Qiu et al. Biol Res (2018) 51:45 https://doi.org/10.1186/s40659-018-0188-1 Biological Research RESEARCH ARTICLE Open Access Identifcation of key genes and miRNAs markers of papillary thyroid cancer Jie Qiu1†, Wenwei Zhang2†, Chuanshan Zang1, Xiaomin Liu1, Fuxue Liu3, Ruifeng Ge1, Yan Sun1* and Qingsheng Xia4* Abstract Objective: In this study, crucial genes and microRNAs (miRNAs) associated with the progression, staging, and prog- nosis of papillary thyroid cancer (PTC) were identifed. Methods: Four PTC datasets, including our own mRNA-sequencing (mRNA-seq) dataset and three public datasets downloaded from Gene Expression Omnibus and The Cancer Genome Atlas, were used to analyze diferentially expressed genes (DEGs) and miRNAs (DEMs) between PTC tumor tissues and paired normal tissues (control). Gene ontology (GO) terms and pathways associated with these DEGs were identifed, and protein–protein interactions (PPIs) were analyzed. Additionally, an miRNA-mRNA regulatory network was constructed and the functions of DEMs were explored. Finally, miRNAs/mRNAs associated with tumor staging and prognosis were identifed. The expression levels of several key genes and miRNAs were validated by qRT-PCR. Results: Numerous DEGs and DEMs were identifed between tumor and control groups in four datasets. The DEGs were signifcantly enriched in cell adhesion and cancer-related GO terms and pathways. In the constructed PPI network, ITGA2, FN1, ICAM1, TIMP1 and CDH2 were hub proteins. In the miRNA-mRNA negative regulatory networks, miR-204-5p regulated the largest number of target genes, such as TNFRSF12A. miR-146b, miR-204, miR-7-2, and FN1 were associated with tumor stage in PTC, and TNFRSF12A and CLDN1 were related to prognosis. Conclusions: Our results suggested the important roles of ITGA2, FN1, ICAM1, TIMP1 and CDH2 in the progression of PTC. miR-204-5p, miR-7-2, and miR-146b are potential biomarkers for PTC staging and FN1, CLDN1, and TNFRSF12A may serve as markers of prognosis in PTC. Keywords: Papillary thyroid cancer, Gene, miRNA, Tumor stage, Prognosis Background common, accounting for 75–85% of all thyroid cancer Tyroid cancer is the most common endocrine neo- cases [3]. PTC is usually curable and has a 5-year survival plasm, accounting for approximately 1.7% of all human rate of over 95% [4]. However, PTC occasionally dedifer- malignancies [1]. Tyroid cancer is usually classifed into entiates into more aggressive and lethal thyroid cancers. four types: papillary thyroid cancer (PTC), anaplastic thy- Additionally, recurrences are observed in approximately roid cancer, follicular thyroid cancer, and medullary thy- 30% of patients with PTC. Terefore, further analyses of roid cancer [2]. Among the four types, PTC is the most the molecular characteristics of this disease are necessary [5]. Genetic mutations have been identifed in the major- *Correspondence: [email protected]; [email protected] ity of PTC cases and are believed to be responsible for †Jie Qiu and Wenwei Zhang are co-frst authors PTC initiation [6]. A high frequency of activating somatic 1 Otolaryngology Head and Neck Surgery, The Afliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao 266071, Shandong alterations in genes encoding efectors in the MAPK Province, China (mitogen-activated protein kinase) signaling pathway, 4 Otolaryngology Head and Neck Surgery, Qingdao Municipal Hospital, such as BRAF, RAS, and RET, has been found in PTC [7– No. 5 Donghai Road, Qingdao 266071, Shandong, China Full list of author information is available at the end of the article 9]; these mutations act as drivers in approximately 70% © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat​iveco​mmons​.org/licen​ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creat​iveco​mmons​.org/ publi​cdoma​in/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Qiu et al. Biol Res (2018) 51:45 Page 2 of 11 of PTC cases [10]. Additionally, galectin-3 (LGALS3), Table 1 Characteristics of patients platelet-derived growth factor (PDGF), and epithelial Samples Hospitalization Sex Age TNM stage Clinical stage muctin-1 (MUC-1) are also potential biomarkers of PTC starting time [11, 12]. A recent study has demonstrated that numerous P1/N1 2014/7/31 Male 26 T1N1aM0 I microRNAs (miRNAs) are transcriptionally up-regulated P2/N2 2014/8/13 Male 43 T1N1aM0 I in PTC compared with normal tissues [13]. miR-181b, P3/N3 2014/8/11 Female 47 T1N1aM0 III miR-221, and miR-222 have been suggested to be overex- P4/N4 2014/8/11 Male 25 T1N1aM0 I pressed in most thyroid tissues originating from patients P5/N5 2014/8/12 Female 46 T1N1aM0 III afected by PTC [14]. Although numerous molecular P6/N6 2017/7/27 Female 45 T1N1aM0 I markers associated with the carcinogenesis of PTC have P7/N7 2017/7/27 Female 61 T1N1aM0 I been identifed, signifcant uncertainty remains regarding P8/N8 2017/8/1 Male 55 T1N1aM0 I its molecular mechanisms. Furthermore, prognosis- or P9N9 2017/8/16 Female 50 T1N1aM0 I staging-related molecular targets need to be explored. P10/N10 2017/8/17 Female 45 T1N1aM0 I Microarrays have made it possible to analyze genome- P11/N11 2017/8/17 Female 66 T1N1aM0 I wide miRNA or mRNA expression [15]. In this study, P12/N12 2017/9/14 Male 33 T1N1aM0 I we combined our mRNA-sequencing (mRNA-seq) data P13N13 2017/9/14 Female 47 T1N1aM0 I with three public datasets (miRNA-seq and mRNA-seq) P14/N14 2017/9/18 Female 54 T1N1aM0 I downloaded from Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) and Te Cancer P papillary thyroid carcinoma, N paired adjacent normal tissues, TNM tumor- node-metastasis Genome Atlas (TCGA, https ://tcga-data.nci.nih.gov/) to analyze diferentially expressed genes (DEGs) and miR- NAs (DEMs) between PTC tumor tissues and normal Illumina HiSeq2000 platform (Illumina, San Diego, CA, tissue samples (control). Common miRNAs and mRNAs USA). After sequencing, RNA-seq data were preproc- between our mRNA-seq dataset and public datasets essed, including the removal of the adaptor sequence were selected for subsequent analyses, including analy- from raw reads and the fltering of reads with a high N ses of gene ontology (GO) biological processes, pathway content and low quality. Te detailed sequencing and enrichment, and protein–protein interactions (PPIs), the preprocessing procedures are described in our previous construction of miRNA-mRNA regulatory networks, as study [16]. well as the identifcation of miRNAs and mRNAs asso- ciated with staging and prognosis. Moreover, the expres- Public data collection sion levels of several key mRNAs and miRNAs identifed miRNA microarray data (accession GSE57780) were in these analyses were confrmed by qRT-PCR. Loci iden- downloaded from the GEO database. A total of 9 samples tifed in this study may act as important therapeutic tar- were available, i.e., 3 PTC tissues, 3 normal tissues, and 3 gets or prognostic markers in PTC. nodal metastases. In this study, 3 PTC and 3 normal tis- sues were selected for further analysis. Te platform used Methods to obtain these data was the Illumina HiSeq2000. Tissue samples and mRNA‑seq data collection Additionally, PTC-associated mRNA-seq, miRNA-seq, Five PTC tumor samples (P1–P5) and paired peritu- and clinical data were downloaded from TCGA using moral thyroid tissue samples as controls (N1–N5) were the TCGAbiolinks R package. In total, 552 samples were obtained from patients with PTC who underwent thy- included. roidectomy. Te tumor samples and paired peritumoral thyroid tissue samples were obtained by thyroidectomy Diferential expression analysis and stored in RNA store Reagent DP408-02 (Tiangen Using our own mRNA-seq data, gene expression levels Biotech Co., Ltd., Beijing, China) at 4 °C in a refrigera- were calculated using the RPKM method. DEGs between tor. Tumor stages were determined based on the tumor- the PTC and control groups were detected using the node-metastasis (TNM) criteria. Detailed patient edgeR [17] tool (version 3.4, http://www.bioco nduct information is provided in Table 1. Informed consent was or.org/packa ges/relea se/bioc/html/edgeR .html), and the obtained from all patients. Tis study was approved by signifcance of DEGs was calculated based on the nega- the local Research Ethics Committee. tive binomial model. Te obtained P-values were adjusted After tissue collection, total RNAs were extracted using using the Benjamini and Hochberg method [5] to obtain the RNAprep Pure Tissue Kit (Tiangen Biotech Co., Ltd.) the false discovery rate (FDR). Te thresholds for DEG according to the manufacturer’s protocol and was then selection were FDR < 0.05 and |log2 fold change (FC)| > 2. converted to Tru-Seq libraries for sequencing using the Qiu et al. Biol Res (2018) 51:45 Page 3 of 11 Te mRNA-seq and miRNA-seq data obtained from to construct the miRNA-mRNA negative regulatory net- TCGA and GEO were also preprocessed using edgeR work. Te key miRNAs and genes were identifed based [17] (version 3.4) in R. Te raw counts were converted on a network topological property analysis. to log2-counts-per-million (logCPM) followed by linear modeling and the mean–variance relationship was mod- miRNA function analysis eled with precision weights using the voom [18] method For genes involved in the miRNA-mRNA negative reg- in limma. Te diferential expression analysis (tumor ulatory network, GO and KEGG pathway enrichment vs. control) was performed using t-tests implemented analyses were performed using clusterProfler [26] in R.