Construction of a competing endogenous RNA network and identification of ITGA2 as a potential target in papillary thyroid carcinoma

Construction of a competing endogenous RNA network and identification of ITGA2 as a potential target in papillary thyroid carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Construction of a competing endogenous RNA network and identification of ITGA2 as a potential target in papillary thyroid carcinoma Guoliang Wu, Xinyu Wang, Yiming Zhu, Shaoyan Liu, Song Ni This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4363244/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Papillary thyroid carcinoma (PTC) stands as the prevalent malignancy within the endocrine system. This study's primary aim is to probe the domain of potential biomarkers associated with PTC Methods Datasets from GEO and TCGA databases were used to analyze the differentially expressed mRNAs (DE-mRNAs), miRNA (DE-miRNAs), and methylated DNAs, which were further integrated to establish a mRNAs-miRNAs-mRNAs competing endogenous RNA (ceRNA) network by the integrative bioinformatics analyses. Additionally, pathway enrichment analysis was performed to reveal the functions of the ceRNAs by means of Metascape. qRT-PCR and western blot were used to evaluate the expression level of several genes. Methylation-specific PCR was used to assess the methylation levels of Integrin Subunit Alpha 2 (ITGA2) promoter. CCK-8 and transwell assays were used to investigate the biological function of ITGA2. Results 160 potential ceRNA pairs were identified from the intersection of mRNA-miRNA-mRNA regulatory network. Simultaneously, 970 methylated genes including 127 hypermethylated and 843 hypomethylated were recognized by overlapping the methylation datasets. Then, we retained 51 methylation-related ceRNA pairs. KEGG pathway enrichment analysis revealed that the 51 genes were primarily involved in ECM-receptor interaction and proteoglycans in cancer. Finally, we demonstrated that ITGA2 acted as an oncogene in thyroid cancer. Conclusion Our study constructed an intricate mRNA-miRNA-mRNA regulatory network as well as pinpointed numerous prospective candidates within the domain of thyroid cancer. Furthermore, our findings suggest that ITGA2 could potentially serve as a viable target in the treatment of thyroid cancer. competing endogenous RNA papillary thyroid carcinoma ITGA2 methylation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Thyroid carcinoma (THCA) constitutes the prevailing malignancy within the endocrine system, encompassing four distinctive four subtypes: papillary thyroid carcinoma (PTC), follicular thyroid cancer (FTC), medullary thyroid cancer (MTC), and anaplastic thyroid cancer (ATC). Among these, papillary and follicular thyroid cancers are the most common THCA and classified as differentiated THCA. PTC accounts for more than 90% of cases and more frequently occurs in women compared with men 1 . While the majority of THCA patients experience favorable prognoses, a subset may confront issues of distant metastasis and recurrence 2 . Consequently, it is imperative to elucidate the molecular pathogenesis of THCA and unearth precise biomarkers to enhance its prognosis. Numerous studies have demonstrated that genetic alterations in PTC, encompassing mutations in BRAF and RAS, as well as translocations involving RET/PTC and PAX8/PPARγ chromosomal rearrangements 3 , 4 . Such insights have paved the way for molecular-guided treatment, offering potential benefits to patients. Nonetheless, despite the emergence of a number of biomarkers, comprehensive exploration of effective diagnostic and therapeutic biomarkers for THCA diagnosis and therapy remains incomplete. To identify therapeutic biomarkers, competing endogenous RNA (ceRNA) hypothesis has attracted more attention 5 , 6 . Salmena et al. introduced the ceRNA hypothesis, which posits that the interplay between mRNA and long noncoding RNAs (lncRNAs) can occur through competitive bidding to shared miRNA response elements (MREs).This concept significantly expands our comprehension of the regulatory network within the human genome 7 . As a result, an expanding body of research is dedicated to exploring ceRNA-related mechanisms in various cancer types, including breast cancer 8 , hepatocellular carcinoma 9 , and THCA 10 . In this study, we obtained differentially expressed mRNAs (DE-mRNAs), miRNAs (DE-miRNAs) through a comprehensive analysis of the GEO and TCGA databases via systematic bioinformatics techniques. Overlapped DE-miRNAs and DE-mRNAs were screened out to construct a ceRNA network. Subsequently, we performed function enrichment analyses and survival analyses. we pinpointed 8 genes as pivotal elements closely associated with the prognosis of THCA prognosis. Among these genes, we conducted quantitative real-time polymerase chain reaction (qRT-PCR) and functional assays to validate the expression levels and biological functions of ITGA2. This validation process provides substantial scientific evidence supporting the identification of potential biomarkers and the exploration of the molecular mechanism underpinning PTC. Materials and Methods Data mining and analyzing mRNA, miRNA expression, and DNA methylation data of PTC patients were obtained from both Gene Expression Omnibus (GEO) datasets 11 and TCGA data portal ( https://cancergenome.nih.gov/ ). TCGA THCA, GSE33630, and GSE113629 datasets were used to analyze RNA expression profiles. TCGA THCA methylation dataset and GSE97466 dataset were used to analyze DNA methylation profiles. Details of these datasets were listed in Table 1 . Table 1 Details of the GEO and TCGA datasets Dataset Type Normal samples Tumor samples GSE33630 mRNA microarray 45 49 GSE113629 miRNA microarray 5 5 GSE97466 DNA methylation 50 60 TCGA-THCA mRNA 58 58 TCGA-THCA DNA methylation 56 56 Identification of differentially expressed miRNA, mRNA, and DNA methylation GEO2R ( http://www.ncbi.nlm.nih.gov/geo/geo2r/ ), an interactive web tool, and EdgeR package in R (version 3.4.1) were used to identify differentially expressed genes (DEGs). DE-mRNAs, DE-miRNAs and differentially expressed DNA methylation were identified between the PTC and adjacent normal thyroid tissues according to the threshold criteria ( p-value 1). Construction of a ceRNA regulatory network To establish a ceRNA network, we firstly chose the top 15 upregulated and 15 downregulated miRNAs ranked by fold change from the GSE113629 dataset. In view of ceRNA pairs targeted by the common miRNA, as a result, the expression levels of one ceRNA would vary with the other ceRNA expression. Therefore, we selected the overlapping DE-mRNAs in the GSE33630 and TCGA THCA datasets and screen out mRNA-mRNA pairs with the positive correlation threshold of Pearson correlation coefficient (R) > 0.4 based on their expression matrix. Then, we used RNA22 7 , a web software, to explore miRNA-mRNA interaction. A hypergeometric test was applied to evaluate the possibility of being authentic ceRNA pairs by calculating the p-value of each ceRNA pair using the ratio of the number of common miRNAs in ceRNA pairs to the total number of miRNAs in miRNA-mRNA interaction. Finally, the ceRNA network was visualized by Cytoscape 12 (version 3.8.1). The p-value was calculated as the following formula: Where, x is the number of common miRNAs that interact with the ceRNA pairs (mRNA1 and mRNA2), whose numerical value must be equal to or greater than three. N is the total number of miRNAs in the ceRNA pairs. K is the number of miRNAs that only regulate mRNA 1, and M is the number of miRNAs that merely regulate mRNA 2. Gene function enrichment analysis 51 intersected genes were identified by overlapping ceRNA pairs and differentially methylated genes, which were integrated to Metascape 13 to perform gene function enrichment analyses including GO biological processes, reactome analysis and KEGG enrichment analysis. Survival analysis Kaplan-Meier Plotter ( https://kmplot.com/analysis/ ), an online tool for meta-analysis of databases from GEO and TCGA 14 , was used to assess the relationship between the expression of mRNA and prognoses of cancer patients. Patients and tissue samples This study was approved and supervised by the Ethics Committee of Cancer Hospital, Chinese Academy of Medical Sciences (Approval number: 22/208–3410). 30 pairs of PTC and adjacent normal tissues were collected from patients that received surgery at our hospital. All patients were pathologically diagnosed with PTC by two pathologists independently. All tissue samples were stored in liquid nitrogen at -80°C. Cell culture The human normal thyroid cell line (Nthy-ori3-1) and PTC cell lines (TPC-1, NPA, and KTC-1) were obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA). All cells were cultured in DMEM (Gibco, Carlsbad, CA, USA) containing 10% fetal bovine serum (FBS) (Gibco) and incubated in a humidified atmosphere with 5% CO 2 at 37°C. Short-hairpin RNA (shRNA) against ITGA2 (sh-ITGA2) and the negative control shRNA (sh-NC) were purchased from RiboBio (Guangzhou, China). Cell transfection was performed in TPC-1 cells using Lipofectamine 2000 reagent (#11668019, Invitrogen, Carlsbad, CA, USA) following the manufacturer’s instructions. Quantitative real-time PCR (qRT-PCR) Total RNA was extracted from PTC tissues and cell lines using TRIzol reagent (Invitrogen, Carlsbad, CA) following the manufacturer’s instructions. The RNA purity was measured at 260 nm (A260/A280 > 1.8). RNA was reversed into complementary DNA using PrimeScript RT Kit (#RK21400, Abclonal, China). qRT-PCR was performed using SYBR Green PCR Master Mix (QuantiFast, Qiagen, Hilden, Germany) and the expression level of mRNAs was calculated using the 2 −ΔΔCt method. GAPDH and U6 were used as internal control. Primers used in this study are listed in Table S1 . Methylation-specific PCR (MSP) Genomic DNA was extracted from Nthy-ori3-1 and TPC-1 using (QIAamp DNA Blood Mini Kit,Qiagen,Germany). DNA was modified with bisulfite using a CpGenome DNA modification kit (CpGenome DNA modification kit, EMD Millipore, USA). MSP-specific methylated and unmethylated primers of ITGA2 were devised and synthesized by Sangon Biotech (Shanghai) Co., Ltd. The primers for unmethylated ITGA2 CpG islands are as follow: (Left U primer: AGGGTGTTATTTTTATTTTTATTGT; Right U primer༚AATTTCTAAACAACTCCTACAACACC); The primers for methylated ITGA2 CpG islands are as follow: (Left M primer༚TTAGGGCGTTATTTTTATTTTTATC; Right M primer༚AAATTTCTAAACAACTCCTACAACG). PCR was conducted in sequence of denaturation at 95 ℃for 10 min; 35 cycles for denaturation (95°C, 30 s), annealing (60°C, 30 s), extension (72℃40 s); extension at 72 ℃for 5 min. Then the PCR amplified products were loaded on 2% agarose gel, stained with ethidium bromide, visualized under ultraviolet light. CCK-8 assay Cell viability was determined using the Cell Counting Kit-8 (DingGuo Bio) according to the manufacturer’s instructions. The transfected TPC-1 cells were plated in 96-well plates (Corning, Corning, NY, USA) at a density of 1.0 × 10 3 cells per well for 24 h. Then, cells incubated for 0 h, 24 h, 48 h, 72 h, and 96 h were added with 10 µL Cell Counting Kit-8 solution. After incubation at 37 ℃for 2 h, the absorbance at 450 nm was measured by a Microplate Reader (BioTek). Transwell assay Transwell chamber (Snapwell™ Inserts,Corning,USA) was used to assess cell migration and invasion. In brief, 2 × 10 4 TPC-1 cells suspended in 200 µL serum-free DMEM were plated into the upper chamber with Matrigel matrix (BD Biosciences, Franklin Lakes, NJ, USA). The lower chamber was treated with complete DMEM medium with 10% FBS. After incubation at 37 ℃for 48 h, the TPC-1 cells in the upper chamber were removed, while those in the lower chamber were fixed with methanol and stained with 0.5% crystal violet. The number of TPC-1 cells was counted using a microscope (NIKON Eclipse Ti2). Western blot assay Total protein was collected from cell lysate using RIPA lysis buffer (Thermo Fisher Scientific) added with protease inhibitor. Protein concentration was measured using the BCA Protein Assay Kit (Pierce, Appleton, WI, USA). Then, the proteins were resolved by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to polyvinylidene fluoride (PVDF) membranes (Millipore, Billerica, MA, USA). Next, the PVDF membranes were blocked by 5% skim milk at room temperature for 2 h and incubated with primary antibodies, anti-FN1(ab109365, Abcam, Cambridge, UK, 1:1000), anti-ITGA2 (ab181548, Abcam, Cambridge, UK, 1:5000), anti-TIAM1(ab211518, Abcam, Cambridge, UK, 1:1000), anti-GAPDH (ab181602, Abcam, Cambridge, UK,1:10000) at 4 ℃overnight. Then, the horseradish peroxidase (HRP)-conjugated secondary antibody Goat Anti-Rabbit IgG H&L (HRP) (ab205718, Abcam, Cambridge, MA, USA, 1:2000) was added to the membranes and incubated at room temperature for 2 h after washing the membrane three times in TBST. Finally, the protein bands were visualized using ECL Kit (Amersham, United Kingdom) and the intensity of the proteins was normalized by GAPDH as an endogenous reference. Statistical analysis For quantitative real-time PCR (qRT-PCR) data, we utilized GraphPad Prism 8 software. Differences between two independent groups were evaluated using Student’s t test. A p-value of less than 0.05 was deemed indicative of statistical significance. To present our data transparently, we expressed all values as means ± standard deviation (SD). All analyses were two-tailed, and a p-value threshold of 0.05 was maintained throughout to discern statistically significant findings. Results Construction of an mRNA-miRNA-mRNA network Using the GEO2R tool and ‘EdgeR’ package, we have screened out thousands of DEGs in PTC from GEO and TCGA datasets with the criteria of p -value 1. As shown in Fig. 1A , DE-miRNAs were identified in the GSE113629 dataset. In view of GSE33630 dataset, a total of 1242 DE-mRNAs were found including 676 upregulated mRNAs and 566 downregulated mRNAs ( Fig. 1B ). Meanwhile, a total of 3169 DE-mRNAs were identified in TCGA THCA dataset, among which 2107 were upregulated and 1062 were downregulated ( Fig. 1C ). In order to construct an mRNA-miRNA-mRNA network, we firstly obtained 491 common upregulated DE-mRNAs and 326 common downregulated DE-mRNAs by overlapping the GSE33630 and TCGA THCA datasets ( Fig. 1D ). Then, we selected the top 15 upregulated and 15 downregulated miRNAs from the GSE113629 miRNA microarray. Finally, 160 ceRNA pairs were recognized by several methods including bioinformatic prediction, correlation analysis, and hypergeometric test. The top 20% mRNAs in miRNA-mRNA interaction from this network was shown in Fig. S1 . Gene function enrichment analyses Further, DNA methylation analyses were performed in two related datasets. 166 mRNAs were hypermethylated and 1274 mRNAs were hypomethylated in the GSE97466 dataset ( Fig. 2A ). And 380 hypermethylated mRNAs and 1638 hypomethylated mRNAs were identified in the TCGA THCA methylation dataset ( Fig. 2B ). We next identified 970 methylated genes, consisting of 127 hypermethylated and 843 hypomethylated genes, through overlapping these two datasets ( Fig. Figure 2 2C ). We intersected above ceRNA pairs and differentially methylated genes in PTC for comprehensive analysis. The results showed that there were 51 dysregulated methylation-related genes including 43 hypomethylated ceRNAs and 8 hypermethylated ceRNAs ( Fig. 2D ). To investigate the function of these 51 genes in PTC, we performed gene function enrichment analysis using Metascape. Figure 2E displayed overall enrichment analysis results. It can be concluded that the 51 genes were mainly involved in cell junction, epithelial morphogenesis, metabolism, and the regulation of cancer-related factors. As we focused on THCA, we implemented KEGG pathway analysis to investigate the cancer-associated pathways and found that the 51 genes significantly enriched in ECM-receptor interaction, proteoglycans in cancer, amoebiasis, tight junction, and pathway in cancer ( Figu. 2F ), which are associated with carcinogenesis. As a result, these 51 genes were consequently linked with tumorigenesis. Notably, 8 genes named FN1, ACTIN1, TNC, ITGA2, TIAM1, TGFB1, CLDN1 and TCF7L1 significantly involved in the KEGG pathway analysis. We therefore concentrated on these 8 genes in the following study. ACTN1, TIAM1 and CLDN1 significantly associated with PTC patients’ prognosis Survival analyses of these 8 genes in PTC patients were carried out by the means of Kaplan-Meier Plotter ( Fig. 3 ). Among them, the expression levels of ACTN1 (p = 0.0059), TIAM1 (p = 0.01) and CLDN1 (0.0015) had close relationship with PTC patients’ prognosis. These 3 genes may be biomarkers of THCA. Survival analyses of Kaplan-Meier curves of 8 genes in PTC patients. FN1, p = 0.26; ACTIN1, p = 0.0059; TNC, p = 0.099; ITGA2, p = 0.13; TIAM1, p = 0.01; TGFB1, p = 0.064; CLDN1, p = 0.0015; TCF7L1, p = 0.15. The red lines represent high expression, while the black lines represent low expression. HR: hazard ratio. Survival analyses of Kaplan-Meier curves of 8 genes in PTC patients. FN1, p = 0.26; ACTIN1, p = 0.0059; TNC, p = 0.099; ITGA2, p = 0.13; TIAM1, p = 0.01; TGFB1, p = 0.064; CLDN1, p = 0.0015; TCF7L1, p = 0.15. The red lines represent high expression, while the black lines represent low expression. HR: hazard ratio. Survival analyses of Kaplan-Meier curves of 8 genes in PTC patients. FN1, p = 0.26; ACTIN1, p = 0.0059; TNC, p = 0.099; ITGA2, p = 0.13; TIAM1, p = 0.01; TGFB1, p = 0.064; CLDN1, p = 0.0015; TCF7L1, p = 0.15. The red lines represent high expression, while the black lines represent low expression. HR: hazard ratio. Fig. 3 Knockout of ITGA2 inhibited proliferation, invasion, and migration of THCA cells As the aforementioned top KEGG pathway was the ECM-receptor interaction, followed by proteoglycan in cancer ( Fig. 2F ), we selected 3 genes (ACTIN1, TIAM1, and CLDN1) that were significantly enriched in the two pathways for experimental validation. qRT-PCR was performed in 30 pairs of PTC tissues and normal thyroid tissues. Interestingly, the mRNA expression levels of FN1 and TIAM1 between PTC and normal thyroid tissues had no significant differences, while the expression level of ITGA2 in PTC tissues was higher than that in the normal tissues (Fig. 4 A). And as expected, the protein level of ITGA2 was notably elevated in three PTC tissues, which was consistent with qRT-PCR result (Fig. 4 B). We therefore chose it for further study. Likewise, the ITGA2 level was also higher in PTC cell lines, including TPC-1, NPA, and KTC-1 cells than that in Nthy-ori3-1 cells (Fig. 4 C). TPC-1 cell line was selected to perform the following experiments, as it possessed the highest level of ITGA2. Knockout of ITGA2 inhibited proliferation, invasion, and migration of THCA cells . (A) mRNA levels of ITGA2, FN1 and TIAM1 in PTC (n = 30) and normal (n = 30) thyroid tissues. (B) Protein levels of ITGA2, FN1 and TIAM1 in PTC and normal thyroid tissues. (C) mRNA levels of ITGA2 in normal thyroid Nthy-ori3-1 and three PTC cell lines including TPC-1, NPA, and KTC-1. (D) P Protein levels of ITGA2 in TPC-1 cells after treated with sh-NC or sh-ITGA2. (E) CCK-8 assay indicated that cell proliferation was suppressed after ITGA2 knockdown. (F) Transwell assay suggested that knockout of ITGA2 inhibited the invasive and migratory ability of TPC-1 cells. * p ≤ 0.05; ** p ≤ 0.01༛*** p ≤ 0.001. To deeply explore the effects of ITGA2 on THCA, we knockout ITGA2 in TPC-1 cells (Fig. 4 D) and we performed functional experiments. CCK8 assay showed that ITGA2 knockout significantly suppressed the proliferation of TPC-1 cells (Fig. 4 E). Moreover, transwell assay revealed that knockout of ITGA2 suppressed the invasive and migratory abilities of THCA cells (Fig. 4 F). Overall, these results suggested that ITGA2 knockout suppressed the THCA cells to proliferate, invade, and migrate, indicating ITGA2 may play critical role on THCA carcinogenesis. Methylation levels of ITGA2 negatively correlated with its expression level Subsequently, we investigated the relationship between ITGA2 methylation and its expression to explore how ITGA2 functions during THCA carcinogenesis though DNA Methylation Interactive Visualization Database( http://119.3.41.228/dnmivd/query_gene/?cancer=THCA&gene=ITGA2&panel=Summary ). The results showed that ITGA2 expression level in tumor was higher than that in normal tissue and negatively correlated with its promoter methylation (Fig. 5 A and 5 B). There are 13 methylation position in ITGA2 genes and they all show dropped methylation tendency as ITGA2 overexpressed. Figure 5 C displayed methylation levels at 4 positions on ITGA2 showing significant negative correlation with ITGA2 expression level. In addition, we further conducted MSP assays and found that the promoter of ITGA2 was hypomethylated in TPC-1 cells. In contrast, the methylation level of the ITGA2 promoter was higher in Nthy-ori3-1 cells (Fig. 5 D). Altogether, these findings revealed that ITGA2 was overexpressed in both PTC tissues and cell lines, and its expression was negatively associated with its methylation levels. Discussion Despite substantial advancements in the diagnosis and therapeutic interventions for THCA, misdiagnoses still afflict a considerable number of individuals. With THCA diagnoses increasingly affecting a younger demographic, there exists an imperative demand for the discovery of dependable biomarkers to enable earlier-stage diagnosis and treatment monitoring. In the present study, our initial endeavor was to establishe an intricate mRNA-miRNA-mRNA network, coupled with an exploration of DNA methylation expression pattern in PTC using rigorous bioinformatics analyses.We systematically scrutinized the functions of potential competing endogenous RNAs (ceRNAs) through pathway enrichment and survival analyses. 8 genes were identified significantly related with THCA prognosis. Finally, we conducted functional assays that affirm ITGA2 contributed to the tumorigenesis of PTC, Moreover, we investigated how the methylation levels of ITGA2 may exert a regulatory influence on the development of THCA. Recent investigations have uncovered pivotal genes and constructed ceRNA regulatory networks across a spectrum of cancers, including bladder cancer 15 , gastric cancer 16 , breast cancer 17 , and lung adenocarcinoma 18 . Wang et al. have established MMP9/ITGB1-miR-29b-3p-HCP5 network in pancreatic cancer, offering valuable prognostic biomarkers for patients with pancreatic cancer 19 . Meanwhile,Zheng et al. demonstrated that the existence STARD13-correlated ceRNA network that curbs the stemness of breast cancer through inhibiting YAP/TAZ activation via Hippo and Rho-GTPase/F-actin signaling 20 ,proposing a novel therapeutic strategy for targeting stemness. In the current study, we constructed an mRNA-miRNA-mRNA ceRNA interactive network by integrated bioinformatics analysis and obtained 51 genes through the intersection of ceRNAs and methylated genes. To further elucidate the function of these genes, we conducted comprehensive functional enrichment analyses. Our KEGG pathway analysis uncovered 8 genes (FN1, ACTIN1, TNC, ITGA2, TIAM1, TGFB1, CLDN1 and TCF7L1) enriched in pathways associated with ECM-receptor interaction, proteoglycans, amoebiasis, tight junction, and pathway in cancer. It is well-acknowledged that extracellular matrix interaction and proteoglycans play essential roles in cancer tumorigenesis and progression 21 , 22 . Therefore, we have established a linkage between these eight genes and the regulation of migration and metastasis of THCA. Among these genes, FN1, ITGA2, and TIAM1 have been previously documented as contributors to cancer progression 23 – 25 . Notably, the overexpression of ITGA2 has been shown to significantly enhance proliferation and invasion of various cancer cells 23 . In addition, Liu et al. revealed that TIAM1 promotes EMT and and metastasis in THCA through the Wnt/β-catenin pathway mediated by Rac1 24 . Furthermore, Cai et al. demonstrated that FN1 promoted cell proliferation, migration, and invasion while inhibiting apoptosis in colorectal cancer, primarily through its interaction with ITGA5 26 . These results substantiate and support the viability of our study to some extent. To further validate the findings from our bioinformatics analysis, we opted to perform experimental investigations on genes enriched in the top KEGG pathway, specifically FN1, ITGA2, and TIAM1, to perform experiments. ITGA2, as the alpha subunit of a transmembrane receptor for collagens, holds a pivotal role in mediating cell adhesion and interactions with the extracellular matrix 27 . The overexpression of ITGA2 has been linked to enhanced cell proliferation, migration, and invasion in various cancer types, including pancreatic cancer, prostate cancer, hepatocellular carcinoma, and gastric cancer 23 , 28 – 30 . In alignment with these previous studies, our analysis revealed elevated expression level of ITGA2 in PTC relative to adjacent normal thyroid tissue, its status as an oncogene in PTC. In addition, knockout of ITGA2 significantly inhibited cell proliferation, migration, and invasion of PTC, demonstrating that ITGA2 may plays an essential role on THCA carcinogenesis and suggesting that it may serve as a biomarker for PTC patients. A recent study demonstrated that Ropivacaine, a local anesthetic, can effectively curb cell proliferation, invasion, migration, and promote apoptosis in PTC cells by regulating ITGA2 expression 11 .This substantiates our findings and underscores the significance of our study providing credible biomarkers for the treatment of thyroid cancer patients. Additionally, we made a simultaneous discovery that the methylation levels of ITGA2 exhibited a negative correlation with its expression level. Our methylation-specific MSP assay revealed that the ITGA2 promoter displayed hypomethylated in TPC-1 cells, which significantly contrasted with the situation in Nthy-ori3-1 cells. This discovery holds great potential for shedding light on the biological role of ITGA2 in THCA, as well as corresponding mechanism. In conclusion, our study has successfully established a novel ceRNA network of THCA. We have pinpointed eight candidate genes that may serve as potential therapeutic biomarkers for individuals diagnosed with thyroid cancer. Furthermore, we have substantiated the significance of ITGA2, which will deeper the understanding of the carcinogenesis and progression of THCA. However, it is important to note that our study has not unveiled the intricate molecular mechanism through which ITGA2 promotes the malignant phenotypes of THCA. Further investigations will necessitate further analyses and experiments to address this knowledge gap. Conclusion Our study constructed an intricate mRNA-miRNA-mRNA regulatory network as well as pinpointed numerous prospective candidates within the domain of thyroid cancer. Furthermore, our findings suggest that ITGA2 could potentially serve as a viable therapeutic target in the treatment of thyroid cancer. Declarations Funding This work was supported by the National Natural Science Foundation of China, Grant/Award Number: 22278349 Competing Interests The authors have no relevant financial or non-financial interests to disclose. Ethic s Statement The study protocols were reviewed and approved by the Ethics Committee of Cancer Hospital, Chinese Academy of Medical Sciences (Approval number: 22/208-3410) All patients provided written informed consent prior to inclusion in the study, following a thorough explanation of the study procedures. Data Availability The RNA expression profiles were derived from the TCGA THCA dataset, as well as GEO datasets GSE33630 and GSE113629. 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Overexpressed ITGA2 promotes malignant tumor aggression by up-regulating PD-L1 expression through the activation of the STAT3 signaling pathway. J Exp Clin Cancer Res . Dec 9 2019;38(1):485. doi:10.1186/s13046-019-1496-1 Liu L, Wu B, Cai H, et al. Tiam1 promotes thyroid carcinoma metastasis by modulating EMT via Wnt/beta-catenin signaling. Exp Cell Res . Jan 15 2018;362(2):532-540. doi:10.1016/j.yexcr.2017.12.019 Qiu J, Zhang W, Zang C, et al. Identification of key genes and miRNAs markers of papillary thyroid cancer. Biol Res . Nov 10 2018;51(1):45. doi:10.1186/s40659-018-0188-1 Cai X, Liu C, Zhang TN, Zhu YW, Dong X, Xue P. Down-regulation of FN1 inhibits colorectal carcinogenesis by suppressing proliferation, migration, and invasion. J Cell Biochem . Jun 2018;119(6):4717-4728. doi:10.1002/jcb.26651 Dong J, Wang R, Ren G, et al. HMGA2-FOXL2 Axis Regulates Metastases and Epithelial-to-Mesenchymal Transition of Chemoresistant Gastric Cancer. Clin Cancer Res . Jul 1 2017;23(13):3461-3473. doi:10.1158/1078-0432.CCR-16-2180 Gaballa R, Ali HEA, Mahmoud MO, et al. Exosomes-Mediated Transfer of Itga2 Promotes Migration and Invasion of Prostate Cancer Cells by Inducing Epithelial-Mesenchymal Transition. Cancers (Basel) . Aug 15 2020;12(8)doi:10.3390/cancers12082300 Wang L, Gao Y, Zhao X, et al. HOXD3 was negatively regulated by YY1 recruiting HDAC1 to suppress progression of hepatocellular carcinoma cells via ITGA2 pathway. Cell Prolif . Aug 2020;53(8):e12835. doi:10.1111/cpr.12835 Min J, Han TS, Sohn Y, et al. microRNA-30a arbitrates intestinal-type early gastric carcinogenesis by directly targeting ITGA2. Gastric Cancer . Jul 2020;23(4):600-613. doi:10.1007/s10120-020-01052-w Additional Declarations No competing interests reported. Supplementary Files Fig.S1.docx TableS1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4363244","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":298585017,"identity":"6dc62a94-f4e0-46df-a9dd-7045a7a5e1cb","order_by":0,"name":"Guoliang Wu","email":"","orcid":"","institution":"National Cancer Center, Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Guoliang","middleName":"","lastName":"Wu","suffix":""},{"id":298585018,"identity":"66686af3-61a9-4b00-9ac3-9ed28849ab76","order_by":1,"name":"Xinyu Wang","email":"","orcid":"","institution":"National Cancer Center, Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Wang","suffix":""},{"id":298585019,"identity":"651a074a-d11e-4c38-95a3-3c71926e33a2","order_by":2,"name":"Yiming Zhu","email":"","orcid":"","institution":"National Cancer Center, Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yiming","middleName":"","lastName":"Zhu","suffix":""},{"id":298585020,"identity":"ad543300-b44b-4c71-9b1d-d8922d2cc281","order_by":3,"name":"Shaoyan Liu","email":"","orcid":"","institution":"National Cancer Center, Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Shaoyan","middleName":"","lastName":"Liu","suffix":""},{"id":298585021,"identity":"93976786-4b4b-4eaa-9f58-4ee9ac58bb6c","order_by":4,"name":"Song Ni","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYDACZjBpw8BGqpY0UrRAwGES1Jqzsz98XPDrvDwf++GnG38w2MkzsJ89gFeLZTOPsfHMvtuGbTxpZrd5GJING3jyEvBqMTjMwybN23M7gU2Cwew20GsJDBI8BgS0sD8DajkH1ML+7eYPhnpitDCYSfP8OADUwmN2g4fhMDFagH7hbUgG+iWn7DaPwXEQg4CW88cfPub5Yycv3358280fFdXy/Oxn8GsBA8Y2uAkMxMbpH+KUjYJRMApGwQgFANr4OWld68z4AAAAAElFTkSuQmCC","orcid":"","institution":"National Cancer Center, Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":true,"prefix":"","firstName":"Song","middleName":"","lastName":"Ni","suffix":""}],"badges":[],"createdAt":"2024-05-03 09:41:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4363244/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4363244/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56175041,"identity":"7f5af8c6-71cd-4e9f-982f-da96f91d91f2","added_by":"auto","created_at":"2024-05-09 12:49:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":542764,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of an mRNA-miRNA-mRNA network. \u003cstrong\u003e(A)\u003c/strong\u003eVolcano plot of DE-miRNAs retrieved from the GSE113629 dataset. \u003cstrong\u003e(B) \u003c/strong\u003eVolcano plot of DE-mRNAs obtained from the GSE33630 dataset. \u003cstrong\u003e(C) \u003c/strong\u003eVolcano plot of DE-mRNAs in TCGA THCA datasets. \u003cstrong\u003e(D)\u003c/strong\u003e Venn diagram showing the intersected DEmRNAs between GSE33630 and TCGA THCA datasets\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4363244/v1/ab066e1c69390d1374ffa1de.png"},{"id":56176293,"identity":"4f4ded14-1e69-4a9d-9e1a-626289ed93c0","added_by":"auto","created_at":"2024-05-09 13:05:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":601750,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene function enrichment analyses. (A)\u003c/strong\u003e Volcano plot of differentially methylated genes in the GSE97466 dataset. \u003cstrong\u003e(B)\u003c/strong\u003e Volcano plot of differentially methylated mRNAs in TCGA THCA datasets. \u003cstrong\u003e(C)\u003c/strong\u003e Venn diagram that indicating the common hypomethylated as well as hypermethylated genes mRNAs in GSE97466 and TCGA THCA methylation datasets. \u003cstrong\u003e(D)\u003c/strong\u003e Venn diagram that showing the intersected ceRNAs between hypomethylated mRNAs as well as hypermethylated mRNAs. \u003cstrong\u003e(E)\u003c/strong\u003e Gene function enrichment analysis of the overlapping methylated ceRNAs using Metascape. \u003cstrong\u003e(F)\u003c/strong\u003e KEGG pathway analysis of the 51 methylated ceRNAs by Metascape.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4363244/v1/c3dcb2c1c2c0b29ba3f23ddb.png"},{"id":56177073,"identity":"085da3f5-a334-40b1-9a62-166282bf2e4b","added_by":"auto","created_at":"2024-05-09 13:21:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":131240,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival\u003cstrong\u003e \u003c/strong\u003eanalyses of\u003cstrong\u003e \u003c/strong\u003eKaplan-Meier curves of 8 genes in PTC patients. FN1, \u003cem\u003ep\u003c/em\u003e=0.26; ACTIN1,\u003cem\u003e p\u003c/em\u003e=0.0059; TNC,\u003cem\u003e p\u003c/em\u003e =0.099; ITGA2,\u003cem\u003e p\u003c/em\u003e =0.13; TIAM1,\u003cem\u003e p\u003c/em\u003e=0.01; TGFB1,\u003cem\u003ep\u003c/em\u003e=0.064; CLDN1,\u003cem\u003e p\u003c/em\u003e=0.0015; TCF7L1,\u003cem\u003e p\u003c/em\u003e=0.15. The red lines represent high expression, while the black lines represent low expression. HR: hazard ratio\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4363244/v1/40a3152294d051bd527a8a65.png"},{"id":56175835,"identity":"32fb9157-7643-4172-b007-5b5dfe26d68e","added_by":"auto","created_at":"2024-05-09 12:57:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":695223,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKnockout of ITGA2 inhibited proliferation, invasion, and migration of THCA cells\u003c/strong\u003e.\u003cstrong\u003e (A)\u003c/strong\u003e mRNA levels of ITGA2, FN1 and TIAM1 in PTC (n=30) and normal (n=30) thyroid tissues. \u003cstrong\u003e(B)\u003c/strong\u003e Protein levels of ITGA2, FN1 and TIAM1 in PTC and normal thyroid tissues. \u003cstrong\u003e(C)\u003c/strong\u003e mRNA levels of ITGA2 in normal thyroid Nthy-ori3-1 and three PTC cell lines including TPC-1, NPA, and KTC-1. \u003cstrong\u003e(D)\u003c/strong\u003e P Protein levels of ITGA2 in TPC-1 cells after treated with sh-NC or sh-ITGA2. \u003cstrong\u003e(E)\u003c/strong\u003e CCK-8 assay indicated that cell proliferation was suppressed after ITGA2 knockdown. \u003cstrong\u003e(F)\u003c/strong\u003e Transwell assay suggested that knockout of ITGA2 inhibited the invasive and migratory ability of TPC-1 cells. *\u003cem\u003ep\u003c/em\u003e≤0.05； **\u003cem\u003ep\u003c/em\u003e≤0.01；***\u003cem\u003ep\u003c/em\u003e≤0.001.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4363244/v1/5a68f1f7a1e6fe832c272f3c.png"},{"id":56175047,"identity":"ca00f730-49ea-4a09-b710-49db66e0c8b8","added_by":"auto","created_at":"2024-05-09 12:49:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":369034,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMethylation levels of ITGA2 negatively correlated with its expression level. (A)\u003c/strong\u003e ITGA2 expression levels in THCA tumor (n=510) and normal tissue (n=58) in online DNA methylation database. \u003cstrong\u003e(B)\u003c/strong\u003e Methylation level of ITGA2 negatively correlated with its expression level. \u003cstrong\u003e(C)\u003c/strong\u003eMethylation levels at 4 representative methylation positions on ITGA2. \u003cstrong\u003e(D)\u003c/strong\u003eMethylation level of the ITGA2 promoter region in Nthy-ori3-1 and TPC-1 cells\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4363244/v1/045590bde1228d6b39d681a4.png"},{"id":57419210,"identity":"dff0d943-82ca-4de3-9775-27b46d7dbefe","added_by":"auto","created_at":"2024-05-30 12:26:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2998141,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4363244/v1/18f1bc68-8a85-4952-9148-eeff0224dc6b.pdf"},{"id":56175830,"identity":"f8d77c2f-b4f2-4161-837c-74b7f4b3885e","added_by":"auto","created_at":"2024-05-09 12:57:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2022410,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4363244/v1/8c7c502b2ad488ad001a3584.docx"},{"id":56176712,"identity":"9e0fb502-0a03-4123-95d3-a79cec7d4f2a","added_by":"auto","created_at":"2024-05-09 13:13:36","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16947,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4363244/v1/acb7264438425ca37e19a221.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Construction of a competing endogenous RNA network and identification of ITGA2 as a potential target in papillary thyroid carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThyroid carcinoma (THCA) constitutes the prevailing malignancy within the endocrine system, encompassing four distinctive four subtypes: papillary thyroid carcinoma (PTC), follicular thyroid cancer (FTC), medullary thyroid cancer (MTC), and anaplastic thyroid cancer (ATC). Among these, papillary and follicular thyroid cancers are the most common THCA and classified as differentiated THCA. PTC accounts for more than 90% of cases and more frequently occurs in women compared with men\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. While the majority of THCA patients experience favorable prognoses, a subset may confront issues of distant metastasis and recurrence\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Consequently, it is imperative to elucidate the molecular pathogenesis of THCA and unearth precise biomarkers to enhance its prognosis.\u003c/p\u003e \u003cp\u003eNumerous studies have demonstrated that genetic alterations in PTC, encompassing mutations in BRAF and RAS, as well as translocations involving RET/PTC and PAX8/PPARγ chromosomal rearrangements \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Such insights have paved the way for molecular-guided treatment, offering potential benefits to patients. Nonetheless, despite the emergence of a number of biomarkers, comprehensive exploration of effective diagnostic and therapeutic biomarkers for THCA diagnosis and therapy remains incomplete. To identify therapeutic biomarkers, competing endogenous RNA (ceRNA) hypothesis has attracted more attention \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Salmena et al. introduced the ceRNA hypothesis, which posits that the interplay between mRNA and long noncoding RNAs (lncRNAs) can occur through competitive bidding to shared miRNA response elements (MREs).This concept significantly expands our comprehension of the regulatory network within the human genome \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. As a result, an expanding body of research is dedicated to exploring ceRNA-related mechanisms in various cancer types, including breast cancer\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, hepatocellular carcinoma\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, and THCA\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we obtained differentially expressed mRNAs (DE-mRNAs), miRNAs (DE-miRNAs) through a comprehensive analysis of the GEO and TCGA databases via systematic bioinformatics techniques. Overlapped DE-miRNAs and DE-mRNAs were screened out to construct a ceRNA network. Subsequently, we performed function enrichment analyses and survival analyses. we pinpointed 8 genes as pivotal elements closely associated with the prognosis of THCA prognosis. Among these genes, we conducted quantitative real-time polymerase chain reaction (qRT-PCR) and functional assays to validate the expression levels and biological functions of ITGA2. This validation process provides substantial scientific evidence supporting the identification of potential biomarkers and the exploration of the molecular mechanism underpinning PTC.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eData mining and analyzing\u003c/h2\u003e\n \u003cp\u003emRNA, miRNA expression, and DNA methylation data of PTC patients were obtained from both Gene Expression Omnibus (GEO) datasets \u003csup\u003e\u003cspan\u003e11\u003c/span\u003e\u003c/sup\u003e and TCGA data portal (\u003cspan\u003e\u003cspan\u003ehttps://cancergenome.nih.gov/\u003c/span\u003e\u003c/span\u003e). TCGA THCA, GSE33630, and GSE113629 datasets were used to analyze RNA expression profiles. TCGA THCA methylation dataset and GSE97466 dataset were used to analyze DNA methylation profiles. Details of these datasets were listed in Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDetails of the GEO and TCGA datasets\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDataset\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNormal samples\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTumor samples\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE33630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emRNA microarray\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE113629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiRNA microarray\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE97466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDNA methylation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTCGA-THCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTCGA-THCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDNA methylation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003eIdentification of differentially expressed miRNA, mRNA, and DNA methylation\u003c/h2\u003e\n \u003cp\u003eGEO2R (\u003cspan\u003e\u003cspan\u003ehttp://www.ncbi.nlm.nih.gov/geo/geo2r/\u003c/span\u003e\u003c/span\u003e), an interactive web tool, and EdgeR package in R (version 3.4.1) were used to identify differentially expressed genes (DEGs). DE-mRNAs, DE-miRNAs and differentially expressed DNA methylation were identified between the PTC and adjacent normal thyroid tissues according to the threshold criteria (\u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log\u003csub\u003e2\u003c/sub\u003e(fold-change) | \u0026gt;1).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003eConstruction of a ceRNA regulatory network\u003c/h2\u003e\n \u003cp\u003eTo establish a ceRNA network, we firstly chose the top 15 upregulated and 15 downregulated miRNAs ranked by fold change from the GSE113629 dataset. In view of ceRNA pairs targeted by the common miRNA, as a result, the expression levels of one ceRNA would vary with the other ceRNA expression. Therefore, we selected the overlapping DE-mRNAs in the GSE33630 and TCGA THCA datasets and screen out mRNA-mRNA pairs with the positive correlation threshold of Pearson correlation coefficient (R)\u0026thinsp;\u0026gt;\u0026thinsp;0.4 based on their expression matrix. Then, we used RNA22\u003csup\u003e7\u003c/sup\u003e, a web software, to explore miRNA-mRNA interaction. A hypergeometric test was applied to evaluate the possibility of being authentic ceRNA pairs by calculating the p-value of each ceRNA pair using the ratio of the number of common miRNAs in ceRNA pairs to the total number of miRNAs in miRNA-mRNA interaction. Finally, the ceRNA network was visualized by Cytoscape\u003csup\u003e\u003cspan\u003e12\u003c/span\u003e\u003c/sup\u003e (version 3.8.1). The p-value was calculated as the following formula:\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003cp\u003eWhere, x is the number of common miRNAs that interact with the ceRNA pairs (mRNA1 and mRNA2), whose numerical value must be equal to or greater than three. N is the total number of miRNAs in the ceRNA pairs. K is the number of miRNAs that only regulate mRNA 1, and M is the number of miRNAs that merely regulate mRNA 2.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003eGene function enrichment analysis\u003c/h2\u003e\n \u003cp\u003e51 intersected genes were identified by overlapping ceRNA pairs and differentially methylated genes, which were integrated to Metascape\u003csup\u003e\u003cspan\u003e13\u003c/span\u003e\u003c/sup\u003e to perform gene function enrichment analyses including GO biological processes, reactome analysis and KEGG enrichment analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003eSurvival analysis\u003c/h2\u003e\n \u003cp\u003eKaplan-Meier Plotter (\u003cspan\u003e\u003cspan\u003ehttps://kmplot.com/analysis/\u003c/span\u003e\u003c/span\u003e), an online tool for meta-analysis of databases from GEO and TCGA\u003csup\u003e\u003cspan\u003e14\u003c/span\u003e\u003c/sup\u003e, was used to assess the relationship between the expression of mRNA and prognoses of cancer patients.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003ePatients and tissue samples\u003c/h2\u003e\n \u003cp\u003eThis study was approved and supervised by the Ethics Committee of Cancer Hospital, Chinese Academy of Medical Sciences (Approval number: 22/208\u0026ndash;3410). 30 pairs of PTC and adjacent normal tissues were collected from patients that received surgery at our hospital. All patients were pathologically diagnosed with PTC by two pathologists independently. All tissue samples were stored in liquid nitrogen at -80\u0026deg;C.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003eCell culture\u003c/h2\u003e\n \u003cp\u003eThe human normal thyroid cell line (Nthy-ori3-1) and PTC cell lines (TPC-1, NPA, and KTC-1) were obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA). All cells were cultured in DMEM (Gibco, Carlsbad, CA, USA) containing 10% fetal bovine serum (FBS) (Gibco) and incubated in a humidified atmosphere with 5% CO\u003csub\u003e2\u003c/sub\u003e at 37\u0026deg;C.\u003c/p\u003e\n \u003cp\u003eShort-hairpin RNA (shRNA) against ITGA2 (sh-ITGA2) and the negative control shRNA (sh-NC) were purchased from RiboBio (Guangzhou, China). Cell transfection was performed in TPC-1 cells using Lipofectamine 2000 reagent (#11668019, Invitrogen, Carlsbad, CA, USA) following the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003eQuantitative real-time PCR (qRT-PCR)\u003c/h2\u003e\n \u003cp\u003eTotal RNA was extracted from PTC tissues and cell lines using TRIzol reagent (Invitrogen, Carlsbad, CA) following the manufacturer\u0026rsquo;s instructions. The RNA purity was measured at 260 nm (A260/A280\u0026thinsp;\u0026gt;\u0026thinsp;1.8). RNA was reversed into complementary DNA using PrimeScript RT Kit (#RK21400, Abclonal, China). qRT-PCR was performed using SYBR Green PCR Master Mix (QuantiFast, Qiagen, Hilden, Germany) and the expression level of mRNAs was calculated using the 2\u003csup\u003e\u0026minus;\u0026Delta;\u0026Delta;Ct\u003c/sup\u003e method. GAPDH and U6 were used as internal control. Primers used in this study are listed in \u003cstrong\u003eTable \u003cspan\u003eS1\u003c/span\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eMethylation-specific PCR (MSP)\u003c/h2\u003e\n \u003cp\u003eGenomic DNA was extracted from Nthy-ori3-1 and TPC-1 using (QIAamp DNA Blood Mini Kit,Qiagen,Germany). DNA was modified with bisulfite using a CpGenome DNA modification kit (CpGenome DNA modification kit, EMD Millipore, USA). MSP-specific methylated and unmethylated primers of ITGA2 were devised and synthesized by Sangon Biotech (Shanghai) Co., Ltd. The primers for unmethylated ITGA2 CpG islands are as follow: (Left U primer: AGGGTGTTATTTTTATTTTTATTGT; Right U primer༚AATTTCTAAACAACTCCTACAACACC); The primers for methylated ITGA2 CpG islands are as follow: (Left M primer༚TTAGGGCGTTATTTTTATTTTTATC; Right M primer༚AAATTTCTAAACAACTCCTACAACG). PCR was conducted in sequence of denaturation at 95 ℃for 10 min; 35 cycles for denaturation (95\u0026deg;C, 30 s), annealing (60\u0026deg;C, 30 s), extension (72℃40 s); extension at 72 ℃for 5 min. Then the PCR amplified products were loaded on 2% agarose gel, stained with ethidium bromide, visualized under ultraviolet light.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eCCK-8 assay\u003c/h2\u003e\n \u003cp\u003eCell viability was determined using the Cell Counting Kit-8 (DingGuo Bio) according to the manufacturer\u0026rsquo;s instructions. The transfected TPC-1 cells were plated in 96-well plates (Corning, Corning, NY, USA) at a density of 1.0 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e cells per well for 24 h. Then, cells incubated for 0 h, 24 h, 48 h, 72 h, and 96 h were added with 10 \u0026micro;L Cell Counting Kit-8 solution. After incubation at 37 ℃for 2 h, the absorbance at 450 nm was measured by a Microplate Reader (BioTek).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eTranswell assay\u003c/h2\u003e\n \u003cp\u003eTranswell chamber (Snapwell\u0026trade; Inserts,Corning,USA) was used to assess cell migration and invasion. In brief, 2 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e TPC-1 cells suspended in 200 \u0026micro;L serum-free DMEM were plated into the upper chamber with Matrigel matrix (BD Biosciences, Franklin Lakes, NJ, USA). The lower chamber was treated with complete DMEM medium with 10% FBS. After incubation at 37 ℃for 48 h, the TPC-1 cells in the upper chamber were removed, while those in the lower chamber were fixed with methanol and stained with 0.5% crystal violet. The number of TPC-1 cells was counted using a microscope (NIKON Eclipse Ti2).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eWestern blot assay\u003c/h2\u003e\n \u003cp\u003eTotal protein was collected from cell lysate using RIPA lysis buffer (Thermo Fisher Scientific) added with protease inhibitor. Protein concentration was measured using the BCA Protein Assay Kit (Pierce, Appleton, WI, USA). Then, the proteins were resolved by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to polyvinylidene fluoride (PVDF) membranes (Millipore, Billerica, MA, USA). Next, the PVDF membranes were blocked by 5% skim milk at room temperature for 2 h and incubated with primary antibodies, anti-FN1(ab109365, Abcam, Cambridge, UK, 1:1000), anti-ITGA2 (ab181548, Abcam, Cambridge, UK, 1:5000), anti-TIAM1(ab211518, Abcam, Cambridge, UK, 1:1000), anti-GAPDH (ab181602, Abcam, Cambridge, UK,1:10000) at 4 ℃overnight. Then, the horseradish peroxidase (HRP)-conjugated secondary antibody Goat Anti-Rabbit IgG H\u0026amp;L (HRP) (ab205718, Abcam, Cambridge, MA, USA, 1:2000) was added to the membranes and incubated at room temperature for 2 h after washing the membrane three times in TBST. Finally, the protein bands were visualized using ECL Kit (Amersham, United Kingdom) and the intensity of the proteins was normalized by GAPDH as an endogenous reference.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eFor quantitative real-time PCR (qRT-PCR) data, we utilized GraphPad Prism 8 software. Differences between two independent groups were evaluated using Student\u0026rsquo;s t test. A p-value of less than 0.05 was deemed indicative of statistical significance. To present our data transparently, we expressed all values as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). All analyses were two-tailed, and a p-value threshold of 0.05 was maintained throughout to discern statistically significant findings.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003eConstruction of an mRNA-miRNA-mRNA network\u003c/h2\u003e\n \u003cp\u003eUsing the GEO2R tool and \u0026lsquo;EdgeR\u0026rsquo; package, we have screened out thousands of DEGs in PTC from GEO and TCGA datasets with the criteria of \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log\u003csub\u003e2\u003c/sub\u003e(fold-change) | \u0026gt;1. As shown in \u003cstrong\u003eFig.\u0026nbsp;1A\u003c/strong\u003e, DE-miRNAs were identified in the GSE113629 dataset. In view of GSE33630 dataset, a total of 1242 DE-mRNAs were found including 676 upregulated mRNAs and 566 downregulated mRNAs (\u003cstrong\u003eFig.\u0026nbsp;1B\u003c/strong\u003e). Meanwhile, a total of 3169 DE-mRNAs were identified in TCGA THCA dataset, among which 2107 were upregulated and 1062 were downregulated (\u003cstrong\u003eFig.\u0026nbsp;1C\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003eIn order to construct an mRNA-miRNA-mRNA network, we firstly obtained 491 common upregulated DE-mRNAs and 326 common downregulated DE-mRNAs by overlapping the GSE33630 and TCGA THCA datasets (\u003cstrong\u003eFig.\u0026nbsp;1D\u003c/strong\u003e). Then, we selected the top 15 upregulated and 15 downregulated miRNAs from the GSE113629 miRNA microarray. Finally, 160 ceRNA pairs were recognized by several methods including bioinformatic prediction, correlation analysis, and hypergeometric test. The top 20% mRNAs in miRNA-mRNA interaction from this network was shown in \u003cstrong\u003eFig. \u003cspan\u003eS1\u003c/span\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003eGene function enrichment analyses\u003c/h2\u003e\n \u003cp\u003eFurther, DNA methylation analyses were performed in two related datasets. 166 mRNAs were hypermethylated and 1274 mRNAs were hypomethylated in the GSE97466 dataset (\u003cstrong\u003eFig.\u0026nbsp;2A\u003c/strong\u003e). And 380 hypermethylated mRNAs and 1638 hypomethylated mRNAs were identified in the TCGA THCA methylation dataset (\u003cstrong\u003eFig.\u0026nbsp;2B\u003c/strong\u003e). We next identified 970 methylated genes, consisting of 127 hypermethylated and 843 hypomethylated genes, through overlapping these two datasets (\u003cstrong\u003eFig. Figure\u0026nbsp;2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2C\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003eWe intersected above ceRNA pairs and differentially methylated genes in PTC for comprehensive analysis. The results showed that there were 51 dysregulated methylation-related genes including 43 hypomethylated ceRNAs and 8 hypermethylated ceRNAs (\u003cstrong\u003eFig.\u0026nbsp;2D\u003c/strong\u003e). To investigate the function of these 51 genes in PTC, we performed gene function enrichment analysis using Metascape. Figure\u0026nbsp;2E displayed overall enrichment analysis results. It can be concluded that the 51 genes were mainly involved in cell junction, epithelial morphogenesis, metabolism, and the regulation of cancer-related factors. As we focused on THCA, we implemented KEGG pathway analysis to investigate the cancer-associated pathways and found that the 51 genes significantly enriched in ECM-receptor interaction, proteoglycans in cancer, amoebiasis, tight junction, and pathway in cancer (\u003cstrong\u003eFigu. 2F\u003c/strong\u003e), which are associated with carcinogenesis. As a result, these 51 genes were consequently linked with tumorigenesis. Notably, 8 genes named FN1, ACTIN1, TNC, ITGA2, TIAM1, TGFB1, CLDN1 and TCF7L1 significantly involved in the KEGG pathway analysis. We therefore concentrated on these 8 genes in the following study.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\"\u003e\n \u003ch2\u003eACTN1, TIAM1 and CLDN1 significantly associated with PTC patients\u0026rsquo; prognosis\u003c/h2\u003e\n \u003cp\u003eSurvival analyses of these 8 genes in PTC patients were carried out by the means of Kaplan-Meier Plotter (\u003cstrong\u003eFig.\u0026nbsp;3\u003c/strong\u003e). Among them, the expression levels of ACTN1 (p\u0026thinsp;=\u0026thinsp;0.0059), TIAM1 (p\u0026thinsp;=\u0026thinsp;0.01) and CLDN1 (0.0015) had close relationship with PTC patients\u0026rsquo; prognosis. These 3 genes may be biomarkers of THCA.\u003c/p\u003e\n \u003cp\u003eSurvival analyses of Kaplan-Meier curves of 8 genes in PTC patients. FN1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.26; ACTIN1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0059; TNC, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.099; ITGA2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.13; TIAM1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01; TGFB1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.064; CLDN1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0015; TCF7L1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.15. The red lines represent high expression, while the black lines represent low expression. HR: hazard ratio.\u003c/p\u003e\n \u003cp\u003eSurvival analyses of Kaplan-Meier curves of 8 genes in PTC patients. FN1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.26; ACTIN1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0059; TNC, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.099; ITGA2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.13; TIAM1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01; TGFB1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.064; CLDN1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0015; TCF7L1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.15. The red lines represent high expression, while the black lines represent low expression. HR: hazard ratio.\u003c/p\u003e\n \u003cp\u003eSurvival analyses of Kaplan-Meier curves of 8 genes in PTC patients. FN1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.26; ACTIN1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0059; TNC, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.099; ITGA2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.13; TIAM1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01; TGFB1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.064; CLDN1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0015; TCF7L1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.15. The red lines represent high expression, while the black lines represent low expression. HR: hazard ratio.\u003cstrong\u003eFig. 3\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\"\u003e\n \u003ch2\u003eKnockout of ITGA2 inhibited proliferation, invasion, and migration of THCA cells\u003c/h2\u003e\n \u003cp\u003eAs the aforementioned top KEGG pathway was the ECM-receptor interaction, followed by proteoglycan in cancer (\u003cstrong\u003eFig.\u0026nbsp;2F\u003c/strong\u003e), we selected 3 genes (ACTIN1, TIAM1, and CLDN1) that were significantly enriched in the two pathways for experimental validation. qRT-PCR was performed in 30 pairs of PTC tissues and normal thyroid tissues. Interestingly, the mRNA expression levels of FN1 and TIAM1 between PTC and normal thyroid tissues had no significant differences, while the expression level of ITGA2 in PTC tissues was higher than that in the normal tissues (Fig. \u003cspan\u003e4\u003c/span\u003eA). And as expected, the protein level of ITGA2 was notably elevated in three PTC tissues, which was consistent with qRT-PCR result (Fig. \u003cspan\u003e4\u003c/span\u003eB). We therefore chose it for further study.\u003c/p\u003e\n \u003cp\u003eLikewise, the ITGA2 level was also higher in PTC cell lines, including TPC-1, NPA, and KTC-1 cells than that in Nthy-ori3-1 cells (Fig. \u003cspan\u003e4\u003c/span\u003eC). TPC-1 cell line was selected to perform the following experiments, as it possessed the highest level of ITGA2.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eKnockout of ITGA2 inhibited proliferation, invasion, and migration of THCA cells\u003c/strong\u003e. \u003cstrong\u003e(A)\u003c/strong\u003e mRNA levels of ITGA2, FN1 and TIAM1 in PTC (n\u0026thinsp;=\u0026thinsp;30) and normal (n\u0026thinsp;=\u0026thinsp;30) thyroid tissues. \u003cstrong\u003e(B)\u003c/strong\u003e Protein levels of ITGA2, FN1 and TIAM1 in PTC and normal thyroid tissues. \u003cstrong\u003e(C)\u003c/strong\u003e mRNA levels of ITGA2 in normal thyroid Nthy-ori3-1 and three PTC cell lines including TPC-1, NPA, and KTC-1. \u003cstrong\u003e(D)\u003c/strong\u003e P Protein levels of ITGA2 in TPC-1 cells after treated with sh-NC or sh-ITGA2. \u003cstrong\u003e(E)\u003c/strong\u003e CCK-8 assay indicated that cell proliferation was suppressed after ITGA2 knockdown. \u003cstrong\u003e(F)\u003c/strong\u003e Transwell assay suggested that knockout of ITGA2 inhibited the invasive and migratory ability of TPC-1 cells. *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05; **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01༛***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.001.\u003c/p\u003e\n \u003cp\u003eTo deeply explore the effects of ITGA2 on THCA, we knockout ITGA2 in TPC-1 cells (Fig. \u003cspan\u003e4\u003c/span\u003eD) and we performed functional experiments. CCK8 assay showed that ITGA2 knockout significantly suppressed the proliferation of TPC-1 cells (Fig. \u003cspan\u003e4\u003c/span\u003eE). Moreover, transwell assay revealed that knockout of ITGA2 suppressed the invasive and migratory abilities of THCA cells (Fig. \u003cspan\u003e4\u003c/span\u003eF). Overall, these results suggested that ITGA2 knockout suppressed the THCA cells to proliferate, invade, and migrate, indicating ITGA2 may play critical role on THCA carcinogenesis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\"\u003e\n \u003ch2\u003eMethylation levels of ITGA2 negatively correlated with its expression level\u003c/h2\u003e\n \u003cp\u003eSubsequently, we investigated the relationship between ITGA2 methylation and its expression to explore how ITGA2 functions during THCA carcinogenesis though DNA Methylation Interactive Visualization Database(\u003cspan\u003e\u003cspan\u003ehttp://119.3.41.228/dnmivd/query_gene/?cancer=THCA\u0026amp;gene=ITGA2\u0026amp;panel=Summary\u003c/span\u003e\u003c/span\u003e). The results showed that ITGA2 expression level in tumor was higher than that in normal tissue and negatively correlated with its promoter methylation (Fig. \u003cspan\u003e5\u003c/span\u003eA and \u003cspan\u003e5\u003c/span\u003eB). There are 13 methylation position in ITGA2 genes and they all show dropped methylation tendency as ITGA2 overexpressed. Figure \u003cspan\u003e5\u003c/span\u003eC displayed methylation levels at 4 positions on ITGA2 showing significant negative correlation with ITGA2 expression level. In addition, we further conducted MSP assays and found that the promoter of ITGA2 was hypomethylated in TPC-1 cells. In contrast, the methylation level of the ITGA2 promoter was higher in Nthy-ori3-1 cells (Fig. \u003cspan\u003e5\u003c/span\u003eD). Altogether, these findings revealed that ITGA2 was overexpressed in both PTC tissues and cell lines, and its expression was negatively associated with its methylation levels.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite substantial advancements in the diagnosis and therapeutic interventions for THCA, misdiagnoses still afflict a considerable number of individuals. With THCA diagnoses increasingly affecting a younger demographic, there exists an imperative demand for the discovery of dependable biomarkers to enable earlier-stage diagnosis and treatment monitoring. In the present study, our initial endeavor was to establishe an intricate mRNA-miRNA-mRNA network, coupled with an exploration of DNA methylation expression pattern in PTC using rigorous bioinformatics analyses.We systematically scrutinized the functions of potential competing endogenous RNAs (ceRNAs) through pathway enrichment and survival analyses. 8 genes were identified significantly related with THCA prognosis. Finally, we conducted functional assays that affirm ITGA2 contributed to the tumorigenesis of PTC, Moreover, we investigated how the methylation levels of ITGA2 may exert a regulatory influence on the development of THCA.\u003c/p\u003e \u003cp\u003eRecent investigations have uncovered pivotal genes and constructed ceRNA regulatory networks across a spectrum of cancers, including bladder cancer\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, gastric cancer\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, breast cancer\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, and lung adenocarcinoma\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Wang et al. have established MMP9/ITGB1-miR-29b-3p-HCP5 network in pancreatic cancer, offering valuable prognostic biomarkers for patients with pancreatic cancer\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Meanwhile,Zheng et al. demonstrated that the existence STARD13-correlated ceRNA network that curbs the stemness of breast cancer through inhibiting YAP/TAZ activation via Hippo and Rho-GTPase/F-actin signaling\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e,proposing a novel therapeutic strategy for targeting stemness. In the current study, we constructed an mRNA-miRNA-mRNA ceRNA interactive network by integrated bioinformatics analysis and obtained 51 genes through the intersection of ceRNAs and methylated genes. To further elucidate the function of these genes, we conducted comprehensive functional enrichment analyses. Our KEGG pathway analysis uncovered 8 genes (FN1, ACTIN1, TNC, ITGA2, TIAM1, TGFB1, CLDN1 and TCF7L1) enriched in pathways associated with ECM-receptor interaction, proteoglycans, amoebiasis, tight junction, and pathway in cancer. It is well-acknowledged that extracellular matrix interaction and proteoglycans play essential roles in cancer tumorigenesis and progression\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Therefore, we have established a linkage between these eight genes and the regulation of migration and metastasis of THCA. Among these genes, FN1, ITGA2, and TIAM1 have been previously documented as contributors to cancer progression \u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Notably, the overexpression of ITGA2 has been shown to significantly enhance proliferation and invasion of various cancer cells\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In addition, Liu et al. revealed that TIAM1 promotes EMT and and metastasis in THCA through the Wnt/β-catenin pathway mediated by Rac1\u003csup\u003e24\u003c/sup\u003e. Furthermore, Cai et al. demonstrated that FN1 promoted cell proliferation, migration, and invasion while inhibiting apoptosis in colorectal cancer, primarily through its interaction with ITGA5\u003csup\u003e26\u003c/sup\u003e. These results substantiate and support the viability of our study to some extent.\u003c/p\u003e \u003cp\u003eTo further validate the findings from our bioinformatics analysis, we opted to perform experimental investigations on genes enriched in the top KEGG pathway, specifically FN1, ITGA2, and TIAM1, to perform experiments. ITGA2, as the alpha subunit of a transmembrane receptor for collagens, holds a pivotal role in mediating cell adhesion and interactions with the extracellular matrix\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The overexpression of ITGA2 has been linked to enhanced cell proliferation, migration, and invasion in various cancer types, including pancreatic cancer, prostate cancer, hepatocellular carcinoma, and gastric cancer\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In alignment with these previous studies, our analysis revealed elevated expression level of ITGA2 in PTC relative to adjacent normal thyroid tissue, its status as an oncogene in PTC.\u003c/p\u003e \u003cp\u003eIn addition, knockout of ITGA2 significantly inhibited cell proliferation, migration, and invasion of PTC, demonstrating that ITGA2 may plays an essential role on THCA carcinogenesis and suggesting that it may serve as a biomarker for PTC patients. A recent study demonstrated that Ropivacaine, a local anesthetic, can effectively curb cell proliferation, invasion, migration, and promote apoptosis in PTC cells by regulating ITGA2 expression \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.This substantiates our findings and underscores the significance of our study providing credible biomarkers for the treatment of thyroid cancer patients. Additionally, we made a simultaneous discovery that the methylation levels of ITGA2 exhibited a negative correlation with its expression level. Our methylation-specific MSP assay revealed that the ITGA2 promoter displayed hypomethylated in TPC-1 cells, which significantly contrasted with the situation in Nthy-ori3-1 cells. This discovery holds great potential for shedding light on the biological role of ITGA2 in THCA, as well as corresponding mechanism.\u003c/p\u003e \u003cp\u003eIn conclusion, our study has successfully established a novel ceRNA network of THCA. We have pinpointed eight candidate genes that may serve as potential therapeutic biomarkers for individuals diagnosed with thyroid cancer. Furthermore, we have substantiated the significance of ITGA2, which will deeper the understanding of the carcinogenesis and progression of THCA. However, it is important to note that our study has not unveiled the intricate molecular mechanism through which ITGA2 promotes the malignant phenotypes of THCA. Further investigations will necessitate further analyses and experiments to address this knowledge gap.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study constructed an intricate mRNA-miRNA-mRNA regulatory network as well as pinpointed numerous prospective candidates within the domain of thyroid cancer. Furthermore, our findings suggest that ITGA2 could potentially serve as a viable therapeutic target in the treatment of thyroid cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China, Grant/Award Number: 22278349\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthic\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStatement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocols were reviewed and approved by the Ethics Committee of Cancer Hospital, Chinese Academy of Medical Sciences (Approval number: 22/208-3410) All patients provided written informed consent prior to inclusion in the study, following a thorough explanation of the study procedures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RNA expression profiles were derived from the TCGA THCA dataset, as well as GEO datasets GSE33630 and GSE113629. DNA methylation profiles were analyzed using both the TCGA THCA methylation dataset and the GSE97466 dataset. Access to the TCGA data was obtained through the Genomic Data Commons data portal (https://portal.gdc.cancer.gov/), and GEO datasets were accessed through the GEO database (https://www.ncbi.nlm.nih.gov/geo/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCabanillas ME, McFadden DG, Durante C. Thyroid cancer. \u003cem\u003eLancet\u003c/em\u003e. Dec 3 2016;388(10061):2783-2795. doi:10.1016/S0140-6736(16)30172-6\u003c/li\u003e\n\u003cli\u003eFiletti S, Durante C, Hartl D, et al. Thyroid cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-updagger. \u003cem\u003eAnn Oncol\u003c/em\u003e. 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Exosomes-Mediated Transfer of Itga2 Promotes Migration and Invasion of Prostate Cancer Cells by Inducing Epithelial-Mesenchymal Transition. \u003cem\u003eCancers (Basel)\u003c/em\u003e. Aug 15 2020;12(8)doi:10.3390/cancers12082300\u003c/li\u003e\n\u003cli\u003eWang L, Gao Y, Zhao X, et al. HOXD3 was negatively regulated by YY1 recruiting HDAC1 to suppress progression of hepatocellular carcinoma cells via ITGA2 pathway. \u003cem\u003eCell Prolif\u003c/em\u003e. Aug 2020;53(8):e12835. doi:10.1111/cpr.12835\u003c/li\u003e\n\u003cli\u003eMin J, Han TS, Sohn Y, et al. microRNA-30a arbitrates intestinal-type early gastric carcinogenesis by directly targeting ITGA2. \u003cem\u003eGastric Cancer\u003c/em\u003e. Jul 2020;23(4):600-613. doi:10.1007/s10120-020-01052-w\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"competing endogenous RNA, papillary thyroid carcinoma, ITGA2, methylation","lastPublishedDoi":"10.21203/rs.3.rs-4363244/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4363244/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePapillary thyroid carcinoma (PTC) stands as the prevalent malignancy within the endocrine system. This study's primary aim is to probe the domain of potential biomarkers associated with PTC\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eDatasets from GEO and TCGA databases were used to analyze the differentially expressed mRNAs (DE-mRNAs), miRNA (DE-miRNAs), and methylated DNAs, which were further integrated to establish a mRNAs-miRNAs-mRNAs competing endogenous RNA (ceRNA) network by the integrative bioinformatics analyses. Additionally, pathway enrichment analysis was performed to reveal the functions of the ceRNAs by means of Metascape. qRT-PCR and western blot were used to evaluate the expression level of several genes. Methylation-specific PCR was used to assess the methylation levels of Integrin Subunit Alpha 2 (ITGA2) promoter. CCK-8 and transwell assays were used to investigate the biological function of ITGA2.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e160 potential ceRNA pairs were identified from the intersection of mRNA-miRNA-mRNA regulatory network. Simultaneously, 970 methylated genes including 127 hypermethylated and 843 hypomethylated were recognized by overlapping the methylation datasets. Then, we retained 51 methylation-related ceRNA pairs. KEGG pathway enrichment analysis revealed that the 51 genes were primarily involved in ECM-receptor interaction and proteoglycans in cancer. Finally, we demonstrated that ITGA2 acted as an oncogene in thyroid cancer.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study constructed an intricate mRNA-miRNA-mRNA regulatory network as well as pinpointed numerous prospective candidates within the domain of thyroid cancer. Furthermore, our findings suggest that ITGA2 could potentially serve as a viable target in the treatment of thyroid cancer.\u003c/p\u003e","manuscriptTitle":"Construction of a competing endogenous RNA network and identification of ITGA2 as a potential target in papillary thyroid carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-09 12:49:24","doi":"10.21203/rs.3.rs-4363244/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"83d8975f-fb0a-4847-8baf-96c2163c0f35","owner":[],"postedDate":"May 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-30T12:18:19+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-09 12:49:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4363244","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4363244","identity":"rs-4363244","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}