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Article

An Integrated Bioinformatics Study of a Novel Niclosamide Derivative, NSC765689, a Potential GSK3β/β-Catenin/STAT3/CD44 Suppressor with Anti-Glioblastoma Properties

by
Ntlotlang Mokgautsi
1,2,†,
Ya-Ting Wen
3,†,
Bashir Lawal
1,2,
Harshita Khedkar
1,2,
Maryam Rachmawati Sumitra
1,2,
Alexander T. H. Wu
4,5,6,7,* and
Hsu-Shan Huang
1,2,7,8,9,*
1
PhD Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University and Academia Sinica, Taipei 11031, Taiwan
2
Graduate Institute for Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan
3
Department of Neurosurgery, Taipei Medical University-Wan Fang Hospital, Taipei 11031, Taiwan
4
TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei 11031, Taiwan
5
The PhD Program of Translational Medicine, College of Science and Technology, Taipei Medical University, Taipei 11031, Taiwan
6
Clinical Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei 11031, Taiwan
7
Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei 11490, Taiwan
8
School of Pharmacy, National Defense Medical Center, Taipei 11490, Taiwan
9
PhD Program in Drug Discovery and Development Industry, College of Pharmacy, Taipei Medical University, Taipei 11031, Taiwan
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Int. J. Mol. Sci. 2021, 22(5), 2464; https://doi.org/10.3390/ijms22052464
Submission received: 26 January 2021 / Revised: 18 February 2021 / Accepted: 24 February 2021 / Published: 28 February 2021
(This article belongs to the Special Issue Novel Therapeutic Treatments to Target Glioblastoma Intratumoral Heterogeneity and Plasticity)
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Abstract

:
Despite management efforts with standard surgery, radiation, and chemotherapy, glioblastoma multiform (GBM) remains resistant to treatment, which leads to tumor recurrence due to glioma stem cells (GSCs) and therapy resistance. In this study, we used random computer-based prediction and target identification to assess activities of our newly synthesized niclosamide-derived compound, NSC765689, to target GBM oncogenic signaling. Using target prediction analyses, we identified glycogen synthase kinase 3β (GSK3β), β-Catenin, signal transducer and activator of transcription 3 (STAT3), and cluster of differentiation 44 (CD44) as potential druggable candidates of NSC765689. The above-mentioned signaling pathways were also predicted to be overexpressed in GBM tumor samples compared to adjacent normal samples. In addition, using bioinformatics tools, we also identified microRNA (miR)-135b as one of the most suppressed microRNAs in GBM samples, which was reported to be upregulated through inhibition of GSK3β, and subsequently suppresses GBM tumorigenic properties and stemness. We further performed in silico molecular docking of NSC765689 with GBM oncogenes; GSK3β, β-Catenin, and STAT3, and the stem cell marker, CD44, to predict protein-ligand interactions. The results indicated that NSC765689 exhibited stronger binding affinities compared to its predecessor, LCC09, which was recently published by our laboratory, and was proven to inhibit GBM stemness and resistance. Moreover, we used available US National Cancer Institute (NCI) 60 human tumor cell lines to screen in vitro anticancer effects, including the anti-proliferative and cytotoxic activities of NSC765689 against GBM cells, and 50% cell growth inhibition (GI50) values ranged 0.23~5.13 μM. In summary, using computer-based predictions and target identification revealed that NSC765689 may be a potential pharmacological lead compound which can regulate GBM oncogene (GSK3β/β-Catenin/STAT3/CD44) signaling and upregulate the miR-135b tumor suppressor. Therefore, further in vitro and in vivo investigations will be performed to validate the efficacy of NSC765689 as a novel potential GBM therapeutic.

1. Introduction

Glioblastoma multiforme (GBM) is a heterogeneous and extremely aggressive intracranial cancer, commonly diagnosed in the adult brain. It is the most lethal malignancy of the central nervous system (CNS) [1], and its aggressive character is associated with poor prognoses despite the efforts of standard surgery, radiation, and chemotherapy [2], in which fewer than 4.3% of late-stage patients reach the 5-year survival mark [3]. GBM represents almost 57% of all gliomas and approximately 48% of all primary tumors of the CNS [4]. The CNS and GBM tumor microenvironment (TME) play significant roles in regulating the effectiveness of antitumor responses and activation of immune responses towards cancer cells [5]. GBM is characterized by vascular proliferation, inflammation, intratumoral heterogeneity (such as necrosis), an invasive mechanism, and an ability to escape immune responses [6]. Moreover, there are also complex interactions between the tumor and the TME [7]. In addition to factors within the TME, GBM tumor cells employ different mechanisms to exert their cancerous potential in patients, and this ultimately leads to poor prognoses [8]. The TME contains a variety of non-malignant cell types, including surrounding stromal cells, such as endothelial cells, immune cells, and fibroblasts [9].
According to recent studies, the most profuse cells within the TME are cancer-associated fibroblasts (CAFs), which progressively influence biological properties of GBM, such as tumor development, and are linked to late cancer stages. CAFs also play a significant role in the development of glioma stem cells (GSCs) by regulating the TME [10]. GSCs, also known as self-renewing cells, were reported to drive tumor progression and recurrence, and are resistant to therapeutic interventions [11]. GSCs can be isolated in a patient’s tumor using single surface markers including cluster of differentiation 44 (CD44) among others [12,13]. Glioma cells secrete immunostimulatory molecules which enhance communication among circulating stem cells, astrocytes, immune cells, and the TME, leading to the production of cytokines and growth factors. This weakens the immune response and promotes tumor development and progression as well as treatment resistance [7,14]. GBM proliferation, migration, and adhesion were shown to be promoted by β-Catenin, a component of the canonical Wnt pathway, which is responsible for regulating cell-cell adhesion and transcription of genes [15]. The β-Catenin expression level was demonstrated to be upregulated in GBM cancer cells, leading to tumorigenesis and progression [16]. Previous studies showed that patients with overexpression of N-Myc downregulated gene 1 (NDRG1) in GBM tumor cells exhibited better prognoses, and its downregulation increased the expression level of glycogen synthase kinase 3β (GSK3β), which promotes cell proliferation [17]. GSK3β is a threonine kinase, which is associated with a variety of physiological cell processes including proliferation and cell death.
In clinical settings, GSK3β was reported to be associated with therapeutic resistance in glioblastoma cancer patients [18]. However, its role in GBM still remains to be fully explored [19]. In addition, others also reported that inhibition of NDRG1 modulates the epithelial-to-mesenchymal transition (EMT), which is a major regulator of the Wnt/β-Catenin pathway [20]. Signal transducer and activator of transcription 3 (STAT3) is a cytoplasmic transcription factor which regulates several cellular processes, such as cell differentiation, migration, and survival [21]. When activated, STAT3 regulates the GBM mesenchymal transition and tumor progression [22,23]. As a result, targeting the STAT3 signaling axis could prove to be significant for managing cancer. In GBM, the STAT3 pathway was shown to interact with GSK3β [24]. Furthermore, accumulating evidence has identified a subset of cancer cells which exhibits glioblastoma stem-like cell (GSC) properties; this subpopulation of cells is highly resistant to treatment [25]. Therefore, there is a great need for novel and effective therapeutic agents for GBM. Mechanisms leading to GBM metastasis and aggressiveness still remain elusive; however, the molecular identification of GBM has revealed a novel therapeutic strategy by specifically targeting oncogenes or microRNAs (miRNAs; miRs) [26,27]. Studies showed that miR-135b is significantly downregulated in GSCs compared to normal stem cells [28]. Additionally, reports showed that by targeting GSK3β, miR-135b expression increases and consequently inhibits GSC proliferation, migration, and invasion [29].
In this study, we used target prediction analyses to identify GSK3β, β-Catenin, STAT3, and CD44 as potential druggable candidates of NSC765689. These signaling pathways were also predicted to be overexpressed in GBM tumor samples compared to adjacent normal samples. In addition, using bioinformatics tools, we also identified miR-135b as one of the most suppressed miRNAs in GBM samples, which was reported to be upregulated through inhibition of its target gene GSK3β, and subsequently suppress GBM tumorigenic properties and stemness. We further performed in silico molecular docking of NSC765689 with the GBM oncogenes; GSK3β, β-Catenin, and STAT3, and the stem cell marker of CD44, to predict protein-ligand interactions, and results indicated that NSC765689 exhibited stronger binding affinities compared to its predecessor, LCC09, which was recently published by our laboratory, and proved to inhibit GBM stemness and resistance. Moreover, we used available US National Cancer Institute (NCI) 60 human tumor cell lines to screen for in vitro anticancer effects, including cytotoxic activities of NSC765689 against GBM cells.

2. Results

2.1. NSC765689 Successfully Meets Required Drug-Likeness Criteria

NSC765689 is a close ring, and its predecessor LCC09 an open ring, both of these compounds are derived from niclosamine (Figure 1A) [30]. To identify potential drug candidates in the early stage of drug discovery and development, we applied criteria specified for the drug-likeness concept [31,32]. By utilizing the BOILED-egg prediction, we evaluated gastrointestinal absorption (GIA) and blood-brain-barrier (BBB) permeability of the compound, and results showed that NSC765689 had the highest probability of having good GIA and BBB permeation Bioavailability radar (Figure 1B), this enabled an evaluation of the drug-likeness of NSC765689. All six physicochemical properties outlined by bioavailability radar showed that NSC765689 passed the minimum requirements of drug-likeness (Figure 1B and accompanying table), based on the molecular weight of the compound, lipophilicity, polarity, solubility, saturation, flexibility, and required criterion of log S (ESOL) = 0~6, which is accepted as good solubility in the body. However, our compound did not meet the required log S (ESOL) criterion with a value of -5.34. Furthermore, pharmacokinetics, drug-likeness, and medicinal chemical properties of NSC765689 (Table 1) indicated that NSC765689 had good synthetic accessibility of 3.27 and met criteria for the Lipinski (Pfizer) rule-of-five, the Ghose, Veber (GSK), Egan (Pharmacia), and muegge for drug likeness and drug discovery. The bioavailability of the compound based on GIA indicated a score of 0.55 (55%) which indicates acceptable pharmacokinetic properties.

2.2. NSC765689 Exhibits Similar Anticancer Fingerprint with NCI Synthetic Compounds and Standard Agents

DTP-COMPARE analysis indicated that NSC765689 share similar anticancer fingerprints with a number of NCI synthetic compounds and NCI standard agents. The Pearson’s correlation coefficient (r) and common cell lines count cell counts (CCLC) of the top 15 NCI correlated standard agents are shown in (Table 1) All NCI synthetic compounds with NSC765689 similar anticancer fingerprints are small molecules with the molecular weight (MW; 177.18~470.17g/mol).

2.3. GSK3β/β-Catenin/STAT3/CD44 Are Potential Druggable Candidates for NSC765689

Using a computer-based target prediction tool (http://www.swisstargetprediction.ch/ (accessed on 20 January 2021)) with NSC765689 as the target compound, we identified several targetable proteins, including kinases, enzymes, transcription factors, proteases, and other unclassified proteins (supplementary Figure S1). Our compound was also shown to target oncogenes such as Janus kinase 2/3 (JAK2/3), epidermal growth factor receptor (EGFR), GSK3β, mitogen-activated protein kinase (MAPK), phosphatidylinositol 3-kinase α (PI3KCA), STAT3, and cyclin-dependent kinases (CDKs) as shown below (Table 2, Supplementary Figure S1).

2.4. Increased GSK3β/β-Catenin/STAT3 Expressions Are Associated with Poor Prognoses

To test our hypothesis, we first utilized bioinformatics analyses of the public databases and found that using the online bioinformatics tool GEPIA2 (http://gepia2.cancer-pku.cn (accessed on 20 January 2021)) with the default settings, messenger (m)RNA levels of GSK3β, β-Catenin, STAT3, and CD44 were highly expressed in tumor tissues of patients with GBM compared to normal tissues with significant p values (<0.05), and the correlational analysis showed positive correlations of STAT3 with GSK3β, CTNNB1 (β-Catenin) with GSK3β, STAT3 with CD44, and CTNNB1 with CD44 in GBM patients (r = 0.28~0.58) (Figure 2). In further analyses using the SurvExpress online tool with default settings, (http://bioinformatica.mty.itesm.mx:8080/Biomatec/SurvivaX.jsp (accessed on 20 January 2021)), higher GSK3β/β-Catenin/STAT3 expression levels were associated with significantly shorter survival times compared to patients with lower GSK3β/β-Catenin/STAT3 expression levels. To explore further, when all four genes associated with immune surveillance escape, viz., GSK3β/β-Catenin/STAT3/CD44, were taken into account, patients with high expression levels of these four genes had significantly shorter survival times, which were even more significant than when just considering GSK3β/β-Catenin/STAT3 signaling, suggesting the predictive power of this signature (Figure 3). In additional analyses, we used the STRING database (https://string-db.org/cgi/ (accessed on 20 January 2021)) under high-confidence (with a minimal interaction score of 0.700) predictions to predict protein-protein interactions (PPIs) among these proteins, and results indicated gene expression correlation networks of GSK3β with CTNNB1, GSK3β with STAT3 CD44 with STAT3, and CD44 with CTNNB1. Correlation networks based on curated data and experimentally validated data existed among all four of these oncogenes. The network has four initial proteins formed and was further expanded by an additional 30 nodes/protein, by using the “more” key on the STRING interface. Furthermore, a PIN enrichment p value of <1.0 × 10−16 was obtained from the network, with a clustering coefficient of 0.711. The table shows all other interacting proteins with GSK3β/β-Catenin/STAT3/CD44, and the confidence cutoff value representing interaction links was adjusted to 0.900 as the highest scoring link (Figure 4A, accompanying table). From the above results we analyzed the gene ontology biological processes and related pathways, (Figure 4B) shows the top 10 most highly associated biological processes in the GSK3β/β-Catenin/STAT3/CD44 networks included glial cell differentiation, response to drug, canonical Wnt signaling, MAPK cascade, brain development, innate immune response, apoptosis processes, positive regulation of threonine kinase, and (Figure 4C) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that were linked with GSK3β/β-Catenin/STAT3/CD44 networks included AMPK signaling, glioma, cellular senescence, microRNAs in cancer, MTOR, PI3K/AKT, JAK-STAT, Wnt signaling and pathways in cancer.

2.5. Increased Expressions of the GSK3β/β-Catenin/STAT3/CD44 Signatures and Reduced miR-135b Are Associated with Poor Prognoses of GBM Patients

GSK3B was reported to promote GBM cancer development and progression and has emerged as a target for drug development [33]. Subsequently, we used the online software TargetScan (http://www.targetscan.org/vert (accessed on 20 January 2021)) to search for potential target genes of miR-135b -5p. We identified an miR-135b -5p site in the 3′ untranslated region (UTR) of GSK3β mRNA, thereby predicting GSK3β to be one of the potential genes. Using the miRandola database (http://mirandola.iit.cnr.it/ (accessed on 20 January 2021)), we identified miR-135b -5p to be significantly downregulated in GBM cancer patients compared to normal cancer samples of “black columns” (Figure 5).

2.6. In Silico Molecular Docking Showed Putative Binding of NSC765689 with GSK3β/β-Catenin/STAT3/CD44

We further performed in silico molecular docking analyses to assess the potential inhibitory effects of NSC765689 compared to its predecessor, LCC09, on oncogene markers including GSK3β, β-Catenin, and STAT3 and the stem cell marker, CD44. Results of protein-ligand interactions obtained through AutoDock Vina showed that NSC765689 displayed the highest respective binding energies of −10.3, −8.2, −9.2, and −8.0 kcal/mol with the GSK3β, β-Catenin, STAT3, and CD44 genes, compared to its predecessor, LCC09, which displayed slightly lower binding affinity values of (−9.3, −7.9, −8.8, and −7.7) kcal/mol, respectively. Visualization by Pymol and interpretation using the discovery studio results revealed that the NSC765689/GSK3β complex and LCC09/GSK3β complex showed similar interactions by conventional hydrogen (H)-bonds with Arg144 and THR138, by Pi-anion interactions with LYS85, and by Pi-Pi stacked interactions with ASP200, and it is further stabilized by Pi-alkyl interactions with VAL70, and also by Van der Waal interactions with SER66 as displayed in their binding pockets. Furthermore, the NSC765689/β-Catenin complex interacted with β-Catenin by H-bonds with Arg457, THR418, ASN415, ASN387, and TRP383, and was further sustained by Pi-anion interactions with ASP459 and by Pi-alkyl interactions with ILE414 as shown in their binding pockets. The NSC765689/STAT3 complex and LCC09/STAT3 complex exhibited the same interactions by conventional H-bond interactions with TRP37 and GLU63 in the binding pockets. The NSC765689/CD44 complex was stabilized by conventional H-bond interactions with ASN94 and GLN113 and further sustained by Pi-anion interactions with ARG90 at their binding sites (Figure 6, accompanying table) and (Figure 7, accompanying table). Moreover, we compared docking results of GSK3β/β-Catenin/STAT3/CD44 in complex with LCC09 and NSC765689 to GSK3β/β-Catenin/STAT3 docking with standard inhibitors of AZD1080, cardamonin, and cryptotanshinone. Interestingly, results showed that NSC765689 exhibited the highest binding energies with GSK3β/β-Catenin/STAT3/CD44 of (−10.3, −8.2, −9.2, and −8.0) kcal/mol, respectively, compared to standard inhibitors with binding energies of (−8.5, −6.0, and −7.0) kcal/mol with GSK3β/β-Catenin/STAT3, respectively (Figure 8) and accompanying table). Collectively, these structural simulations predicted NSC765689 to be a multi-target inhibitor with high confidence.

2.7. NSC765689 Exhibits Cytotoxic Activities Obtained from Single High-Dose Testing of 60 Human Cancer Cell Lines (NCI)

We investigated the 60 human tumor cell line available from the US NCI to screen for anticancer effects, including cytotoxicity of NSC765689 toward multiple cell lines of leukemia, non-small cell lung cancer (NSCLC), colon cancer, central nervous system (CNS) cancers, melanomas, ovarian cancers, renal cancers, prostate cancer, and breast cancer based on the developed therapeutic program of the US NCI [34]. Primarily, we evaluated the anti-proliferation and cytotoxic effects of NSC765689 against the NCI-60 cell lines, and we discovered that NSC765689 showed anticancer activities against these cancer cell lines (Figure 9). The cytotoxic activities of NSC765689 were shown after an initial single dose (10 μM), through which the effects of the compound are represented by the percentage growth modified by the treatment (Table 3). Melanoma cell lines were more sensitive to NSC765689, and the activities of the drug growth inhibition (GI) were 67.21%, 29.37%, 28.31% for the SK-MEL-5, MALME-3M, and SK-MEL-2 cell lines, respectively, followed by 39.25%, 34.46%, and 28.76% GI for the HL-60 (TB), MOLT-4, and RPMI-8226 leukemia cell lines, respectively. Renal cancer GI was 35.34% for the A498 and 23.92% for the UO-31 cell lines. The MDA-MB-231/ATCC breast cancer cell line was also sensitive to the drug at 27.93% as was the MDA-MB-468 cell line with −18.67% GI. A CNS cancer cell line exhibited −19.35% GI, followed by NSCLC, ovarian cancer, colon cancer, and prostate cancer which were less sensitive to NSC765689 compared to the above-mentioned cancer types, with the GI percentages of −13.04%, −7.61%, and −4.67%, 0% respectively, however the compound also exhibited satisfactory anti-proliferative effects on all the above mentioned cancer cells lines (Figure 10). The initial single dose of NSC765689 of 10 μM exhibited anticancer effects against multiple cancer cell lines, and proved that more investigation of the dose-dependent effects would be worthwhile. For the purpose of this study, however, we mainly focused on the CNS cancer data, since our main target cancer type was GBM, which was reported to account for approximately 45% of all primary CNS tumors [35].

2.8. NSC765689 Exhibits Dose-Dependent Anticancer Activities against NCI-60 Human Cancer Cell Lines

Since NSC765689 exhibited satisfactory anticancer activities against the 60 NCI cancer cell lines, at an initial single dose of 10 μM, further exploration was performed. However, this time, we focused on the CNS results as a way of targeting GBM cancer. Results showed that by treating cell lines with five doses of NSC765689, CNS cell lines (SF-268, SF-295, U251, and SNB-75) were more sensitive in dose-dependent manners (Figure 10). The 50% GI (GI50) concentrations of NSC765689 against most of the NCI-60 human cell lines ranged 0.23~5.13 μM; the most sensitive cell line was a melanoma (SK-MEL-5) cell line which showed a small sub-micromolar GI50 value of 0.23 μM, followed breast cancer (BT-549), leukemia (MOLT-4), NSCLC (NCI-H46-), ovarian cancer (OVCAR-4), renal cancer (A489), and CNS (SNB-75) with values of 0.29, 0.30, 0.36, 0.38, 0.40 and 0.61 μM respectively. The lowest GI activity was for the COLO 205 colon cancer cell line with a value of 5.13 μM (Figure 9 and Figure 10).

3. Discussion

GBM is the leading type of glial tumor and most aggressive primary brain tumor in adults [36]. Despite considerable advances in treatment modalities including surgery, radiation, and chemotherapy, GBM patients eventually develop resistance to therapy, and the overall survival (OS) rate remains under 2 years, due to the heterogeneous character of GBM [11]. Therapeutic resistance is associated with the development of glioma stem cells (GSCs), which were demonstrated to be capable of withstanding treatment interventions [37]. Emerging studies demonstrated several challenges in treating GBM, including intertumoral heterogeneity, insufficient drug efficacy, and poor drug permeability across the BBB [38]. In addition, GBM cells seldom metastasize; however, cells move from the tumor to penetrate neighboring brain tissues, hence rendering treatment inadequate [6,39]. This necessitates the need to develop novel treatments with the ability to target GBM cellular heterogeneity and GSC therapeutic resistance. In the current study, we demonstrated the anticancer activities of a novel compound NSC765689 against a panel of 60 human tumor multiple cell lines of the US national cancer institute (NCI). NSC765989 (closed ring), is a niclosamide-derived compound synthesized in our laboratory. Based on an initial single dose of 10 μM, the antiproliferative and cytotoxic effects of the tumor cell lines represented by the percentage growth showed the malignant cells to be sensitive to NSC765689, with GI50 values ranging 0.23~5.13 μM against most of the HCI-60 human cell lines. Melanoma cell lines were more sensitive, followed breast cancer, leukemia, NSCLC, ovarian cancer, renal cancer, and CNS and prostate cancer cell lines which were less sensitive to NSC765689. For the purpose of this study, since we were focusing on GBM, which is the most common malignancy of the CNS [1], We further explored the dose-dependency at five doses on CNS cell lines and found that the SF-268, SF-295, U251, and SNB-75 CNS cell lines were more sensitive to NSC765689 (Figure 10).
The development therapeutics program (DTP) of the NCI has demonstrated that cell lines growth inhibition pattern was common among compounds with the same mechanisms, herein, we applied the DTP-COMPARE analysis to identify other compounds which shares similar patterns of percentage growth inhibition with NSC765689 [40]. The results from the analysis indicated that NSC765689 share similar anticancer mechanisms with a number of NCI standard agent with pearson’s correlation (r = 0.25~0.4). The compound also shared the similar anticancer fingerprints with a number of NCI synthetic compounds with (r = 0.54~0.59). The Pearson’s correlation coefficient (r) and (CCLC) of the top 15 NCI correlated standard agents are shown in Table 2. All NCI synthetic compounds with NSC765689 similar anticancer fingerprints are small molecules with the molecular weight (MW; 177.18~470.17g/mol). In addition, COMPARE prediction tool and swiss target software predicted GSK3β/β-Catenin/STAT3/CD44 as target gene for NSC765689. For furthermore analysis, we utilized The Cancer Genome Atlas (TCGA) public database and identified GSK3β, β-Catenin, STAT3, and CD44 oncogenes to be overexpressed in GBM tumor samples compared to adjacent normal tissue groups, and this was associated with poor cancer prognoses (Figure 2). Moreover, we explored interactions among these oncogenes using the STRING analytical tool. PPI networks (PINs) were previously used as a blueprint to analyze different biological activities, including identifying signaling pathways and mechanisms through which cellular processes are activated, such as cell development and immunity [41,42]. As anticipated, we found a clustering network of GSK3β/β-Catenin/STAT3/CD44 and other proteins displayed in the string interface, which were related to the cell cycle. A PIN enrichment p value < 1.0 × 10−16 was obtained from the network, with a clustering coefficient of 0.711, and the confidence cutoff value representing the interaction links was adjusted to 0.900 as the highest scoring link (Figure 4). Accumulating reports have demonstrated that GSK3β is highly expressed in GBM compared to non-malignant brain tissues, and plays a significant role as a promoter of neoplastic GBM phenotypes, making it a potential biomarker for targeted therapy [19,43,44]. Therefore, STRING analytical results showed that proteins which interacted the most with GSK3β in the network were STAT3, AKT, β-Catenin, SNAI1, and adenomatous polyposis coli (APC), thus supporting our hypothesis.
There are several other factors that contribute to GBM tumorigenesis, progression, and therapeutic resistance, including microRNAs (miRs) [45]. miRs are approximately 22-nucleotide-long non-coding RNAs, which play significant roles in modulating gene expressions after DNA transcription [46]. Recent studies showed another important function of miRs as a tumor suppressor depending on their target genes [47]. Using miR target prediction online software, we identified significantly lower expression of the miR-135b tumor suppressor in GBM tumor cohorts compared to normal samples, with the fold change (LogFC = −0.733) and (p-value < 0.001). In addition, we discovered that miR-135b is downregulated in GBM patients (Figure 5A,B), and according to recent studies, it plays a pivotal role in GBM progression, and its downregulation is also associated GBM stemness [29]. This suggests that restoration of miR-135b could be important strategy to reduce tumorigenesis and cancer recurrence. Since miRs function as tumor suppressors depending on their target gene, we further explored this aspect with an miR target prediction tool, and identified the miR-135b-5p site on the 3′UTR region of GSK3β, which suggests that GSK3β may be a target gene for miR-135b-5P (Figure 5C). Thus we suggest further in vitro and in vivo investigations into the potential roles of NSC765689 in targeting GSK3β as a way of increasing the miR-135b expression level in GBM patients.
In our previous study, one of our small molecule, LCC-09, was shown to inhibit GBM stem cell-related oncogenic activities and ultimately increased the expression level of miR-34a in GBM cells both in vitro and in vivo [37]. From our recent findings, we rationalized that this open-ring niclosamide-derived compound (LCC-09) could be further optimized; thus, we synthesized the closed-ring niclosamide small-molecule NSC765689, which might yield better results, as close ring structures has been proven to be more stable [30,48]. Since GSK3β/β-Catenin/STAT3 showed to be potential NSC765689 druggable targets, we performed in silico molecular docking to analyze ligand-protein interactions. Interestingly, we discovered that NSC765689 was able to bind to GSK3β/β-Catenin/STAT3/CD44, and exhibited stronger binding affinities as compared to its predecessor LCC09 which displayed slightly lower respective binding affinities (Figure 6 and Figure 7 and accompanying tables). Additionally, we compared docking results of GSK3β/β-Catenin/STAT3/CD44 in complexes with LCC09 and NSC765689 by docking with standard inhibitors of GSK3β/β-Catenin/STAT3 of AZD1080, cardamonin, and cryptotanshinone, respectively. Interestingly, results showed that NSC765689 exhibited the highest binding affinities than all the aforementioned inhibitors (Figure 8 and accompanying table). NSC765689 successfully met the physicochemical properties as outlined by the bioavailability radar such as lipophilicity, molecular weight, polarity, solubility, saturation, and flexibility. The pharmacokinetics, drug-likeness, and medicinal chemical properties of NSC765689 indicated that NSC765689 had good synthetic accessibility of 3.27 and met criteria for the Lipinski (Pfizer) rule-of-five, and the Ghose, Veber (GSK), Egan (Pharmacia), and muegge for drug likeness and drug discovery. The bioavailability of the compound based on the GIA showed a score of 0.55 (55%), which indicates acceptable pharmacokinetic properties. Therefore, the efficacy of NSC765689 is worthy of further investigation in both in vitro and in vivo studies, which are currently in progress in our laboratory.

4. Materials and Methods

4.1. Pharmacokinetics, Drug-Likeness, and Medicinal Chemical Analyses

The drug-likeness, medicinal chemistry, and pharmacokinetics investigation examined the adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of NSC765689 as analyzed using SwissADME software developed by the Swiss Institute of Bioinformatics (http://www.swissadme.ch (accessed on 20 January 2021)) [49]. The drug-likeness properties were analyzed in terms of Ghose (Amgen), Egan (Pharmacia), Veber (GSK), and more importantly, the Lipinski (Pfizer) rule-of-five: cLogP, molecular mass, hydrogen acceptor, hydrogen donor, and molar refractive index [50] for drug likeness and drug discovery. The Abbot Bioavailability Score was calculated based on the probability of the compound to have at least 0.1 (10%) oral bioavailability in rats or measurable Caco-2 permeability [51], while gastrointestinal absorption and brain penetration properties were analyzed using the Brain Or IntestinaL EstimateD permeation (BOILED-Egg) model [32].

4.2. Bioinformatics Predictions

To analyze the overall survival (OS) and disease-free survival (DFS) ratios between cancer cohorts with high and those with low GSK2B/β-Catenin/STAT3/CD44 expressions, we utilized Kaplan-Meier survival plots on the comprehensive gene expression database SurvExpress with default settings (http://bioinformatica.Biomatec/SurvivaX.jsp (accessed on 20 January 2021)). For further analysis, we used the STRING database (https://string-db.org/ (accessed on 20 January 2021)) under high confidence (minimal interaction score of 0.700) predictions to predict the PPIs among these proteins, and results indicated gene expression correlation networks of GSK3β with CTNNB1, GSK3β with STAT3, and CD44, and STAT3 and CD44 with CTNNB1. We used the UALCAN bioinformatics tool (http://ualcan.path.uab.edu/ (accessed on 20 January 2021)) with default settings for a differential expression analysis of GSK3β/β-Catenin/STAT3/CD44 expressions in tumor vs. normal tissues from various cancers in The Cancer Genome Atlas (TCGA) database.

4.3. In Silico Molecular Docking Analyses

In silico molecular docking analyses were used to assess the potential inhibitory effects of NSC765689 compared to its predecessor, LCC09, on oncogene markers, including GSK3β, β-Catenin, and STAT3, and the stem cell marker, CD44. Results of protein-ligand interactions obtained through AutoDock Vina showed that NSC765689 displayed the highest binding energy values of (−10.3, −8.2, −9.2, and −8.0) kcal/mol with the GSK3β, β-Catenin, STAT3, and CD44 genes, respectively, compared to its predecessor LCC09 which displayed slightly lower binding affinity values of −9.3, −7.9, −8.8, and −7.7 kcal/mol, respectively. Visualization by Pymol and interpretation using the discovery studio results revealed that the NSC765689/GSK3β complex and LCC09/GSK3β complex showed similar interactions by conventional hydrogen (H)-bonds with Arg144 and THR138, by Pi-anion interactions with LYS85, and Pi-Pi stacked interactions with ASP200, and were further stabilized by Pi-alkyl interactions with VAL70, and also by Van der Waals interactions with SER66 as displayed in their binding pockets. Furthermore, the NSC765689/β-Catenin complex interacted with β-Catenin by H-bonds with Arg457, THR418, ASN415, ASN387, and TRP383, and was further sustained by Pi-anion interactions with ASP459 and by Pi-alkyl interactions with ILE414 as shown in their binding pockets. The NSC765689/STAT3 complex and LCC09/STAT3 complex exhibited the same interactions by conventional H-bond interactions with TRP37 and GLU63 in their binding pockets. The NSC765689/CD44 complex was stabilized by conventional H-bond interactions with ASN94 and GLN113 and further sustained by Pi-anion interactions with ARG90 at the binding site (Figure 7 and Figure 8 and accompanying tables). Moreover, we compared docking results of GSK3β/β-Catenin/STAT3/CD44 in complex with LCC09 and NSC765689 to GSK3β/β-Catenin/STAT3 docking with standard inhibitors of AZD1080, cardamonin, and cryptotanshinone.

4.4. In Vitro Screening Of NSC765689 against the Full NCI-60 Cell Panels of Human Tumor Cell Lines

NSC765689 was submitted to the NCI for screening on its panel of NCI-60 cancer cell lines. The preliminary single-dose screening of the two compounds were conducted against all 60 NCI cell line panels comprised of melanomas, leukemia, CNS cancers, NSCLC, renal cancer, breast cancer, ovarian cancer, and prostate cancer in agreement with the protocol of the NCI (http://dtp.nci.nih.gov (accessed on 20 January 2021)). The NSC765689 compound was tested against 60 cell lines of NCI, with an initial single dose of 10 μM. Results are presented in Figure 10. Melanoma cell lines were more sensitive to NSC765689 and the activities of drug growth inhibition (GI) were 67.21%, 29.37%, and 28.31% for SK-MEL-5, MALME-3M, and SK-MEL-2 cells, respectively. Next were 39.25%, 34.46%, and 28.76% GI levels for the HL-60 (TB), MOLT-4, and RPMI-8226 leukemia cell lines, respectively. Renal cancer GI was 35.34% for the A498 and 23.92% for the UO-31 cell line. The breast cancer cell line of MDA-MB-231/ATCC was also sensitive to the drug at 27.93% as was the MDA-MB-468 cell line with −18.67% GI. A CNS cancer cell line exhibited GI of −19.35%. followed by NSCLC, ovarian cancer, colon cancer, and prostate cancer which were less sensitive to NSC765689 compared to the above-mentioned cancer types, with GI levels of −13.04%, −7.61%, and −4.67% and 0% respectively.

4.5. Data Analysis

Pearson’s correlation was used to assess correlations of GSK3β/β-Catenin/STAT3/CD44 expressions in GBM. The statistical significance of differentially expressed genes was evaluated using the Wilcoxon test. * p < 0.05 was considered statistically significant. The Kaplan-Meier curve was employed to present the patient survival from different cancer cohorts. GI by NSC765689 in the single-dose assay was obtained by subtracting the positive value on the plot from 100, i.e., a value of 40 would mean 60% growth inhibition.

5. Conclusions

In summary, overexpression of the GSK3β, β-Catenin, STAT3, and CD44 oncogenes and downregulation of the miR-135b tumor suppressor in GBM tumor samples compared to adjacent normal tissue were associated with poor patient prognoses, stemness, and therapeutic resistance. Recently we published a paper showing that LCC09, a predecessor of our present compound NSC765689, inhibited GBM stem cell-related oncogenesis and ultimately increased the expression level of the tumor suppressor in glioblastoma cells, in vitro and in vivo. Herein, we predicted that NSC765689 could successfully target GSK3β, β-Catenin, STAT3, and CD44 oncogenic signaling, showing its potential role in regulating expressions of these oncogenic signatures in GBM. In addition, through targeting these signaling pathways, will possibly increase the expression of the miR-135b tumor suppressor increased in GBM cells. Finally, we reported the in vitro anti-proliferative and cytotoxic effects of the NSC765689 compound in multiple cells line, specifically in GBM cancer cell lines. We are currently conducting in vitro and in vivo studies in our laboratory for further validation of NSC765689 as a GBM therapeutic.

Supplementary Materials

The following are available online at https://www.mdpi.com/1422-0067/22/5/2464/s1.

Author Contributions

N.M. wrote the manuscript; B.L. and H.K. helped with data collection and analyses; H.-S.H. and M.R.S. synthesized and provided NSC765689; Y.-T.W., H.-S.H. and A.T.H.W. designed and oversaw the study. All authors have read and agreed to the published version of the manuscript.

Funding

Hsu-Shan Huang is funded by the Ministry of Science and Technology (MOST109-2113-M-038-003). Ya-Ting Wen and Hsu-Shan Huang are funded by a Taipei Medical University and Wan-Fang Hospital research grant (109TMU-WFH-18). Ya-Ting Wen is also funded by the Ministry of Science and Technology (MOST110-2314-B-038-024).

Data Availability Statement

The datasets generated and/or analyzed in this study are available on reasonable request.

Acknowledgments

The NCI Developmental Therapeutics Program (DTP) for the 60-cancer-cell-line screening of selected compounds described in this paper was funded by the National Cancer Institute, National Institutes of Health (NIH-NCI). We also acknowledged the editing services provided by the Office of Research and Development, Taipei Medical University.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Serrano-Garrido, O.; Peris-Torres, C.; Redondo-Garcia, S.; Asenjo, H.G.; Plaza-Calonge, M.D.C.; Fernandez-Luna, J.L.; Rodriguez-Manzaneque, J.C. ADAMTS1 Supports Endothelial Plasticity of Glioblastoma Cells with Relevance for Glioma Progression. Biomolecules 2020, 11, 44. [Google Scholar] [CrossRef]
  2. Weller, M.; van den Bent, M.; Tonn, J.C.; Stupp, R.; Preusser, M.; Cohen-Jonathan-Moyal, E.; Henriksson, R.; Le Rhun, E.; Balana, C.; Chinot, O.; et al. European Association for Neuro-Oncology (EANO) guideline on the diagnosis and treatment of adult astrocytic and oligodendroglial gliomas. Lancet Oncol. 2017, 18, e315–e329. [Google Scholar] [CrossRef] [Green Version]
  3. Da Hora, C.C.; Schweiger, M.W.; Wurdinger, T.; Tannous, B.A. Patient-Derived Glioma Models: From Patients to Dish to Animals. Cells 2019, 8, 1177. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Tan, A.C.; Ashley, D.M.; López, G.Y.; Malinzak, M.; Friedman, H.S.; Khasraw, M. Management of glioblastoma: State of the art and future directions. CA Cancer J. Clin. 2020, 70, 299–312. [Google Scholar] [CrossRef]
  5. Desland, F.A.; Hormigo, A. The CNS and the Brain Tumor Microenvironment: Implications for Glioblastoma Immunotherapy. Int. J. Mol. Sci. 2020, 21, 7358. [Google Scholar] [CrossRef]
  6. Louis, D.N.; Perry, A.; Reifenberger, G.; von Deimling, A.; Figarella-Branger, D.; Cavenee, W.K.; Ohgaki, H.; Wiestler, O.D.; Kleihues, P.; Ellison, D.W. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: A summary. Acta Neuropathol. 2016, 131, 803–820. [Google Scholar] [CrossRef] [Green Version]
  7. Piperi, C.; Papavassiliou, K.A.; Papavassiliou, A.G. Pivotal Role of STAT3 in Shaping Glioblastoma Immune Microenvironment. Cells 2019, 8, 1398. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Quail, D.F.; Joyce, J.A. The Microenvironmental Landscape of Brain Tumors. Cancer Cell 2017, 31, 326–341. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Quail, D.F.; Joyce, J.A. Microenvironmental regulation of tumor progression and metastasis. Nat. Med. 2013, 19, 1423–1437. [Google Scholar] [CrossRef] [PubMed]
  10. Truffi, M.; Mazzucchelli, S.; Bonizzi, A.; Sorrentino, L.; Allevi, R.; Vanna, R.; Morasso, C.; Corsi, F. Nano-Strategies to Target Breast Cancer-Associated Fibroblasts: Rearranging the Tumor Microenvironment to Achieve Antitumor Efficacy. Int. J. Mol. Sci. 2019, 20, 1263. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  11. Galdieri, L.; Jash, A.; Malkova, O.; Mao, D.D.; DeSouza, P.A.; Chu, Y.E.; Salter, A.; Campian, J.L.; Naegle, K.M.; Brennan, C.W.; et al. Defining phenotypic and functional heterogeneity of glioblastoma stem cells by mass cytometry. JCI Insight 2021, 6, 128456. [Google Scholar] [CrossRef] [PubMed]
  12. Son, M.J.; Woolard, K.; Nam, D.H.; Lee, J.; Fine, H.A. SSEA-1 is an enrichment marker for tumor-initiating cells in human glioblastoma. Cell Stem Cell 2009, 4, 440–452. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Dirkse, A.; Golebiewska, A.; Buder, T.; Nazarov, P.V.; Muller, A.; Poovathingal, S.; Brons, N.H.C.; Leite, S.; Sauvageot, N.; Sarkisjan, D.; et al. Stem cell-associated heterogeneity in Glioblastoma results from intrinsic tumor plasticity shaped by the microenvironment. Nat. Commun. 2019, 10, 1787. [Google Scholar] [CrossRef] [PubMed]
  14. Xia, S.; Lal, B.; Tung, B.; Wang, S.; Goodwin, C.R.; Laterra, J. Tumor microenvironment tenascin-C promotes glioblastoma invasion and negatively regulates tumor proliferation. Neuro Oncol. 2016, 18, 507–517. [Google Scholar] [CrossRef] [Green Version]
  15. Moon, R.T.; Kohn, A.D.; De Ferrari, G.V.; Kaykas, A. WNT and beta-catenin signalling: Diseases and therapies. Nat. Rev. Genet. 2004, 5, 691–701. [Google Scholar] [CrossRef]
  16. Nager, M.; Bhardwaj, D.; Cantí, C.; Medina, L.; Nogués, P.; Herreros, J. β-Catenin Signalling in Glioblastoma Multiforme and Glioma-Initiating Cells. Chemother. Res. Pract. 2012, 2012, 192362. [Google Scholar] [CrossRef] [Green Version]
  17. Ito, H.; Watari, K.; Shibata, T.; Miyamoto, T.; Murakami, Y.; Nakahara, Y.; Izumi, H.; Wakimoto, H.; Kuwano, M.; Abe, T.; et al. Bidirectional Regulation between NDRG1 and GSK3β Controls Tumor Growth and Is Targeted by Differentiation Inducing Factor-1 in Glioblastoma. Cancer Res. 2020, 80, 234–248. [Google Scholar] [CrossRef]
  18. Xiao, S.; Yang, Z.; Lv, R.; Zhao, J.; Wu, M.; Liao, Y.; Liu, Q. miR-135b contributes to the radioresistance by targeting GSK3β in human glioblastoma multiforme cells. PLoS ONE 2014, 9, e108810. [Google Scholar] [CrossRef]
  19. Kotliarova, S.; Pastorino, S.; Kovell, L.C.; Kotliarov, Y.; Song, H.; Zhang, W.; Bailey, R.; Maric, D.; Zenklusen, J.C.; Lee, J.; et al. Glycogen synthase kinase-3 inhibition induces glioma cell death through c-MYC, nuclear factor-kappaB, and glucose regulation. Cancer Res. 2008, 68, 6643–6651. [Google Scholar] [CrossRef] [Green Version]
  20. Sun, J.; Zhang, D.; Bae, D.H.; Sahni, S.; Jansson, P.; Zheng, Y.; Zhao, Q.; Yue, F.; Zheng, M.; Kovacevic, Z.; et al. Metastasis suppressor, NDRG1, mediates its activity through signaling pathways and molecular motors. Carcinogenesis 2013, 34, 1943–1954. [Google Scholar] [CrossRef] [Green Version]
  21. Rawlings, J.S.; Rosler, K.M.; Harrison, D.A. The JAK/STAT signaling pathway. J. Cell Sci. 2004, 117, 1281–1283. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Segerman, A.; Niklasson, M.; Haglund, C.; Bergström, T.; Jarvius, M.; Xie, Y.; Westermark, A.; Sönmez, D.; Hermansson, A.; Kastemar, M.; et al. Clonal Variation in Drug and Radiation Response among Glioma-Initiating Cells Is Linked to Proneural-Mesenchymal Transition. Cell Rep. 2016, 17, 2994–3009. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Wu, L.; Bernal, G.M.; Cahill, K.E.; Pytel, P.; Fitzpatrick, C.A.; Mashek, H.; Weichselbaum, R.R.; Yamini, B. BCL3 expression promotes resistance to alkylating chemotherapy in gliomas. Sci. Transl. Med. 2018, 10, eaar2238. [Google Scholar] [CrossRef] [Green Version]
  24. Moh, A.; Zhang, W.; Yu, S.; Wang, J.; Xu, X.; Li, J.; Fu, X.Y. STAT3 sensitizes insulin signaling by negatively regulating glycogen synthase kinase-3 beta. Diabetes 2008, 57, 1227–1235. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Eramo, A.; Ricci-Vitiani, L.; Zeuner, A.; Pallini, R.; Lotti, F.; Sette, G.; Pilozzi, E.; Larocca, L.M.; Peschle, C.; De Maria, R. Chemotherapy resistance of glioblastoma stem cells. Cell Death Differ. 2006, 13, 1238–1241. [Google Scholar] [CrossRef] [Green Version]
  26. Mohyeldin, A.; Chiocca, E.A. Gene and viral therapy for glioblastoma: A review of clinical trials and future directions. Cancer J. 2012, 18, 82–88. [Google Scholar] [CrossRef]
  27. Skalsky, R.L.; Cullen, B.R. Reduced expression of brain-enriched microRNAs in glioblastomas permits targeted regulation of a cell death gene. PLoS ONE 2011, 6, e24248. [Google Scholar] [CrossRef]
  28. Munding, J.B.; Adai, A.T.; Maghnouj, A.; Urbanik, A.; Zöllner, H.; Liffers, S.T.; Chromik, A.M.; Uhl, W.; Szafranska-Schwarzbach, A.E.; Tannapfel, A.; et al. Global microRNA expression profiling of microdissected tissues identifies miR-135b as a novel biomarker for pancreatic ductal adenocarcinoma. Int. J. Cancer 2012, 131, E86–E95. [Google Scholar] [CrossRef]
  29. Lulli, V.; Buccarelli, M.; Martini, M.; Signore, M.; Biffoni, M.; Giannetti, S.; Morgante, L.; Marziali, G.; Ilari, R.; Pagliuca, A.; et al. miR-135b suppresses tumorigenesis in glioblastoma stem-like cells impairing proliferation, migration and self-renewal. Oncotarget 2015, 6, 37241–37256. [Google Scholar] [CrossRef]
  30. Barbosa, E.J.; Löbenberg, R.; de Araujo, G.L.B.; Bou-Chacra, N.A. Niclosamide repositioning for treating cancer: Challenges and nano-based drug delivery opportunities. Eur. J. Pharm. Biopharm. 2019, 141, 58–69. [Google Scholar] [CrossRef] [PubMed]
  31. Ritchie, T.J.; Ertl, P.; Lewis, R. The graphical representation of ADME-related molecule properties for medicinal chemists. Drug Discov. Today 2011, 16, 65–72. [Google Scholar] [CrossRef]
  32. Daina, A.; Zoete, V. A BOILED-Egg To Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules. ChemMedChem 2016, 11, 1117–1121. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Korur, S.; Huber, R.M.; Sivasankaran, B.; Petrich, M.; Morin, P., Jr.; Hemmings, B.A.; Merlo, A.; Lino, M.M. GSK3β eta regulates differentiation and growth arrest in glioblastoma. PLoS ONE 2009, 4, e7443. [Google Scholar] [CrossRef]
  34. Shoemaker, R.H. The NCI60 human tumour cell line anticancer drug screen. Nat. Rev. Cancer 2006, 6, 813–823. [Google Scholar] [CrossRef] [PubMed]
  35. Ellingson, B.M.; Wen, P.Y.; van den Bent, M.J.; Cloughesy, T.F. Pros and cons of current brain tumor imaging. Neuro Oncol. 2014, 16 (Suppl. 7), vii2–vii11. [Google Scholar] [CrossRef] [PubMed]
  36. Birzu, C.; Tran, S.; Bielle, F.; Touat, M.; Mokhtari, K.; Younan, N.; Psimaras, D.; Hoang-Xuan, K.; Sanson, M.; Delattre, J.Y.; et al. Leptomeningeal Spread in Glioblastoma: Diagnostic and Therapeutic Challenges. Oncologist 2020, 25, e1763–e1776. [Google Scholar] [CrossRef]
  37. Wen, Y.T.; Wu, A.T.; Bamodu, O.A.; Wei, L.; Lin, C.M.; Yen, Y.; Chao, T.Y.; Mukhopadhyay, D.; Hsiao, M.; Huang, H.S. A Novel Multi-Target Small Molecule, LCC-09, Inhibits Stemness and Therapy-Resistant Phenotypes of Glioblastoma Cells by Increasing miR-34a and Deregulating the DRD4/Akt/mTOR Signaling Axis. Cancers 2019, 11, 1442. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Ou, A.; Yung, W.K.A.; Majd, N. Molecular Mechanisms of Treatment Resistance in Glioblastoma. Int. J. Mol. Sci. 2020, 22, 351. [Google Scholar] [CrossRef]
  39. Hess, K.R. Extent of resection as a prognostic variable in the treatment of gliomas. J. Neurooncol. 1999, 42, 227–231. [Google Scholar] [CrossRef]
  40. Holbeck, S.L.; Collins, J.M.; Doroshow, J.H. Analysis of Food and Drug Administration-approved anticancer agents in the NCI60 panel of human tumor cell lines. Mol. Cancer Ther. 2010, 9, 1451–1460. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  41. Hao, T.; Zhao, L.; Wu, D.; Wang, B.; Feng, X.; Wang, E.; Sun, J. The Protein-Protein Interaction Network of Litopenaeus vannamei Haemocytes. Front. Physiol. 2019, 10, 156. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Navlakha, S.; Gitter, A.; Bar-Joseph, Z. A network-based approach for predicting missing pathway interactions. PLoS Comput. Biol. 2012, 8, e1002640. [Google Scholar] [CrossRef] [PubMed]
  43. Miyashita, K.; Kawakami, K.; Nakada, M.; Mai, W.; Shakoori, A.; Fujisawa, H.; Hayashi, Y.; Hamada, J.; Minamoto, T. Potential therapeutic effect of glycogen synthase kinase 3beta inhibition against human glioblastoma. Clin. Cancer Res. 2009, 15, 887–897. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Miyashita, K.; Nakada, M.; Shakoori, A.; Ishigaki, Y.; Shimasaki, T.; Motoo, Y.; Kawakami, K.; Minamoto, T. An emerging strategy for cancer treatment targeting aberrant glycogen synthase kinase 3 beta. Anticancer Agents Med. Chem. 2009, 9, 1114–1122. [Google Scholar] [CrossRef]
  45. Toraih, E.A.; El-Wazir, A.; Abdallah, H.Y.; Tantawy, M.A.; Fawzy, M.S. Deregulated MicroRNA Signature Following Glioblastoma Irradiation. Cancer Control 2019, 26, 1073274819847226. [Google Scholar] [CrossRef] [PubMed]
  46. Toraih, E.A.; Ibrahiem, A.T.; Fawzy, M.S.; Hussein, M.H.; Al-Qahtani, S.A.M.; Shaalan, A.A.M. MicroRNA-34a: A Key Regulator in the Hallmarks of Renal Cell Carcinoma. Oxid. Med. Cell. Longev. 2017, 2017, 3269379. [Google Scholar] [CrossRef] [Green Version]
  47. Toraih, E.A.; Mohammed, E.A.; Farrag, S.; Ramsis, N.; Hosny, S. Pilot Study of Serum MicroRNA-21 as a Diagnostic and Prognostic Biomarker in Egyptian Breast Cancer Patients. Mol. Diagn. Ther. 2015, 19, 179–190. [Google Scholar] [CrossRef]
  48. Ma, R.; Ma, Z.G.; Zhen, C.L.; Shen, X.; Li, S.L.; Li, L.; Zheng, Y.F.; Dong, D.L.; Sun, Z.J. Design, synthesis and characterization of poly (methacrylic acid-niclosamide) and its effect on arterial function. Mater. Sci. Eng. C Mater. Biol. Appl. 2017, 77, 352–359. [Google Scholar] [CrossRef]
  49. Daina, A.; Michielin, O.; Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep. 2017, 7, 42717. [Google Scholar] [CrossRef] [Green Version]
  50. Lipinski, C.A. Lead-and drug-like compounds: The rule-of-five revolution. Drug Discov. Today Technol. 2004, 1, 337–341. [Google Scholar] [CrossRef]
  51. Martin, Y.C. A bioavailability score. J. Med. Chem. 2005, 48, 3164–3170. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. NSC765689 successfully met the required drug-likeness criteria. (A) Flow diagram of LCC09 and NSC765689 chemical and structural synthesis with reference to previous studies. (B) Bioavailability radar (BA), displaying the six physicochemical properties of absorption of the NSC765689 compound. The pink area represents the optimal range for each property. The accompanying table displays physicochemical properties of lipophilicity of XLOGP3 between −0.7 and 5.0; molecular weight of ≤500 g/mol; polarity of TPSA ≤ 140 Å (angstrom); flexibility of ≤9 rotatable bonds; and solubility of log S between 0 and 6, and saturation sp3 hybridization of ≤0.25.
Figure 1. NSC765689 successfully met the required drug-likeness criteria. (A) Flow diagram of LCC09 and NSC765689 chemical and structural synthesis with reference to previous studies. (B) Bioavailability radar (BA), displaying the six physicochemical properties of absorption of the NSC765689 compound. The pink area represents the optimal range for each property. The accompanying table displays physicochemical properties of lipophilicity of XLOGP3 between −0.7 and 5.0; molecular weight of ≤500 g/mol; polarity of TPSA ≤ 140 Å (angstrom); flexibility of ≤9 rotatable bonds; and solubility of log S between 0 and 6, and saturation sp3 hybridization of ≤0.25.
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Figure 2. Glycogen synthase kinase 3β (GSK3B)/β-Catenin/signal transduction and activator of transcription 3 (STAT3)/cluster of differentiation 44 (CD44) mRNA levels were highly expressed in glioblastoma multiforme (GBM) tumor cohorts compared to normal samples with significant p values (A). When all four genes were combined for analysis, there were positive correlations of STAT3 with GSK3β, CTNNB1 (β-Catenin) with GSK3β, STAT3 with CD44, and CTNNB1 with CD44 in GBM patients (r = 0.28~0.58) (B). The statistical significance of differentially expressed genes was evaluated using the Wilcoxon test. * p < 0.05.
Figure 2. Glycogen synthase kinase 3β (GSK3B)/β-Catenin/signal transduction and activator of transcription 3 (STAT3)/cluster of differentiation 44 (CD44) mRNA levels were highly expressed in glioblastoma multiforme (GBM) tumor cohorts compared to normal samples with significant p values (A). When all four genes were combined for analysis, there were positive correlations of STAT3 with GSK3β, CTNNB1 (β-Catenin) with GSK3β, STAT3 with CD44, and CTNNB1 with CD44 in GBM patients (r = 0.28~0.58) (B). The statistical significance of differentially expressed genes was evaluated using the Wilcoxon test. * p < 0.05.
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Figure 3. Prediction of shorter survival times using glycogen synthase kinase 3β (GSK3β)/β-Catenin/signal transduction and activator of transcription 3 (STAT3)/cluster of differentiation 44 (CD44) signatures. Elevated mRNA levels of GSK3β/β-Catenin/STAT3/CD44 were found to be associated with shorter survival times in patients with glioblastoma multiforme (GBM) (GBM Metabase and SurvExpress). p values are indicated in each panel.
Figure 3. Prediction of shorter survival times using glycogen synthase kinase 3β (GSK3β)/β-Catenin/signal transduction and activator of transcription 3 (STAT3)/cluster of differentiation 44 (CD44) signatures. Elevated mRNA levels of GSK3β/β-Catenin/STAT3/CD44 were found to be associated with shorter survival times in patients with glioblastoma multiforme (GBM) (GBM Metabase and SurvExpress). p values are indicated in each panel.
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Figure 4. STRING database predicted protein-protein interacting networks (PINs) among the oncogenic markers of glycogen synthase kinase 3β (GSK3B)/β-Catenin/signal transduction and activator of transcription 3 (STAT3)/cluster of differentiation 44 (CD44). (A) The network has four initial proteins formed and was further expanded by an additional 30 nodes/protein, by using the “more” key on the STRING interface. Furthermore, a PIN enrichment p value of <1.0 × 10−16 was obtained from the network, with a clustering coefficient of 0.711. The accompanying table shows all other interacting proteins with GSK3β/β-Catenin/STAT3/CD44, and the confidence cutoff value representing interaction links was adjusted to 0.900 as the highest scoring link. (B) Biological processes and (C) KEGG pathways associated with of GSK3β/β-Catenin/STAT3/CD44 clustering networks.
Figure 4. STRING database predicted protein-protein interacting networks (PINs) among the oncogenic markers of glycogen synthase kinase 3β (GSK3B)/β-Catenin/signal transduction and activator of transcription 3 (STAT3)/cluster of differentiation 44 (CD44). (A) The network has four initial proteins formed and was further expanded by an additional 30 nodes/protein, by using the “more” key on the STRING interface. Furthermore, a PIN enrichment p value of <1.0 × 10−16 was obtained from the network, with a clustering coefficient of 0.711. The accompanying table shows all other interacting proteins with GSK3β/β-Catenin/STAT3/CD44, and the confidence cutoff value representing interaction links was adjusted to 0.900 as the highest scoring link. (B) Biological processes and (C) KEGG pathways associated with of GSK3β/β-Catenin/STAT3/CD44 clustering networks.
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Figure 5. Prediction of expression levels and long-term survival using miRNA-135b-5p and glycogen synthase kinase 3β (GSK3β) in glioblastoma multiforme (GBM). (A) The miRDB prediction tool revealed low expression of miR-135b in tumor cohorts compared to adjacent normal tissue samples with p < 0.05. (B) miRNA-135b-5p expression was low in GBM patients (miRondala-DB). (C) Using targetscan online software, we identified the miR-135 -5p site on the 3′-untranslated region of GSK3β mRNA, which predicted GSK3β as a target gene of miR-135b-5P.
Figure 5. Prediction of expression levels and long-term survival using miRNA-135b-5p and glycogen synthase kinase 3β (GSK3β) in glioblastoma multiforme (GBM). (A) The miRDB prediction tool revealed low expression of miR-135b in tumor cohorts compared to adjacent normal tissue samples with p < 0.05. (B) miRNA-135b-5p expression was low in GBM patients (miRondala-DB). (C) Using targetscan online software, we identified the miR-135 -5p site on the 3′-untranslated region of GSK3β mRNA, which predicted GSK3β as a target gene of miR-135b-5P.
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Figure 6. NSC765689 could serve as a potential small-molecule inhibitor of multi-oncogenic proteins. (A) 3D structures of glycogen synthase kinase 3β (GSK3β)/β-Catenin/signal transduction and activator of transcription 3 (STAT3)/cluster of differentiation 44 (CD44)-NSC765689 interactions (left). (B) Surface representations of the active-site flap of the GSK3β/β-Catenin/STAT3/CD44-NSC765689 complex in the binding pocket (middle). (C) 2D structural analysis of amino acids and distances of GSK3β/β-Catenin/STAT3/CD44-NSC765689 complexes using Discovery Studio (right). Accompanying Table shows binding affinities, ligand-receptor interactions, atoms involved in H-bonding, bond distances between NSC765689 and target proteins, and interacting amino acid residues.
Figure 6. NSC765689 could serve as a potential small-molecule inhibitor of multi-oncogenic proteins. (A) 3D structures of glycogen synthase kinase 3β (GSK3β)/β-Catenin/signal transduction and activator of transcription 3 (STAT3)/cluster of differentiation 44 (CD44)-NSC765689 interactions (left). (B) Surface representations of the active-site flap of the GSK3β/β-Catenin/STAT3/CD44-NSC765689 complex in the binding pocket (middle). (C) 2D structural analysis of amino acids and distances of GSK3β/β-Catenin/STAT3/CD44-NSC765689 complexes using Discovery Studio (right). Accompanying Table shows binding affinities, ligand-receptor interactions, atoms involved in H-bonding, bond distances between NSC765689 and target proteins, and interacting amino acid residues.
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Figure 7. Docking results of LCC09 with glycogen synthase kinase 3β (GSK3β)/β-Catenin/signal transduction and activator of transcription 3 (STAT3)/cluster of differentiation 44 (CD44). (A) 3D structures of GSK3β/β-Catenin/STAT3/CD44-LCC09 interactions (left). (B) Surface representations of the active-site flap of the GSK3β/β-Catenin/STAT3/CD44-LCC09 complex in the binding pocket (middle). (C) 2D structural analysis of amino acids and distances of the GSK3β/β-Catenin/STAT3/CD44-LCC09 complex using Discovery Studio (right). Accompanying table shows binding affinities, ligand-receptor interactions, atoms involved in H-bond distances between LCC09 and target proteins, and the interacting amino acid residues.
Figure 7. Docking results of LCC09 with glycogen synthase kinase 3β (GSK3β)/β-Catenin/signal transduction and activator of transcription 3 (STAT3)/cluster of differentiation 44 (CD44). (A) 3D structures of GSK3β/β-Catenin/STAT3/CD44-LCC09 interactions (left). (B) Surface representations of the active-site flap of the GSK3β/β-Catenin/STAT3/CD44-LCC09 complex in the binding pocket (middle). (C) 2D structural analysis of amino acids and distances of the GSK3β/β-Catenin/STAT3/CD44-LCC09 complex using Discovery Studio (right). Accompanying table shows binding affinities, ligand-receptor interactions, atoms involved in H-bond distances between LCC09 and target proteins, and the interacting amino acid residues.
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Figure 8. Docking results of AZD1080 with glycogen synthase kinase 3β (GSK3β)/β-Catenin/signal transduction and activator of transcription 3 (STAT3). (A) 3D structures of GSK3β/β-Catenin/STAT3 -AZD1080 interactions (left). (B) Surface representations of the active-site flap of the GSK3β/β-Catenin/STAT3 -AZD1080 complex in the binding pocket (middle). (C) 2D structural analysis of amino acid and distances of the GSK3β/β-Catenin/STAT3 -AZD1080 complex using Discovery Studio (right). Accompanying table shows binding affinities, ligand-receptor interactions, atoms involved in H-bonding, bond distances between AZD1080 and target proteins, and interacting amino acid residues.
Figure 8. Docking results of AZD1080 with glycogen synthase kinase 3β (GSK3β)/β-Catenin/signal transduction and activator of transcription 3 (STAT3). (A) 3D structures of GSK3β/β-Catenin/STAT3 -AZD1080 interactions (left). (B) Surface representations of the active-site flap of the GSK3β/β-Catenin/STAT3 -AZD1080 complex in the binding pocket (middle). (C) 2D structural analysis of amino acid and distances of the GSK3β/β-Catenin/STAT3 -AZD1080 complex using Discovery Studio (right). Accompanying table shows binding affinities, ligand-receptor interactions, atoms involved in H-bonding, bond distances between AZD1080 and target proteins, and interacting amino acid residues.
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Figure 9. Sensitivity of the NCI-60 cancer cell lines to inhibitory activities of NSC765689. An initial single dose of 10 μM was used on each cell line to test the compound. The zero on the x-axis indicates the mean percentage of cell growth of the cell lines. The percentage growth of each cell line relative to the mean is represented by horizontal bars, which are elongated to the right side indicating greater sensitivity and to the left side indicating reduced sensitivity.
Figure 9. Sensitivity of the NCI-60 cancer cell lines to inhibitory activities of NSC765689. An initial single dose of 10 μM was used on each cell line to test the compound. The zero on the x-axis indicates the mean percentage of cell growth of the cell lines. The percentage growth of each cell line relative to the mean is represented by horizontal bars, which are elongated to the right side indicating greater sensitivity and to the left side indicating reduced sensitivity.
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Figure 10. Dose-response curves of the cytotoxic activity of NSC765689 against central nervous system (CNS) cancer cell lines. Growth percentage value of 100 represents the growth of untreated cells, while a value of 0 represents no net growth throughout the period of the experiment, and a value of -100 indicates that all of the cells had been killed by the end of the experiment.
Figure 10. Dose-response curves of the cytotoxic activity of NSC765689 against central nervous system (CNS) cancer cell lines. Growth percentage value of 100 represents the growth of untreated cells, while a value of 0 represents no net growth throughout the period of the experiment, and a value of -100 indicates that all of the cells had been killed by the end of the experiment.
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Table
Table 1. NCI synthetic compounds and standard anticancer agent sharing similar anti-cancer fingerprints and mechanistic correlation with NSC765689.
Table 1. NCI synthetic compounds and standard anticancer agent sharing similar anti-cancer fingerprints and mechanistic correlation with NSC765689.
NSC-Synthetic Compounds NSC-Standard Agents
RankrCCLCTarget NSCTarget DescriptorMWrCCLCTarget Descriptor
10.5959757391DICHLOROPHENE269.12 g/mol0.457bryostatin 1
20.595929027TETRACHLOROCATECHOL247.92 g/mol0.37584-ipomeanol
30.5859825282CPI455314.77 g/mol0.3658Pentamethyl melamine
40.58567684215-CHLORO-2-(3-(3,4,5-TRIMETHOX383.01 g/mol0.3346rhizoxin
50.5854406472OCTOXYNOL 9(USAN)250.19 g/mol0.2958gallium nitrate
60.57587670633-(1-(3,4-DICHLOROPHENYL)-2,5-308.89 g/mol0.2858flavoneacetic acid
70.5759767322RO3200934-000371.41 g/mol 0.2746didemnin B
80.56567684065-FLUORO-2-(3-(3,4,5-TRIMETHOX470.17 g/mol0.2758ARA-6-MP
90.5656755806XL147448.52 g/mol0.2756tamoxifen
100.5656756322GW701032X413.51 g/mol 0.2754doxorubicin (Adriamycin)
110.5659755984PNU-74654320.34 g/mol0.2657pibenzimol hydrochloride
120.5556772889MK-2048461.87 g/mol0.2646cyanomorpholino-ADR
130.5556750742(E)-3-(6-(4-METHOXYPHENYL)-2-M177.18 g/mol0.2646S-trityl-L-cysteine
140.5452328477BENZAMIDE, N-(3-CHLORO-2-METHY134.08 g/mol0.2558chromomycin A3
150.5449401443PHLORETIN274.26 g/mol0.2558mitramycin
r—Pearson’s correlation coefficient: value ranges between −1 and 1 (values becomes more significant as they increase above 1), CCLC—common cell lines count cell counts, MW—Molecular weight (g/mol).
Table
Table 2. Showing common names, Uniprot and ChEMBL IDs as well as target classes of specific. protein targets of NSC765689.
Table 2. Showing common names, Uniprot and ChEMBL IDs as well as target classes of specific. protein targets of NSC765689.
TargetCommon
Name		Uniprot
ID		ChEMBL IDTarget Class
Cyclin-dependent kinase 9CDK9P50750CHEMBL3116Kinase
Tyrosine-protein kinase JAK3JAK3P52333CHEMBL2148Kinase
6-phosphofructo-2-kinase/fructose-2,6- bisphosphatase 3PFKFB3Q16875CHEMBL2331053Enzyme
Epidermal growth factor receptor erbB1EGFRP00533CHEMBL203Kinase
Glycogen synthase kinase-3 betaGSK3BP49841CHEMBL262Kinase
MAP kinase p38 alphaMAPK14Q16539CHEMBL260Kinase
Cyclin-dependent kinase 2CDK2P24941CHEMBL301Kinase
Cyclin-dependent kinase 1CDK1P06493CHEMBL308Kinase
Hexokinase type IVGCKP35557CHEMBL3820Enzyme
Tankyrase-2TNKS2Q9H2K2CHEMBL6154Enzyme
Tankyrase-1TNKSO95271CHEMBL6164Enzyme
Fructose-1,6- bisphosphataseFBP1P09467CHEMBL3975Enzyme
11-beta-hydroxysteroidHSD11B1P28845CHEMBL4235Enzyme
Poly [ADP-ribose]PARP1P09874CHEMBL3105Enzyme
NicotinamideNAMPTP43490CHEMBL1744525Enzyme
Signal transducer and activator of transcription 3STAT3P40763CHEMBL4026Transcription factor
PI3-kinase p110-delta subunitPIK3CDO00329CHEMBL3130Enzyme
ThrombinF2P00734CHEMBL204Protease
Table
Table 3. Cytotoxic activities of NSC765689 against 60 NCI human cancer cell lines. GI50, 50% growth inhibition; TGI, total growth inhibition; LC50, 50% loss of cells.
Table 3. Cytotoxic activities of NSC765689 against 60 NCI human cancer cell lines. GI50, 50% growth inhibition; TGI, total growth inhibition; LC50, 50% loss of cells.
Panel/Cell line (μM)NSC765689
GI50TGILC50
Leukemia
CCRF-CEM0.6520.8615.70
HL-60(TB)0.5210.80>100
K-5622.4058.2>100
MOLT-40.3017.743.90
RPMI-82260.6938.047.90
Non-Small Cell Lung Cancer
A549/ATCC1.8710.80>100
EKVX1.886.9496.20
HOP-621.240.67>100
HOP-920.555.48>100
NCI-H2260.412.7753.40
NCI-H231.808.91>100
NCU-H322M0.8512.30>100
NCI-H46-0.362.3143.40
Colon cancer
COLON 2055.1317.9053.30
HCC29982.887.9150.40
HCT-1161.146.0138.80
HCT-151.2010.70>100
HT-294.0515.10>100
KM121.737.31>100
SW-6202.0831.10<100
CNS cancer
SF-2951.5318.908.01
SF-5391.813.8155.50
SNB-191.518.06>100
SNB-750.6117.40>100
U2511.034.9961.40
Melanoma
LOX IMVI1.233.918.26
MALME-3M0.382.71>100
M140.249.96>100
MDA-MB-4351.685.13>100
SK-MEL-20.391.837.81
SK-MEL-280.434.76>100
SK-MEL-50.230.103.08
UACC-2570.733.71>100
UACC-620.361.9135.80
Ovarian cancer
IGROV10.8821.50>100
OVCAR-30.5512.50>100
OVCAR-40.385.74>100
OVCAR-54.76100.00>100
OVCAR-81.7712.00>100
NCI/ADR-RES0.541.00>100
SK-OV-30.750.70>100
Renal cancer
786-02.942.9441.40
A4890.400.4015.80
ACHN1.181.1862.00
CAKI-12.352.35>100
RXF 3931.101.1092.30
SN12C0.920.92>100
TK-102.892.89>100
UO-311.621.62>100
Prostate cancer
PC-31.141.14>100
DU-1452.572.57>100
Breast cancer
MCF70.364.26>100
MDA-MB-231/ATCC1.284.8968.10
HS 578-T0.665.28>100
BT-5490.290.99>100
T-47D0.372.88>100
MDA-MB-4681.173.21>100
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Mokgautsi, N.; Wen, Y.-T.; Lawal, B.; Khedkar, H.; Sumitra, M.R.; Wu, A.T.H.; Huang, H.-S. An Integrated Bioinformatics Study of a Novel Niclosamide Derivative, NSC765689, a Potential GSK3β/β-Catenin/STAT3/CD44 Suppressor with Anti-Glioblastoma Properties. Int. J. Mol. Sci. 2021, 22, 2464. https://doi.org/10.3390/ijms22052464

AMA Style

Mokgautsi N, Wen Y-T, Lawal B, Khedkar H, Sumitra MR, Wu ATH, Huang H-S. An Integrated Bioinformatics Study of a Novel Niclosamide Derivative, NSC765689, a Potential GSK3β/β-Catenin/STAT3/CD44 Suppressor with Anti-Glioblastoma Properties. International Journal of Molecular Sciences. 2021; 22(5):2464. https://doi.org/10.3390/ijms22052464

Chicago/Turabian Style

Mokgautsi, Ntlotlang, Ya-Ting Wen, Bashir Lawal, Harshita Khedkar, Maryam Rachmawati Sumitra, Alexander T. H. Wu, and Hsu-Shan Huang. 2021. "An Integrated Bioinformatics Study of a Novel Niclosamide Derivative, NSC765689, a Potential GSK3β/β-Catenin/STAT3/CD44 Suppressor with Anti-Glioblastoma Properties" International Journal of Molecular Sciences 22, no. 5: 2464. https://doi.org/10.3390/ijms22052464

APA Style

Mokgautsi, N., Wen, Y.-T., Lawal, B., Khedkar, H., Sumitra, M. R., Wu, A. T. H., & Huang, H.-S. (2021). An Integrated Bioinformatics Study of a Novel Niclosamide Derivative, NSC765689, a Potential GSK3β/β-Catenin/STAT3/CD44 Suppressor with Anti-Glioblastoma Properties. International Journal of Molecular Sciences, 22(5), 2464. https://doi.org/10.3390/ijms22052464

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