Molecular Mechanisms of Cassia fistula against Epithelial Ovarian Cancer Using Network Pharmacology and Molecular Docking Approaches

Abstract

Epithelial ovarian cancer (EOC) is one of the deadliest reproductive tract malignancies that form on the external tissue covering of an ovary. Cassia fistula is popular for its anti-inflammatory and anticarcinogenic properties in conventional medications. Nevertheless, its molecular mechanisms are still unclear. The current study evaluated the potential of C. fistula for the treatment of EOC using network pharmacology approach integrated with molecular docking. Eight active constituents of C. fistula were obtained from two independent databases and the literature, and their targets were retrieved from the SwissTargetPrediction. In total, 1077 EOC associated genes were retrieved from DisGeNET and GeneCardsSuite databases, and 800 potential targets of eight active constituents of C. fistula were mapped to the 1077 EOC targets and intersected targets from two databases. Ultimately, 98 potential targets were found from C. fistula for EOC. Finally, the protein–protein interaction network (PPI) topological interpretation revealed AKT1, CTNNB1, ESR1, and CASP3 as key targets. This is the first time four genes have been found against EOC from C. fistula. The major enriched pathways of these candidate genes were established by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) investigations. To confirm the network pharmacology findings, the molecular docking approach demonstrated that active molecules have higher affinity for binding to putative targets for EOC suppression. More pharmacological and clinical research is required for the development of a drug to treat EOC.

Full text

Citation: Kanwal, A.; Azeem, F.;
Nadeem, H.; Ashfaq, U.A.; Aadil,
R.M.; Kober, A.K.M.H.; Rajoka,
M.S.R.; Rasul, I. Molecular
Mechanisms of Cassia fistula against
Epithelial Ovarian Cancer Using
Network Pharmacology and
Molecular Docking Approaches.
Pharmaceutics 2022,14, 1970.
https://doi.org/10.3390/
pharmaceutics14091970
Academic Editor: Tatjana
P. Stanojkovi´c
Received: 29 July 2022
Accepted: 9 September 2022
Published: 19 September 2022
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4.0/).
pharmaceutics
Article
Molecular Mechanisms of Cassia fistula against Epithelial
Ovarian Cancer Using Network Pharmacology and Molecular
Docking Approaches
Aqsa Kanwal 1, †, Farrukh Azeem 1 ,† , Habibullah Nadeem 1, Usman Ali Ashfaq 1, Rana Muhammad Aadil 2,
A. K. M. Humayun Kober 3, Muhammad Shahid Riaz Rajoka 4and Ijaz Rasul 1, *
1Department of Bioinformatics and Biotechnology, Government College University Faisalabad,
Faisalabad 38000, Pakistan
2National Institute of Food Science and Technology, University of Agriculture Faisalabad,
Faisalabad 38000, Pakistan
3Department of Dairy and Poultry Science, Chittagong Veterinary and Animal Sciences University,
Chittagong 4225, Bangladesh
4Laboratory of Animal Food Function, Graduate School of Agricultural Science, Tohoku University,
Sendai 980-8572, Japan
*Correspondence: ijazrasul@gcuf.edu.pk
† These authors contributed equally to this work.
Abstract:
Epithelial ovarian cancer (EOC) is one of the deadliest reproductive tract malignancies that
form on the external tissue covering of an ovary. Cassia fistula is popular for its anti-inflammatory and
anticarcinogenic properties in conventional medications. Nevertheless, its molecular mechanisms are
still unclear. The current study evaluated the potential of C. fistula for the treatment of EOC using
network pharmacology approach integrated with molecular docking. Eight active constituents of
C. fistula were obtained from two independent databases and the literature, and their targets were
retrieved from the SwissTargetPrediction. In total, 1077 EOC associated genes were retrieved from
DisGeNET and GeneCardsSuite databases, and 800 potential targets of eight active constituents
of C. fistula were mapped to the 1077 EOC targets and intersected targets from two databases.
Ultimately, 98 potential targets were found from C. fistula for EOC. Finally, the protein–protein
interaction network (PPI) topological interpretation revealed AKT1, CTNNB1, ESR1, and CASP3 as
key targets. This is the first time four genes have been found against EOC from C. fistula. The major
enriched pathways of these candidate genes were established by Gene Ontology (GO) and Kyoto
Encyclopedia of Genes and Genomes (KEGG) investigations. To confirm the network pharmacology
findings, the molecular docking approach demonstrated that active molecules have higher affinity
for binding to putative targets for EOC suppression. More pharmacological and clinical research is
required for the development of a drug to treat EOC.
Keywords:
epithelial ovarian cancer; Cassia fistula; anticarcinogenic; network pharmacology; active
constituents; gene ontology; molecular docking
1. Introduction
Ovarian cancer (OC) is the world’s seventh highest prevalent lethal gynecological
malignancy and accounted for 2.5% of new cancers in women. In 2019, the number of
new cases was predicted to be 222,240, while the death toll was estimated to be 14,170 [
1
].
The most frequent type of OC is epithelial ovarian cancer (EOC), having a 45.6% survival
rate [
2
]. EOC originates from the ovaries, ovarian surface epithelial (OSE), oviduct, and
pelvic epithelium locales. About 75% individuals are examined at later phases because
EOC symptoms are non-specific and typically include abdominal bloating, distention, early
satiety, nausea, changes in bowel function, urinary tract problems, back pain, fatigue, and
weight loss [
3
]. EOCs are listed at different stages based on conformation of the cancer. The
Pharmaceutics 2022,14, 1970. https://doi.org/10.3390/pharmaceutics14091970 https://www.mdpi.com/journal/pharmaceutics

Pharmaceutics 2022,14, 1970 2 of 17
malignancy is restricted to either one or both ovaries at phase I. In phase II, the tumor also
extends to the uterus and other epithelial tissues in the pelvic region. In phase III, the tumor
advances at lymphoid tissue and is confined to the abdominopelvic cavity. In phase IV, both
or one of the ovaries are implicated in remote metastases [
4
]. EOC is not a single disease,
while studies show that it is composed of tumors categorized on the basis of molecular
genetics. EOC composed of subtypes on the basis of closely resembled tissues encompasses
serous, mucinous, endometriosis, clear-cell and transitional cell types. EOC is treated
surgically, followed by chemotherapy [
4
]. The etiology of EOC includes stress-induced
recurrent ovulation [
5
,
6
], elevated levels of estrogen [
7
], high levels of androgens [
7
], and
stromal hyperactivity [
8
]. However, the etiology and pathogenesis of EOC is very complex
and not yet fully understood.
The use of plants and their components to control different diseases in humans has an
age-old history. Several therapeutic plants have been demonstrated to be effective in the
administration of quality of life through antioxidant, anti-inflammation, and antidiabetic
biological effects [
9
–
11
]. These herbal medicines, composed of hundreds of phytochemicals,
are derived from many herbs, though their effects and molecular mechanisms are not well
known. Network pharmacology, a new drug development technique, was introduced in
Nature Biotechnology by Hopkins in 2007, updated to the present day “one-target-on-one-
drug” approach to the advanced “network targets, multiple-constituent” technique [
12
].
Network pharmacology analyzes the complex biological mechanisms and selects specific
signaling nodes to develop multi-target drug molecules. It highlights the active constituents,
improves therapeutic effects of drugs, reduces toxicity and maps active constituents to
disease gene to seek potential targets to develop PPI network and carry out gene annotation
analysis [
13
,
14
]. It focuses to demonstrate the interaction of active constituents, potential
targets, and disease-related genes by constructing active constituent/potential target sig-
naling pathway networks [
15
]. In this way, it shows how the screened potential targets
work to cure a disease.
C. fistula belongs to the subfamily Caesalpinioideae of the legume family, Fabaceae
popular as Amaltas. C. fistula is indigenous to the Indian subcontinent, Mauritius, Thailand,
China, Brazil, Sri Lanka, and southern Pakistan. It plays a central role in disease preven-
tion because of its valuable constituents such as flavonoids, anthraquinone, chromones,
coumarins, alkaloids, phytosterols, long-chain hydrocarbons, phenolic, and triterpenes.
Former research confirmed that C. fistula showed roles in antifertility [
16
], antimicrobial [
17
],
hepatoprotective [
18
], improved tissue regeneration and wound healing activities [
19
,
20
].
C. fistula also played a wide range of activities including hypoglycemic activities [
21
,
22
],
antipyretic [
23
], larvicidal [
24
], anti-inflammatory [
25
], antioxidant and antitumor activi-
ties [
26
]. C. fistula is prescribed against epidermis, hepatic, and lung problems as well as
hematemesis, chronic itchy skin, hypopigmentation, and diabetes mellitus [27]. The exact
activities of its molecular mechanisms to prevent a disease are yet entirely unknown. In this
investigation, a C. fistula-target EOC network is constructed, from analysis of compound
to the interaction of potential targets, integrated with pathway analysis to investigate the
molecular mechanism of C. fistula in order to treat EOC.
2. Materials and Methods
2.1. Phytochemical Library Construction
Phyto-constituents of C. fistula were acquired from the literature [28,29], KNApSAcK
Family Core System database [
30
] (http://www.knapsackfamily.com/, accessed on 8
August 2022) and Traditional Chinese Medicine System Pharmacology (TCMSP) (https:
//tcmsp-e.com/tcmsp.php, accessed on 8 August 2022), which was performed by using
the plant name Cassia fistula as a search term in kNAPSAcK database [
31
] and literature
mining performed via Google Scholar and PubMed. Their 2D structures in .sdf file format
were collected through PubChem database (https://pubchem.ncbi.nlm.nih.gov/, accessed
on 10 August 2022) to construct phytochemical library. Meanwhile, their SMILES were also
collected in order to investigate the pharmacodynamics attributes. Active constituents of

Pharmaceutics 2022,14, 1970 3 of 17
C. fistula were obtained from admetSAR (http://lmmd.ecust.edu.cn/admetsar2, accessed
on 13 August 2022) and TCMSP database, i.e., OB and DL greater than 30% and 0.18,
respectively [
32
]. OB stands for oral bioavailability of pharmacological components, while
DL stands for drug-component similarity, which might suggest a prospective drug.
2.2. Drug Target Profiles for C. fistula
SwissTargetPrediction tool (http://www.swisstargetprediction.ch/, accessed on 15
August 2022) was used to find out prospective genes targeting active compounds via
providing the SMILES, while Homo sapiens were selected as the species. SwissTargetPre-
diction is extensively used program for reverse screening of chemical compounds and
predicting the bioactivity of the chemicals. Protein IDs were aligned with UniProtKB
(https://www.uniprot.org/help/uniprotkb, accessed on 16 August 2022) [
33
], performed
in order to eliminate duplication.
2.3. Candidate Targets of C. fistula for EOC
EOC-related Human targets were retrieved from DisGeNET (https://www.disgenet.
org/, accessed on 20 August 2022) and GeneCards (https://www.genecards.org/, accessed
on 20 August 2022) using keyword “epithelial ovarian cancer” [
34
,
35
]. A Venn illustration
was built to map drug-targets profiles of active constituents of C. fistula to the EOC-related
targets [
36
] in order to obtain prospective genes of C. fistula to treat EOC. These mapped
genes were chosen to advance the investigations.
2.4. Compound-Target Network
The network of active constituents of C. fistula with associated genes was built and
analyzed through Cytoscape v3.8.2 [
37
]. Cytoscape is a freely available bioinformatics
platform to visualize, incorporate complex networks from different types of information.
Nodes of network formed represent the phyto-constituents and genes. The edges depict the
interrelationship between them. A plug-in “Network Analyzer” was employed to assess
the network’s topology [38]. The network was evaluated based on “degree” [39].
2.5. Protein–Protein Interaction (PPI) Network
To evaluate the interaction between the prospective genes of C. fistula, these were
imported into STRING v11.5 database (https://string-db.org/, accessed on 25 August 2022)
to construct a PPI network [
40
]. The multiple protein option was taken and potential targets
were added using the “Homo sapiens” as the target species. The network was built with a
confidence of 0.4. This network was further analyzed using Cytoscape for visualization
and topological analysis [
37
]. A plug-in “CytoHubba” was used to obtain targets of higher
degree. In fact, higher the degree means they are linked more [
41
]. “Network Analyzer”
was utilized to analyze the topological properties [38].
2.6. Gene Functional Annotation
The gene and pathway enrichment investigation was performed through DAVID
(https://david.ncifcrf.gov/home.jsp, accessed on 26 August 2022). DAVID is a database of
functional annotations that can be accessed online that helps researchers comprehends the
biological meanings of a huge number of genes. The gene enrichment analysis categorized
the gene functions that incorporate Biological Process (BP), Cellular Component (CC) and
Molecular Function (MF). Moreover, enriched pathways were filtered out through KEGG
analysis [
42
]. The potential genes were copied into DAVID, selecting the species as the
Homo sapiens. The probability value was set to p< 0.05 to select enriched pathways. To
illustrate the GO annotation and KEGG pathways, bubble plots were created in R via
ggplot2 package.
The highest 20 enriched pathways were chosen to construct the pathway–target net-
work, which was constructed and examined by using Cytoscape v3.8.2 to understand
interactions of pathways with the potential targets to evaluate the key targets [37].

Pharmaceutics 2022,14, 1970 4 of 17
2.7. Compound–Target–Signaling Pathway Network
The compound–target–signaling pathways network was built by integration of compound–
target and pathway–target networks. The Cytoscape program was used to understand the
interaction of active constituents with prospective targets and signaling pathways in order
to evaluate the primary targets.
2.8. Molecular Docking
Molecular docking makes it easier to figure out how ligands interact with their cor-
responding proteins. Finally, the results of this network pharmacological study were
verified through molecular docking approach. For that, the 3D structures of active com-
ponents were retrieved from the PubChem search in the .sdf format and optimized [
43
].
The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSBPDB)
(https://www.rcsb.org/, accessed on 29 August 2022) was used to retrieve receptor protein
data, and their PDB files were downloaded [
44
]. The input protein was preprocessed by the
Chimera to eliminate ligand molecules from the source file in order to fulfill the docking
criteria.
Auto-dock Vina is used to carry out the protein–ligand docking for the prediction
of predominant binding mode of the ligand with a protein [
45
]. The active constituents
acted as ligands, while the targets acted as receptors. Finally, the best docking results were
selected for the visualization using the Chimera. The graphical framework of the network
pharmacology and molecular docking techniques is shown (Figure 1).
Pharmaceutics 2022, 14, x FOR PEER REVIEW 5 of 20
Figure 1. The network pharmacology and molecular docking techniques employed in order to
predict C. fistula’s potential drug targets for the treatment of EOC depicted graphically.
3. Results
3.1. Active Constituents of C. fistula
A phytochemical library was constructed with the help of previous knowledge and
multiple databases. That library contained 78 phytochemicals isolated from different
parts of C. fistula such as leaves, fruit, bark, stem, as well as seeds (Table S1). Eight phy-
tochemicals (Rhein, Ellagic Acid, Quercetin, Kaempferol, Gibberellin A3, Licoisoflavone,
β-Sitosterol and Stigmasterol) were predicted with pharmacokinetic criteria (≥30% OB
and ≥0.18 DL. These eight phytochemicals were filtered as effective constituents and
their properties are presented (Table 1).
Figure 1.
The network pharmacology and molecular docking techniques employed in order to predict
C. fistula’s potential drug targets for the treatment of EOC depicted graphically.
3. Results
3.1. Active Constituents of C. fistula
A phytochemical library was constructed with the help of previous knowledge and
multiple databases. That library contained 78 phytochemicals isolated from different parts

Pharmaceutics 2022,14, 1970 5 of 17
of C. fistula such as leaves, fruit, bark, stem, as well as seeds (Table S1). Eight phytochemicals
(Rhein, Ellagic Acid, Quercetin, Kaempferol, Gibberellin A3, Licoisoflavone,
β
-Sitosterol
and Stigmasterol) were predicted with pharmacokinetic criteria (
≥
30% OB and
≥
0.18 DL.
These eight phytochemicals were filtered as effective constituents and their properties are
presented (Table 1).
Table 1. Properties of active phytochemicals C. fistula.
Sr.No. Phytochemicals Molecular
Formula
Molecular
Weight (Dalton)
Drug Likeness
(>0.18)
Oral Bioavailability
(>30%) 2D Structures PubChem
ID
01 Rhein C15H8O6284.22 0.28 47.07
Pharmaceutics 2022, 14, x FOR PEER REVIEW 6 of 20
Table 1. Properties of active phytochemicals C. fistula.
Sr.No.PhytochemicalsMolecular
Formula
Molecular
Weight (Dalton)
Drug
Likeness
(>0.18)
Oral
Bioavailability
(>30%)
2D Structures PubChem
ID
01 Rhein C15H8O6 284.22 0.28 47.07 10168
02 Ellagic Acid C14H6O8 302.19 0.43 43.06 5281855
03 Quercetin C15H10O7 302.23 0.28 46.43 5280343
04 Kaempferol C15H10O6 286.24 0.24 41.88
5280863
05 Gibberellin A3 C19H22O6 346.4 0.53 81.59
6466
06 Licoisoflavone C20H18O6 354.4 0.42 41.61 5281789
07 β-Sitosterol C29H50O 414.7 0.75 36.91
222284
10168
02 Ellagic Acid C14H6O8302.19 0.43 43.06
Pharmaceutics 2022, 14, x FOR PEER REVIEW 6 of 20
Table 1. Properties of active phytochemicals C. fistula.
Sr.No.PhytochemicalsMolecular
Formula
Molecular
Weight (Dalton)
Drug
Likeness
(>0.18)
Oral
Bioavailability
(>30%)
2D Structures PubChem
ID
01 Rhein C15H8O6 284.22 0.28 47.07 10168
02 Ellagic Acid C14H6O8 302.19 0.43 43.06 5281855
03 Quercetin C15H10O7 302.23 0.28 46.43 5280343
04 Kaempferol C15H10O6 286.24 0.24 41.88
5280863
05 Gibberellin A3 C19H22O6 346.4 0.53 81.59
6466
06 Licoisoflavone C20H18O6 354.4 0.42 41.61 5281789
07 β-Sitosterol C29H50O 414.7 0.75 36.91
222284
5281855
03 Quercetin C15 H10O7302.23 0.28 46.43
Pharmaceutics 2022, 14, x FOR PEER REVIEW 6 of 20
Table 1. Properties of active phytochemicals C. fistula.
Sr.No.PhytochemicalsMolecular
Formula
Molecular
Weight (Dalton)
Drug
Likeness
(>0.18)
Oral
Bioavailability
(>30%)
2D Structures PubChem
ID
01 Rhein C15H8O6 284.22 0.28 47.07 10168
02 Ellagic Acid C14H6O8 302.19 0.43 43.06 5281855
03 Quercetin C15H10O7 302.23 0.28 46.43 5280343
04 Kaempferol C15H10O6 286.24 0.24 41.88
5280863
05 Gibberellin A3 C19H22O6 346.4 0.53 81.59
6466
06 Licoisoflavone C20H18O6 354.4 0.42 41.61 5281789
07 β-Sitosterol C29H50O 414.7 0.75 36.91
222284
5280343
04 Kaempferol C15H10 O6286.24 0.24 41.88
Pharmaceutics 2022, 14, x FOR PEER REVIEW 6 of 20
Table 1. Properties of active phytochemicals C. fistula.
Sr.No.PhytochemicalsMolecular
Formula
Molecular
Weight (Dalton)
Drug
Likeness
(>0.18)
Oral
Bioavailability
(>30%)
2D Structures PubChem
ID
01 Rhein C15H8O6 284.22 0.28 47.07 10168
02 Ellagic Acid C14H6O8 302.19 0.43 43.06 5281855
03 Quercetin C15H10O7 302.23 0.28 46.43 5280343
04 Kaempferol C15H10O6 286.24 0.24 41.88
5280863
05 Gibberellin A3 C19H22O6 346.4 0.53 81.59
6466
06 Licoisoflavone C20H18O6 354.4 0.42 41.61 5281789
07 β-Sitosterol C29H50O 414.7 0.75 36.91
222284
5280863
05 Gibberellin A3 C19H22 O6346.4 0.53 81.59
Pharmaceutics 2022, 14, x FOR PEER REVIEW 6 of 20
Table 1. Properties of active phytochemicals C. fistula.
Sr.No.PhytochemicalsMolecular
Formula
Molecular
Weight (Dalton)
Drug
Likeness
(>0.18)
Oral
Bioavailability
(>30%)
2D Structures PubChem
ID
01 Rhein C15H8O6 284.22 0.28 47.07 10168
02 Ellagic Acid C14H6O8 302.19 0.43 43.06 5281855
03 Quercetin C15H10O7 302.23 0.28 46.43 5280343
04 Kaempferol C15H10O6 286.24 0.24 41.88
5280863
05 Gibberellin A3 C19H22O6 346.4 0.53 81.59
6466
06 Licoisoflavone C20H18O6 354.4 0.42 41.61 5281789
07 β-Sitosterol C29H50O 414.7 0.75 36.91
222284
6466
06 Licoisoflavone C20H18 O6354.4 0.42 41.61
Pharmaceutics 2022, 14, x FOR PEER REVIEW 6 of 20
Table 1. Properties of active phytochemicals C. fistula.
Sr.No.PhytochemicalsMolecular
Formula
Molecular
Weight (Dalton)
Drug
Likeness
(>0.18)
Oral
Bioavailability
(>30%)
2D Structures PubChem
ID
01 Rhein C15H8O6 284.22 0.28 47.07 10168
02 Ellagic Acid C14H6O8 302.19 0.43 43.06 5281855
03 Quercetin C15H10O7 302.23 0.28 46.43 5280343
04 Kaempferol C15H10O6 286.24 0.24 41.88
5280863
05 Gibberellin A3 C19H22O6 346.4 0.53 81.59
6466
06 Licoisoflavone C20H18O6 354.4 0.42 41.61 5281789
07 β-Sitosterol C29H50O 414.7 0.75 36.91
222284
5281789
07 β-Sitosterol C29H50 O 414.7 0.75 36.91
Pharmaceutics 2022, 14, x FOR PEER REVIEW 6 of 20
Table 1. Properties of active phytochemicals C. fistula.
Sr.No.PhytochemicalsMolecular
Formula
Molecular
Weight (Dalton)
Drug
Likeness
(>0.18)
Oral
Bioavailability
(>30%)
2D Structures PubChem
ID
01 Rhein C15H8O6 284.22 0.28 47.07 10168
02 Ellagic Acid C14H6O8 302.19 0.43 43.06 5281855
03 Quercetin C15H10O7 302.23 0.28 46.43 5280343
04 Kaempferol C15H10O6 286.24 0.24 41.88
5280863
05 Gibberellin A3 C19H22O6 346.4 0.53 81.59
6466
06 Licoisoflavone C20H18O6 354.4 0.42 41.61 5281789
07 β-Sitosterol C29H50O 414.7 0.75 36.91
222284
222284
08 Stigmasterol C29H48 O 412.7 0.76 43.83
Pharmaceutics 2022, 14, x FOR PEER REVIEW 7 of 20
08 Stigmasterol C29H48O 412.7 0.76 43.83
5280794
3.2. Drug Target Profiling for C. fistula
In total, 800 target genes of corresponding active phyto-constituents of C. fistula
were collected from the SwissTargetPrediction that showed the bioactivity of active con-
stituents of C. fistula. Protein ID’s of the targets were aligned from UniProtKB to remove
the duplicates. In the end, 415 unique targets are selected for further analysis.
3.3. Potential Targets of C. fistula for EOC
A total of 1077 EOC-related targets (Table S2) were acquired by using DisGeNET &
Genecard databases. These were intersected to the active constituent’s target genes. Out
of those, 98 potential targets (Table S3) were predicted for the treatment of EOC (Figure
2).
Figure 2. Venn diagram of potential targets.
3.4. Compound–Target Network
The compound–target network of eight active constituents to corresponding 98 po-
tential targets was constructed by using Cytoscape software. That network had 106
nodes and 217 edges. Each node represented active constituents or potential targets, and
lines showed interaction between them (Figure 3).
5280794

Pharmaceutics 2022,14, 1970 6 of 17
3.2. Drug Target Profiling for C. fistula
In total, 800 target genes of corresponding active phyto-constituents of C. fistula were
collected from the SwissTargetPrediction that showed the bioactivity of active constituents
of C. fistula. Protein ID’s of the targets were aligned from UniProtKB to remove the
duplicates. In the end, 415 unique targets are selected for further analysis.
3.3. Potential Targets of C. fistula for EOC
A total of 1077 EOC-related targets (Table S2) were acquired by using DisGeNET &
Genecard databases. These were intersected to the active constituent’s target genes. Out of
those, 98 potential targets (Table S3) were predicted for the treatment of EOC (Figure 2).
Pharmaceutics 2022, 14, x FOR PEER REVIEW 7 of 20
08 Stigmasterol C29H48O 412.7 0.76 43.83
5280794
3.2. Drug Target Profiling for C. fistula
In total, 800 target genes of corresponding active phyto-constituents of C. fistula
were collected from the SwissTargetPrediction that showed the bioactivity of active con-
stituents of C. fistula. Protein ID’s of the targets were aligned from UniProtKB to remove
the duplicates. In the end, 415 unique targets are selected for further analysis.
3.3. Potential Targets of C. fistula for EOC
A total of 1077 EOC-related targets (Table S2) were acquired by using DisGeNET &
Genecard databases. These were intersected to the active constituent’s target genes. Out
of those, 98 potential targets (Table S3) were predicted for the treatment of EOC (Figure
2).
Figure 2. Venn diagram of potential targets.
3.4. Compound–Target Network
The compound–target network of eight active constituents to corresponding 98 po-
tential targets was constructed by using Cytoscape software. That network had 106
nodes and 217 edges. Each node represented active constituents or potential targets, and
lines showed interaction between them (Figure 3).
Figure 2. Venn diagram of potential targets.
3.4. Compound–Target Network
The compound–target network of eight active constituents to corresponding 98 poten-
tial targets was constructed by using Cytoscape software. That network had 106 nodes and
217 edges. Each node represented active constituents or potential targets, and lines showed
interaction between them (Figure 3).
Topological analysis of network revealed the network characteristics: the density is
0.039, network centralization is 0.271, network heterogeneity is 1.668, and characteristic
path length is 3.042. Furthermore, the active constituents with respective degrees were
found as: Rhein (32), Ellagic Acid (32), Quercetin (30), Kaempferol (29), Gibberellin A3 (28),
Licoisoflavone (26),
β
-Sitosterol (21), and Stigmasterol (19). They interacted with multiple
targets (Table 2).
Table 2. Degrees of 8 active constituents analyzed by the Cytoscape.
Sr. No. Phytochemical Name Categories Degree
01 Rhein Anthraquinone 32
02 Ellagic Acid Polyphenol 32
03 Quercetin Flavonoid 30
04 Kaempferol Flavonoid 29
05 Gibberellin A3 Hormone 28
06 Licoisoflavone Flavonoid 26
07 ß-Sitosterol Phytosterols 21
08 Stigmasterol Phytosterols 19

Pharmaceutics 2022,14, 1970 7 of 17
Pharmaceutics 2022, 14, x FOR PEER REVIEW 8 of 20
Figure 3. Compound–target network of active constituents and potential targets (circle shape
shows active constituents and diamond shape shows potential targets).
Topological analysis of network revealed the network characteristics: the density is
0.039, network centralization is 0.271, network heterogeneity is 1.668, and characteristic
path length is 3.042. Furthermore, the active constituents with respective degrees were
found as: Rhein (32), Ellagic Acid (32), Quercetin (30), Kaempferol (29), Gibberellin A3
(28), Licoisoflavone (26), β-Sitosterol (21), and Stigmasterol (19). They interacted with
multiple targets (Table 2).
Table 2. Degrees of 8 active constituents analyzed by the Cytoscape.
Sr. No. Phytochemical Name Categories Degree
01 Rhein Anthraquinone 32
02 Ellagic Acid Polyphenol 32
03 Quercetin Flavonoid 30
04 Kaempferol Flavonoid 29
05 Gibberellin A3 Hormone 28
06 Licoisoflavone Flavonoid 26
Figure 3.
Compound–target network of active constituents and potential targets (circle shape shows
active constituents and diamond shape shows potential targets).
3.5. Protein–Protein Interaction (PPI) Network
The 98 potential target genes of C. fistula which may be the potential targeting genes
to treat EOC were copied to STRING v11.5to build a PPI network with a score of 0.4
confidence interaction. That was performed to predict the interactions of potential proteins
with Homo sapiens proteins and their physiological functions. In that network, the nodes
are representing the targets, and the lines connecting the nodes are edges that represent
the intermolecular interactions between multiple targets during a disease development
(Figure S1). There were 98 nodes and 1153 edges in the network. In addition, the density,
heterogeneity, network centralization, and path length were 0.243, 0.721, 0.521, and 1.862,
respectively (Figure 4A). Later, the “Network Analyzer” tool was used to examine the
PPI network. The five highest degree targets were AKT1 (73), ALB (65), CTNNB1 (64),
ESR1 (64), and CASP3 (62). The higher degree depicted that the target genes are extremely
connected; hence, these five genes might be important targets (Figure 4B). When the data
were compared to the data obtained from enrichment analysis of 98 potential targets,
specifically four out of those five genes, AKT1, CTNNB1, ESR1, and CASP3, were identified
as the principal anticancerous targets of C. fistula.

Pharmaceutics 2022,14, 1970 8 of 17
Pharmaceutics 2022, 14, x FOR PEER REVIEW 9 of 20
07 ß-Sitosterol Phytosterols 21
08 Stigmasterol Phytosterols 19
3.5. Protein–Protein Interaction (PPI) Network
The 98 potential target genes of C. fistula which may be the potential targeting genes
to treat EOC were copied to STRING v11.5to build a PPI network with a score of 0.4 con-
fidence interaction. That was performed to predict the interactions of potential proteins
with Homo sapiens proteins and their physiological functions. In that network, the nodes
are representing the targets, and the lines connecting the nodes are edges that represent
the intermolecular interactions between multiple targets during a disease development
(Figure S1). There were 98 nodes and 1153 edges in the network. In addition, the density,
heterogeneity, network centralization, and path length were 0.243, 0.721, 0.521, and
1.862, respectively (Figure 4A). Later, the “Network Analyzer” tool was used to examine
the PPI network. The five highest degree targets were AKT1 (73), ALB (65), CTNNB1
(64), ESR1 (64), and CASP3 (62). The higher degree depicted that the target genes are ex-
tremely connected; hence, these five genes might be important targets (Figure 4B). When
the data were compared to the data obtained from enrichment analysis of 98 potential
targets, specifically four out of those five genes, AKT1, CTNNB1, ESR1, and CASP3,
were identified as the principal anticancerous targets of C. fistula.
(A)
Pharmaceutics 2022, 14, x FOR PEER REVIEW 10 of 20
(B)
Figure 4. (A) Analysis and Visualization of PPI network in Cytoscape. (B) Top 10 targets of C. fis-
tula on EOC analyzed by Cytoscape.
3.6. Gene Functional Annotation
The gene and enriched pathways were analyzed through DAVID to predict gene
role and signaling pathways of eight active constituents of C. fistula for the EOC treat-
ment. The GO enrichment analysis contained 324 Biological Processes (BPs), 51 Cellular
Components (CCs) and 85 Molecular Functions (MFs) as conformed screening criteria,
with count ≥ 2 and p ≤ 0.05. Moreover, 133 enriched pathways were identified on the ba-
sis of criteria p < 0.05. A pathway–target network was constructed by using Cytoscape
(Figure 5). GO annotations and KEGG pathway were plotted by using ggplot2 in R lan-
guage (Figures 6 and 7).
Figure 4.
(
A
) Analysis and Visualization of PPI network in Cytoscape. (
B
) Top 10 targets of C. fistula
on EOC analyzed by Cytoscape.
3.6. Gene Functional Annotation
The gene and enriched pathways were analyzed through DAVID to predict gene role
and signaling pathways of eight active constituents of C. fistula for the EOC treatment. The
GO enrichment analysis contained 324 Biological Processes (BPs), 51 Cellular Components
(CCs) and 85 Molecular Functions (MFs) as conformed screening criteria, with count
≥
2
and p
≤
0.05. Moreover, 133 enriched pathways were identified on the basis of criteria
p< 0.05. A pathway–target network was constructed by using Cytoscape (Figure 5). GO

Pharmaceutics 2022,14, 1970 9 of 17
annotations and KEGG pathway were plotted by using ggplot2 in R language (Figures 6
and 7).
Pharmaceutics 2022, 14, x FOR PEER REVIEW 11 of 20
Figure 5. Hub genes enriched in top 20 signaling pathways. Signaling pathways are represented as
hexagons and targets are represented as ellipses.
(A) (B)
Figure 5.
Hub genes enriched in top 20 signaling pathways. Signaling pathways are represented as
hexagons and targets are represented as ellipses.
Pharmaceutics 2022, 14, x FOR PEER REVIEW 11 of 20
Figure 5. Hub genes enriched in top 20 signaling pathways. Signaling pathways are represented as
hexagons and targets are represented as ellipses.
(A) (B)
Figure 6. Cont.

Pharmaceutics 2022,14, 1970 10 of 17
Pharmaceutics 2022, 14, x FOR PEER REVIEW 12 of 20
(C)
Figure 6. GO analysis of C. fistula’s potential targets on EOC. (A) Biological Processes (BP), (B) Cel-
lular Components (CC), and (C) Molecular Functions (MF).
Figure 6.
GO analysis of C. fistula’s potential targets on EOC. (
A
) Biological Processes (BP), (
B
)
Cellular Components (CC), and (C) Molecular Functions (MF).
Pharmaceutics 2022, 14, x FOR PEER REVIEW 12 of 20
(C)
Figure 6. GO analysis of C. fistula’s potential targets on EOC. (A) Biological Processes (BP), (B) Cel-
lular Components (CC), and (C) Molecular Functions (MF).
Figure 7. KEGG pathway enrichment analysis: 20 enriched pathways plotted through R language.

Pharmaceutics 2022,14, 1970 11 of 17
3.7. Compound–Target–Signaling Pathway Network
A compound–target–signaling pathways network was built by integration of compound–
target and pathway–target networks using the Cytoscape. The “Network Analyzer” anal-
ysis showed that it included 126 nodes, 570 edges, 8 active phytochemicals, 98 potential
targets and 20 associated pathways. The targets of active phytochemicals were intercon-
nected with pathways (Figure 8).
Pharmaceutics 2022, 14, x FOR PEER REVIEW 13 of 20
Figure 7. KEGG pathway enrichment analysis: 20 enriched pathways plotted through R language.
3.7. Compound–Target–Signaling Pathway Network
A compound–target–signaling pathways network was built by integration of com-
pound–target and pathway–target networks using the Cytoscape. The “Network Ana-
lyzer” analysis showed that it included 126 nodes, 570 edges, 8 active phytochemicals, 98
potential targets and 20 associated pathways. The targets of active phytochemicals were
interconnected with pathways (Figure 8).
Figure 8. C. fistula’s compound–target–signaling pathway network to EOC. Candidate active phy-
tochemicals shown as ellipses, potential targets represented as diamonds, and pathways repre-
sented as hexagons.
3.8. Molecular Docking
Molecular docking was used to screen potential targets of components with the ca-
pacity to decrease the prevalence of EOC. The highest four target genes, AKT1,
CTNNB1, ESR1, and CASP3 were selected via the topological examination of PPI net-
work. The three-dimensional (3D) structures of these target proteins (AKT1 (PDB id:
3QKK), CTNNB1 (PDB id: 1jdh), ESR1 (PDB id: 1pcg), CASP3 (PDB id: 3kjf) were re-
trieved through the PDB. These structures were refined by the UCSF Chimera tool with
Figure 8.
C. fistula’s compound–target–signaling pathway network to EOC. Candidate active phyto-
chemicals shown as ellipses, potential targets represented as diamonds, and pathways represented as
hexagons.
3.8. Molecular Docking
Molecular docking was used to screen potential targets of components with the
capacity to decrease the prevalence of EOC. The highest four target genes, AKT1, CTNNB1,
ESR1, and CASP3 were selected via the topological examination of PPI network. The
three-dimensional (3D) structures of these target proteins (AKT1 (PDB id: 3QKK), CTNNB1
(PDB id: 1jdh), ESR1 (PDB id: 1pcg), CASP3 (PDB id: 3kjf) were retrieved through the PDB.
These structures were refined by the UCSF Chimera tool with 1000 decent steps of energy
minimization [
46
]. In order to avoid collisions and erroneous compositions, non-standard
residues were eliminated from the proteins. The docking analysis was used to accurately
predict the significant binding affinity between active constituents and four target protein’s’
binding pockets. The eight active components of C. fistula were docked with the four EOC
potential targets (Figure 9). All compounds and targets showed binding scores ranging
from
−
5.8 to
−
9.2 kcal/mole. According to the docking studies, eight active constituents
reported greater binding energy with key EOC targets. Among those, more specifically,
two phytochemicals, β- Sitosterol and Stigmasterol, were prominent (Table 3).

Pharmaceutics 2022,14, 1970 12 of 17
Pharmaceutics 2022, 14, x FOR PEER REVIEW 14 of 20
1000 decent steps of energy minimization [46]. In order to avoid collisions and erroneous
compositions, non-standard residues were eliminated from the proteins. The docking
analysis was used to accurately predict the significant binding affinity between active
constituents and four target protein’s’ binding pockets. The eight active components of
C. fistula were docked with the four EOC potential targets (Figure 9). All compounds
and targets showed binding scores ranging from −5.8 to −9.2 kcal/mole. According to the
docking studies, eight active constituents reported greater binding energy with key EOC
targets. Among those, more specifically, two phytochemicals, β- Sitosterol and Stigmas-
terol, were prominent (Table 3).
Table 3. The binding affinities of prospective target genes with phytochemicals in the docking
analysis.
Sr. No Compound Binding Affinities (kcal/mol)
AKT1 CTNNB1 ESR1 CASP3
01 Rhein −7.5 −6.9 −6.8 −6.2
02 Ellagic acid −7.9 −7.2 −6.6 −6.4
03 Quercetin −7.7 −6.5 −7.7 −6.1
04 Kaempferol −7.6 −6.7 −7.3 −5.8
05 Gibberelin A3 −8.2 −6.7 −6.6 −6.7
06 Licoisoflavone −7.9 −6.1 −6.6 −6.5
07 β- Sitosterol −8.8 −7.0 −6.7 −6.8
08 Stigmasterol −9.2 −6.7 −7.0 −6.8
(A)
Pharmaceutics 2022, 14, x FOR PEER REVIEW 15 of 20
(B)
(C)
(D)
Figure 9.
The binding site residues with the four proteins are shown in the docking complex of four
targets with their best binding components: (A) AKT1; (B) CTNNB1; (C) ESR1; (D) CASP3.

Pharmaceutics 2022,14, 1970 13 of 17
Table 3.
The binding affinities of prospective target genes with phytochemicals in the docking
analysis.
Sr. No Compound Binding Affinities (kcal/mol)
AKT1 CTNNB1 ESR1 CASP3
01 Rhein −7.5 −6.9 −6.8 −6.2
02 Ellagic acid −7.9 −7.2 −6.6 −6.4
03 Quercetin −7.7 −6.5 −7.7 −6.1
04 Kaempferol −7.6 −6.7 −7.3 −5.8
05 Gibberelin
A3 −8.2 −6.7 −6.6 −6.7
06
Licoisoflavone
−7.9 −6.1 −6.6 −6.5
07 β- Sitosterol −8.8 −7.0 −6.7 −6.8
08 Stigmasterol −9.2 −6.7 −7.0 −6.8
4. Discussion
The etiology and pathogenesis of EOC is very complex and not yet fully understood.
It is characterized by sneaking symptoms. That is why individuals are examined at a
later stage. Cyto-reduction surgeries (CRS) are used to treat this, followed by platinum-
based chemotherapy [
1
]. These types of medications incorporate adverse side effects on
the individual’s well-being [
2
]. Owing to the progress in the treatment of OC, targeted
medicines are available now. Nevertheless, targeted/focused medications are expensive
and the options are frequently restricted. Therefore, more effective, safe, and affordable
therapeutic medications are urgently needed for the OC.
Natural phytochemicals, especially plant-derived compounds, are widely employed
as alternative therapeutics, such as taxol for cancers [
47
]. The reason is that they are
inexpensive, more accessible, multi-targeting, and contain low toxicity. Herbal medicines
have been utilized in order to cure human illness since ancient times and are thought to be
the rich origin of drug discovery. C. fistula is a versatile plant that has shown the antioxidant,
anti-inflammatory, immunological, hepatoprotective, antipyretic, analgesic, and antitumor
properties [
30
]. More than 60 phytochemicals, including flavonoids, anthraquinones,
chromones, coumarins, alkaloids, phytosterols, long-chain hydrocarbons, phenolic, and
other phytochemicals have been found in C. fistula.
This study has provided a baseline for the screening of C. fistula’s bioactive compounds.
That was a unique therapeutic idea for future research of C. fistula’s processes for EOC
treatment. Anthraquinone, polyphenols, hormones, phytosterols, and flavonoids were the
most abundant bioactive chemicals discovered in C. fistula. These played a critical role in
the development of EOC. This is a strong indicator that a number of targets may work
together to provide a synergistic impact.
By PPI network topological analysis, the four key targets AKT1, CTNNB1, ESR1, and
CASP3 were evaluated for anti-EOC activity. This is a strong indicator that a number of
targets may work together to provide a synergistic impact. AKT1 promotes OC cell prolif-
eration, immigration, epithelial transformation to mesenchyme (EMT), gluconeogenesis,
and resistance to therapeutics and serves as a biomarker for OC therapy response [48–51].
CTNNB1 is involved in the growth of OC due to its deregulated impact [
52
–
54
]. ESR1 is
implicated in amplification and penetration, and its hereditary characteristics are linked to
OC probability and advancement [
55
–
57
]. CASP3 activity and expression are linked to cell
death, immigration, amplification, susceptibility of OC cells to anticarcinogenic drugs, as
well as metastasis and prognostic outcomes in OC patients [55–58].
Gene enrichment analysis reported that anti-EOC target genes of C. fistula were pri-
marily involved in different molecular processes such as protein serine activity, protein
serine/threonine kinase activity, transmembrane receptor protein tyrosine kinase activity,

Pharmaceutics 2022,14, 1970 14 of 17
and transmembrane receptor protein kinase activity. The pathway enrichment analysis
evaluate that genes were focused to EOC-associated signaling pathways: the ‘PI3K-Akt
signaling pathway, proteoglycans in cancer, the TNF signaling pathway, and the IL-17
signaling pathway. The PI3K-Akt signaling pathway might boost tumor progression,
amplification, and uncontrolled mitosis OC cells [
56
,
57
,
59
]. Proteoglycans in cancer mod-
ulate adhesion and migration are also associated to mutagenic and angiogenic growth
factors [
60
]. TNF signaling pathways promote unregulated inflammation, which can be sup-
pressed with medication, and their expression profiles are linked to OC cancer [
61
,
62
]. The
pro-inflammatory cytokine interleukin-17 (IL-17) has been linked to low tumor grade [
63
].
Through the network analysis, the four main targets AKT1, CTNNB1, ESR1, and
CASP3 were evaluated for their binding energies to eight active components of C. fistula. It
exemplifies the expression of multiple targets, components, and pathways. In molecular
docking, more negative the binding energy, the greater the expected affinity for binding
of the ligand to the target [
64
]. The
β
-Sitosterol showed lower binding affinities to AKT1
and CTNNB1. Stigmasterol interacted to AKT1 and ESR1 with lower binding affinities.
This revealed that C. fistula has anti-EOC properties, inhibiting EOC main targeting genes.
Network pharmacology integrated to molecular docking approach appeared to be effective
for the identification of biologically active phytochemicals, associated potential genes, and
linked signaling pathways to treat EOC. It provided a systematic framework for subsequent
investigation of phytochemicals against a wide range of disorders.
5. Conclusions
This research set the foundation to establish the efficacy of multicomponent, multi-
target chemical formulae and identification of genes that target to treat EOC. Network
pharmacology and molecular docking approaches were incorporated in order to reveal
the underlying mechanisms of C. fistula to treat EOC. Furthermore, our data suggest that
the AKT1, CTNNB1, ESR1, and CASP3 targets are intriguingly beneficial to slow down
the prevalence of EOC, potentially resulting in therapeutic benefits in EOC. This is the
first time four genes have been reported against the EOC from C. fistula. However, more
pharmacological and clinical studies are required to confirm our findings. This method
lays the framework for future research into C. fistula for EOC-protective mechanisms and
the use of network pharmacology in drug development.
Supplementary Materials:
The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/pharmaceutics14091970/s1. Figure S1: PPI network constructed
by STRING. It represents the interaction of potential genes of C. fistula against EOC. Table S1:
Phytochemical library of C. fistula. Table S2: The unique genes and variants of EOC fetched from
DisGeNET & Genecards databases. Table S3: Potential targets of C. fistula for EOC.
Author Contributions:
Conceptualization, U.A.A. and I.R.; methodology, H.N.; software, U.A.A.;
validation, F.A., H.N. and A.K.M.H.K.; formal analysis, A.K. and F.A.; investigation, A.K.; resources,
R.M.A.; data curation, A.K.; writing—original draft preparation, A.K. and R.M.A.; writing—review
and editing, I.R. and M.S.R.R.; visualization, R.M.A.; supervision, I.R.; project administration, I.R. All
authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments:
We are thankful to Department of Bioinformatics and Biotechnology, Govern-
ment College University, Faisalabad, Pakistan for providing computational facilities for successful
completion of this project. Another acknowledgement is due to Aamer Shaheen, Department of
English Literature, Government College University Faisalabad, Pakistan. He helped us in correcting
the English language and grammatical errors. Moreover, Najma Hameed and Hira Saleem deserve
special thanks for their assistance during formal analysis and docking.

Pharmaceutics 2022,14, 1970 15 of 17
Conflicts of Interest:
The funders had no role in the design of the study; in the collection, analyses,
or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
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