Integrative systems biology and in-vitro analysis of cryptolepine's therapeutic role in breast cancer

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.

Keywords: BC; Cryptolepine; P53; STAT3; Signaling pathways.

Fig. 1

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

Fig. 2

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

Bubble chart of the top 20 enriched KEGG pathways, with bubble size indicating gene count and color representing enrichment significance

Fig. 4

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

Fig. 5

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

Fig. 6

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)