Content uploaded by Partha Biswas
Author content
All content in this area was uploaded by Partha Biswas on Nov 30, 2022
Content may be subject to copyright.
Vol.:(0123456789)
1 3
Molecular Diversity
https://doi.org/10.1007/s11030-022-10573-8
ORIGINAL ARTICLE
Oncoinformatic screening ofthegene clusters involved
intheHER2‑positive breast cancer formation alongwiththein silico
pharmacodynamic profiling ofselective long‑chain omega‑3 fatty
acids asthemetastatic antagonists
AKMHelalMorshed1· SalauddinAlAzad2 · Md.AbdurRashidMia3· MohammadFahimUddin4·
TanzilaIsmailEma5· RukaiyaBinteYeasin5· SanjidaAhmedSrishti6· PallabSarker7· RubaitaYounusAurthi8·
FarhanJamil9· NureSharafNowerSamia10· ParthaBiswas11· IatAraSharmeen12· RaselAhmed13·
MahbubaSiddiquy14· Nurunnahar15
Received: 22 September 2022 / Accepted: 17 November 2022
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022
Abstract
The HER2-positive patients occupy ~ 30% of the total breast cancer patients globally where no prevalent drugs are available
to mitigate the frequent metastasis clinically except lapatinib and neratinib. This scarcity reinforced researchers' quest for
new medications where natural substances are significantly considered. Valuing the aforementioned issues, this research
aimed to study the ERBB2-mediated string networks that work behind the HER2-positive breast cancer formation regarding
co-expression, gene regulation, GAMA-receptor-signaling pathway, cellular polarization, and signal inhibition. Following
the overexpression, promotor methylation, and survivability profiles of ERBB2, the super docking position of HER2 was
identified using the quantum tunneling algorithm. Supramolecular docking was conducted to study the target specificity
of EPA and DHA fatty acids followed by a comprehensive molecular dynamic simulation (100ns) to reveal the RMSD,
RMSF, Rg, SASA, H-bonds, and MM/GBSA values. Finally, potential drug targets for EPA and DHA in breast cancer were
* Salauddin Al Azad
sci.01866952382@gmail.com
1 Pathology andPathophysiology Major, Academy ofMedical
Science, Zhengzhou University, Zhengzhou450001,
HenanProvince, People’sRepublicofChina
2 Key Laboratory ofIndustrial Biotechnology, Ministry
ofEducation, School ofBiotechnology, Jiangnan University,
Wuxi214122, JiangsuProvince, People’sRepublicofChina
3 Department ofPharmaceutical Technology, Faculty
ofPharmacy, International Islamic University Malaysia,
25200Pahang, Kuantan, Malaysia
4 College ofMaterial Science andEngineering, Zhejiang
Sci-Tech University, Hangzhou310018, Zhejiang,
People’sRepublicofChina
5 Department ofBiochemistry andMicrobiology, North South
University, Dhaka1229, Bangladesh
6 School ofPharmacy, BRAC University, 66 Mohakhali,
Dhaka1212, Bangladesh
7 Department ofMedicine, Sher-E-Bangla Medical College
Hospital, South Alekanda, Barisal8200, Bangladesh
8 Department ofChemical Engineering, Bangladesh University
ofEngineering andTechnology, Palashi, Dhaka1205,
Bangladesh
9 Department ofPharmacy, University ofAsia Pacific,
Farmgate, Dhaka1205, Bangladesh
10 School ofEnvironment andLife Sciences, Independent
University, Dhaka1219, Bangladesh
11 Laboratory ofPharmaceutical Biotechnology
andBioinformatics, Department ofGenetic Engineering
andBiotechnology, Jashore University ofScience
andTechnology, Jashore7408, Bangladesh
12 School ofData Sciences, Department ofMathematics
& Natural Sciences, BRAC University, 66 Mohakhali,
Dhaka1212, Bangladesh
13 School ofComputing, Engineering andDigital Technologies,
Teesside University, MiddlesbroughTS13BX, TeesValley,
UK
14 State Key Laboratory ofFood Science andTechnology,
Jiangnan University, Wuxi214122, JiangsuProvince,
People’sRepublicofChina
15 Department ofMathematics, Mawlana Bhashani Science
andTechnology University, Santosh, Tangail1902,
Bangladesh
Molecular Diversity
1 3
constructed to determine the drug–protein interactions (DPI) at metabolic stages. Considering the values resulting from the
combinational models of the oncoinformatic, pharmacodynamic, and metabolic parameters, long-chain omega-3 fatty acids
like EPA and DHA can be considered as potential-targeted therapeutics for HER2-positive breast cancer treatment.
Graphical abstract
Keywords HER2-positive breast cancer· ERBB2 overexpression· Long-chain omega-3 fatty acids· G protein-coupled
receptors· EPA and DHA· STRING and STITCH
Introduction
Breast cancer is an uncontrol cell growth and proliferation
that take place in the human mammary glands [1]. During
this malignant condition, fast-paced cells from the vari-
ous mammary gland tissues abnormally and uncontrolla-
bly expand for the creation of a tumor that is capable of
metastasis [2]. According to breast cancer statistics, annually
about 2.3 million female patients are newly diagnosed, and
685,000 females die worldwide, which makes it the second
greatest cause of carcinoma-related death among women [3].
Males account for 0.5–1% of all breast cancer cases identi-
fied globally, with a male-to-female ratio of 1:100. The fact
that the majority of cases are identified at a late stage con-
tributes to a poor prognosis and an extremely low survival
rate [2]. The incidence rate of cancer remains high, although
Molecular Diversity
1 3
the mortality can be reduced by improvements in the aware-
ness of cancer prevention and early diagnosis. Among all
types of breast cancer, almost 25–30% account for human
epidermal growth factor receptor 2 (HER2)-positive cases
[4]. The p53 is mutated in 75%, and HER2 protein is highly
expressed in this type of breast cancer, which leads to poor
prognosis, metastasis, and earlier recurrence and increases
the degree of malignancy [5]. Also, genetic mutations and
breast cancer initiation are directly correlated [1]. For dec-
ades, breast cancer has been deemed as a complicated ill-
ness with an unclear explanation of its progression routes.
In terms of biology and patient response to therapy, breast
cancers exhibit substantial disease heterogeneity [3]. Breast
cancer is classified into subtypes based on two hormonal
receptors and one protein receptor. The classification of
breast cancer depends on the existence and non-existence
of progesterone receptor HER2 and estrogen receptor HER2
[6]. To win the war against breast cancer, scientists need to
have a better understanding of the exact molecular pathways
causing disease development and the creation of efficient
targeted therapy. Due to modern development in medical sci-
ence, several treatment techniques are applied frequently for
any type of breast cancer diagnosis and treatment, including
digital mammography, ultrasound, PET-CT, MRI, radiother-
apy, hormone therapy, and chemotherapy that have proven
to be less efficient and also reduced precision causing more
harm to patients [7]. According to current guidelines, neo-
adjuvant chemotherapy and anti-HER2 treatment are recom-
mended for early-stage HER2-positive breast cancer, which
are subsequently prescribed surgery, radiotherapy, and a
further 48weeks course of HER2-targeted treatment based
on the patient’s conditions [8].
HER2, also known as ERBB2, is a transmembrane pro-
tein that belongs to a family called Epidermal Growth Factor
Receptors (EGFR); the other three members of the EGFR
are ERBB1/HER1, ERBB2/HER2, and ERBB4/HER4. The
HER receptor family regulates normal breast growth and
development through cell growth followed by differentia-
tion via their direct role play in signal transduction path-
ways [9]. The HER receptors are present in various cell
and tissue types, excluding those of hematopoietic origin.
HER2 is encoded by the ERBB2 gene located in chromo-
some region 17q12, and it possesses intrinsic tyrosine kinase
activities. This ERBB2 gene plays a crucial role in human
malignancies for its amplification and/or overexpression
has been responsible for 15–30% of aggressive metastatic
breast cancer, although its presence has also been witnessed
in other malignancies [10]. Before HER2 amplifies, ductal
carcinoma insitu attains major alterations in transcriptome
and copy number variation events. These incidents indicate
that before amplification, DCIS may develop a state of cell
prepared to gain HER2 amplification in favor of growth
[11]. Various downstream-signaling pathways such as
phosphatidylinositol-4,5-biphosphate 3-kinase (PI3K) and
mitogen-activated protein kinase (MAPK) that promotes cell
proliferation, invasion, survival, and migration are activated
by hetero- and homodimerization of the HER family proteins
(EGFR, HER2, HER3, HER4), which mostly involves HER2
as the preferred dimerization partner [12]. In the transduc-
tion signal cascades, HER2 relays signals to cancer cells by
acting as a networking receptor, hence, causing uncontrol-
lable cancer cell proliferation. No certain ligand has been
found to date for ERBB2, but it is the preferred heterodi-
merization partner in the carcinoma formation of ERBB1
and ERBB3, whose potent mitogenic activity gets triggered
via this heterodimerization. This often leads to hyper-acti-
vated signaling causing avoidance of apoptotic pathways,
and dysregulation of the cell cycle homeostatic machinery
along with the increased expression of the active cyclin-D/
CDK complexes [13]. Mounting evidence has demonstrated
that patients with overly expressed ERBB2 genes have a sig-
nificant power survival rate and a shorter life span to relapse
than individuals whose tumors consisted of low-expressing
ERBB2 genes [14]. Different therapeutic methods have been
created to suppress this overexpression, like the ERBB2-
targeted antibody–drug Herceptin/Trastuzumab. Currently,
the US FDA has allowed the use of a combination of EGFR/
ERBB2 kinase inhibitor with capecitabine for those patients
who had no success with Herceptin [15, 16].
The epidemiological studies have been conducted for the
past few decades have suggested that dietary fatty acids (FA)
can play a critical role in the prevention of breast cancer and
even improve the quality of life of the survivors when anti-
cancer drugs, specifically designed to kill cancer cells and
reduce the tumor burden, become ineffective against some
phases of tumorigenesis. Both invivo and invitro study
results have suggested that ω-3 polyunsaturated fatty acids
(PUFA) have anti-cancer effects [17]. The most common
ω-3 FA to have an impact on breast cancer are long-chained
ω-3 Eicosapentaenoic acid (EPA) and Docosahexaenoic
acid (DHA). In a review of the recent systematic prospec-
tive cohort studies, it has been shown that an increment of
0.1g of ω-3 FA every day lowers the risks of developing
breast cancer by 5%, and for those who are already suffer-
ing from breast cancer, ω-3-rich diet can improve the patient
survival [18]. Lipid rafts are microdomains of the plasma
membrane which optimize the signaling proteins and are
particularly important for the function of EGFR, tyrosine
kinase receptors, and HER2 proteins. EPA and DHA inter-
fere with the lipid rafts and the function of EGFR and HER2,
thus, reducing the growth and proliferation of the benign and
malignant cancerous mammary cells resulting in the preven-
tion of metastasis [19].
The purpose of this research work is to figure out the
target specificity of the EPA, and DHA forms of long-chain
omega-3 fatty acids towards the ERBB2 protein to prove that
Molecular Diversity
1 3
those complexes can be used to disrupt the whole mutated
ERBB2 responsible for HER2-positive breast cancer for-
mation. Quantum tunneling followed by molecular docking
is performed to determine the complexity formed between
EPA, DHA, and HER2 proteins. Afterward, molecular
dynamics simulation was implemented to analyze the ligand
stability inside the HER2 protein. Gene–gene interactions
(GGI), and the drug–protein interactions (DI) disrupting the
mutated ERBB2 gene network itself were performed by net-
work visualization analysis where oncoinformatic, in silico
pharmacodynamic, and complicated metabolic parameters
were considered simultaneously.
Materials andmethods
Molecular strings behindtheHER2‑mediated
multiple gene regulation
The HER2 gene-mediated string networks were developed to
figure out the co-expressed and overexpressed genes, which
are responsible for controlling the ERBB2 protein synthesis
in living systems. First, the HER2 receptor protein string
was extracted from the STRING database (https:// string- db.
org/). The individual nodes and edges of the protein were
identified and characterized using Cytoscape 3.8.2 (https://
cytos cape. org/) [20], which is operated through the Java
Runtime Environment (https:// www. oracle. com/ java/ techn
ologi es/ downl oads/). Second, the genes responsible for
encoding the HER2 receptor, along with the others who are
significantly interconnected with the HER2 protein, were
screened using GeneMANIA (https:// string- db. org/) [21],
and Morpheus web-based interface (https:// softw are. broad
insti tute. org/ morph eus) [22].
The genetic networking of HER2 with a group of genes
responsible for different grades of breast cancer forma-
tion was studied, where both the overall and co-expressed
gene–gene interactions (GGI) were studied. In addition,
the genetic data normalization and clustering were con-
ducted to understand the evolutionary relationship of the
HER2 gene with the others. The string properties of HER2
(ERBB2) were profiled considering the factors mean inter-
action sources; genes for protein tyrosine kinase activity;
regulatory genes for the epidermal growth factors; genes for
the GAMA-receptor-signaling pathway; genes for cellular
polarization; and the genes for the negative regulation of
HER2 (ERBB2)-signaling pathway [21, 22].
Oncoinformatic profiling ofHER2 gene
In the oncoinformatic analysis, various important factors
were considered to determine the overexpression and pro-
motor methylation of the HER2 gene in case of breast cancer
patients were studied, such as sample type; individual can-
cer stages; racial impacts; gender; age ranges; menopausal
status; p53 mutation, nodal metastasis; and so on using the
ULCAN interface (http:// ualcan. path. uab. edu/ index. html).
The survivability assessment of the HER2-positive cancer
patients was analyzed in six different ways through CAN-
CERTOOL [23], where the survivability (%) of the breast
cancer patients was estimated in months.
Library construction forligands
A total of 200 bioactive compounds including several
Omega-3 fatty acids were constructed considering their
pharmacokinetic and pharmacodynamic profiles from the
previously established literature reviews and comprehensive
database mining [24]. Based on the target specificity and the
binding affinity properties, eicosapentaenoic acid (EPA), and
docosahexaenoic acid (DHA) were selected as the most sig-
nificant ligand candidates for further studies against breast
cancer [19].
ADMET andQSAR profiling forthepharmacokinetic
analysis
The pharmacokinetic profiles of EPA and DHA were
assessed to screen their disposition inside the human body
(Durán-Iturbide etal. 2020). To understand the absorption,
distribution, metabolism, and excretion, server tools like-
‘Molinspiration Cheminformatics' (https:// www. molin spira
tion. com/ cgi- bin/ prope rties), and the ‘Swiss ADME’ online
interface (http:// www. swiss adme. ch/ index. php) were used as
preliminary [25]. Afterward, 'admetSAR 2.0' (http:// lmmd.
ecust. edu. cn/ admet sar2/) was used for secondary verifica-
tion purposes. Finally, the toxicity profiles of the compounds
were conducted through 'pkCSM' (http:// biosig. unime lb.
edu. au/ pkcsm/ predi ction) [26]. In the case of ADMET and
QSAR analysis, parameters like molecular weight, number
of the hydrogen bond acceptor and donor, predicted octanol/
water partition coefficient, number of rotatable bonds, intes-
tinal absorption, total clearance, LD50, blood–brain barrier,
hepatotoxicity, AEMS toxicity, maximum human tolerant
doses, druglikeness, and number of Lipinski's rule viola-
tions, were considered [26]. The QSAR (quantitative struc-
ture–activity relationship) analysis of the desired ligands was
analyzed using the PASS server (http:// www. way2d rug. com/
passo nline/) to finally validate the anti-microbial, anti-viral,
and anti-infective characteristics [24].
Receptor protein target forbreast cancer
The crystal structure of the kinase domain of the human
HER2 protein (PDB ID: 3PP0) was retrieved from the PDB
database (https:// www. rcsb. org/) [27], also remarked as
Molecular Diversity
1 3
ERBB2, which is considered the most responsible recep-
tor for breast cancer formation [28–30]. The FASTA for-
mat of the HER2 receptor was assessed in ‘SEQATOMs’
(https:// www. bioin forma tics. nl/ tools/ seqat oms/) to detect
the missing residues of the crystal structure of the protein.
More importantly, the missing residues of the middle, C,
and N-terminus regions of the protein’s resolved structure
were assessed by the BLAST function of SEQATOMs [24].
Optimization ofcomponents
Optimization ofreceptor protein
The crystal structure of the receptor macromolecule- HER2
(PDB ID: 3PP0) was optimized using ‘UCSF Chimera ver-
sion 1.14’ (https:// www. cgl. ucsf. edu/ chime ra/ downl oad.
html) [27], where non-interactive residues, ions, water
molecules, unwanted ligands, and side chains except the
‘A chain’ of HER2 were removed. Besides, the missing
hydrogen atoms were added. ‘Gasteiger method’ was used
for energy minimization of the protein and the output file
was saved as a ‘PDB file’ format. Finally, the quantitative
measurement of the minimized energy was accomplished
through the ‘YASARA’ (http:// www. yasara. org/ minim izati
onser ver. htm) [24].
Optimization ofligands
The structures of the test ligands of interest mean-EPA and
DHA (PubChem CID: 446284 and 445580, respectively)
were downloaded as SDF files from PubChem, following
their ADMET and QSAR profiles. To discard the accumu-
lative charge of the ligands up to zero, the energy minimi-
zation process is conducted as an essential part of optimi-
zation, following the Gasteiger method of ‘UCSF Chimera
version 1.14’ [26]. After energy minimization, each of the
test ligands was saved as a ‘mol2 file.’
Active site prediction oftheHER2 receptor protein
To conduct a point-specific molecular docking, the active
site/s of the receptor were initially identified using 'CASTp
Server' [31], where only a single region of 1151.074Å sur-
face area was spotted. Secondary identification of the active
sites was accomplished using ‘Maestro’ (Schrödinger,
LLC) (https:// www. schro dinger. com/ produ cts/ desmo nd),
where four different active sites have resulted. The high-
throughput prediction of the exposed and hidden active sites
of HER2 using the ‘COACH-D’ (https:// yangl ab. nankai. edu.
cn/ COACH-D/) algorithm was conducted finally [32] to
verify the authenticity of those sites identified by ‘Maestro-
Desmond.’ The ‘COACH-D’ algorithm revealed the three
active sites (Fig.6A–C) along with the nearby amino acid
residues, their positional strength, and binding energy (Kcal/
mol) simultaneously and also in comparison to each other
(Fig.6D).
Quantum tunneling onthebest active site ofHER2
To understand the morphological features of the best active
site of the HER2 receptor, quantum tunneling parameters
were analyzed considering the protein tunnel length (Å), cur-
vature (radius), and bottleneck (radius) (Fig.7). In that case,
five different protein tunnels were identified (Fig.7A–E),
where each containing several sub-tunnel clusters at different
configurations (Fig.7F). In all the parameters, the second
tunnel (Fig.7B) is more viable than all the others along with
the sub-tunnel clusters it possesses. Thus, the second tunnel
region of the active site of HER2 predicts the supramolecu-
lar docking point for any ligands. In all aspects of quantum
tunneling of HER2, ‘CAVER 3’ (http:// www. caver. cz) [26,
32] and ‘Site Map 2.6’ (Schrödinger, LLC) were used [33].
Point‑specific molecular docking
Individual molecular docking of each of the optimized
ligands with the best active site of the HER2 receptor was
conducted through Maestro (Schrödinger, LLC) [34]. At the
time of molecular docking operation, the macromolecule
and the ligands were converted into ‘pdbqt file’ format. The
RMSD values were assessed (Å) and the binding affinities
of each of the ligand–protein complexes were conserved in
‘CSV file’ format for further studies.
Post‑molecular docking analysis
The qualitative analysis and initial visualization of the
ligand–protein complexes were undertaken by ‘PyMOL
version 2.5 (https:// pymol. org/2/), and ‘Discovery Studio
Visualizer version 3.0’ (https:// disco ver. 3ds. com/ disco very-
studio- visua lizer- downl oad). At each time, the ligand–pro-
tein complex files were saved in ‘PDB file’ format. As the
secondary study, a quantitative assessment of the number of
hydrophobic interactions (non-covalent) and the number of
hydrogen bond formations between each ligand and receptor
complex was studied through ‘LigPlot + version 2.2’ [35],
36, before subjecting into the molecular dynamic simulation
(MDS) (Fig.8).
Molecular dynamic simulation (100ns)
At the very beginning, the physical alterations and frequent
interactions of the ligand-free HER2 receptor with the neigh-
boring water molecules, and ions were observed for 10ns,
operating the ‘CABS-flex 2.0’ web-based simulator (http://
bioco mp. chem. uw. edu. pl/ CABSfl ex2/) [37]. Afterward, the
Molecular Diversity
1 3
protein–ligand complexes were simulated up to 3.1ns in
the ‘LARMD’ simulation system (http:// chemy ang. ccnu.
edu. cn/ ccb/ server/ LARMD/ index. php) to understand the
Debye–Waller factor for thermostability (B-factor), residual
cross-correlations (RCC), and a clustering dendrogram from
the principal component analysis [38]. Finally, a compre-
hensive molecular dynamic simulation of the ligand–protein
complexes was conducted by ‘Desmond Simulation Pack-
age’ (Schrödinger, LLC, NY, USA) for 100ns to analyze
RMSD (Å), Cα, RMSF (Å), MolSA (Å2), Rg (nm), PSA (Å),
SASA (Å2), and the MM-GBSA dG-binding score (Fig.9)
(Ivanova etal. 2018). The grid box dimension was fixed at
‘X:Y:Z,’ and Na+ was added as the nullifying ion, required
to get the expected results. To assess the SASA and MolSA
values, the probe radius was adjusted to 1.4Å. All the result-
ing data were converted and conserved into ‘CSV’ format.
Statistical analysis andgraphical representation
The resulting data from the molecular dynamic simulation
(100ns) were statistically analyzed using ‘R programming’
(version R-4.0.2 for Linux) [39–41], and ‘GraphPad Prism
version 8.0.1’ software package (for Mac OS) [42–44].
Chemical association network establishment forEPA
andDHA inhuman
The interactive network among EPA, DHA, and the diver-
sified breast cancer protein targets was developed using
STITCH [45]; and Cytoscape 3.8.2 [20] comprehensively
to understand the tentative drug metabolism. Inputting the
canonical SMILES of EPA and DHA in the STITCH, the
lists of different breast cancer-related proteins were derived.
The protein list was then navigated into the Cytoscape 3.8.2
software to produce a unique drug-protein interaction (DPI)
network involving both EPA and DHA, along with various
other breast cancer protein targets on which the drugs can
work [20].
Results
Gene–gene interaction (GGI) betweenERBB2
andother evolutionary‑related gene clusters
ERBB2, along with other interactive genes, are repre-
sented by nodes, and their in-between interactions are indi-
cated by edges, each having a sender and a receiver side
(Fig.1). Among the related genes, GRB2, ERBB3, SRC,
BTC, GRB7, ERBIN, NRG4, HSP90AA1, EZR, MATK,
and PTK2B are found to be much more interactive by the
edges inside the network of overall gene–gene interaction
with ERBB2. Most of the highly interactive genes are found
in the near vicinity of the ERBB2 gene node, and the less
interactive ones are relatively far from ERBB2 (Fig.1A).
Interestingly out of these 11 highly interactive genes, ten
genes (GRB2, ERBB3, SRC, BTC, GRB7, ERBIN, NRG4,
HSP90AA1, MATK, and PTK2B) are also found to be
co-expressed with the ERBB2 remarked as golden nodes
(Fig.1B). However, the TGFA gene, which was less inter-
active (Fig.1A), has been found to co-express with ERBB2
(Fig.1B).
Horizontally, from a recent common ancestral gene per-
spective, ERBB2 is most closely related to ERBB2IP, asso-
ciated with the CRB2 gene, followed by GRB7, HSP90AA1,
and NRG1 genes related, respectively. All six of them
branched from a single ancestral gene. Having a different
ancestral gene, CD44, CTNNB1, EGF, and EGFR are less
closely related to ERBBB2 than others (Fig.1C).
Analysis ofERBB2‑mediated protein–protein
interaction (PPI) instrings
The signaling pathways associated with ERBB2 contrib-
ute to the progression of breast cancer. Upon network
analysis, 11 such significant biological processes related
to ERBB2-signaling pathways were identified (Fig.2A).
The genes that serve as stimulants in breast cancer in addi-
tion to ERBB2 have been identified (Fig.2B). These are
the receptor tyrosine kinases that simulate the expression
of the ERBB2 gene. Their interaction with ligand-free
ERBB2 is believed to form an effective signaling com-
plex. The genes like betacellulin (BTC), transforming
growth factor alpha (TGFA), and growth factor recep-
tor bound protein 2 (GRB2) that regulate the epidermal
growth factors are illustrated (Fig.2C). These genes
affect cell division and survival by binding to the ERBB2
receptor in an antagonistic or cooperative manner. BTC
is the most potent activator of both clathrin-dependent
and clathrin-independent mechanisms, while TGFA only
causes clathrin-dependent endocytosis (CME). However,
the activation of signaling cascades is aided by the inter-
action of GRB2 with the EGFR. The genes that interact
most with the-GAMA-receptor-signaling pathway through
overexpressing themselves are SRC proto-oncogene, non-
receptor tyrosine kinase, growth factor receptor bound
protein 2 (GRB2), and Heat Shock Protein 90 Alpha Fam-
ily Class A Member 1 (HSP90AA1). (Fig.2D). Most of
the complex networks of intracellular signal transduction
pathways are either initiated by SRC genes or controlled
by them. On the other hand, the GRB2 gene is thought to
play a key role in the transformation of cancer cells and
the activation of pathways like MAP kinase, RAS path-
ways, etc. HSP90AA1 is a chaperone that works directly
with ERBB2 to keep it stable, send signals, and make
Molecular Diversity
1 3
dimers. On the other hand, the gamma-signaling path-
way does not involve the ERBB2 gene. The genes EZR,
ERBIN, PTK2B, and HSP90AA1 control how cells line up
(Fig.2E). By interacting with proteins involved in cell–cell
adhesion, these genes control the recruitment of polarity
complexes to specific sites of cell–cell adhesion, which
leads to the polarization of cells. The genes BTC, TGFA,
and GRB2, which are implicated in the regulation of epi-
dermal growth factors, also inhibit the signaling pathways
of ERBB2 (Fig.2F). High-binding ligands such as BTC
and TGFA activate EGFR, and EGFR mutations activate
downstream-signaling pathways that support cancer cell
survival and proliferation. Combining a GRB2 inhibitor
with a cytotoxic agent can help treat HER2-dependent
tumors. These regulatory genes can serve as biomarkers
for breast cancer therapy by targeting the pathways they
control.
HER2 expression assessment ofTGCA samples
andtheir clinical associations
In this research, the HER2 genetic expression of the TCGA
breast cancer samples was analyzed using the ULCAN
database (Fig.3). According to the findings, the HER2
gene expression was significantly upregulated in primary
breast tumors compared to the normal breast cell (Fig.3A).
Surprisingly, the expression of the HER2 gene was found
to be higher in cancerous breast cells (stages 1–4) than
in normal breast cells throughout the progression of the
disease. Among the cancer stages, HER2 expression was
found slightly downregulating in cancer stage 4 (Fig.3B).
Fig. 1 Molecular strings of ERBB2 referring to the peripheral (A)
and co-expressed genes (B), along with its evolutionary relationship
with the other clusters in a gene–gene interaction (GGI) system (C).
Vertically, the experimentally determined interaction (EI) shows the
most highly expressed genes followed by co-expression (CEx), com-
bined score (CS), and database annotation (DA), respectively. These
four grades show high expression values compared to the other five
less expressive profiles consisting of phylogenetic co-occurrence
(PO), the neighborhood on the chromosome (NC); gene fusion (GF);
homology (H), automated text mining (AT). Legends CD44 (*);
CTNNB1 (**); EGF (#); EGFR (##); ERBB2 (***); ERBB2IP ($);
CRB2 ($$); GRB7 (α*); HSP90AA1 (β*); NRG1 ($*)
Molecular Diversity
1 3
Regarding races, Asian patients had a higher level of HER2
gene expression than Caucasian and African-American
patients (Fig.3C). As regards gender, HER2 was upregu-
lated in both male and female breast cancer patients when
compared to normal people. Although male patients were
rarely affected by breast cancer more than females. Interest-
ingly, it was noticed that HER2 overexpression was rela-
tively higher in male than female patients (Fig.3D). Age
is another factor of breast cancer where HER2 overexpres-
sion was significantly increased after 60years compared to
normal patients (Fig.5E). Moreover, HER2 gene expression
was higher in pre-menopause, peri-menopause, and post-
menopause compared to normal patients, while pre-men-
opause was found to have significantly higher overexpres-
sion of genes among TGCA samples (Fig.3F). Clinically,
the HER2 gene expression was significantly higher in P53
mutated patients than in non-mutated P53 and normal breast
cells (Fig.3G). Likewise, HER2 gene expression was upreg-
ulated with the progression of nodal metastasis in breast
cancer patients (Fig.3H). HER2 expression in histological
Fig. 2 Construction of HER2 network with the neighboring genes
based on mean interaction sources (A), protein tyrosine kinase (B),
regulatory genes for EGF (C), GAMA-receptor-signaling pathways
(D), cellular polarization (E), and inhibition of HER2 (ERBB2)-
signaling pathway (F). EPA eicosapentaenoic acid; DHA Docosahex-
aenoic acid; MoW molecular weight, (g/mol); H/A No. of hydrogen
bond acceptor; H/D No. of hydrogen bond donor; L/P Predicted
octanol/water partition coefficient; NRB No. of rotatable bonds; MoR
molar refractivity; I/A intestinal absorption, % absorbed; T/C Total
clearance, log ml/min/kg; LD50 oral rat acute toxicity; BBB blood–
brain barrier; H/T hepatotoxicity; A/T AMES toxicity; Max/TD Maxi-
mum tolerated dose for a human, log mg/kg/day; NLV no. of Lipin-
ski’s rule violations; D/L druglikeness; GPCRKI G-protein-coupled
receptor kinase inhibitor (Pa)
Molecular Diversity
1 3
subtype (IDC, ILC, mixed, and others) of TGCA samples
were also significantly higher, whereas metaplastic, INOS,
and medullary were downregulation compared to the normal
patients.
Promoter methylation ofHER2 expression analysis
ofTCGA samples
In this study, for the determination of promoter methyla-
tion of the HER2 gene in the case of breast cancer patients,
different factors were considered by using the ULCAN
interface. Here, the beta value describes the promotor meth-
ylation quantification. The determination of promotor meth-
ylation demonstrates the overexpression of the HER2 gene
(Fig.4). In this study, the error bar shows TCGA samples
from normal patients (controlled group) and patients with
primary tumors (Fig.4A). The error bars of the two groups
do overlap each other. So, the other group is not significantly
different from the control group. In the primary tumor group,
the promotor methylation is higher (4.97E−09) than in the
control group. However, different stages of cancer compared
to the control group were analyzed (Fig.4B). The error bars
show that stage 1, stage 2, stage 3, and stage 4 overlap with
the control group, and each is not significantly different from
the control group. So, promotor methylation is higher in
stage 4 > stage 2 > stage 1 > stage 3. As a result, gene overex-
pression is higher (7.71E−02) in stage 4. Moreover, different
racial impacts compared to the controlled group were stud-
ied (Fig.4C). The error bars overlap with each other, and the
values are not significantly different. Results show that the
beta value is higher for African-American > Asian > Cau-
casian. So. the promoter methylation or the overexpression
of the gene is higher (3.22E−04) in African-Americans. In
terms of the female patients and the controlled group, the
Fig. 3 HER2 expression assessment of TGCA samples with various
factors, namely sample types (A), individual cancer stages (B), racial
impacts (C), gender (D), age ranges (E), menopause stage of indi-
viduals (F), level of the P53 mutation (G), nodal metastasis (H), and
comparison of histological subtype between normal and other vari-
ables (I)
Molecular Diversity
1 3
error bars do overlap with each other. So, both groups are
not significantly different. A significant portion of data from
female patients can be lower or the same as normal patients.
However, the error bars of male patients and the control
group do not overlap with each other, and they are signifi-
cantly different (Fig.4D). The result shows that the methyla-
tion value is higher (2.38E−08) in male patients compared
to female patients. Beta value or the overexpression of gene
is higher in male than female patients. Again, the error bars
of the controlled group and the group of different ages do
overlap with each other (Fig.4E). Their values are not sig-
nificantly different. The beta value is higher (7.69E−08)
in people aged 41–60years old. So, the overexpression of
genes is higher in this group of people. The beta value is
higher in 41–60years > 61–80years > 21–40years > 81–1
00years. Similarly, the error bars of the controlled group
and different nodal metastasis stages do overlap each other
(Fig.4F). The higher beta value of 8.02−E09 is for the N2
group, which means overexpression of ERBB2 is higher in
this group. The error bars of the three groups do overlap
each other, and the beta value for the TP53 group is higher
(2.08E−12) (Fig.4G). So, compared to the controlled group,
the overexpression of genes is higher than in the male group.
Moreover, the error bars of the groups overlap each other,
and a significant portion of data from pre-menopausal, peri-
menopausal, and post-menopausal have the same or lower
values than the control group (Fig.4H). The beta value is
higher (8.28E−03) in the perimenopausal group. So, over-
expression of genes is higher in the perimenopausal group.
Lastly, the error bars of different factors and control group
is shown (Fig.4I). Surprisingly, not all the error bars of
groups overlap with each other. They have a significant value
Fig. 4 Promoter methylation of HER2 expression assessment of
TCGA samples in risk profiling of cancer with different factors such
as sample type (A), individual cancer stages (B), racial impacts (C),
gender (D), age ranges (E), nodal metastasis (F), p53 mutation (G),
menopausal status (H) and other variables (I)
Molecular Diversity
1 3
Fig. 5 Kaplan Meier (KM) plot representing the survivability of HER2 breast cancer patients in 6 different databases
Fig. 6 Visualization of the quantum tunnels of the HER2 protein considering the tunnel length (Å) and bottleneck radius (Å) and curvature (Å)
for assigning the tentative super docking position/s for ligands
Molecular Diversity
1 3
different from the control group. The beta value of the med-
ullary group (9.27E−02) and other values (7.42E−04) show
a higher value. Therefore, promotor methylation is higher,
and overexpression of genes is greater in those groups.
Survivability assessment oftheHER2‑positive
breast cancer patients
Data from the Kaplan Meier plots were analyzed to identify
the survivability of HER 2-positive breast cancer patients
across six different databases. The primary focus was on
TCGA database samples with a hazard ratio of 1.421 and
a p value of 0429. It showed a general trend of decrease in
survival of HER2-positive breast cancer among the quartiles
as duration increased. A similar decreasing trend in surviv-
ability was observed in the remaining five databases as well.
The Kaplan Meier plots' hazard ratio of most data-
bases had a value greater than one except for the Lu
database (hazard ratio 0.98). The differences among the
quartiles were significant in Ivshina (p = 0.045), Pawitan
(p = 0.011), and Metabric (p = 0.018) were substantial. No
significant differences between the quartile were observed
in TCGA (p = 0.429), Wang (p = 0.348), and Lu (p = 0.42)
(Fig.5).
Quantum tunnel profiling ofHER2
Five distinct protein tunnels were found after a Quantum
tunneling mechanism was performed to the HER2 recep-
tor's optimum active location (Fig.6). Considering the
factors such as tunnel length (Å), curvature radius, and
bottleneck radius, each tunnel was different and supreme
from the others. There are varied numbers of amino acids
close to each tunnel's bottleneck point. The foremost tun-
nel's bottleneck site contained Thr798, Thr862, Asp863,
Leu852, Phe864, Ala751, Gly729, Ala730, Lys753, and
Val734 (Fig.6A). Similar to the first tunnel, Cys805,
Leu852, Met801, Pro802, Gly804, Leu800, and 751Ala
are seen in the second tunnel (Fig.6B). In addition, the
third tunnel—Lys753, Leu785, Phe864, Asp863, Gly865,
and Glu770 were closed to its bottleneck region (Fig.6C).
Additionally, Ala771, Leu785, Leu796, Ile788, Leu790,
and Val734encircledthe bottleneck site of the fifth tunnel
(Fig.6E). The fourth tunnel is longer and more curved than
the other fourdue to the participation of Thr798, Gln799,
Leu800, Met801, Thr862, and Ser783 in its peripheral
region (Fig.6D). Table1 showsthe length, bottleneck
radius, and curvature radius of each tunnel accordingly.
Table 1 Predictive quantum tunnel profiles of the tunnels of the
HER2 receptor considering their length (Å), curvature (Å), and bot-
tleneck radius (Å)
No. of
tunnels Length
(Å) Bottleneck radius (Å) Curvature radius (Å)
1 11.12286399 1.844466271 1.097082849
2 14.8529118 1.48513821 1.124179785
3 13.74219749 1.417483612 1.188360575
4 17.03680405 1.357627697 1.706636678
Predicted HER2
active sites
Predicted
binding energy
Predicted residue positions Predicted
positional strength
A-4.1 114,117,118,121,205,208,209,238,239,240,241,244 *
B-7.5 21,22,29,46,94,95,96,97,102,103,144,145,147,158,180 **
C-8.2 21,22,23,29,46,48,78,93,94,95,96,97,99,100,144,145,147,157,158 ***
ABC
D
Fig. 7 Illustration of the predicted active sites of HER2 based on the COACH-D algorithm, where the involvement of different amino acid resi-
dues and their complexing strength with the ligands were profiled
Molecular Diversity
1 3
Detection oftheactive site ontheHER2 receptor
protein
A total of three active sites (Fig.7) were predicted on the
HER2 receptor protein, exhibiting energies binding affinities
resulting in − 4.1kcal/mol, − 7.5kcal/mol, and − 8.2kcal/
mol (Fig.7D), respectively. The highest number of predicted
residue numbers were found on active site C (19), followed
by active site B (15) and A (12).
Pharmacokinetic exploration oftheligands
The findings of two compounds namely EPA and DHA
have been shown in Table1 according to the SwissADME,
pharmacokinetic web tool (pkCSM), and way2drug PASS
GO-predicted analyses. Both EPA and DHA ligands found
an equal number of hydrogen bond acceptors, donors,
Lipinski’s rule violations, druglikeness, AMES toxicity,
blood–brain barrier, and hepatotoxicity. In contrast, the rest
of the parameters in the physiochemical and pharmacoki-
netic analysis showed a slight difference from each other.
Another critical parameter was the G-protein-coupled recep-
tor kinase inhibitor whose possible activity was found at
0,890 and 0,891 for EPA and DHA, respectively (Table2).
Supramolecular andPost‑molecular docking
analysis
The weakest binding affinity (− 7.86kcal/mol) is found for
the ligand DHAandas opposed to EPA exhibited a higher
interaction affinity (− 7.93kcal/mol). The EPA was deter-
mined to be worthierto the DHA when taking into account
the binding affinities (Kcal/mol) and the RMSD (Ǻ) val-
ues following both the superior and inferior bound ratings
(Table3). Provided amino acids’ interaction with the ligand
compounds in their hydrogen and hydrophobic bond for-
mations (Table4), the candidate ligand compound EPA
developed single-hydrogen bond interaction with Asp 808
Table 2 ADMET and QSAR profiling for the physiochemical and pharmacokinetic properties of EPA and DHA
*GPCR KI (Pa) values for EPA and DHA are 0.890 and 0.891 respectively
*ADMET: absorption, distribution, metabolism, excretion, and toxicity; QSAR: quantitative structure–activity relationship; MW: molecular
weight; HAc: No. of hydrogen bond acceptor; HD: No. of hydrogen bond donor; LogP: predicted octanol/water partition coefficient; NRB: No.
of rotatable bonds; IA: intestinal absorption; TC: total clearance, log mL/(min·kg); LD50: oral rat acute toxicity, mg/kg; BBB: blood brain bar-
rier; HT: hepatotoxicity; AT: AMES toxicity; MTD: maximum tolerated dose for a human, log mg/(kg·day); NLV: No. of Lipinski's rule viola-
tions; DL: druglikeness
Physiochemical Pharmacological
Ligands MoW L/P HAc HD NRB MoR SA NLV DL IA BBB TC AT LD50 HT MTD
EPA 302.46 5.99 2 1 13 97.66 135.18 1 Yes 93.16 No 2.15 No 1.45 Yes − 0.94
DHA 328.50 6.55 2 1 14 106.80 147.22 1 Yes 92.98 No 2.26 No 1.46 Yes − 0.98
Table 3 Molecular docking
analysis of the targeted
macromolecule HER2 receptor
with the tested ligands, EPA
and DHA
MM/GBSA values resulted in − 103.1937467 and − 95.74179402 for EPA and DHA, respectively
Receptor Ligands Binding affinity
(Kcal/mol) RMSD (Ǻ) MM/GBSA
Upper bound (Ǻ) Lower bound (Ǻ)
HER2 EPA − 7.93 6.604 3.875 − 103.194
HER2 DHA − 7.86 4.248 2.836 − 95.742
Table 4 The involvements of different hydrophobic (non-covalent bonds) interactions and hydrogen bonds at the point of complexing HER2
with EPA and DHA individually
Macromolecule Ligands Amino acid interactions
Hydrogen bonds Hydrophobic interactions
HER2 EPA Asp 808 (2.59Ǻ) Leu785, Gly804, Ala771, Glu770, Met774, Phe864, Asp863, Leu796, Thr862, Lys753, Val734,
Gly729, Ser728, Cys805, Leu726
HER2 DHA Cys 805 (3.29Ǻ),
Asp 808
(2.45Ǻ)
Leu726, Ser728, Gly729, Asp863, Val734, Ser783, Glu770, Met774, Phe864, Thr798, Lys753,
Leu796, Thr862
Molecular Diversity
1 3
amino acid residue on the HER2 receptor with a bond length
of 2.59Å (Fig.8). It exhibited fifteen hydrophobic bond
associations on Leu785, Gly804, Ala771, Glu770, Met774,
Phe864, Asp863, Leu796, Thr862, Lys753, Val734, Gly729,
Ser728, Cys805, and Leu726 residues (Fig.8A). Another
ligand, DHA, showed two hydrogen bond interactions with
Cys805 and Asp808 amino acid residues with bond lengths
of 3.29Å and 2.45Å (Fig.8B), respectively. It produced
thirteen hydrophobic bond interlinkages with ligand–pro-
tein complex on Leu726, Ser728, Gly729, Asp863, Val734,
Ser783, Glu770, Met774, Phe864, Thr798, Lys753, Leu796,
and Thr862 (Fig.8B).
Molecular dynamic simulation (100ns)
In this research, the RMSD (Å) values resulted in ran-
dom fluctuation for each of the ligands, whereas the
threshold-spectrum ranged between 0.139462 (Å) and
0.320852 (Å) for EPA (Fig.9). Similarly, DHA resulted in
0.1379235 (Å) and 0.4217227 (Å) as the respective lowest
and highest threshold values (Fig.9A) in response to the
100ns of MD simulation. On the other hand, the RMSF (Å)
values scored 0.07 to 0.8574 (Å), and 0.0662 to 1.0331 (Å),
respectively, for the EPA and DHA upon the MD simula-
tion process (Fig.9B). The radius of gyration is another
important MDS parameter, where EPA demonstrated a value
range between 1.87 to 1.98nm. For DHA, the value range
for Rg was comparatively higher which is between 1.91 and
2.04nm (Fig.9C).
In the case of SASA analysis for EPA and DHA, the val-
ues fluctuated in a narrow range. For EPA, the lowest SASA
value obtained was 133.965 Ǻ2 and the highest SASA value
obtained was 152.694 Ǻ2. (Fig.9D) Additionally, the lowest
SASA value found for DHA was 124.48 Ǻ2 and the highest
Fig. 8 Supramolecular docking of the candidate ligands within the
HER2 macromolecule with their simultaneous qualitative and quan-
titative complexing profiles means EPA–HER2 (A) and DHA–HER2
(B). The hydrogen bonds and hydrophobic interactions are repre-
sented with the green and red lines, respectively
Molecular Diversity
1 3
value was 152.45 Ǻ2. (Fig.9D) The MDS analysis for both
precursors resulted in almost similar SASA values.
During the MDS analysis, the number of hydrogen bonds
was counted for both EPA and DHA precursors for 100ns.
At 2.1ns and 27.8ns, the lowest number of hydrogen bonds
was obtained for EPA, which was valued at 180, and at
97.8ns, the highest number of hydrogen bonds was obtained,
which was 236. (Fig.9E) For DHA, the lowest number of
hydrogen bonds was obtained at 0.8ns, 7.6ns, 19.5ns,
24.7ns, 50.8ns, 51.1ns, 55.5ns, and 57.2ns and the value
was 180, while the highest value of hydrogen bonds was
obtained at 96.5ns and the value was 219 (Fig.9E).
The hydrogen bond analysis for ligand was performed for
EPA and DHA complexes for 100ns. For EPA, the lowest
value for ligand hydrogen bond was found 0 and the highest
value obtained was 2.0 (Fig.9F) Furthermore, the lowest
value for ligand hydrogen bond obtained for DHA was 0
while the highest value obtained was 2.0 (Fig.9F) The val-
ues were similar for both EPA and DHA in this case.
Network ofdrug–protein interactions (DPI) created
using STITCH andcytoscape
The drug–protein circuit network shows the connection
between five different drugs, with the significant key drugs
EPA (Eicosapentaenoic acid) and DPA (Docosahexaenoic
acid) highlighted in yellow and 17 other breast cancer-related
protein targets presented here as nodes (Fig.10). All the 57
edges show the connection between the drugs and protein
targets. EPA has both strong and weak edges with most of
the nodes, indicating its potential as a drug that can effec-
tively work on numerous breast cancer protein targets.
Discussion
ERBB2‑mediated genetic interaction networks
The genetic string network shows the interaction of ERBB2
with other significantly related genes. The 11 highly interac-
tive genes are GRB2, ERBB3, SRC, BTC, GRB7, ERBIN,
NRG4, HSP90AA1, EZR, MATK, and PTK2B. Further sort-
ing reveals that ERBB3, BTS, GRB2, EZR, and HSP90AA1
show the highest interaction [46, 47] (Fig.1A). Most of the
co-expressed genes are highly interactive. But the level
of interaction between genes does not always translate to
equivalent co-expression. Out of these 11 highly interactive
genes, 10 are found to be co-expressed with the ERBB2.
TGFA gene co-expresses with ERBB2 despite having less
interaction than genes like EZR which is highly interactive
[48, 49]. The EZR gene is not co-expressed with ERBB2
(Fig.1B). The heatmap indicates 8 distinct expression pat-
terns in each column. Evolutionarily, there are two ancestral
prime clusters for all 46 expressions [50]. From the recent
0255075 100
0.00
0.14
0.28
0.42
Time (ns)
RMSD (Å)
EPA
DHA
A
700 800 900 1000
0.0
0.4
0.8
1.2
@Frames
RMSF (Å)
EPA
DHA
B
0 25000 50000 75000 100000
1.85
1.90
1.95
2.00
2.05
Time (ps)
Rg
(nm)
EPA
DHA
C
0 25000 50000 75000 100000
127
136
145
154
Time (ps)
SASA (Å
2
)
EPA
DHA
D
0 2500050000 75000100000
160
180
200
220
240
Time (ps)
No.ofH-bonds
E
EPA
DHA
0 25000 50000 75000 100000
0.0
0.5
1.0
1.5
2.0
2.5
Time (ps)
Ligand H-bonds
EPA
DHA
F
Fig. 9 Demonstration of the outcomes of the molecular dynamic simulation (MDS) variables for all ligands crosslinked with the ERBB2 recep-
tor after 100ns of runtimes, including the RMSD (A), RMSF (B), Rg (C), SASA (D), No. of H-bonds (E), and the Ligand H-bonds (F)
Molecular Diversity
1 3
common ancestral gene perspective, ERBB2 is most closely
related to ERBB2IP and then related to the CRB2 gene and
followed by GRB7, HSP90AA1, and NRG1 genes, respec-
tively. Homology and phylogenetic co-occurrence columns
both show similar but extremely low expression patterns.
The neighborhood of chromosomes shows very few ele-
vated and mostly insignificant gene expressions. Fusion of
genes has minor expression changes compared to other low
expressive ones but still nothing significant. In contrast, the
co-expression column is highly expressive except for a few
unchanged expressions. Experimentally determined interac-
tion between genes indicates the highest of all expression
values. Data annotation and combined score show a low
and high expression mixture of genes. However, along with
co-expression, these three profiles show elevated expression
of the ERBB2 gene (Fig.1C) [51, 52].
The human genome encodes more than 800 G-protein-
coupled receptors (GPCRs), many of which exhibit overex-
pression in conditions like tumors. GLP570 platform from
GEO (Gene Expression Omnibus) repository was screened,
and the application of various inclusion criteria reveals a set
of 12 GPCR genes having 9 responsible for breast cancer
[53]. Sphingosine 1-phosphate receptor 2 (one of the G-pro-
tein-coupled receptors) has its N-terminus removed, which is
likely to improve G protein coupling and offer the first proof
that S1P2 is released from breast cancer cells in exosomes
and processed by fibroblasts to encourage ERK signaling
and the growth of these cells [54]. One member of the LGR
family within the rhodopsin GPCR superfamily, LGR4, had
significantly higher levels of protein in breast tumors than in
surrounding breast tissue in the individuals evaluated [55].
Breast cancer that has been triggered by the PI3Kinase/Akt
pathway and PAR1 is linked to GPCR and is susceptible to
inhibition by 2-amino-1-methyl-1H-imidazole-4(5H)-one
derivative [56]. In the case of TNBC, it is found that GPCR
116 intensifies cell invasion [57], and the fold increases in
expression for 11 of the commonly expressed GPCRs with
a greater than twofold increase in expression compared to
the control breast epithelial cell line [58]. A class A G-pro-
tein-coupled receptor (GPCR) Endothelin receptor A (ETA)
is involved in breast cancer metastasis and progression
[59]. Downstream signaling of G protein-coupled estrogen
receptor-1 (GPER) also plays a vital part in sustaining the
stemness of Breast Cancer Stem Cells [60].
ERBB2 gene amplification is observed in 20–25% of
breast tumors, and its therapeutic targeting has significantly
improved breast cancer patients’ survival. Based on factor
mean interaction sources, 11 genes are seen to be allocated
within the network associated with the ERBB2 gene in dif-
ferent signaling pathways to promote breast cancer (Fig.2).
Among them, ERBB3, which lacks intrinsic tyrosine kinase
activity, is unable to produce signaling without the ERBB2
receptor) [61]. However, MAP kinase was turned off in
EGFR breast cancer cells by GRB2 inhibition but not in
ERBB2 cells. This suggests that EGFR and ERBB2 use
different pathways to control the growth of breast cancer
[62]. Moreover, ERBB2 overexpression and activation of its
downstream-signaling pathways can result in the upregula-
tion and activation of the SRC protein, both of which are
essential for ERBB2-mediated breast cancer invasion and
metastasis (Fig.2). According to studies, PKCα and SRC
co-immunoprecipitate, and Src kinase activity helps ERBB2
to activate PKCα in human breast cancer cells [63]. BTC,
TGFα, and GRB2 genes are the most potent activators of
clathrin-mediated and clathrin-independent mechanisms
[64]. In addition to that, many genes are observed to inter-
act with the GAMA-receptor-signaling pathways except
Fig. 10 Construction of the drug-protein interactions (DPI) using
STITCH and Cytoscape mapping to demonstrate multiple protein
targets for EPA and DHA. The rectangles refer to drug molecules
whereas the circular nodes are the protein targets
Molecular Diversity
1 3
for the ERBB2 gene, which indicates that SRC and GRB2
genes initiate or control those signal transduction pathways.
On the other hand, GRB2 is required for the heregulin or
overexpression-induced activation of ERBB2 to activate
the Akt pathway and disseminate mitogenic signals [65].
Additionally, EGFR is activated by high-binding ligands like
BTC and TGFA, and EGFR mutations activate downstream-
signaling pathways that favor cancer cell survival and prolif-
eration [66]. By targeting the pathways that these regulatory
genes control and associating with the ERBB2 receptor, they
can act as biomarkers for breast cancer treatment. The most
prevalent class of cell-surface receptors, G-protein-coupled
receptors (GPCRs), is implicated in the initiation and devel-
opment of several malignancies, including breast cancer.
In heterologous cells, co-expression of ERBB2 with these
GPCRs induces the formation of ligand-dependent com-
plexes and activates MAPK [67]. However, PAR1 is a GPCR
stimulated by tumor-generated proteases and enhances breast
cancer cell invasion during ERBB-signaling hyperactivation
[68]. The contribution of PAR1 to tumor progression will
yield fresh insights into the molecular basis of breast can-
cer and give new targets for the development of anti-cancer
drugs.
Oncoinformatic profiling ofHER2
According to TCGA ULCAN database analysis (Fig.3),
HER2 expression was elevated in all stages of breast cancer
tissues when compared to normal tissues. A similar finding
was also found for breast cancer that happened due to the
activation of the PI3K-AKT-mTOR pathway by expression
of HER2 [69, 70]. According to our findings, breast cancer
can occur in adults to older ages, of any race and gender that
happened due to the expression of the HER2 gene by dif-
ferent factors like hormones, lifestyle, diet, hereditary, and
demographic as reported previously [71]. Another research
found that Asian people were more affected by breast cancer
than other natives which were also noticed in our findings
[72]. Considering the breast cancer status, HER2 showed
more expression in pre-menopause compared to the nor-
mal, peri, and post-menopause stages (Fig.3). This conclu-
sion is consistent with previous research that discovered a
greater frequency of breast cancer among Asian-American
pre-menopausal women [73]. HER2 expression also depends
on TP53 and nodal metastasis found in this study for breast
cancer patients. This finding is in line with a study that found
the expression of HER2 is linked to a substantial increase in
the nodal metastatic potential of breast cancer cells and has
the potential to serve as a reliable indicator of breast cancer
[74]. Furthermore, at the transcriptional level of the HER2
protein, p53 mutations cause overexpression of the HER2
gene, which leads to cancer [75].
The promoter methylation as HER2 expression assess-
ment of the TCGA samples is done by considering different
factors through the ULCAN interface (Fig.4). Surprisingly,
studies show abnormal promoter methylation of numerous
tumor suppressor genes in Breast cancer precursor lesions,
suggesting that DNA methylation occurs early in the devel-
opment of breast cancer [76]. Our study shows that promoter
methylation is higher in the primary tumor group than in the
control group. DNA methylation has also been suggested
as a useful biomarker for cancer detection and prognosis
due to its connection to tissue-specific gene silencing [77].
Moreover, our study shows that the rate of methylation var-
ies in different stages and that the overexpression of genes
is higher in patients in stage 4. Gender, race, age, tumor
type, tumor stage, and menopause status have been linked to
prognosis in breast cancer [78]. Studies show the rate of pro-
motor methylation is African American > Asian > Caucasian
accordingly. African Americans have a higher value for pro-
moter methylation than the normal patient group. Similarly,
the promotor methylation value is higher in male and female
patients as well as in patients of different ages (41–60yea
rs > 61–80years > 21–40years > 81–100years) compared
to the normal patient group (Fig.4). Moreover, in different
nodal metastasis stages, the TP53 group denotes the higher
beta value as overexpression of genes. Pre-menopausal, peri-
menopausal, and post-menopausal have the same or lower
values than the control group. However, the beta value is
higher in the perimenopausal group. Furthermore, the beta
value of the medullary group and others show a higher value
as overexpression of genes is greater in those groups which
leads to the prognosis of breast cancer (Fig.4). As a result
of methylation, it alters gene function and has an impact
on gene expression. The promoter methylation is noticeably
higher in breast invasive carcinoma tumors in the TCGA
compared to normal patients [79].
Kaplan–Meier plot is undoubtedly one of the best tools at
our disposal to measure the fraction of subjects who survived
after the intervention has been introduced, especially ERBB
2-positive breast cancer in Fig.5. It is also the simplest way
of calculating and representing the survival over time of
subjects in the trial [80]. Kaplan Meier curve puts time into
consideration of many short intervals and is the probability
of survival in a given length of time [81]. The Kaplan–Meier
curve most definitely goes in a series of steps, i.e., vertical
and horizontal. Vertical step-downs reflect increased mortal-
ity, while horizontal reflects stationary level mortality [82].
Numerous studies have been conducted using Kaplan Meier
to establish a comparison in survivability between treatment
protocols or even living conditions such as the study done
by Tesfay with regard to breast cancer survivability in a hos-
pital in Ethiopia [83]. Similarly, Ouyang DJ conducted a
study to see the effect of pyrotinib on HER2-positive breast
cancer and related survival analysis to show its effects in
Molecular Diversity
1 3
comparison to lapatinib naïve and lapatinib-treated group
[84]. In addition to the ERBB2 protein, other biomarker
proteins related to breast cancer prognosis have also been
analyzed, through gene mapping and Kaplan–Meier plots
in studies previously [85].
Pharmacokinetic analysis ofEPA andDHA
The pkCSM and the Swiss ADME approaches provide a
framework for the investigation and optimization of pharma-
cokinetic features. Both EPA and DHA compounds matched
Lipinski's rule of five and had acceptable GPCRKI and bio-
availability ratings, which were considered for finding poten-
tial therapeutic targets (Table1).
Active site prediction ofHER2 protein
Quantum tunnel properties, like tunnel length, curvature,
and bottleneck radius, validated the best potentialities of
accommodating the test ligands with the best possible bind-
ing affinities (Fig.6). In this study, five major tunnels are
found in the study of protein receptor HER2, established on
the number of amino acid residues at their bottleneck point.
From this qualitative analysis of these tunnels, it is promi-
nent that the second major tunnel has a higher length and
curvature involved with more amino acids than the other two
tunnels (Fig.6A–C). The length range of the five sub-tunnel
cluster was between 11.12 and 17.03Å, the bottleneck radius
ranged between 1.04 to 1.84Å, and the curvature radius was
1.09Å to 1.7Å. These parameters mean- tunnel length, cur-
vature, and bottleneck radius are the major factors required
for initiating the predictive super docking position of any
protein’s active site. The active sites on the HER2 receptor
were predicted to understand the best active binding sites for
the candidate ligands (Fig.7). Among the active sites, the
third one was found more viable (Fig.7C) according to the
predicted binding energy, amino acid residues, and binding
strength obtained (Fig.7D).
Post‑molecular docking analysis
The hydrophobic and hydrogen bond interactions of dif-
ferent atoms of the ligands with the amino acid residues
of HER2 were observed (Fig.8). The candidate ligand
compound EPA developed single-hydrogen bond interac-
tion and fifteen hydrophobic bond associations with differ-
ent amino acid residues (Fig.8A). Another ligand, DHA,
showed two hydrogen bond interactions with Cys805 and
Asp808 amino acid residues with bond lengths of 3.29Å and
2.45Å (Fig.8B), respectively. It produced thirteen hydro-
phobic bond interlinkages with ligand–protein complexes
(Table4). Both hydrogen bond and hydrophobic interactions
(non-covalent) are needed to validate the drug efficacy of
the test ligands, where the increased amount of hydrogen
bonds stands for the increased targeted-binding strength of
the ligands of interest [86].
Molecular dynamic simulation (100ns)
The current research revealed the complexing strength of
the ligands EPA and DHA with the neighboring amino
acid residues of the ERBB2/HER2 protein considering the
parameters like RMSD (Å); RMSF (Å); Rg (nm); SASA
(Å2); and intramolecular hydrogen bonds [26, 32]. In terms
of the RMSD analysis, eicosapentaenoic acid (EPA) ranged
between 0.139462 and 0.320852Å, which is considered a
convenient simulation spectrum range for any ligands to be
certified as target-specific pharmacophore according to the
standard protocols [24, 27], while docosahexaenoic acid
(DHA) resulted in 0.1379235Å and 0.4217227 Å alike
EPA values (Fig.9A). In the case of RMSF, the EPA-HER2
complex showed fluctuation between 0.07 and 0.8574 (Å)
(Fig.9B). This fluctuation was comparatively preferable to
DHA which scored 0.0662 to 1.0331 (Å) (Fig.9B). This is
because from recent literature, it is deduced that large dif-
ferences during simulation represent weak protein–ligand
complex and, thus, comprehended as unstable [24].
The radius of gyration (RG) parameter is the standard
indicator of the stability and flexing capability of a molecu-
lar structure. In this criteria, eicosapentaenoic acid (EPA)
excelled within 1.87889nm and 1.9852nm, which is
extremely stable considering the control value. A similar
trend is also followed by docosahexaenoic acid (DHA) rang-
ing from 1.91149 to 2.04407nm showing a slightly upper
range compared to eicosapentaenoic acid (EPA) (Fig.9C).
The SASA values were examined to have a better under-
standing of the effective interaction in receptor-ligand com-
plexes [24]. From the SASA analysis of EPA, the obtained
result was between 133.965 and 152.694 Ǻ2, and for DHA,
it resulted in between 124.48 and 152.45Ǻ2 (Fig.9D). Gen-
erally, the SASA values range between 55 to 308 Ǻ2 in the
case of MDS analysis [24, 26, 32]. The obtained SASA val-
ues ranged moderately at the lower bound, which means
the formed complexes were stable and the values were con-
venient. Additionally, the values represent that the complex
formed by DHA was more durable than the complex formed
by EPA. Hydrogen bond analysis was performed for both
drug receptors to obtain the final profile and significance
of the complexes during MD simulation. As the number of
hydrogen bonds increases for a drug-receptor complex that
becomes more significant [32]. In the hydrogen bond analy-
sis for EPA and DHA, it was obtained that the number of
hydrogen bonds ranged from 180 to 236 for the EPA com-
plex while the values ranged from 180 to 219 for the DHA
complex (Fig.9E) These findings indicate that the EPA com-
plex is more significant than the DHA complex. The ligand
Molecular Diversity
1 3
hydrogen bond is an important parameter to identify the pro-
tein–ligand interaction characteristics in MDS analysis [87,
88]. This study showed that the values of ligand hydrogen
bonds for EPA ranged between 0 and 2 and the range was
similar for DHA (Fig.9F).
Protein targets theEPA andDHA metabolism
The main drugs, eicosapentaenoic acid (EPA), and doco-
sahexaenoic acid (DHA) are highlighted in yellow boxes.
Three other drugs, such as Pirinixic acid, Rosiglitazone,
and Zileuton associated with the related breast cancer target
proteins are also shown in the DPI network in Fig.10 in rec-
tangular white boxes (unhighlighted ones). The circles are
the nodes that represent the 17 different breast cancer target
proteins, all of which are connected or the five drugs by the
lines of interaction produced. These lines of interaction are
called the edges with variable thickness for different interac-
tions. The darker and thicker lines depict stronger interaction
whereas the lighter and thinner ones show otherwise. It is
very evident that out of all five drugs, EPA has the most
edges connected with the majority of the proteins directly,
proving that it is a highly potent drug with the ability to
interact with different breast cancer-related proteins in the
human body (Fig.10). DHA is the second-most potent drug
to have a drug-like effect on the target proteins (Fig.10).
GPCRs, being the most important transmembrane protein
family, can recognize a variety of different drugs and other
ligands like amines and bioactive peptides, etc., making the
binding of the ligand selective. GPCRs play a crucial role in
the ADMET of any drug including the target drugs in this
study—EPA and DHA [89]. The main drugs, eicosapentae-
noic acid, and docosahexaenoic acid are highlighted in yel-
low boxes. Three other drugs such as Pirinixic acid, Rosigl-
itazone, and Zileuton associated with the related breast
cancer target proteins are also shown in the DPI network in
rectangular white boxes (unhighlighted ones) (Fig.10). The
circles are the nodes that represent the 17 different breast
cancer target proteins ADNP, ALOX5, CMKLR1, CYP2U1,
FADS1, FAT1, FPR1, FPR2, HSP90AA1, NCOA1, NCOR1,
NCOR2, PPARA, PPARGC1A, RARRES2, SCD, and UBC,
all of which are connected or the five drugs by the lines of
interaction produced. These lines of interaction are called
the edges with variable thicknesses for different interac-
tions. The darker and thicker lines depict stronger interaction
whereas the lighter and thinner ones show otherwise. It is
very evident from Fig.10 that out of all five drugs, EPA has
the most edges connected with the majority of the proteins
directly, with DHA being the second (Fig.10).
Functional bioactive components like fatty acids, anti-
microbial peptides, and secondary therapeutic derivatives
mainly come from microorganisms [90], where many
of which are clinically considered probiotics [91]. These
bioactive compounds can render proper opsonization as part
of our secondary immune response against infectious dis-
eases including cancer [92]. Bioactive components like EPA,
DHA, and Quercetin are getting more space in the ROS-
mediated cancer formation and cancer stem cell research
[93].
Conclusion
The present experiment sought to determine the antitumor
activity and therapeutic response of eicosapentaenoic acid
(EPA) and docosahexaenoic acid (DHA) in light of the
prevalence and lethality of HER2-positive breast cancer.
Considering the pharmacokinetic and pharmacodynamic
investigation of the aforementioned ligands, many in silico
variables were evaluated. The most effective location for
supramolecular interaction of the EPA and DHA was found
relying on the quantum tunneling of the HER2 receptor pro-
tein. Following that, a thorough 100ns molecular dynamic
simulation was performed to disclose every in silico factor
needed to forecast ligands as a prospective anti-cancer drug.
Subsequently, the clustering of the ERBB2 gene and protein
string was completed. Besides, the drug–protein interaction
(DPI) network was developed to recognize the potential-
ity of the selected ligands. The current study indicates that
EPA and DHA could be utilized as chemotherapeutic agents
for breast cancer in humans as a specifically aimed drug on
the HER2 receptor protein after taking into account all the
data of the parameters like molecular docking, molecular
dynamic simulation, and the ultimate string and STITCH
network exploration. The ligands must be employed invivo
for additional validation and substantiation of their efficacy
considering the comprehensive in silico bioprospecting.
References
1. Giovannelli P, Di Donato M, Galasso G, Di Zazzo E, Bilancio
A, Migliaccio A (2018) The androgen receptor in breast cancer
[Review]. Front Endocrinol. https:// doi. org/ 10. 3389/ fendo. 2018.
00492
2. Del Carmen OJM, Emilia GRD, Mares BH, Marcela OJ (2021)
Educational interventions on breast cancer in men and women: a
necessity in primary healthcare. Ecancermedicalscience 15:1255
3. Farshbafnadi M, Pastaki Khoshbin A, Rezaei N (2021) Immune
checkpoint inhibitors for triple-negative breast cancer: from
immunological mechanisms to clinical evidence. Int Immunop-
harmacol 98:107876
4. Abdel-Razeq H, Mansour A, Jaddan D (2020) Breast cancer care
in Jordan. JCO Glob Oncol. https:// doi. org/ 10. 1200/ jgo. 19. 00279
5. Fedorova O, Daks A, Shuvalov O, Kizenko A, Petukhov A,
Gnennaya Y, Barlev N (2020) Attenuation of p53 mutant as an
approach for treatment HER2-positive cancer. Cell Death Discov
6(1):1–8
Molecular Diversity
1 3
6. Barzaman K, Moradi-Kalbolandi S, Hosseinzadeh A, Kazemi
MH, Khorramdelazad H, Safari E, Farahmand L (2021) Breast
cancer immunotherapy: Current and novel approaches. Int Immu-
nopharmacol 98:107886. https:// doi. org/ 10. 1016/j. intimp. 2021.
107886
7. Zurrida S, Veronesi U (2014) Milestones in breast cancer treat-
ment. Breast J 21(1):3–12. https:// doi. org/ 10. 1111/ tbj. 12361
8. Tong CWS, Wu M, Cho WCS, To KKW (2018) Recent advances
in the treatment of breast cancer [mini review]. Front Oncol.
https:// doi. org/ 10. 3389/ fonc. 2018. 00227
9. Oh D-Y, Bang Y-J (2020) HER2-targeted therapies—a role beyond
breast cancer. Nat Rev Clin Oncol 17(1):33–48
10. Schroeder RL, Stevens CL, Sridhar J (2014) Small molecule tyros-
ine kinase inhibitors of ERBB2/HER2/Neu in the treatment of
aggressive breast cancer. Molecules 19(9):15196–15212
11. Lu P, Foley J, Zhu C, McNamara K, Sirinukunwattana K, Vennam
S, Varma S, Fehri H, Srivastava A, Zhu S, Rittscher J, Mallick
P, Curtis C, West R (2021) Transcriptome and genome evolu-
tion during HER2-amplified breast neoplasia. Breast Cancer Res
23(1):73. https:// doi. org/ 10. 1186/ s13058- 021- 01451-6
12. Lyu H, Han A, Polsdofer E, Liu S, Liu B (2018) Understanding
the biology of HER3 receptor as a therapeutic target in human
cancer. Acta Pharmaceutica Sinica B 8(4):503–510. https:// doi.
org/ 10. 1016/j. apsb. 2018. 05. 010
13. Prat AP-C (2020) HER2-enriched subtype and ERBB2 expression
in HER2-positive breast cancer treated with dual HER2 blockade.
JNCI 112(1):46–54
14. Prat A, Fan C, Fernández A, Hoadley KA, Martinello R, Vidal M,
Viladot M, Pineda E, Arance A, Muñoz M (2015) Response and
survival of breast cancer intrinsic subtypes following multi-agent
neoadjuvant chemotherapy. BMC Med 13(1):1–11
15. Chmielecki JR (2015) Oncogenic alterations in ERBB2/HER2
represent potential therapeutic targets across tumors from diverse
anatomic sites of origin. Oncologist 20(1):7–12
16. Conlon NT, Kooijman JJ, van Gerwen SJC, Mulder WR,
Zaman GJR, Diala I, Eli LD, Lalani AS, Crown J, Collins DM
(2021) Comparative analysis of drug response and gene profil-
ing of HER2-targeted tyrosine kinase inhibitors. Br J Cancer
124(7):1249–1259. https:// doi. org/ 10. 1038/ s41416- 020- 01257-x
17. Liu J, Ma DWL (2014) The role of n-3 polyunsaturated fatty
acids in the prevention and treatment of breast cancer. Nutrients
6:5184–5223. https:// doi. org/ 10. 3390/ nu611 5184
18. Paixão EM, Oliveira AC, Pizato N, Muniz-Junqueria MI, Magal-
hães KG, Nakano EY, Ito MK (2017) The effects of EPA and
DHA enriched fish oil on nutritional and immunological mark-
ers of treatment naïve breast cancer patients: a randomized
double-blind controlled trial. Nutr J. https:// doi. org/ 10. 1186/
s12937- 017- 0295-9
19. Fabian CJ, Kimler BF, Hursting SD (2015) Omega-3 fatty acids
for breast cancer prevention and survivorship. Breast Cancer Res.
https:// doi. org/ 10. 1186/ s13058- 015- 0571-6
20. Li M, Li D, Tang Y, Wu F, Wang J (2017) CytoCluster: a
cytoscape plugin for cluster analysis and visualization of biologi-
cal networks. Int J Mol Sci 18(9):1880. https:// doi. org/ 10. 3390/
ijms1 80918 80
21. Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi
R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT, Maitland A
(2010) The GeneMANIA prediction server: biological network
integration for gene prioritization and predicting gene function.
Nucleic Acids Res 38(2):W214–W220
22. Molinari MD, Mendonça J, Barbosa D, Marin D, Mertz-Henning
LM, Nepomuceno A (2021) Transcriptome analysis using RNA-
Seq from experiments with and without biological replicates: a
review. Embrapa Soja-Artigo em periódico indexado (ALICE)
23. Cortazar AR, Torrano V, Martín-Martín N, Caro-Maldonado
A, Camacho L, Hermanova I, Guruceaga E, Lorenzo-Martín
LF, Caloto R, Gomis RR, Apaolaza I, Quesada V, Trka J,
Gomez-Muñoz A, Vincent S, Bustelo XR, Planes FJ, Aransay
AM, Carracedo A (2018) CANCERTOOL: a visualization
and representation interface to exploit cancer datasets. Cancer
Res 78(21):6320–6328. https:// doi. org/ 10. 1158/ 0008- 5472.
CAN- 18- 1669
24. Arefin A, Ismail Ema T, Islam T, Hossen S, Islam T, Al Azad S,
Uddin Badal N, Islam A, Biswas P, Alam NU, Islam E, Anjum
M, Masud A, Kamran S, Rahman A, Kumar PP (2021) Tar-
get specificity of selective bioactive compounds in blocking
α-dystroglycan receptor to suppress Lassa virus infection: an
insilico approach. J Biomed Res 35(6):459–473. https:// doi. org/
10. 7555/ JBR. 35. 20210 111
25. Daina A, Michielin O, Zoete V (2017) SwissADME: a free web
tool to evaluate pharmacokinetics, drug-likeness and medicinal
chemistry friendliness of small molecules. Sci Rep 7:42717.
https:// doi. org/ 10. 1038/ srep4 2717
26. Nipun TS, Ema TI, Mia M, Hossen MS, Arshe FA, Ahmed
SZ, Masud A, Taheya FF, Khan AA, Haque F, Azad SA, Al
Hasibuzzaman M, Tanbir M, Anis S, Akter S, Mily SJ, Dey D
(2021) Active site-specific quantum tunneling of hACE2 recep-
tor to assess its complexing poses with selective bioactive com-
pounds in co-suppressing SARS-CoV-2 influx and subsequent
cardiac injury. J Adv Vet Anim Res 8(4):540–556. https:// doi.
org/ 10. 5455/ javar. 2021. h544
27. Dey D, Paul PK, Al Azad S, Al Mazid MF, Khan AM, Sharif
MA, Rahman MH (2021) Molecular optimization, docking, and
dynamic simulation profiling of selective aromatic phytochemi-
cal ligands in blocking the SARS-CoV-2 S protein attachment to
ACE2 receptor: an in silico approach of targeted drug designing.
J Adv Vet Anim Res 8(1):24–35. https:// doi. org/ 10. 5455/ javar.
2021. h481
28. Krishnamurti U, Silverman JF (2014) HER2 in breast cancer:
a review and update. Adv Anat Pathol 21(2):100–107. https://
doi. org/ 10. 1097/ PAP. 00000 00000 000015
29. Loibl S, Gianni L (2017) HER2-positive breast cancer. Lancet
389(10087):2415–2429
30. Figueroa-Magalhães MC, Jelovac D, Connolly RM, Wolff
AC (2014) Treatment of HER2-positive breast cancer. Breast
23(2):128–136
31. Tian W, Chen C, Lei X, Zhao J, Liang J (2018) CASTp 3.0:
computed atlas of surface topography of proteins. Nucleic Acids
Res 46(W1):W363–W367. https:// doi. org/ 10. 1093/ nar/ gky473
32. Ferdausi N, Islam S, Rimti FH, Quayum ST, Arshad EM, Ibnat
A etal (2022) Point specific interactions of isovitexin with the
neighboring amino acid residues of the hACE2 receptor as a tar-
geted therapeutic agent in suppressing the SARS-CoV-2 influx
mechanism. J Adv Vet Anim Res 9(2):230–240
33. John A, Umashankar V, Krishnakumar S, Deepa PR (2015)
Comparative modeling and molecular dynamics simulation of
substrate binding in human fatty acid synthase: enoyl reduc-
tase and β-ketoacyl reductase catalytic domains. Genomics Inf
13(1):15–24. https:// doi. org/ 10. 5808/ GI. 2015. 13.1. 15
34. Lyne PD, Lamb ML, Saeh JC (2006) Accurate prediction of the
relative potencies of members of a series of kinase inhibitors
using molecular docking and MM-GBSA scoring. J Med Chem
49(16):4805–4808
35. Paul PK, Azad SA, Rahman MH, Farjana M, Uddin MR, Dey
D etal (2022) Catabolic profiling ofselective enzymesin the
saccharification of nonfood lignocellulose parts of biomassinto
functional edible sugars and bioenergy: an in silico bioprospect-
ing. J Adv Vet Anim Res 9(1):19–32
36. Sharif MA, Hossen MS, Shaikat MM etal (2021) Molecular
optimization, docking and dynamic simulation study of selec-
tive natural aromatic components to block E2-CD81 complex
Molecular Diversity
1 3
formation in predating protease inhibitor resistant HCV influx.
Int J Pharm Res. https:// doi. org/ 10. 31838/ ijpr/ 2021. 13. 02. 408
37. Kuriata A, Gierut AM, Oleniecki T, Ciemny MP, Kolinski A,
Kurcinski M, Kmiecik S (2018) CABS-flex 2.0: a web server for
fast simulations of flexibility of protein structures. Nucleic Acids
Res 46(W1):W338–W343. https:// doi. org/ 10. 1093/ nar/ gky356
38. Yang JF, Wang F, Chen YZ, Hao GF, Yang GF (2020) LARMD:
integration of bioinformatic resources to profile ligand-driven pro-
tein dynamics with a case on the activation of estrogen receptor.
Brief Bioinform 21(6):2206–2218. https:// doi. org/ 10. 1093/ bib/
bbz141
39. Akter KM, Tushi T, Jahan Mily S, Mohona RA, Anis S,
Chakraborty AK, Tabassum E, Islam TU, Akhi OJ, Nishe IS, Laxy
BN, Zerin SS, Roble AJ, Hossain MI, Ahmed S, Azad SA (2020)
RT-PCR mediated identification of SARS-CoV-2 patients from
particular regions of Bangladesh and the multi-factorial analysis
considering their pre and post infection health conditions. Bio-
technol J Int 24(6):43–56. https:// doi. org/ 10. 9734/ bji/ 2020/ v24i6
30121
40. Islam R, Akter KM, Rahman A, Khanam NN, Al Azad S, Islam
MR, Farjana M, Rahman MH, Badal MN, Ahmed S (2021) The
serological basis of the correlation between iron deficiency ane-
mia and thyroid disorders in women: a community based study. J
Pharm Res Int 30(19A):69–81
41. Al Azad S, Ahmed S, Biswas P etal (2022) Quantitative analysis
of the factors influencing IDA and TSH downregulation in cor-
relation to the fluctuation of activated vitamin D3 in women. J
Adv Biotechnol Exp Ther 5(2):320–333. https:// doi. org/ 10. 5455/
jabet. 2022. d118
42. Al Azad S, Moazzem Hossain K, Rahman SM, Al Mazid MF,
Barai P, Gazi MS (2020) In ovo inoculation of duck embryos
with different strains of Bacillus cereus to analyse their synergistic
post-hatch anti-allergic potentialities. Vet Med Sci 6(4):992–999
43. Azad SA, Farjana M, Mazumder B, Abdullah-Al-Mamun M,
Haque A (2020) Molecular identification of a Bacillus cereus
strain from Murrah buffalo milk showed invitro bioremedia-
tion properties on selective heavy metals. J Adv Vet Anim Res
7(1):62–68. https:// doi. org/ 10. 5455/ javar. 2020. g394
44. Rashaduzzaman M, Kamrujjaman M, Islam MA, Ahmed S, Al
Azad S. An experimental analysis of different point specific mus-
culoskeletal pain among selected adolescent-club cricketers in
Dhaka city.
45. Beljaards L, Elmahdy MS, Verbeek F, Staring M (2020) A cross-
stitch architecture for joint registration and segmentation in adap-
tive radiotherapy. InMedical imaging with deep learning, PMLR.
pp 62–74
46. Rezaei-Tavirani M, Zamanian Azodi M, Bashash D, Ahmadi
N, Rostaim-Nejad M (2019) Breast cancer interaction network
concept from mostly related components. Galen Med J 8:1298.
https:// doi. org/ 10. 31661/ gmj. v8i0. 1298
47. Arjmand B, Khodadoost M, Sherafat SJ, Tavirani MR, Moghadam
MH, Tavirani SR, Khanabadi B, Iranshahi M (2021) Assessment
of colon cancer molecular mechanism: a system biology approach.
Gastroenterol Hepatol Bed Bench 14:S51–S57
48. Yin W, Mendoza L, Monzon-Sandoval J, Urrutia AO, Gutierrez H
(2021) Emergence of co-expression in gene regulatory networks.
PLoS ONE 16(4):e0247671. https:// doi. org/ 10. 1371/ journ al. pone.
02476 71
49. De Moraes CL, Cruz e Melo N, Valoyes MAV, Naves do Amaral
W (2022) AGR2 and AGR3 play an important role in the clinical
characterization and prognosis of basal like breast cancer. Clin
Breast Cancer 22(2):e242–e252. https:// doi. org/ 10. 1016/j. clbc.
2021. 07. 008
50. Barma N, Stone TC, Carmona Echeverria LM, Heavey S (2021)
Exploring the value of BRD9 as a biomarker, therapeutic target
and co-target in prostate cancer. Biomolecules 11(12):1794.
https:// doi. org/ 10. 3390/ biom1 11217 94
51. Azumah R, Hummitzsch K, Hartanti MD, St. John JC, Ander-
son RA, Rodgers RJ (2022) Analysis of upstream regulators,
networks, and pathways associated with the expression patterns
of polycystic ovary syndrome candidate genes during fetal ovary
development. Front Genet 12:762177. https:// doi. org/ 10. 3389/
fgene. 2021. 762177
52. Bhyan SB, Wee Y, Liu Y, Cummins S, Zhao M (2019) Integra-
tive analysis of common genes and driver mutations implicated in
hormone stimulation for four cancers in women. PeerJ 7:e6872.
https:// doi. org/ 10. 7717/ peerj. 6872
53. Kübler E, Albrecht H (2018) Large set data mining reveals overex-
pressed GPCRs in prostate and breast cancer: potential for active
targeting with engineered anti-cancer nanomedicines. Oncotarget
9(38):24882–24897
54. El Buri A, Adams DR, Smith D, Tate RJ, Mullin M, Pyne S,
Pyne NJ (2018) The sphingosine 1-phosphate receptor 2 is shed in
exosomes from breast cancer cells and is N-terminally processed
to a short constitutively active form that promotes extracellular
signal regulated kinase activation and DNA synthesis in fibro-
blasts. Oncotarget 9(50):29453–29467
55. Yue Z, Yuan Z, Zeng L, Wang Y, Lai L, Li J, Sun P, Xue X, Qi
J, Yang Z, Zheng Y, Fang Y, Li D, Siwko S, Li Y, Luo J, Liu M
(2018) LGR4 modulates breast cancer initiation, metastasis, and
cancer stem cells. FASEB J 32(5):2422–2437
56. Ashok G, Miryala SK, Saju MT, Anbarasu A, Ramaiah S (2022)
FN1 encoding fibronectin as a pivotal signaling gene for therapeu-
tic intervention against pancreatic cancer. Mol Genet Genomics
18:1–6
57. Muthiah I, Rajendran K, Dhanaraj P, Vallinayagam S (2021) In
silico structure prediction, molecular docking and dynamic simu-
lation studies on G Protein-Coupled Receptor 116: a novel insight
into breast cancer therapy. J Biomol Struct Dyn 39(13):4807–4815
58. Insel PA, Sriram K, Wiley SZ, Wilderman A, Katakia T, McCann
T, Yokouchi H, Zhang L, Corriden R, Liu D, Feigin ME, French
RP, Lowy AM, Murray F (2018) GPCRomics: GPCR expression
in cancer cells and tumors identifies new, potential biomarkers and
therapeutic targets. Front Pharmacol 22(9):431
59. Ju MS, Ahn HM, Han SG, Ko S, Na JH, Jo M, Lim CS, Ko BJ, Yu
YG, Lee WK, Kim YJ, Jung ST (2021) A human antibody against
human endothelin receptor type A that exhibits antitumor potency.
Exp Mol Med 53(9):1437–1448
60. Chan YT, Lai AC, Lin RJ, Wang YH, Wang YT, Chang WW, Wu
HY, Lin YJ, Chang WY, Wu JC, Yu JC, Chen YJ, Yu AL (2020)
GPER-induced signaling is essential for the survival of breast
cancer stem cells. Int J Cancer 146(6):1674–1685
61. Agus DB, Akita RW, Fox WD, Lewis GD, Higgins B, Pisacane
PI, Lofgren JA, Tindell C, Evans DP, Maiese K, Scher HI, Sli-
wkowski MX (2002) Targeting ligand-activated ERBB2 sign-
aling inhibits breast and prostate tumor growth. Cancer Cell
2(2):127–137
62. Tari A, Hung MC, Li K etal (1999) Growth inhibition of breast
cancer cells by Grb2 downregulation is correlated with inacti-
vation of mitogen-activated protein kinase in EGFR, but not in
ERBB2, cells. Oncogene 18:1325–1332
63. Tan M, Li P, Sun M etal (2006) Upregulation and activation of
PKCα by ERBB2 through Src promotes breast cancer cell inva-
sion that can be blocked by combined treatment with PKCα and
Src inhibitors. Oncogene 25:3286–3295
64. Henriksen L (2013) Internalization mechanisms of the epider-
mal growth factor receptor after activation with different ligands.
PLoS ONE 8:e58148
65. Lim SJ, Lopez-Berestein G, Hung MC etal (2000) Grb2 down-
regulation leads to Akt inactivation in heregulin-stimulated
Molecular Diversity
1 3
and ERBB2-overexpressing breast cancer cells. Oncogene
19:6271–6276
66. Sanders JM, Wampole ME, Thakur ML, Wickstrom E (2013)
Molecular determinants of epidermal growth factor binding: a
molecular dynamics study. PLoS ONE 8(1):e54136
67. Negro A, Brar BK, Gu Y, Peterson KL, Vale W, Lee KF (2006)
ERBB2 is required for G protein-coupled receptor signaling in the
heart. Proc Natl Acad Sci USA 103(43):15889–15893
68. Arora P, Cuevas B, Russo A etal (2008) Persistent transactiva-
tion of EGFR and ERBB2/HER2 by protease-activated receptor-1
promotes breast carcinoma cell invasion. Oncogene 27:4434–4445
69. Fujimoto Y, Morita TY, Ohashi A, Haeno H, Hakozaki Y, Fujii
M, Mukohara T (2020) Combination treatment with a PI3K/Akt/
mTOR pathway inhibitor overcomes resistance to anti-HER2
therapy in PIK3CA-mutant HER2-positive breast cancer cells.
Sci Rep 10(1):1–16
70. Miricescu D, Totan A, Stanescu-Spinu II, Badoiu SC, Stefani C,
Greabu M (2020) PI3K/AKT/mTOR signaling pathway in breast
cancer: From molecular landscape to clinical aspects. Int J Mol
Sci 22(1):173
71. Momenimovahed Z, Salehiniya H (2019) Epidemiological char-
acteristics of and risk factors for breast cancer in the world. Breast
Cancer 11:151
72. Lei S, Zheng R, Zhang S, Wang S, Chen R, Sun K, Wei W (2021)
Global patterns of breast cancer incidence and mortality: a pop-
ulation-based cancer registry data analysis from 2000 to 2020.
Cancer Commun 41(11):1183–1194
73. Morey BN, Gee GC, von Ehrenstein OS, Shariff-Marco S, Can-
chola AJ, Yang J, Gomez SL (2019) Peer reviewed: Higher breast
cancer risk among immigrant Asian American women than among
US-born Asian American Women. Prev Chronic Dis 16:E20
74. Ahmed AR (2016) HER2 expression is a strong independent pre-
dictor of nodal metastasis in breast cancer. J Egypt Natl Canc Inst
28(4):219–227
75. Roman-Rosales AA, García-Villa E, Herrera LA, Gariglio P, Díaz-
Chávez J (2018) Mutant p53 gain of function induces HER2 over-
expression in cancer cells. BMC Cancer 18(1):1–12
76. Salta S, Nunes SP, Fontes-Sousa M, Lopes P, Freitas M, Caldas
M, Antunes L, Castro F, Antunes P, de Sousa SP, Henrique R,
Jerónimo C (2018) A DNA methylation-based test for breast can-
cer detection in circulating cell-free DNA. J Clin Med. https:// doi.
org/ 10. 3390/ jcm71 10420
77. Constâncio V, Nunes SP, Henrique R, Jerónimo C (2020) DNA
methylation-based testing in liquid biopsies as detection and prog-
nostic biomarkers for the four major cancer types. Cells. https://
doi. org/ 10. 3390/ cells 90306 24
78. Hung CS, Wang SC, Yen YT, Lee TH, Wen WC, Lin RK (2018)
Hypermethylation of CCND2 in lung and breast cancer is a poten-
tial biomarker and drug target. Int J Mol Sci. https:// doi. org/ 10.
3390/ ijms1 91030 96
79. Mou MA, Keya NA, Islam M, Hossain MJ, Al Habib MS, Alam
R, Rana S, Samad A, Ahammad F (2020) Validation of CSN1S1
transcriptional expression, promoter methylation, and prognostic
power in breast cancer using independent datasets. Biochem Bio-
phys Rep. https:// doi. org/ 10. 1016/j. bbrep. 2020. 100867
80. Goel MK, Khanna P, Kishore J (2010) Understanding survival
analysis: Kaplan–Meier estimate. Int J Ayurveda Res 1(4):274
81. Altman DG (1992) Analysis of survival times. Practical statistics
for medical research. Chapman and Hall, London, pp 365–393
82. Stel VS, Dekker FW, Tripepi G, Zoccali C, Jager KJ (2011) Sur-
vival analysis I: the Kaplan–Meier method. Nephron Clin Pract
119(1):c83–c88. https:// doi. org/ 10. 1159/ 00032 4758
83. Tesfay B, Getinet T, Assefa E (2021) Survival analysis of time
to death of breast cancer patients: in case of ayder comprehen-
sive specialized hospital tigray. Ethiopia Cogent Med 8:1908648.
https:// doi. org/ 10. 1080/ 23312 05X. 2021. 19086 48
84. Ouyang DJ, Chen QT, Anwar M etal (2021) The efficacy of
pyrotinib as a third- or higher-line treatment in HER2-positive
metastatic breast cancer patients exposed to lapatinib compared
to lapatinib-naive patients: a real-world study. Front Pharmacol
12:682568
85. Andrade F, Nakata A, Gotoh N, Fujita A (2020) Large miRNA
survival analysis reveals a prognostic four-biomarker signature for
triple negative breast cancer. Genet Mol Biol 43(1):e20180269.
https:// doi. org/ 10. 1590/ 1678- 4685- GMB- 2018- 0269
86. Rakshit G, Jayaprakash V (2022) Tuberculosis and HIV responses
threatened by nCOVID-19: a situation prompting an in-silico
investigation of reported MbtA inhibitors for combined inhibition
of SARS-CoV-2 and HIV-TB co-infection. Struct Chem. https://
doi. org/ 10. 1007/ s11224- 022- 02013-y
87. Malik FK, Guo JT (2022) Insights into protein-DNA interactions
from hydrogen bond energy-based comparative protein-ligand
analyses. Proteins 90(6):1303–1314
88. Naha A, Banerjee S, Debroy R, Basu S, Ashok G, Priyamvada P,
Kumar H, Preethi AR, Singh H, Anbarasu A, Ramaiah S (2022)
Network metrics, structural dynamics and density functional the-
ory calculations identified a novel ursodeoxycholic acid derivative
against therapeutic target Parkin for Parkinson’s disease. Comput
Struct Biotechnol J 1(20):4271–4287
89. Basith S, Cui M, Macalino SJY, Park J, Clavio NAB, Kang S,
Choi S (2018) Exploring G protein-coupled receptors (GPCRs)
ligand space via cheminformatics approaches: impact on rational
drug design. Front Pharmacol. https:// doi. org/ 10. 3389/ fphar. 2018.
00128
90. Dipta DE, Tanzila Ismail EM, Partha Biswas SA etal (2021) Anti-
viral effects of bacteriocin against animal-to-human transmitta-
ble mutated sars-cov-2: a systematic review. Front Agric Sci Eng
8:603–622
91. Abdullah-Al-Mamun M, Jakir Hasan M, Al Azad S etal (2016)
Evaluation of potential probiotic characteristics of isolated lactic
acid bacteria from goat milk. Biotechnol J Int 14(2):1–7. https://
doi. org/ 10. 9734/ BBJ/ 2016/ 26397
92. Al Azad S, Shahriyar S, Mondal KJ (2016) Opsonin and its
mechanism of action in secondary immune. J Mol Stud Med Res
1(02):48–56. https:// doi. org/ 10. 18801/ jmsmr. 010216. 06
93. Biswas P, Dey D, Biswas PK, Rahaman TI, Saha S, Parvez A,
Khan DA, Lily NJ, Saha K, Sohel M, Hasan MM, Al Azad S,
Bibi S, Hasan MN, Rahmatullah M, Chun J, Rahman MA, Kim
B (2022) A comprehensive analysis and anti-cancer activities of
quercetin in ROS-mediated cancer and cancer stem cells. Int J Mol
Sci 23(19):11746. https:// doi. org/ 10. 3390/ ijms2 31911 746
Publisher's Note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.
Springer Nature or its licensor (e.g. a society or other partner) holds
exclusive rights to this article under a publishing agreement with the
author(s) or other rightsholder(s); author self-archiving of the accepted
manuscript version of this article is solely governed by the terms of
such publishing agreement and applicable law.
A preview of this full-text is provided by Springer Nature.
Content available from Molecular Diversity
This content is subject to copyright. Terms and conditions apply.
