RNA Sequencing Reveals Key Genes and Pathways Associated with Thyroid Cancer

RNA Sequencing Reveals Key Genes and Pathways Associated with Thyroid Cancer | 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 RNA Sequencing Reveals Key Genes and Pathways Associated with Thyroid Cancer Poongkodi Karunakaran, Gangaraj Karyath Palliyath, Ashok Kumar Jangam, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8742634/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract BACKGROUND: Molecular distinctions between malignant and benign thyroid lesions remain inadequately characterised, particularly in Indian populations, while preoperative cytology has recognised diagnostic limitations. Transcriptomic profiling may uncover pathways of diagnostic and therapeutic relevance. METHODS: In this prospective study, fresh intraoperative thyroid tissue was obtained from 103 patients undergoing thyroidectomy (83 benign lesions, 20 malignant tumours). Paired-end RNA sequencing (150 bp) on live frozen samples was performed on the Illumina NovaSeq 6000 platform. Differential expression analysis was conducted using DESeq2, with functional enrichment assessed using KEGG and Gene Ontology databases. RESULTS: Thirty-one genes were differentially expressed between malignant and benign lesions (adjusted P ≤ 0.05; |log₂ fold change| ≥ 1), including 24 upregulated and 7 downregulated genes in malignant tissue. Upregulated transcripts were predominantly ribosomal protein–encoding genes, indicating enhanced ribosome biogenesis and translational capacity. Notably, MET and CDH6 were overexpressed among non-ribosomal genes. Downregulated genes included KIT, KDR, BCL2, PHLDA2, MLLT3 and RPS6KA1. Pathway analysis revealed enrichment of ribosome-related pathways with relative suppression of MAPK, PI3K–Akt, Ras, Rap1, focal adhesion and p53 signalling. CONCLUSION: Malignant thyroid lesions demonstrate a distinct transcriptomic signature characterised by enhanced ribosome biogenesis and translational reprogramming, highlighting protein synthesis–associated pathways as potential diagnostic and therapeutic targets. Funding: Indian Council of Medical Research (ICMR) Trial Registration: CTRI/2020/09/027607. Transcriptomic landscape RNA-sequencing Thyroid cancer Differential gene expression Translational reprogramming Ribosome biogenesis Highlights Malignant thyroid lesions exhibit enhanced ribosome biogenesis & translational reprogramming. Ribosomal genes dominate the malignant thyroid transcriptome. MET and CDH6 emerge as key non-ribosomal drivers of aggressive tumour biology. Several canonical oncogenic pathways are transcriptionally attenuated. Translational control represents a potential diagnostic and therapeutic target. Introduction Thyroid cancer represents the most common endocrine malignancy worldwide, with papillary thyroid carcinoma (PTC) accounting for the majority of cases.[1,2] The incidence of thyroid cancer has steadily increased, driven partly by improved diagnostic methods, greater access to imaging, and enhanced molecular testing techniques. Despite global advances, molecular data from Indian populations remain insufficient, although environmental exposures, iodine nutrition, and genetic factors differ substantially across regions, potentially influencing tumour biology.[3,4] Current diagnostic modalities for thyroid nodules have several shortcomings. Fine-needle aspiration cytology (FNAC), the primary diagnostic tool, has limitations, particularly in distinguishing follicular adenoma from carcinoma and in Bethesda indeterminate categories, often necessitating diagnostic thyroidectomy. Molecular profiling provides complementary insights that improve diagnostic certainty and may refine risk stratification. The landmark TCGA study established the genomic framework of PTC, identifying key drivers such as BRAF, RAS mutations, RET/PTC fusions, and alterations in the MAPK and PI3K pathways.[5] However, TCGA predominantly represents Western populations, and its applicability to Indian cohorts remains unclear. Population-specific transcriptomic studies are thus warranted. A major challenge in thyroid molecular research is the acquisition of high-quality RNA, as formalin-fixed tissues often yield degraded RNA that limits expression analysis. This study, therefore, utilized fresh intraoperative thyroid tissue preserved in RNAlater under stringent conditions to obtain high-integrity RNA suitable for next-generation sequencing (NGS). The objective was to examine differential gene expression patterns and enriched pathways distinguishing malignant from benign thyroid lesions in an Indian surgical cohort. METHODOLOGY A prospective observational study was performed from 2020 to 2024 at Government Mohan Kumaramangalam Medical College and Hospital, Salem, India. Institutional Ethical Committee approval (no. GMKMC&H/1733/1EC/2018-6) was obtained and written informed consent was secured from all participants. Consecutive patients with malignant or benign thyroid nodules who were surgical candidates were enrolled in the study. Surgical indication for total thyroidectomy included cytology with suspicion for malignancy, progressive pressure symptoms, retrosternal extension, Graves’ Disease or Toxic goitre refractory to anti-thyroid drugs or the patient's preference for surgery on cosmetic/logistic reasons. During thyroidectomy, a 0.5 × 0.5 cm tissue fragment was harvested from the representative tumour region, immediately immersed in RNA stabilization solution, kept at 4 °C for 24 hours, and subsequently stored at −90 °C. RNA extraction was performed using the Trizol method. RNA concentration and purity were assessed using the Qubit RNA BR Assay and QIAxpert system, while integrity was confirmed with Agilent RNA ScreenTape. Only samples with an RNA Integrity Number (RIN) of at least 7.0 were included. Sequencing libraries were prepared with custom Illumina RNA probes, and high-throughput sequencing was conducted on the Illumina NovaSeq 6000 platform using 150 bp paired-end reads. Sequence quality was evaluated using FastQC, and reads were aligned to the human reference genome (hg38) using STAR aligner. Differential expression analysis was performed with DESeq2, applying thresholds of log₂ fold change ≥1 or ≤−1 and an adjusted p-value ≤0.05.[6,7] Functional annotation and pathway enrichment were conducted using KOBAS with KEGG and Gene Ontology databases, applying Fisher’s exact test and false discovery rate correction. Results Of 116 patients enrolled, 103 were included in the final analysis (mean [SD] age, 42.9 [13.7] years; 14 men and 89 women), comprising 83 benign lesions and 20 histologically confirmed malignant tumours. Benign lesions included colloid goiter (15, 18%), follicular adenoma (12 14%), Hashimoto thyroiditis/chronic lymphocytic thyroiditis (24, 29%), Graves’ disease (9, 11%), and toxic multinodular goiter (11, 13%). Malignant tumours comprised papillary thyroid carcinoma (13, 65%), follicular thyroid carcinoma (2, 10%), medullary thyroid carcinoma (1, 5%), poorly differentiated thyroid carcinoma (2, 10%), anaplastic thyroid carcinoma (1, 5%), and carcinoma of uncertain histogenesis (1, 5%). A total of 4.05 billion sequencing reads were generated across all samples, yielding approximately 39.3 terabases of high-depth transcriptomic data. Alignment to the human reference genome (hg38) exceeded 90% across samples, reflecting excellent sequencing quality and uniform coverage suitable for robust differential expression and pathway analyses. All analysed sequence reads were validated and archived in the NCBI Sequence Read Archive (Supplementary Table 1). Among the samples analysed, malignant thyroid lesions demonstrated a distinct transcriptomic profile compared with benign counterparts. Differential expression analysis identified 24 significantly upregulated genes (Table 1) and 7 significantly downregulated genes (Table 2) in malignant lesions (adjusted p ≤ 0.05). Upregulated genes Upregulation was strikingly dominated by ribosomal protein genes, particularly large subunit components including RPL3, RPL8, RPL9, RPL10, RPL10A, RPL12, RPL13, RPL13A, RPL19, RPL27, RPL27A, RPL28, RPL30, RPL31, RPL36, RPL37, RPL37A, and RPLP0 , with log₂ fold changes ranging from ~1.2 to 2.4. This coordinated enrichment indicates enhanced ribosome biogenesis and translational activity as a defining feature of malignant thyroid tissue. Among non-ribosomal genes, MET showed robust overexpression (log₂ fold change >2.0), and CDH6 was also significantly upregulated, suggesting alterations in receptor tyrosine kinase signalling and cell–cell adhesion dynamics. Additional transcripts, including ARMCX3 and METRL , reflected broader changes in cellular differentiation and metabolic regulation. Downregulated genes Seven genes were significantly downregulated in malignant lesions, including KIT, KDR (VEGFR2), BCL2, RPS6KA1, AARS1, MLLT3, and PHLDA2 . These genes are involved in growth factor signalling, apoptotic regulation, and translational fidelity, indicating coordinated transcriptional suppression of survival and signalling pathways. Pathway enrichment KEGG analysis of upregulated genes demonstrated strong and exclusive enrichment of the ribosome pathway (19 genes; p = 4.25 × 10⁻³⁸), confirming a dominant translational phenotype (Table 3). Minor enrichment was observed in proteoglycans in cancer and calcium-handling pathways. Conversely, downregulated genes were enriched across multiple canonical oncogenic and stress-response pathways, including MAPK, PI3K–Akt, Ras, Rap1, focal adhesion, VEGF, mTOR, p53, apoptosis, and endocrine resistance pathways (Table 4). Collectively, these findings indicate that malignant thyroid lesions are characterised by upregulated translational machinery alongside transcriptional attenuation of classical signalling pathways, suggesting fundamental reprogramming of cellular resource allocation during tumorigenesis. Discussion This comprehensive transcriptomic analysis demonstrates that malignant transformation in thyroid tissue is characterized by a distinctive molecular phenotype dominated by enhanced ribosomal biogenesis and translational capacity, rather than global transcriptional activation of classical oncogenic signalling pathways. Leveraging high-depth RNA sequencing with robust alignment metrics, our study revealed that malignant thyroid lesions exhibit a metabolic reprogramming, favouring protein synthesis and translational efficiency over growth factor–driven signalling cascades.[8,9] The most consistent and biologically dominant finding was the marked upregulation of ribosomal protein genes, with strong enrichment of ribosome-related pathways across malignant samples. Ribosome biogenesis is increasingly recognized as a hallmark of cancer, supporting elevated biosynthetic demands, proliferative capacity, and adaptive stress responses.[10] Beyond structural roles, ribosomal proteins actively modulate cell-cycle control, metabolic plasticity, and tumour aggressiveness.[11,12] In thyroid cancer, this translational bias may facilitate rapid cellular turnover and adaptation to oncogenic stress, even in the absence of overt transcriptional activation of downstream signalling pathways. While similar ribosomal signatures have been reported in Western datasets, including TCGA, this study provides important population-specific validation in an Indian cohort, addressing a notable gap in regional molecular data.[5,13] These findings are concordant with emerging proteogenomic evidence indicating that translational output rather than transcript abundance often governs tumour behaviour in advanced thyroid cancer.[14–17] Among non-ribosomal transcripts, consistent overexpression of MET emerged as a clinically relevant signal. MET activation has been linked to invasive behaviour, lymph node metastasis, radioiodine refractoriness, and adverse outcomes in differentiated and advanced thyroid cancers, supporting its role as a driver of tumour progression and a potential therapeutic target across populations.[18,19] Upregulation of CDH6 , a cadherin involved in cell-to-cell adhesion and epithelial–mesenchymal dynamics, suggests altered cellular plasticity in malignant lesions.[20] Although thyroid-specific functional evidence remains limited, its reproducible overexpression warrants further mechanistic investigation. Additional perturbations involving genes linked to mitochondrial regulation and differentiation may contribute to the metabolic plasticity and the biological heterogeneity of malignant thyroid tissue.[21] In parallel, malignant lesions demonstrated coordinated downregulation of genes central to growth factor signalling, apoptosis, and angiogenesis, including KIT, KDR, BCL2, and RPS6KA1 . Loss of KIT expression and altered apoptotic regulation have been described as molecular features accompanying thyroid cancer progression, particularly in more aggressive or dedifferentiated tumours.[22,23] Suppression of angiogenic and MAPK-associated transcripts further highlights a transcriptional uncoupling between signalling cascades and downstream biological output. At the pathway level, this transcriptional landscape translated into suppression of multiple canonical oncogenic and stress-response pathways, including MAPK, PI3K–Akt, Ras, Rap1, focal adhesion, VEGF, mTOR, and p53 signalling. This finding is clinically pertinent, given the high prevalence of activating driver mutations such as BRAF and RAS in thyroid cancer.[24,25] The observed dissociation between mutation status and transcript-level pathway activation suggests that signalling output in malignant thyroid lesions may be maintained predominantly through post-transcriptional or post-translational mechanisms.[26] Similar patterns have been reported in radioiodine-refractory, poorly differentiated, and anaplastic thyroid carcinomas, where translational and proteomic regulation appear to dominate tumour behaviour.[27,28] Collectively, these data support a model in which malignant thyroid lesions prioritize enhanced translational efficiency and cellular economy through ribosomal upregulation, while selectively attenuating multiple signalling networks at the transcriptomic level. This paradigm shift may partly explain the variable and often modest clinical efficacy of pathway-targeted therapies in radioiodine-refractory thyroid cancer. It also highlights ribosome biogenesis and translational control as underexplored but potentially actionable therapeutic vulnerabilities.[29,30] From a diagnostic perspective, ribosomal and translation-related markers may also aid in distinguishing malignant from benign lesions, particularly in cytologically indeterminate thyroid nodules where surgical decision-making remains challenging.[12] The principal strengths of this study include the use of fresh intraoperatively collected thyroid tissue and high-quality RNA sequencing, enabling robust transcriptomic profiling. Limitations include tumour heterogeneity and a relatively small malignant cohort, precluding subtype-specific analyses, as well as the inherent inability of bulk transcriptomics to capture post-translational regulation or tumour microenvironmental interactions. Future studies integrating single-cell transcriptomics, proteomics, and long-term clinical outcomes will be essential to validate the translational relevance of these findings.[31,32] Thus, this study expands current understanding of thyroid tumorigenesis by revealing a ribosome-centric translational signature alongside transcriptional suppression of multiple oncogenic pathways. These insights provide a rationale for therapeutic strategies and biomarker development focused on translational control and metabolic vulnerability in thyroid cancer.[33] Conclusion Malignant thyroid lesions in this Indian cohort are characterised by enhanced ribosome biogenesis and translational reprogramming, accompanied by transcriptional attenuation of multiple canonical oncogenic pathways. This molecular architecture suggests a shift in thyroid tumorigenesis towards prioritisation of protein synthesis and cellular economy rather than sustained transcriptional signalling. These insights provide a coherent biological framework with potential implications for diagnostic refinement, risk stratification, and therapeutic development, particularly in aggressive and treatment-refractory thyroid cancers. Declarations ACKNOWLEDGMENT We thank MedGenome Pvt Ltd, Bengaluru, for sequencing and bioinformatics work. We thank the multi‐disciplinary research unit, GMKMC, Salem-30, for their assistance in data collection. We thank Dr. Vinayakumar Kataneni, ICAR Central Institute of Brackish water Aquaculture, Chennai‐28, for his assistance in bioinformatics, data analysis, and interpretation. AUTHOR’S CONTRIBUTION PK contributed to conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, resources, software, supervision, validation, visualization, literature search, and writing of the original draft. AKJ and GKP contributed to conceptualization, data curation, formal analysis, study design, software, visualization, and critical review and editing of the manuscript. DBR contributed to the literature search, figures, study design, data collection, data analysis, data interpretation, and critical review and editing of the manuscript. All authors read and approved the final version of the manuscript. ETHICS STATEMENT Approval (no. GMKMC&H/1733/1EC/2018-6) was granted by Institutional Ethics Committee, Government Mohan Kumaramangalam Medical College, Salem‐636030. All procedures performed in this study were in accordance with the ethical standards of the Institutional Ethics Committee and the 1964 Helsinki declaration and its later amendments. CONSENT TO PARTICIPATE Informed written consent was obtained from all the participants included in the study. CONSENT FOR PUBLICATION Informed written consent was obtained from all the patients in the study regarding participation in this research and the publication of their data. DECLARATION OF GENERATIVE AI IN SCIENTIFIC WRITING The authors declared none DATA AVAILABILITY STATEMENT The dataset generated during the current study is archived in the SRA database of the NCBI library and will be available from the corresponding author on reasonable request. CONFLICT OF INTEREST STATEMENT The authors declared that they have no conflicts of interest. FUNDING PK received extramural funding by the Indian Council of Medical Research, India, for the current study. (Ref No. 5/4/5‐3/Diab./19‐NCD‐II). References C. M. Kitahara and J. A. Sosa, Nat. Rev. Endocrinol. 12 , 646 (2016). A. Miranda-Filho, J. Lortet-Tieulent, F. Bray, B. Cao, S. Franceschi, S. Vaccarella, and L. Dal Maso, Lancet Diabetes Endocrinol. 9 , 225 (2021). L. Xu, Z. X. Cao, X. Weng, and C. F. Wang, Front. Endocrinol. 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Tables Table 1: Upregulated genes in malignant thyroid lesions Gene ID (Ensemble ID) log2FoldChange padj Description ENSG00000071082 1.411149 0.007262 ribosomal protein L31 ENSG00000089157 1.847072 0.000155 ribosomal protein lateral stalk subunit P0 ENSG00000091831 2.410644 0.000989 ribosomal protein lateral stalk subunit P0 ENSG00000100316 1.424379 0.00099 ribosomal protein L3 ENSG00000102401 2.03121 0.007008 armadillo repeat containing X-linked 3 ENSG00000105976 2.07281 0.00099 MET proto-oncogene, receptor tyrosine kinase ENSG00000108107 1.440083 0.012642 ribosomal protein L28 ENSG00000108298 1.334889 0.03308 ribosomal protein L19 ENSG00000113361 2.338621 0.006506 cadherin 6 ENSG00000130255 1.535584 0.03308 ribosomal protein L36 ENSG00000131469 1.370198 0.008791 ribosomal protein L27 ENSG00000142541 1.342475 0.00211 ribosomal protein L13a ENSG00000145592 1.300931 0.010849 ribosomal protein L37 ENSG00000147403 1.197095 0.003088 ribosomal protein L10 ENSG00000148303 1.209323 0.011931 ribosomal protein L7a ENSG00000156482 1.414217 0.013835 ribosomal protein L30 ENSG00000161016 1.591417 0.001402 ribosomal protein L8 ENSG00000163682 1.29413 0.010849 ribosomal protein L9 ENSG00000166441 1.229113 0.013871 ribosomal protein L27a ENSG00000167526 1.424586 0.002537 Ribosomal protein L13 ENSG00000176845 1.59595 0.033276 meteorin like, glial cell differentiation regulator ENSG00000197756 1.415879 0.007008 ribosomal protein L37a ENSG00000197958 1.597052 0.003088 ribosomal protein L12 ENSG00000198755 1.439705 0.002257 ribosomal protein L10a Table 2: Downregulated genes in malignant thyroid lesions Gene ID (Ensemble ID) log2FoldChange P adj Description ENSG00000090861 -1.80276 0.016983 alanyl-tRNA synthetase 1 ENSG00000117676 -2.073 0.042875 ribosomal protein S6 kinase A1 ENSG00000128052 -1.00384 0.042446 kinase insert domain receptor ENSG00000157404 -1.74856 0.003088 KIT proto-oncogene, receptor tyrosine kinase ENSG00000169499 -1.21755 0.03308 pleckstrin homology domain containing A2 ENSG00000171791 -1.10606 0.003559 BCL2 apoptosis regulator ENSG00000171843 -1.18615 0.00099 MLLT3 super elongation complex subunit Table 3. The enriched KEGG pathways associated with upregulated genes in malignant thyroid lesions Term ID Input number Background number P-Value Enrich_ratio Ribosome hsa03010 19 150 4.25E-38 0.126667 Proteoglycans in cancer hsa05205 2 203 0.015303 0.009852 Malaria hsa05144 1 48 0.044387 0.020833 Endocrine and other factor-regulated calcium reabsorption hsa04961 1 50 0.046158 0.02 Table 4. The enriched KEGG pathways associated with downregulated genes in malignant thyroid lesions Term ID Input number Background number P-Value Enrich_ratio MAPK signalling pathway hsa04010 3 296 5.14E-05 0.010135135 PI3K-Akt signalling pathway hsa04151 3 354 8.70E-05 0.008474576 EGFR tyrosine kinase inhibitor resistance hsa01521 2 79 0.0002 0.025316456 Neurotrophin signaling pathway hsa04722 2 119 0.000447 0.016806723 Fluid shear stress and atherosclerosis hsa05418 2 138 0.000598 0.014492754 Focal adhesion hsa04510 2 199 0.001225 0.010050251 Rap1 signalling pathway hsa04015 2 211 0.001374 0.009478673 Ras signalling pathway hsa04014 2 232 0.001654 0.00862069 Pathways in cancer hsa05200 2 530 0.008246 0.003773585 Apoptosis - multiple species hsa04215 1 35 0.009683 0.028571429 Aminoacyl-tRNA biosynthesis hsa00970 1 44 0.012091 0.022727273 Hedgehog signalling pathway hsa04340 1 47 0.012892 0.021276596 Amyotrophic lateral sclerosis (ALS) hsa05014 1 51 0.01396 0.019607843 VEGF signalling pathway hsa04370 1 59 0.016093 0.016949153 Acute myeloid leukemia hsa05221 1 66 0.017956 0.015151515 Long-term potentiation hsa04720 1 67 0.018222 0.014925373 Central carbon metabolism in cancer hsa05230 1 70 0.019019 0.014285714 p53 signalling pathway hsa04115 1 72 0.01955 0.013888889 Platinum drug resistance hsa01524 1 72 0.01955 0.013888889 Colorectal cancer hsa05210 1 86 0.023262 0.011627907 Small cell lung cancer hsa05222 1 93 0.025113 0.010752688 Hematopoietic cell lineage hsa04640 1 94 0.025377 0.010638298 Endocrine resistance hsa01522 1 96 0.025906 0.010416667 Progesterone-mediated oocyte maturation hsa04914 1 97 0.02617 0.010309278 Prostate cancer hsa05215 1 97 0.02617 0.010309278 NF-kappa B signaling pathway hsa04064 1 99 0.026698 0.01010101 AGE-RAGE signalling pathway in diabetic complications hsa04933 1 100 0.026961 0.01 Melanogenesis hsa04916 1 101 0.027225 0.00990099 Parathyroid hormone synthesis, secretion and action hsa04928 1 106 0.028543 0.009433962 Insulin resistance hsa04931 1 108 0.02907 0.009259259 HIF-1 signalling pathway hsa04066 1 110 0.029597 0.009090909 Cholinergic synapse hsa04725 1 112 0.030123 0.008928571 Toxoplasmosis hsa05145 1 112 0.030123 0.008928571 Sphingolipid signalling pathway hsa04071 1 119 0.031963 0.008403361 Yersinia infection hsa05135 1 122 0.032751 0.008196721 Oocyte meiosis hsa04114 1 126 0.0338 0.007936508 Autophagy - animal hsa04140 1 128 0.034325 0.0078125 Oestrogen signalling pathway hsa04915 1 137 0.036681 0.00729927 Apoptosis hsa04210 1 138 0.036943 0.007246377 Measles hsa05162 1 140 0.037466 0.007142857 Phospholipase D signalling pathway hsa04072 1 147 0.039294 0.006802721 Breast cancer hsa05224 1 147 0.039294 0.006802721 Gastric cancer hsa05226 1 148 0.039555 0.006756757 Adrenergic signalling in cardiomyocytes hsa04261 1 149 0.039816 0.006711409 mTOR signalling pathway hsa04150 1 153 0.040859 0.006535948 Necroptosis hsa04217 1 163 0.043462 0.006134969 Jak-STAT signalling pathway hsa04630 1 163 0.043462 0.006134969 Hepatitis B hsa05161 1 164 0.043721 0.006097561 Protein processing in endoplasmic reticulum hsa04141 1 165 0.043981 0.006060606 Tuberculosis hsa05152 1 175 0.046577 0.005714286 NOD-like receptor signalling pathway hsa04621 1 179 0.047614 0.005586592 Transcriptional misregulation in cancer hsa05202 1 185 0.049167 0.005405405 Additional Declarations No competing interests reported. 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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-8742634","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596475235,"identity":"740c9a40-04e4-4e0f-bd6e-a0074c7ab2e2","order_by":0,"name":"Poongkodi Karunakaran","email":"data:image/png;base64,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","orcid":"","institution":"Government Mohan Kumaramangalam Medical College","correspondingAuthor":true,"prefix":"","firstName":"Poongkodi","middleName":"","lastName":"Karunakaran","suffix":""},{"id":596475236,"identity":"7efda2db-bee8-4903-91c9-d392a8e8715c","order_by":1,"name":"Gangaraj Karyath Palliyath","email":"","orcid":"","institution":"Former Young Professional II, ICAR-Central Institute of Brackish water Aquaculture","correspondingAuthor":false,"prefix":"","firstName":"Gangaraj","middleName":"Karyath","lastName":"Palliyath","suffix":""},{"id":596475237,"identity":"592837f9-13e3-4802-80fb-0f537013e29c","order_by":2,"name":"Ashok Kumar Jangam","email":"","orcid":"","institution":"ICAR-Central Institute of Brackish water Aquaculture","correspondingAuthor":false,"prefix":"","firstName":"Ashok","middleName":"Kumar","lastName":"Jangam","suffix":""},{"id":596475238,"identity":"59a12c15-aaf9-4f55-ac03-5e217bb7a0ee","order_by":3,"name":"Dinesh Babu Ramalingam","email":"","orcid":"","institution":"Government Mohan Kumaramangalam Medical College","correspondingAuthor":false,"prefix":"","firstName":"Dinesh","middleName":"Babu","lastName":"Ramalingam","suffix":""}],"badges":[],"createdAt":"2026-01-30 15:12:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8742634/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8742634/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104399255,"identity":"fbc35297-2395-4fb7-a873-5345722ef27c","added_by":"auto","created_at":"2026-03-11 12:05:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":914464,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8742634/v1/dceb63c6-4ba9-4873-88f1-11acbd804dfc.pdf"},{"id":103624851,"identity":"d7ea10a2-3fcd-4269-8a93-60d7b3e514a1","added_by":"auto","created_at":"2026-02-27 19:54:12","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":16140,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8742634/v1/83cc5ab8468e7855f17eab67.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"RNA Sequencing Reveals Key Genes and Pathways Associated with Thyroid Cancer","fulltext":[{"header":"Highlights","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003eMalignant thyroid lesions exhibit enhanced ribosome biogenesis \u0026amp; translational reprogramming.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eRibosomal genes dominate the malignant thyroid transcriptome.\u003c/li\u003e\n \u003cli\u003eMET and CDH6 emerge as key non-ribosomal drivers of aggressive tumour biology.\u003c/li\u003e\n \u003cli\u003eSeveral canonical oncogenic pathways are transcriptionally attenuated.\u003c/li\u003e\n \u003cli\u003eTranslational control represents a potential diagnostic and therapeutic target.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eThyroid cancer represents the most common endocrine malignancy worldwide, with papillary thyroid carcinoma (PTC) accounting for the majority of cases.[1,2] The incidence of thyroid cancer has steadily increased, driven partly by improved diagnostic methods, greater access to imaging, and enhanced molecular testing techniques. Despite global advances, molecular data from Indian populations remain insufficient, although environmental exposures, iodine nutrition, and genetic factors differ substantially across regions, potentially influencing tumour biology.[3,4]\u003c/p\u003e\n\u003cp\u003eCurrent diagnostic modalities for thyroid nodules have several shortcomings. Fine-needle aspiration cytology (FNAC), the primary diagnostic tool, has limitations, particularly in distinguishing follicular adenoma from carcinoma and in Bethesda indeterminate categories, often necessitating diagnostic thyroidectomy. Molecular profiling provides complementary insights that improve diagnostic certainty and may refine risk stratification. The landmark TCGA study established the genomic framework of PTC, identifying key drivers such as BRAF, RAS mutations, RET/PTC fusions, and alterations in the MAPK and PI3K pathways.[5] However, TCGA predominantly represents Western populations, and its applicability to Indian cohorts remains unclear. Population-specific transcriptomic studies are thus warranted.\u003c/p\u003e\n\u003cp\u003eA major challenge in thyroid molecular research is the acquisition of high-quality RNA, as formalin-fixed tissues often yield degraded RNA that limits expression analysis. This study, therefore, utilized fresh intraoperative thyroid tissue preserved in RNAlater under stringent conditions to obtain high-integrity RNA suitable for next-generation sequencing (NGS). The objective was to examine differential gene expression patterns and enriched pathways distinguishing malignant from benign thyroid lesions in an Indian surgical cohort.\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cp\u003eA prospective observational study was performed from 2020 to 2024 at Government Mohan Kumaramangalam Medical College and Hospital, Salem, India. Institutional Ethical Committee approval (no. GMKMC\u0026amp;H/1733/1EC/2018-6) was obtained and written informed consent was secured from all participants. Consecutive patients with malignant or benign thyroid nodules who were surgical candidates were enrolled in the study. Surgical indication for total thyroidectomy included cytology with suspicion for malignancy, progressive pressure symptoms, retrosternal extension, Graves\u0026rsquo; Disease or Toxic goitre refractory to anti-thyroid drugs or the patient\u0026apos;s preference for surgery on cosmetic/logistic reasons. During thyroidectomy, a 0.5 \u0026times; 0.5 cm tissue fragment was harvested from the representative tumour region, immediately immersed in RNA stabilization solution, kept at 4 \u0026deg;C for 24 hours, and subsequently stored at \u0026minus;90 \u0026deg;C. RNA extraction was performed using the Trizol method. RNA concentration and purity were assessed using the Qubit RNA BR Assay and QIAxpert system, while integrity was confirmed with Agilent RNA ScreenTape. Only samples with an RNA Integrity Number (RIN) of at least 7.0 were included.\u003c/p\u003e\n\u003cp\u003eSequencing libraries were prepared with custom Illumina RNA probes, and high-throughput sequencing was conducted on the Illumina NovaSeq 6000 platform using 150 bp paired-end reads. Sequence quality was evaluated using FastQC, and reads were aligned to the human reference genome (hg38) using STAR aligner. Differential expression analysis was performed with DESeq2, applying thresholds of log₂ fold change \u0026ge;1 or \u0026le;\u0026minus;1 and an adjusted p-value \u0026le;0.05.[6,7] Functional annotation and pathway enrichment were conducted using KOBAS with KEGG and Gene Ontology databases, applying Fisher\u0026rsquo;s exact test and false discovery rate correction.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOf 116 patients enrolled, 103 were included in the final analysis (mean [SD] age, 42.9 [13.7] years; 14 men and 89 women), comprising 83 benign lesions and 20 histologically confirmed malignant tumours. Benign lesions included colloid goiter (15, 18%), follicular adenoma (12 14%), Hashimoto thyroiditis/chronic lymphocytic thyroiditis (24, 29%), Graves\u0026rsquo; disease (9, 11%), and toxic multinodular goiter (11, 13%). Malignant tumours comprised papillary thyroid carcinoma (13, 65%), follicular thyroid carcinoma (2, 10%), medullary thyroid carcinoma (1, 5%), poorly differentiated thyroid carcinoma (2, 10%), anaplastic thyroid carcinoma (1, 5%), and carcinoma of uncertain histogenesis (1, 5%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA total of 4.05 billion sequencing reads were generated across all samples, yielding approximately 39.3 terabases of high-depth transcriptomic data. Alignment to the human reference genome (hg38) exceeded 90% across samples, reflecting excellent sequencing quality and uniform coverage suitable for robust differential expression and pathway analyses. All analysed sequence reads were validated and archived in the NCBI Sequence Read Archive (Supplementary Table 1). Among the samples analysed, malignant thyroid lesions demonstrated a distinct transcriptomic profile compared with benign counterparts. Differential expression analysis identified 24 significantly upregulated genes (Table 1) and 7 significantly downregulated genes (Table 2) in malignant lesions (adjusted \u003cem\u003ep\u003c/em\u003e \u0026le; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUpregulated genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpregulation was strikingly dominated by ribosomal protein genes, particularly large subunit components including \u003cem\u003eRPL3, RPL8, RPL9, RPL10, RPL10A, RPL12, RPL13, RPL13A, RPL19, RPL27, RPL27A, RPL28, RPL30, RPL31, RPL36, RPL37, RPL37A,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;RPLP0\u003c/em\u003e, with log₂ fold changes ranging from ~1.2 to 2.4. This coordinated enrichment indicates enhanced ribosome biogenesis and translational activity as a defining feature of malignant thyroid tissue. Among non-ribosomal genes, \u003cem\u003eMET\u003c/em\u003e showed robust overexpression (log₂ fold change \u0026gt;2.0), and \u003cem\u003eCDH6\u003c/em\u003e was also significantly upregulated, suggesting alterations in receptor tyrosine kinase signalling and cell\u0026ndash;cell adhesion dynamics. Additional transcripts, including \u003cem\u003eARMCX3\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;METRL\u003c/em\u003e, reflected broader changes in cellular differentiation and metabolic regulation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDownregulated genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeven genes were significantly downregulated in malignant lesions, including \u003cem\u003eKIT, KDR (VEGFR2), BCL2, RPS6KA1, AARS1, MLLT3,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;PHLDA2\u003c/em\u003e. These genes are involved in growth factor signalling, apoptotic regulation, and translational fidelity, indicating coordinated transcriptional suppression of survival and signalling pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePathway enrichment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKEGG analysis of upregulated genes demonstrated strong and exclusive enrichment of the ribosome pathway (19 genes; \u003cem\u003ep\u003c/em\u003e = 4.25 \u0026times; 10⁻\u0026sup3;⁸), confirming a dominant translational phenotype (Table 3). Minor enrichment was observed in proteoglycans in cancer and calcium-handling pathways. Conversely, downregulated genes were enriched across multiple canonical oncogenic and stress-response pathways, including MAPK, PI3K\u0026ndash;Akt, Ras, Rap1, focal adhesion, VEGF, mTOR, p53, apoptosis, and endocrine resistance pathways (Table 4).\u003c/p\u003e\n\u003cp\u003eCollectively, these findings indicate that malignant thyroid lesions are characterised by upregulated translational machinery alongside transcriptional attenuation of classical signalling pathways, suggesting fundamental reprogramming of cellular resource allocation during tumorigenesis.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis comprehensive transcriptomic analysis demonstrates that malignant transformation in thyroid tissue is characterized by a distinctive molecular phenotype dominated by enhanced ribosomal biogenesis and translational capacity, rather than global transcriptional activation of classical oncogenic signalling pathways. Leveraging high-depth RNA sequencing with robust alignment metrics, our study revealed that malignant thyroid lesions exhibit a metabolic reprogramming, favouring protein synthesis and translational efficiency over growth factor–driven signalling cascades.[8,9]\u003c/p\u003e\n\u003cp\u003eThe most consistent and biologically dominant finding was the marked upregulation of ribosomal protein genes, with strong enrichment of ribosome-related pathways across malignant samples. Ribosome biogenesis is increasingly recognized as a hallmark of cancer, supporting elevated biosynthetic demands, proliferative capacity, and adaptive stress responses.[10] Beyond structural roles, ribosomal proteins actively modulate cell-cycle control, metabolic plasticity, and tumour aggressiveness.[11,12] In thyroid cancer, this translational bias may facilitate rapid cellular turnover and adaptation to oncogenic stress, even in the absence of overt transcriptional activation of downstream signalling pathways. While similar ribosomal signatures have been reported in Western datasets, including TCGA, this study provides important population-specific validation in an Indian cohort, addressing a notable gap in regional molecular data.[5,13] These findings are concordant with emerging proteogenomic evidence indicating that translational output rather than transcript abundance often governs tumour behaviour in advanced thyroid cancer.[14–17]\u003c/p\u003e\n\u003cp\u003eAmong non-ribosomal transcripts, consistent overexpression of MET emerged as a clinically relevant signal. \u003cem\u003eMET\u0026nbsp;\u003c/em\u003eactivation has been linked to invasive behaviour, lymph node metastasis, radioiodine refractoriness, and adverse outcomes in differentiated and advanced thyroid cancers, supporting its role as a driver of tumour progression and a potential therapeutic target across populations.[18,19] Upregulation of \u003cem\u003eCDH6\u003c/em\u003e, a cadherin involved in cell-to-cell adhesion and epithelial–mesenchymal dynamics, suggests altered cellular plasticity in malignant lesions.[20] Although thyroid-specific functional evidence remains limited, its reproducible overexpression warrants further mechanistic investigation. Additional perturbations involving genes linked to mitochondrial regulation and differentiation may contribute to the metabolic plasticity and the biological heterogeneity of malignant thyroid tissue.[21]\u003c/p\u003e\n\u003cp\u003eIn parallel, malignant lesions demonstrated coordinated downregulation of genes central to growth factor signalling, apoptosis, and angiogenesis, including \u003cem\u003eKIT, KDR, BCL2, and RPS6KA1\u003c/em\u003e. Loss of KIT expression and altered apoptotic regulation have been described as molecular features accompanying thyroid cancer progression, particularly in more aggressive or dedifferentiated tumours.[22,23] Suppression of angiogenic and MAPK-associated transcripts further highlights a transcriptional uncoupling between signalling cascades and downstream biological output.\u003c/p\u003e\n\u003cp\u003eAt the pathway level, this transcriptional landscape translated into suppression of multiple canonical oncogenic and stress-response pathways, including MAPK, PI3K–Akt, Ras, Rap1, focal adhesion, VEGF, mTOR, and p53 signalling. This finding is clinically pertinent, given the high prevalence of activating driver mutations such as BRAF and RAS in thyroid cancer.[24,25] The observed dissociation between mutation status and transcript-level pathway activation suggests that signalling output in malignant thyroid lesions may be maintained predominantly through post-transcriptional or post-translational mechanisms.[26] Similar patterns have been reported in radioiodine-refractory, poorly differentiated, and anaplastic thyroid carcinomas, where translational and proteomic regulation appear to dominate tumour behaviour.[27,28]\u003c/p\u003e\n\u003cp\u003eCollectively, these data support a model in which malignant thyroid lesions prioritize enhanced translational efficiency and cellular economy through ribosomal upregulation, while selectively attenuating multiple signalling networks at the transcriptomic level. This paradigm shift may partly explain the variable and often modest clinical efficacy of pathway-targeted therapies in radioiodine-refractory thyroid cancer. It also highlights ribosome biogenesis and translational control as underexplored but potentially actionable therapeutic vulnerabilities.[29,30] From a diagnostic perspective, ribosomal and translation-related markers may also aid in distinguishing malignant from benign lesions, particularly in cytologically indeterminate thyroid nodules where surgical decision-making remains challenging.[12]\u003c/p\u003e\n\u003cp\u003eThe principal strengths of this study include the use of fresh intraoperatively collected thyroid tissue and high-quality RNA sequencing, enabling robust transcriptomic profiling. Limitations include tumour heterogeneity and a relatively small malignant cohort, precluding subtype-specific analyses, as well as the inherent inability of bulk transcriptomics to capture post-translational regulation or tumour microenvironmental interactions. Future studies integrating single-cell transcriptomics, proteomics, and long-term clinical outcomes will be essential to validate the translational relevance of these findings.[31,32]\u003c/p\u003e\n\u003cp\u003eThus, this study expands current understanding of thyroid tumorigenesis by revealing a ribosome-centric translational signature alongside transcriptional suppression of multiple oncogenic pathways. These insights provide a rationale for therapeutic strategies and biomarker development focused on translational control and metabolic vulnerability in thyroid cancer.[33] \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eMalignant thyroid lesions in this Indian cohort are characterised by enhanced ribosome biogenesis and translational reprogramming, accompanied by transcriptional attenuation of multiple canonical oncogenic pathways. This molecular architecture suggests a shift in thyroid tumorigenesis towards prioritisation of protein synthesis and cellular economy rather than sustained transcriptional signalling. These insights provide a coherent biological framework with potential implications for diagnostic refinement, risk stratification, and therapeutic development, particularly in aggressive and treatment-refractory thyroid cancers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank MedGenome Pvt Ltd, Bengaluru, for sequencing and bioinformatics work. We thank the multi‐disciplinary research unit, GMKMC, Salem-30, for their assistance in data collection. We thank Dr. Vinayakumar Kataneni, ICAR Central Institute of Brackish water Aquaculture, Chennai‐28, for his assistance in bioinformatics, data analysis, and interpretation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR’S CONTRIBUTION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePK contributed to conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, resources, software, supervision, validation, visualization, literature search, and writing of the original draft. AKJ and GKP contributed to conceptualization, data curation, formal analysis, study design, software, visualization, and critical review and editing of the manuscript. DBR contributed to the literature search, figures, study design, data collection, data analysis, data interpretation, and critical review and editing of the manuscript. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval (no. GMKMC\u0026amp;H/1733/1EC/2018-6) was granted by Institutional Ethics Committee, Government Mohan Kumaramangalam Medical College, Salem‐636030. All procedures performed in this study were in accordance with the ethical standards of the Institutional Ethics Committee and the 1964 Helsinki declaration and its later amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONSENT TO PARTICIPATE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed written consent was obtained from all the participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONSENT FOR PUBLICATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed written consent was obtained from all the patients in the study regarding participation in this research and the publication of their data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDECLARATION OF GENERATIVE AI IN SCIENTIFIC WRITING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared none\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset generated during the current study is archived in the SRA database of the NCBI library and will be available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePK received extramural funding by the Indian Council of Medical Research, India, for the current study. (Ref No. 5/4/5‐3/Diab./19‐NCD‐II).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eC. M. Kitahara and J. A. Sosa, Nat. Rev. Endocrinol. \u003cb\u003e12\u003c/b\u003e, 646 (2016).\u003c/li\u003e\n\u003cli\u003eA. Miranda-Filho, J. Lortet-Tieulent, F. Bray, B. Cao, S. Franceschi, S. Vaccarella, and L. Dal Maso, Lancet Diabetes Endocrinol. \u003cb\u003e9\u003c/b\u003e, 225 (2021).\u003c/li\u003e\n\u003cli\u003eL. Xu, Z. X. Cao, X. Weng, and C. F. Wang, Front. Endocrinol. (Lausanne). \u003cb\u003e14\u003c/b\u003e, (2023).\u003c/li\u003e\n\u003cli\u003eK. Poongkodi, S. Periyasamy, R. A. Gurunathan, V. Krishnasamy, D. Jayakumar, R. Subburaman, S. Jayaraman, and S. K. Prabhudas, World J. Surg. \u003cb\u003e48\u003c/b\u003e, 2880 (2024).\u003c/li\u003e\n\u003cli\u003eN. Agrawal, R. Akbani, B. A. Aksoy, A. Ally, H. Arachchi, S. L. Asa, J. T. Auman, M. Balasundaram, S. Balu, S. B. Baylin, M. Behera, B. Bernard, R. Beroukhim, J. A. Bishop, A. D. Black, T. 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Commun. \u003cb\u003e10\u003c/b\u003e, (2019).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Upregulated genes in malignant thyroid lesions\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene ID (Ensemble ID)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elog2FoldChange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003epadj\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000071082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e1.411149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.007262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003eribosomal protein L31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000089157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e1.847072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.000155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003eribosomal protein lateral stalk subunit P0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000091831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e2.410644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.000989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003eribosomal protein lateral stalk subunit P0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000100316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e1.424379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.00099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003eribosomal protein L3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000102401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e2.03121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.007008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003earmadillo repeat containing X-linked 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000105976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e2.07281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.00099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003eMET proto-oncogene, receptor tyrosine kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000108107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e1.440083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.012642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003eribosomal protein L28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000108298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e1.334889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.03308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003eribosomal protein L19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000113361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e2.338621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.006506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003ecadherin 6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000130255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e1.535584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.03308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003eribosomal protein L36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000131469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e1.370198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.008791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003eribosomal protein L27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000142541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e1.342475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.00211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003eribosomal protein L13a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000145592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e1.300931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.010849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003eribosomal protein L37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000147403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e1.197095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.003088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003eribosomal protein L10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000148303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e1.209323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.011931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003eribosomal protein L7a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000156482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e1.414217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.013835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003eribosomal protein L30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000161016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e1.591417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.001402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003eribosomal protein L8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000163682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e1.29413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.010849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003eribosomal protein L9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000166441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e1.229113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.013871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003eribosomal protein L27a\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000167526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e1.424586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.002537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003eRibosomal protein L13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000176845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e1.59595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.033276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003emeteorin like, glial cell differentiation regulator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000197756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e1.415879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.007008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003eribosomal protein L37a\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000197958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e1.597052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.003088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003eribosomal protein L12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000198755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e1.439705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.002257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003eribosomal protein L10a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Downregulated genes in malignant thyroid lesions\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene ID (Ensemble ID)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003elog2FoldChange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eP adj\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eENSG00000090861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-1.80276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.016983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ealanyl-tRNA synthetase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eENSG00000117676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-2.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.042875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eribosomal protein S6 kinase A1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eENSG00000128052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-1.00384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.042446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ekinase insert domain receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eENSG00000157404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-1.74856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.003088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eKIT proto-oncogene, receptor tyrosine kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eENSG00000169499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-1.21755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.03308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003epleckstrin homology domain containing A2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eENSG00000171791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-1.10606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.003559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBCL2 apoptosis regulator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eENSG00000171843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-1.18615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.00099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMLLT3 super elongation complex subunit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. The enriched KEGG pathways associated with upregulated genes in malignant thyroid lesions\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTerm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eInput number\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBackground number\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnrich_ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRibosome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ehsa03010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.25E-38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.126667\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eProteoglycans in cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ehsa05205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.015303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.009852\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMalaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ehsa05144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.044387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.020833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEndocrine and other factor-regulated calcium reabsorption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ehsa04961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.046158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e \u003cstrong\u003eThe enriched KEGG pathways associated with downregulated genes in malignant thyroid lesions\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTerm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eInput number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBackground number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eEnrich_ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMAPK signalling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5.14E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.010135135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePI3K-Akt signalling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8.70E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.008474576\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eEGFR tyrosine kinase inhibitor resistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa01521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.025316456\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNeurotrophin signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.000447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.016806723\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFluid shear stress and atherosclerosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa05418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.000598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.014492754\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFocal adhesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.001225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.010050251\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRap1 signalling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.001374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.009478673\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRas signalling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.001654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.00862069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePathways in cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa05200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.008246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.003773585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eApoptosis - multiple species\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.009683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.028571429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAminoacyl-tRNA biosynthesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa00970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.012091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.022727273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHedgehog signalling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.012892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.021276596\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAmyotrophic lateral sclerosis (ALS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa05014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.01396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.019607843\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eVEGF signalling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.016093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.016949153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAcute myeloid leukemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa05221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.017956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.015151515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLong-term potentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.018222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.014925373\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCentral carbon metabolism in cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa05230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.019019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.014285714\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ep53 signalling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.01955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.013888889\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePlatinum drug resistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa01524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.01955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.013888889\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eColorectal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa05210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.023262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.011627907\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSmall cell lung cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa05222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.025113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.010752688\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHematopoietic cell lineage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.025377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.010638298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eEndocrine resistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa01522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.025906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.010416667\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eProgesterone-mediated oocyte maturation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.02617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.010309278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eProstate cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa05215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.02617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.010309278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNF-kappa B signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.026698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.01010101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAGE-RAGE signalling pathway in diabetic complications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.026961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMelanogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.027225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.00990099\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eParathyroid hormone synthesis, secretion and action\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.028543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.009433962\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eInsulin resistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.02907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.009259259\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHIF-1 signalling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.029597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.009090909\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCholinergic synapse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.030123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.008928571\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eToxoplasmosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa05145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.030123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.008928571\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSphingolipid signalling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.031963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.008403361\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYersinia infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa05135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.032751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.008196721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOocyte meiosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.007936508\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAutophagy - animal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.034325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0078125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOestrogen signalling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.036681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.00729927\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eApoptosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.036943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.007246377\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMeasles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa05162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.037466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.007142857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePhospholipase D signalling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.039294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.006802721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBreast cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa05224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.039294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.006802721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGastric cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa05226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.039555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.006756757\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAdrenergic signalling in cardiomyocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.039816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.006711409\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003emTOR signalling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.040859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.006535948\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNecroptosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.043462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.006134969\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eJak-STAT signalling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.043462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.006134969\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHepatitis B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa05161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.043721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.006097561\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eProtein processing in endoplasmic reticulum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.043981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.006060606\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTuberculosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa05152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.046577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.005714286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNOD-like receptor signalling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa04621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.047614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.005586592\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTranscriptional misregulation in cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehsa05202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.049167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.005405405\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Transcriptomic landscape, RNA-sequencing, Thyroid cancer, Differential gene expression, Translational reprogramming, Ribosome biogenesis","lastPublishedDoi":"10.21203/rs.3.rs-8742634/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8742634/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBACKGROUND:\u003c/p\u003e\n\u003cp\u003eMolecular distinctions between malignant and benign thyroid lesions remain inadequately characterised, particularly in Indian populations, while preoperative cytology has recognised diagnostic limitations. Transcriptomic profiling may uncover pathways of diagnostic and therapeutic relevance.\u003c/p\u003e\n\u003cp\u003eMETHODS:\u003c/p\u003e\n\u003cp\u003eIn this prospective study, fresh intraoperative thyroid tissue was obtained from 103 patients undergoing thyroidectomy (83 benign lesions, 20 malignant tumours). Paired-end RNA sequencing (150 bp) on live frozen samples was performed on the Illumina NovaSeq 6000 platform. Differential expression analysis was conducted using DESeq2, with functional enrichment assessed using KEGG and Gene Ontology databases.\u003c/p\u003e\n\u003cp\u003eRESULTS:\u003c/p\u003e\n\u003cp\u003eThirty-one genes were differentially expressed between malignant and benign lesions (adjusted P ≤ 0.05; |log₂ fold change| ≥ 1), including 24 upregulated and 7 downregulated genes in malignant tissue. Upregulated transcripts were predominantly ribosomal protein–encoding genes, indicating enhanced ribosome biogenesis and translational capacity. Notably, MET and CDH6 were overexpressed among non-ribosomal genes. Downregulated genes included KIT, KDR, BCL2, PHLDA2, MLLT3 and RPS6KA1. Pathway analysis revealed enrichment of ribosome-related pathways with relative suppression of MAPK, PI3K–Akt, Ras, Rap1, focal adhesion and p53 signalling.\u003c/p\u003e\n\u003cp\u003eCONCLUSION:\u003c/p\u003e\n\u003cp\u003eMalignant thyroid lesions demonstrate a distinct transcriptomic signature characterised by enhanced ribosome biogenesis and translational reprogramming, highlighting protein synthesis–associated pathways as potential diagnostic and therapeutic targets.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding:\u003c/em\u003e Indian Council of Medical Research (ICMR)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTrial Registration:\u003c/em\u003eCTRI/2020/09/027607.\u003c/p\u003e","manuscriptTitle":"RNA Sequencing Reveals Key Genes and Pathways Associated with Thyroid Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-27 19:53:40","doi":"10.21203/rs.3.rs-8742634/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"14f2f759-6fd7-422b-bf38-fca05bfca542","owner":[],"postedDate":"February 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-03T19:39:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-27 19:53:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8742634","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8742634","identity":"rs-8742634","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}