Int J App Pharm, Vol 17, Issue 1, 2025, 161-173Original Article

NETWORK PHARMACOLOGY-BASED COMPUTATIONAL STUDY TO INVESTIGATE THE POTENTIAL MECHANISM OF SYZYGIUM CARYOPHYLLATUM AGAINST COLON CANCER

RAMADEVI PEMMEREDDY1, AJAY MILI1, BHARATH HAROHALLI BYREGOWDA2, JYOTHI GIRIDHAR3, SREEDHARA RANGANATH PAI K.2, ANNA MATHEW1, VASUDEV PAI1, CHANDRASHEKAR K. S.1*

1,2Department of Pharmacognosy, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal-576104, Karnataka, India. 3Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal-576104, Karnataka, India
*Corresponding author: Chandrashekar K. S.; *Email: cks.bhat@manipal.edu

Received: 28 Aug 2024, Revised and Accepted: 04 Nov 2024

ABSTRACT

Objective: Syzygium caryophyllatum, a traditional medicinal plant from the Myrtaceae family, is rich in potential phytoconstituents. Based on its ethnobotanical uses and documented pharmacological activities, present work was conducted to evaluate the probable mechanism of action of S. caryophyllatum to manage colon cancer by integrating network pharmacology and computational studies.

Methods: The plant extract was prepared by Soxhlet extraction method and in vitro screening was performed using Sulforhodamine (SRB) Assay on HT 29 cancer cell lines. We have used super-PRED database, Cytoscape network analyser tool, string database and CytoHubba for performing network analysis for the extract compounds reported in GC-MS analysis. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and DAVID databases were used for gene set enrichment analysis. We have used Schrödinger suite Version 11.4's to perform computational studies.

Results: The extract has demonstrated significant in vitro cytotoxic activity (IC50 value is 49.01 µg/ml) and the GC-MS analysis identified seventy-six distinct compounds. The Gene Ontology (GO) and KEGG demonstrated that the shared targets were strongly associated with key processes involved in colon cancer. The current study has identified Estrogen Receptor Alpha (ESR1), Heat Shock Protein 90 Alpha Family Class A Member 1 (HSP90AA1), Mitogen-activated protein kinase 3 (MAP3K), Epidermal Growth Factor Receptor (EGFR) and Signal transducer and activator of transcription 3 (STAT3) proteins as essential targets and 5,7-Dihydroxy-2-undecyl-4H-chromen-4-one, 7a,12-Dihydroindolo[2,3-a] quinolizine, 5-hydroxy-7-methoxy-2-methyl-8-(3-methylbutyl) chromen-4-one as key compounds. Docking studies of the compounds with core proteins completely supplemented their binding affinity and suggested strong interactions at the binding site.

Conclusion: These outcomes highlight the multi-target, multi-compound, and multi-pathway approaches of S. caryophyllatum against colon cancer.

Keywords: Syzygium caryophyllatum, GC-MS analysis, SRB assay, Network pharmacology, Molecular docking

© 2025 The Authors. Published by Innovare Academic Sciences Pvt Ltd. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/)
DOI: https://dx.doi.org/10.22159/ijap.2025v17i1.52490 Journal homepage: https://innovareacademics.in/journals/index.php/ijap

INTRODUCTION

Cancer is the most commonly occurring disease in the world. In accordance with World Health Organization (WHO) data from 2023 reports, colorectal cancer comes in second in terms of cancer-related fatalities globally, with more than 1.9 million new instances and 930,000 deaths predicted for the disease in 2020. By 2040, there would be 3.2 million new instances of colorectal cancer and 1.6 million deaths annually [1].

The lethality of colon cancer remains a cause for concern, in spite of existence of current therapeutic alternatives, including intensive resection surgeries, radiotherapy, palliative care, neoadjuvant chemotherapies, and intensive laparoscopic surgeries for primary tumors [2, 3]. However natural compounds/agents used as chemotherapeutic medications have shown numerous advantages compared to synthetic ones, mostly due to their lower risk of adverse effects, and greater therapeutic efficacy [4, 5]. Today's most effective and curative anticancer treatments are obtained from plant-based sources. Since the NCI program began, approximately 35,000 plant species have been studied, leading to the development of anticancer medications, including Etoposide analogues, Vincristine, Vinblastine, Camptothecin, Indicine–N-oxide, and Taxol [6].

Syzygium caryophyllatum, belonging to the family Myrtaceae, is a folkloric herb. The tender leaves of this plant have been used traditionally in India to cure wounds, ulcers, diarrhea, and stomatitis [7]. The traditional practitioners in Sri Lanka use this plant for treatment of diabetes mellitus and inflammation [8]. Studies conducted on this plant revealed that the plant has shown antimicrobial activity [9], antidiabetic [10, 11], antioxidant and anticancer activities [12]. The anticancer study conducted on the methanolic extract of S. caryophyllatum plant by rohit et al. has led to the isolation of few phytocompounds such as 3,7-Dihydroxy-4-methoxy flavones, quercetin, and 6,4 dihydroxy 3’propen chalcone, claimed to possess cytotoxic activity on HeLa cancer cell lines [12], However, It's yet unclear how the therapeutic targets and the beneficial phytoconstituents interact. Additionally, there may be some undiscovered chemical moieties present in the plant that may have anticancer activities.

The current strategy of drug discovery, which focuses on using one medicine to treat one specific ailment, is currently encountering significant problems in terms of safety, effectiveness, and long-term survivability. Bioinformatics advancements have transformed medication design and discovery by integrating network biology and polypharmacology to create medicines targeting multiple diseases. Network pharmacology, a combination of network biology and polypharmacology has evolved as an efficient method for elucidating the pharmacological mechanisms underlying multitarget drugs in a variety of disorders [13, 14].

In the current investigation, the ethanolic extract of S. caryophyllatum leaves was screened using the Sulforhodamine (SRB) Assay and then subjected to GC-MS analysis to identify the phytoconstituents. Secondly, we predicted the putative active constituents, associated target genes and the pathways of the compounds against colon cancer by network pharmacology. Finally, we conducted docking and simulation studies to evaluate the therapeutic potential of S. caryophyllatum plant constituents in Colon cancer.

MATERIALS AND METHODS

Preparation of ethanolic leaf extract

The leaves of S. caryophyllatum were collected from Manipal, Karnataka, India in the month of March to August and authenticated. The voucher specimen (PP 652) was deposited in Manipal College of Pharmaceutical Sciences, Department of Pharmacognosy. The leaves of the plant S. caryophyllatum were shade-dried, coarsely ground, and then extracted using ethanol in a Soxhlet extractor, the excess solvent was extracted and concentrated.

Evaluation of cytotoxic activity by SRB assay

To test the cytotoxicity of the extract, 5,000 cells per well were seeded in 96-well plates using 100 µl media. Following a 24-hour period of cell seeding, the cells were exposed to treatment with extract at various concentrations (500 to 15.62 µg/ml). Meanwhile, cells that were not exposed to any treatment were added with an equivalent volume of media, functioning as the control group. Using the SRB assay, cell growth inhibition was performed following a 48 h incubation period. The cell monolayer formed is coloured for 60 min after being fixed with 10% trichloroacetic acid. Following the incubation period, trichloroacetic acid was removed and the wells were dried after three rounds of washing with distilled water. 100 µl** of 0.057% sulforhodamine B solution in acetic acid (1% v/v) was added, kept in dark for 30 min. 200 µl** of Tris base (10 mmol) was added to the wells to dissolve the dye bound to the protein. A multiplate reader (BioTek Instruments Inc. ELx800) was used to examine the absorbance at 540 nm. ​The IC50 values were calculated using graph pad prism 7 [15, 16].

GC-MS analysis

GC-MS Analysis of S. caryophyllatum extract was analysed using GC/MS Clarus 500 (Perkin Elmer) equipment coupled with Restek RtxR – 5 column having dimensions of 30 mm X 0.25 mm. The oven temperature was set as follows: 40 degrees Celsius for 5 min, then gradually increased to 280 degrees Celsius after 15 min and the injector was set to 280 degrees Celsius. The chemicals were identified using the National Institute Standard and Technology (NIST) database coupled with the GC-MS instrument.

Prediction of compound targets and colon cancer targets

Total 76 compounds obtained from GC-MS analysis of the extract (after removing the duplicates) are selected for network pharmacology. The probable targets were identified using Super-PRED, an online compound target prediction engine. The PubChem Compound names were used as search input. Uniprot ID mapping tool was then used to match the predicted chemical targets to their standard gene names [17]. Gene Cards database was used to find the Colon cancer target genes using the phrase "Colon cancer" with a relevance score ≥20 [18].

Building a protein-protein interaction (PPI) network

The PPI network was depicted by Cytoscape v3.10.1. and its topological parameters were examined using Cytoscape Network Analyzer tool [19]. Using the String database v2.0.1, the PPI network of shared targets between the active drugs and colon cancer was constructed; a confidence score of 0.7 is determined as the optimum score. The software extension CytoHubba was subsequently employed to compute the hub targets based on network node's topological characteristics and target genes [20].

Gene set enrichment analysis

Enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway (updated 1st May 2021) and DAVID ver. 6.8 was used to perform pathway and functional annotation [21].

Molecular docking studies

Molecular docking investigations were conducted to comprehend the mechanism of binding of the chemical constituents discovered in S. caryophyllatum to five hub Colon cancer targets, identified by network pharmacology. For the study, the modules of Schrödinger suite Version 11.4's were utilized.

The co-crystallized protein-ligand complex structures of colon cancer targets namely Estrogen Receptor Alpha (ESR1), Heat Shock Protein 90 Alpha Family Class A Member 1 (HSP90AA1), Mitogen-activated protein kinase 3 (MAP3K), Epidermal Growth Factor Receptor (EGFR), Signal transducer and activator of transcription 3 (STAT3), bearing PDB ID’s 5GTR, 4BQG, 2ZOQ, 6VHN and 6NJS, respectively were downloaded from RCSB protein data bank. The protein structure was processed using "protein preparation wizard" in the Schrodinger suite. It consists of subsequent phases: “import and process”, “review and modify”, and “refine”. The missing atoms and the sidechains were substituted using Prime tool during the initial stage, required pH adjustments were made for individual proteins, following this, the hydrogen bonds were optimized and assigned and water molecules larger than 3 Å were eliminated. A low-energy state protein was generated through Restrain minimization utilizing the “OPLS3e (optimization potential for liquid simulation) force field”. The energy optimization methodology employed in this stage of protein preparation generates the protein in its most energy-efficient state, thereby facilitating subsequent in silico investigations [22]. Utilizing the "Receptor grid generation" interface, a grid representing the chosen protein structures was generated [23]. The structures of the compounds were downloaded from the PubChem database. The “LigPrep” tool was used to prepare ligands. Structures with the lowest energy and associated chirality at pH specific to the proteins were built using the OPLS3e force field. Tautomerization, H-bond addition, ionization Epik, neutralization of charged group, and optimisation of ligand geometry were performed for all the selected ligands [24]. The "Glide module" was utilized for all docking experiments. The Glide module attached prepared ligands on the protein's selected site. In extreme precision (XP) mode, prepared ligands were docked and docking score was calculated [25].

ADME analysis

The docking score, ligand interactions were utilized to determine the five most optimal ligands for each protein. The QikProp tool was used to perform ADME analysis. Using this procedure, the following parameters were established: QPlogPo/w (predicted octanol/water coefficient), QPlogBB (predicted brain/blood partition coefficient), QPlogS (predicted aqueous solubility), QPPCaco (cell permeability), QPlogHERG (cardiotoxicity), and Lipinski's rule of five [26, 27].

Induced-fit docking studies (IFD)

The IFD procedure involved specification of 20 poses for an individual ligand and 0.50 Van der Waals scaling. Energy minimization and estimation of the Prime side chain were subsequently conducted. Every residue in the side chain and ligand conformation was refined to a value of 5 Å. All ligand poses were docked thoroughly, and IFD was determined for each pose [28].

Molecular dynamic simulations (MDS)

Protein-ligand complex dynamics and functionality can often be studied using MDS. Based on the ADME profile, IFD data, and ligand docking score, one compound for each protein will be chosen for MDS. Before starting the MDS, the complete system was submerged in a simple point charge/E solvent model. The counter ions were included to maintain overall electrical neutrality. The buffer box size calculation procedure was applied during the building process; the distance (Å) was a = 10, b = 10, and c = 10, and the corresponding angles were α = 90, β = 90, and γ = 90. The system was configured for MD simulation using the System Builder tool, with the OPLS2005 force field being used constantly throughout the procedure. For system minimization, a minimization tool was employed. Simulation time was set to 100 ns with one frame taken from the trajectory every 100 picoseconds, and 1000 frames produced throughout the process. The NPT ensemble (constant particle number (N), pressure (P) 1.01325 bar, and temperature (T) 310 K) was utilized in the production run, with the force field being OPLS2005. Finally, MDS report was created using the Simulation interaction diagram [29, 30].

RESULTS

SRB assay and GC-MS results

The experimental procedure is depicted in fig. 1. The extract is assessed for cytotoxicity by SRB assay on HT-29 cancer cell lines. The extract has demonstrated a significant and dose-dependent cytotoxic effect (IC50 value of 49.01 µg/ml). The fig. 2 illustrates the viability of HT-29 cell lines following treatment with S. caryophyllatum leaf extract at various concentrations. GC-MS chromatogram of the extract, shown in fig. 3 confirmed the presence of 76 compounds. Table 1 illustrates the names of the compounds, retention times and percentage area.

Fig. 1: The framework of study

Fig. 2: Cell viability of S. caryophyllatum ethanolic extract at various concentrations on HT-29 cancer cell lines

Fig. 3: GCMS Chromatogram of S. caryophyllatum leaf extract

Table 1: GC-MS data of S. caryophyllatum plant leaf extract

Peak no Name of the compound CID Retention time Percentage area
1 7-Tetradecenal, (Z)- CID: 5364468 3.193 1.89
2 Glycerin CID: 753 4.683 0.26
3 2-Pyrrolidinone, 1-methyl- CID: 13387 5.746 0.07
4 Ethyl hydrogen succinate CID: 70610 7.38 0.18
5 5-Hydroxymaltol CID: 70627 7.738 0.08
6 1,2-Benzenediol CID: 289 7.887 0.13
7 Benzofuran, 2,3-dihydro- CID: 10329 8.114 0.39
8 Butanedioic acid, hydroxy-, diethyl ester, (.+/-.)- CID: 24197 8.757 0.16
9 Malic Acid CID: 525 8.906 0.07
10 Hexadecanoic acid, 2,3-dihydroxypropyl ester CID: 14900 10.094 0.24
11 1,2,3-Benzenetriol CID: 1057 10.4 4.44
12 3,5-Dimethyl-1-dimethylphenylsilyloxybenzene CID: 532641 10.747 0.24
13 Trehalose CID: 7427 11.112 0.28
14 5-Isopropenyl-3-isopropyl-2,2-dimethyl-2,5-dihydrofuran CID: 596568 11.497 0.27
15 Butylated Hydroxytoluene CID: 31404 12.035 0.88
16 Eicosane CID: 8222 12.243 0.08
17 1-Chloroeicosane CID: 39150 12.932 0.06
18 Methyl 9-(2-[(2-butylcyclopropyl)methyl]cyclopropyl)nonanoate CID: 544392 12.983 0.08
19 N-Methyl-3,5-dihydroxyaniline CID: 587240 13.115 0.08
20 Tricyclo [3.1.0.0(2,4)] hexane, 3,3,6,6-tetraethyl-, trans-- CID: 572173 14.842 0.07
21 (1E)-2-(2,2,6-Trimethyl-7-oxabicyclo [4.1.0]hept-1-yl)-1-propenyl acetate CID: 5363712 15.153 0.97
22 2-Cyclohexen-1-one, 4-hydroxy-3,5,6-trimethyl-4-(3-oxo-1-butenyl)- CID: 5371378 15.309 0.33
23,26,27 cis-9-Hexadecenal CID: 5364643 15.576, 15.982, 16.078 3.54
24 2-Pentadecanone, 6,10,14-trimethyl- CID: 10408 15.643 0.3
25 Stigmasta-5,24(28)-dien-3-ol, (3 beta) CID: 5281326 15.831 0.11
28,31 l-(+)-Ascorbic acid 2,6-dihexadecanoate CID: 54722209 16.245, 16.837 2.46
29 tert-Hexadecanethiol CID: 109858 16.457 0.07
30 Cyclopentadecanone, 2-hydroxy-$$ 2-Hydroxycyclopentadecanone CID: 543400 16.678 0.58
32 Hexadecanoic acid, ethyl ester CID: 12366 17.123 0.61
33 Cholest-5-en-3-ol (3. beta.)- CID: 5997 17.663 0.67
34 9-Undecen-2-one, 6,10-dimethyl- CID: 102604 18.033 0.47
35,36 Phytol CID: 5280435 18.288, 18.383 0.76
37 Octadecanoic acid CID: 5281 18.708 0.93
38 Octadecanoic acid, ethyl ester CID: 8122 18.964 0.09
39 9,12-Octadecadienoic acid (Z, Z)-, 2,3-dihydroxypropyl ester CID: 5283469 19.076 0.15
40 E, Z-1,3,12-Nonadecatriene CID: 5365680 19.808 18.41
41 Methyl alpha linolinate CID: 5319706 19.957 6.04
42, 43 Gamma.-Tocopherol CID: 92729 20.223, 20.583 4.59
44,54,66,75 Gamma.-Sitosterol CID: 457801 20.682, 22.985, 25.689, 28.156 9.6
45 Cholestane-3,6-diol, (3. beta.,5. alpha.,6. alpha.,17. alpha.,20S)- CID: 22213509 21.054 0.47
46 E, E, Z-1,3,12-Nonadecatriene-5,14-diol CID: 5364768 21.268, 25.284, 25.846 1.46
47 Linoleoyl-rac-glycerol CID: 5365676 21.418 0.47
48 Tetracosamethyl-cyclododecasiloxane CID: 167767 21.682 0.62
49 2,6,10,14,18,22-Tetracosahexaene, 2,6,10,15,19,23-hexamethyl-, (all-E)- CID: 638072 21.887 0.25
50,57,69 Vitamin E acetate $$ dl-. alpha.-Tocopherol acetate CID: 2117 22.071, 23.718, 26.16 3.5
51 9-Octadecenoic acid, 1,2,3-propanetriyl ester, (E, E, E)- CID: 5364673 22.342 0.13
52 Ergost-5-en-3-ol, (3. beta.)- CID: 5283637 22.477, 24.119 3.19
53 2,6-Heptadienal, 2,4-dimethyl- CID: 5370098 22.819 0.25
55 Stigmasterol CID: 5280794 23.269 1.54
56 2H-1-Benzopyran-6-ol, 3,4-dihydro-2,8-dimethyl-2-(4,8,12-trimethyltridecyl)-, [2R-[2R* CID: 12444418 23.429 0.74
59 Lanosterol CID: 246983 24.427 4.34
60 Beta.-Tocopherol CID: 6857447 24.566 0.9
61 Stigmasta-4,22-dien-3. beta.-ol CID: 15215516 24.681, 27.269 2.05
62 Stigmast-5-en-3-ol, oleate CID: 20831071 25.152 0.1
64 Alpha-tocopherol-beta-D-mannoside CID: 597057 25.435 1.5
65 4,4,8-Trimethyl-non-5-enal CID: 5365831 25.535 0.31
68 Triacontane, 1-bromo- CID: 521082 26.01 0.41
70 Lupeol CID: 259846 26.831 1.5
71 Cholestanol CID: 6665 26.981 0.28
73 Linoleic acid ethyl ester CID: 5282184 27.671 0.33
74 (R)-(-)-14-Methyl-8-hexadecyn-1-ol CID: 10944926 27.865 0.26
76 Stigmastanol CID: 241572 28.296 0.33
77 Fucosterol CID: 5281328 28.399 0.15
78,81 9,19-cyclolanost-24-en-3-ol, (3 beta)- CID: 129660864 28.696, 29.484 0.1
79 Dihydrochondrillaterol CID: 5283639 29.006 0.09
80 12-oleanen-3yl acetate, (3 alpha)- CID: 45044112 29.297 1.24
82 2,3-Benzocyclododecenedimethanol, 1,2,3,4,5,6,7,8,9,10,11,12,13,14-tetradecahydro-, (2 CID: 569165 29.792 0.13
83 alpha-Amyrin acetate CID: 293754 30.021 0.12
84 2-Ethyl-4-(2-methylcyclohexyl)-5,6,7,8-tetrahydro-4H-1,3,2-benzodioxaborinine CID: 609622 30.861 4.59
85 5,7-Dihydroxy-2-undecyl-4H-chromen-4-one CID: 5375797 30.987 1.03
86 Dihydroindolo[2,3-a] quinolizine CID: 613040 31.772 2.28
87 4-Methyl-1-(2,6,6-trimethyl-2-cyclohexen-1-yl) pent-1-en-3-one CID: 5376281 31.93 0.09
88 5-hydroxy-7-methoxy-2-methyl-8-(3-methylbutyl)chromen-4-one CID: 613101 32.384 3.4
89 2-piperaine-4-amino-6,7-dimethoxyquinaoline CID: 616267 32.848 0.54
90 D; B-Friedo-B; A-neogammacer-5-en-3-ol, (3-beta)- CID: 12442794 32.934 0.37
91 Friedelan-3-one CID: 91472 33.296 0.31

CID: Compound identifier

Network pharmacology analysis

Network Pharmacology has been performed for the 76 chemicals found in the extract and the putative targets of the Colon cancer. The Super-PRED server identified a total of 528 distinct targets from 76 compounds found in the S. caryophyllatum plant leaf extract. In the same manner, A total of 1131 targets relevant to colon cancer were found, having a relevance threshold score of 20 or above. A total of 135 overlapping targets between the chemicals and the Colon cancer were determined using an online Venn Diagram tool shown in fig. 4A.

A PPI network was constructed utilizing String Database v2.0.1 to reveal the interactive connection among the 135 shared targets. In the PPI network, all 135 targets displayed interactions with a confidence score exceeding 0.70. In this experimental investigation, the degree of a node denotes the count of its immediate neighbours. It is widely accepted that the influence exerted by a node increases in proportion to the number of nodes that are directly connected to it. Betweenness centrality quantifies the shortest path traversed by a given node between every pair of nodes. Closeness centrality is a metric that quantifies the centrality of a node in a network [31]. A higher number of closeness centrality denotes a greater centrality of node, suggesting that signals are transmitted more quickly from that node to other nodes. As shown in fig. 4B, the degree of interaction between the targets is represented by the distinct colours of the nodes of PPI network (green ˂ blue˂ yellow), the size of the nodes shows their betweenness centrality, and their shape shows their closeness centrality (Square ˂hexagon ˂circle). The network consisted of 135 nodes and 962 edges. The CytoHubba software was used to determine the network's five most important targets, as determined by the degree of interactions, betweenness centrality and closeness centrality; the targets are HSP90AA1, STAT3, EGFR, MAPK3 and ESR1(fig. 4C). Table 2 represents the data values of top five targets of network analysis.

A

B

C

Fig. 4: (A) Venn diagram depicts the most commonly targeted genes for chemical compounds and disease. (B) PPI network of 135 colon cancer targets of S. caryophyllatum. (C) The top five targets of PPI networks

Table 2: Data values of top five targets of network analysis

Protein name Degree Betweenness centrality Closeness centrality
HSP90AA1 60 0.12588496 0.610328638
STAT3 55 0.09496326 0.616113744
EGFR 45 0.060568805 0.587777778
MAPK3 45 0.053612779 0.580357143
ESR1 44 0.040844799 0.555555556

The DAVID ver 6.8 database was used to perform GO analysis on 135 gene targets. There were 567 identified biological processes (BP), 86 cell compositions (CC), and 150 molecular functions (MF). Based on fold enrichment values (P ˂ 0.01), the top five BP, CC, and MF were chosen and are listed in table 3. BPs were primarily associated with regulation of Golgi inheritance, positive regulation of cyclase activity and control of APC-dependent catabolic process in colon cancer. Phosphatidylinositol 3-kinase (PI3K) complex-class IA caspase complex, cyclin E1-CDK2 complex, PI3K complex-class IB, peptidase inhibitor complex is among the CCs. The MF are primarily involved in cyclin-dependent protein kinase activity, peptide N-acetyltransferase activity, prostaglandin-endoperoxide synthase activity, vitamin D response element binding, JUN kinase activity. The KEGG pathway evaluation of the shared targets identified the specific path in which the common targets are significantly enriched. The top 20 signalling pathways include various cancer-related pathways: prolactin, Vascular endothelial growth factor (VEGF), Fc epsilon RI, Erythroblastic leukaemia viral oncogene homologue (ErbB), Hypoxia-Inducible Factor (HIF-1), and EGFR signalling pathways (table 3).

Table 3: GO and KEGG pathway enrichment analysis of S. caryophyllatum compound targets

ID Category Description Fold enrichment P value Count
GO: 0060440 GO Biological Process Trachea formation 102.7195767 7.47e-08 5
GO: 0090170 GO Biological Process Regulation of Golgi inheritance 143.8074074 1.28e-06 4
GO: 0031281 GO Biological Process Positive regulation of cyclase activity 143.8074074 1.41e-04 3
GO: 1905784 GO Biological Process Regulation of anaphase-promoting complex-dependent catabolic process 143.8074074 0.013757 2
GO: 0036269 GO Biological Process Swimming behaviour 143.8074074 0.013757 2
GO: 0097134 GO Cell Compositions Cyclin E1-CDK2 complex 101.8469136 0.019366 2
GO: 1904090 GO Cell Compositions Peptidase inhibitor complex 76.38518519 0.025739 2
GO: 0005944 GO Cell Compositions Phosphatidylinositol 3-kinase complex, class IB 76.38518519 0.025739 2
GO: 0005943 GO Cell Compositions Phosphatidylinositol 3-kinase complex, class IA 67.89794239 2.19e-05 4
GO: 0008303 GO Cell Compositions Caspase complex 65.47301587 8.61e-04 3
GO: 0004666 GO Molecular Functions Prostaglandin-endoperoxide synthase activity 140.3333333 0.014097 2
GO: 0034212 GO Molecular Functions Peptide N-acetyltransferase activity 140.3333333 0.014097 2
GO: 0097472 GO Molecular Functions Cyclin-dependent protein kinase activity 140.3333333 0.014097 2
GO: 0004705 GO Molecular Functions JUN kinase activity 93.55555556 0.021071 2
GO: 0070644 GO Molecular Functions Vitamin D response element binding 93.55555556 0.021071 2
hsa05219 KEGG Pathways Bladder cancer 23.64085 4.82e-16 15
hsa05230 KEGG Pathways Central carbon metabolism in cancer 23.07797 6.23e-27 25
hsa05215 KEGG Pathways Prostate cancer 22.64972 7.26e-37 34
hsa01521 KEGG Pathways EGFR tyrosine kinase inhibitor resistance 22.08474 1.33e-28 27
hsa05212 KEGG Pathways Pancreatic cancer 21.25603 6.20e-26 25
hsa05223 KEGG Pathways Non-small cell lung cancer 20.64196 1.84e-23 23
hsa05220 KEGG Pathways Chronic myeloid leukemia 20.40579 2.17e-24 24
hsa05221 KEGG Pathways Acute myeloid leukemia 20.2535 3.65e-21 21
hsa04917 KEGG Pathways Prolactin signaling pathway 19.3855 9.79e-21 21
hsa05214 KEGG Pathways Glioma 18.95471 1.58e-21 22
hsa05211 KEGG Pathways Renal cell carcinoma 18.72995 2.12e-19 20
hsa01522 KEGG Pathways Endocrine resistance 18.46238 2.60e-27 28
hsa05213 KEGG Pathways Endometrial cancer 17.82574 4.71e-15 16
hsa05235 KEGG Pathways PD-L1 expression and PD-1 checkpoint pathway in cancer 17.42517 1.26e-22 24
hsa04664 KEGG Pathways Fc epsilon RI signaling pathway 17.10485 1.13e-16 18
hsa05218 KEGG Pathways Melanoma 17.05206 1.35e-17 19
hsa04012 KEGG Pathways ErbB signaling pathway 16.72474 2.87e-20 22
hsa05210 KEGG Pathways Colorectal cancer 16.53027 3.74e-20 22
hsa04066 KEGG Pathways HIF-1 signaling pathway 16.00637 1.60e-24 27
hsa04370 KEGG Pathways VEGF signaling pathway 15.33316 3.20e-12 14

GO: Gene ontology, KEGG: Kyoto encyclopedia of genes and genomes

Molecular docking results

Molecular docking studies were performed to determine the binding affinity of bioactive compounds and the core colon cancer targets. The top five colon cancer target proteins after network pharmacology (5GTR, 4BQG, 2ZOQ, 6VHN and 6NJS) were docked with the 76 compounds of S. caryophyllatum plant. A greater negative docking score indicates high binding affinity of the ligand to the target protein [32]. Table 4 illustrates the docking score and the interactions of ligands with the proteins of the top five compounds for top five targets. The amino acids GLU353, ARG394 and HIE524 are the key target residues of estrogen receptor antagonists [33]. The compound5375797 showed the highest docking score of-11.823 kcal/mol and hydrogen bond interactions with GLU353, ARG 394 residues of 5GTR protein. The key residues LEU48, SER52, ASP93, GLY97, LEU107, PHE138, TYR139, TRP162, THR184 present in the ATPaseN-terminal domain of HSP90AA1 protein form the active site of HSP90AA1 protein [34]. With the residues at the active site, compound 613040 has demonstrated the highest docking score of-10.523 kcal/mol and has established stable H-bond interactions and Pi-Pi interactions. The aminoacids ASP123, MET125, SER170 and ASP184 serves as active sites for MAPK3 inhibitors [35]. The molecule 5375797 forms H-bonds with ASP123, MET125, and ASP184 of the protein 2ZOQ (MAPK3), showed a docking score of-8.130 kcal/mol. The residues THR790, MET793, and CYS797 serve as the active sites for EGFR inhibitors [36, 37]. The compound 5371378 shows hydrogen bonds with THR790 MET793 residues of 6VHN (EGFR) protein. The amino acid residues ARG609, SER611, SER613, SER636, GLU638 present in the SH2 domain of 6NJS protein are the key targets for STAT3 inhibitors [38]. The compound 597057 forms stable H-bond interaction with GLU594, ARG595, SER636 residues with a docking score of-7.542 kcal/mol.

Table 4: Docking score, and interactions of top five compounds for top five target proteins

Protein Compound id Docking score (kcal/mol) Interactions
ESR1 (5GTR) 5375797 -11.823 Hydrogen bond interaction: LEU346, GLU353, ARG 394
569165 -11.229 Hydrogen bond interaction: GLU353, ARG394, Pi-Pi stacking: PHE404
5988 -10.286 Hydrogen bond interaction: THR347, GLU353, GLY521, HIE524
5371378 -8.324 Hydrogen bond interaction: LEU346, ARG204
5365831 -7.314 Hydrogen bond interaction: ARG394
HSP90AA1 (4BQG) 613040 -10.523

Hydrogen bond interaction: ASP93, PHE 138, Pi-Pi stacking: PHE138

Pi-cation: PHE 138
5283469 -8.356 Hydrogen bond interaction: ASP54, LYS58, PHE138
613101 -7.988 Hydrogen bond interaction: TYR139, Pi-Pi stacking: PHE138, TRP162
616267 -7.603 Pi-Pi stacking: PHE138, Pi-cation: PHE138,
12444418 -6.757 Water mediated hydrogen bond interaction: SER52.
MAPK3(2ZOQ) 5375797 -8.130 Hydrogen bond interaction: ASP123, MET125, ASP184
70627 -7.0 Hydrogen bond interaction: GLN122, ASP123, MET125
613040 -6.164 Hydrogen bond interaction: ASP128, Salt bridge: ASP184
14900 -4.213 Hydrogen bond interaction: MET125
587240 -3.177 Hydrogen bond interaction: GLN122, ASP123, MET125
EGFR (6VHN) 613101 -9.302 Hydrogen bond interaction: LYS745, THR790, GLN791, MET793
5371378 -7.202 Hydrogen bond interaction: THR790, MET793
569165 -7.026 Hydrogen bond interaction: MET793, Water mediated hydrogen bond interaction: CYS797, ASP800
5376281 -6.308 Hydrogen bond interaction: MET793
616267 -6.203 Hydrogen bond interaction: MET793, ASN842, ASP855
STAT3(6NJS) 597057 -7.542 Hydrogen bond interaction: GLU594, ARG595, SER636
5988 -5.202 Hydrogen bond interaction: LYS591, ARG609, SER636, GLU638
587240 -4.714 Hydrogen bond interaction: GLN635, SER636, Pi-Pi stacking: TRP623
1057 -4.095 Hydrogen bond interaction: GLU612, SER613, Pi-cation: LYS591
13387 -3.023 Hydrogen bond interaction: LYS591, SER636

ADME analysis

Drug-like characteristics and ADME properties are important filtration criteria in the drug-design process, and these properties for the top five compounds for each targetted protein were assessed using QikProp tool of maestro module are shown in tables 5 and 6. According to the predicted data, all of the chosen compounds exhibited favorable human oral absorption and both the H-bond donor and acceptor atoms are within the acceptable range. There were no substantial violations of the Lipinski rule of five, with only two compounds, 7427 and 597057, found to violate the rule. With the exception of compound 7427, the Caco-2 permeability of all the compounds was satisfactory (values ˃ 25). The acceptable range for QPlogBB is-3.0 to 1.2, and these values for all the compounds were witin the range. The compound 12444418 has showed higher QlogKhsa and QlogPo/w than the recommended range, while the acceptable range of QlogKhsa and QlogPo/w is-1.5 to 1.5 and-2.0 to 6.5, respectively. The compounds 12444418 and 597057 has shown lower QlogS values than the acceptable limit of-6.5 to 0.5.

Table 5: ADME analysis of compounds

Compound ID

Molecular weight

(g/mol)

 Hydrogen bond donor Hydrogen bond acceptor Psa Metab % Human oral absorption Rule of five
569165 280.45 2 3.4 44.119 6 100 0
5375797 332.439 1 3 78.349 3 100 0
7427 342.299 6 16.8 186.758 8 100 2
5371378 222.283 1 4.75 75.482 3 85.653 0
5365831 182.305 0 2 38.322 2 100 0
613040 220.273 1 0.5 8.984 2 100 0
5283469 354.529 2 5.4 80.995 6 100 0
613101 276.332 0 3 61.048 4 100 0
14900 330.507 2 5.4 79.764 3 100 0
616267 289.336 3 6 78.823 3 78.408 0
70627 142.111 2 4 79.634 3 70.818 0
587240 139.154 3 2.5 58.879 5 78.44 0
12444418 402.659 1 1.5 28.129 3 100 1
5376281 220.354 0 2 25.854 4 100 0
597057 592.855 4 10 97.374 8 83.757 2
1057 126.112 3 2.25 65.652 3 73.803 0
13387 99.132 0 3 32.942 1 81.208 0

Psa: Polar Surface Area, Metab: Number of possible metabolic reactions

Table 6: ADME characteristics

Compound ID QPlog Po/w QPlogS QPlog HERG QPP Caco QP logBB QPlogKhsa
569165 3.368 -4.4 -3.544 1156.859 -0.53 0.49
5375797 4.867 -6.391 -5.931 376.67 -1.849 0.871
7427 -3.669 -0.134 -2.924 14.831 -2.597 -1.118
5371378 1.498 -2.624 -3.288 616.461 -0.663 -0.203
5365831 3.064 -3.204 -3.505 2039.601 -0.423 0.16
613040 2.792 -2.976 -4.833 7591.505 0.456 0.032
5283469 4.962 -6.328 -5.989 432.399 -2.346 0.592
613101 3.502 -3.651 -4.121 1252.582 -0.58 0.386
14900 4.49 -5.732 -5.73 482.566 -2.264 0.395
616267 1.333 -2.501 -5.188 274.904 -0.163 -0.114
70627 -0.338 -0.521 -2.907 364.597 -0.708 -0.751
587240 0.408 -0.964 -3.47 554.264 -0.668 -0.674
12444418 8.298 -9.538 -5.928 3005.087 -0.863 2.111
5376281 3.97 -4.431 -3.72 4731.596 0.087 0.557
597057 6.096 -7.351 -5.642 424.508 -2.342 0.98
1057 0.093 -0.323 -3.225 387.014 -0.738 -0.812
13387 -0.362 1.015 -1.15 1412.992 0.14 -1.228

QPlog Po/w: Predicted octanol/water partition coefficient, QPlogS: Predicted aqueous solubility, QPlog HERG: Predicted IC50 value for blockage of HERG Kþ channels, QPP Caco: Predicted apparent Caco-2 cell permeability, QP logBB: Predicted brain/blood partition coefficient, QPlogKhsa: Prediction of binding to human serum albumin

IFD analysis

Top five compounds for each protein have been chosen for IFD depending on their interactions with the protein targets docking score. After IFD, the compound 5375797 has been chosen for MD simulation studies for the proteins 5GTR and 2ZOQ, as this compound has shown highest negative IFD score and also retained bonds. The compounds 613040 and 613101 are selected for MD analysis for 4BQG and 6VHN, respectively based on their IFD scores and bond retention. For the protein target 6NJS, MD analysis was not carried out as the compounds did not retain bonds during IFD analysis. The 3D interaction image of the compounds seleced for MD simulation studies are displayed in fig. 5.

Molecular dynamics (MD) simulation study results

The primary goal of MD studies is to subject the receptor-ligand complex to physiologic settings that were not possible with ligand docking or IFD. Four protein-compound complexes (5375795-5GTR complex, 613040-4BQG complex, 5375797-2ZOQ complex, 613101-6VHN complex) were subjected to MD studies. During MDS, a trajectory frame was generated every 100 ps. 1000 frames were generated when MDS was performed for 100 ns. Analysis of “Root mean square deviation (RMSD),” and “Root mean square fluctuation (RMSF)” was also carried out. By aligning generated frames of protein and ligand-protein complex with the reference frame, the RMSD was computed. The plot of RMSD of proteins on the Y-axis indicates that the variations in protein RMSD are in the range of 1-3Å, which is widely acknowledged as a measure of protein stability during the simulation [39]. For complex 5375797-5GTR, some modest and considerable deviations were seen at about 40-45 ns, and the complex remained stable after 90 ns and no significant conformational change was seen (fig. 6A). In 613040-4BQG complex, significant deviations were observed till 20ns, following that, it remained stable throughout the simulation run, with no significant conformational changes seen (fig. 6B). The complex 5375797-2ZOQ exhibited an initial deviation at around 30–45 ns, following that, it remained stable throughout the simulation run, with no significant conformational changes (fig. 6C). The complex 613101-6VHN remains stable throughout the simulation run (fig. 6D).

Fig. 5: IFD 3D interaction diagram (A) 5375797-5GTR (IFD score:-487.09), (B) 613040-4BQG (IFD score:-450.20), (C) 5375797-2ZOQ (IFD score:-737.56), (D) 613101-6VHN (-587.54)

Fig. 6: RMSD Plots (A) 5375797-5GTR complex, (B) 613040-4BQG complex, (C) 5375797-2ZOQ complex, (D) 613101-6VHN complex

RMSF determines the residues responsible for fluctuations in the complexes and illustrates localized variations throughout the protein chain. The RMSF of the protein in relation to all ligands was shown in fig. 7A, 7B, 7C, and 7D. Our analysis revealed that all the four protein-ligand complexes (5375797-5GTR complex, 613040-4BQG complex, 5375797-2ZOQ complex, 613101-6VHN complex) exhibit several regions of significant flexibility, as shown by the peaks in their RMSF profiles. The RMSF values of the interacting residues, represented by green lines, in all the complexes, are below 1.8 Å. This suggests that the interactions formed between the ligands and the proteins are stable.

Fig. 7: RMSF Plots (A) 5375797-5GTR complex, (B) 613040-4BQG complex, (C) 5375797-2ZOQ complex, (D) 613101-6VHN complex

The protein-ligand contact pertains to the length of time during which the protein and the ligand interact during MD simulation run. A value of 0.4 signifies that the contact persisted for 40% of the total duration of the simulation, but value higher than 1 implies that there may be more interactions between the ligand and the protein's amino acid residue. The compound 5375797 demonstrated H-bond interactions with LEU349 and GLU 353 of 5GTR protein, which persisted for 60% and 65% of the simulation run, respectively. The compound 5375797 also formed a pi-pi interaction with PHE 404 residue of 5GTR protein, which lasted for 70% of the simulation run (fig. 8A). Several sustained interactions occurred between compound 613040 and residues TRP 162 and PHE 170 of 4BQG throughout the experiment. Pi interactions were also established between compound 613040 and PHE 138 and TYR 139 of 4BQG for a duration of 65% (fig. 8B). The compound 5375797 shared H-bond interactions with ASP123 and MET125 of 2ZOQ and these interactions lasted for 40% of simulation run. The compound 5375797 had multiple interactions with the residues ASP 184 of 2ZOQ that lasted throughout the experiment (fig. 8C). The compound 613101 formed hydrogen bonds with the key amino acid residues THR 790 and MET 793. These interactions were present for 50% and 100% of the entire simulation duration, respectively (fig. 8D).

Fig. 8: Histogram of Protein-ligand complex (A) 5375797-5GTR complex, (B) 613040-4BQG complex, (C) 5375797-2ZOQ complex, (D) 613101-6VHN complex

DISCUSSION

Network pharmacology research focuses on the identification of genes associated to compounds and diseases, the building of a PPI network, and the subsequent analysis and visualization of the network [40, 41]. Constructing molecular networks from extensive databases is a straightforward beginning. Subsequently, by employing network analysis, crucial nodes are determined, and essential biological pathways are forecasted [42, 43]. In the current research, we examined the possible molecular mechanisms of the active constituents present in S. caryophyllatum leaf extract responsible for its anticancer activity by integrating the network pharmacology and computational approach. In vitro cytotoxic assay conducted on the extract revealed that it has shown significant cytotoxic activity against HT 29 cell lines and subsequent GC-MS analysis indicated that the extract contained 76 compounds. The network analysis identified 528 targets of 76 compounds, and a total of 1131 gene targets linked to colon cancer were extracted from the Gene cards database. The Cytoscape software was utilized to identify the five most important hub targets selected based on the degree of interaction, closeness centrality and betweenness centrality and the targets include ESR1, HSP90AA1, MAP3K, EGFR and STAT3. ESR1 is involved in the signalling cascade associated with NOD-like receptors (NLR) in cancer. By inhibiting NLRP3 expression and inflammasome activity, a selective ER antagonist might drastically reduce pro-inflammatory cytokine expression, inhibit cell proliferation, and promote apoptosis [44, 45]. The MAPK pathway is a key target for developing colon cancer therapies. Abnormal MAPK pathway activation in colon cancer can lead to uncontrolled proliferation, resistance to therapy, and the spread of cancer cells to other tissues and organs [46, 47]. According to several studies, MAPK3 overexpression has been linked to the beginning, development and metastasis of several carcinomas, including colon cancer [48]. The HSP90, encoded by HSP90AA1 gene is overexpressed in a number of malignancies, including CRC, and promotes neoplastic transformation by preventing cancer cell invasiveness [49, 50]. Since the discovery of the first Hsp 90 inhibitors, natural compounds have shown enormous potential as a source for creating new inhibitors for Hsp90. As a result, several innovative natural and semi-synthetic HSP90 inhibitors have been developed and are currently being tested in clinical studies [51, 52]. In colitis-associated cancer (CRC), STAT3 over-activates and promotes tumour development, angiogenesis, cancer cell invasion, and migration [53–55].

Our study utilized the KEGG and GO functional enrichment analysis to examine 135 targets. The analysis revealed that the genes are involved in various signalling pathways such as EGFR, PD-L1 expression, Fc epsilon RI, ErbB, HIF-1 and VEGF, which had a role in the development of colon cancers [56]. This suggests that the phytocompounds present in S. caryophyllatum plant have a multi-faceted impact on many pathways involved in the genesis of colon cancer.

Furthermore, using the Schrodinger suite Version 11.4 software, an in silico docking analysis of the 76 compounds with the target proteins (ESR1, HSP90AA1, MAP3K, EGFR and STAT3) was done to validate the findings of network pharmacology. Further, the results of molecular docking confirmed the presence of a high binding energy between the selected compounds and the prospective targets. Based on their interactions with the protein targets, docking score, ADME characteristics, IFD score and bond retention, a single compound has been selected for each target protein for simulation studies. The compound 5,7-Dihydroxy-2-undecyl-4H-chromen-4-one (CID: 5375797) was selected for MD simulation research for the proteins 2ZOQ and 5GTR. Based on their IFD scores and bond retention, the compounds 7a,12-Dihydroindolo[2,3-a]quinolizine (CID: 613040) and 5-hydroxy-7-methoxy-2-methyl-8-(3-methylbutyl)chromen-4-one (CID: 613101) are chosen for MD analysis for 4BQG and 6VHN, respectively. The compound 5,7-Dihydroxy-2-undecyl-4H-chromen-4-one (CID: 5375797). The ADME predicted by the QikProp tool indicates that the three compounds (CID: 5375797, CID: 613040 and CID: 613101) adhere to Lipinski's rule of five in terms of druggability, transport properties, and pharmacokinetics. The two compounds 5,7-Dihydroxy-2-undecyl-4H-chromen-4-one and 5-hydroxy-7-methoxy-2-methyl-8-(3-methylbutyl) chromen-4-one are chromone derivatives. Chromones are a type of heterocyclic compounds that include oxygen. Previous research findings show that the chromone derivatives exhibit anticancer activity through a varied array of pathways, including anti-metastasis, cytotoxicity, chemoprevention, anti-angiogenesis, and immune modulation properties [57, 58]. Therefore, to create novel approaches for cancer prevention and treatment, more investigation is necessary to pinpoint the precise molecular pathways underlying the anticancer activity of the bioactive compounds studied in this work.

This study was based on the application of a network pharmacology approach to explore the possible anticancer compounds discovered in the plant S. caryophyllatum, an area that had previously been unexplored. Regrettably, the inaccessibility of the compounds for commercial utilization rendered comprehensive in vitro and in vivo analysis impracticable for our research.

CONCLUSION

The present investigation employed the network pharmacology approach to analyse possible molecular mechanisms of 76 active compounds present in the S. caryophyllatum plant leaf extract and the analysis has identified five significant hub target proteins, namely, ESR1, HSP90AA1, MAP3K, EGFR and STAT3. Further Molecular docking studies revealed that the compound 5,7-Dihydroxy-2-undecyl-4H-chromen-4-one docked favourably with the MAPK3 and ESR-1 proteins, the compound 7a,12-Dihydroindolo[2,3-a] quinolizine docked effectively with HSP90AA1 protein, and the compound 5-hydroxy-7-methoxy-2-methyl-8-(3-methylbutyl)chromen-4-one docked with EGFR protein active site. Thus, these findings indicate that the three identified compounds may have a significant role in mitigating colon cancer by affecting the key protein targets that are involved in colon carcinogenesis. Therefore, network pharmacology and computational investigations provide a diverse array of applications and hold immense potential as a strategy for future drug development aimed at reducing the occurrence of colon cancer. Nevertheless, it is crucial to note that further clinical and pharmacological investigation is necessary to substantiate the findings of this study.

ACKNOWLEDGEMENT

All authors express gratitude to Manipal College of Pharmaceutical Sciences (MCOPS) and Manipal Academy of Higher Education (MAHE) for their provision of facilities for this research. The authors express their gratitude to the Manipal-Schrödinger Centre for Molecular Simulations. Ms. Ramadevi Pemmereddy acknowledges the Manipal Academy of Higher Education (MAHE) for providing Dr. T. M. A. Pai Doctoral Fellowship. Mr. Jyothi Giridhar acknowledges the Council of Scientific and Industrial Research (CSIR), Government of India, for providing the Junior Research Fellowship.

FUNDING

Nil

AUTHORS CONTRIBUTIONS

Conceptualization: Ramadevi Pemmereddy and Chandrashekar K. S; Methodology: Ramadevi Pemmereddy and Ajay Mili; Software: Ramadevi Pemmereddy, Bharath Harohalli Byregowda, Jyothi Giridhar and Ajay Mili; Formal analysis: Ramadevi Pemmereddy, Chandrashekar K. S and Sreedhara Ranganath Pai K; Data curation: Ramadevi Pemmereddy, Anna Mathew and Vasudev Pai; Writing-original draft preparation: Ramadevi Pemmereddy and Chandrashekar K. S; Writing, review and editing: Ramadevi Pemmereddy, Ajay Mili, Bharath Harohalli Byregowda and Jyothi Giridhar. All authors have read and agreed to the published version of the manuscript.

CONFLICT OF INTERESTS

The authors confirm that they do not have any competing financial interests or personal relationships that could have impacted the work provided in this study.

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