*Article* **Identification of DPP4/CTNNB1/MET as a Theranostic Signature of Thyroid Cancer and Evaluation of the Therapeutic Potential of Sitagliptin**

**Sheng-Yao Cheng 1,†, Alexander T. H. Wu 2,3,4,5,† , Gaber El-Saber Batiha <sup>6</sup> , Ching-Liang Ho <sup>7</sup> , Jih-Chin Lee <sup>1</sup> , Halimat Yusuf Lukman <sup>8</sup> , Mohammed Alorabi <sup>9</sup> , Abdullah N. AlRasheedi <sup>10</sup> and Jia-Hong Chen 7,\***


**Simple Summary:** In recent years, the incidence of thyroid cancer has been increasing globally, with papillary thyroid cancer (PTCa) being the most prevalent pathological type. Although PTCa has been regarded to be slow growing and has a good prognosis, in some cases, PTCa can be aggressive and progress despite surgery and radioactive iodine treatment. Therefore, searching for new targets and therapies is required. We utilized bioinformatics analyses to identify critical theranostic markers for PTCa. We found that DPP4/CTNNB1/MET is an oncogenic signature that is overexpressed in PTCa and associated with disease progression, distant metastasis, treatment resistance, immunoevasive phenotypes, and poor clinical outcomes. Interestingly, our in silico molecular docking results revealed that sitagliptin, an antidiabetic drug, has strong affinities and potential for targeting DPP4/CTNNB1/MET signatures, even higher than standard inhibitors of these genes. Collectively, our findings suggest that sitagliptin could be repurposed for treating PTCa.

**Abstract:** In recent years, the incidence of thyroid cancer has been increasing globally, with papillary thyroid cancer (PTCa) being the most prevalent pathological type, accounting for approximately 80% of all cases. Although PTCa has been regarded to be slow growing and has a good prognosis, in some cases, PTCa can be aggressive and progress despite surgery and radioactive iodine treatment. In addition, most cancer treatment drugs have been shown to be cytotoxic and nonspecific to cancer cells, as they also affect normal cells and consequently cause harm to the body. Therefore, searching for new targets and therapies is required. Herein, we explored a bioinformatics analysis to identify important theranostic markers for THCA. Interestingly, we identified that the *DPP4/CTNNB1/MET* gene signature was overexpressed in PTCa, which, according to our analysis, is associated with immuno-invasive phenotypes, cancer progression, metastasis, resistance, and unfavorable clinical outcomes of thyroid cancer cohorts. Since most cancer drugs were shown to exhibit cytotoxicity

**Citation:** Cheng, S.-Y.; Wu, A.T.H.; Batiha, G.E.-S.; Ho, C.-L.; Lee, J.-C.; Lukman, H.Y.; Alorabi, M.; AlRasheedi, A.N.; Chen, J.-H. Identification of DPP4/CTNNB1/ MET as a Theranostic Signature of Thyroid Cancer and Evaluation of the Therapeutic Potential of Sitagliptin. *Biology* **2022**, *11*, 324. https:// doi.org/10.3390/biology11020324

Academic Editors: Shibiao Wan, Yiping Fan, Chunjie Jiang and Shengli Li

Received: 11 December 2021 Accepted: 14 February 2022 Published: 17 February 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

and to be nonspecific, herein, we evaluated the anticancer effects of the antidiabetic drug sitagliptin, which was recently shown to possess anticancer activities, and is well tolerated and effective. Interestingly, our in silico molecular docking results exhibited putative binding affinities of sitagliptin with *DPP4/CTNNB1/MET* signatures, even higher than standard inhibitors of these genes. This suggests that sitagliptin is a potential THCA therapeutic, worthy of further investigation both in vitro and in vivo and in clinical settings.

**Keywords:** sitagliptin; thyroid cancer (THCA); papillary thyroid cancer (PTCa); thyroidectomy; metastasis; drug resistance

#### **1. Introduction**

Thyroid cancer (THCA) is the most prevalent malignancy of the endocrine system, and the 9th most common cancer in the world [1,2], accounting for approximately 600,000 newly diagnosed cases annually on a global scale [3], with high rates of morbidity reported in recent years [4]. THCA is divided into various subtypes, including anaplastic thyroid cancer (ATC), papillary thyroid carcinoma (PTCa), and follicular thyroid carcinoma (FTC), with PTCa being the most prevalent, as it accounts for approximately 85% of THCA [5,6]. PTC and FTC are well-differentiated thyroid cancers with an optimal prognosis of about 10 years disease-specific survival [7]. However, the ATC is poorly differentiated with proliferative stem-cell-like properties, resistance to therapies, and accounts for the majority of thyroid-cancer-related deaths [8,9]. The rapid increase in thyroid cancer, particularly PTCa, has been accredited to the availability and sensitive use of ultrasonography and other diagnostic imaging modalities [10,11], which have likely led to a massive detection and diagnosis of a large reservoir of subclinical, indolent lesions of the thyroid [12,13]. Studies have also implicated obesity, hormonal imbalance, metabolic syndromes, and environmental pollutants in the development of PTCa [14].

Patients with PTCa usually show good clinical outcomes compared with other cancers; however, there is also a very high rate of relapse post-treatment, leading to distant metastasis [15,16]. About 11% of patients with PTC present with distant metastases outside the neck and mediastinum [17]. Moreover, long-term survival outcomes for aggressive PTC subgroups exhibit heterogeneous clinical behavior and a wide range of mortality risks, suggesting that treatment should be tailored to specific histologic subtypes [18]. The diagnostic criteria for PTC allow it to demonstrate various histological features and growth patterns; different variants of PTCa are recognized, including classic, microcarcinoma, encapsulated, follicular, diffuse sclerosing, tall cell, columnar cell, cribriform-morular, hobnail, solid, oncocytic, spindle cell, clear cell, and Warthin-like variants [19]. However, among these variants, tall cell, columnar cells, and hobnail variants are of undoubted clinical significance, since they are aggressive variants associated with aggressive clinicopathological features and worse prognosis than for classic and encapsulated PTC [20–22].

Surgery, endocrine therapy, and radioiodine therapy are well-known therapy regimens for PTCa, offering a good prognosis; however, the aggressive variants of PTCa progress despite surgery and radioactive iodine treatment [23]. In addition, tumor recurrence in PTCa is associated with therapeutic resistance which increases the death toll in patients [24–26]. Unfortunately, an upsurge in the incidence of aggressive PTCs was observed at a rate higher than that seen in well-differentiated PTCs or anaplastic thyroid carcinomas (ATCs) in the past two decades in a study of a large cohort of thyroid cancers [22]; therefore, there is an urgent need to identify novel diagnostic and prognostic molecular biomarkers that could also be used as molecular targets for the development of new drugs or in repurposing existing drugs for the treatment of PTCa.

Increasing evidence shows that dipeptidyl aminopeptidase IV (*DPP IV*) is associated with cancer development and progression [27,28]; DPP4 is an adenosine deaminase complex protein, and was demonstrated to be upregulated in THCA, particularly in PTCa,

and is associated with tumor aggression and poor prognoses [29–31]. Moreover, high expression of *DPP4* was shown to promote distance metastasis and stemness in esophageal adenocarcinoma and colorectal cancer [32,33]. However, the prognostic role of *DPP4* expression and its role in THCA metastasis remains elusive [7,29,31]. Studies have shown that DPP4 and b-catenin crosstalk to regulate critical cellular processes, including motility and invasion [34]. A study involving lung cancer patients has revealed that the expression levels of β-catenin correlate with DPP4 expression [35] and contributed to tumor metastasis [34,36]. An experimental study has also reported that activating mutation of *Ctnnb1* induced DPP4 overexpression in epidermal keratinocytes of LRIG1<sup>+</sup> stem cells [37]. Research has illuminated that inhibitors of DPP4 exert their therapeutic effect via modulation of the Wnt/β-catenin signaling pathway [38]. Sitagliptin, an inhibitor of DPP4, has also been reported to provide renal protection via inhibition of the tubulointerstitial Wnt/β-catenin signaling pathway in diabetic nephropathy [39].

Accumulating studies demonstrated a pivotal correlation between distant metastasis in PTCa and *MET* (MET proto-oncogenic receptor tyrosine kinase) [40]. Approximately 70% of PTCas were reported to overexpress the *MET* gene, and it is associated with poor prognoses [41]. In addition, Rossana et al. also demonstrated that higher expression levels of *MET* in PTCa promoted cancer growth and distance metastasis [42,43]. *MET* is a transmembrane tyrosine kinase identified as a high-affinity receptor for hepatocyte growth factor (HGF), and both *MET* and *HGF* were demonstrated to be expressed in PTCa [42], and consequently promote progression and secondary metastasis [44]. Additionally, *MET* was shown to activate β-catenin (*CTNNB1*), an important component of the canonical Wnt pathway [45,46]. *CTNNB1* was recently reported to be mutated in PTCa, and to ultimately promote cancer development and stemness [47,48]. Moreover, upregulated *MET* was also demonstrated to regulate the expression of mitogen-activated protein kinase (*MAPK*), phosphatidylinositol 3-kinase (*PI3K*)*/AKT*, signal transducer and activator of transcription 3 (*STAT3*), and nuclear factor (*NF*)*-κB* pathways in THCA [40,49]. This suggests that *MET* is a crucial target gene in THCA, and worthy of further investigation. To date, most drugs used for cancer treatment are cytotoxic and usually not specific to cancer cells, but also affect normal cells; therefore, there is still a huge gap in finding more sensitive and specific drugs for cancer. Recent studies suggested an association between cancer occurrence and antidiabetic medicaments. Sitagliptin is a standard inhibitor of *DPP4*, widely used for treating diabetes, and was shown to possess anticancer activities, as well as being efficacious and well tolerated [50]. In the present study, we predicted the potential anticancer activities of sitagliptin as a target for *DPP4/CTNNB1/MET* oncogenic signatures, which are overexpressed in THCA.

#### **2. Materials and Methods**

#### *2.1. Microarray Data Acquisition and Identification of Differentially Expressed Genes (DEGs)*

Gene expressions of four THCA datasets (GEO3467, GEO36787, GEO6004, and GEO33630) were extracted from the NCBI gene expression omnibus. The acquired datasets were further analyzed using GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/ accessed on 5 September 2021), and results contained DEG profiles from THCA patients compared to normal samples. To control the false discovery rate (FDR), the Benjamini–Hochberg adjustment was applied to *p* values (adjusted (adj.) *p* values), to moderate the balance between detection of significant genes and possible false-positive values. The fold-change (FC) threshold was set to 1.5, and adj. *p* < 0.05 was considered statistically significant. Venn diagrams were constructed using the Bioinformatics and Evolutionary Genomics (BEG) online tool (http://bioinformatics.psb. ugent.be/webtools/Venn/ accessed on 6 September 2021).

#### *2.2. Differential Expression of the THCA Gene Hub*

Differential expressions of THCA gene profiles between tumor tissues and normal adjacent tissues of the Cancer Genome Atlas (TCGA) database were analyzed using UALCAN (http://ualcan.path.uab.edu accessed on 12 September 2021), an online web portal used to

identify gene expression levels between primary tumors compared to normal tissue samples [51]. Moreover, we explored the cBioPortal online web tool (https://www.cbioportal.org accessed on 19 September 2021), which categorizes gene alterations based on percentages of individual genes due to amplification [52]. For further analysis, we used the cBioPortal correlation sub-tool to determine gene expression correlations with positive Spearman and Pearson correlation coefficients with *p* < 0.05 as statistically significant.

## *2.3. Comparisons of DPP4/CTNNB1/MET Expressions in Normal, Primary, and Metastatic Tumor of Thyroid Cancer Cohorts*

To compare expression levels of the *DPP4/CTNNB1/MET* oncogenes among normal, tumor, and metastatic tissues, we explored the tumor, normal, and metastatic plot (TNMplot), (https://tnmplot.com/analysis/ accessed on 21 September 2021), an RNA-sequence-based rapid analysis, which is used to compare data of selected genes [53]. Data were compared using the Kruskal–Wallis test, which is a method used to test samples originally from the same distribution of specimens, followed by Dunn's test, which assesses the significance of gene expressions in promoting THCA tumor metastasis, with *p* < 0.05 considered statistically significant.

#### *2.4. Interaction Network and Gene Enrichment Analysis*

An interaction network analysis was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING, https://string-db.org/ accessed on 25 September 2021) database [54], and GeneMANIA [55] (http://genemania.org/data accessed on 28 September 2021), which are online web tools developed to analyze interaction networks. The STRING database was used under a high confidence of 0.700, and protein enrichment of *<sup>p</sup>* < 6.0 <sup>×</sup> <sup>10</sup>−<sup>03</sup> was obtained. Interactions among genes were analyzed according to correlations based on experimental data (pink), gene neighborhoods (green), gene fusion (red), gene co-occurrences (blue), and gene co-expression (black). Moreover, we explored the Network Analyst user-friendly online tool (https://www.networkanalyst.ca/ accessed on 5 October 2021) to analyze co-expressed gene enrichment from the biological processes databases; herein we applied the Igraph R package visualization tool for analysis [56]. Furthermore, gene ontology (GO), biological processes (BPs), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were analyzed using FunRich software (http://www.funrich.org accessed on 9 October 2021), an open access, stand-alone functional enrichment and network analytical tool [57].
