medicina Article Gene Expression Patterns Unveil New Insights in Papillary Thyroid Cancer Mihai Saftencu 1, Cornelia Braicu 2, Roxana Cojocneanu 2 , Mihail Buse 3, Alexandru Irimie 4,5, Doina Piciu 6,7,* and Ioana Berindan-Neagoe 2,3,8 1 Faculty of Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 40015 Cluj-Napoca, Romania 2 Research Center for Functional Genomics and Translational Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 23 Marinescu Street, 40015 Cluj-Napoca, Romania 3 MEDFUTURE-Research Center for Advanced Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania, 23 Marinescu Street, 40015 Cluj-Napoca, Romania 4 Department of Surgery, The Oncology Institute “Prof. Dr. Ion Chiricuta”, 40015 Cluj-Napoca, Romania 5 Department of Surgical Oncology and Gynecological Oncology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania, 40015 Cluj-Napoca, Romania 6 Department of Nuclear Medicine and Endocrinology, The Oncology Institute “Prof. Dr. Ion Chiricuta”, 40015 Cluj-Napoca, Romania 7 Department of Oncology, Iuliu Hatieganu University of Medicine and Pharmacy, 40015 Cluj-Napoca, Romania 8 Department of Functional Genomics and Experimental Pathology, The Oncology Institute “Prof. Dr. Ion Chiricuta”, Republicii 34th street, 400015 Cluj-Napoca, Romania * Correspondence: [email protected] Received: 16 May 2019; Accepted: 13 August 2019; Published: 19 August 2019 Abstract: Background and objectives: Papillary thyroid carcinoma is the most frequent variety of all malignant endocrine tumors. It represents a heterogeneous malignancy with various clinical outcomes, emphasizing the need to identify powerful biomarkers with clinical relevance. Materials and Methods: Available gene expression data (level 3) for thyroid cancers were downloaded from the Cancer Genome Atlas (TCGA), followed by bioinformatic analyses performed on the data set. Results: Based on gene expression analysis, we were able to identify common and specific gene signatures for the three main types of papillary thyroid carcinoma (classical, follicular variant, and tall-cell). The survival rate was not significantly different among the main subtypes, but we were able to identify a biological adhesion signature with impact in patient prognostic. Conclusions: Taken together, the gene expression signature and particular adhesion signature, along with ITGA10 and MSLN in particular, could be used as a prognostic tool with important clinical relevance. Keywords: thyroid papillary cancer; Cancer Genome Atlas (TCGA); gene expression and biological adhesion signature 1. Introduction Thyroid cancer is the most common endocrine malignancy, showing an increasing incidence during the last years [1]. Papillary thyroid carcinoma (PTC) has the highest prevalence among all thyroid malignancies, representing around 80% of all thyroid carcinomas [2]. There are three common subtypes of papillary thyroid cancer: classical (conventional), follicular, and tall-cell [2]. The tall-cell phenotype is the most aggressive subtype, showing an increased risk of metastatic dissemination to cervical lymph nodes [2,3]. Genetic and epigenetic alterations are the driving forces of thyroid cancer. There has been an exciting advance in comprehending its molecular pathogenesis during recent years [1,3,4]. In spite of Medicina 2019, 55, 500; doi:10.3390/medicina55080500 www.mdpi.com/journal/medicina Medicina 2019, 55, 500 2 of 13 this fact, there are still various issues related to the pathogenic mechanisms behind thyroid cancer that need to be solved, such as the identification of various genes, mutations, epigenetic alterations, and environmental factors implicated in its progress [5–7]. To manage the limitations related to molecular pathogenesis, many researchers have attempted to identify useful genetic tools [1,4,8]. Such tools for transcriptomic analysis are expected to provide novel information with clinical relevance, in order to improve the management and the outcome of this disease [9–11]. The Cancer Genome Atlas (TCGA) is a large database of molecular data for a wide range of cancer types, including genome and transcriptome evaluation, selected in a laborious and consistent manner [7,12,13]. These furnish remarkable possibilities for complex data analyses and for detecting trends of alteration of transcriptomic patterns. This kind of analysis will lead to novel insight into the management of thyroid malignancies. The current study was focused on the evaluation of global gene expression pattern in papillary thyroid carcinoma tissue and its related main types (classical, follicular, and tall-cell) compared with adjacent normal tissue, in order to identify differentially expressed genes. Then, we integrated the altered gene expression signature in biological context, trying to identify the main pathways and biological processes impacting the development of new biomarkers of thyroid cancer. 2. Materials and Methods 2.1. Gene Expression Evaluation Based on TCGA Data Papillary thyroid cancer RNA sequencing data (systematized on its main subtypes) as well as matched adjacent noncancerous control data was used to evaluate the global alteration in gene expression pattern. This data was downloaded from the TCGA module database of the UCSC (University of California Santa Cruz) Xena browser, in the form of data matrices containing log2, normalized expression data, together with clinical and demographic information of the patients. We included 357 patients with classical PTC, 102 with the follicular variant of PTC, and 36 with tall-cell PTC (Table2). We used the GeneSpring Gx analysis software (provided by Agilent Technologies, Santa Clara, CA, USA) in order to evaluate both the global and the characteristic subtypical alterations in PTC. As a cut-off value, we used a fold change of 2 ± and a p-value < 0.05, corrected using the Benjamini–Hochberg method to restrain the false discovery rate (FDR) resulted in multiple testing. Table 1. Demographic, pathologic and clinical characteristics of TCGA samples for PTC. Papillary Thyroid Carcinoma (n = 505) Demographics Classical (n = 358) Follicular (n = 102) Tall Cell (n = 36) Other (n = 9) Nr % Nr % Nr % Nr % Males 98 27.37 24 23.53 9 25.00 5 55.56 Sex Females 260 72.63 78 76.47 27 75.00 4 44.44 Median, Range 45.5, 15–88 46, 16–83 46, 28–89 46, 22–62 Age Median, Range 51, 15–88 51, 16–80 51.5, 38–85 37, 22–60 Median, Range♂ 43.5, 15–87 46, 17–83 46, 28–89 46, 42–62 Unifocal♀ 203 56.70 51 50.00 11 30,56 3 33.33 Focus type Multifocal 148 41.34 50 49.02 23 63.89 6 66.67 Unknown 7 1.96 1 0.98 2 5.56 - T1 103 28.77 32 31.37 7 19.44 1 11.11 T2 122 34.08 40 39.22 3 8.33 1 11.11 T3 111 31.01 28 27.45 25 69.44 7 77.78 T4 20 5.59 2 1.96 1 2.78 - Tx 2 0.56 - - - T unknown - - - - N0 149 41.62 65 63.73 13 36.11 3 33.33 TNM N1 184 51.40 13 12.75 22 61.11 6 66.67 N2 - - - - Nx 25 6.98 24 23.53 1 2.78 - N unknown - - - - M0 222 62.01 32 31.37 23 63.89 5 55.56 M1 4 1.12 5 4.90 - - Mx 131 36.59 65 63.73 13 36.11 4 44.44 M unknown 1 0.28 - - - Medicina 2019, 55, 500 3 of 13 Table 2. Cont. Papillary Thyroid Carcinoma (n = 505) Demographics Classical (n = 358) Follicular (n = 102) Tall Cell (n = 36) Other (n = 9) I 212 59.22 56 54.90 10 27.78 6 66.67 II 29 8.10 22 21.57 - 1 11.11 Tumor stage III 73 20.39 18 17.65 20 55.56 1 11.11 IV 43 12.01 5 4.90 6 16.67 1 11.11 Unknown 1 0.28 1 0.98 - - Right lobe 141 39.39 59 57.84 12 33.33 3 33.33 Left lobe 132 36.87 30 29.41 12 33.33 2 22.22 Anatomic site Bilateral 63 17.60 10 9.80 9 25.00 4 44.44 Isthmus 17 4.75 2 1.96 3 8.33 - Unknown 5 1.40 1 0.98 - - Goiter 4 1.12 - - - Graves’ disease 1 0.28 - 1 2.78 - Hashimoto 1 0.28 - - - Thyroiditis Hashimoto Thyroiditis & - 1 0.98 - - Hypothyroidism Hyperthyroidism 3 0.84 - - - Hypothyroidism 5 1.40 3 2.94 2 5.56 1 11.11 Lymphocytic 32 8.94 11 10.78 - - Thyroiditis Lymphoid - 1 0.98 - - hyperplasia Nodular 47 13.13 35 34.31 7 19.44 2 22.22 Hyperplasia Other 8 2.2 2 1.96 - - Normal 209 58.38 43 42.16 22 61.11 6 66.67 Unknown 48 13.41 6 5.88 4 11.11 - Complete 107 29.89 31 30.39 20 55.56 5 55.56 response Response to Partial response 15 4.19 3 2.94 - - therapy Radiological progressive 5 1.40 - 1 2.78 - disease Stable disease 2 0.56 - 2 5.56 - Unknown 229 63.97 68 66.67 13 36.11 4 44.44 TNM-tumour stage, lymph nodes, metastases. 2.2. Pathway Enrichment and Biological Process Analysis, Gene Ontology Classification, and Network Visualization Pathway enrichment analysis, biological process analysis and gene ontology classification were performed for genes with an altered expression level by using the online Panther tool (http://www. pantherdb.org)[14]. STRING (https://string-db.org) and miRNET (https://www.mirnet.ca/miRNet/ faces/home.xhtml) were used to determine gene involvement in PTC pathogenesis and for the inclusion of altered genes in key regulatory network. 3. Results 3.1. Differential Gene Expression in Tumor Tissues versus Normal Tissues for PTC Global gene expression was evaluated in tumor tissues (n = 505) versus normal tissues (n = 59), considering as cut-off value the FC (fold change) of 2 and corrected p-value 0.05.