Background: Nigella sativa, generally known as black seed or black cumin, has a rich historical background in traditional medicine for its diverse health benefits. Objective: The aim of the study explore the potential effects of Nigella sativa (NS) on colorectal cancer (CC) through the application of network pharmacology and molecular docking. Methods: Network pharmacology (NP) analysis was conducted to identify pertinent colorectal cancer targets and compounds sourced from relevant databases. Subsequently, protein-protein interaction (PPI) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were employed to delineate critical molecular pathways, while molecular docking simulations study were employed to validate the binding interactions at active sites. Results: Network analysis of compound-target interactions revealed a network comprising 319 nodes and 410 edges, indicating a complex interplay between the derivatives and their associated targets. The PPI analysis underscored the significant interactions within the network, particularly with targets known to be involved in colorectal cancer regulation. KEGG pathway analysis highlighted the importance of EGFR and PI3K pathways in the context of colorectal cancer. Notably, molecular docking studies identified Nigellicine as having the highest affinity for key colorectal cancer-related targets, including AKT1 (-7.6 kcal/mol), IL6 (-6.2 kcal/mol), ALB (-7.1 kcal/mol) and HSP90AA1 (-8.2). Conclusion: The integration of network analysis and molecular docking studies provided collectively support the colorectal cancer characteristics of the compounds, emphasizing the need for further research to develop novel pharmacological interventions for colorectal cancer management.
Keywords: Network Pharmacology; Molecular Docking; Colorectal Cancer; Nigella sativa
Vol. 11 (1): 10-24, Jan-Mar, 2024.
DOI: http://doi.org/10.56511/JIPBS.2024.11102
Cancer is characterized by excessive cell proliferation that can metastasis to other parts of the body and cause disease.
The rising population, the aging population, and the effects of lifestyle changes all contribute to a rise in cancer incidence and mortality rates. Because cancer has such farreaching effects, it degrades the quality of human resources - colorectal cancer ranks third in terms of prevalence worldwide [2]. Hereditary disorders such as familial adenomatous polyposis (FAP) and hereditary nonpolyposis colorectal cancer (HNPCC) account for 10% of all cases of colorectal cancer [3]. The remaining 70% of cases arise at random.
Colorectal cancer treatment is complex and calls for input from other fields of expertise. There is a higher recurrence especially for metastatic disease, and the adverse effects of chemotherapy and monoclonal antibodies are distinct [4]. This issue is mostly attributable to the fact that the medications being utilized are not selective, causing harm to both cancer cells and healthy cells. Numerous sets of genes are involved in most disorders. However, creating a new medication is a lengthy and expensive process [5]. Because of this issue, the focus of drug development has shifted from developing single-drug therapies to creating plant-based medicines that utilize many chemical compounds directed at different targets (multicomponent - network targets) [6]. Multicomponent medications have more than one active ingredient, and this can have a positive impact on the pharmacological effects. In silico computational methods have a synergistic effect on multicomponent approaches, allowing for faster execution and more encouraging outcomes. The concepts of network theory between chemicals and biological systems, as well as the outcomes of in vitro and in vivo testing for drug development, are explained by network pharmacology [7].
For correct findings and productive preclinical research, an early investigation providing an initial view is crucial. Compound content and mechanism of action are predicted using computational methods based on the compound's link to proteins in existing databases. The purpose of this research is to foretell Nigella sativa network pharmacology in colorectal cancer. Our findings can be used as a foundation for developing safe and effective new medicines or improvements in cancer treatment based on target genes.
The constituents employed in this investigation were procured from the KNApSAcK Family database (http://www.KNApSAcKfamily.com/) and Dr. Duke’s Phytochemical and Ethnobotanical Databases (https://phytochem.nal.usda.gov/phytochem/search). The selection of active compounds adhered to specific criteria, including a molecular weight (MW) within the range of 180 to 500 Dalton, oral bioavailability (OB) equal to or greater than 20%, drug-likeness (DL) equal to or greater than 0.1, and a blood–brain barrier (BBB) value equal to or greater than −0.3. The chemical structure validation of Nigella sativa components was conducted utilizing the PubChem database (https://pubchem.ncbi.nlm.nih.gov) and was generated using ChemDraw.
The protein targets associated with Nigella sativa constituents were sourced from the Swiss Target Prediction database (http://www.swisstargetprediction.ch/). Target information relevant to colorectal cancer was extracted from the Gene Card Database (https://www.genecards.org/) and the Online Mendelian Inheritance in Man (OMIM) database sativa compounds were identified through a screening process that involved detecting common targets among the genes represented in a Venn plot (https://bioinformatics.psb.ugent.be/webtools/Venn/).
The exploration of protein-protein interactions (PPI) serves as a valuable approach to discern potential therapeutic targets in the management of disorders, given the pivotal role of PPI in orchestrating biological processes [8]. In this investigation, the shared targets of Nigella sativa compounds and colorectal cancer were delineated utilizing the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) V11.5 platform (https://string-db.org/) designed for multiple proteins in Homo sapiens [9]. A stringent minimal interaction score of 0.9 (indicative of high confidence) was applied in the establishment of the PPI network. Subsequent extraction of p-values was conducted to assess the statistical significance of the PPI network and its enrichment.
The investigation of signal pathways, biological processes, molecular functions, and cellular component terms associated with the intersecting targets between the drug and disease was conducted through Gene Ontology (GO) functional annotations and the Kyoto Encyclopedia of Genes and Genomes (KEGG), considering a significance threshold of p < 0.05. Enrichment analysis of these pathways was performed using Input 2.0 (http://cbcb.cdutcm.edu.cn/INPUT/Home/) and Shinygo (http://bioinformatics.sdstate.edu/go/) as analytical tools [10- 11].
The compounds derived from Nigella sativa were systematically linked to construct a compound-target network. Cytoscape 3.9.1 (Cytoscape Consortium, San Diego, CA, USA) [12], a software tool designed for visualizing interaction networks, was employed for the network construction. In this network, edges denote the interactions between the compounds and their corresponding protein targets, while nodes represent the individual compounds and their associated targets.
AutoDock Vina was employed to assess the interactions between Nigella sativa compounds and target proteins [13]. The three-dimensional (3D) structures of the target proteins, in Protein Data Bank (PDB) format, were retrieved from (https://www.rcsb.org) for subsequent docking analysis [14]. Additionally, the three-dimensional (3D) structures of Nigella sativa compounds were sourced from the PubChem database (https://pubchem.ncbi.nlm.nih.gov). AutoDock and docking scores (binding affinities) and binding interaction between the compounds and target proteins [13].
Ten components inherent in Nigella sativa were anticipated through a comprehensive analysis utilizing the KNApSAcK Family and Dr. Duke's Phytochemical and Ethnobotanical databases. Specifically, five components were identified within the seed oil of Nigella sativa L., as detailed in Table 1. The KNApSAcK database, referenced in [15], provides a wealth of information encompassing the interrelation of components, biological activities, and associated species. Additionally, Dr. Duke's Phytochemical and Ethnobotanical database, accessible online at no cost, offers valuable ethno medicinal data of high significance. Nigella sativa seeds have historically played a pivotal role in treating various diseases, including cancer, within traditional medical systems prevalent in South and Southeast Asia, Arabia, Africa, and the Mediterranean [16]. Employing pharmacokinetic criteria and literature reviews, we identified nine potentially active chemicals from Nigella sativa, detailed in Table 2. These compounds exhibit molecular weights (MW) ranging from 180 to 500, oral bioavailability (OB) between 20 and 30, and blood–brain barrier (BBB) binding affinity constants between 0.3 and 0. The chemical structures of these selected compounds were delineated using ChemDraw, as illustrated in Figure 1, showcasing a diversity of structures encompassing glucosides, long-chain lipids, and simple chemical molecules.
The study reveals the capacity of Nigella sativa components to interact with a diverse array of proteins, including those implicated in cancer, as determined through protein target prediction utilizing the Swiss Target Prediction database. The database identified 410 potential chemical targets associated with Nigella sativa. To identify potential targets pertinent to colorectal cancer, the Gene Card Database was employed. The combined analysis of Swiss Target Prediction and GeneCards Database yielded a comprehensive list of Nigella sativa compound protein targets, as detailed in Table 3.
The selection of 410 targets predicted by the Swiss system was visually represented using Cytoscape 3.9.1, resulting in a compound-target network displayed in Figure 2A-2B. The Venn diagram illustrates shared targets and genes, involving 9 chemicals and 410 interacting target proteins. Notably, multiple components within this network were observed to target the same proteins. The network comprises 319 nodes and 410 edges, suggesting a potential synergistic effect of Nigella sativa's active biochemicals on various targets. This aligns with the plant's documented therapeutic efficacy against a spectrum of diseases and ailments. Furthermore, topological characteristics, including betweenness centrality, closeness centrality, and degree, were assessed to elucidate in Table 4. These findings underscore the multifaceted potential of Nigella sativa compounds in targeting specific proteins, thereby presenting a promising avenue for therapeutic interventions against diverse diseases, including
network depicting potential colorectal cancer targets associated with Nigella sativa. The PPI enrichment analysis yielded a p-value of 0.0272, while the average node degree was determined to be 8.6. Notably, specific targets such as AKT1 exhibited a node degree of 120, IL6 demonstrated a node degree of 112, ALB exhibited a node degree of 110, and HSP90AA1 showed a node degree of 88. These findings suggest that these identified targets, particularly AKT1, IL6, ALB, and HSP90AA1, may play pivotal roles in mediating the effects of Nigella sativa on colorectal cancer.
Genes with potential relevance to colorectal cancer treatment were identified through a Gene Ontology (GO) enrichment study of Nigella sativa. These genes were associated with various cellular components, molecular functions, and biological processes. Noteworthy biological processes included positive regulation of gene expression, response to endogenous stimulus, cellular localization, intracellular signal transduction, nitric oxide biosynthetic process, protein phosphorylation, epidermal growth factor response, apoptotic process, and acute inflammatory response, as indicated in Table 5. Molecular functions associated with these genes encompassed nitric-oxide synthase regulator activity, enzyme binding, binding of identical proteins, binding of phosphatases, ions, proteins, protein phosphatases, and small molecules, detailed in Table 6a. Additionally, Table 6b highlighted the predominant cellular component term as the Endoplasmic reticulum lumen in the GO analysis.
Furthermore, the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis Table 6c and Figure 4 KEGG Pathways revealed several pathways linked to the potential colorectal cancer impact of Nigella sativa. These pathways included Pathways in cancer, EGFR tyrosine kinase inhibitor resistance, the IL-17 signaling pathway, prostate cancer, the ErbB signaling pathway, the TNF signaling pathway, the Estrogen signaling pathway, gastric cancer, Th17 cell differentiation, the MAPK signaling pathway, apoptosis, and the Rap1 signaling pathway (Figure 5A-E). The implicated genes within these pathways encompassed the epidermal growth factor receptor, nitrogen metabolism, cancerous pathway, PI3K, and vascular endothelial growth factor.
c1cc(c(cc1C)O)C(C)C CCCCCCCC/C=C c12c(c(cc(c1)CC=C)OC)OCO2 c1cc(ccc1C)C(C)C CC1(CCC2(CCC3(C(=CCC4C3(CCC5C4(CCC(C5( C)C)O)C)C)C2C1)C)C)C OC(CCCCCCC/C=CC/C=CC/C=CCC)=O C1=C(C(=O)C=C(C1=O)C)C(C)C C1=CC(=CC=C1C2=C(C(=O)C3=C(C=C(C=C3O2) O)O)OC4C(C(C(C(O4)CO)O)O)OC5C(C(C(C(O5)C O)O)O)OC6C(C(C(C(O6)CO)O)O)O)O C1=CC(=C(C=C1C2=C(C(=O)C3=C(C=C(C=C3O2 )O)O)OC4C(C(C(C(O4)CO)O)O)OC5C(C(C(C(O5) CO)O)O)OC6C(C(C(C(O6)CO)O)O)O)O)O COC1=C(C=CC(=C1)C=CC(=O)OCC2C(C(C(C(O2 )OC3C(C(C(OC3OC4C(C(C(OC4OC5=C(OC6=CC(
m‐Thymol 89‐83‐8 Oleic acid 112‐80‐1 Myristicin 607‐91‐0 p‐Cymene 99‐87‐6 beta‐Amyrin 559‐70‐6 Linolenic acid 463‐40‐1 Thymoquinone 490‐91‐5 Kaempferol 197250‐98‐9 3‐glucosyl‐(1‐>2)‐ galactosyl‐(1‐>2)‐glu coside Quercetin 197250‐97‐8 3‐glucosyl‐(1‐>2)‐ galactosyl‐(1‐>2)‐glu coside Quercetin 197294‐29‐4 3‐(6””‐feruloylgluco
Seed Oil \CCCCCCCC(=O)O Seed Seed Oil Seed Seed Oil Seed Seed Oil Seed Oil Seed Seed O)CO)O)O)O)O)O)O CC1=CC(=O)C2=C(N3CCCCN3C2=C1)C(=O)O CC1=CC(=O)C2=C(N3CCCCN3C2=C1)C4=CC=C( CC1=NC=CC2=CC(=C(C=C12)OC)OC CC1=CCC(CC1=O)C(=C)C CC1=CC2=C(C(=C1)OS(=O)(=O)O)C(=[N+]3N2C CCC3)C4=CC=C(C=C4)O PPARG, PPARA, PPARD, FABP4, FFAR1, FABP3, PTGS1, PTGES, LTB4R, POLB, ESR2, PTPN6, RORC, TOP1, PTGER2, ALOX12, CDC25A, PTPRF, PTGS2, NOS2, PGR, SRD5A2, ‐galactosyl‐(1‐>2)‐gl ucoside Nigellicine 98063‐20‐8 Nigellidine 120993‐86‐4 C=C4)O Nigellimine 4594‐02‐9 Carvone 99‐49‐0 Nigellidine 1032622‐86‐8 4‐O‐sulfite
Linolenic acid 5280934 FABP5, PDE4D, Seed Seed Seed Seed Seed TERT, FABP1, CNR1, PTPN1, ALOX5, PTPN2, PTGER1, PLA2G1B, ESR1, CDC25B, NR3C1, RXRB, SERPINA6, SHBG, G6PD, ITGAL, PTGER4, CYP26A1, NPC1L1, CYP17A1, AKR1B10, IMPDH2, 1L6, HTR2B, MAPK14, MAPK1, PREP, AR, MDM2, FFAR4, ALOX15, TOP2A, RORA, MMP2 PLK1, GLI2, GLI1, ALOX5, CYP19A1, PTPN2, CHRM2, CA2, CDK5R1, CDK5, AR, EGFR, KDR, AURKA, AKT1, SIGMAR1, MMP9, FGFR1, DYRK1B, GABRA1, CCNE2, CDK2, CCNE1, CDK1, CCNB1, CCNB2, PIM1, MAPKAPK2, GSK3B, CTSK, CA12, AKR1B10, RBP4, PIK3CG, PIK3CA,TGFBR1, SIRT2, RORC, MAPK1, CASP3 DYRK1A, DYRK1B, JAK1, JAK2, CTSK, GSK3B, CSNK1D, GRM5, ADORA2B, IDO1, PDE5A, CDK1, CCNB1, CCNB2 CYP19A1, SRD5A1, NR3C1, PGR, SERPINA6, SHBG, FABP1, NR1I3, SRD5A2, AR, ADH1A, SIGMAR1, ADH1C, NPC1L1, PTGES, CA2, CYP17A1, PARP1, ADORA3, MAPK3, PRKCH, PTPN11, AKR1B10, PTPN2, PTGS1, NR1H3, TOP1
ENPP2, Thymoquinone 10281 ACHE, SHBG Nigellicine 11402337 HTR2A, TSPO, JAK3, CSF1R, CDK2, CDK1, Nigellimine 20725 ACHE, NQO1, Carvone 7439
Positive regulation of gene expression Response to endogenous stimulus Regulation of cellular localization Regulation of intracellular signal transduction Positive regulation of nitric oxide biosynthetic process Positive regulation of protein phosphorylation Response to epidermal growth factor Regulation of acute inflammatory response ICAM1, ITGB2, MAPK3, RBP4, PRKAG1, PRKAB1, PRKAA2, ADRA1D, HTR2B, ADRA1A, HTR2C, ADRA1B, DBF4, CDC7, MMP3, HPGD, DYRK1A, MMP13, MMP2, MIF, ALOX15, ALOX12, CNR1, GABRA5, ROCK1, ERN1, AKR1B1, JAK2, CYP1A2, GRM4, CCNE2, CDK2, CCNE1, .
9 1.18E-06 9 2.75E-06 8 7.27E-06 9 1.13E-05 4 1.85E-05 7 2.84E-05 3 0.00031 3 0.00035
6M4R), and HSP90AA1 (PDB: 3O0I) were obtained. Auto
3O0I 4ZS7
AKT1 IL6 In contrast to the conventional paradigm of "one drug, one target" in drug design, network pharmacology adopts a perspective of multi-targeted therapy to explore the intricate relationships between pharmaceuticals and diseases [17].
This unconventional approach leverages principles from systems biology, including network analysis, connectivity, and redundancy. Researchers have increasingly turned to network pharmacology studies [18-19] as a means to elucidate how medications interact with yet-to-be-identified signaling pathways. The Network Pharmacology framework [20] introduces novel insights into the systemic interplay between therapeutic targets and the entirety of a disease, rendering it a potent and promising tool for elucidating disease mechanisms at the systemic level and identifying potential bioactive ingredients. In the present study, a pioneering network was constructed to delve into the molecular processes associated with Nigella sativa.
ALB HSP90AA1 and oral bioavailability (OB) specifications was ascertained. Nigella sativa underwent further investigation, with emphasis on identifying bioactive targets related to colorectal cancer and their integration into a disease-centric network. The constructed network elucidated protein-protein interactions and various pathways, underscoring the potential of Nigella sativa bioactives in colorectal cancer inhibition. Key genes implicated in colorectal cancer, notably AKT1, IL6, ALB, HSP90AA1, ESR1, PTGS2, PPARG, MAPK3, GSK3B, and ERBB2, were identified through protein-protein interaction (PPI) analysis. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses unveiled additional pathways, diseases, and disorders associated with these selected genes. Results from the GO enrichment study suggested a significant preventive effect of bioactives in colorectal cancer. Pathway analysis utilizing the KEGG database provided supportive evidence for the therapeutic 17 signaling, TNF signaling, Estrogen signaling, MAPK signaling, and Apoptosis emerged as significant nodes in the network. The findings indicated that Nigella sativa influenced multiple signaling pathways, suggesting its applicability beyond prostate cancer, stomach cancer, and Th17 cell deficiency. Colorectal cancer, along with cancers of the colon, pancreas, endometrium, prostate, melanoma, bladder, lungs (small and large cell), liver, and stomach, demonstrated susceptibility to the anticancer effects of Nigella sativa. Validation of target accuracy was conducted through an additional docking port assessment. The evaluation of the components' affinity for their respective targets provided immediate insights into the structureactivity relationship.
This computational investigation utilizes network pharmacology and docking analysis to systematically explore the pharmacological mechanisms underlying the preventative and therapeutic effects of Nigella sativa in colorectal cancer. Emphasis is placed on elucidating the significant contributions of network pharmacology in unraveling the intricate mechanisms associated with Nigella sativa. The insights gained from this study may extend to benefit our understanding of analogous mechanisms in other related cancer types. As the current study has been conducted in line with the principle of network pharmacology but it is necessary to carry out validation study.
Abbreviations: FAP, Familial adenomatous polyposis; HNPCC, Hereditary nonpolyposis colorectal cancer; MW, Molecular Weight; OB, Oral bioavailability; DL, Druglikeness; BBB, Blood Brain Barrier; KEGG, Kyoto Encyclopedia of Genes and Genomes; GO, Gene Ontology; 3D, Three Dimensional; PDB, Protein Data Bank; CAS, Chemical Abstracts Service; PPI, Protein Protein Interaction; DPED, Duke’s Phytochemical and Ethnobotanical Databases; NS, Nigella sativa; CC, Colorectal Cancer; MD, Molecular Docking; SWP, Swiss Target Prediction.
As this study does not involve animal and patient experiments, the ethical approval and consent to participate are not applicable.
We are thankful to management and principal of Y.B. Chavan College of pharmacy, Aurangabad (M.S), India for providing the facilities and infrastructure to carry out the research work.
All authors agreed with the content and that all gave explicit consent to submit and publish.
The authors declare no competing interests
There are no conflicts to declare.
All authors have carefully reviewed and given their approval for final version of the manuscript. The all authors contributed equally.
The author has no relevant financial or non-financial interest to disclose.
All data generated or analyzed during this study are included in this published article.