Adunctin E from Conamomum rubidum Induces Apoptosis in Lung Cancer via HSP90AA1 Modulation: A Network Pharmacology and In Vitro Study

Abstract

Lung cancer stands out as a leading cause of death among various cancer types, highlighting the urgent need for effective anticancer drugs and the discovery of new compounds with potent therapeutic properties. Natural sources, such as the Conamomum genus, offer various bioactive compounds. Adunctin E (AE), a dihydrochalcone derived from Conamomum rubidum, exhibited several pharmacological activities, and its potential as an anticancer agent remains largely unexplored. Thus, this study aimed to elucidate its apoptotic-inducing effect and identify its molecular targets. The network pharmacology analysis led to the identification of 71 potential targets of AE against lung cancer. Subsequent gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome pathway enrichment analyses revealed the involvement of these targets in cancer-associated signaling pathways. Notably, HSP90AA1, MAPK1, and PIK3CA emerged as key players in apoptosis. In silico molecular docking and dynamic simulations suggested a strong and stable interaction between AE and HSP90AA1. In vitro experiments further confirmed a significant apoptotic-inducing effect of AE on lung cancer cell lines A549 and H460. Furthermore, immunoblot analysis exhibited a substantial decrease in HSP90AA1 levels in response to AE treatment. These findings support the potential anticancer activity of AE through the HSP90AA1 mechanism, underscoring its promise as a novel compound worthy of further research and development for anti-lung cancer therapy.

Full text

Citation: Iksen, I.; Singharajkomron,
N.; Nguyen, H.M.; Hoang, H.N.T.; Ho,
D.V.; Pongrakhananon, V. Adunctin E
from Conamomum rubidum Induces
Apoptosis in Lung Cancer via
HSP90AA1 Modulation: A Network
Pharmacology and In Vitro Study. Int.
J. Mol. Sci. 2024,25, 11368. https://
doi.org/10.3390/ijms252111368
Academic Editor: Moon Nyeo Park
Received: 17 September 2024
Revised: 17 October 2024
Accepted: 21 October 2024
Published: 22 October 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
International Journal of
Molecular Sciences
Article
Adunctin E from Conamomum rubidum Induces Apoptosis in
Lung Cancer via HSP90AA1 Modulation: A Network
Pharmacology and In Vitro Study
Iksen Iksen
1
, Natsaranyatron Singharajkomron
1
, Hien Minh Nguyen
2
, Hanh Nhu Thi Hoang
3,4
, Duc Viet Ho
4
and Varisa Pongrakhananon 1,5, *
1
Department of Pharmacology and Physiology, Faculty of Pharmaceutical Sciences, Chulalongkorn University,
Bangkok 10330, Thailand; ikseniksen08@gmail.com (I.I.); natsaranyatron.s@gmail.com (N.S.)
2Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam;
ngyenminhhien@tdtu.edu.vn
3Faculty of Engineering and Food Technology, Hue University of Agriculture and Forestry, Hue University,
Hue City 49000, Vietnam; htnhanh@hueuni.edu.vn
4Faculty of Pharmacy, Hue University of Medicine and Pharmacy, Hue University, Hue City 49000, Vietnam;
hvietduc@hueuni.edu.vn
5Preclinical Toxicity and Efficacy Assessment of Medicines and Chemicals Research Unit, Chulalongkorn
University, Bangkok 10330, Thailand
*Correspondence: varisa.p@pharm.chula.ac.th
Abstract: Lung cancer stands out as a leading cause of death among various cancer types, highlighting
the urgent need for effective anticancer drugs and the discovery of new compounds with potent
therapeutic properties. Natural sources, such as the Conamomum genus, offer various bioactive
compounds. Adunctin E (AE), a dihydrochalcone derived from Conamomum rubidum, exhibited
several pharmacological activities, and its potential as an anticancer agent remains largely unexplored.
Thus, this study aimed to elucidate its apoptotic-inducing effect and identify its molecular targets.
The network pharmacology analysis led to the identification of 71 potential targets of AE against lung
cancer. Subsequent gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and
Reactome pathway enrichment analyses revealed the involvement of these targets in cancer-associated
signaling pathways. Notably, HSP90AA1, MAPK1, and PIK3CA emerged as key players in apoptosis.
In silico molecular docking and dynamic simulations suggested a strong and stable interaction
between AE and HSP90AA1.
In vitro
experiments further confirmed a significant apoptotic-inducing
effect of AE on lung cancer cell lines A549 and H460. Furthermore, immunoblot analysis exhibited a
substantial decrease in HSP90AA1 levels in response to AE treatment. These findings support the
potential anticancer activity of AE through the HSP90AA1 mechanism, underscoring its promise as a
novel compound worthy of further research and development for anti-lung cancer therapy.
Keywords: adunctin E; apoptosis; Conamomum rubidum; lung cancer; network pharmacology;
molecular docking
1. Introduction
Lung cancer remains a significant global health concern, with high incidence and
mortality rates [
1
]. It is primarily categorized into two main subtypes: non-small cell lung
cancer (NSCLC), which constitutes approximately 85% of cases, and small-cell lung cancer
(SCLC), which accounts for the remaining 15% [
2
,
3
]. Unfortunately, nonspecific lung cancer-
associated symptoms often lead to its late diagnosis or diagnosis at an advanced stage [
4
].
Current standard therapies for lung cancer include surgery, radiotherapy, chemotherapy,
and targeted therapy [
5
]. Despite advances in lung cancer therapy, therapeutic resistance,
cancer recurrence, and metastasis often develop after multiple treatments, and the mortality
rate is gradually increasing [6–8], highlighting the urgent need for new anticancer drugs.
Int. J. Mol. Sci. 2024,25, 11368. https://doi.org/10.3390/ijms252111368 https://www.mdpi.com/journal/ijms

Int. J. Mol. Sci. 2024,25, 11368 2 of 16
Plants are promising sources of natural biological actives for drug development.
Compounds, particularly those extracted from the genus Conamomum, a synonym of
the genus Amomum, in the Zingiberaceae family, have demonstrated pharmacological
activities such as antimicrobial and antioxidant activities and have long been used in
traditional medicine for their anti-inflammatory and fever-reducing properties [
9
–
12
]. A
recent study has reported the potent cytotoxicity of compounds such as adunctin E (AE),
a dihydrochalcone isolated from Conamomum rubidum in lung cancers [
13
]. However, the
anticancer activity of AE and its underlying molecular mechanism remain unexplored.
Recently, bioinformatics has significantly advanced in drug research and development
by not only identifying potential therapeutic targets but also aiding in drug design [
14
,
15
].
Network pharmacology approaches have emerged as invaluable tools in this process and
facilitated the identification of potential molecular targets of novel compounds promptly
and precisely [
16
,
17
]. These approaches, through integration with multiple bioinformatic
databases, provide insights into the molecular targets of diseases and predict targets of new
compounds. Furthermore, the mechanism of action was investigated through the pathway
analysis. In silico molecular docking allows for the prediction of interactions between new lead
compounds and identified target molecules [
18
,
19
]. Importantly, targets can be validated using
in vitro
experimental models, providing essential preclinical data of new lead compounds for
further investigation and ultimately expediting the drug discovery [20].
In this study, we aimed to identify the molecular targets of AE by integrating network
pharmacology methodology, investigate its interaction with molecular targets using in silico
molecular docking, and validate these molecular targets through
in vitro
lung cancer cell-
based experiments. The findings of this study could offer insights into the anticancer activity
of AE and elucidate its molecular mechanisms of action for potential therapeutic development.
2. Results
2.1. Pharmacokinetic Parameters and Target Identification of AE in NSCLC1
The workflow is illustrated in Figure 1. Pharmacokinetic parameters were analyzed by
pkCSM, indicating that AE has high intestinal absorption, low blood–brain barrier permeabil-
ity, and less toxic (Table S1). Additionally, AE was predicted to inhibit CYP enzymes, which
may influence both its therapeutic efficacy and safety profile. Molecular targets of AE were
retrieved from the Swiss Target Prediction database and SEA, extracting 160 predicted targets
(Table S2). NSCLC-associated targets were obtained from GeneCards, OMIM, and DisGeNET,
yielding a total of 5693 targets after removing duplicates. A Venn diagram identified 71 com-
mon targets of AE and NSCLC (Figure 2A and Table S3). Subsequently, a compound–target
interaction network was constructed using Cytoscape 3.9.1 (Figure 2B). In the network, active
components were labeled in yellow, whereas the 71 common targets were highlighted in blue.
Int. J. Mol. Sci. 2024, 25, x FOR PEER REVIEW 3 of 16
Figure 1. Workflow for the investigation of the molecular targets of adunctin E (AE).
Figure 2. Molecular target identification of adunctin E. (A) This Venn diagram represents targets of
adunctin E (blue), targets in non-small cell lung cancer (NSCLC, yellow), and common targets be-
tween the compound and the disease. (B) A compound–target network was constructed by
Figure 1. Workflow for the investigation of the molecular targets of adunctin E (AE).

Int. J. Mol. Sci. 2024,25, 11368 3 of 16
Int. J. Mol. Sci. 2024, 25, x FOR PEER REVIEW 3 of 16
Figure 1. Workflow for the investigation of the molecular targets of adunctin E (AE).
Figure 2. Molecular target identification of adunctin E. (A) This Venn diagram represents targets of
adunctin E (blue), targets in non-small cell lung cancer (NSCLC, yellow), and common targets be-
tween the compound and the disease. (B) A compound–target network was constructed by
Figure 2. Molecular target identification of adunctin E. (A) This Venn diagram represents targets
of adunctin E (blue), targets in non-small cell lung cancer (NSCLC, yellow), and common targets
between the compound and the disease. (B) A compound–target network was constructed by
Cytoscape 3.9. Active components were labeled in yellow, whereas the 71 common targets were
highlighted in blue. (C) The protein–protein interaction (PPI) network of the common targets was
analyzed by importing 71 common targets to the search tool from the STRING.
2.2. Construction of PPI Network and Enrichment Analyses of GO, KEGG and
Reactome Pathways
To evaluate potential associations among targets, the PPI network of the 71 common
targets was analyzed using the STRING (Figure 2C). In the network, nodes represented
common targets, and edges indicated an association between nodes, including neighboring,
fusion, and co-occurring genes. Sixty-four common targets were connected, whereas seven
nodes did not show interactions. Subsequently, the common targets were subjected to
GO functional annotation analyses which were performed by mapping the target genes
to Gene Ontology categories and conducting enrichment analysis to identify significantly
enriched terms. The top 20 enriched biological processes associated with cancer included
responses to chemical, metabolic process regulation, regulation of cell death and apoptotic
process, and protein phosphorylation, according to the degree of significance (Figure 3A).
In cellular component associations with lung cancer, the targets were mainly found in the
cell projection membrane, cell periphery, plasma membrane, catalytic complex, and cyclin
E1-cyclin-dependent kinase 2 (CDK2) complex (Figure 3B). For molecular functions, the
targets were involved in ribonucleotide binding, protein kinase activity, catalytic activity,
carbohydrate derivative binding, and ion binding (Figure 3C).

Int. J. Mol. Sci. 2024,25, 11368 4 of 16
Int. J. Mol. Sci. 2024, 25, x FOR PEER REVIEW 5 of 16
Figure 3. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway
enrichment analyses were conducted on the potential targets of adunctin E in non-small cell lung
cancer (NSCLC). Data analyzed in STRING were imported to RStudio with the ggplot2 package.
The GO terms examined include (A) biological process, (B) cellular component, and (C) molecular
function. (D) The KEGG pathway associated with these common targets was analyzed.
2.3. Potential Target Identification
The common target network obtained from STRING was imported into Cytoscape
(Figure 4A), and the topology analysis highlighted the top 16 molecules based on their
degree of connectivity (Figure 4B and Table S4). These molecules, appearing dark to light
green according to their degree scores, are potential targets of AE in NSCLC. Among the
identified targets, the key regulators implicated in NSCLC pathogenesis included
HSP90AA1, MAPK1, CDK2, cyclin-dependent kinase 1 (CDK1), phosphatidylinositol-4,5-
bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), heat shock protein 90 alpha fam-
ily class B member 1 (HSP90AB1), toll-like receptor 4 (TLR4), aurora kinase A (AURKA),
cyclin B1 (CCNB1), polo-like kinase 1 (PLK1), glycogen synthase kinase 3 beta (GSK3B),
cyclin E2 (CCNE2), cyclin E1 (CCNE1), histone deacetylase 2 (HDAC2), nitric oxide syn-
thase 2 (NOS2), and phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit
delta (PIK3CD).
Because of the crucial role of apoptosis induction in anticancer therapy, we focused
primarily on potential targets classified as apoptosis regulators. Among these targets,
HSP90AA1, MAPK1, and PIK3CA emerged as promising candidates based on their de-
gree scores in the network analysis.
Figure 3. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway
enrichment analyses were conducted on the potential targets of adunctin E in non-small cell lung
cancer (NSCLC). Data analyzed in STRING were imported to RStudio with the ggplot2 package.
The GO terms examined include (A) biological process, (B) cellular component, and (C) molecular
function. (D) The KEGG pathway associated with these common targets was analyzed.
In the KEGG pathway analysis, these targets participated in cancer-related pathways
such as viral carcinogenesis, the PI3K–AKT signaling pathway, microRNAs in cancer, cellular
senescence, cell cycle, autophagy, and apoptosis (Figure 3D). Furthermore, the Reactome
pathway analysis identified several significantly enriched pathways, highlighting key bio-
logical processes related to the input gene set. The analysis used p-adjust values to ensure
statistical significance. The data suggested that the molecular targets of AE in NSCLC were
associated with signal transduction, receptor tyrosine kinase signaling, extracellular matrix
degradation, regulation of transcription of cell cycle genes by p53, MAPK family signaling
cascades, apoptosis, cell cycle, and PI3K/AKT signaling in cancer (Table 1).
Table 1. Pathways of adunctin E’s targets in non-small cell lung cancer by the Reactome pathway analysis.
Pathways Targets
Signal transduction
HDAC7, DD4, MAPK1, HDAC5, CTSD, MMP7, ITGAV, CCNE1, PIK3CA, CDK2,
PAK1, NOS3, PLK1, HDAC3, ADORA2B, F2, PAK2, GSK3B, PDE2A, HSP90AA1,
PDE5A, RET, RAC1, DRD2, HSP90AB1, PIK3CD, CDK1, MAPK8, RXRA, HDAC2,
NTRK1, JAK3, CCKBR, ITGB3, ADORA2A, JAK1

Int. J. Mol. Sci. 2024,25, 11368 5 of 16
Table 1. Cont.
Pathways Targets
Signaling by receptor tyrosine kinases
MAPK1, CTSD, ITGAV, PIK3CA, PAK1, NOS3, HDAC3, PAK2, HSP90AA1, RAC1,
HDAC2, NTRK1, JAK3, ITGB3, ADORA2A
Degradation of the extracellular matrix CTSD, MMP7, MMP13, CAPN2, PRSS1, MMP1, CTSB, CAPN1, CAPNS1
TP53 regulates transcription of cell cycle
genes AURKA, CCNB1, CCNE1, CDK2, CDK1, CCNE2
MAPK family signaling cascades MAPK1, PIK3CA, PAK1, PAK2, RET, RAC1, CDK1, JAK3, ITGB3, JAK1
Apoptosis HSP90AA1, MAPK1, PAK2, TLR4, MAPK8, DAPK1, DAPK3, DAPK2
Cell cycle
MAPK1, AURKA, CCNB1, CCNE1, CDK2, PLK1, GSK3B, HSP90AA1, HSP90AB1,
CDK1, CCNE2
PI3K/AKT signaling in cancer PIK3CA, GSK3B, RAC1, PIK3CD, NTRK1
2.3. Potential Target Identification
The common target network obtained from STRING was imported into Cytoscape
(Figure 4A), and the topology analysis highlighted the top 16 molecules based on their
degree of connectivity (Figure 4B and Table S4). These molecules, appearing dark to light
green according to their degree scores, are potential targets of AE in NSCLC. Among
the identified targets, the key regulators implicated in NSCLC pathogenesis included
HSP90AA1, MAPK1, CDK2, cyclin-dependent kinase 1 (CDK1), phosphatidylinositol-4,5-
bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), heat shock protein 90 alpha family
class B member 1 (HSP90AB1), toll-like receptor 4 (TLR4), aurora kinase A (AURKA),
cyclin B1 (CCNB1), polo-like kinase 1 (PLK1), glycogen synthase kinase 3 beta (GSK3B),
cyclin E2 (CCNE2), cyclin E1 (CCNE1), histone deacetylase 2 (HDAC2), nitric oxide syn-
thase 2 (NOS2), and phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit delta
(PIK3CD).
Int. J. Mol. Sci. 2024, 25, x FOR PEER REVIEW 6 of 16
Figure 4. (A) The association of common molecular targets of adunctin E and non-small cell lung
cancer was constructed by Cytoscape 3.9.1. The top 16 common targets with the highest degree
scores were generated using the cytoHubba plugin. Colors ranging from dark blue to light green
indicate a higher to lower score of degree. (B) Plot of the degree values of the top 16 common targets.
2.4. Molecular Docking and Molecular Dynamic Analysis of AE Target Interactions
Molecular docking experiments were performed to analyzed potential interactions
between AE and the identified apoptosis regulators. The results revealed that AE binds to
these targets through a combination of hydrogen bonding, van der Waals forces, hydro-
phobic interaction, and electrostatic interactions (Figure 5A–C). Specifically, the binding
energies of AE with HSP90AA1, MAPK1, and PIK3CA were −10.1, −7.7 and −8.1 kcal/mol,
respectively (Table 2). The ligand efficiency between AE and HSP90AA1 was notably
high, indicating significant contributions from each heavy atom in the ligand to the bind-
ing interaction with the target protein. Conversely, the interactions between AE and either
MAPK1 or PIK3CA exhibited moderate ligand efficiency.
Furthermore, molecular dynamics simulations were performed to investigate the sta-
bility and conformational changes of the ligand–target protein complexes over time (Fig-
ure 5D,E). The ligand movements for AE with HSP90AA1, MAPK1, and PIK3CA were
4.53 ± 0.07 Å, 4.44 ± 0.06 Å, and 6.58 ± 0.1 Å. The conformational changes in the ligands
were 1.64 ± 0.01 Å, 1.64 ± 0.03 Å, and 2.33 ± 0.03 Å for HSP90AA1, MAPK1, and PIK3CA,
respectively. Notably, the RMSD of the ligand’s movement and conformation with
HSP90AA1 remained relatively stable up to the 25 ns mark. In contrast, greater fluctua-
tions were observed in the interactions between AE and both MAPK1 and PIK3CA. These
findings suggest that AE forms particularly stable complexes with HSP90AAA1, indicat-
ing the strongest interaction among the tested proteins. This highlights the potential of
AE as a therapeutic agent in NSCLC by targeting key signaling pathways involved in
apoptosis regulation.
Table 2. Interaction strength between 22-(40py)-JA adunctin E and potential targets.
Targets PDB
Binding Energy
(kcal/mol)
Ligand Efficiency
(kcal/mol per Heavy Atom)
Number of Interactions
Hydrogen van der Waals Hydrophobic Electrostatic
HSP90AA1 4BQG −10.1 0.32 - 12 9 -
MAPK1 (ERK2) 1WZY −7.7 0.24 1 11 11 -
PIK3CA 6DGT −8.1 0.25 2 7 3 1
Figure 4. (A) The association of common molecular targets of adunctin E and non-small cell lung
cancer was constructed by Cytoscape 3.9.1. The top 16 common targets with the highest degree scores
were generated using the cytoHubba plugin. Colors ranging from dark blue to light green indicate a
higher to lower score of degree. (B) Plot of the degree values of the top 16 common targets.
Because of the crucial role of apoptosis induction in anticancer therapy, we focused
primarily on potential targets classified as apoptosis regulators. Among these targets,
HSP90AA1, MAPK1, and PIK3CA emerged as promising candidates based on their degree
scores in the network analysis.

Int. J. Mol. Sci. 2024,25, 11368 6 of 16
2.4. Molecular Docking and Molecular Dynamic Analysis of AE Target Interactions
Molecular docking experiments were performed to analyzed potential interactions
between AE and the identified apoptosis regulators. The results revealed that AE binds
to these targets through a combination of hydrogen bonding, van der Waals forces, hy-
drophobic interaction, and electrostatic interactions (Figure 5A–C). Specifically, the binding
energies of AE with HSP90AA1, MAPK1, and PIK3CA were
−
10.1,
−
7.7 and
−
8.1 kcal/mol,
respectively (Table 2). The ligand efficiency between AE and HSP90AA1 was notably high,
indicating significant contributions from each heavy atom in the ligand to the binding
interaction with the target protein. Conversely, the interactions between AE and either
MAPK1 or PIK3CA exhibited moderate ligand efficiency.
Int. J. Mol. Sci. 2024, 25, x FOR PEER REVIEW 7 of 16
Figure 5. Molecular docking and molecular dynamics between adunctin E and protein targets: 2D
and 3D interactions between adunctin E and HSP90AA1 (A), MAPK1 (B), and PIK3CA (C). Root
mean square deviation for ligand movement (D) and ligand conformation (E). Simulation between
22 adunctin E and HSP90AA1 (red), MAPK1 (green), and PIK3CA (blue) for 25 ns were plotted.
Figure 5. Molecular docking and molecular dynamics between adunctin E and protein targets: 2D
and 3D interactions between adunctin E and HSP90AA1 (A), MAPK1 (B), and PIK3CA (C). Root
mean square deviation for ligand movement (D) and ligand conformation (E). Simulation between
22 adunctin E and HSP90AA1 (red), MAPK1 (green), and PIK3CA (blue) for 25 ns were plotted.

Int. J. Mol. Sci. 2024,25, 11368 7 of 16
Table 2. Interaction strength between 22-(40py)-JA adunctin E and potential targets.
Targets PDB Binding Energy
(kcal/mol)
Ligand Efficiency
(kcal/mol per Heavy Atom)
Number of Interactions
Hydrogen van der
Waals
Hydrophobic Electrostatic
HSP90AA1
4BQG −10.1 0.32 - 12 9 -
MAPK1
(ERK2)
1WZY
−7.7 0.24 1 11 11 -
PIK3CA 6DGT −8.1 0.25 2 7 3 1
Furthermore, molecular dynamics simulations were performed to investigate the
stability and conformational changes of the ligand–target protein complexes over time
(Figure 5D,E). The ligand movements for AE with HSP90AA1, MAPK1, and PIK3CA
were 4.53
±
0.07 Å, 4.44
±
0.06 Å, and 6.58
±
0.1 Å. The conformational changes in the
ligands were 1.64
±
0.01 Å, 1.64
±
0.03 Å, and 2.33
±
0.03 Å for HSP90AA1, MAPK1, and
PIK3CA, respectively. Notably, the RMSD of the ligand’s movement and conformation
with HSP90AA1 remained relatively stable up to the 25 ns mark. In contrast, greater
fluctuations were observed in the interactions between AE and both MAPK1 and PIK3CA.
These findings suggest that AE forms particularly stable complexes with HSP90AAA1,
indicating the strongest interaction among the tested proteins. This highlights the potential
of AE as a therapeutic agent in NSCLC by targeting key signaling pathways involved in
apoptosis regulation.
2.5. In Vitro Apoptosis-Inducing Effect of AE on Nsclc
To assess the
in vitro
cytotoxic effects of AE on NSCLC, A549 and H460 cells were
treated with varying concentrations of AE (0–100
µ
M) for 48 h, and a cytotoxic assay using
MTT was performed. The results demonstrated a significant reduction in the viability
of NSCLC, with an IC
50
of 15.72
±
3.37 and 15.71
±
3.43
µ
M in A549 and H460 cells,
respectively (Figure 6A). Furthermore, AE-induced cell apoptosis was evaluated by treating
cells with similar conditions, followed by analysis with annexin-V/propidium iodide (PI)
staining. The result revealed that the number of early (annexin-V
+
, PI
–
) and late (annexin-
V
+
, PI
+
) apoptotic cells gradually increased in a dose-dependent manner, with 48% and
64% observed in A549 cells and 34% and 68% in H460 cells treated with 10 and 20
µ
M of
AE, respectively (Figure 6B,C).
2.6. AE Downregulates HSP90AA1 Expression
Based on the above results, HSP90AA1 emerged as a top potential candidate targeted
by AE in NSCLC. To underscore the clinical relevance of HSP90AA1 in lung cancer, an
analysis of its differential expression and overall survival was performed. Data revealed a
significant upregulation of HSP90AA1 mRNA expression in lung cancer tissues compared
with lung normal tissues in all datasets (Figure 7A). Furthermore, lung cancer patients with
high HSP90AA1 levels exhibited lower overall survival than those with low HSP90AA1
expression (Figure 7B). These finding suggests the prognostic significance of HSP90AA1 in
lung cancer, highlighting it as a potential therapeutic target.
Further, to investigate the effect of AE on HSP90AA1 levels, cells were treated with
various concentrations of AE. Immunoblot analysis demonstrated a gradual decrease in
HSP90AA1 levels in a dose-dependent manner (Figure 8). At a concentration of 20
µ
M,
AE substantially reduced HSP90AA1 expression levels to 0.15- and 0.12-fold in A549 and
H460 cells, respectively, compared with the untreated control cells. This finding confirms
that HSP90AA1 serves as a molecular target of AE in facilitating lung cancer cell apoptosis.

Int. J. Mol. Sci. 2024,25, 11368 8 of 16
Figure 6.
In vitro
cytotoxicity and apoptosis induction of adunctin E. (A) A549 and H460 cells
were treated with adunctin E (0–100
µ
M) for 48 h. Cell viability was determined by MTT assay.
Plots are presented as a percentage of cell viability. (B) Apoptosis cells were evaluated by annexin-
V/propidium iodide (PI) staining. Representative histograms from the flow cytometry analysis are
shown. (C) The number of early (annexin-V
+
, PI
–
) and late (annexin-V
+
, PI
+
) apoptotic cells were
plotted. Data are presented as the mean ±SEM (n = 3). * p< 0.05 vs. untreated control cells.

Int. J. Mol. Sci. 2024,25, 11368 9 of 16
Int. J. Mol. Sci. 2024, 25, x FOR PEER REVIEW 9 of 16
(C) The number of early (annexin-V
+
, PI
–
) and late (annexin-V
+
, PI
+
) apoptotic cells were plotted.
Data are presented as the mean ± SEM (n = 3). * p < 0.05 vs. untreated control cells.
2.6. AE Downregulates HSP90AA1 Expression
Based on the above results, HSP90AA1 emerged as a top potential candidate targeted
by AE in NSCLC. To underscore the clinical relevance of HSP90AA1 in lung cancer, an
analysis of its differential expression and overall survival was performed. Data revealed
a significant upregulation of HSP90AA1 mRNA expression in lung cancer tissues com-
pared with lung normal tissues in all datasets (Figure 7A). Furthermore, lung cancer pa-
tients with high HSP90AA1 levels exhibited lower overall survival than those with low
HSP90AA1 expression (Figure 7B). These finding suggests the prognostic significance of
HSP90AA1 in lung cancer, highlighting it as a potential therapeutic target.
Figure 7. HSP90AA1 is a molecular target of adunctin E. (A) HSP90AA1 expression was upregulated
in lung cancer. HSP90AA1 expressions in both normal lung (blue circles) and lung tumor (red cir-
cles) tissues were assessed utilizing GEO data. (B) Kaplan–Meier survival analysis of lung cancer
patients who had high and low HSP90AA1 expressions from the GEO cohort. HR, hazard ratio.
Further, to investigate the effect of AE on HSP90AA1 levels, cells were treated with
various concentrations of AE. Immunoblot analysis demonstrated a gradual decrease in
HSP90AA1 levels in a dose-dependent manner (Figure 8). At a concentration of 20 μM,
AE substantially reduced HSP90AA1 expression levels to 0.15- and 0.12-fold in A549 and
H460 cells, respectively, compared with the untreated control cells. This finding confirms
that HSP90AA1 serves as a molecular target of AE in facilitating lung cancer cell apopto-
sis.
Figure 7. HSP90AA1 is a molecular target of adunctin E. (A)HSP90AA1 expression was upregulated
in lung cancer. HSP90AA1 expressions in both normal lung (blue circles) and lung tumor (red circles)
tissues were assessed utilizing GEO data. (B) Kaplan–Meier survival analysis of lung cancer patients
who had high and low HSP90AA1 expressions from the GEO cohort. HR, hazard ratio.
Int. J. Mol. Sci. 2024, 25, x FOR PEER REVIEW 10 of 16
Figure 8. (A) A549 and H460 cells were treated with adunctin E (0–20 μM) for 48 h. The expression
of HSP90AA1 was analyzed by immunoblotting. Blots were reprobed with anti-GAPDH antibody
to ensure equal loading. Representative blots from triplicate independent experiments are shown.
(B) HSP90AA1 protein levels were quantified and normalized with those of GAPDH. Relative
HSP90AA1 protein levels were plotted. Data are presented as mean ± SEM (n = 3). * p < 0.05 vs.
untreated control cells.
3. Discussion
The landscape of anticancer drug discovery and research, particularly in the context
of lung cancer, presents ongoing challenges. Despite advances in current therapeutic in-
terventions, the OS rates have gradually increased [1]. Our findings shed light on the re-
markable anticancer properties of AE against lung cancer. Using the network pharmacol-
ogy approach, HSP90AA1 was identified as a significant molecular target of AE. In silico
assays revealed a potent and stable interaction between AE and HSP90AA1. Furthermore,
AE induced apoptosis of lung cancer cells through this mechanism. This underscores AE
as a promising candidate for further anticancer drug research and development.
Network pharmacology offers various advantages in drug research and discovery.
This high-throughput approach has significantly accelerated the drug discovery process
while minimizing costs [21,22]. Enabling the identification of potential molecular targets
of various biologically active compounds has garnered significant interest in the field of
drug discovery [16]. In this study, we identified 71 possible AE targets in lung cancer.
Pathway analysis using GO, KEGG, and Reactome further narrowed down potential mo-
lecular targets, showing notable associations with apoptosis signaling pathways in cancer,
particularly for HSP90AA1, MAPK1, and PIK3CA.
Apoptosis dysregulation is a recognized hallmark of cancer [23]. Apoptosis, or pro-
grammed cell death, is crucially involved in normal physiologies, including embryonic
development and tissue homeostasis, without mediating inflammatory responses or be-
ing harmful to neighboring cells [24,25]. An abnormal apoptosis mechanism contributes
to the pathogenesis of various diseases including cancers [26]. Cancer cells often acquire
deregulated apoptotic signaling either by the upregulation of antiapoptotic and/or pro-
survival proteins or the downregulation of proapoptotic signaling [27–29]. Therefore, can-
cer therapeutics target the editing of these apoptotic signaling pathways. This study also
demonstrated the potent cytotoxic effect of AE on lung cancer cells, mediated through
apoptosis mechanisms.
Notably, HSP90AA1, MAPK1, and PIK3CA emerged as potential molecular targets
for AE in inducing apoptosis. Notably, in silico experiments revealed that AE formed the
strongest and most stable interaction with HSP90AA1, suggesting that this protein may
play a critical role in the mediating of apoptotic effects by AE. Despite strong support
from molecular docking and dynamic simulation studies, there is still a possibility of
Figure 8. (A) A549 and H460 cells were treated with adunctin E (0–20
µ
M) for 48 h. The expression
of HSP90AA1 was analyzed by immunoblotting. Blots were reprobed with anti-GAPDH antibody
to ensure equal loading. Representative blots from triplicate independent experiments are shown.
(B)HSP90AA1 protein levels were quantified and normalized with those of GAPDH. Relative
HSP90AA1 protein levels were plotted. Data are presented as mean
±
SEM (n = 3). * p< 0.05 vs.
untreated control cells.

Int. J. Mol. Sci. 2024,25, 11368 10 of 16
3. Discussion
The landscape of anticancer drug discovery and research, particularly in the context of
lung cancer, presents ongoing challenges. Despite advances in current therapeutic interven-
tions, the OS rates have gradually increased [
1
]. Our findings shed light on the remarkable
anticancer properties of AE against lung cancer. Using the network pharmacology ap-
proach, HSP90AA1 was identified as a significant molecular target of AE. In silico assays
revealed a potent and stable interaction between AE and HSP90AA1. Furthermore, AE
induced apoptosis of lung cancer cells through this mechanism. This underscores AE as a
promising candidate for further anticancer drug research and development.
Network pharmacology offers various advantages in drug research and discovery.
This high-throughput approach has significantly accelerated the drug discovery process
while minimizing costs [
21
,
22
]. Enabling the identification of potential molecular targets
of various biologically active compounds has garnered significant interest in the field of
drug discovery [
16
]. In this study, we identified 71 possible AE targets in lung cancer.
Pathway analysis using GO, KEGG, and Reactome further narrowed down potential
molecular targets, showing notable associations with apoptosis signaling pathways in
cancer, particularly for HSP90AA1, MAPK1, and PIK3CA.
Apoptosis dysregulation is a recognized hallmark of cancer [
23
]. Apoptosis, or pro-
grammed cell death, is crucially involved in normal physiologies, including embryonic
development and tissue homeostasis, without mediating inflammatory responses or being
harmful to neighboring cells [
24
,
25
]. An abnormal apoptosis mechanism contributes to
the pathogenesis of various diseases including cancers [
26
]. Cancer cells often acquire
deregulated apoptotic signaling either by the upregulation of antiapoptotic and/or prosur-
vival proteins or the downregulation of proapoptotic signaling [
27
–
29
]. Therefore, cancer
therapeutics target the editing of these apoptotic signaling pathways. This study also
demonstrated the potent cytotoxic effect of AE on lung cancer cells, mediated through
apoptosis mechanisms.
Notably, HSP90AA1, MAPK1, and PIK3CA emerged as potential molecular targets
for AE in inducing apoptosis. Notably, in silico experiments revealed that AE formed the
strongest and most stable interaction with HSP90AA1, suggesting that this protein may
play a critical role in the mediating of apoptotic effects by AE. Despite strong support
from molecular docking and dynamic simulation studies, there is still a possibility of
indirect effects. Additional experimental validation is required to confirm the specificity
and mechanism of this interaction. Moreover, it is plausible that AE exerts its effects
through multiple pathways, and indirect modulation of other proteins cannot be excluded.
HSP90AA1, a member of the molecular chaperone network, is upregulated in response
to cellular stress [
30
] and has been implicated in cancer aggressiveness and poor survival
outcomes in various cancers [
31
–
33
]. It interacts with key signaling targets such as MAPK
and AKT that regulate the stability and functions of the targets, contributing to cancer
aggressiveness including apoptosis resistance and metastasis [
34
–
37
]. An inhibitor of
HSP90AA1 was reported to remarkably induce apoptosis and suppress cell proliferation in
lung cancer through an ERK/AKT-dependent mechanism [
34
] and to induce autophagic
cell death in osteosarcoma by suppressing AKT/mTOR signaling [
37
], highlighting its
importance in cancer biology.
In this study, we demonstrated that AE downregulates HSP90AA1, a critical protein
involved in cancer cell survival. Our findings indicate that AE binds to the N-terminal
domain of HSP90AA1, specifically interacting with THR184 (Figure S1). This interaction is
likely to disrupt the ATP-binding activity of HSP90AA1, which is essential for its chaperone
function. By interfering with the N-terminal ATPase activity, AE may induce structural
instability in HSP90AA1, impairing its proper folding and function. Consequently, the
misfolded HSP90AA1 is likely targeted for degradation through the proteasome pathway,
which plays a significant role in the clearance of misfolded proteins [
38
]. While our
current study primarily focuses on protein-level effects, we acknowledge the need for
further mechanistic investigations, such as RNA-based assays, to fully elucidate how AE

Int. J. Mol. Sci. 2024,25, 11368 11 of 16
modulates protein levels specifically, whether this modulation occurs via the inhibition of
translation or through targeted degradation.
MAPK1 (also known as ERK2) is a crucial target of HSP90AA1, whereas PIK3CA serves
as an upstream kinase for AKT signaling [
37
,
39
]. This suggests that HSP90AA1 inhibition
holds significant promise as a therapeutic approach for cancers, given its central role in
regulating key signaling pathways involved in cancer progression. However, it is important
to acknowledge that HSP90AA1 operates within broader, interconnected signaling networks
that include the MAPK and PI3K/AKT pathways [
34
,
37
]. These proteins are frequently
co-regulated in various cancer contexts, and their overlapping roles may limit the specificity
of targeting HSP90AA1 alone. Future investigations will focus on elucidating the effects of
AE on the MAPK, PI3K, and AKT pathways, thereby enhancing the therapeutic potential
of AE by demonstrating its influence on multiple cancer-related mechanisms. This multi-
target approach could provide a more comprehensive strategy for addressing the complex
signaling networks in lung cancer. In addition, further studies, particularly in animal
models, are warranted to validate the anticancer efficacy of AE and elucidate its mechanism
of action.
4. Materials and Methods
4.1. Chemicals and Reagents
3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) was purchased
from Sigma-Aldrich (St. Louis, MO, USA). AE (Figure 1) was isolated from Conamomum
rubidum, and its 1H and 13C nuclear magnetic resonance (NMR) spectra were reported
previously [
13
]. Briefly, the isolation of AE from Conamomum rubidum involved extracting
with methanol and partitioning with n-hexane. In addition, the n-hexane fraction was
separated using silica gel chromatography by using an n-hexane-acetone solvent system.
The pharmacokinetic parameters were analyzed by pkCSM [40].
4.2. Identification of the Targets of AE and Nsclc-Related Genes
The possible targets of AE were retrieved from the Swiss Target Prediction database [
41
]
and the Similarity ensemble approach (SEA) [
42
]. Key NSCLC-associated molecular targets
were obtained from GeneCards [
43
], Online Mendelian Inheritance in Man (OMIM) [
44
],
and DisGeNET [
45
]. The compound–target network was visualized using Cytoscape ver-
sion 3.9.1 [
46
]. The common targets of AE and NSCLC were identified by Venny version
2.1.0 [47] and presented in a Venn diagram.
4.3. Construction of the Protein–Protein Interaction Network
The protein–protein interaction network was constructed using STRING version
11.5 [
48
] by incorporating the common targets of AE and NSCLC. The protein type was
specified as “Homo sapiens” with a confidence level set to 0.7, and other parameters were
set to default values. To create the interaction network, protein interaction relationships
were imported into Cytoscape version 3.9.1. Subsequently, top targets were analyzed using
the cytoHubba plugin [
49
]. Core proteins with the highest degree values were subjected to
further analyses.
4.4. Bioinformatic Analyses of Gene Ontology (Go), and Kyoto Encyclopedia of Genes and Genomes
(Kegg), and Reactome Pathways
GO and KEGG were retrieved from the STRING version 11.5 database by importing the
common targets of AE and NSCLC. GO analysis was performed to explore the functionality
of genes, including biological processes, cellular components, and molecular functions [
50
].
In NSCLC, the putative molecular mechanisms of AE were elucidated through KEGG
pathway enrichment analyses [
51
]. Data were visualized by R software version 1.4.1717
with ggplot2 [
52
]. Results were visualized as a bubble plot, with the X-axis representing
the gene ratio, the Y-axis denoting the GO terms, and bubble size and color indicating the
number of associated genes and their statistical significance.

Int. J. Mol. Sci. 2024,25, 11368 12 of 16
Reactome pathway analysis was also employed to investigate pathways associated
with the identified targets [
53
]. Targets were mapped to corresponding pathways in the
Reactome database. A statistical method was applied to assess pathway enrichment,
using an adjusted p-value threshold (e.g., <0.05) to determine significance. The enriched
pathways were then visualized and interpreted to understand potential alterations in
biological processes and signaling pathways relevant to the study.
4.5. Molecular Docking and Dynamics
The X-ray crystal structures of the potential targets were retrieved from the Protein
Data Bank (PDB). The structure of AE was drawn by ChemDraw Ultra version 15.0 (Perkin
Elmer, Waltham, MA, USA). Molecular docking studies of AE with the protein targets
were conducted using the PyRx Virtual Screening Tool version 0.8. Ligand conformations
that exhibited the highest clusters were analyzed to determine their free binding energies
(
∆
G). The binding interactions between the ligands and target proteins were evaluated
using PyMOL version 2.4 (Schrödinger, Portland, OR, USA) and BIOVIA Discovery Studio
Visualizer 2022 (Biovia, San Diego, CA, USA). Molecular dynamics simulations were
performed using Yasara software (https://www.yasara.org/) with the AMBER14 force
field. The simulations were conducted at a temperature of 298 K and pH of 7.4, lasting for
25 ns. The default macro md_run.mcr and md_analyse.mcr. were employed for analyses.
The root mean square deviation (RMSD) graphic was generated using RStudio software
version 1.4.1717 [54].
4.6. Gene Expression Datasets and Differential Expression Analysis
mRNA expression data were obtained from the Gene Expression Omnibus (GEO)
database [
54
]. Two GEO datasets, namely, GSE30219 (tumors, n = 239; normal lung tissues,
n = 14) and GSE31210 (tumors, n = 226; normal lung tissues, n = 19), were analyzed. The
expression levels of HSP90AA1 in normal and tumor lung tissues were compared.
4.7. Survival Analysis
mRNA expression data of HSP90AA1 and lung cancer survival information were
obtained from the GEO databases (GSE30219, n = 239; GSE31210, n = 226) [
55
]. According
to the median expression level, patients were categorized into high and low HSP90AA1
expression groups. The overall survival rates in these two groups were compared using
Kaplan–Meier plots generated by Prism 10 version 10.2.3 (GraphPad Software, Boston, MA,
USA). A log-rank p-value < 0.05 was considered statistically significant.
4.8. Cell Culture
Human NSCLC H460 and A549 cells were purchased from the American Type Culture
Collection (ATCC, Manassas, VA, USA). H460 were cultured in Roswell Park Memorial
Institute (RPMI) Medium, while A549 cells were maintained in Dulbecco’s Modified Eagle
Medium (DMEM). Both media were supplemented with 10% fetal bovine serum (FBS),
100 U/mL penicillin–streptomycin antibiotic solution, and 2 mM L-glutamine. The cell
cultures were incubated in a humidified incubator at 37
◦
C with 5% CO
2
. All media and
supplements were sourced from Gibco (Waltham, MA, USA).
4.9. Cytotoxicity Assay
Cells at a density of 5
×
10
3
cells/well were seeded onto a 96-well plate. After
overnight incubation to allow for cell attachment, the cells were treated with various
concentrations of AE for 48 h. Following treatment, 100
µ
L of the MTT solution (0.5 mg/mL)
was added to each well and incubated for another 4 h. The formazan crystals formed were
then solubilized using dimethylsulfoxide, and the optical intensity was measured at a
wavelength of 570 nm using a microplate reader (VICTOR3/Wallac 1420, Perkin Elmer,
Waltham, MA, USA). The percentage of viable cells was calculated as a percentage relative

Int. J. Mol. Sci. 2024,25, 11368 13 of 16
to the control cells. The inhibitory concentration at 50% (IC
50
) was determined using Prism
10 version 10.2.3 (GraphPad Software, Boston, CA, USA).
4.10. Apoptosis Assay
For the apoptosis evaluation by annexin-V/PI staining, apoptotic cells were assessed
using an apoptosis detection kit (Invitrogen, Waltham, MA, USA). Cells were treated with
various concentrations (0–20
µ
M) of AE for 48 h, washed with cold phosphate-buffered saline,
and resuspended in a binding buffer. The cells were then incubated with annexin-V-FITC/PI
solution for 15 min at room temperature. The fluorescence intensity of each cell was then
analyzed using an EPICS-XL flow cytometer (Beckman Coulter, Indianapolis, IN, USA).
4.11. Immunoblot Analysis
Cells were lysed with a lysis buffer composed of 20 mM Tris-HCl (pH 7.5), 1 mM
MgCl
2
, 150 mM NaCl, 20 mM NaF, 0.5% sodium metavanadate, 1% nonidet-P40, 0.1 mM
phenylmethylsulfonyl fluoride, and protease inhibitor cocktail. Lysis was carried out for
45 min at 4
◦
C. Following lysis, protein concentrations were measured using a BCA Pro-
tein Assay Reagent Kit (Thermo Fisher Scientific, Waltham, MA, USA). The lysates were
subsequently separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis
(SDS-PAGE) and transferred to polyvinylidene difluoride (PVDF) membranes (Bio-Rad
Laboratories, Hercules, CA, USA). The membranes were blocked with 5% skim milk in
Tris-buffer saline containing 0.075% Tween-20 and incubated with specific primary anti-
bodies overnight at 4
◦
C, followed by incubation with corresponding secondary antibodies
at room temperature for 2 h. Rabbit HSP90AA1 (1:1000, CST#8165), mouse anti-GAPDH
(1:1000, CST# 97166), HRP-conjugated anti-rabbit (1:1000, CST#7074), and HRP-conjugated
anti-mouse (1:1000, CST#7076) were the antibodies used. Protein expression was visualized
using an enhanced chemiluminescence system with Immobilon Western chemilumines-
cent HRP substrate (Merck Millipore, Burlington, MA, USA). Densitometry analysis was
performed using Image J software (https://imagej.net/ij/).
4.12. Statistical Analysis
Data are presented as the mean
±
standard deviation, derived from three independent
experiments. Statistical analysis was conducted using Prism 10 version 10.2.3 (GraphPad
Software, Boston, CA, USA). One-way analysis of variance (ANOVA) followed by Tukey’s
multiple comparison test were employed to evaluate statistical significance, with p-value < 0.05.
5. Conclusions
This study highlights the potential of AE as a promising candidate for anticancer drug
development, particularly in lung cancer. AE significantly induced lung cancer cell death
via apoptosis mechanisms. Through network pharmacology and in silico molecular docking
and dynamic analyses, HSP90AA1 was identified as a potential molecular target of AE.
Subsequent
in vitro
experiments confirmed that AE induces apoptosis via an HSP90AA1-
dependent mechanism. These findings provide valuable scientific insights into the potential
of AE for further anticancer drug research and development targeting lung cancer.
Supplementary Materials: The following supporting information can be downloaded at:
https://www.mdpi.com/article/10.3390/ijms252111368/s1.
Author Contributions: Conceptualization, V.P.; methodology, V.P.; validation, V.P. and I.I.; formal
analysis, V.P., I.I. and N.S.; investigation, V.P., I.I., N.S., H.M.N., H.N.T.H. and D.V.H.; resources,
V.P.; data curation, V.P. and I.I.; writing—original draft preparation, V.P. and I.I.; writing—review
and editing, V.P.; visualization, V.P. and I.I.; supervision, V.P.; project administration, V.P.; funding
acquisition, V.P. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by Thailand Science research and Innovation Fund Chula-
longkorn University for the
in vitro
and in silico experiments (HEAF67330003 to V.P.), the Second
Century Fund, Chulalongkorn University (C2F to I.I.), Hue University for adunctin E isolation

Int. J. Mol. Sci. 2024,25, 11368 14 of 16
(DHH2023-04-197), and the Postdoctoral Scholarship Programme of Vingroup Innovation Foundation
(VINIF, VINIF.2023.STS.30 to H.N.T.H.).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: All data supporting the findings of this study as well as Supplementary
Materials are available within the paper and published online.
Acknowledgments: We thank the Pharmaceutical Research Instrument Center (Faculty of Pharma-
ceutical Sciences, Chulalongkorn University, Bangkok, Thailand) for the research facility.
Conflicts of Interest: The authors declare no conflicts of interest.
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