1%) DMSO as a negative control, across all evaluated cell lines, treatment with cryptolepine resulted in a significant reduction in cell viability that was connected with both dose and concentration, as shown in the Fig. [165]6A. We used DAPI staining, a fluorescent dye that makes apoptotic cells visible, to examine the nuclear effects of cryptolepine on BC cells. Using this technique, distinctive nuclear changes such as chromatin condensation and fragmentation were discovered. DAPI staining revealed increased nuclear permeability in MDA-MB-231 cells which coincided with the presence of nuclear apoptotic bodies, indicating that the cells were undergoing apoptosis. We also evaluated apoptotic alterations at the mRNA level to support and expand on our findings. According to our findings, pro-caspase levels declined while cytochrome C (cyt. c) and BAX (a pro-apoptotic protein) levels significantly increased. This implies that pro-caspase was triggered and cleaved into its active forms, particularly cleaved caspase 3, which is essential for starting apoptosis that is further elucidated by increasing BAX levels triggering apoptosis being a pro-apoptotic protein. Prior research has shown that STAT3 may decrease the apoptosis of BC cells by upregulating cyclin D-1, c-myc, and bcl-2, suggesting a possible role for STAT3 in cell cycle and survival [[166]14]. Consistent with our research, STAT3 can also inhibit caspase-dependent apoptosis linked to Bax/Bcl-2 when it is triggered by the IL-6/JAK2 pathway [[167]102]. In order to verify our computational research, we examined the significant potential of cryptolepine to induce apoptosis in BC cells. The complicated signalling pathways were the focus of our investigation into this compound's anti-cancer effects on BC stemness. Fig. 6. [168]Fig. 6 [169]Open in a new tab A CRP inhibits proliferation of MDA-MB-231 and MCF-7 BC cells in a dose- and time-dependent manner (Ctrl = 0.1% DMSO, Placebo = Untreated, C1 = 2.5 μM, C2 = 5 μM, C3 = 10 μM, C4 = 20 μM, C5 = 40 μM, C6 = 60 μM) B DAPI staining showing nuclear changes in CRP-treated cells, including increased permeability, chromatin condensation, and apoptotic bodies, indicating apoptosis C qRT-PCR analysis shows increased mRNA levels of pro-apoptotic markers BAX and cytochrome C, and decreased pro-caspase-3 mRNA after 24 h of CRP treatment (C1 = 2.3 μM, C2 = 3.4 μM), supporting CRP’s role in activating apoptosis in BC cells (“****” represents the P value of < 0.0001, “***” represents the P value of < 0.001) In conclusion, our research combines computational drug design through network pharmacology and in-vitro validation to investigate cryptolepine's role in targeting key signaling pathways in BC. Building on our previous findings that cryptolepine interacts with mutant p53 in triple-negative BC this study emphasizes the potential for combination therapies targeting multiple pathways, including P53, STAT3, and PI3K-Akt. Notably, we identified STAT3 as a downstream effector of wild-type p53 (wtp53), suggesting that enhancing wtp53 functionality may synergize with cryptolepine to inhibit STAT3 signaling, offering a potential therapeutic strategy for BC. These insights into cryptolepine’s mechanisms contribute to a broader understanding of cancer signaling and pave the way for developing effective treatments that address resistance and improve patient outcomes [[170]50, [171]73]. Discussion The most common cancer in women globally and a leading cause of cancer-related mortality is BC. Conventional treatments such as hormone therapy, radiation, chemotherapy, surgery, and small-molecule targeted therapy sometimes fail to appropriately address the complexity and heterogeneity of certain subtypes of BC. This can result in drug resistance and the spread of the disease to other locations. Therefore, it is crucial to find new treatment targets and agents. Natural products and their derivatives are becoming more and more valued sources for small-molecule anticancer medications due to their low toxicity and wide diversity [[172]75, [173]105]. To explore new avenues for BC treatment, we investigated the natural compound cryptolepine (CRP). Cryptolepine was chosen due to its significant medicinal properties and potential for repurposing as an anti-cancer agent. With growing interest in drug repurposing for cancer therapies, CRP emerged as a compelling candidate because it has shown preliminary anti-cancer activity and has established uses as an anti-malarial drug. Furthermore, cryptolepine was assessed in TNBC in our previous published study that marked its importance and promising attribute for triple-negative BC (TNBC), a challenging subtype that lacks targeted therapies. In this study, we conducted a thorough evaluation of cryptolepine’s (CRP) efficacy and mechanisms as a potential therapy for breast cancer broadly, addressing its heterogeneity rather than focusing solely on triple-negative breast cancer (TNBC). Our investigation emphasizes CRP’s distinct properties and its ability to target key pathways involved in cancer progression. A number of CRP's pharmacological characteristics have been reported [[174]101], such as anti-diuretic, anti-inflammatory [[175]7], anti-malarial [[176]1, [177]27], anti-bacterial, antifungal, anti-hyperglycemic, hypotensive/antipyretic [[178]84] and anticancer effects such as skin cancer [[179]66]. Later, several in-vitro and in-vivo studies evaluated the anti-tumor effects of CRP and reported it is promising therapeutic agent for the treatment of melanoma [[180]67], hepatocellular carcinoma [[181]22], colorectal carcinoma [[182]76] and breast adenocarcinomas [[183]109]. However, keeping into consideration the heterogeneity of BC and to find the other potential targets of cryptolepine in BC we performed the network pharmacology approach and in-vitro analysis that revealed STAT3, P53, PI3K-Akt and metabolic pathways as potential targets of cryptolepine in BC. By activating the PI3K/AKT and IL6/JAK2/STAT3 signalling pathways, metastatic BCs have been linked to the acquisition of the epithelial-mesenchymal transition (EMT) program and self-renewing trait (CSCs), which results in clinically incurable disease and poor survival [[184]40, [185]45]. Signal transduction and activator of transcription 3 (STAT 3) is a member of the STAT family and is constitutively active in a number of malignancies, including head and neck squamous cell carcinoma, breast, lung, ovarian, colorectal, cervical, gastric, and prostate cancers [[186]11, [187]41, [188]49, [189]60, [190]80]. One of the most extensively researched tumor suppressor genes is TP53 (tumor protein p53). Wild-type p53 (wtp53) has long been referred to be the “guardian of the genome” [[191]46] due to its critical function in preventing cancer. By triggering cell cycle arrest, DNA repair, or death, p53 is known to inhibit tumor growth and provide protection against DNA damage [[192]111]. P53 mutation is frequently seen in cancer, particularly in late stages of the disease's development [[193]62, [194]79]. Both upregulation of STAT3 activity and downregulation of wtp53 expression are necessary for the growth and survival of tumor cells. On the other hand, wtp53 decreases DNA-binding activity and STAT3 activation in breast and prostate cancer cells [[195]50, [196]51]. Furthermore, TP53 expression is suppressed by STAT3 activity, according to another investigation [[197]63]. As a result, active STAT3 and wtp53 adversely regulate one another. Given that wtp53 works as a tumor suppressor [[198]6] and activated STAT3 functions as an oncogene [[199]96], the conflicting biological activities of these proteins can account for this detrimental regulation. Normal cells may thus have developed systems to modify STAT3 and p53 expression in order to meet the requirements for cell proliferation, whereas tumor cells may take advantage of this negative regulation in order to survive [[200]63]. Studies have revealed that expression of wt p53 but not mutant p53 dramatically decreased tyrosine phosphorylation of Stat3 and hindered Stat3 DNA binding activity in both DU145 and Tsu prostate cancer cell lines, which produce constitutively active Stat3, which is a known oncogene, hence providing a strong link between Wild type P53 (Tumor suppressor) and STAT3 (Oncogene) (Jiayuh [[201]50, [202]51]). Moreover, p53 expression is upregulated when Stat3 is blocked in cancer cells, which results in p53-mediated tumor cell death. Stat3 is a prospective molecular target for cancer therapy and is constitutively active at high frequency in a wide variety of malignancies, serving as a site of confluence for several oncogenic signalling pathways. Therefore, Stat3's suppression of p53 expression is probably crucial to the growth of tumors, and targeting Stat3 provides a unique therapeutic strategy for p53 reactivation in a variety of malignancies that do not contain p53 mutations [[203]63, [204]70] Through STAT3-regulated signalling, tumors develop preferentially in the early stages of progression [[205]99]. Even while p53 mutations have been shown to arise early and play a function in the development of tumors, it seems that some malignancies may obtain p53 mutations later on and play important roles in later stages of carcinogenesis [[206]78]. Additionally, the accumulation of mutant p53 (mtp53) and the loss of wtp53 activity can promote STAT3-mediated tumor cell survival and growth [[207]70]. Clinical studies are underway for a number of inhibitors that target either p53 or STAT3, but their effectiveness has been constrained by resistance to targeted cancer therapy [[208]23]. A combinational therapy that co-targets STAT3 and p53 may be able to overcome drug resistance since resistance frequently arises because of the intricacy of cancer signalling pathways, which makes it challenging for single-target inhibitors to produce satisfying clinical results [[209]108]. Conclusion To effectively halt cancer progression, many therapeutic approaches focus on small-molecule drugs that specifically target key regulators such as p53 and STAT3, along with their associated signaling pathways. While drug resistance remains a significant challenge that can limit clinical success, substantial and growing evidence underscores the critical role of both STAT3 and p53 as viable and promising molecular targets for cancer treatment. Consequently, co-targeting p53 and STAT3 may be a viable strategy to combat medication resistance and expedite clinical trials. There are two components to the STAT3–p53 regulatory loop: positive regulation between STAT3 and mtp53 and negative regulation between STAT3 and wtp53. For cancer treatments, proteins that are a part of this feedback loop may be used to control p53-mediated and STAT3-mediated signalling. Additional drug development work is required, and greater proof of the effectiveness of medication combination therapy should be given to. In the fight against BC, a growing number of natural compounds and their derivatives are showing promise such as cryptolepine as revealed from our study. Strategic improvement of natural substances' anticancer characteristics, optimization for focused action, increased bioavailability, and reduced adverse effects can all help future treatment approaches. To optimize the therapeutic potential of natural products, these aspects should be given priority in future study. Limitation of the study The primary limitation of our study was the inability to address BC heterogeneity, particularly in relation to therapy-resistant models. This limitation arose due to the lack of comprehensive datasets that capture the full spectrum of heterogeneity and resistance profiles. As a result, this critical aspect could not be thoroughly examined in the current study. However, we plan to address this limitation in future research. Given the complexity of BC heterogeneity and the promising therapeutic potential of cryptolepine observed through our network pharmacology analysis, we believe that continued investigation in this area is both necessary and valuable. Studying BC heterogeneity in in vivo models remains a significant challenge, but we are committed to pursuing this line of research in the near future. Additionally, we will incorporate studies on off-target effects and examine responses across different BC cell line subtypes to enhance the robustness of our findings. Supplementary Information [210]Supplementary Material 1.^ (2.8MB, xlsx) [211]Supplementary Material 2.^ (93.7KB, xlsx) [212]Supplementary Material 3.^ (25.5KB, xlsx) [213]Supplementary Material 4.^ (37.6KB, xlsx) [214]Supplementary Material 5.^ (21.9KB, xlsx) [215]Supplementary Material 6.^ (7.8KB, xlsx) [216]Supplementary Material 7.^ (80.3MB, zip) [217]Supplementary Material 8.^ (84.9MB, zip) Acknowledgements