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(DPP) Family Genes in Clinical Breast Cancer Patients Via an Integrated Bioinformatics Approach

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(DPP) Family Genes in Clinical Breast Cancer Patients Via an Integrated Bioinformatics Approach diagnostics Article Identification of Dipeptidyl Peptidase (DPP) Family Genes in Clinical Breast Cancer Patients via an Integrated Bioinformatics Approach Tak-Kee Choy 1, Chih-Yang Wang 2,3, Nam Nhut Phan 4, Hoang Dang Khoa Ta 2,3, Gangga Anuraga 2,5 , Yen-Hsi Liu 6, Yung-Fu Wu 7, Kuen-Haur Lee 2,3,8 , Jian-Ying Chuang 9,10,11 and Tzu-Jen Kao 9,10,11,* 1 Department of Surgery, Division of Gastroenterologic Surgery, Yuan’s General Hospital, Kaohsiung 80249, Taiwan; [email protected] 2 PhD Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; [email protected] (C.-Y.W.); [email protected] (H.D.K.T.); [email protected] (G.A.); [email protected] (K.-H.L.) 3 Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan 4 NTT Institute of Hi-Technology, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam; [email protected] 5 Department of Statistics, Faculty of Science and Technology, PGRI Adi Buana University, East Java, Surabaya 60234, Indonesia 6 School of Chinese Medicine for Post-Baccalaureate, I-Shou University, Kaohsiung 82445, Taiwan; [email protected] 7 National Defense Medical Center, Department of Medical Research, School of Medicine, Tri-Service General Hospital, Taipei 11490, Taiwan; [email protected] Citation: Choy, T.-K.; Wang, C.-Y.; 8 Cancer Center, Wan Fang Hospital, Taipei Medical University, Taipei 11031, Taiwan Phan, N.N.; Khoa Ta, H.D.; Anuraga, 9 Graduate Institute of Neural Regenerative Medicine, College of Medical Science and Technology, G.; Liu, Y.-H.; Wu, Y.-F.; Lee, K.-H.; Taipei Medical University, Taipei 11031, Taiwan; [email protected] Chuang, J.-Y.; Kao, T.-J. Identification 10 Research Center of Neuroscience, Taipei Medical University, Taipei 11031, Taiwan of Dipeptidyl Peptidase (DPP) Family 11 TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei 11031, Taiwan Genes in Clinical Breast Cancer * Correspondence: [email protected] Patients via an Integrated Bioinformatics Approach. Diagnostics Abstract: Breast cancer is a heterogeneous disease involving complex interactions of biological 2021, 11, 1204. https://doi.org/ processes; thus, it is important to develop therapeutic biomarkers for treatment. Members of the 10.3390/diagnostics11071204 dipeptidyl peptidase (DPP) family are metalloproteases that specifically cleave dipeptides. This family comprises seven members, including DPP3, DPP4, DPP6, DPP7, DPP8, DPP9, and DPP10; Academic Editor: Antonio Maccio however, information on the involvement of DPPs in breast cancer is lacking in the literature. As such, we aimed to study their roles in this cancerous disease using publicly available databases Received: 27 May 2021 such as cBioportal, Oncomine, and Kaplan–Meier Plotter. These databases comprise comprehensive Accepted: 29 June 2021 high-throughput transcriptomic profiles of breast cancer across multiple datasets. Furthermore, Published: 2 July 2021 together with investigating the messenger RNA expression levels of these genes, we also aimed to correlate these expression levels with breast cancer patient survival. The results showed that DPP3 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in and DPP9 had significantly high expression profiles in breast cancer tissues relative to normal breast published maps and institutional affil- tissues. High expression levels of DPP3 and DPP4 were associated with poor survival of breast cancer iations. patients, whereas high expression levels of DPP6, DPP7, DPP8, and DPP9 were associated with good prognoses. Additionally, positive correlations were also revealed of DPP family genes with the cell cycle, transforming growth factor (TGF)-beta, kappa-type opioid receptor, and immune response signaling, such as interleukin (IL)-4, IL6, IL-17, tumor necrosis factor (TNF), and interferon (IFN)- Copyright: © 2021 by the authors. alpha/beta. Collectively, DPP family members, especially DPP3, may serve as essential prognostic Licensee MDPI, Basel, Switzerland. biomarkers in breast cancer. This article is an open access article distributed under the terms and Keywords: DPP family genes; breast cancer; bioinformatics conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Diagnostics 2021, 11, 1204. https://doi.org/10.3390/diagnostics11071204 https://www.mdpi.com/journal/diagnostics Diagnostics 2021, 11, 1204 2 of 15 1. Introduction Approximately 30% of all cancers that occurred in women in the United States in 2020 were breast cancer [1]. Breast cancer is subtyped by the expression levels of the estrogen receptor (ER, the gene of which is named ESR1), progesterone receptor (PR), and human epidermal growth factor receptor (HER)-2. Many genetic therapies are applied to breast cancer patients, such as fulvestrant [2,3], cyclin-dependent kinase inhibitors [4], aromatase-related inhibitors [5], and histone deacetylase (HDAC) inhibitors [6]. It has been reported that 70% of metastatic breast cancer cases have high expression of B-cell lymphoma 2 (BCL2). Using BCL2 inhibitors for these metastatic cases improved cancer cell apoptosis in a preclinical model of breast cancer [7,8]. Genes that are involved in this biological process are dipeptidyl peptidase (DPP) family genes, extracellular-signal- regulated kinase (ERK), GATA-binding protein 3 (GATA3), signal transducer and activator of transcription 3 (STAT3), phosphatidylinositol 3-kinase (PI3K), and NOTCH [9–12]. Members of the DPP family are metalloproteases that specifically cleave dipeptides, and this family is comprised of seven members, including DPP3, DPP4, DPP6, DPP7, DPP8, DPP9, and DPP10, which are zinc-dependent hydrolases involved in degrading oligopep- tides. Many biological processes involve these proteins, including cancer cell defense against oxidative stress. A previous study demonstrated that DPP3 and DPP7 are highly expressed in multiple myelomas [13]. DPP3 overexpression was positively associated with KEAP1 mutant tumors, and it further promoted lung cancer development [14]. DPP4 attenuates C-X-C motif ligand 10 (CXCL10) and atypical chemokine receptor 2 (ACKR2) activity by regulating N-terminal processing [15], while DPP4 inhibitors may serve as second-line treatment for epithelial ovarian cancer [16]. High expression levels of DPP4 in some types of cancer patients can increase susceptibility to severe acute respiratory syndrome coronavirus (SARS-CoV)-2 infection and further cause cytokine storms [17]. DPP6 promoter activity was significantly higher in pancreatic ductal adenocarcinoma tissues compared to normal tissues [18]. Knockdown of DPP7 increased apoptosis by upregulating Bax–Bcl2 signaling in the HepG2 liver cancer cell line [19]. NLR family pyrin domain-containing 1 (NLRP1) can interact with DPP8 and DPP9, which can serve as a checkpoint for activating the NLRP1 inflammasome [20]. A DPP8 and DPP9 inhibitor can promote apoptosis by activating poly(ADP ribose) polymerase (PARP) and caspase-3 in multiple myelomas [21]. DPP10 inhibits colon cancer stem cell proliferation by regulating microRNAs such as miR-127-3p [22]. Although correlations between breast cancer and DPP family members0 messenger (m)RNA expression levels still remain unclear, it is important to investigate this corre- lation with a comprehensive, holistic approach. It is well known that high-throughput technologies provide thousands to millions of data points from a single run, making them highly suitable tools for rapidly and efficiently screening potential biomarkers [23–25]. Significant alterations in transcriptomic levels of genes imply their roles in a certain disease, such as oncogenic or tumor suppressors in cancerous diseases [26]. Utilizing this concept, we queried publicly available transcriptomic databases for DPP mRNA expression levels in many breast cancer datasets, including multiple breast cancer subtypes. Furthermore, protein and gene interaction networks were evaluated to screen for downstream molecules associated with DPP family member genes. 2. Materials and Methods 2.1. Oncomine and GEPIA Analyses To search for mRNA expression levels of DPP genes in 20 types of common cancers relative to normal matched tissue, we used Oncomine (www.oncomine.org, accessed on 01 May 2021) and GEPIA (http://gepia.cancer-pku.cn/, accessed on 01 May 2021) [27–32]. Search thresholds included the multiple of change (>2.0), p value (<10−4), and gene ranking percentile (top 10%). Search results displayed the number of datasets qualified for the above thresholds with up- and downregulated expression levels in different types and Diagnostics 2021, 11, 1204 3 of 15 subtypes of cancers. Red- and blue-colored gradients were used to show these genes0 up- and downregulated expression levels based on the top-ranking percentiles. 2.2. Cancer Cell Line Encyclopedia (CCLE) Analysis Additionally, we used the CCLE database (https://portals.broadinstitute.org/ccle, accessed on 1 May 2021) to search for expression levels of DPP genes in cancer cell lines [33]. The CCLE is comprised of many human cancer cell lines (n = 1457) with large numbers of unique datasets (n = 136,488). Gene expression levels were retrieved using an RNA sequencing method in 60 breast cancer cell lines and the data were plotted with default settings as we previously described [34–37]. 2.3. Kaplan–Meier (KM) Plot
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