Bioinformatics Studies Provide Insight into Possible Target and Mechanisms of Action of Nobiletin against Cancer Stem Cells

Abstract

Objective:

Nobiletin treatment on MDA-MB 231 cells reduces the expression of CXC chemokine receptor type 4 (CXCR4), which is highly expressed in cancer stem cell populations in tumor patients. However, the mechanisms of nobiletin in cancer stem cells (CSCs) remain elusive. This study was aimed to explore the potential target and mechanisms of nobiletin in cancer stem cells using bioinformatics approaches.

Methods:

Gene expression profiles by public COMPARE predicting the sensitivity of tumor cells to nobiletin. Functional annotations on gene lists are carried out with The Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.8, and WEB-based GEne SeT Analysis Toolkit (WebGestalt). The protein-protein interaction (PPI) network was analyzed by STRING-DB and visualized by Cytoscape.

Results:

Microarray analyses reveal many genes involved in protein binding, transcriptional and translational activity. Pathway enrichment analysis revealed breast cancer regulation of estrogen signaling and Wnt/ß-catenin by nobiletin. Moreover, three hub genes, i.e. ESR1, NCOA3, and RPS6KB1 and one significant module were filtered out and selected from the PPI network.

Conclusion:

Nobiletin might serve as a lead compound for the development of CSCs-targeted drugs by targeting estrogen and Wnt/ß-catenin signaling. Further studies are needed to explore the full therapeutic potential of nobiletin in cancer stem cells.

Introduction

Recent studies have shown that the ability of tumors to develop and propagate depends on a small population of cells called cancer stem cells (CSCs) (Pan et al., 2018; Zhu and Fan, 2018). CSCs are responsible for resistance to chemotherapy and radiotherapy (Toledo-Guzman et al., 2018). Conventional chemotherapy has proved to be able to reduce tumor size; however most of the tumor relapsed because the population of CSCs that were able to survive and grow into tumor bulk (Zhu and Fan, 2018). The CSC-targeted therapy will target the CSCs population whose slowly growth (Moltzahn et al., 2008), and thus the effectiveness of cancer therapy will be achieved. Collectively, CSC-targeted therapy is needed to prevent relapse after chemotherapy.

Flavonoid compounds have been shown to overcome chemoresistance (Meiyanto et al., 2012) and to inhibit CSCs (Hermawan and Putri, 2018). One potential flavonoid compound to be developed as CSC-targeted drugs is nobiletin (Figure 1A). Previous studies showed that polymetoxiflavone citrus flavonoids namely nobiletin exhibits cytotoxic effects on several cancer cells, e.g. TMK-1, MKN-45, MKN-74 and KATO-III stomach cancer cells (Yoshimizu et al., 2004), MH1C1 and HepG2 human hepatocellular carcinoma (Ohnishi et al., 2004), MDA-MB-435 breast cancer cells, MCF-7 and in HT-29 colon cancer cells (Morley et al., 2007). Studies on the combination of nobiletin and conventional chemotherapy agents have also been carried out. Nobiletin is reported to increase the uptake of chemotherapy vinblastine through inhibition of P-gp in Caco-2 cells (Takanaga et al., 2000). Nobiletin also increased doxorubicin cytotoxicity in MCF-7 breast cancer cells but not T47D cells (Meiyanto et al., 2011). In addition, nobiletin showed the effect of inhibiting metastasis by downregulating CXC chemokine receptor type 4 (CXCR4) and matrix metallopeptidase-9 on MDA-MB 231 breast cancer cells (Baek et al., 2012). Therefore, it has been proven that nobiletin is able to overcome chemoresistance and also inhibit CXCR4 which is one of the regulators of CSCs, but its molecular mechanism on CSCs need to be clarified further.

Figure 1.

(A) Chemical Structure of Nobiletin. (B) Cytotoxicity of Nobiletin on the NCI-60 Tumor Cell Line Panel

In this study, we used comprehensive bioinformatics analysis to explore nobiletin cytotoxicity and mechanism in CSCs. Analysis of the public library from the COMPARE database was done to produce a list of drugs that have similarities with nobiletin, as well as a gene list that was influenced by nobiletin on the NCI 60 cell line panel. From the microarray data, functional annotations are then carried out to predict molecular mechanisms, functions and roles of these genes. Furthermore, an analysis of protein-protein interaction was performed from the gene list. Hence we provide information about the possible molecular mechanisms of the nobiletin and its molecular targets against cancer stem cells.

Materials and Methods

Data collection and processing

Cytotoxicity and mRNA arrays data were obtained from the NCI 60 DTP website (http.dtp.nci.nih.gov) (Monks et al., 1997). COMPARE analysis with the public library produces a list of drugs that have similarities with nobiletin, as well as a list of gene expressions on the NCI 60 cell line panel (Mahmoud et al., 2018). The similarity pattern is expressed as the Pearson correlation coefficient. In this study, the list of compounds and genes was limited to the Pearson correlation coefficient <-0.5 and> 0.5.

Functional and pathway enrichment analysis

Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were carried out by The Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.8 (Huang da et al., 2009), with p<0.05 was selected as the cutoff value. Moreover, pathway enrichment was also conducted busing Overrepresentation Enrichment Analysis (ORA) from WEB-based GEne SeT AnaLysis Toolkit (WebGestalt) with FDR<0.05 was selected as the cutoff value (Wang et al., 2017a).

Construction of PPI network and module analysis

Protein-protein interaction (PPI) network was constructed with STRING-DB v11.0 (Szklarczyk et al., 2015). Confidence scores >0.7 were considered significant. PPI network was visualized by Cytoscape software. Genes with a degree score more than 5, analyzed by CytoHubba plugin, were selected as hub genes.

Results

COMPARE analysis reveals mRNA target list and standard agent

This study explored the molecular mechanism of nobiletin in CSCs. Analysis of cytotoxicity with a public database of COMPARE showed that nobiletin exhibits cytotoxicity at the same level in the NCI-60 cells panel showing by similar IC₅₀ value (Figure 1B). COMPARE analysis identified 11 standard agents which have a correlation with nobiletin (Table 1). Tamoxifen, triciribine phosphate and 4-ipomeanol are standard drugs with the highest score of a Pearson correlation coefficient.

Table 1.

Correlation of Nobiletin to Standard Agent by COMPARE Analyses with Log IC₅₀ of Nobiletin

No	Correlation coefficient	NSC Code	Drugs
1	0.525	S180973	Tamoxifen
2	0.501	S280594	Triciribine Phosphate
3	0.453	S349438	4-ipomeanol
4	0.386	S95580	Hexamethylenebisace Tamide
5	0.339	S180973	Tamoxifen
6	0.331	S118994	Diglycoaldehyde
7	0.327	S73754	Fluorodopan
8	0.31	S51143	Impy
9	0.309	S349156	Pancratiastatin
10	0.307	S141540	VP-16 (Etoposide)
11	0.301	S357704	Cyanomorpholino- ADR

Level of mRNA expression analysed by COMPARE showed 108 genes regulated by nobiletin (Table 2), which 104 and 4 genes with positive and negative Pearson correlation coefficient, respectively. FRAT2, AANAT, DYM and SNHG8 are genes with direct correlation, whereas BLOC1S6, NR2F6, VANGL1 and SEPT2 are genes with inverse correlation. Direct correlation indicates that the higher mRNA expression, the higher the chemoresistance, while inverse correlation indicates that the higher mRNA expression, the higher the chemosensitivity. FRAT2 has the highest Pearson correlation coefficient (0.612) while FRAT1 shows the Pearson correlation coefficient of 0.543. Both FRAT1 and FRAT2 are regulatory genes of Wnt/ß-catenin signaling. ESR1 shows the Pearson correlation coefficient of 0.525.

Table 2.

mRNA Expression Analysed by COMPARE with log IC₅₀ of Nobiletin on the NCI-60 Cell Line Panel

No	Pearson Correlation Coefficient	Gene Symbol	Gene Name
1	0.612	FRAT2	Frequently Rearranged In Advanced T-Cell Lymphomas 2
2	0.586	AANAT	Aralkylamine N-Acetyltransferase
3	0.582	DYM	Dymeclin
4	0.576	SNHG8	Small Nucleolar RNA Host Gene 8
5	0.574	LETMD1	LETM1 Domain Containing 1
6	0.571	ATXN7L3B	Ataxin 7 Like 3B
7	0.559	EPB41L5	Erythrocyte Membrane Protein Band 4.1 Like 5
8	0.557	PISD	Phosphatidylserine Decarboxylase
9	0.557	ALDH3B2	Aldehyde Dehydrogenase 3 Family Member B2
10	0.555	VPS37C	VPS37C, ESCRT-I Subunit
11	0.555	HEATR6	HEAT Repeat Containing 6
12	0.554	LARP4B	La Ribonucleoprotein Domain Family Member 4B
13	0.554	FBP1	Fructose-Bisphosphatase 1
14	0.554	AIF1L	Allograft Inflammatory Factor 1 Like
15	0.55	SMARCD2	SWI/SNF Related, Matrix Associated, Actin Dependent Regulator of Chromatin, Subfamily D, Member 2
16	0.548	GRTP1	Growth Hormone Regulated TBC Protein 1
17	0.546	C21orf33	Chromosome 21 Open Reading Frame 33
18	0.545	WDR25	WD Repeat Domain 25
19	0.544	TEAD2	TEA Domain Transcription Factor 2
20	0.544	EIF4B	Eukaryotic Translation Initiation Factor 4B
21	0.543	FRAT1	Frequently Rearranged In Advanced T-Cell Lymphomas 1
22	0.542	TREH	Trehalase
23	0.542	NCOA3	Nuclear Receptor Coactivator 3
24	0.541	RMND1	Required for Meiotic Nuclear Division 1 Homolog
25	0.541	ALOX15	Arachidonate 15-Lipoxygenase
26	0.54	TRIM37	Tripartite Motif Containing 37
27	0.538	TMEM241	Transmembrane Protein 241
28	0.538	APRT	Adenine Phosphoribosyltransferase
29	0.537	SP1	Sp1 Transcription Factor
30	0.536	USP32	Ubiquitin Specific Peptidase 32
31	0.535	RNF44	Ring Finger Protein 44
32	0.535	BRIP1	BRCA1 Interacting Protein C-Terminal Helicase 1
33	0.534	RLN2	Relaxin 2
34	0.534	NPY1R	Neuropeptide Y Receptor Y1
35	0.534	ITGA2B	Integrin Subunit Alpha 2b
36	0.533	ZNF282	Zinc Finger Protein 282
37	0.533	RHPN1	Rhophilin Rho Gtpase Binding Protein 1
38	0.533	MEPCE	Methylphosphate Capping Enzyme
39	0.532	SPTSSB	Serine Palmitoyltransferase Small Subunit B
40	0.53	SPDEF	SAM Pointed Domain Containing ETS Transcription Factor
41	0.53	PIK3R2	Phosphoinositide-3-Kinase Regulatory Subunit 2
42	0.53	EIF3E	Eukaryotic Translation Initiation Factor 3 Subunit E
43	0.529	NUP210L	Nucleoporin 210 Like
44	0.528	ELP2	Elongator Acetyltransferase Complex Subunit 2
45	0.527	GATA3	GATA Binding Protein 3
46	0.526	PPM1D	Protein Phosphatase, Mg2+/Mn2+ Dependent 1D
47	0.526	IRX5	Iroquois Homeobox 5
48	0.525	TFF1	Trefoil Factor 1
No	Pearson Correlation Coefficient	Gene Symbol	Gene Name
49	0.525	RAD51C	RAD51 Paralog C
50	0.525	ESR1	Estrogen Receptor 1
51	0.524	RFX1	Regulatory Factor X1
52	0.524	C15orf59	Chromosome 15 Open Reading Frame 59
53	0.523	ZNF277	Zinc Finger Protein 277
54	0.523	PABPC1	Poly(A) Binding Protein Cytoplasmic 1
55	0.523	CYB561	Cytochrome B561
56	0.522	SCAMP1	Secretory Carrier Membrane Protein 1
57	0.522	PLEKHF2	Pleckstrin Homology And FYVE Domain Containing 2
58	0.522	KIAA1324	Kiaa1324
59	0.522	DSCAM	DS Cell Adhesion Molecule
60	0.521	XBP1	X-Box Binding Protein 1
61	0.521	TUBD1	Tubulin Delta 1
62	0.52	EMCN	Endomucin
63	0.52	APPBP2	Amyloid Beta Precursor Protein Binding Protein 2
64	0.519	TMEM18	Transmembrane Protein 18
65	0.519	ST6GALNAC4	ST6 N-Acetylgalactosaminide Alpha-2,6-Sialyltransferase 4
66	0.519	SPPL2B	Signal Peptide Peptidase Like 2B
67	0.518	TMEM183A	Transmembrane Protein 183A
68	0.517	PSMD6	Proteasome 26S Subunit, Non-Atpase 6
69	0.517	ECSIT	ECSIT Signalling Integrator
70	0.516	SIAH2	Siah E3 Ubiquitin Protein Ligase 2
71	0.516	POU6F2-AS2	POU6F2 Antisense RNA 2
72	0.516	MAX	MYC Associated Factor X
73	0.516	GNAO1	G Protein Subunit Alpha O1
74	0.515	SPATA17	Spermatogenesis Associated 17
75	0.514	STARD10	Star Related Lipid Transfer Domain Containing 10
76	0.513	PATZ1	POZ/BTB And AT Hook Containing Zinc Finger 1
77	0.512	PDZD3	PDZ Domain Containing 3
78	0.512	CYP2J2	Cytochrome P450 Family 2 Subfamily J Member 2
79	0.512	COX6C	Cytochrome C Oxidase Subunit 6C
80	0.511	PLXNA4	Plexin A4
81	0.511	PCDHB4	Protocadherin Beta 4
82	0.51	TBC1D30	TBC1 Domain Family Member 30
83	0.51	PREX1	Phosphatidylinositol-3,4,5-Trisphosphate Dependent Rac Exchange Factor 1
84	0.51	MKS1	Meckel Syndrome, Type 1
85	0.509	ZNF768	Zinc Finger Protein 768
86	0.509	PARD6B	Par-6 Family Cell Polarity Regulator Beta
87	0.509	PABPC3	Poly(A) Binding Protein Cytoplasmic 3
88	0.509	GATC	Glutamyl-Trna Amidotransferase Subunit C
89	0.508	TRPC5OS	TRPC5 Opposite Strand
90	0.508	KREMEN2	Kringle Containing Transmembrane Protein 2
91	0.508	HOOK2	Hook Microtubule Tethering Protein 2
92	0.507	RPS6KB1	Ribosomal Protein S6 Kinase B1
93	0.507	CEACAM21	Carcinoembryonic Antigen Related Cell Adhesion Molecule 21
94	0.507	ABCA12	ATP Binding Cassette Subfamily A Member 12
95	0.506	MVK	Mevalonate Kinase
96	0.505	DHTKD1	Dehydrogenase E1 And Transketolase Domain Containing 1
No	Pearson Correlation Coefficient	Gene Symbol	Gene Name
97	0.504	TDRD5	Tudor Domain Containing 5
98	0.504	ENTHD2	Tepsin
99	0.503	SLC29A2	Solute Carrier Family 29 Member 2
100	0.501	MRPL4	Mitochondrial Ribosomal Protein L4
101	0.501	MATK	Megakaryocyte-Associated Tyrosine Kinase
102	0.501	FBXW9	F-Box And WD Repeat Domain Containing 9
103	0.501	EIF3C	Eukaryotic Translation Initiation Factor 3 Subunit C
104	0.5	RXRA	Retinoid X Receptor Alpha
105	-0.505	BLOC1S6	Biogenesis Of Lysosomal Organelles Complex 1 Subunit 6
106	-0.51	NR2F6	Nuclear Receptor Subfamily 2 Group F Member 6
107	-0.514	VANGL1	VANGL Planar Cell Polarity Protein 1
108	-0.564	SEPT2	Septin 2

Positive correlation coefficients indicate direct correlation to logIC₅₀ value whereas negative correlation coeficients showed inverse correlation.

Gene ontology analysis of potential nobiletin target genes

Gene ontology analysis was classified into biological process, cellular component and molecular function (Table 3). There are no significant GO analysis results of the genes with a negative Pearson correlation coefficient. We found that the upregulated genes mostly involved in the biological process related to negative regulation of transcription, translational initiation, phosphatidylinositol 3-kinase signaling and cellular response to insulin stimulus. Moreover, the upregulated genes located in the cellular component of nuclear chromatin, cytosol and cytoplasm (e.g. ESR1 and NCOA3), and play a role in the molecular function of protein binding, transcriptional and translational activity, as well as steroid hormone activity, e.g. ESR1.

Table 3.

The Top Five Gene Ontology and KEGG Pathway Enrichment of DEGs, Analysed by DAVID

ID	Term	Count	P value	Genes
Biological Process
GO:0001731	Formation of translation preinitiation complex	3	0.006970337	EIF3C, EIF4B, EIF3E
GO:0032869	Cellular response to insulin stimulus	4	0.008691289	SP1, XBP1, APRT, PIK3R2
GO:0014065	Phosphatidylinositol 3-kinase signaling	3	0.009534075	XBP1, GATA3, PIK3R2
GO:0006446	Regulation of translational initiation	3	0.016576226	EIF3C, EIF4B, EIF3E
GO:0009267	Cellular response to starvation	3	0.027361724	MAX, PPM1D, KIAA1324
Cellular component
GO:0000790	Nuclear chromatin	6	0.003712494	SP1, NCOA3, SMARCD2, RXRA, GATA3, ESR1
GO:0005829	Cytosol	29	0.006647911	RHPN1, PREX1, STARD10, RPS6KB1, BLOC1S6, EIF3C, HOOK2, XBP1, EIF3E, FRAT1, AANAT, FRAT2, PABPC1, DHTKD1, PSMD6, PIK3R2, ABCA12, MATK, PARD6B, FBP1, LARP4B, APRT, EIF4B, TRIM37, MKS1, ALOX15, MVK, SIAH2, PDZD3
GO:0005654	Nucleoplasm	25	0.009918189	RAD51C, RPS6KB1, BLOC1S6, MAX, SMARCD2, XBP1, EIF3E, GATA3, NR2F6, PATZ1, PSMD6, SCAMP1, RXRA, ESR1, SPPL2B, BRIP1, TEAD2, ECSIT, APRT, RNF44, NCOA3, SP1, TUBD1, RFX1, SIAH2
Molecular function
GO:0005515	Protein binding	62	0.007869723	RAD51C, SEPT2, PREX1, RPS6KB1, VPS37C, HOOK2, MAX, SMARCD2, GATA3, NR2F6, FRAT1, PSMD6, RMND1, DSCAM, MATK, SCAMP1, MRPL4, VANGL1, RXRA, ESR1, FBP1, ECSIT, TRIM37, MKS1, PPM1D, ALOX15, NCOA3, MVK, SIAH2, ITGA2B, GATC, RHPN1, STARD10, EIF3C, BLOC1S6, FBXW9, XBP1, EIF3E, AANAT, LETMD1, TFF1, PABPC1, APPBP2, TMEM183A, USP32, ABCA12, PIK3R2, PARD6B, SPTSSB, SPPL2B, BRIP1, TEAD2, NPY1R, LARP4B, CYB561, EIF4B, PLEKHF2, SP1, WDR25, RFX1, DYM, PDZD3
GO:0004879	RNA polymerase II transcription factor activity, ligand-activated sequence-specific DNA binding	3	0.017092174	RXRA, NR2F6, ESR1
GO:0001046	Core promoter sequence-specific DNA binding	3	0.02389184	SP1, GATA3, ESR1
GO:0043565	Sequence-specific DNA binding	8	0.025500996	MAX, IRX5, SP1, XBP1, RXRA, NR2F6, SPDEF, ESR1
GO:0003707	Steroid hormone receptor activity	3	0.038898918	RXRA, NR2F6, ESR1
KEGG pathway enrichment analysis
hsa03013	RNA transport	6	0.005016166	EIF3C, EIF4B, EIF3E, PABPC3, PABPC1, NUP210L
hsa05222	Small cell lung cancer	4	0.017791047	MAX, RXRA, ITGA2B, PIK3R2
hsa04915	Estrogen signaling pathway	4	0.026525517	GNAO1, SP1, ESR1, PIK3R2
hsa04919	Thyroid hormone signaling pathway	4	0.038861334	NCOA3, RXRA, ESR1, PIK3R2
hsa04150	mTOR signaling pathway	3	0.054897616*	EIF4B, RPS6KB1, PIK3R2
hsa03013	RNA transport	6	0.005016166	EIF3C, EIF4B, EIF3E, PABPC3, PABPC1, NUP210L

*, not significant

KEGG pathway enrichment, protein-protein interaction (PPI) network construction and module selection

KEGG pathway enrichment indicated several pathways regulated by nobiletin (Table 3) such as RNA transport, small cell lung cancer, estrogen signaling pathway and thyroid hormone signaling pathway. Pathway enrichment analysed by WebGestalt showed breast cancer signaling regulated by nobiletin (Figure 2A). In addition, several genes involved in breast cancer regulation by targeting estrogen receptor and Wnt/ß-catenin signaling (Table 4). A total of 108 genes were constructed to PPI network complex containing 105 nodes and 40 edges, with average node degree 0.762 (Figure 2B). Three nodes with a degree score more than five were identified as hub genes, e.g. ESR1, NCOA3 and RPS6KB1 (Figure 2C and Table 5).

Figure 2.

(A), Pathway enrichment analysis of DEGs with Webgestalt; (B), Protein-protein interaction networks of DEGs, analyzed with STRING-DB and Cytoscape; (C), Top 10 hub genes with the highest degree score, analyzed by Cytoscape

Table 4.

DEGs Involved in Breast Cancer Regulation, Pathway Enrichment Analysis by WebGestalt

User ID	Gene Symbol	Gene Name
RPS6KB1	RPS6KB1	ribosomal protein S6 kinase B1
ESR1	ESR1	estrogen receptor 1
PIK3R2	PIK3R2	phosphoinositide-3-kinase regulatory subunit 2
SP1	SP1	Sp1 transcription factor
NCOA3	NCOA3	nuclear receptor coactivator 3
FRAT1	FRAT1	FRAT1, WNT signaling pathway regulator
FRAT2	FRAT2	FRAT2, WNT signaling pathway regulator

Table 5.

The Hub Genes Identified by PPI Networks, Possessing Degree more than 5

Gene Symbol	Gene name	Degree score
ESR1	estrogen receptor 1	8
NCOA3	nuclear receptor coactivator 3	7
RPS6KB1	ribosomal protein S6 kinase B1	6

Discussion

This study analyzed the molecular mechanism of nobiletin in CSCs using bioinformatics approaches. A pharmacological network analysis using bioinformatics approach can help to explain the potential target and mechanism of compounds in several diseases (Lee et al., 2018). Analysis of cytotoxicity with a public database of COMPARE showed that nobiletin exhibits cytotoxicity at the same level in NCI-60 cells panel showing by similar IC50 value. Nobiletin cytotoxicity does not depend on particular tissue. The low IC₅₀ value indicates the potential of nobiletin for CSCs-targeted agents in combinatorial chemotherapy. The ideal compounds for combinatorial therapy should be potent, have low toxicity and selective (Wang et al., 2014).

COMPARE analysis identified 11 standard agents which have a correlation with nobiletin (Table 1). Tamoxifen, triciribine phosphate and 4-ipomeanol are standard drugs with the highest score of a Pearson correlation coefficient. Tamoxifen is a classical selective estrogen receptor modulator (SERM) for adjuvant chemotherapy of estrogen receptor-positive (Daurio et al., 2016). Tamoxifen activates tumor suppressor gene maspin in breast cancer (Liu et al., 2004). 4-ipomeanol, a lung-toxic furanoterpenoid produced by sweet potatoes (Ipomoea batatas) infected with the fungus Fusarium solani (Boyd and Wilson, 1972; Lakhanpal et al., 2001), is the first agent to undergo preclinical study at the National Cancer Institute (NCI) based on a specific biochemical-biological rationale for clinical investigation as an antineoplastic agent targeted lung cancer (Christian et al., 1989). Phase I and phase II clinical trial of 4-ipomeanol in patients with non-small cell lung cancer and advanced hepatocellular carcinoma, respectively showed that 4-ipomeanol is not recommended for those diseases (Kasturi et al., 1998; Lakhanpal et al., 2001). Triciribine, an inhibitor of Akt phosphorylation and activation, reduces CSC population in T-cell acute lymphoblastic leukemia cells (Evangelisti et al., 2011) and human breast cancer cells SKBR3 cells (Jain et al., 2015). Accordingly, nobiletin probably acts as a kinase inhibitor in inhibiting CSCs.

COMPARE analysis showed that FRAT1 and FRAT2 are genes with positive, while VANGL1 is genes with negative Pearson correlation coefficient, respectively. Those genes also involve in the Wnt/ß-catenin signaling pathway. The frequently rearranged in advanced T-cell lymphomas 1 (Frat 1) and 2 (Frat 2) are positively regulator of the Wnt signaling pathway by stabilizing ß-catenin through the association with GSK-3 (Saitoh et al., 2001). Upon binding to GSK3, Frat prevents the phosphorylation and accompanying degradation of ß-catenin and allows the activation of downstream target genes (van Amerongen and Berns, 2005; Luan et al., 2008). Wnt/ß-catenin signalling may be aberrantly activated through Frat1 overexpression in ovarian serous adenocarcinomas (Wang et al., 2006). The expression of Frat is also positively correlated with the degree of tumor differentiation and the abnormal cell expression of ß-catenin in lung cancer (Luan et al., 2008). Overexpression of Frat1 and abnormal expression of β-catenin were found to represent a poor prognosis for the non-small cell lung cancer patients (Zhang et al., 2012). Frat1 demonstrates oncogenic properties in prostate cancer by inhibiting GSK 3β against β-catenin and thus promoting cell growth (Zhang et al., 2016), while Frat2 mediates the oncogenic activation of Rac by mixed lineage leukemia fusions (Walf-Vorderwulbecke et al., 2012). VANGL1 encodes a transmembrane protein that interacts with Frizzeld a receptor of Wnt (Jenny et al., 2003) and negatively regulates canonical Wnt/β-catenin signaling in mammalian cells. FRAT1 and FRAT2 are tumor promoting genes whereas VANGL1 is a tumor suppressor gene which involved in the Wnt/ß-catenin signaling pathway and thus posses as a molecular target of nobiletin.

KEGG pathway enrichment analysis revealed that estrogen and Wnt/ß-catenin signaling are regulated by nobiletin. There is only a few studies on the role of estrogen in BCSCs. A study demonstrated that estrogen treatment reduces mammosphere formation from estrogen receptor-positive breast cancer cells (Simoes et al., 2011). Other studies showed that estrogen signaling blocking by tamoxifen induces chemoresistance due to EGFR and estrogen receptor cross talk (Shou et al., 2004). Expression of Wnt/ß-catenin signaling pathway-regulated genes correlates with estrogen receptor expression (Lamb et al., 2013). Activation of Wnt/ß-catenin signaling and CSCs properties are associated with advanced progression of ER-positive breast cancer (Sun et al., 2018). The Wnt/ß-catenin pathway is considered to be one of the most important pathways in the regulation of CSCs (Wang et al., 2016). A study showed that Wnt/ß-catenin and estrogen signaling pathways cross-talk in vivo through functional interaction between ERα and β-catenin (Kouzmenko et al., 2004). Therefore it is interesting to further explore the effect of nobiletin in estrogen and Wnt/ß-catenin signaling as well as its cross-talk in CSCs.

Pathway enrichment analysis with KEGG also showed the mTOR pathway regulated by nobiletin even the p-value is slightly greater than the cut off (p= 0.0548). The PI3K/Akt/mTOR signaling pathway is important for CSCs maintenance and could be a promising target for development of CSC-target drugs (Matsubara et al., 2013; Xia and Xu, 2015; Dandawate et al., 2016; Francipane and Lagasse, 2016). Rapamycin and triciribine target CSC population, and inhibits migration and invasion on glioblastoma and neuroblastoma cells (Bahmad et al., 2018). COMPARE analysis showed that triciribin is one of the compounds with the highest similarity to nobiletin, and therefore the effect of nobiletin in mTOR signaling is also potential to be further explored.

There are three hub genes identified from PPI networks, i.e. ESR1, NCOA3, RPS6KB1 ESR1 encodes estrogen receptor alpha which regulates estrogen signaling upon estrogen binding. Abnormal estrogen signaling leads to the development of a variety of diseases, such as cancer, metabolic and cardiovascular disease, neurodegeneration, inflammation, and osteoporosis (Jia et al., 2015). NCOA3 encodes nuclear receptor coactivator 3, a member of the nuclear receptor co-activator family known to be overexpressed in breast cancer and essentially involved in estrogen-mediated cancer cell proliferation (Wagner et al., 2013). Overexpression of NCOA3 promotes breast cancer chemoresistance to tamoxifen (Burwinkel et al., 2005) and paclitaxel (Ao et al., 2016). NCOA3 also drives the formation of cancer stem-like cells and supports tumor outgrowth in breast cancer models (Rohira et al., 2017). Moreover, NCOA3 is a selective co-activator of ERα-mediated transactivation of PLAC1, novel cancer-associated placental in MCF-7 breast cancer cells (Wagner et al., 2013). RPS6KB1 encodes ribosomal protein S6 kinase B1 which plays a key role in regulating protein translation and progression of hepatocellular carcinoma (Li et al., 2012), prostate cancer (Cai et al., 2015) and small cell lung cancer (Chen et al., 2017). S6K1 also activates ERα and promotes the proliferation of estrogen receptor-positive breast cancer cells (Holz, 2012). Taken together, those three genes regulate estrogen signaling in breast cancer and could be evaluated for further studies of marker and target genes of nobiletin in breast cancer stem cells.

Previous studies showed the role of nobiletin in estrogen, Wnt/ß-catenin and mTOR signaling. In estrogen signaling, nobiletin prevents bone loss due induced by estrogen deficiency in rats (Harada et al., 2011; Matsumoto et al., 2018) and inhibits lower cytotoxicity on MCF-7 estrogen receptor-positive breast cancer cells than on SKBR3 HER2 positive and MDA-MB 468 triple-negative breast cancer cells (Chen et al., 2014). Moreover, treatment of nobiletin in lower dose decreases activity and expression of aromatase on MCF-7 cells (Rahideh et al., 2017). In Wnt/ß-catenin signaling, nobiletin inhibits its signaling pathway in hypoxia stimulated Caki-1 and 786-O renal cell carcinoma (Liu et al., 2019), and inhibits invasion via inhibition of AKT/GSK3ß/ß-catenin pathway in glioblastoma cells (Zhang et al., 2017). Nobiletin shows inhibition of mTOR signaling on MDA-MB-468 triple-negative breast cancer cells (Chen et al., 2014). On the mTOR signaling pathway, nobiletin also protects cadmium-induced neurotoxicity induced by cadmium (Qu et al., 2018) and increases the sensitivity of colorectal cancer to oxiplatin (Li et al., 2019). Accordingly, those studies support the present study and enhance the development potential of nobiletin as CSCs-drugs by targeting estrogen, Wnt/ß-catenin and mTOR signaling.

This present study showed that nobiletin target estrogen signaling and Wnt/ß-catenin signaling. Protein interaction networks showed three hub genes regulates estrogen signaling. A previous study also showed functional interaction between estrogen and Wnt/ß-catenin signaling (Kouzmenko et al., 2004). Estradiol not only stimulates the estrogen signaling pathway but also increases the cancer stem cell (CSC) population in estrogen receptor-positive breast cancer cells (Kurebayashi et al., 2017). Treatment with hormone antagonist in estrogen receptor-positive breast cancer cells may repress their estrogen receptors and be resistant to hormone therapy (Simoes et al., 2015). However, a recent study showed that tamoxifen-resistant cells exhibit increased stemness properties via activation of Wnt/ß-catenin signaling (Leung et al., 2017). The interaction of CXC chemokine receptor type 4 (CXCR4) with its ligand CXC motif ligand 12 (CXCL12) plays important roles in maintaining CSCs properties in tamoxifen-resistant breast cancer cells (Dubrovska et al., 2012), nasopharyngeal CSCs (Tian et al., 2017), esophageal CSCs (Wang et al., 2017b), and stimulates the angiogenesis in vascular endothelial cells through upregulation of the MAPK/ERK and PI3K/AKT and Wnt/β-catenin pathways. (Song et al., 2018). A study showed that nobiletin decreases the expression of CXCR4 in breast cancer cells (Baek et al., 2012). Accordingly, nobiletin is potential to target CSCs by inhibiting estrogen and Wnt/ß-catenin signaling.

This present study has several limitations, including the mRNA data used for the PPI network. This might give different results because the expression of mRNA is not always correlated to the protein level. This study is also using bioinformatics approaches, therefore further in vitro and in vivo studies are needed to validate the results as well as to explore the full therapeutic potential of nobiletin on CSCs.

In conclusion, we found that tamoxifen, triciribine phosphate and 4-ipomeanol are standard drugs with the highest score of Pearson correlation coefficient to nobiletin. Moreover, many genes involved in protein binding, transcriptional and translational activity. Importantly, pathway enrichment analysis revealed breast cancer regulation of estrogen signaling and Wnt/ß-catenin by nobiletin. In addition, three hub genes, i.e. ESR1, NCOA3, and RPS6KB1 and one significant module were filtered out and selected from the PPI network. Taken together, using a bioinformatics approach, we showed that nobiletin might serve as a lead compound for the development of cancer stem cells-targeted drugs by targeting targets estrogen and Wnt/ß-catenin signaling.

Author contribution

AH-conception and design of the study, acquisition, analysis and interpretation of data, drafting and revising the article and final approval of the version to be published, HP-acquisition and analysis of data, drafting the article and final approval of the version to be published

Availability of material

The datasets analysed during the present study are online available in the public database.

Funding statement

This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Conflict of interest

The authors declare no conflict of interest.