An Insilico Analysis: Three Upregulated microRNAs as Potential Diagnostic Biomarkers of Papillary Thyroid Carcinoma (PTC)

An Insilico Analysis: Three Upregulated microRNAs as Potential Diagnostic Biomarkers of Papillary Thyroid Carcinoma (PTC) | 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 An Insilico Analysis: Three Upregulated microRNAs as Potential Diagnostic Biomarkers of Papillary Thyroid Carcinoma (PTC) Maharani Putri Hermawan, Sari Eka Pratiwi, Ridha Ulfah, Eko Rustianto Suhardiman, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7920609/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Apr, 2026 Read the published version in Journal of the Egyptian National Cancer Institute → Version 1 posted 9 You are reading this latest preprint version Abstract Background Papillary Thyroid Carcinoma (PTC) is a variant of thyroid cancer with the highest incidence. Many studies have proven that specific microRNAs are differentially expressed in PTC and have high potential as biomarkers. Therefore, this study aims to identify upregulated microRNAs that have the potential to be a diagnostic biomarkers of Papillary Thyroid Carcinoma (PTC) through an in silico approach. Methods This study conducted a comprehensive analysis of miRNA expression patterns using datasets available through A Database of Differentially Expressed miRNAs in Human Cancers (dbDEMC). The dataset was then processed through a data mining approach with a cutoff of P-value 1.5 to identify miRNAs that were significantly and consistently upregulated in the datasets. The target genes are predicted through miRDIP, miRTarBase, and miRPathDB. Gene ontology and pathway enrichment analysis were performed in ShinyGO and EnrichR. To assess the diagnostic ability of the three miRNAs, CancerMIRNome is used to identify the ROC curve analysis results of each miRNA. Results Our study found 85 differentially regulated miRNAs in PTC. Among those, 3 miRNAs, namely hsa-miR-221-3p, hsa-miR-222-3p, and hsa-miR-146b-5p, were significantly and consistently upregulated in all datasets. Functional enrichment on the target gene set also found that these three miRNAs have a significant contribution to PTC carcinogenesis. ROC curve analysis through CancermiRNome showed that each of the three miRNAs has excellent diagnostic performance with the AUC values respectively 0,93, 0,93, and 0,91. Conclusion In summary, our study identified hsa-miR-221-3p, hsa-miR-222-3p, and hsa-miR-146b-5p as promising diagnostic biomarkers for PTC. MicroRNA Papillary Thyroid Carcinoma In silico¸ Diagnosis Biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Thyroid cancer is the most common malignancy in the endocrine system. 1 The incidence of thyroid cancer has increased significantly between 2018 and 2022. Globally, there was an increase by 1,4 times, while in Indonesia alone the incidence of thyroid cancer increased by 3,7 times. 2 , 3 Thyroid cancer is classified into four major subtypes: papillary, follicular, medullary, and anaplastic. Of the four types, the papillary subtype, which will be referred to as papillary thyroid carcinoma (PTC), is the variant of thyroid cancer with the highest incidence, accounting for 80–85% of all thyroid cancer cases. Although PTC is considered a low-mortality cancer with a 5-year survival rate of 80–95%, metastasis in PTC is very common. The metastasis of PTC is 30–40% to regional lymph nodes in the neck area, but it may reach distant organs such as the lungs and the bone. 1 In general, the diagnosis of PTC is established through several supporting examinations, such as ultrasonography, thyroid function, and Fine Needle Aspiration Biopsy (FNAB). 1 The gold standard in the diagnosis of thyroid cancer is the FNAB procedure. 4 FNAB is often combined with ultrasonography to improve the accuracy of the results. However, according to the American Thyroid Association (ATA) guidelines, the FNAB procedure is recommended to be performed only on nodules > 1cm in size. 5 The requirement of performing FNAB is a hurdle in detecting PTCs that are evolving into small-sized carcinomas over the years, even less than 1 cm. 6 In addition, the cytology results through the FNAB procedure do not always indicate malignancy, and other tests, such as molecular testing, are needed to determine the appropriate course of action in treating thyroid nodules. 7 During this phase, microRNA (miRNA) can play a role as one of the supports for the diagnosis of PTC. MicroRNA is a type of non-coding RNA and plays a role as a post-transcriptional regulator that can suppress the process of gene expression by degrading the messenger RNA (mRNA) involved. 7 In cancer tissue, miRNA can be categorized as oncomiR, which has a high expression level and oncosuppressormiR, which has a low expression level. By analyzing the differences of miRNA expression in PTC tissues and comparing them with normal tissues, several miRNA variants will be obtained that have the potential to become diagnostic biomarker candidates for PTC. 8 In addition, miRNAs have high stability and specificity and can be found in tissues and body fluids such as blood, urine, and other body fluids, thus having great potential to become a minimally to non-invasive diagnostic tool. 9 Consistent differentially expressed microRNA in PTC can be identified with the in-silico method, where researchers are able to reinvestigate previous studies and with the help of bioinformatics technology. In silico methods can be used as a preliminary study in identifying biomarker candidates without having to incur a great expense or require a lengthy duration of time. 10 Therefore, this study aims to identify upregulated microRNAs that might serve as potential diagnostic biomarkers of Papillary Thyroid Carcinoma (PTC) with an in silico approach. Methods Data Collection Data collection begins with retrieving datasets through Database of Differentially Expressed miRNAs in Human Cancers (dbDEMC) 11 by choosing "thyroid cancer", "cancer vs normal", and "microarray". The data that appears will be sorted again and adjusted to the predetermined criteria. Then, the obtained data will be processed through GEO2R 12 to be grouped according to the type of sample, namely, normal and cancer. Identification of Differentially Expressed MiRNAs (DEMs) Datasets that have been downloaded through GEO2R will be visualized as a volcano plot through VolcaNoseR 13 with a cut off of P-value 1.5 (Fig. 1 ). In addition to forming a volcano plot, the GEO2R data will be reprocessed using the Orange app 14 to obtain miRNAs that are upregulated in cancer samples with a cut-off of P-value 1.5. MiRNA candidates were then selected based on log2FC value, significance, and number of occurrences in the three datasets that are used in this study. Identification of differentially regulated genes (DEGs) Once the candidate miRNAs were found, the target genes by each candidate miRNA were identified through miRDIP 15 , miRTarBase 16 , and miRPathDB 17 . In addition, GEPIA2 18 was used to collect genes differentially expressed in thyroid cancer with LIMMA, |log2FC| >1.5, and p-value 0.05. To increase the reliability of this finding, overlapping target genes will be identified in the form of a Venn diagram using Bioinformatics and Evolutionary Genomics (Fig. 2). Signaling pathways of Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were analyzed through EnrichR 19 and ShinyGo 20 . Results Distinguishing the miRNAs Candidate The eligible datasets acquired from dbDEMC are analyzed through the Orange app to obtain the significantly upregulated miRNA with a cut-off value of P-value 1.5. The data analyzed through the Orange app will then be processed in Microsoft Excel to calculate the average Log 2 FC and P-value, also to determine the number of miRNA occurrences using the Pivot Table feature. A total of 85 miRNAs were then identified. Based on the Log 2 FC value, significance (P-value < 0.05), and number of occurrences in the three datasets, hsa-miR-221-3p, hsa-miR-222-3p, and hsa-miR-146b-5 are selected for further analysis (Table 1 ). Table 1 Top three upregulated miRNAs MiRNA Average Log2FC Average P-value CancermiRnome P-value&AUC score Occurence hsa-miR-146b-5p 5.50 0.00003 ***&0.91 GSE73182 GSE103996 GSE113629 hsa-miR-221-3p 3.72 0.000044 ***&0.93 GSE73182 GSE103996 GSE113629 hsa-miR-222-3p 3.44 0.0001 ***&0.93 GSE73182 GSE103996 GSE113629 (*) represent t-test results, ***=P-value < 0.0001 Significance and Diagnostic Performance of miRNA Candidates In order to assess the discriminatory values and the significance of the selected miRNAs between thyroid cancer tissues and matched normal tissue, CancermiRNome is used to identify the corresponding AUC score of each miRNA and its expression significance on the Cancer Genome Atlas (TCGA) tumor and normal samples. Hsa-miR-221-3p, hsa-miR-222-3p, and hsa-miR-146b-5p exhibit excellent diagnostic performances with the AUC score > 0,9 (Fig. 3 ). Whereas for their significance in thyroid cancer, a p-value < 0,0001 was obtained for each of the miRNAs as shown in Fig. 4. This finding shows that each of the miRNA are significantly upregulated in thyroid cancer. Figure 4. Significance of (A) Hsa-miR0146b-5p, (B) Hsa-miR-221-3p, (C) Hsa-miR-222-3p on various cancers. Thyroid cancer is abbreviated as THCA (highlighted in pink). (*) represent t-test results;*=P-valure < 0.01, **=P-value < 0.001, ***=P-value < 0.0001, ns = not significant. Target genes prediction and functional enrichment analysis The overlapping target genes of each miRNA that were discovered are used to perform enrichment analysis. Hsa-miR-221-3p target genes are involved in phospholipase activity, cell migration, differentiation, chemotaxis, and epithelial cell migration-related biological processes (S1). The thyroid was also one of the top 10 tissues that involved hsa-miR-221-3p target genes (Fig. 5(A)). The enrichment KEGG pathways were mainly associated with pathways in cancers, involving 3 genes and a -log10 2.0 false discovery rate (FDR) (Fig. 6(A)). Hsa-miR-222-3p target genes are involved in epithelial cell migration, DNA replication, actin cytoskeleton reorganization, etc. They were also found in carcinoma disease with a p-value of 0,037185, but were not included in the top 10 diseases and were not significantly expressed in the thyroid gland (Fig. 5 (D)). The enrichment of KEGG pathways was mainly associated with central carbon metabolism in cancer, involving a single gene (Fig. 6(B)). Hsa-miR-146b-5 target genes were not significantly found in thyroid gland tissue and carcinoma diseases. But they are involved in epithelial cell migration, MAPK cascade positive regulator, and stem cell differentiation-related biological processes. The enrichment KEGG pathways were mainly associated with central carbon metabolism in cancer, involving 2 genes, a 0,0038 FDR, and a fold enrichment score of 109 (Fig. 6(C)). Figure 5. Top 10 tissues and diseases of the target genes of (A,B) Hsa-miR-221-3p, (C,D) Hsa-miR-222-3p, (E,F) Hsa-miR-146b-5p. The color and the length indicate the significance of the term; the longer and the lighter the bar is, the more significant the term is. Figure 6. Signaling pathways diagram of (A) Hsa-miR-221-3p, (B) Hsa-miR-222-3p, and (C) Hsa-miR-146b-5p target genes. The size of the circle represents the number of genes involved, the length of the line represents the fold enrichment value, and the color difference represents the False Discovery Rate (FDR) value. Discussion MicroRNAs are small pieces of RNA with a size of 21–23 nucleotides that are not translated into proteins and function as regulators of gene expression. During the transcription process, microRNA (miRNA) attaches to the 3′ untranslated region (3′ UTR) of messenger RNA (mRNA). This mechanism will cause the mRNA to be degraded and suppress the gene transcription process. 7 Various studies have proven that miRNAs have a major involvement in the development of cancer cells, especially in the focus of this study, namely Papillary Thyroid Carcinoma (PTC). Research conducted by Celakovsky et al found that increased expression of miR-146b was specifically found in PTC patients associated with BRAF gene mutations, and increased expression of miR-221 in recurrent PTC patients. 21 This study reported that hsa-miR-221-3p, hsa-miR-222-3p, and hsa-miR-146b-p are the top three significantly upregulated miRNAs in dbDEMC’s PTC sample using an in silico approach. A previous in silico study unveiled a three-microRNA signature (miR-21, miR-584, and miR-155) as a diagnostic marker in clear cell renal cancer (KIRC) using univariate Cox regression analysis. 2 2 In the present study, the eligible dataset acquired from dbDEMC was analyzed through the Orange app with a cut-off of P-value 1.5 to identify upregulated miRNAs. These upregulated miRNAs were then sorted based on their occurrence in the used datasets, average P-value, and log 2 FC value. We found that hsa-miR-221-3p, hsa-miR-222-3p, and hsa-miR-146b-p matched our criteria of a significantly upregulated miRNA as they appear in all three datasets along with a high average log 2 FC value and proved to be statistically significant (P-value <0.05). These top three upregulated miRNAs were then confirmed to be significantly upregulated in TCGA samples that are stored in the CancermiRNome repository. A diagnostic biomarker then must went through diagnostic accuracy analyses, which requires a physician to report its sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and accuracy. These evaluations are used to ensure that the diagnostic biomarker is able to detect the disease in question while differentiating it from a healthy condition. Receiver Operating Characteristic (ROC) analysis is one of the methods that are used in diagnostic studies in medicine. This technique involves physicians in evaluating the ability of a diagnostic test by assessing each of the possible test results' sensitivity, specificity, PPV, NPV, PLR, and NLR. The results were then plotted on a graph with TPF (sensitivity) and FPF (1-specificity) as the x-y coordinates. This process will create a ROC curve. Scores generated from the Area Under the Curve (AUC) were then used to rate the diagnostic test’s performance in distinguishing a disease. An AUC value of 1 indicates perfect diagnostic ability, while an AUC of 0.5 signifies the test performs no better than flipping a coin. 23 Our study uses an AUC value generated from the ROC curve that we retrieved through CancermiRNome to rate each of the miRNA candidates' diagnostic performance. The summary of our miRNA candidate credibility is shown in Table 1 . Our findings are consistent with previous studies that stated these three miRNAs are also significantly upregulated in PTC. In vivo and in vitro studies by Diao et al suggest that miR-221 promotes proliferation and invasion of PTC cells by targeting the TIMP3 gene, a tumor suppressor gene. 24 Jahanbani et al stated that hsa-mir-222-3p has an increased expression in tissue and blood samples of PTC patients. 25 Jia et al revealed that hsa-miR-146b-5p has been proven in vivo and in vitro to be upregulated and act as an oncomiR in PTC carcinogenesis by targeting the CCDC6 gene that codes a mutated chimeric protein of the RET proto-oncogene. 26 To increase our findings, target genes are identified, and functional enrichment is analyzed. We found that hsa-miR-221-3p targeted 18 genes, and three of them, namely MMP2, KIT, and CXCL12, are involved in cancer cells ability to sustains angiogenesis (S1(A)). The target genes of hsa-miR-146b-5p, especially KIT and PDGFRA, play a role in the central carbon metabolism in cancer, particularly in the ERK/MAPK pathway (S1(B)). Our study also discovered that hsa-miR-222-3p targeted the KIT gene. The previous explanation states that these three miRNAs have one target gene in common, the KIT gene. KIT or KIT proto-oncogene encodes a mitogen-activated receptor tyrosine kinase (RTK). These RTKs are the activators of the ERK/MAPK pathway. Activation of this pathway plays an essential role in cell proliferation, angiogenesis, and invasion. 27 In PTC, several studies have shown that KIT gene expression is decreased when compared to benign samples. 28 – 30 On the contrary, overexpression of KIT will decrease the malignant features of thyroid cancer cells and the proliferation ability of tumor cells, suggesting that this gene is involved in the differentiation of normal cells into cancer. 28 , 30 This gene is also found to inhibit the immune escape ability of thyroid cancer cells by blocking the activation of the MAPK pathway. 29 In summary, KIT might be a tumor suppressor gene in PTC. Our findings also show that each of our top three upregulated microRNAs has an excellent diagnostic ability, with the area under the curve (AUC) score above 0.9. 23 Conclusion In summary, by performing extensive analysis on differentially expressed miRNA and each of their biological process information, our study proposes hsa-miR-221-3p, hsa-miR-222-3p, and hsa-miR-146b as potential diagnostic biomarkers for PTC based on their high average log 2 FC value, which are also statistically significant (P-value < 0.05) and included in the three datasets that we use. Regarding their diagnostic ability, TCGA AUC scores showed that they have an excellent diagnostic ability (AUC ≥ 0.9). Through this study, we also found that each of the corresponding miRNAs targeted the KIT gene, which was found to be downregulated in PTC. This gene is involved in cancer cells' proliferation, invasion, migration, angiogenesis, and immune escape through controlling the ERK/MAPK pathway. Nonetheless, further in vitro and in vivo studies are needed to confirm our findings to be used in the clinical setting. Declarations Ethics approval and consent to participate This research was conducted after obtaining approval through the Ethics Committee of the Faculty of Medicine, Tanjungpura University (NO. 7367/UN22.9/PG/2024). Consent for publication Not applicable Competing interests The authors declare no competing interests. Funding This study doesn’t receive any external funding. Author Contribution MPH : Writing and editing the manuscript, data analysisSEP : Designing the method, analyzing, writing and editing the manuscriptRU: Analyzing and editing the manuscriptERS: Analysing and editing the manuscriptMM: Analysing data Data Availability 1. DEMs are acquired through dbDEMC database under experiment number EXP00548 and are available at the following URL: [https://www.biosino.org/dbDEMC/experiment/detail/EXP00548](https:/www.biosino.org/dbDEMC/experiment/detail/EXP00548) .2. DEMs data are acquired through dbDEMC database under experiment number EXP00585 and are available at the following URL: [https://www.biosino.org/dbDEMC/experiment/detail/EXP00585](https:/www.biosino.org/dbDEMC/experiment/detail/EXP00585) .3. DEMs data are acquired through dbDEMC database under experiment number EXP00452 and are available at the following URL: [https://www.biosino.org/dbDEMC/experiment/detail/EXP00452](https:/www.biosino.org/dbDEMC/experiment/detail/EXP00452) .4. DEMs microarray data were deposited into Gene Expression Omnimbus database under accession number GSE113629 and are available at the following URL: [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE113629](https:/www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE113629)5. DEMs microarray data were deposited into Gene Expression Omnimbus database under accession number GSE103996 and are available at the following URL: [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE103996](https:/www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE103996)6. DEMs microarray data were deposited into Gene Expression Omnimbus database under accession number GSE73182 and are available at the following URL: [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE73182](https:/www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE73182)7. Target genes datasets are acquired through mirDIP: [https://ophid.utoronto.ca/mirDIP/](https:/ophid.utoronto.ca/mirDIP) . , miRPathDB: [https://mpd.bioinf.uni-sb.de/](https:/mpd.bioinf.uni-sb.de) ., miRTarBase: [https://mirtarbase.cuhk.edu.cn/~miRTarBase/miRTarBase\_2025/php/index.php](https:/mirtarbase.cuhk.edu.cn/~miRTarBase/miRTarBase_2025/php/index.php) . , , and GEPIA2 : [https://gepia.cancer-pku.cn/](https:/gepia.cancer-pku.cn) .8. Target genes Venn diagram is made through Bioinformatics & Evolutionary Genomics web tool and are available at the following URL: [https://bioinformatics.psb.ugent.be/webtools/Venn/](https:/bioinformatics.psb.ugent.be/webtools/Venn)9. Functional enrichment analysis is done through ShinyGO: [https://bioinformatics.sdstate.edu/go/](https:/bioinformatics.sdstate.edu/go) . and Enrichr repository: [https://maayanlab.cloud/Enrichr/](https:/maayanlab.cloud/Enrichr) . References Siregar KB. Kanker Tiroid: Penjelasan Komprehensif Tentang Kanker Tiroid. USU Press: Medan; 2023. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018;68(6):394–424; doi: 10.3322/caac.21492 . Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Supplementary Files Supplementarydata.docx Cite Share Download PDF Status: Published Journal Publication published 13 Apr, 2026 Read the published version in Journal of the Egyptian National Cancer Institute → Version 1 posted Editorial decision: Revision requested 05 Jan, 2026 Reviews received at journal 15 Dec, 2025 Reviews received at journal 16 Nov, 2025 Reviewers agreed at journal 16 Nov, 2025 Reviewers agreed at journal 13 Nov, 2025 Reviewers invited by journal 09 Nov, 2025 Editor assigned by journal 29 Oct, 2025 Submission checks completed at journal 29 Oct, 2025 First submitted to journal 22 Oct, 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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1","display":"","copyAsset":false,"role":"figure","size":87247,"visible":true,"origin":"","legend":"\u003cp\u003eEligible datasets volcano plot. Red indicates miRNAs with increased expression, blue indicates decreased expression, and grey indicates insignificant changes.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7920609/v1/45972d4846625925d74e27c9.png"},{"id":96365386,"identity":"0bb6ee6f-7c0b-41b2-bde2-8693ac383acd","added_by":"auto","created_at":"2025-11-20 10:10:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":409025,"visible":true,"origin":"","legend":"\u003cp\u003emiRNAs candidates target genes venn diagram (A) Hsa-miR0146b-5p, (B) Hsa-miR-221-3p, (C) Hsa-miR-222-3p.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7920609/v1/a46e1f0375cabac3ca65234e.png"},{"id":96315735,"identity":"2000d1c7-1b73-43bb-95e0-6e713cd7de11","added_by":"auto","created_at":"2025-11-19 17:35:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":71699,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of (A) Hsa-miR0146b-5p, (B) Hsa-miR-221-3p, (C) Hsa-miR-222-3p.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7920609/v1/2c4221252786873e24d06e9e.png"},{"id":96315737,"identity":"3cf1385b-66ca-4121-a273-275443d8fe50","added_by":"auto","created_at":"2025-11-19 17:35:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":257490,"visible":true,"origin":"","legend":"\u003cp\u003eSignificance of\u003cstrong\u003e \u003c/strong\u003e(A) Hsa-miR0146b-5p, (B) Hsa-miR-221-3p, (C) Hsa-miR-222-3p on various cancers.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7920609/v1/d95920292ebc42976a7a550c.png"},{"id":96366016,"identity":"9457dea7-2dde-4cb4-897e-d1648acabf53","added_by":"auto","created_at":"2025-11-20 10:11:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":290346,"visible":true,"origin":"","legend":"\u003cp\u003eTop 10 tissues and diseases of the target genes of\u003cstrong\u003e \u003c/strong\u003e(A,B) Hsa-miR-221-3p, (C,D) Hsa-miR-222-3p, (E,F) Hsa-miR-146b-5p.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7920609/v1/d7f151338088ba0510aa61b9.png"},{"id":96315748,"identity":"91a17511-6505-4e5f-9449-148bc28980c9","added_by":"auto","created_at":"2025-11-19 17:35:33","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":388682,"visible":true,"origin":"","legend":"\u003cp\u003eSignaling pathways diagram of (A) Hsa-miR-221-3p, (B) Hsa-miR-222-3p, and (C) Hsa-miR-146b-5p target genes.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7920609/v1/72fdbcbbfb83b2a1252a540e.png"},{"id":107350783,"identity":"74b05839-58aa-408d-b88d-d859126fa008","added_by":"auto","created_at":"2026-04-20 16:04:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1648289,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7920609/v1/482b3e3a-388b-4a2f-babe-83012ae1bfd9.pdf"},{"id":96315740,"identity":"2cc57359-4d91-4ca1-a4d7-f0f49fd719ab","added_by":"auto","created_at":"2025-11-19 17:35:33","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":392211,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-7920609/v1/5299bee3483935d53e5f7333.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Insilico Analysis: Three Upregulated microRNAs as Potential Diagnostic Biomarkers of Papillary Thyroid Carcinoma (PTC)","fulltext":[{"header":"Background","content":"\u003cp\u003eThyroid cancer is the most common malignancy in the endocrine system.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e The incidence of thyroid cancer has increased significantly between 2018 and 2022. Globally, there was an increase by 1,4 times, while in Indonesia alone the incidence of thyroid cancer increased by 3,7 times.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Thyroid cancer is classified into four major subtypes: papillary, follicular, medullary, and anaplastic. Of the four types, the papillary subtype, which will be referred to as papillary thyroid carcinoma (PTC), is the variant of thyroid cancer with the highest incidence, accounting for 80\u0026ndash;85% of all thyroid cancer cases. Although PTC is considered a low-mortality cancer with a 5-year survival rate of 80\u0026ndash;95%, metastasis in PTC is very common. The metastasis of PTC is 30\u0026ndash;40% to regional lymph nodes in the neck area, but it may reach distant organs such as the lungs and the bone.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eIn general, the diagnosis of PTC is established through several supporting examinations, such as ultrasonography, thyroid function, and Fine Needle Aspiration Biopsy (FNAB).\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e The gold standard in the diagnosis of thyroid cancer is the FNAB procedure.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e FNAB is often combined with ultrasonography to improve the accuracy of the results. However, according to the American Thyroid Association (ATA) guidelines, the FNAB procedure is recommended to be performed only on nodules\u0026thinsp;\u0026gt;\u0026thinsp;1cm in size.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e The requirement of performing FNAB is a hurdle in detecting PTCs that are evolving into small-sized carcinomas over the years, even less than 1 cm.\u003csup\u003e6\u003c/sup\u003e In addition, the cytology results through the FNAB procedure do not always indicate malignancy, and other tests, such as molecular testing, are needed to determine the appropriate course of action in treating thyroid nodules.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eDuring this phase, microRNA (miRNA) can play a role as one of the supports for the diagnosis of PTC. MicroRNA is a type of non-coding RNA and plays a role as a post-transcriptional regulator that can suppress the process of gene expression by degrading the messenger RNA (mRNA) involved.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e In cancer tissue, miRNA can be categorized as oncomiR, which has a high expression level and oncosuppressormiR, which has a low expression level. By analyzing the differences of miRNA expression in PTC tissues and comparing them with normal tissues, several miRNA variants will be obtained that have the potential to become diagnostic biomarker candidates for PTC.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e In addition, miRNAs have high stability and specificity and can be found in tissues and body fluids such as blood, urine, and other body fluids, thus having great potential to become a minimally to non-invasive diagnostic tool.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eConsistent differentially expressed microRNA in PTC can be identified with the \u003cem\u003ein-silico\u003c/em\u003e method, where researchers are able to reinvestigate previous studies and with the help of bioinformatics technology. In silico methods can be used as a preliminary study in identifying biomarker candidates without having to incur a great expense or require a lengthy duration of time.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Therefore, this study aims to identify upregulated microRNAs that might serve as potential diagnostic biomarkers of Papillary Thyroid Carcinoma (PTC) with an \u003cem\u003ein silico\u003c/em\u003e approach.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Collection\u003c/h2\u003e\u003cp\u003eData collection begins with retrieving datasets through Database of Differentially Expressed miRNAs in Human Cancers (dbDEMC)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e by choosing \"thyroid cancer\", \"cancer vs normal\", and \"microarray\". The data that appears will be sorted again and adjusted to the predetermined criteria. Then, the obtained data will be processed through GEO2R\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e to be grouped according to the type of sample, namely, normal and cancer.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIdentification of Differentially Expressed MiRNAs (DEMs)\u003c/h3\u003e\n\u003cp\u003eDatasets that have been downloaded through GEO2R will be visualized as a volcano plot through VolcaNoseR\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e with a cut off of P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2FC| \u0026gt;1.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition to forming a volcano plot, the GEO2R data will be reprocessed using the Orange app\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e to obtain miRNAs that are upregulated in cancer samples with a cut-off of P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2FC| \u0026gt;1.5. MiRNA candidates were then selected based on log2FC value, significance, and number of occurrences in the three datasets that are used in this study.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eIdentification of differentially regulated genes (DEGs)\u003c/h3\u003e\n\u003cp\u003eOnce the candidate miRNAs were found, the target genes by each candidate miRNA were identified through miRDIP\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, miRTarBase\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, and miRPathDB\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In addition, GEPIA2\u003csup\u003e18\u003c/sup\u003e was used to collect genes differentially expressed in thyroid cancer with LIMMA, |log2FC| \u0026gt;1.5, and p-value 0.05. To increase the reliability of this finding, overlapping target genes will be identified in the form of a Venn diagram using Bioinformatics and Evolutionary Genomics (Fig.\u0026nbsp;2). Signaling pathways of Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were analyzed through EnrichR\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e and ShinyGo\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eDistinguishing the miRNAs Candidate\u003c/h2\u003e\n \u003cp\u003eThe eligible datasets acquired from dbDEMC are analyzed through the Orange app to obtain the significantly upregulated miRNA with a cut-off value of P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and Log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;\u0026gt;\u0026thinsp;1.5. The data analyzed through the Orange app will then be processed in Microsoft Excel to calculate the average Log\u003csub\u003e2\u003c/sub\u003eFC and P-value, also to determine the number of miRNA occurrences using the Pivot Table feature. A total of 85 miRNAs were then identified. Based on the Log\u003csub\u003e2\u003c/sub\u003eFC value, significance (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and number of occurrences in the three datasets, hsa-miR-221-3p, hsa-miR-222-3p, and hsa-miR-146b-5 are selected for further analysis (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTop three upregulated miRNAs\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMiRNA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage Log2FC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage P-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCancermiRnome P-value\u0026amp;AUC score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOccurence\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-146b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e***\u0026amp;0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE73182 GSE103996 GSE113629\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-221-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e***\u0026amp;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE73182 GSE103996 GSE113629\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-222-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e***\u0026amp;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE73182 GSE103996 GSE113629\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e(*) represent t-test results, ***=P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eSignificance and Diagnostic Performance of miRNA Candidates\u003c/h3\u003e\n\u003cp\u003eIn order to assess the discriminatory values and the significance of the selected miRNAs between thyroid cancer tissues and matched normal tissue, CancermiRNome is used to identify the corresponding AUC score of each miRNA and its expression significance on the Cancer Genome Atlas (TCGA) tumor and normal samples. Hsa-miR-221-3p, hsa-miR-222-3p, and hsa-miR-146b-5p exhibit excellent diagnostic performances with the AUC score\u0026thinsp;\u0026gt;\u0026thinsp;0,9 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Whereas for their significance in thyroid cancer, a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0,0001 was obtained for each of the miRNAs as shown in Fig. 4. This finding shows that each of the miRNA are significantly upregulated in thyroid cancer.\u003c/p\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFigure 4.\u003c/strong\u003e Significance of (A) Hsa-miR0146b-5p, (B) Hsa-miR-221-3p, (C) Hsa-miR-222-3p on various cancers.\u003c/p\u003e\n \u003cp\u003eThyroid cancer is abbreviated as THCA (highlighted in pink). (*) represent t-test results;*=P-valure\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **=P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ***=P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, ns\u0026thinsp;=\u0026thinsp;not significant.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eTarget genes prediction and functional enrichment analysis\u003c/h2\u003e\n \u003cp\u003eThe overlapping target genes of each miRNA that were discovered are used to perform enrichment analysis. Hsa-miR-221-3p target genes are involved in phospholipase activity, cell migration, differentiation, chemotaxis, and epithelial cell migration-related biological processes (S1). The thyroid was also one of the top 10 tissues that involved hsa-miR-221-3p target genes (Fig. 5(A)). The enrichment KEGG pathways were mainly associated with pathways in cancers, involving 3 genes and a -log10 2.0 false discovery rate (FDR) (Fig. 6(A)). Hsa-miR-222-3p target genes are involved in epithelial cell migration, DNA replication, actin cytoskeleton reorganization, etc. They were also found in carcinoma disease with a \u003cem\u003ep-value\u003c/em\u003e of 0,037185, but were not included in the top 10 diseases and were not significantly expressed in the thyroid gland (Fig. 5 (D)). The enrichment of KEGG pathways was mainly associated with central carbon metabolism in cancer, involving a single gene (Fig. 6(B)). Hsa-miR-146b-5 target genes were not significantly found in thyroid gland tissue and carcinoma diseases. But they are involved in epithelial cell migration, MAPK cascade positive regulator, and stem cell differentiation-related biological processes. The enrichment KEGG pathways were mainly associated with central carbon metabolism in cancer, involving 2 genes, a 0,0038 FDR, and a fold enrichment score of 109 (Fig. 6(C)).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFigure 5.\u003c/strong\u003e Top 10 tissues and diseases of the target genes of (A,B) Hsa-miR-221-3p, (C,D) Hsa-miR-222-3p, (E,F) Hsa-miR-146b-5p.\u003c/p\u003e\n \u003cp\u003eThe color and the length indicate the significance of the term; the longer and the lighter the bar is, the more significant the term is.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFigure 6.\u003c/strong\u003e Signaling pathways diagram of (A) Hsa-miR-221-3p, (B) Hsa-miR-222-3p, and (C) Hsa-miR-146b-5p target genes.\u003c/p\u003e\n \u003cp\u003eThe size of the circle represents the number of genes involved, the length of the line represents the fold enrichment value, and the color difference represents the False Discovery Rate (FDR) value.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMicroRNAs are small pieces of RNA with a size of 21\u0026ndash;23 nucleotides that are not translated into proteins and function as regulators of gene expression. During the transcription process, microRNA (miRNA) attaches to the 3\u0026prime; untranslated region (3\u0026prime; UTR) of messenger RNA (mRNA). This mechanism will cause the mRNA to be degraded and suppress the gene transcription process.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Various studies have proven that miRNAs have a major involvement in the development of cancer cells, especially in the focus of this study, namely Papillary Thyroid Carcinoma (PTC). Research conducted by Celakovsky et al found that increased expression of miR-146b was specifically found in PTC patients associated with BRAF gene mutations, and increased expression of miR-221 in recurrent PTC patients.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThis study reported that hsa-miR-221-3p, hsa-miR-222-3p, and hsa-miR-146b-p are the top three significantly upregulated miRNAs in dbDEMC\u0026rsquo;s PTC sample using an \u003cem\u003ein silico\u003c/em\u003e approach. A previous \u003cem\u003ein silico\u003c/em\u003e study unveiled a three-microRNA signature (miR-21, miR-584, and miR-155) as a diagnostic marker in clear cell renal cancer (KIRC) using univariate Cox regression analysis.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e2\u003c/sup\u003e In the present study, the eligible dataset acquired from dbDEMC was analyzed through the Orange app with a cut-off of P-value \u0026lt;0.05 and log\u003csub\u003e2\u003c/sub\u003eFC \u0026gt;1.5 to identify upregulated miRNAs. These upregulated miRNAs were then sorted based on their occurrence in the used datasets, average P-value, and log\u003csub\u003e2\u003c/sub\u003eFC value. We found that hsa-miR-221-3p, hsa-miR-222-3p, and hsa-miR-146b-p matched our criteria of a significantly upregulated miRNA as they appear in all three datasets along with a high average log\u003csub\u003e2\u003c/sub\u003eFC value and proved to be statistically significant (P-value \u0026lt;0.05). These top three upregulated miRNAs were then confirmed to be significantly upregulated in TCGA samples that are stored in the CancermiRNome repository.\u003c/p\u003e\u003cp\u003eA diagnostic biomarker then must went through diagnostic accuracy analyses, which requires a physician to report its sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and accuracy. These evaluations are used to ensure that the diagnostic biomarker is able to detect the disease in question while differentiating it from a healthy condition. Receiver Operating Characteristic (ROC) analysis is one of the methods that are used in diagnostic studies in medicine. This technique involves physicians in evaluating the ability of a diagnostic test by assessing each of the possible test results' sensitivity, specificity, PPV, NPV, PLR, and NLR. The results were then plotted on a graph with TPF (sensitivity) and FPF (1-specificity) as the x-y coordinates. This process will create a ROC curve. Scores generated from the Area Under the Curve (AUC) were then used to rate the diagnostic test\u0026rsquo;s performance in distinguishing a disease. An AUC value of 1 indicates perfect diagnostic ability, while an AUC of 0.5 signifies the test performs no better than flipping a coin.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Our study uses an AUC value generated from the ROC curve that we retrieved through CancermiRNome to rate each of the miRNA candidates' diagnostic performance. The summary of our miRNA candidate credibility is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eOur findings are consistent with previous studies that stated these three miRNAs are also significantly upregulated in PTC. In vivo and in vitro studies by Diao et al suggest that miR-221 promotes proliferation and invasion of PTC cells by targeting the TIMP3 gene, a tumor suppressor gene.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Jahanbani et al stated that hsa-mir-222-3p has an increased expression in tissue and blood samples of PTC patients.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Jia et al revealed that hsa-miR-146b-5p has been proven \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e to be upregulated and act as an oncomiR in PTC carcinogenesis by targeting the CCDC6 gene that codes a mutated chimeric protein of the RET proto-oncogene. \u003csup\u003e26\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eTo increase our findings, target genes are identified, and functional enrichment is analyzed. We found that hsa-miR-221-3p targeted 18 genes, and three of them, namely MMP2, KIT, and CXCL12, are involved in cancer cells ability to sustains angiogenesis (S1(A)). The target genes of hsa-miR-146b-5p, especially KIT and PDGFRA, play a role in the central carbon metabolism in cancer, particularly in the ERK/MAPK pathway (S1(B)). Our study also discovered that hsa-miR-222-3p targeted the KIT gene. The previous explanation states that these three miRNAs have one target gene in common, the KIT gene. KIT or KIT proto-oncogene encodes a mitogen-activated receptor tyrosine kinase (RTK). These RTKs are the activators of the ERK/MAPK pathway. Activation of this pathway plays an essential role in cell proliferation, angiogenesis, and invasion.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e In PTC, several studies have shown that KIT gene expression is decreased when compared to benign samples.\u003csup\u003e\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e On the contrary, overexpression of KIT will decrease the malignant features of thyroid cancer cells and the proliferation ability of tumor cells, suggesting that this gene is involved in the differentiation of normal cells into cancer.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e This gene is also found to inhibit the immune escape ability of thyroid cancer cells by blocking the activation of the MAPK pathway.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e In summary, KIT might be a tumor suppressor gene in PTC. Our findings also show that each of our top three upregulated microRNAs has an excellent diagnostic ability, with the area under the curve (AUC) score above 0.9.\u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, by performing extensive analysis on differentially expressed miRNA and each of their biological process information, our study proposes hsa-miR-221-3p, hsa-miR-222-3p, and hsa-miR-146b as potential diagnostic biomarkers for PTC based on their high average log\u003csub\u003e2\u003c/sub\u003eFC value, which are also statistically significant (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and included in the three datasets that we use. Regarding their diagnostic ability, TCGA AUC scores showed that they have an excellent diagnostic ability (AUC\u0026thinsp;\u0026ge;\u0026thinsp;0.9). Through this study, we also found that each of the corresponding miRNAs targeted the KIT gene, which was found to be downregulated in PTC. This gene is involved in cancer cells' proliferation, invasion, migration, angiogenesis, and immune escape through controlling the ERK/MAPK pathway. Nonetheless, further \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e studies are needed to confirm our findings to be used in the clinical setting.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003eThis research was conducted after obtaining approval through the Ethics Committee of the Faculty of Medicine, Tanjungpura University (NO. 7367/UN22.9/PG/2024).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study doesn\u0026rsquo;t receive any external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMPH : Writing and editing the manuscript, data analysisSEP : Designing the method, analyzing, writing and editing the manuscriptRU: Analyzing and editing the manuscriptERS: Analysing and editing the manuscriptMM: Analysing data\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003e1. DEMs are acquired through dbDEMC database under experiment number EXP00548 and are available at the following URL: [https://www.biosino.org/dbDEMC/experiment/detail/EXP00548](https:/www.biosino.org/dbDEMC/experiment/detail/EXP00548) .2. DEMs data are acquired through dbDEMC database under experiment number EXP00585 and are available at the following URL: [https://www.biosino.org/dbDEMC/experiment/detail/EXP00585](https:/www.biosino.org/dbDEMC/experiment/detail/EXP00585) .3. DEMs data are acquired through dbDEMC database under experiment number EXP00452 and are available at the following URL: [https://www.biosino.org/dbDEMC/experiment/detail/EXP00452](https:/www.biosino.org/dbDEMC/experiment/detail/EXP00452) .4. DEMs microarray data were deposited into Gene Expression Omnimbus database under accession number GSE113629 and are available at the following URL: [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE113629](https:/www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE113629)5. DEMs microarray data were deposited into Gene Expression Omnimbus database under accession number GSE103996 and are available at the following URL: [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE103996](https:/www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE103996)6. DEMs microarray data were deposited into Gene Expression Omnimbus database under accession number GSE73182 and are available at the following URL: [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE73182](https:/www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE73182)7. Target genes datasets are acquired through mirDIP: [https://ophid.utoronto.ca/mirDIP/](https:/ophid.utoronto.ca/mirDIP) . , miRPathDB: [https://mpd.bioinf.uni-sb.de/](https:/mpd.bioinf.uni-sb.de) ., miRTarBase: [https://mirtarbase.cuhk.edu.cn/~miRTarBase/miRTarBase\\_2025/php/index.php](https:/mirtarbase.cuhk.edu.cn/~miRTarBase/miRTarBase_2025/php/index.php) . , , and GEPIA2 : [https://gepia.cancer-pku.cn/](https:/gepia.cancer-pku.cn) .8. Target genes Venn diagram is made through Bioinformatics \u0026amp;amp; Evolutionary Genomics web tool and are available at the following URL: [https://bioinformatics.psb.ugent.be/webtools/Venn/](https:/bioinformatics.psb.ugent.be/webtools/Venn)9. Functional enrichment analysis is done through ShinyGO: [https://bioinformatics.sdstate.edu/go/](https:/bioinformatics.sdstate.edu/go) . and Enrichr repository: [https://maayanlab.cloud/Enrichr/](https:/maayanlab.cloud/Enrichr) .\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiregar KB. Kanker Tiroid: Penjelasan Komprehensif Tentang Kanker Tiroid. USU Press: Medan; 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Front Oncol 2023;12; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fonc.2022.1101530\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2022.1101530\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":"journal-of-the-egyptian-national-cancer-institute","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jeci","sideBox":"Learn more about [Journal of the Egyptian National Cancer Institute](http://jenci.springeropen.com)","snPcode":"43046","submissionUrl":"https://submission.springernature.com/new-submission/43046/3","title":"Journal of the Egyptian National Cancer Institute","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"MicroRNA, Papillary Thyroid Carcinoma, In silico¸ Diagnosis, Biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-7920609/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7920609/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePapillary Thyroid Carcinoma (PTC) is a variant of thyroid cancer with the highest incidence. Many studies have proven that specific microRNAs are differentially expressed in PTC and have high potential as biomarkers. Therefore, this study aims to identify upregulated microRNAs that have the potential to be a diagnostic biomarkers of Papillary Thyroid Carcinoma (PTC) through an \u003cem\u003ein silico\u003c/em\u003e approach.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study conducted a comprehensive analysis of miRNA expression patterns using datasets available through A Database of Differentially Expressed miRNAs in Human Cancers (dbDEMC). The dataset was then processed through a data mining approach with a cutoff of P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;\u0026gt;\u0026thinsp;1.5 to identify miRNAs that were significantly and consistently upregulated in the datasets. The target genes are predicted through miRDIP, miRTarBase, and miRPathDB. Gene ontology and pathway enrichment analysis were performed in ShinyGO and EnrichR. To assess the diagnostic ability of the three miRNAs, CancerMIRNome is used to identify the ROC curve analysis results of each miRNA.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOur study found 85 differentially regulated miRNAs in PTC. Among those, 3 miRNAs, namely hsa-miR-221-3p, hsa-miR-222-3p, and hsa-miR-146b-5p, were significantly and consistently upregulated in all datasets. Functional enrichment on the target gene set also found that these three miRNAs have a significant contribution to PTC carcinogenesis. ROC curve analysis through CancermiRNome showed that each of the three miRNAs has excellent diagnostic performance with the AUC values respectively 0,93, 0,93, and 0,91.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eIn summary, our study identified hsa-miR-221-3p, hsa-miR-222-3p, and hsa-miR-146b-5p as promising diagnostic biomarkers for PTC.\u003c/p\u003e","manuscriptTitle":"An Insilico Analysis: Three Upregulated microRNAs as Potential Diagnostic Biomarkers of Papillary Thyroid Carcinoma (PTC)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-19 17:35:28","doi":"10.21203/rs.3.rs-7920609/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-05T19:45:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-15T08:36:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-17T02:33:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278108599832446110474745919640207664604","date":"2025-11-17T00:25:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199581330487698684077610927749630432859","date":"2025-11-13T07:39:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-09T12:43:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-29T04:10:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-29T04:09:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of the Egyptian National Cancer Institute","date":"2025-10-22T08:06:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-the-egyptian-national-cancer-institute","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jeci","sideBox":"Learn more about [Journal of the Egyptian National Cancer Institute](http://jenci.springeropen.com)","snPcode":"43046","submissionUrl":"https://submission.springernature.com/new-submission/43046/3","title":"Journal of the Egyptian National Cancer Institute","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6447e1b7-b8cc-4cd3-9e10-d7ac35d41e30","owner":[],"postedDate":"November 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:01:51+00:00","versionOfRecord":{"articleIdentity":"rs-7920609","link":"https://doi.org/10.1186/s43046-026-00350-1","journal":{"identity":"journal-of-the-egyptian-national-cancer-institute","isVorOnly":false,"title":"Journal of the Egyptian National Cancer Institute"},"publishedOn":"2026-04-13 15:57:41","publishedOnDateReadable":"April 13th, 2026"},"versionCreatedAt":"2025-11-19 17:35:28","video":"","vorDoi":"10.1186/s43046-026-00350-1","vorDoiUrl":"https://doi.org/10.1186/s43046-026-00350-1","workflowStages":[]},"version":"v1","identity":"rs-7920609","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7920609","identity":"rs-7920609","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}