A novel long noncoding RNA, PICSAR, promotes thyroid cancer progression through the hsa-miR-320A/hsa-miR-485/RAPGEFL1 axis

A novel long noncoding RNA, PICSAR, promotes thyroid cancer progression through the hsa-miR-320A/hsa-miR-485/RAPGEFL1 axis | 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 A novel long noncoding RNA, PICSAR, promotes thyroid cancer progression through the hsa-miR-320A/hsa-miR-485/RAPGEFL1 axis Maryam Hejazi, Tahmineh jafari, AmirHossein Yari, Ramin Heshmat, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6403368/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Aug, 2025 Read the published version in Medical Oncology → Version 1 posted 11 You are reading this latest preprint version Abstract Background Among endocrine cancers, thyroid carcinoma (TC) is the most prevalent and ranks sixth in global mortality rates. Aberrant expression of long noncoding RNA (lncRNAs) is associated with the progression of various human cancers, including TC. The role of PICSAR lncRNA (LINC00162) has been validated in different human cancers. Therefore, this study aimed to assess the expression levels and functions of lncRNA PICSAR in thyroid cancer tumorigenesis. This comprehensive approach combined in silico and in vitro methods to explore the molecular mechanisms and clinical significance of PICSAR in thyroid cancer. Materials and Methods This work assessed the expression of the long non-coding RNA LINC00162 and identified differentially expressed genes (DEGs) using the Cancer Genome Atlas (TCGA) database. Interactions among LINC00162, hsa-miR-320A, hsa-miR-485, and RAPGEFL1 were investigated using the LncACT and miRDB databases. Bioinformatics techniques were employed to conduct functional enrichment analysis to clarify the relevant molecular pathways. For the investigation of LINC00162 expression in TC samples, 50 matched samples of thyroid carcinoma and adjacent normal tissue were gathered. Real-time PCR was used to objectively evaluate the expression levels of the targeted genes. Every tissue sample was examined pathologically. A specific siRNA was transfected into a thyroid cancer cell line to examine the functional role of LINC00162. The impact of LINC00162 silencing was then assessed by measuring the level of target genes expression following the transfection. Results Based on TCGA-THCA analysis and qRT-PCR on tissue samples, LINC00162 (PICSAR) was markedly overexpressed in thyroid cancer tissues compared to normal samples. However, no discernible correlation was found between LINC00162 expression and the pathological characteristics of thyroid cancer. Our bioinformatics predictions based on lncRNA-microRNA interactions demonstrate that LINC00162 acts as a molecular sponge for the downregulated microRNAs hsa-miR-320A and hsa-miR-485 in thyroid cancer. RAPGEFL1, a gene associated with the development of thyroid cancer, is upregulated in conjunction with this downregulation. The LINC00162-miRNA-RAPGEFL1 axis is involved in critical carcinogenic processes, including thiamine metabolism, cell cycle control, and folate biosynthesis, according to functional enrichment analysis. Additionally, a bioinformatics study revealed a negative association between PICSAR and the NUDT3 gene, while a positive correlation was found with the SNX18P14 gene. Thyroid cancer cells transfected with LINC00162-specific siRNA showed significant downregulation of LINC00162 and RAPGEFL1, alongside an increase in hsa-miR-320A and hsa-miR-485, ultimately inhibiting the growth of thyroid cancer. These findings suggest that targeting PICSAR may offer a treatment strategy for thyroid cancer by altering important biological processes. Conclusion In conclusion, LINC00162, which is overexpressed in thyroid cancer, acts as a molecular sponge for hsa-miR-320A and hsa-miR-485, regulating key oncogenic pathways and leading to the upreglation of RAPGEFL1. These effects are reversed upon siRNA-mediated silencing of LINC00162, indicating its potential as a promising therapeutic target for thyroid cancer. RAPGEFL1 regulates the Rap signaling pathway, controlling adhesion, migration, polarity, and metabolism to maintain cellular and tissue homeostasis. Its dysregulation is linked to various diseases, highlighting its potential as a therapeutic target. Thyroid cancer LncRNA PICSAR (LINC00162) microRNA ceRNA Computational biology Systems biology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 1. Introduction Globally, thyroid cancer ranks sixth, and among all cancers, it ranks thirteenth [ 1 ]. The rise in the incidence of large and innovative thyroid cancers, as well as mortality from thyroid cancer, suggests that etiological factors may contribute to the increased prevalence of this disease [ 2 ]. Imperative recent advancements in identifying molecular subtypes of thyroid cancer and genetic predisposition factors provide insights into the etiology of the disease [ 3 , 4 ]. Histologically, thyroid tumors are categorized into several classifications: Papillary thyroid carcinoma (PTC), Follicular thyroid carcinoma (FTC), Medullary thyroid carcinoma (MTC), and Anaplastic thyroid carcinoma (ATC) [ 5 ]. Although most thyroid cancers are relatively benign, in some cases they can develop into aggressive and lethality malignant tumors [ 6 ]. Thyroid tumorigenesis is a multistep process that involves the accumulation of numerous hereditary and epigenetic changes. Therefore, understanding the mechanisms of tumorigenesis is essential for the treatment of thyroid cancer [ 7 ]. Recently, it has been observed that long non-coding RNAs (LncRNAs) are involved in cancer progression by interacting with microRNAs and sponging them. Conversely, their roles and molecular mechanisms in thyroid cancer are fundamentally unclear [ 8 ]. For example, microRNAs like hsa-miR-320A and hsa-miR-485, as well as genes like RAPGEFL1, play crucial roles in the development of cancer by influencing the expression of genes and the routes that cells take. With differentiated thyroid cancer, hsa-miR-485 is responsible for regulating metabolic processes such as glycolysis, which in turn promotes tumor development. Within the context of tumor suppression, RAPGEFL1 has an impact on signaling pathways. The RAPGEF1 gene encodes a guanine nucleotide exchange factor critical for activating Rap proteins involved in cell signaling, development, and disease processes. It plays roles in neurodevelopment, cancer progression, and insulin signaling, with mutations linked to developmental abnormalities and type 2 diabetes[ 9 – 11 ]. There is a lack of clarity on the role that hsa-miR-320A plays in thyroid cancer; related miRNAs influence proliferation and differentiation in thyroid cancers[ 12 – 15 ]. The function of various LncRNAs in carcinogenesis has been established [ 16 ]. In thyroid cancer, lncRNAs accomplished as oncogenes or tumor suppressor genes. For example, overexpression of NEAT1 has been identified in PTC tissues and cell lines [ 17 ]. NEAT1 lncRNA enhances cell proliferation, invasion, and migration. Functional studies have demonstrated interactions between this lncRNA and miR-129-5p, resulting in KLK7 overexpression in PTC tissues [ 18 ]. PTCSC3 has been well-known as a tumor suppressor for LncRNAs in thyroid tissues [ 19 ]. Overexpression of PTCSC3 LncRNAs in thyroid cancer cells has led to inhibition of cell growth, cell cycle arrest, and increased apoptosis. The inverse correspondence between miR-574-5p and PTCSC3 has also been confirmed at in vitro conditions [ 20 ]. Studies have highlighted the role of lncRNA PICSAR(LINC00126) in the progression of cancers such as pancreatic cancer (PC)[ 21 ], DA neurons[ 22 ], rheumatoid arthritis (RA) [ 23 ], gastric cancer (GC) [ 24 ], and cutaneous squamous cell carcinomas (cSCCs) [ 25 ]. PICSAR(LINC00126) is upregulated in these cells. However, no study has been conducted to evaluate the expression of LINC00126 in TC. This research aimed to investigate the function of PICSAR in thyroid cancer by examining its relationships with microRNAs and its potential impact on TC progression. To assess the expression of PICSAR and associated genes, the research employed bioinformatics techniques to analyze data from the TCGA database. The LncACT and miRDB databases were utilized to evaluate lncRNA-microRNA interactions, followed by functional enrichment analysis. RNA extraction, cDNA synthesis, and real-time PCR were performed on 50 matched thyroid cancer and normal tissue samples to validate the results. Additionally, thyroid cancer cell lines were transfected with PICSAR-specific siRNA to evaluate changes in gene expression and the impact on pathways related to thyroid cancer. 2. Methods and Materials 2.1. In-silico analysis 2.1.1. Bulk RNA analysis The TCGAbiolinks package [ 26 ] was used to obtain the RNA-seq gene expression data for the Thyroid Cancer Genome Atlas (TCGA-THCA), which was then normalized and analyzed using the limma package[ 27 ] in the R programming language. Using the Gepia online tool ( https://gepia.cancer-pku.cn/ ) normal tissues were obtained from the GTEX dataset, whereas tumoral tissues were sourced from the TCGA dataset, to compare RNA expression. The expression level and ROC curve of the LINC00162 non-coding gene were analyzed using GraphPad Prism software ( https://www.graphpad.com/ ). To explore the relationship between LINC00162 expression and the pathogenic data as well as pan-cancer analysis, the TCGA RNA seq was queried using the Ualcan web tool ( https://ualcan.path.uab.edu/ )[ 28 , 29 ]. 2.1.2. lncRNA-microRNA interaction The competing endogenous RNA network, also known as the LncRNA-microRNA network, was investigated to identify the competing molecules that act as microRNA sponges following the significant dysregulation of lncRNA in thyroid cancer. To achieve this, the LncACT online tool[ 30 , 31 ] ( http://bio-bigdata.hrbmu.edu.cn/LncACTdb/ ) was employed to analyze the interaction between lncRNAs and microRNAs interact. The TCGA-THCA was queried using the Ualcan online tool to evaluate the expression levels of the microRNAs'. 2.1.3. microRNA-mRNA interaction The microRNA-mRNA interaction network was reconstructed following the identification of an LncRNA-microRNA axis that exhibits significant dysregulation and coordination across pathways. The miRDB database[ 32 ] ( https://mirdb.org/ ) was employed for this specific task. This database was employed to identify the microRNAs selected to target the candidate mRNAs. The study's findings indicate that microRNAs might be employed as both therapeutic and diagnostic markers for thyroid cancer. The Ualcan web tool was used to query the TCGA-THCA and determine mRNA expression levels. 2.1.4. Functional enrichment analysis To classify the pathways associated with PICSAR (LINC00162),. we first obtained PICSAR’s co-expressed LncRNAs and co-expressed genes (both negatively and positively correlated) using the web resources lncHUB2 ( https://maayanlab.cloud/lncHUB2/ ). The LNCHUB2 online tool was also used for query the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to identify biological pathways influenced by this lncRNA. All data were visualized graphically in the Cytoscape software ( https://cytoscape.org/ ). 2.2. Experimental validation 2.2.1. Thyroid cancer tissue samples 2.2.1.1. Samples collection and preparation In this study, fifty TC and fifty paired normal tissue samples were obtained from several hospital of Tabriz and Shariati Hospital, Tehran, from 2021 to 2022 with their consent. Prior to surgery, no patient underwent chemotherapy or radiation treatment. Samples were frozen in liquefied nitrogen and stored at − 80°C until RNA extraction. All clinicopathological features including tumor size, age, lymphatic and vascular invasion, capsular invasion were availablein patient profiles. 2.2.1.2. RNA extraction and cDNA synthesis The tissue specimens were ground in liquid nitrogen using a mortar and pestle, then transferred to a lysis buffer, and homogenized using a needle and syringe. RNA extraction was performed using Trizol reagent (GeneAll, Korea) in according to tothe provided procedure instructions. The quantification and assessment of RNA quality were performed using a NanoDrop spectrophotometer.Using the BioFACT cDNA synthesis kit (Korea), cDNA was synthesized from 1 µg of total RNA in a final volume of 20 µl, following the typical procedure (50°C in 30 min for Reverse transcription, 95° C in 5 min for Inactivation of RT enzyme, 10°C in infinitive for end) with the thermocycler PCR. 2.2.1.3. Quantitive Real Time PCR (qRT-PCR) The BioFACT™ 2X Real-Time PCR Master Mix and gene-specific primers were used to conduct Real Time PCR. The total volume of the reaction was established at 20 µl. The reaction was conducted in a tripartite steps, as outlined below: Step 1: Heat the holding stage or first heat to 95°C for a duration of 2 minutes. Step 2 involves doing 45 cycles of denaturation at 95°C for 10 seconds, primer annealing at 60°C for 30 seconds, and extension at 72°C for 30 seconds. Step 3: Melting curves were generated at the end of each run. Data normalization was performed using the GAPDH gene. The 2 − ΔΔCt technique was used to estimate the expression of target genes. The primers were designed using the NCBI Primer 3 online facility ( https://www.ncbi.nlm.nih.gov/tools/primer-blast/ ). Table 6 presents the qRT-PCR primer sequences. Table 6 qPCR primer sequences Target gene Forward (5′ – 3′) Reverse (5′ – 3′) U6 GCTTCGGCAGCACATATACTAAAAT CGCTTCACGAATTTGCGTGTCAT GAPDH AAGGTGAAGGTCGGAGTCAAC GGGGTCATTGATGGCAACAA LINC00162(PICSAR) GCACCTAAACGGGGAGATGG GATTCCGCAGCAAGTTGGTG hsa-mir-320A CGACGGAAAAGCTGGGTTGAGA CCAGTGCAGGGTCCGAGGTA hsa-mir-485 TGTTTTTTAGAGGCTGGCCG CCAGTGCAGGGTCCGAGGTA RAPGEFL1 TCCGTAAAGATTCCAGAGAAG GGTGGACATAAATTCTACTGC U6 Stem-loop 5´-GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACAAAAATAT − 3´ hsa-mir-320A Stem loop 5´-GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTCGCCC-3´ hsa-mir-485 Stem loop 5´- GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACAGAATTC-3´ 2.2.2. Thyroid cancer cell line 2.2.2.1. Cell culture The human thyroid cancer cell line BCPAP was purchased from the Pasteur Institute of Iran. The cells were cultured in RPMI-1640 medium (Gibco, USA) supplemented with 10% fetal bovine serum (FBS) (Gibco, USA) and 1% penicillin-streptomycin (100 U/mL penicillin and 100 µg/mL streptomycin) (Sigma-Aldrich, USA). Cells were maintained at 37°C in a humidified atmosphere with 5% CO₂. The medium was replaced every 2–3 days, and the cells were passaged when they reached 80–90% confluency using 0.25% trypsin-EDTA (Gibco, USA). For experimental assays, 1.5 × 10 5 cells were seeded per well and allowed to adhere overnight before the siRNA transfection. 2.2.2.2. siRNA transfection Once the BCPAP cells reached optimal confluency, they underwent siRNA transfection with PICSAR-specific siRNA to evaluate its effect on gene expression. Transfection was performed using Lipofectamine™ 3000 Transfection Reagent (Thermo Fisher Scientific, USA), following the manufacturer’s instructions. A PICSAR-specific siRNA (sequence:) was used, while a scramble siRNA (sequence: 5' GACGATAATTAACATTCTTCACTA 3') served as a negative control to assess transfection efficiency and specificity. Seeded cells transfected with 60 pM of siRNA. Optimize dose and time of transfection are investigated in our another unpublished paper. 2.2.2.3. Quantitative PCR After 48 hours of incubation, total RNA was extracted from three groups: untransfected cells, negative control (scramble siRNA-transfected) cells, and PICSAR siRNA-transfected cells, using the appropriate RNA isolation kit (eGeneAll, Korea). The extracted RNA was quantified, and cDNA synthesis was performed for each group using a reverse transcription kit (AddScript cDNA Synthesis Kit)). qRT-PCR was then conducted to assess the expression levels of PICSAR, hsa-miR-320A, hsa-miR-485, and RAPGEFL1, to evaluate silencing efficiency and biological effect. 2.3. Statistical analysis The assessment of qRT-PCR outcomes and visualizing the graphs were conducted using GraphPad Prism software V8. Differences in PICSAR(LINC00162) expression levels between samples were finalized using an unpaired t-test and one way-Anova. A significant P value was quantified to less than 0.01. 3. Results 3.1. In-silico analysis 3.1.1. LINC00162 is noticeably upregulated in thyroid cancer. The information obtained from TCGA regarding the transcriptome of thyroid cancer indicates that LINC00162 is significantly upregulated in this cancer. Analysis of RNA-seq datasets from the TCGA database reveals that the expression level of LINC00162 markedly increases in thyroid cancer compared to normal tissues [Figure 1 a]. The potential of this lncRNA to serve as an investigative biomarker was subsequently evaluated. The accuracy of the data collecting process was demonstrated by plotting the receiver operating characteristic curve (ROC) and Area Under Curve (AUC) using GraphPad Prism software [Figure 1 b and c]. The study results indicate that the expression level of LINC00126 is significantly increased across various types and stages of thyroid cancer [Figure 2 ]. 3.1.2. Dysregulation of LINC00162 is significant in all cancers. PICSAR (P38 inhibited cutaneous squamous cell carcinoma-associated lincRNA), also known as LINC00162, is dysregulated in various cancer types according to the TCGA database. In a comprehensive pan-cancer investigation, thyroid cancer was included. To thoroughly understand the dysregulation of LINC00162, data from over 11,000 patients across more than 30 kinds of cancer were collected. This extensive study revealed consistent patterns of LINC00162 expression and demonstrated strong statistical capabilities to differentiate between malignant and healthy tissues. A pan-cancer analysis using the Ualcan database discovered that LINC00162 was dysregulated in all malignancies, including thyroid carcinoma (Fig. 3 ). 3.1.3. lncRNA-microRNA interaction A significant connection between the lncRNA LINC00162 and the miRNAs hsa-miR-320A and hsa-miR-485 in thyroid cancer was uncovered through the LncACT database's exploration of the LncRNA-miRNA network. LINC00162, a notably dysregulated lncRNA, may act as a competitive endogenous RNA (ceRNA), influencing the progression of thyroid cancer by modulating the activity of hsa-miR-320A and hsa-miR-485. The expression of LINC00162 is negatively correlated with hsa-miR-320A/hsa-miR-485, indicating that the LINC00162-miRNA axis plays a crucial role in the pathophysiology of thyroid cancer. Data from the TCGA-THCA database show that hsa-miR-320A and hsa-miR-485 are significantly downregulated across thyroid cancer stages and subtypes. These miRNAs are associated to advanced tumor characteristics and poorer clinical outcomes, suggesting that they could serve as valuable targets for diagnosis and treatment. Table 2 outlines their interactions with LINC00162, while Figs. 4 and 5 illustrate their persistent downregulation. These results highlight the need for more research to fully understand how miRNA downregulation affects thyroid carcinoma survival and disease progression. Table 2 Detailed display of the interaction between PICSAR and hsa-miR-320A and hsa-mir-485. LncRNA microRNA species experimental Pubmed ID reference LINC00162 hsa-miR-320A Homo sapiens (human) luciferase reporter assays;qRT-PCR;Northern blot assay 26539909 [ 33 ] LINC00162 hsa-miR-485 Homo sapiens (human) qPCR;Western blot 34255190 [ 34 ] 3.1.4. microRNA-mRNA interaction Using the miRDB database, the thyroid cancer microRNA-mRNA interaction network was analyzed. RAPGEFL1 mRNA was identified as a common target of hsa-miR-320A and hsa-miR-485. Due to the strong negative correlation between RAPGEFL1 mRNA and the levels of hsa-miR-320A and hsa-miR-485, a reduction in these levels may lead to an increase in RAPGEFL1 mRNA, potentially inducing thyroid cancer. These findings suggest that hsa-miR-320A, hsa-miR-485, and RAPGEFL1 mRNA could serve as therapeutic targets and diagnostic biomarkers for thyroid cancer. Further research is required to explore their therapeutic applications in managing thyroid cancer and to understand the mechanisms of this regulatory axis. An extensive analysis of the interactions between hsa-miR-320A, hsa-miR-485, and RAPGEFL1 is provided in Table 3 . RAPGEFL1 expression is significantly upregulated in thyroid cancer (TCGA-THCA) (Fig. 6 ) and remains overexpressed across all types and stages of thyroid cancer (Fig. 7 ). Table 3 Detailed display of the interaction between hsa-miR-320A/hsa-miR-485 and RAPGEFL1. miRNA Target Gene Gene Description miRDB target score hsa-miR-320a RAPGEFL1 Rap guanine nucleotide exchange factor-like 1 61 hsa-miR-485 RAPGEFL1 Rap guanine nucleotide exchange factor-like 1 85 3.1.5. LINC00126 is vital in a broad number of biological functions To identify co-expressed genes associated with LINC00126, we searched the TCGA database using the available tool lncHUB2. Consequently, 200 genes were found to have expression levels that are correlated with LINC00126. Figure 8 illustrates the network of positively co-expressed genes, while Fig. 9 presents the network of negatively co-expressed genes. Table 4 lists the highly ranked co-expressed lncRNAs, along with negatively and positively co-expressed genes with LINC00162 including their Pearson's Correlation Coefficient. The analysis of the KEGG pathway database using the LNCHUB2 online tool showed that LINC00162 is involved in various biological pathways including folate biosynthesis, thiamine metabolism, cell cycle, nucleotide excision repair, pyrimidine metabolism, DNA replication, and various essential pathways. Figure 10 illustrates the pathways associated with the LINC00162. Table 4 High ranking co-expressed lncRNAs, negatively and positively co-expressed genes with LINC00162 Positively co-expressed genes Pearson's Correlation Coefficient Negatively co-expressed genes Pearson's Correlation Coefficient Co-expressed LncRNAs Pearson's Correlation Coefficient SNX18P14 0.45959943532943726 NUDT3 -0.14219123125076294 LINC00165 0.5769169926643372 SLC9A3P2 0.45317313075065613 SCAF8 -0.1392144411802292 RP11-126O1.2 0.4507526457309723 RP11-126O1.2 0.4507526457309723 HNRNPK -0.13595622777938843 ENSG00000287791 0.43986520171165466 ENSG00000287791 0.43986520171165466 C16orf72 -0.13571712374687195 RP11-473E2.3 0.43413230776786804 ENSG00000236750 0.4384653866291046 FGD5-AS1 -0.13548369705677032 ENSG00000288860 0.43351155519485474 RP11-473E2.3 0.43413230776786804 ADD1 -0.13486593961715698 RP11-158I23.1 0.43316972255706787 ENSG00000288860 0.43351155519485474 PTBP3 -0.13475295901298523 ENSG00000290024 0.4269241392612457 VTA1P1 0.4334138333797455 WAC -0.13172028958797455 CTD-2139B15.5 0.4231734871864319 RP11-158I23.1 0.43316972255706787 TM9SF3 -0.13125644624233246 CTD-2062F14.3 0.42309385538101196 MIR3619 0.43224406242370605 MTPN -0.13018518686294556 MYO16-AS1 0.4193621575832367 3.2. Experimental validation 3.2.1. Thyroid cancer tissue samples 3.2.1.1. LINC00162 is significantly overexpressed in thyroid cancer tissue samples. The expression of LINC00162 was examined in 50 TC tumors and 50 normal samples using qRT-PCR. The analysis of qRT-PCR data revealed that the expression of LINC00162 was significantly increased (P-value < 0.0001) in TC tissue samples compared to paired normal samples, which confirmed the results of the TCGA-THCA dataset (Fig. 11a). ROC curve analysis suggested that the expression of LINC00162 serves as a biomarker for diagnostic purposes (Fig. 11b). It is important to note that the TCGA samples which which encompass a large number of patient roles, could be significant and distinctly differentiate between patients and normal individuals. 3.2.1.2. Pathology characteristics of LINC00162. The correlation between pathological characteristics and LINC00162 expression was examined in TC patients. There was not significant association between LINC00162 expression and age (p = 0.1256) (Fig. 12 a), Tumor size (p = 0.4068) (Fig. 12 b), lymph node metastasis (p = 0.0780) (Fig. 12 c), Vascular invasion (p = 0.6809) (Fig. 12 d), Gender (p = 0.3711) (Fig. 12 e), Capsular invasion (p = 0.9142) (Fig. 12 f), and hemorrhage(p = 0.9859) (Fig. 12 g). Table 5 presents the relationship between LINC00162 expression and pathological characteristics. Table 5 association between LINC00162 expression and pathological characteristics. Properties p-value expression Age 40 Gender Male 0.3711 Female Tumor size (mm) 0.4068 < 15 ≥ 15 lymph node metastasis 0.0780 YES NO Vascular invasion Yes 0.6809 No capsular invasion Yes 0.9142 No hemorrhage YES 0.9859 NO 3.2.1.3. hsa-miR-320A and hsa-miR-485 are significantly downregulated in thyroid cancer tissue samples qRT-PCR analysis revealed that thyroid cancer tissues expressed significantly lower levels of hsa-miR-320A (Fig. 13 a) and hsa-miR-485 (Fig. 13 c) compared to matched normal tissues, so tumor samples showing an average expression about four times less (P < 0.0001). ROCcurve analysis was employed to assess the diagnostic capabilities of these miRNAs. The area under the curve (AUC) for hsa-miR-320A indicated strong diagnostic accuracy at 0.9648, (Fig. 13 b), while hsa-miR-485 demonstrated an AUC of 0.8016 (Fig. 13 d), both with P-values < 0.0001. Given their high sensitivity and specificity, our findings suggest that hsa-miR-320A and hsa-miR-485 could serve as valuable biomarkers for distinguishing thyroid cancer from healthy tissues, potentially facilitating early detection and disease monitoring. 3.2.1.4. RAPGEFL1 is significantly upregulated in thyroid cancer tissue samples. qRT-PCR analysis shows that RAPGEFL1 expression is significantly increased in thyroid cancer, with a mean expression level 6.7 times higher in tumor samples compared in matched normal tissues (P < 0.0001). ROC curve analysis was conducted to further illustrate RAPGEFL1's diagnostic capability. The results showed an area under the curve (AUC) of 0.9972 (P < 0.0001), which indicates exceptional diagnostic accuracy. According to these results, RAPGEFL1 is a gene that is substantially elevated in thyroid cancer. It may also help with early detection and disease monitoring, and it may serve as a viable biomarker for distinguishing between malignant and normal tissues (Fig. 14 ). 3.2.2. Thyroid cancer cell line 3.2.2.1. PICSAR Silencing Downregulates RAPGEFL1 and Upregulates hsa-miR-320A and hsa-miR-485 in BCPAP Thyroid Cancer Cells BCPAP cells were utilized to evaluate the silencing of PICSAR (LINC00162) overexpression by transfection of PICSAR-specific siRNA and its effects on expression of RAPGEFL1, hsa-miR-320A, and hsa-miR-485. Our results show that PICSAR siRNA transfection significantly decreased PICSAR expression, as illustrated in Fig. 15 , with a fold change reduction of approximately 0.2 in comparison to the untransfected (P < 0.0001) and negative control (scramble siRNA-transfected) groups. These results indicate that PICSAR in the siRNA-transfected cells has been effectively silenced. Similarly, PICSAR siRNA transfection led to a notable downregulation of RAPGEFL1 expression when compared to the control groups (P < 0.0001), suggesting a potential link between PICSAR silencing and RAPGEFL1 regulation. In contrast, PICSAR siRNA-transfected cells exhibited a significant upregulation of hsa-miR-320A and hsa-miR-485 expression. Compared to the control groups, hsa-miR-320A expression changed by around 2.5 fold (P < 0.001), While hsa-miR-485 expression increased more dramatically, changing by about 5.5 fold (P < 0.0001). Our findings demonstrate that PICSAR silencing has a substantial impact on the expression of both coding (RAPGEFL1) and non-coding (hsa-miR-320A, hsa-miR-485) RNAs, highlighting its regulatory role in gene regulation within BCPAP cells (Fig. 15 ). 4. Discussion TC is the most common malignant internal secretion tumor, and its incidence has rapidly increased in recent years [ 35 ]. The precise pathogenesis of this disease remains fundamentally unknown. Therefore, early diagnosis and identifying genetic and environmental factors will aid in developing diagnostic, treatment, and cancer prevention strategies [ 36 ]. In addition to genetic changes, other procedures, such as lncRNA, are involved in nearly all stages of tumor development, including cell proliferation, survival, and metastasis.LncRNAs are a group of RNAs longer than 200 nucleotides that do not encode proteins to date. Numerous cancer-related lncRNAs have been identified that play a significant role in thyroid tumorigenesis [ 37 ]. Given the fundamental role of lncRNAs in cancer progression and tumorigenesis, this study selected a lncRNA called PICSAR (LINC00126) to investigate its role in thyroid cancer. PICSAR, which is also called LINC00126, is a long noncoding RNA (lncRNA) that has important parts in how cancer grows and in inflammatory diseases. PICSAR helps cutaneous squamous cell cancer (cSCC) grow and move by turning on the ERK1/2 signaling pathway and decreasing DUSP6, a protein that stops tumors from growing. This control system shows its cancerous role in the growth of skin cancer[ 38 ]. To date, no study has evaluated the involvement of PICSAR(LINC00126) gene expression in TC. The findings indicated that, compared to normal tissues, TC tumor tissue exhibited significantly higher expression of PICSAR(LINC00126). Increased expression of PICSAR(LINC00126) was not associated with clinical pathological variables such as age, stage, grade, and lymph node metastasis. Bioinformatics and experimental analysis revealed that PICSAR may influence thyroid cancer progression through interactions with microRNAs hsa-miR-320A and hsa-miR-485, acting as a molecular sponge. This sponging leads to the downregulation of these tumor-suppressive miRNAs, which, in turn, upregulates the expression of RAPGEFL1, a gene associated with cancer progression. Functional enrichment analysis indicated that the PICSAR-hsa-miR-320A/hsa-miR-485-RAPGEFL1 axis is implicated in critical oncogenic pathways, including thiamine metabolism and DNA replication. The RAPGEFL1 gene (C3G) is an important part of the Rap signaling system because it helps Rap proteins, especially Rap1 and Rap2, exchange guanine nucleotides. These small GTPases play a key role in many biological processes, such as binding, movement, growth, and differentiation. Rap proteins are turned on by RAPGEFL1, which speeds up the exchange of GDP for GTP. This changes the proteins from inactive to active. Once they are turned on, Rap proteins work with different effectors further down the line to make important biological reactions happen. Controlling how cells stick together and move is one of the main jobs of RAPGEFL1 in the Rap signaling system. When RAPGEFL1 turns on Rap1, it improves integrin-mediated binding, which is necessary for keeping cell-cell and cell-matrix interactions going. For example, when Rap1 is turned on in endothelial cells, it increases adherens junctions, keeps cell-cell bonds stable, and helps keep the vascular barrier intact. These steps are very important for the growth of blood vessels. Rap1 helps organize endothelial cells and make nitric oxide, which makes sure that the vessels work right and respond properly to shear stress[ 39 ]. In addition to its structural roles, RAPGEFL1 connects Rap signals to other pathways to change how cells work. For instance, turning on Rap1 improves PI3K-Akt signaling, which helps cells stay alive and grow. It is also connected to the ERK pathway, which changes how cells differentiate and how the cytoskeleton is organized. RAPGEFL1 is part of the Rap signaling system and helps control cell movement and growth in unhealthy conditions like cancer. In some situations, it can either cause cancer or stop tumors from growing. In some cancers, its abundance helps the tumor grow, while in others, its methylation state affects how well the tumor is suppressed[ 10 ]. The RAPGEFL1 gene, as well as microRNAs like hsa-mir-320A and hsa-mir-485, have been implicated in the progression of cancer, including thyroid cancer, according to recent research. MicroRNAs regulate cellular functions by influencing the expression of target genes that are engaged in critical pathways, including cell growth, differentiation, and metabolism. This regulation is especially important in malignancies, where dysregulated microRNAs contribute to uncontrolled proliferation and other malignant characteristics. As an example, it has been discovered that hsa-miR-485 plays a significant part in the metabolic processes that occur inside cancer cells. It has an effect on the pathways that are associated with glucose absorption and glycolysis. hsa-miR-485 interacts with circular RNAs, such as hsa_circ_0023990, in dedifferentiated thyroid tumors. These circular RNAs further control oncogenic proteins, such as the FOXM1 protein. The hsa-miR-485/FOXM1 axis is responsible for increased glycolysis and cell proliferation, which in turn promotes the formation and progression of tumors in aggressive kinds of thyroid cancer. The existence of this association highlights the potential of hsa-miR-485 as a therapeutic target, whereby the modification of its levels has the potential to hinder the metabolic advantage that the cancer has and to lessen its aggressiveness [ 12 ]. In spite of the fact that there has been very little direct study conducted on hsa-mir-320A in thyroid cancer, studies on microRNAs that are comparable to it indicate that it may have a role in cellular processes such as differentiation and proliferation. As an example, it has been discovered that some microRNAs, such as hsa-miR-152-3p and hsa-miR-196a, control pathways that are essential for the advancement of thyroid cancer. These microRNAs affect cell adhesion, migration, and proliferation. These microRNAs normally work by interacting with certain signaling pathways. Furthermore, they have the ability to act as biomarkers for aggressive phenotypes of thyroid cancer, which further emphasizes the therapeutic potential of microRNA regulation [ 14 ]. It has also been shown that the gene RAPGEFL1 can restrict tumor development in research conducted on renal cell carcinoma. Specifically, a circular RNA that is generated from this gene, known as cRAPGEF5, was discovered to impede the growth and migration of cancer cells. Even though RAPGEFL1's direct effect in thyroid cancer has not yet been completely elucidated, the fact that it regulates important signaling pathways that are crucial in cell proliferation and survival implies that it may have a comparable impact on thyroid cancer. An example of this would be the interaction between cRAPGEF5 and miR-27a-3p in renal cell carcinoma. This interaction targets TXNIP, a gene that helps restrict the proliferation of cancer cells. The information provided by this pathway demonstrates that RAPGEFL1 may be relevant to a variety of cancer types and highlights the potential of this protein as a therapeutic target [ 13 ]. Additionally, significant correlations were identified between PICSAR and other genes, such as a positive correlation with SNX18P14 and a negative correlation with NUDT3. Silencing PICSAR in thyroid cancer cell lines resulted in the downregulation of RAPGEFL1 and upregulation of hsa-miR-320A and hsa-miR-485, indicating its regulatory role in gene expression (Fig. 16 ). ROC curve analysis validated the potential of PICSAR as a highly accurate diagnostic biomarker for thyroid cancer. The findings suggest that targeting PICSAR could represent a viable therapeutic strategy in thyroid cancer, necessitating further research into its biological functions and therapeutic applications. This work discovers new insights into the role of long noncoding RNA PICSAR (LINC00162) in TC and compelling evidence that it may serve as both a biomarker and a treatment target. Several key conclusions have emerged from a comprehensive investigation employing both experimental and bioinformatics approaches; these are detailed in the following sections. Utilizing qRT-PCR on paired samples and analyzing the Cancer Genome Atlas (TCGA) data, this study confirms that PICSAR (LINC00162) is significantly elevated in thyroid cancer tissues compared to normal tissues. A related study by Chen et al. (2022) looked at LINC00162 expression in pancreatic cancer. According to their research, advanced clinical stages and metastases in pancreatic cancer are strongly correlated with elevated expression of LINC00162. The expression of LINC00162 in pancreatic cancer tissues was evaluated in this work using qRT-PCR and ROC curve analysis. Their findings suggest that LINC00162 has potential as a non-invasive diagnostic tool for early-stage pancreatic cancer [ 40 ]. Through sponging miR-4701-5p in fibroblast-like synoviocytes, Bi et al. (2019) discovered that LINC00162 enhances cell proliferation and migration in RA patients. The research found that LINC00162 may be a therapeutic target in RA, using qRT-PCR and cell proliferation tests [ 23 ]. The function of LINC00162 in diabetic nephropathy was studied by Fan et al. (2020), who found that its expression is increased and linked to the miR-383/HDAC9 signaling pathway. Findings from the qRT-PCR and pathway analysis studies point to LINC00162's potential role as a biomarker for diabetic nephropathy [ 41 ]. According to research by Wang et al. (2020), LINC00162 is upregulated in cutaneous squamous cell carcinoma (SCC) tissues and promotes cell migration and proliferation through MAPK/ERK and cyclin-dependent kinases. Using qRT-PCR and cell migration and proliferation assays, they concluded that LINC00162 may serve as a biomarker for SCC and a possible target for therapy [ 42 ]. Another study by Wang et al. (2020) examined LINC00162 in bladder cancer and found that it was significantly overexpressed in bladder cancer cell lines,correlating with increased proliferative activity through interacting with chromatin modifiers and activating oncogenic pathways. The findings, which relied on qPCR and cell proliferation tests, suggest that LINC00162 act as an oncogene in bladder cancer and has therapeutic potential [ 43 ]. In summary, PICSAR (LINC00162) is elevated across a range of cancers and may serve as a diagnostic biomarker or treatment target, although its precise role may vary depending on the pathology.. Its potential significance in cancer biology and treatment is underscored by its consistent upregulation. Table 7 details the role of LINC00162 in various diseases. This pioneering work marks a significant advancement in our understanding of this prevalent disease, revealing the crucial role of the long noncoding RNA PICSAR (LINC00162) in thyroid cancer. This study establishes PICSAR as a highly accurate diagnostic biomarker by demonstrating its substantial increase in thyroid carcinoma tissuesUtilizing advanced bioinformatics techniques, the research identifies PICSAR as a viable therapeutic target by illustrating its nvolvement in critical biological processes like DNA repair and cell cycle control. Novel gene correlations, including NUDT3 and SNX18P14, provide new information on the molecular dynamics of thyroid cancer and may lead to innovative therapeutic strategies. This study not only enhances our understanding of thyroid cancer's molecular basis of thyroid cancer but also paves the way for future research Table 7 The role of LINC00162 in various diseases in detail. Study Disease Main Findings Methodology Conclusion Reference Present study (2024) Thyroid Cancer Thyroid cancer increased PICSAR (LINC00162) more than normal tissues. No meaningful pathogenic connection. qPCR, TCGA analysis The potential diagnostic biomarker for thyroid cancer might be PICSAR (LINC00162) upregulation. (Hejazi et al., 2024) Chen et al. (2022) Pancreatic Cancer There was a strong correlation between advanced clinical stages and metastases and elevated LINC00162 expression. qPCR, ROC curve analysis for early pancreatic cancer using LINC00162 as a non-invasive diagnostic marker. [ 40 ] Bannon et al. (2015) Cocaine Abuse Midbrain regions of cocaine users with dysregulated long non-coding RNAs, including LINC00162. RNA sequencing, transcript analysis Expression of LINC00162 could have a function in neuroadaptation and is linked to cocaine usage. [ 22 ] Bi et al. (2019) Rheumatoid Arthritis In fibroblast-like synoviocytes, LINC00162 sponging miR-4701-5p enhances cell proliferation and migration. qPCR, cell proliferation assays A possible therapeutic target for rheumatoid arthritis is LINC00162. [ 23 ] Fan et al. (2020) Diabetic Nephropathy The miR-383/HDAC9 signaling pathway is related with increased LINC00162 expression. qPCR, pathway analysis Involvement of LINC00162 in the development of diabetic nephropathy, a possible biomarker. [ 41 ] Wang et al. (2020) Cutaneous SCC There was an upregulation in LINC00162 expression, which promotes cell migration and proliferation, in cutaneous squamous cell carcinomas. qPCR, cell migration and proliferation assays Skin squamous cell carcinoma biomarker LINC00162: a promising new avenue for treatment. [ 42 ] Wang et al. (2020) Bladder Cancer Increased proliferative activity and a significant overexpression of LINC00162 in bladder cancer cell lines. qPCR, cell proliferation assays A possible therapeutic target for bladder cancer, LINC00162 is an oncogene. [ 43 ] 5. Conclusion This study identifies PICSAR (LINC00162) as an overexpressed long non-coding RNA in thyroid cancer, playing a key role in promoting tumor progression. PICSAR acts as a molecular sponge for specific microRNAs (hsa-miR-320A and hsa-miR-485), leading to increased expression of RAPGEFL1, a gene associated with cancer development. Silencing PICSAR in thyroid cancer cells reduced RAPGEFL1 levels while increased hsa-miR-320A and hsa-miR-485 levels, highlighting PICSAR’s potential as a therapeutic target. These findings position PICSAR as a valuable biomarker and possible therapeutic target for thyroid cancer, encouraging further research to confirm its clinical utility. The novelty of this study is the identification of PICSAR (LINC00162) as a previously unexplored long non-coding RNA that is overexpressed in thyroid cancer. It functions as a molecular sponge for tumor-suppressive microRNAs, thereby enhancing RAPGEFL1 expression and promoting cancer cell growth, highlighting its potential as a therapeutic target in thyroid cancer treatment. Declarations Ethics approval The ethical committee of Tabriz University of Medical Sciences approved the study(IR.TBZMED.REC.1402.188). Written informed consent was obtained from all patients. All methods were carried out by relevant guidelines and regulations. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding Declaration The authors are thankful for the supports of the Immunology Research Center, Tabriz University of Medical Science (grant number: 71287) and Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences (grant number: 62540) Author Contribution Conceptualization: A.A.M.,and S. M.T. Data curation: M.H , T. j., and A.H. YFormal analysis: A.A.M.,and S. M.T.Investigation: M.H , T. j., and O.PMethodology: M.H , A.A.M., S. M.T. , G.Sh, R.H, and B. LProject administration: A.A.MSoftware: M.H , and A.H. YSupervision: A.A.M., S. M.T. , and G.ShValidation: A.A.M., R.H, and B. LVisualization: M.H , T. j., and A.H. YWriting–original draft: A.A.M.,and S. M.T. Acknowledgment The authors are thankful for the supports of the Immunology Research Center, Tabriz University of Medical Science Data Availability Data is provided within the manuscript or supplementary information files. References Carling T, Udelsman R. Thyroid cancer. Annu Rev Med. 2014;65:125–37. Nguyen QT, et al. Diagnosis and treatment of patients with thyroid cancer. Am health drug benefits. 2015;8(1):30. Nikiforov YE, Nikiforova MN. Molecular genetics and diagnosis of thyroid cancer. Nat Reviews Endocrinol. 2011;7(10):569–80. Laha D, Nilubol N, Boufraqech M. New therapies for advanced thyroid cancer. Front Endocrinol. 2020;11:82. Habchi Y et al. AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions. Systems, 2023. 11(10). Grimm D. Recent Advances in Thyroid Cancer Research. Int J Mol Sci, 2022. 23(9). 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Lu Y, et al. The overexpression of long intergenic ncRNA00162 induced by RelA/p65 promotes growth of pancreatic ductal adenocarcinoma. Cell Prolif. 2020;53(5):e12805. Bannon MJ, et al. Identification of long noncoding RNA s dysregulated in the midbrain of human cocaine abusers. J Neurochem. 2015;135(1):50–9. Bi X, et al. LncRNA PICSAR promotes cell proliferation, migration and invasion of fibroblast-like synoviocytes by sponging miRNA-4701-5p in rheumatoid arthritis. EBioMedicine. 2019;50:408–20. Zong L, et al. LINC00162 confers sensitivity to 5-Aza-2′-deoxycytidine via modulation of an RNA splicing protein, HNRNPH1. Oncogene. 2019;38(26):5281–93. Piipponen M, et al. Long noncoding RNA PICSAR promotes growth of cutaneous squamous cell carcinoma by regulating ERK1/2 activity. J Invest Dermatology. 2016;136(8):1701–10. Colaprico A, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 2015;44(8):e71–71. 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Downregulation of miR-320a/383-sponge-like long non-coding RNA NLC1-C (narcolepsy candidate-region 1 genes) is associated with male infertility and promotes testicular embryonal carcinoma cell proliferation. Cell Death Dis, 2015. 6(11): p. e1960. Xu S, Dong W, Shi Y. LncRNA PICSAR binds to miR-485-5p and activates TGF-β1/Smad to promote abnormal proliferation of hypertrophic scar fibroblasts (HSFs) and excessive deposition of extracellular matrix (ECM). Med Mol Morphol. 2021;54(4):337–45. Lebastchi AH, Callender GG. Thyroid cancer. Curr Probl Cancer. 2014;38(2):48–74. Segev DL, Umbricht C, Zeiger MA. Molecular pathogenesis of thyroid cancer. Surg Oncol. 2003;12(2):69–90. Tano K, Akimitsu N. Long non-coding RNAs in cancer progression. Front Genet. 2012;3:219. Piipponen M, et al. Long Noncoding RNA PICSAR Promotes Growth of Cutaneous Squamous Cell Carcinoma by Regulating ERK1/2 Activity. J Invest Dermatol. 2016;136 8:1701–10. Chrzanowska-Wodnicka M. Rap1 in endothelial biology. Curr Opin Hematol. 2017;24:248–55. Chen M, et al. Long Intergenic Non-Coding 00162 as Diagnostic Biomarker for Early-Stage Pancreatic Cancer. Annals Clin Lab Sci. 2022;52(4):533–43. Fan W et al. LINC00162 participates in the pathogenesis of diabetic nephropathy via modulating the miR-383/HDAC9 signalling pathway. Artificial cells, nanomedicine, and biotechnology, 2020. 48(1): pp. 1047–54. Wang Y, et al. The roles of lncRNA in cutaneous squamous cell carcinoma. Front Oncol. 2020;10:158. Wang X et al. Super-enhancer LncRNA LINC00162 promotes progression of bladder cancer. Iscience, 2020. 23(12). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Aug, 2025 Read the published version in Medical Oncology → Version 1 posted Editorial decision: Revision requested 25 Jun, 2025 Reviews received at journal 22 Jun, 2025 Reviews received at journal 13 Jun, 2025 Reviewers agreed at journal 09 Jun, 2025 Reviewers agreed at journal 09 Jun, 2025 Reviews received at journal 26 May, 2025 Reviewers agreed at journal 26 May, 2025 Reviewers invited by journal 06 May, 2025 Editor assigned by journal 14 Apr, 2025 Submission checks completed at journal 14 Apr, 2025 First submitted to journal 08 Apr, 2025 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. 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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-6403368","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":454082136,"identity":"2fbae939-f9f6-4a9f-92dc-3a9622745524","order_by":0,"name":"Maryam Hejazi","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Maryam","middleName":"","lastName":"Hejazi","suffix":""},{"id":454082137,"identity":"33ea4e95-276e-4b65-bd0e-58398e835ddb","order_by":1,"name":"Tahmineh jafari","email":"","orcid":"","institution":"Tabriz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Tahmineh","middleName":"","lastName":"jafari","suffix":""},{"id":454082138,"identity":"20c83215-f796-4104-bc97-9b9d72799fc6","order_by":2,"name":"AmirHossein Yari","email":"","orcid":"","institution":"Tabriz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"AmirHossein","middleName":"","lastName":"Yari","suffix":""},{"id":454082139,"identity":"d0d4af4b-9653-48c7-9e4f-724e7115b6ed","order_by":3,"name":"Ramin Heshmat","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ramin","middleName":"","lastName":"Heshmat","suffix":""},{"id":454082140,"identity":"89dc2466-bad7-478c-813a-71379686e01c","order_by":4,"name":"Bagher Larijani","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Bagher","middleName":"","lastName":"Larijani","suffix":""},{"id":454082141,"identity":"6fb73da9-d55e-44e7-94d1-70ccd1501d75","order_by":5,"name":"Samaneh Ahvaz","email":"","orcid":"","institution":"Tabriz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Samaneh","middleName":"","lastName":"Ahvaz","suffix":""},{"id":454082142,"identity":"a6920f6b-f0eb-4112-aba2-f5f6d5fe4c37","order_by":6,"name":"Omid Pourbagherian","email":"","orcid":"","institution":"Tabriz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Omid","middleName":"","lastName":"Pourbagherian","suffix":""},{"id":454082143,"identity":"8f9b3f76-ee6d-426c-afad-ba94f6ad5450","order_by":7,"name":"Seyed Mohammad Tavangar","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Seyed","middleName":"Mohammad","lastName":"Tavangar","suffix":""},{"id":454082144,"identity":"9966b898-fc33-451b-b5b8-1bd8e3cfb81e","order_by":8,"name":"Gita Shafiee","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Gita","middleName":"","lastName":"Shafiee","suffix":""},{"id":454082145,"identity":"414fce98-b6d3-4827-9b5e-67dee1efd501","order_by":9,"name":"Amir Ali Mokhtarzadeh","email":"data:image/png;base64,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","orcid":"","institution":"Tabriz University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Amir","middleName":"Ali","lastName":"Mokhtarzadeh","suffix":""}],"badges":[],"createdAt":"2025-04-08 12:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6403368/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6403368/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12032-025-02987-9","type":"published","date":"2025-08-26T15:57:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82602985,"identity":"a9aad5c1-241b-41b5-8708-b6fb4a20b3ad","added_by":"auto","created_at":"2025-05-13 09:52:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":129062,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression level of LINC00126 in TCGA dataset (a), and Gepia webtool with pvalue less than 0.001 (b). The association between the Roc curve and TCGA level to discriminate between patients and normal human beings (c).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6403368/v1/da8ec95a034243b5ca0af15a.png"},{"id":82605327,"identity":"0e554960-00b3-4e06-9621-527cb054c83d","added_by":"auto","created_at":"2025-05-13 10:00:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62634,"visible":true,"origin":"","legend":"\u003cp\u003eLINC00126 expression and its connection with Stages(a), and tumor histology (b).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6403368/v1/63570c7bc7f60d5c1903ec56.png"},{"id":82605328,"identity":"bd1f75f4-bdd5-41bd-9de2-a6a7c28435b9","added_by":"auto","created_at":"2025-05-13 10:00:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":75992,"visible":true,"origin":"","legend":"\u003cp\u003eLINC00162 dysregulation was identified in all cancers, including thyroid cancer, through Ualcan's pan-cancer study.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6403368/v1/657bfe3495335d0155276f6d.png"},{"id":82605330,"identity":"5aa9a7b4-cd91-4b17-80a9-c5a1f4919e35","added_by":"auto","created_at":"2025-05-13 10:00:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":79025,"visible":true,"origin":"","legend":"\u003cp\u003ehsa-miR-320A (a) and hsa-miR-485 (b) expression are significantly downregulated in thyroid cancer (TCGA-THCA).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6403368/v1/c386a87bd348cea62ba75915.png"},{"id":82602991,"identity":"c660345f-a3c6-4b9f-a7b7-774fb898f68f","added_by":"auto","created_at":"2025-05-13 09:52:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":305751,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression levels of hsa-miR-320A in thyroid cancer in various stages (a) and various histological subtypes (b). The expression value of hsa-miR-485 in thyroid cancer in various stages (c) and various histological subtypes (d).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6403368/v1/bfca07c7ca17d0182fbed7b9.png"},{"id":82602988,"identity":"badce0a7-88bf-4afa-8ddc-923f8a648358","added_by":"auto","created_at":"2025-05-13 09:52:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":33152,"visible":true,"origin":"","legend":"\u003cp\u003eRAPGEFL1 expression is considerably upregulated in thyroid cancer (TCGA-THCA).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6403368/v1/f6d85bb078c5babe0474addf.png"},{"id":82606693,"identity":"57ba6e79-cfe2-4ecf-96e1-ec39ddf6934f","added_by":"auto","created_at":"2025-05-13 10:08:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":73780,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression value of RAPGEFL1 in thyroid cancer various stages (a) and various histological subtypes (b).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6403368/v1/12612cc4c056425d8a7aa0ea.png"},{"id":82605336,"identity":"8c7b87f0-56bc-4cf8-96dc-3ddca36a1441","added_by":"auto","created_at":"2025-05-13 10:00:38","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":715778,"visible":true,"origin":"","legend":"\u003cp\u003eThe positively co-expressed genes with LIN00162. Orange nodes represent the positively co-expressed genes and the red node indicates the high-ranking gene that positively correlates with LINC00162 according to Pearson's Correlation Coefficient.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6403368/v1/47f95f164f0079ac76f4ccc5.png"},{"id":82605331,"identity":"08744b58-d23c-42ca-a129-2325a59e08c6","added_by":"auto","created_at":"2025-05-13 10:00:38","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":648314,"visible":true,"origin":"","legend":"\u003cp\u003eThe negatively co-expressed genes with LIN00162. Cyan nodes show the negatively co-expressed genes and the red node shows the high rank gene that negatively correlated with LINC00162 according to Pearson's Correlation Coefficient.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-6403368/v1/296269e445c8788b33f37bd1.png"},{"id":82605333,"identity":"4af3b80e-4dad-4f7e-88f4-905a78475ec1","added_by":"auto","created_at":"2025-05-13 10:00:38","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":82721,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted KEGG pathways associations for the lncRNA PICSAR. Terms are classified by the right-tailed p-value for the mean Pearson association measurement calculated alongside each gene set and PICSAR.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-6403368/v1/620d8562f1484214ae89645b.png"},{"id":82605337,"identity":"854601d0-0ba8-4f5f-a005-87ea7d55eed4","added_by":"auto","created_at":"2025-05-13 10:00:38","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":119465,"visible":true,"origin":"","legend":"\u003cp\u003eThe analysis of LINC00162 expression in tumor (N=50) and normal tissues (N=50)using qRT-PCR technique (a). ROC curve for LINC00162 to differentiate tumor tissues from normal samples (b).\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-6403368/v1/94a627040978683891886963.png"},{"id":82603010,"identity":"d158a00f-e07b-44d1-9332-2fdae4fd783c","added_by":"auto","created_at":"2025-05-13 09:52:38","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":76912,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between LINC00162expression and clinical characteristics in TC patients. (a) age (p =0.1256), (b) tumor size (p =0.4068), (c) lymph node metastasis (p =0.0780), (d) vascular invasion (p=0.6809), (e) gender (p=0.3711), (f) capsular invasion (p=0.9142), and (g) hemorrhage (p =0.9859). The unpaired t-test was performed for the numerical investigation.\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-6403368/v1/dacb867d9f78e7f189d420d7.png"},{"id":82603030,"identity":"98e9e950-613b-495c-8efa-65231675d1db","added_by":"auto","created_at":"2025-05-13 09:52:39","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":109530,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Thyroid cancer tissues show significantly lower levels of hsa-miR-320A than normal tissues, as indicated by qPCR analysis (P \u0026lt; 0.0001). (b) ROC analysis reveals that hsa-miR-320A has strong diagnostic accuracy, with an AUC of 0.9648 (P \u0026lt; 0.0001). (c) qPCR indicates that thyroid cancer tissues have significantly lower levels of hsa-miR-485 (P \u0026lt; 0.0001). (d) ROC analysis demonstrates the diagnostic capability of hsa-miR-485's with an AUC of 0.8016 (P \u0026lt; 0.0001). Both miRNAs may serve as useful indicators for the identification of thyroid carcinoma.\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-6403368/v1/4032dc73f89e396fc1aa9240.png"},{"id":82605338,"identity":"5a688dd2-ea56-46a1-a37c-4f9481eb10fe","added_by":"auto","created_at":"2025-05-13 10:00:38","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":81347,"visible":true,"origin":"","legend":"\u003cp\u003e(a) RAPGEFL1 is significantly upregulated in thyroid cancer tissues compared to normal tissues, as shown by qRT-PCR analysis (P \u0026lt; 0.0001). (b) AUC of 0.9972 (P \u0026lt; 0.0001) for RAPGEFL1 indicates excellent diagnostic accuracy based on ROC curve analysis. RAPGEFL1 could be a valuable biomarker for tracking and detecting thyroid cancer.\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-6403368/v1/5d1ac28574067655a70506e0.png"},{"id":82603016,"identity":"67fe6fec-e2f0-4a7b-94ca-1893b418eca4","added_by":"auto","created_at":"2025-05-13 09:52:38","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":225313,"visible":true,"origin":"","legend":"\u003cp\u003eThe impact of PICSAR (LINC00162) siRNA transfections on the expression of PICSAR s', RAPGEFL1, hsa-miR-320A, and hsa-miR-485 in BCPAP cell. According to qPCR analysis, siRNA-transfected cells show a marked overexpression of hsa-miR-320A (P \u0026lt; 0.001) and hsa-miR-485 (P \u0026lt; 0.0001), alongside downregulation of PICSAR (P \u0026lt; 0.0001) and RAPGEFL1 (P \u0026lt; 0.0001). The data show the RNA fold change ± SD. NC stands for the scramble siRNA-transfected group.\u003c/p\u003e","description":"","filename":"floatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-6403368/v1/d10f1be7d805404be3d79f59.png"},{"id":82605345,"identity":"923c2d61-54b4-4e6b-bb68-aa7f134bee8d","added_by":"auto","created_at":"2025-05-13 10:00:39","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":63541,"visible":true,"origin":"","legend":"\u003cp\u003eThe regulatory effects of PICSAR siRNA transfection. Before transfection, PICSAR is upregulated, suppressing hsa-miR-320A and hsa-miR-485 and activating RAPGEFL1. After PICSAR knockdown with siRNA, hsa-miR-320A and hsa-miR-485 are upregulated, while RAPGEFL1 is downregulated.\u003c/p\u003e","description":"","filename":"floatimage16.png","url":"https://assets-eu.researchsquare.com/files/rs-6403368/v1/518beefadde6459315986c71.png"},{"id":90344880,"identity":"22abb252-b5c5-480c-9166-4d54f958a4d1","added_by":"auto","created_at":"2025-09-01 16:07:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4860940,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6403368/v1/b801dc7c-c9cd-4c3a-9746-223a5c50c6a4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A novel long noncoding RNA, PICSAR, promotes thyroid cancer progression through the hsa-miR-320A/hsa-miR-485/RAPGEFL1 axis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobally, thyroid cancer ranks sixth, and among all cancers, it ranks thirteenth [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The rise in the incidence of large and innovative thyroid cancers, as well as mortality from thyroid cancer, suggests that etiological factors may contribute to the increased prevalence of this disease [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Imperative recent advancements in identifying molecular subtypes of thyroid cancer and genetic predisposition factors provide insights into the etiology of the disease [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Histologically, thyroid tumors are categorized into several classifications: Papillary thyroid carcinoma (PTC), Follicular thyroid carcinoma (FTC), Medullary thyroid carcinoma (MTC), and Anaplastic thyroid carcinoma (ATC) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although most thyroid cancers are relatively benign, in some cases they can develop into aggressive and lethality malignant tumors [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Thyroid tumorigenesis is a multistep process that involves the accumulation of numerous hereditary and epigenetic changes. Therefore, understanding the mechanisms of tumorigenesis is essential for the treatment of thyroid cancer [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Recently, it has been observed that long non-coding RNAs (LncRNAs) are involved in cancer progression by interacting with microRNAs and sponging them. Conversely, their roles and molecular mechanisms in thyroid cancer are fundamentally unclear [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. For example, microRNAs like hsa-miR-320A and hsa-miR-485, as well as genes like RAPGEFL1, play crucial roles in the development of cancer by influencing the expression of genes and the routes that cells take. With differentiated thyroid cancer, hsa-miR-485 is responsible for regulating metabolic processes such as glycolysis, which in turn promotes tumor development. Within the context of tumor suppression, RAPGEFL1 has an impact on signaling pathways. The RAPGEF1 gene encodes a guanine nucleotide exchange factor critical for activating Rap proteins involved in cell signaling, development, and disease processes. It plays roles in neurodevelopment, cancer progression, and insulin signaling, with mutations linked to developmental abnormalities and type 2 diabetes[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. There is a lack of clarity on the role that hsa-miR-320A plays in thyroid cancer; related miRNAs influence proliferation and differentiation in thyroid cancers[\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The function of various LncRNAs in carcinogenesis has been established [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In thyroid cancer, lncRNAs accomplished as oncogenes or tumor suppressor genes. For example, overexpression of NEAT1 has been identified in PTC tissues and cell lines [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. NEAT1 lncRNA enhances cell proliferation, invasion, and migration. Functional studies have demonstrated interactions between this lncRNA and miR-129-5p, resulting in KLK7 overexpression in PTC tissues [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. PTCSC3 has been well-known as a tumor suppressor for LncRNAs in thyroid tissues [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Overexpression of PTCSC3 LncRNAs in thyroid cancer cells has led to inhibition of cell growth, cell cycle arrest, and increased apoptosis. The inverse correspondence between miR-574-5p and PTCSC3 has also been confirmed at in vitro conditions [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStudies have highlighted the role of lncRNA PICSAR(LINC00126) in the progression of cancers such as pancreatic cancer (PC)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], DA neurons[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], rheumatoid arthritis (RA) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], gastric cancer (GC) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and cutaneous squamous cell carcinomas (cSCCs) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. PICSAR(LINC00126) is upregulated in these cells. However, no study has been conducted to evaluate the expression of LINC00126 in TC. This research aimed to investigate the function of PICSAR in thyroid cancer by examining its relationships with microRNAs and its potential impact on TC progression. To assess the expression of PICSAR and associated genes, the research employed bioinformatics techniques to analyze data from the TCGA database. The LncACT and miRDB databases were utilized to evaluate lncRNA-microRNA interactions, followed by functional enrichment analysis. RNA extraction, cDNA synthesis, and real-time PCR were performed on 50 matched thyroid cancer and normal tissue samples to validate the results. Additionally, thyroid cancer cell lines were transfected with PICSAR-specific siRNA to evaluate changes in gene expression and the impact on pathways related to thyroid cancer.\u003c/p\u003e"},{"header":"2. Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. In-silico analysis\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1. Bulk RNA analysis\u003c/h2\u003e \u003cp\u003eThe TCGAbiolinks package [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] was used to obtain the RNA-seq gene expression data for the Thyroid Cancer Genome Atlas (TCGA-THCA), which was then normalized and analyzed using the limma package[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] in the R programming language. Using the Gepia online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gepia.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"https://gepia.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) normal tissues were obtained from the GTEX dataset, whereas tumoral tissues were sourced from the TCGA dataset, to compare RNA expression. The expression level and ROC curve of the LINC00162 non-coding gene were analyzed using GraphPad Prism software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.graphpad.com/\u003c/span\u003e\u003cspan address=\"https://www.graphpad.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To explore the relationship between LINC00162 expression and the pathogenic data as well as pan-cancer analysis, the TCGA RNA seq was queried using the Ualcan web tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ualcan.path.uab.edu/\u003c/span\u003e\u003cspan address=\"https://ualcan.path.uab.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2. lncRNA-microRNA interaction\u003c/h2\u003e \u003cp\u003eThe competing endogenous RNA network, also known as the LncRNA-microRNA network, was investigated to identify the competing molecules that act as microRNA sponges following the significant dysregulation of lncRNA in thyroid cancer. To achieve this, the LncACT online tool[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bio-bigdata.hrbmu.edu.cn/LncACTdb/\u003c/span\u003e\u003cspan address=\"http://bio-bigdata.hrbmu.edu.cn/LncACTdb/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was employed to analyze the interaction between lncRNAs and microRNAs interact. The TCGA-THCA was queried using the Ualcan online tool to evaluate the expression levels of the microRNAs'.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3. microRNA-mRNA interaction\u003c/h2\u003e \u003cp\u003eThe microRNA-mRNA interaction network was reconstructed following the identification of an LncRNA-microRNA axis that exhibits significant dysregulation and coordination across pathways. The miRDB database[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mirdb.org/\u003c/span\u003e\u003cspan address=\"https://mirdb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was employed for this specific task. This database was employed to identify the microRNAs selected to target the candidate mRNAs. The study's findings indicate that microRNAs might be employed as both therapeutic and diagnostic markers for thyroid cancer. The Ualcan web tool was used to query the TCGA-THCA and determine mRNA expression levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.1.4. Functional enrichment analysis\u003c/h2\u003e \u003cp\u003eTo classify the pathways associated with PICSAR (LINC00162),. we first obtained PICSAR\u0026rsquo;s co-expressed LncRNAs and co-expressed genes (both negatively and positively correlated) using the web resources lncHUB2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/lncHUB2/\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/lncHUB2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The LNCHUB2 online tool was also used for query the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to identify biological pathways influenced by this lncRNA. All data were visualized graphically in the Cytoscape software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cytoscape.org/\u003c/span\u003e\u003cspan address=\"https://cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Experimental validation\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Thyroid cancer tissue samples\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section4\"\u003e \u003ch2\u003e2.2.1.1. Samples collection and preparation\u003c/h2\u003e \u003cp\u003eIn this study, fifty TC and fifty paired normal tissue samples were obtained from several hospital of Tabriz and Shariati Hospital, Tehran, from 2021 to 2022 with their consent. Prior to surgery, no patient underwent chemotherapy or radiation treatment. Samples were frozen in liquefied nitrogen and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until RNA extraction. All clinicopathological features including tumor size, age, lymphatic and vascular invasion, capsular invasion were availablein patient profiles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section4\"\u003e \u003ch2\u003e2.2.1.2. RNA extraction and cDNA synthesis\u003c/h2\u003e \u003cp\u003eThe tissue specimens were ground in liquid nitrogen using a mortar and pestle, then transferred to a lysis buffer, and homogenized using a needle and syringe. RNA extraction was performed using Trizol reagent (GeneAll, Korea) in according to tothe provided procedure instructions. The quantification and assessment of RNA quality were performed using a NanoDrop spectrophotometer.Using the BioFACT cDNA synthesis kit (Korea), cDNA was synthesized from 1 \u0026micro;g of total RNA in a final volume of 20 \u0026micro;l, following the typical procedure (50\u0026deg;C in 30 min for Reverse transcription, 95\u0026deg;\u003csup\u003eC\u003c/sup\u003e in 5 min for Inactivation of RT enzyme, 10\u0026deg;C in infinitive for end) with the thermocycler PCR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section4\"\u003e \u003ch2\u003e2.2.1.3. Quantitive Real Time PCR (qRT-PCR)\u003c/h2\u003e \u003cp\u003eThe BioFACT\u0026trade; 2X Real-Time PCR Master Mix and gene-specific primers were used to conduct Real Time PCR. The total volume of the reaction was established at 20 \u0026micro;l. The reaction was conducted in a tripartite steps, as outlined below: Step 1: Heat the holding stage or first heat to 95\u0026deg;C for a duration of 2 minutes. Step 2 involves doing 45 cycles of denaturation at 95\u0026deg;C for 10 seconds, primer annealing at 60\u0026deg;C for 30 seconds, and extension at 72\u0026deg;C for 30 seconds. Step 3: Melting curves were generated at the end of each run. Data normalization was performed using the GAPDH gene. The 2\u003csup\u003e\u0026minus; ΔΔCt\u003c/sup\u003e technique was used to estimate the expression of target genes. The primers were designed using the NCBI Primer 3 online facility (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/tools/primer-blast/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/tools/primer-blast/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the qRT-PCR primer sequences.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eqPCR primer sequences\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget gene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eForward (5\u0026prime; \u0026ndash; 3\u0026prime;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReverse (5\u0026prime; \u0026ndash; 3\u0026prime;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eU6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGCTTCGGCAGCACATATACTAAAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCGCTTCACGAATTTGCGTGTCAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGAPDH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAAGGTGAAGGTCGGAGTCAAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGGGGTCATTGATGGCAACAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLINC00162(PICSAR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGCACCTAAACGGGGAGATGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGATTCCGCAGCAAGTTGGTG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ehsa-mir-320A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCGACGGAAAAGCTGGGTTGAGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCCAGTGCAGGGTCCGAGGTA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ehsa-mir-485\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGTTTTTTAGAGGCTGGCCG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCCAGTGCAGGGTCCGAGGTA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRAPGEFL1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTCCGTAAAGATTCCAGAGAAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGGTGGACATAAATTCTACTGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eU6 Stem-loop\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e5\u0026acute;-GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACAAAAATAT \u0026minus;\u0026thinsp;3\u0026acute;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ehsa-mir-320A Stem loop\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e5\u0026acute;-GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTCGCCC-3\u0026acute;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ehsa-mir-485 Stem loop\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e5\u0026acute;- GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACAGAATTC-3\u0026acute;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Thyroid cancer cell line\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section4\"\u003e \u003ch2\u003e2.2.2.1. Cell culture\u003c/h2\u003e \u003cp\u003eThe human thyroid cancer cell line BCPAP was purchased from the Pasteur Institute of Iran. The cells were cultured in RPMI-1640 medium (Gibco, USA) supplemented with 10% fetal bovine serum (FBS) (Gibco, USA) and 1% penicillin-streptomycin (100 U/mL penicillin and 100 \u0026micro;g/mL streptomycin) (Sigma-Aldrich, USA). Cells were maintained at 37\u0026deg;C in a humidified atmosphere with 5% CO₂. The medium was replaced every 2\u0026ndash;3 days, and the cells were passaged when they reached 80\u0026ndash;90% confluency using 0.25% trypsin-EDTA (Gibco, USA). For experimental assays, 1.5 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells were seeded per well and allowed to adhere overnight before the siRNA transfection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section4\"\u003e \u003ch2\u003e2.2.2.2. siRNA transfection\u003c/h2\u003e \u003cp\u003eOnce the BCPAP cells reached optimal confluency, they underwent siRNA transfection with PICSAR-specific siRNA to evaluate its effect on gene expression. Transfection was performed using Lipofectamine\u0026trade; 3000 Transfection Reagent (Thermo Fisher Scientific, USA), following the manufacturer\u0026rsquo;s instructions. A PICSAR-specific siRNA (sequence:) was used, while a scramble siRNA (sequence: 5' GACGATAATTAACATTCTTCACTA 3') served as a negative control to assess transfection efficiency and specificity. Seeded cells transfected with 60 pM of siRNA. Optimize dose and time of transfection are investigated in our another unpublished paper.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section4\"\u003e \u003ch2\u003e2.2.2.3. Quantitative PCR\u003c/h2\u003e \u003cp\u003eAfter 48 hours of incubation, total RNA was extracted from three groups: untransfected cells, negative control (scramble siRNA-transfected) cells, and PICSAR siRNA-transfected cells, using the appropriate RNA isolation kit (eGeneAll, Korea). The extracted RNA was quantified, and cDNA synthesis was performed for each group using a reverse transcription kit (AddScript cDNA Synthesis Kit)). qRT-PCR was then conducted to assess the expression levels of PICSAR, hsa-miR-320A, hsa-miR-485, and RAPGEFL1, to evaluate silencing efficiency and biological effect.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Statistical analysis\u003c/h2\u003e \u003cp\u003eThe assessment of qRT-PCR outcomes and visualizing the graphs were conducted using GraphPad Prism software V8. Differences in PICSAR(LINC00162) expression levels between samples were finalized using an unpaired t-test and one way-Anova. A significant P value was quantified to less than 0.01.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.1. In-silico analysis\u003c/h2\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1. LINC00162 is noticeably upregulated in thyroid cancer.\u003c/h2\u003e \u003cp\u003eThe information obtained from TCGA regarding the transcriptome of thyroid cancer indicates that LINC00162 is significantly upregulated in this cancer. Analysis of RNA-seq datasets from the TCGA database reveals that the expression level of LINC00162 markedly increases in thyroid cancer compared to normal tissues [Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea]. The potential of this lncRNA to serve as an investigative biomarker was subsequently evaluated. The accuracy of the data collecting process was demonstrated by plotting the receiver operating characteristic curve (ROC) and Area Under Curve (AUC) using GraphPad Prism software [Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb and c]. The study results indicate that the expression level of LINC00126 is significantly increased across various types and stages of thyroid cancer [Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. Dysregulation of LINC00162 is significant in all cancers.\u003c/h2\u003e \u003cp\u003e PICSAR (P38 inhibited cutaneous squamous cell carcinoma-associated lincRNA), also known as LINC00162, is dysregulated in various cancer types according to the TCGA database. In a comprehensive pan-cancer investigation, thyroid cancer was included. To thoroughly understand the dysregulation of LINC00162, data from over 11,000 patients across more than 30 kinds of cancer were collected. This extensive study revealed consistent patterns of LINC00162 expression and demonstrated strong statistical capabilities to differentiate between malignant and healthy tissues. A pan-cancer analysis using the Ualcan database discovered that LINC00162 was dysregulated in all malignancies, including thyroid carcinoma (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3. lncRNA-microRNA interaction\u003c/h2\u003e \u003cp\u003eA significant connection between the lncRNA LINC00162 and the miRNAs hsa-miR-320A and hsa-miR-485 in thyroid cancer was uncovered through the LncACT database's exploration of the LncRNA-miRNA network. LINC00162, a notably dysregulated lncRNA, may act as a competitive endogenous RNA (ceRNA), influencing the progression of thyroid cancer by modulating the activity of hsa-miR-320A and hsa-miR-485. The expression of LINC00162 is negatively correlated with hsa-miR-320A/hsa-miR-485, indicating that the LINC00162-miRNA axis plays a crucial role in the pathophysiology of thyroid cancer. Data from the TCGA-THCA database show that hsa-miR-320A and hsa-miR-485 are significantly downregulated across thyroid cancer stages and subtypes. These miRNAs are associated to advanced tumor characteristics and poorer clinical outcomes, suggesting that they could serve as valuable targets for diagnosis and treatment. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e outlines their interactions with LINC00162, while Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrate their persistent downregulation. These results highlight the need for more research to fully understand how miRNA downregulation affects thyroid carcinoma survival and disease progression.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetailed display of the interaction between PICSAR and hsa-miR-320A and hsa-mir-485.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLncRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emicroRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003especies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eexperimental\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePubmed ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC00162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-320A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHomo sapiens (human)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eluciferase reporter assays;qRT-PCR;Northern blot assay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26539909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC00162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHomo sapiens (human)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eqPCR;Western blot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34255190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.1.4. microRNA-mRNA interaction\u003c/h2\u003e \u003cp\u003eUsing the miRDB database, the thyroid cancer microRNA-mRNA interaction network was analyzed. RAPGEFL1 mRNA was identified as a common target of hsa-miR-320A and hsa-miR-485. Due to the strong negative correlation between RAPGEFL1 mRNA and the levels of hsa-miR-320A and hsa-miR-485, a reduction in these levels may lead to an increase in RAPGEFL1 mRNA, potentially inducing thyroid cancer. These findings suggest that hsa-miR-320A, hsa-miR-485, and RAPGEFL1 mRNA could serve as therapeutic targets and diagnostic biomarkers for thyroid cancer. Further research is required to explore their therapeutic applications in managing thyroid cancer and to understand the mechanisms of this regulatory axis. An extensive analysis of the interactions between hsa-miR-320A, hsa-miR-485, and RAPGEFL1 is provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. RAPGEFL1 expression is significantly upregulated in thyroid cancer (TCGA-THCA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) and remains overexpressed across all types and stages of thyroid cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetailed display of the interaction between hsa-miR-320A/hsa-miR-485 and RAPGEFL1.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTarget Gene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGene Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emiRDB target score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-320a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRAPGEFL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRap guanine nucleotide exchange factor-like 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRAPGEFL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRap guanine nucleotide exchange factor-like 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.1.5. LINC00126 is vital in a broad number of biological functions\u003c/h2\u003e \u003cp\u003eTo identify co-expressed genes associated with LINC00126, we searched the TCGA database using the available tool lncHUB2. Consequently, 200 genes were found to have expression levels that are correlated with LINC00126. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e illustrates the network of positively co-expressed genes, while Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e presents the network of negatively co-expressed genes. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e lists the highly ranked co-expressed lncRNAs, along with negatively and positively co-expressed genes with LINC00162 including their Pearson's Correlation Coefficient. The analysis of the KEGG pathway database using the LNCHUB2 online tool showed that LINC00162 is involved in various biological pathways including folate biosynthesis, thiamine metabolism, cell cycle, nucleotide excision repair, pyrimidine metabolism, DNA replication, and various essential pathways. Figure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e illustrates the pathways associated with the LINC00162.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHigh ranking co-expressed lncRNAs, negatively and positively co-expressed genes with LINC00162\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositively\u003c/p\u003e \u003cp\u003eco-expressed genes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson's Correlation Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegatively\u003c/p\u003e \u003cp\u003eco-expressed genes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePearson's Correlation Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCo-expressed LncRNAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePearson's Correlation Coefficient\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNX18P14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.45959943532943726\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNUDT3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.14219123125076294\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLINC00165\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5769169926643372\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLC9A3P2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.45317313075065613\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCAF8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1392144411802292\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRP11-126O1.2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4507526457309723\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRP11-126O1.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.4507526457309723\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eHNRNPK\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.13595622777938843\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eENSG00000287791\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.43986520171165466\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eENSG00000287791\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.43986520171165466\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eC16orf72\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.13571712374687195\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eRP11-473E2.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.43413230776786804\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eENSG00000236750\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.4384653866291046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eFGD5-AS1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.13548369705677032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eENSG00000288860\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.43351155519485474\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRP11-473E2.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.43413230776786804\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eADD1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.13486593961715698\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eRP11-158I23.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.43316972255706787\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eENSG00000288860\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.43351155519485474\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePTBP3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.13475295901298523\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eENSG00000290024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.4269241392612457\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVTA1P1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.4334138333797455\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eWAC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.13172028958797455\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eCTD-2139B15.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.4231734871864319\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRP11-158I23.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.43316972255706787\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTM9SF3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.13125644624233246\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eCTD-2062F14.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.42309385538101196\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMIR3619\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.43224406242370605\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMTPN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.13018518686294556\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMYO16-AS1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.4193621575832367\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Experimental validation\u003c/h2\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Thyroid cancer tissue samples\u003c/h2\u003e \u003cdiv id=\"Sec27\" class=\"Section4\"\u003e \u003ch2\u003e3.2.1.1. LINC00162 is significantly overexpressed in thyroid cancer tissue samples.\u003c/h2\u003e \u003cp\u003eThe expression of LINC00162 was examined in 50 TC tumors and 50 normal samples using qRT-PCR. The analysis of qRT-PCR data revealed that the expression of LINC00162 was significantly increased (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) in TC tissue samples compared to paired normal samples, which confirmed the results of the TCGA-THCA dataset (Fig.\u0026nbsp;11a). ROC curve analysis suggested that the expression of LINC00162 serves as a biomarker for diagnostic purposes (Fig.\u0026nbsp;11b). It is important to note that the TCGA samples which which encompass a large number of patient roles, could be significant and distinctly differentiate between patients and normal individuals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section4\"\u003e \u003ch2\u003e3.2.1.2. Pathology characteristics of LINC00162.\u003c/h2\u003e \u003cp\u003eThe correlation between pathological characteristics and LINC00162 expression was examined in TC patients. There was not significant association between LINC00162 expression and age (p\u0026thinsp;=\u0026thinsp;0.1256) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003ea), Tumor size (p\u0026thinsp;=\u0026thinsp;0.4068) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003eb), lymph node metastasis (p\u0026thinsp;=\u0026thinsp;0.0780) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003ec), Vascular invasion (p\u0026thinsp;=\u0026thinsp;0.6809) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003ed), Gender (p\u0026thinsp;=\u0026thinsp;0.3711) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003ee), Capsular invasion (p\u0026thinsp;=\u0026thinsp;0.9142) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003ef), and hemorrhage(p\u0026thinsp;=\u0026thinsp;0.9859) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003eg). Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the relationship between LINC00162 expression and pathological characteristics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eassociation between LINC00162 expression and pathological characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProperties\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eexpression\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor size (mm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.4068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003elymph node metastasis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.0780\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVascular invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ecapsular invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ehemorrhage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section4\"\u003e \u003ch2\u003e3.2.1.3. hsa-miR-320A and hsa-miR-485 are significantly downregulated in thyroid cancer tissue samples\u003c/h2\u003e \u003cp\u003eqRT-PCR analysis revealed that thyroid cancer tissues expressed significantly lower levels of hsa-miR-320A (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e13\u003c/span\u003ea) and hsa-miR-485 (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e13\u003c/span\u003ec) compared to matched normal tissues, so tumor samples showing an average expression about four times less (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). ROCcurve analysis was employed to assess the diagnostic capabilities of these miRNAs. The area under the curve (AUC) for hsa-miR-320A indicated strong diagnostic accuracy at 0.9648, (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e13\u003c/span\u003eb), while hsa-miR-485 demonstrated an AUC of 0.8016 (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e13\u003c/span\u003ed), both with P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.0001. Given their high sensitivity and specificity, our findings suggest that hsa-miR-320A and hsa-miR-485 could serve as valuable biomarkers for distinguishing thyroid cancer from healthy tissues, potentially facilitating early detection and disease monitoring.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section4\"\u003e \u003ch2\u003e3.2.1.4. RAPGEFL1 is significantly upregulated in thyroid cancer tissue samples.\u003c/h2\u003e \u003cp\u003eqRT-PCR analysis shows that RAPGEFL1 expression is significantly increased in thyroid cancer, with a mean expression level 6.7 times higher in tumor samples compared in matched normal tissues (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). ROC curve analysis was conducted to further illustrate RAPGEFL1's diagnostic capability. The results showed an area under the curve (AUC) of 0.9972 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), which indicates exceptional diagnostic accuracy. According to these results, RAPGEFL1 is a gene that is substantially elevated in thyroid cancer. It may also help with early detection and disease monitoring, and it may serve as a viable biomarker for distinguishing between malignant and normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Thyroid cancer cell line\u003c/h2\u003e \u003cdiv id=\"Sec32\" class=\"Section4\"\u003e \u003ch2\u003e3.2.2.1. PICSAR Silencing Downregulates RAPGEFL1 and Upregulates hsa-miR-320A and hsa-miR-485 in BCPAP Thyroid Cancer Cells\u003c/h2\u003e \u003cp\u003eBCPAP cells were utilized to evaluate the silencing of PICSAR (LINC00162) overexpression by transfection of PICSAR-specific siRNA and its effects on expression of RAPGEFL1, hsa-miR-320A, and hsa-miR-485. Our results show that PICSAR siRNA transfection significantly decreased PICSAR expression, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e15\u003c/span\u003e, with a fold change reduction of approximately 0.2 in comparison to the untransfected (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and negative control (scramble siRNA-transfected) groups. These results indicate that PICSAR in the siRNA-transfected cells has been effectively silenced. Similarly, PICSAR siRNA transfection led to a notable downregulation of RAPGEFL1 expression when compared to the control groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), suggesting a potential link between PICSAR silencing and RAPGEFL1 regulation. In contrast, PICSAR siRNA-transfected cells exhibited a significant upregulation of hsa-miR-320A and hsa-miR-485 expression. Compared to the control groups, hsa-miR-320A expression changed by around 2.5 fold (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), While hsa-miR-485 expression increased more dramatically, changing by about 5.5 fold (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Our findings demonstrate that PICSAR silencing has a substantial impact on the expression of both coding (RAPGEFL1) and non-coding (hsa-miR-320A, hsa-miR-485) RNAs, highlighting its regulatory role in gene regulation within BCPAP cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTC is the most common malignant internal secretion tumor, and its incidence has rapidly increased in recent years [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The precise pathogenesis of this disease remains fundamentally unknown. Therefore, early diagnosis and identifying genetic and environmental factors will aid in developing diagnostic, treatment, and cancer prevention strategies [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In addition to genetic changes, other procedures, such as lncRNA, are involved in nearly all stages of tumor development, including cell proliferation, survival, and metastasis.LncRNAs are a group of RNAs longer than 200 nucleotides that do not encode proteins to date. Numerous cancer-related lncRNAs have been identified that play a significant role in thyroid tumorigenesis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Given the fundamental role of lncRNAs in cancer progression and tumorigenesis, this study selected a lncRNA called PICSAR (LINC00126) to investigate its role in thyroid cancer. PICSAR, which is also called LINC00126, is a long noncoding RNA (lncRNA) that has important parts in how cancer grows and in inflammatory diseases. PICSAR helps cutaneous squamous cell cancer (cSCC) grow and move by turning on the ERK1/2 signaling pathway and decreasing DUSP6, a protein that stops tumors from growing. This control system shows its cancerous role in the growth of skin cancer[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. To date, no study has evaluated the involvement of PICSAR(LINC00126) gene expression in TC. The findings indicated that, compared to normal tissues, TC tumor tissue exhibited significantly higher expression of PICSAR(LINC00126). Increased expression of PICSAR(LINC00126) was not associated with clinical pathological variables such as age, stage, grade, and lymph node metastasis. Bioinformatics and experimental analysis revealed that PICSAR may influence thyroid cancer progression through interactions with microRNAs hsa-miR-320A and hsa-miR-485, acting as a molecular sponge. This sponging leads to the downregulation of these tumor-suppressive miRNAs, which, in turn, upregulates the expression of RAPGEFL1, a gene associated with cancer progression. Functional enrichment analysis indicated that the PICSAR-hsa-miR-320A/hsa-miR-485-RAPGEFL1 axis is implicated in critical oncogenic pathways, including thiamine metabolism and DNA replication. The RAPGEFL1 gene (C3G) is an important part of the Rap signaling system because it helps Rap proteins, especially Rap1 and Rap2, exchange guanine nucleotides. These small GTPases play a key role in many biological processes, such as binding, movement, growth, and differentiation. Rap proteins are turned on by RAPGEFL1, which speeds up the exchange of GDP for GTP. This changes the proteins from inactive to active. Once they are turned on, Rap proteins work with different effectors further down the line to make important biological reactions happen. Controlling how cells stick together and move is one of the main jobs of RAPGEFL1 in the Rap signaling system. When RAPGEFL1 turns on Rap1, it improves integrin-mediated binding, which is necessary for keeping cell-cell and cell-matrix interactions going. For example, when Rap1 is turned on in endothelial cells, it increases adherens junctions, keeps cell-cell bonds stable, and helps keep the vascular barrier intact. These steps are very important for the growth of blood vessels. Rap1 helps organize endothelial cells and make nitric oxide, which makes sure that the vessels work right and respond properly to shear stress[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In addition to its structural roles, RAPGEFL1 connects Rap signals to other pathways to change how cells work. For instance, turning on Rap1 improves PI3K-Akt signaling, which helps cells stay alive and grow. It is also connected to the ERK pathway, which changes how cells differentiate and how the cytoskeleton is organized. RAPGEFL1 is part of the Rap signaling system and helps control cell movement and growth in unhealthy conditions like cancer. In some situations, it can either cause cancer or stop tumors from growing. In some cancers, its abundance helps the tumor grow, while in others, its methylation state affects how well the tumor is suppressed[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The RAPGEFL1 gene, as well as microRNAs like hsa-mir-320A and hsa-mir-485, have been implicated in the progression of cancer, including thyroid cancer, according to recent research. MicroRNAs regulate cellular functions by influencing the expression of target genes that are engaged in critical pathways, including cell growth, differentiation, and metabolism. This regulation is especially important in malignancies, where dysregulated microRNAs contribute to uncontrolled proliferation and other malignant characteristics. As an example, it has been discovered that hsa-miR-485 plays a significant part in the metabolic processes that occur inside cancer cells. It has an effect on the pathways that are associated with glucose absorption and glycolysis. hsa-miR-485 interacts with circular RNAs, such as hsa_circ_0023990, in dedifferentiated thyroid tumors. These circular RNAs further control oncogenic proteins, such as the FOXM1 protein. The hsa-miR-485/FOXM1 axis is responsible for increased glycolysis and cell proliferation, which in turn promotes the formation and progression of tumors in aggressive kinds of thyroid cancer. The existence of this association highlights the potential of hsa-miR-485 as a therapeutic target, whereby the modification of its levels has the potential to hinder the metabolic advantage that the cancer has and to lessen its aggressiveness [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In spite of the fact that there has been very little direct study conducted on hsa-mir-320A in thyroid cancer, studies on microRNAs that are comparable to it indicate that it may have a role in cellular processes such as differentiation and proliferation. As an example, it has been discovered that some microRNAs, such as hsa-miR-152-3p and hsa-miR-196a, control pathways that are essential for the advancement of thyroid cancer. These microRNAs affect cell adhesion, migration, and proliferation. These microRNAs normally work by interacting with certain signaling pathways. Furthermore, they have the ability to act as biomarkers for aggressive phenotypes of thyroid cancer, which further emphasizes the therapeutic potential of microRNA regulation [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. It has also been shown that the gene RAPGEFL1 can restrict tumor development in research conducted on renal cell carcinoma. Specifically, a circular RNA that is generated from this gene, known as cRAPGEF5, was discovered to impede the growth and migration of cancer cells. Even though RAPGEFL1's direct effect in thyroid cancer has not yet been completely elucidated, the fact that it regulates important signaling pathways that are crucial in cell proliferation and survival implies that it may have a comparable impact on thyroid cancer. An example of this would be the interaction between cRAPGEF5 and miR-27a-3p in renal cell carcinoma. This interaction targets TXNIP, a gene that helps restrict the proliferation of cancer cells. The information provided by this pathway demonstrates that RAPGEFL1 may be relevant to a variety of cancer types and highlights the potential of this protein as a therapeutic target [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Additionally, significant correlations were identified between PICSAR and other genes, such as a positive correlation with SNX18P14 and a negative correlation with NUDT3. Silencing PICSAR in thyroid cancer cell lines resulted in the downregulation of RAPGEFL1 and upregulation of hsa-miR-320A and hsa-miR-485, indicating its regulatory role in gene expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e16\u003c/span\u003e). ROC curve analysis validated the potential of PICSAR as a highly accurate diagnostic biomarker for thyroid cancer. The findings suggest that targeting PICSAR could represent a viable therapeutic strategy in thyroid cancer, necessitating further research into its biological functions and therapeutic applications. This work discovers new insights into the role of long noncoding RNA PICSAR (LINC00162) in TC and compelling evidence that it may serve as both a biomarker and a treatment target. Several key conclusions have emerged from a comprehensive investigation employing both experimental and bioinformatics approaches; these are detailed in the following sections. Utilizing qRT-PCR on paired samples and analyzing the Cancer Genome Atlas (TCGA) data, this study confirms that PICSAR (LINC00162) is significantly elevated in thyroid cancer tissues compared to normal tissues. A related study by Chen et al. (2022) looked at LINC00162 expression in pancreatic cancer. According to their research, advanced clinical stages and metastases in pancreatic cancer are strongly correlated with elevated expression of LINC00162. The expression of LINC00162 in pancreatic cancer tissues was evaluated in this work using qRT-PCR and ROC curve analysis. Their findings suggest that LINC00162 has potential as a non-invasive diagnostic tool for early-stage pancreatic cancer [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Through sponging miR-4701-5p in fibroblast-like synoviocytes, Bi et al. (2019) discovered that LINC00162 enhances cell proliferation and migration in RA patients. The research found that LINC00162 may be a therapeutic target in RA, using qRT-PCR and cell proliferation tests [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The function of LINC00162 in diabetic nephropathy was studied by Fan et al. (2020), who found that its expression is increased and linked to the miR-383/HDAC9 signaling pathway. Findings from the qRT-PCR and pathway analysis studies point to LINC00162's potential role as a biomarker for diabetic nephropathy [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. According to research by Wang et al. (2020), LINC00162 is upregulated in cutaneous squamous cell carcinoma (SCC) tissues and promotes cell migration and proliferation through MAPK/ERK and cyclin-dependent kinases. Using qRT-PCR and cell migration and proliferation assays, they concluded that LINC00162 may serve as a biomarker for SCC and a possible target for therapy [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Another study by Wang et al. (2020) examined LINC00162 in bladder cancer and found that it was significantly overexpressed in bladder cancer cell lines,correlating with increased proliferative activity through interacting with chromatin modifiers and activating oncogenic pathways. The findings, which relied on qPCR and cell proliferation tests, suggest that LINC00162 act as an oncogene in bladder cancer and has therapeutic potential [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In summary, PICSAR (LINC00162) is elevated across a range of cancers and may serve as a diagnostic biomarker or treatment target, although its precise role may vary depending on the pathology.. Its potential significance in cancer biology and treatment is underscored by its consistent upregulation. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e details the role of LINC00162 in various diseases. This pioneering work marks a significant advancement in our understanding of this prevalent disease, revealing the crucial role of the long noncoding RNA PICSAR (LINC00162) in thyroid cancer. This study establishes PICSAR as a highly accurate diagnostic biomarker by demonstrating its substantial increase in thyroid carcinoma tissuesUtilizing advanced bioinformatics techniques, the research identifies PICSAR as a viable therapeutic target by illustrating its nvolvement in critical biological processes like DNA repair and cell cycle control. Novel gene correlations, including NUDT3 and SNX18P14, provide new information on the molecular dynamics of thyroid cancer and may lead to innovative therapeutic strategies. This study not only enhances our understanding of thyroid cancer's molecular basis of thyroid cancer but also paves the way for future research\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe role of LINC00162 in various diseases in detail.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMain Findings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMethodology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eConclusion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePresent study\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(2024)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThyroid Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThyroid cancer increased PICSAR (LINC00162) more than normal tissues. No meaningful pathogenic connection.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eqPCR, TCGA analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThe potential diagnostic biomarker for thyroid cancer might be PICSAR (LINC00162) upregulation.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(Hejazi et al., 2024)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChen et al. (2022)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePancreatic Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThere was a strong correlation between advanced clinical stages and metastases and elevated LINC00162 expression.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eqPCR, ROC curve analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003efor early pancreatic cancer using LINC00162 as a non-invasive diagnostic marker.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBannon et al. (2015)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCocaine Abuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMidbrain regions of cocaine users with dysregulated long non-coding RNAs, including LINC00162.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRNA sequencing, transcript analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExpression of LINC00162 could have a function in neuroadaptation and is linked to cocaine usage.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBi et al. (2019)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRheumatoid Arthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIn fibroblast-like synoviocytes, LINC00162 sponging miR-4701-5p enhances cell proliferation and migration.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eqPCR, cell proliferation assays\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA possible therapeutic target for rheumatoid arthritis is LINC00162.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFan et al. (2020)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetic Nephropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe miR-383/HDAC9 signaling pathway is related with increased LINC00162 expression.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eqPCR, pathway analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInvolvement of LINC00162 in the development of diabetic nephropathy, a possible biomarker.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWang et al. (2020)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCutaneous SCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThere was an upregulation in LINC00162 expression, which promotes cell migration and proliferation, in cutaneous squamous cell carcinomas.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eqPCR, cell migration and proliferation assays\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSkin squamous cell carcinoma biomarker LINC00162: a promising new avenue for treatment.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWang et al. (2020)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBladder Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIncreased proliferative activity and a significant overexpression of LINC00162 in bladder cancer cell lines.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eqPCR, cell proliferation assays\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA possible therapeutic target for bladder cancer, LINC00162 is an oncogene.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study identifies PICSAR (LINC00162) as an overexpressed long non-coding RNA in thyroid cancer, playing a key role in promoting tumor progression. PICSAR acts as a molecular sponge for specific microRNAs (hsa-miR-320A and hsa-miR-485), leading to increased expression of RAPGEFL1, a gene associated with cancer development. Silencing PICSAR in thyroid cancer cells reduced RAPGEFL1 levels while increased hsa-miR-320A and hsa-miR-485 levels, highlighting PICSAR\u0026rsquo;s potential as a therapeutic target. These findings position PICSAR as a valuable biomarker and possible therapeutic target for thyroid cancer, encouraging further research to confirm its clinical utility. The novelty of this study is the identification of PICSAR (LINC00162) as a previously unexplored long non-coding RNA that is overexpressed in thyroid cancer. It functions as a molecular sponge for tumor-suppressive microRNAs, thereby enhancing RAPGEFL1 expression and promoting cancer cell growth, highlighting its potential as a therapeutic target in thyroid cancer treatment.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval\u003c/strong\u003e \u003cp\u003e The ethical committee of Tabriz University of Medical Sciences approved the study(IR.TBZMED.REC.1402.188). Written informed consent was obtained from all patients.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e All methods were carried out by relevant guidelines and regulations.\u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding Declaration\u003c/h2\u003e \u003cp\u003eThe authors are thankful for the supports of the Immunology Research Center, Tabriz University of Medical Science (grant number: 71287) and Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences (grant number: 62540)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: A.A.M.,and S. M.T. Data curation: M.H , T. j., and A.H. YFormal analysis: A.A.M.,and S. M.T.Investigation: M.H , T. j., and O.PMethodology: M.H , A.A.M., S. M.T. , G.Sh, R.H, and B. LProject administration: A.A.MSoftware: M.H , and A.H. YSupervision: A.A.M., S. M.T. , and G.ShValidation: A.A.M., R.H, and B. LVisualization: M.H , T. j., and A.H. YWriting\u0026ndash;original draft: A.A.M.,and S. M.T.\u003c/p\u003e\u003ch2\u003eAcknowledgment\u003c/h2\u003e \u003cp\u003eThe authors are thankful for the supports of the Immunology Research Center, Tabriz University of Medical Science\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCarling T, Udelsman R. Thyroid cancer. Annu Rev Med. 2014;65:125\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen QT, et al. Diagnosis and treatment of patients with thyroid cancer. 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J Invest Dermatology. 2016;136(8):1701\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColaprico A, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 2015;44(8):e71\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitchie ME, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChandrashekar DS, et al. UALCAN: An update to the integrated cancer data analysis platform. Neoplasia. 2022;25:18\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChandrashekar DS, et al. UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia. 2017;19(8):649\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang P, et al. 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Annals Clin Lab Sci. 2022;52(4):533\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan W et al. \u003cem\u003eLINC00162 participates in the pathogenesis of diabetic nephropathy via modulating the miR-383/HDAC9 signalling pathway.\u003c/em\u003e Artificial cells, nanomedicine, and biotechnology, 2020. 48(1): pp. 1047\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, et al. The roles of lncRNA in cutaneous squamous cell carcinoma. Front Oncol. 2020;10:158.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X et al. Super-enhancer LncRNA LINC00162 promotes progression of bladder cancer. Iscience, 2020. 23(12).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"medical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"medo","sideBox":"Learn more about [Medical Oncology](https://www.springer.com/journal/12032)","snPcode":"12032","submissionUrl":"https://submission.nature.com/new-submission/12032/3","title":"Medical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Thyroid cancer, LncRNA, PICSAR (LINC00162), microRNA, ceRNA, Computational biology, Systems biology","lastPublishedDoi":"10.21203/rs.3.rs-6403368/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6403368/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAmong endocrine cancers, thyroid carcinoma (TC) is the most prevalent and ranks sixth in global mortality rates. Aberrant expression of long noncoding RNA (lncRNAs) is associated with the progression of various human cancers, including TC. The role of PICSAR lncRNA (LINC00162) has been validated in different human cancers. Therefore, this study aimed to assess the expression levels and functions of lncRNA PICSAR in thyroid cancer tumorigenesis. This comprehensive approach combined in silico and in vitro methods to explore the molecular mechanisms and clinical significance of PICSAR in thyroid cancer.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eThis work assessed the expression of the long non-coding RNA LINC00162 and identified differentially expressed genes (DEGs) using the Cancer Genome Atlas (TCGA) database. Interactions among LINC00162, hsa-miR-320A, hsa-miR-485, and RAPGEFL1 were investigated using the LncACT and miRDB databases. Bioinformatics techniques were employed to conduct functional enrichment analysis to clarify the relevant molecular pathways. For the investigation of LINC00162 expression in TC samples, 50 matched samples of thyroid carcinoma and adjacent normal tissue were gathered. Real-time PCR was used to objectively evaluate the expression levels of the targeted genes. Every tissue sample was examined pathologically. A specific siRNA was transfected into a thyroid cancer cell line to examine the functional role of LINC00162. The impact of LINC00162 silencing was then assessed by measuring the level of target genes expression following the transfection.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBased on TCGA-THCA analysis and qRT-PCR on tissue samples, LINC00162 (PICSAR) was markedly overexpressed in thyroid cancer tissues compared to normal samples. However, no discernible correlation was found between LINC00162 expression and the pathological characteristics of thyroid cancer. Our bioinformatics predictions based on lncRNA-microRNA interactions demonstrate that LINC00162 acts as a molecular sponge for the downregulated microRNAs hsa-miR-320A and hsa-miR-485 in thyroid cancer. RAPGEFL1, a gene associated with the development of thyroid cancer, is upregulated in conjunction with this downregulation. The LINC00162-miRNA-RAPGEFL1 axis is involved in critical carcinogenic processes, including thiamine metabolism, cell cycle control, and folate biosynthesis, according to functional enrichment analysis. Additionally, a bioinformatics study revealed a negative association between PICSAR and the NUDT3 gene, while a positive correlation was found with the SNX18P14 gene. Thyroid cancer cells transfected with LINC00162-specific siRNA showed significant downregulation of LINC00162 and RAPGEFL1, alongside an increase in hsa-miR-320A and hsa-miR-485, ultimately inhibiting the growth of thyroid cancer. These findings suggest that targeting PICSAR may offer a treatment strategy for thyroid cancer by altering important biological processes.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn conclusion, LINC00162, which is overexpressed in thyroid cancer, acts as a molecular sponge for hsa-miR-320A and hsa-miR-485, regulating key oncogenic pathways and leading to the upreglation of RAPGEFL1. These effects are reversed upon siRNA-mediated silencing of LINC00162, indicating its potential as a promising therapeutic target for thyroid cancer. RAPGEFL1 regulates the Rap signaling pathway, controlling adhesion, migration, polarity, and metabolism to maintain cellular and tissue homeostasis. Its dysregulation is linked to various diseases, highlighting its potential as a therapeutic target.\u003c/p\u003e","manuscriptTitle":"A novel long noncoding RNA, PICSAR, promotes thyroid cancer progression through the hsa-miR-320A/hsa-miR-485/RAPGEFL1 axis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 09:52:33","doi":"10.21203/rs.3.rs-6403368/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-25T19:49:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-22T08:15:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-13T14:36:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49274934133447397110930156120316503819","date":"2025-06-09T13:13:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313073310542380237907443797308309319427","date":"2025-06-09T05:03:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-26T23:34:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224875504156233395271171768884045697850","date":"2025-05-26T23:24:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-07T02:55:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-15T01:56:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-15T01:54:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Medical Oncology","date":"2025-04-08T12:27:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"medical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"medo","sideBox":"Learn more about [Medical Oncology](https://www.springer.com/journal/12032)","snPcode":"12032","submissionUrl":"https://submission.nature.com/new-submission/12032/3","title":"Medical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"791783a2-2748-4138-8b18-f78231a4ca93","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-01T16:01:24+00:00","versionOfRecord":{"articleIdentity":"rs-6403368","link":"https://doi.org/10.1007/s12032-025-02987-9","journal":{"identity":"medical-oncology","isVorOnly":false,"title":"Medical Oncology"},"publishedOn":"2025-08-26 15:57:35","publishedOnDateReadable":"August 26th, 2025"},"versionCreatedAt":"2025-05-13 09:52:33","video":"","vorDoi":"10.1007/s12032-025-02987-9","vorDoiUrl":"https://doi.org/10.1007/s12032-025-02987-9","workflowStages":[]},"version":"v1","identity":"rs-6403368","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6403368","identity":"rs-6403368","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}