A Pan-omics Systems Biology Perspective Identifies Calcium Signaling Pathway Critical in Medullary Thyroid Cancer

A Pan-omics Systems Biology Perspective Identifies Calcium Signaling Pathway Critical in Medullary 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 A Pan-omics Systems Biology Perspective Identifies Calcium Signaling Pathway Critical in Medullary Thyroid Cancer Febby Payva, Santhy K. S., Remya James This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6926629/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 : Medullary thyroid carcinoma (MTC) is an uncommon type of thyroid cancer that occurs in the thyroid gland's parafollicular C cell. The pan-omics approach aids in determining the molecular pathways and elements important to MTC carcinogenesis. Methodology : SNPs associated with medullary thyroid carcinoma (MTC) were extracted from the DisGeNET database, while differentially expressed genes were obtained from the Gene Expression Omnibus (GEO). Proteomic and metabolomic datasets were retrieved from published studies available in the EMBL-EBI database. The functional implications of the SNPs; and the pathways and gene ontology analysis of SNPs, differentially expressed genes (DEGs), proteins (DEPs), and metabolites (DEMs) were performed. Important kinases and transcription factors (TFs) were also found. To identify the core dysregulated pathways in MTC carcinogenesis, an integrated approach was employed to study the pan-omics data. Results: Calcium signaling pathway was identified as the core dysregulated pathway in MTC. The parent pathways linked with MTC oncogenesis were metabolism, signal transduction, apoptosis, disease, cancer, and cell–extracellular matrix (ECM) interaction and adhesion. Fourteen TFs and eighty-seven kinases that are essential for the development and progression of MTC were identified by the commonality analysis. The core pathway, TFs, and kinases were influential on the clinical outputs like proliferation, metastasis, angiogenesis, EMT, and apoptosis in MTC. Conclusion - The core deregulated pathway, TFs, and kinases deciphered may help identify potential biomarkers and druggable targets for improved patient care and management in MTC. Medullary thyroid cancer systems biology multi-omics calcium signaling pathway Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 INTRODUCTION Medullary thyroid cancer (MTC), an uncommon form of thyroid cancer (TC) that affects the thyroid gland's parafollicular C cells, accounts for about 1 to 5% of all thyroid malignancies [ 1 , 2 ]. The incidence of MTC rose from 0.41 to 0.57/100000 persons between 1975 and 2018, a moderate increase, according to studies. With a sex ratio of 0.73, the average age at which MTC is diagnosed is 53. A complete remission of 58.5% and a five-year survival rate of approximately 88.4% were reported. Between the hereditary and sporadic types of MTC, the prognosis is poorer for sporadic [ 3 ]. Most (70%) of sporadic MTC cases with palpable nodules may have metastases to cervical lymph nodes, with up to 10% harbouring distant metastases. About 20–25% of cases are hereditary MTC, which is defined by a genetic mutation in RET proto-oncogene and manifests as either familial MTC (FMTC) or multiple endocrine neoplasia (MEN) II A or II B. Early prophylaxis includes family screening of RET mutation [ 4 ]. The common symptoms of MTC include nodules and pain in the neck, swallowing difficulty, cough, and dyspnoea followed by some unusual signs like severe diarrhoea, Cushing syndrome, facial flushing, bone pain, lethargy, and weight loss. Calcitonin and carcinoembryonic antigen (CEA) act as serum biomarkers while RET mutation is a molecular MTC biomarker. The physical examination followed by determination of basal and stimulated calcitonin, CEA, RET mutation analysis, fine-needle aspiration biopsy (FNAB), ultrasonography of the neck, computed tomography (CT) scan, and MRI are the widely used methods of screening MTC [ 5 ]. Positron emission tomography/computed tomography (PET/CT) with radiotracers 18 F-FDG [ 6 ], [ 18 F] F-DOPA [ 7 ] and 68Ga-DOTATATE [ 8 ] are efficient nuclear medicine techniques for metastatic and recurrent MTC. Pharmacotherapy and surgery are the main techniques used to manage MTC. High levels of toxicity and drug resistance are caused by the pharmacotherapy now in use. Research on the potential application of immunotherapy, radiation, tyrosine kinase inhibitors, and RET-specific kinase inhibitors in the therapeutic management of MTC has advanced recently [ 9 ]. Selpercatinib and Pralsetinib are potent RET-specific kinase inhibitors used in MTC [ 10 ]. Owing to the low tumor mutational burden (TMB) in MTC the efficacy of immunotherapy in MTC management is a challenge [ 11 ]. Advancements in multi-omics analysis and molecular high throughput technologies have enhanced the understanding and management of TC via modifying patient risk stratification, identifying significant target therapy, and facilitating personalized treatment strategies [ 12 ]. Multi-omics data analysis was used in various diseases like rheumatoid arthritis [ 13 ] and PTC [ 14 ]. Notably, an integrated proteogenomic analysis has been conducted for MTC [ 15 ]. However, comprehensive system-based multi-omics studies remain limited for MTC. The present study aims to address this gap by identifying the key dysregulated metabolic pathways in the pathophysiology of MTC at multiple omics levels by assembling MTC-associated single nucleotide polymorphisms (SNPs), differentially expressed genes (DEGs), proteins (DEPs), and metabolites (DEMs). Furthermore, the kinases and transcription factors (TFs) essential to MTC were found. The clinical outcome in MTC was associated with these putative pathways. Hence, a systems biology approach provides a comprehensive overview of the MTC, offering insights that could potentially be translated into clinical applications for improved patient care and management. MATERIALS AND METHODS MTC ASSOCIATED SINGLE NUCLEOTIDE POLYMORPHISM (SNP) ANALYSIS SNPs allied to MTC were screened from DisGeNET ( http://www.disgenet.org/ ) and verified with ClinVar database ( https://www.ncbi.nlm.nih.gov/clinvar/ ) for its association with MTC or MEN 2A/ 2B. One hundred fifty-one missense mutations were curated. DisGeNET is a comprehensive platform for the disease associated genes and variants [ 16 ]. The variant analysis was performed by SIFT and PolyPhen-2. SIFT ( https://sift.bii.a-star.edu.sg/ ) forecasts how a nucleotide alteration in the coding area will affect a protein's function [ 17 ]. The curated dbSNPs were predicted in this study based on their potential influence on MTC genesis. A SIFT value of less than 0.05 was considered "deleterious," demonstrating a high conservation, while a value of greater than 0.05 was considered "tolerated," indicating low conservation, for amino acid changes. PolyPhen-2 ( http://genetics.bwh.harvard.edu/pph2/ ) is a program for identifying the SNPs in the coding sequence. It examines how the SNVs affect the protein's functionality. SNVs with scores near 1.0 are most likely harmful, while those near 0.0 are projected to be benign [ 18 ]. Another annotation tool for predicting genome sequence variation is SNPnexus ( https://www.snp-nexus.org ). Among the several applications of SNPnexus are genome mapping, impacts on protein function, connections between phenotype and disease, structural variation, pathway analysis, and clinical interpretation [ 19 ]. Enrichr ( https://maayanlab.cloud/Enrichr/ ) was applied for the pathway enrichment and gene ontology (GO) of the biological process (BP), molecular function (MF), and cellular component (CC) [ 20 ]. MTC TRANSCRIPTOMIC DATA ANALYSIS Two gene expression datasets from the Gene Expression Omnibus (GEO) repository ( http://www.ncbi.nlm.nih.gov/geo)—GSE2715 5[ 21 ] and GSE196264 [ 22 ]—were used to analyse MTC transcriptome data by comparing sick and healthy controls. Significant DEGs for GSE27155 were identified using GEO2R ( http://www.ncbi.nlm.nih.gov/geo/geo2r ) with an adjusted P-value threshold of < 0.05, while DEGs for GSE196264 were obtained from the corresponding published study. The online tool Enrichr ( https://maayanlab.cloud/Enrichr/ ) was used to do the enrichment analysis of the DEGs. CC, BP, and MF were annotated by a gene ontology (GO) analysis. Molbiotools( https://maayanlab.cloud/Enrichr/ ) was used to identify the dysregulated pathways that were shared by the datasets. IDENIFICATION OF TRANSCRIPTION FACTORS (TFS) AND KINASES IN MTC DEGs were examined using eXpression2Kinases (X2K) ( https://maayanlab.cloud/X2K/ ) to identify potential TFs and kinases associated with MTC. It is a web resource for transcription factor enrichment analysis (TFEA), kinase enrichment research, protein-protein interactions subnetwork building, and TFs-kinase interaction network design [ 23 ]. MTC PROTEOMIC DATA ANALYSIS The proteomic data decipher the amount, nature, location, interaction, post translational modification, and biological function of protein in an organism. Two published MTC-related proteomic data sets in serum [ 24 ] and tissue [ 25 ], were mined from the EMBL-EBI ( https://www.ebi.ac.uk/ ) database to identify MTC-associated significant DEPs (adj. P-values < 0.05) with a two-fold change. To determine the dysregulated pathways in MTC, DEPs were assessed using Enrichr. MTC METABOLOMIC DATA ANALYSIS The metabolomic analysis is a bio-analytical tool for comprehending the biological functionality within an organism in a healthy state instead of a diseased state [ 26 ]. This article analysed two metabolomic studies, one conducted in serum[ 27 ] and the other in plasma [ 28 ]. Together with other omics analyses, metabolomics—the final stage of the gene expression process—is essential to comprehensively understanding the illness [ 29 ]. To determine the key metabolic pathways linked to MTC, metabolite enrichment analysis was performed using the web-based Metaboanalyst 6.0 ( https://www.metaboanalyst.ca/ ). MULTI-OMICS DATA INTEGRATION IN MTC Dysregulated pathways in the SNP analysis, transcriptomics, proteomics, and metabolomics were collated. Pathways potentially involved in the MTC disease mechanism were identified through an analysis of shared pathways across MTC-linked human SNPs, DEGs, DEPs, and DEMs. The key altered pathways were further correlated with clinical parameters in MTC. RESULT STUDY OF SNP REVEAL SIGNIFICANT MUTATIONS AND ALTERED PATHWAYS IN MTC PATHOGENESIS The DisGeNET database was used to source the SNPs linked to MTC tumorigenesis. A total of 151 missense variants were chosen for SNP analysis and critical pathway enrichment. SNP analysis explains the mutation led functional modifications of the proteins resulting in MTC (Fig. 1 a and Supplementary Table 1). Enrichment analysis identifies potential dysregulated pathways associated with MTC and streamline the MF, CC, and BP in MTC pathogenesis (Fig. 1 b). GO MF observed were transmembrane receptor protein kinase activity, transmembrane receptor protein tyrosine kinase activity, protein tyrosine kinase activity, neurotrophin binding and calcium ion binding. However, the cellular components enriched were axon, endosome membrane, cytoplasmic vesicle membrane, neuron projection, bounding membrane of organelle, and endosome. Positive regulation of neuron projection development and cell projection organization, enzyme-linked receptor protein signaling pathway, regulation of phosphatidylinositol 3-kinase/protein kinase B signal transduction, cell surface receptor protein tyrosine kinase signaling pathway, phosphorylation, positive regulation of MAPK cascade and protein phosphorylation were the primary biological processes involved. Twelve KEGG pathways were found significant in the pathway enrichment analysis of MTC associated SNPs as shown in Fig. 1 c and Supplementary table 2 . SIGNIFICANT DIFFERENTIALLY EXPRESSED GENES (DEGS), TRANSCRIPTION FACTORS, KINASES, AND DYSREGULATED PATHWAYS IN MTC IDENTIFIED USING TRANSCRIPTOMIC DATA ANALYSIS Significant DEGs associated with transcriptomic datasets GSE27155 and GSE196264 comparing the diseased with the normal were used for the study. GSE27155 and GSE196264 analysis resulted 2013 and 1666 DEGs respectively. The DEGs were mapped to pathways potentially implicated in MTC. GO assessment of the DEGs were also conducted for both the GEO datasets. (Supplementary Table 3) DEGs from GSE27155 dataset was binned into one hundred fifty-eight significant KEGG pathways including MAPK signaling pathway, pathways in cancer, focal adhesion, PI3K-Akt signaling pathway, axon guidance, dopaminergic synapse, Rap1 signaling pathway, proteoglycans in cancer, human papillomavirus infection, and relaxin signaling pathway (Fig. 2 a and Supplementary Table 3b). S-methylmatonate semialdehyde, gamma- aminobutyric acid, NAD + , 5-hydroxyindolacetaldehyde and beta alanine were the predicted metabolites (Supplementary Fig. 1A). GSE196264 dataset was enriched in major KEGG pathways like dopaminergic synapse, circadian entrainment, adrenergic signaling in cardiomyocytes, calcium signaling pathway, thyroid hormone synthesis, and ECM-receptor interaction (Fig. 3 a and Supplementary Table 3b). Glycine, glucose 1 phosphate, retinal, 9-cis retina, and tyrosine were among the predicted metabolites (Supplementary Fig. 1B). A comparative analysis of transcriptomic dataset resulted in fifteen common pathways like adrenergic signaling in cardiomyocytes, arrhythmogenic right ventricular cardiomyopathy, calcium signaling pathway, circadian entrainment, cocaine addiction, dilated cardiomyopathy, dopaminergic synapse, ECM-receptor interaction, gastric acid secretion, glutamatergic synapse, hypertrophic cardiomyopathy, protein digestion and absorption, Rap1 signaling pathway, thyroid hormone synthesis, and cAMP signaling pathway (Fig. 4 a). The key TFs and kinases linked to MTC carcinogenesis were identified in two datasets (Supplementary Table 3e), and it was observed that fourteen transcription factors were common (Fig. 4 b). Between the two MTC datasets, eighty-seven kinases were remarkable (Fig. 4 c). SIGNIFICANT DIFFERENTIALLY EXPRESSED PROTEINS (DEP) AND DYSREGULATED PATHWAYS ASSOCIATED WITH MTC FROM PROTEOMIC DATA ANALYSIS MTC proteomic data comparing the diseased with normal in serum and tissue samples were analysed to derive DEPs. The serum and tissue datasets yielded 73 and 338 significant DEPs, respectively. The DEPs were evaluated for the critical pathways dysregulated in MTC (Fig. 5 and Supplementary Table 4). SIGNIFICANT MTC-ASSOCIATED METABOLITES AND METABOLIC PATHWAYS FROM METABOLOMIC DATA ANALYSIS Analysing metabolomic data is essential to comprehending how the main metabolic pathways contribute to the pathophysiology of disease. Significant metabolites were screened in the MTC-based metabolomic datasets. According to Supplementary Table 5, these metabolites were mapped into important metabolic pathways associated with MTC tumorigenesis (adj. P value < 0.05 and FDR < 0.25) (Fig. 6 ). Glycerophospholipid metabolism; sphingolipid metabolism; biosynthesis of unsaturated fatty acids; arginine biosynthesis, alanine aspartate and glutamate metabolism; and valine leucine and isoleucine biosynthesis, were the pooled significant metabolic pathways. DECIPHERING DYSREGULATED PATHWAYS IN MTC USING AN INTEGRATED MULTI-OMIC DATA ANALYSIS To identify the primary dysregulated pathways in MTC, the important pathways at the multi-omics level were combined. Calcium signaling pathway was the most significant pathway in MTC, common to transcriptomic, proteomic, and SNP datasets of MTC (Fig. 7 ). The other significant dysregulated pathways common to at least two omics data were enlisted and categorised according to their parent pathways as shown in Supplementary Table 6. The parent pathways linked with MTC oncogenesis were metabolism, signal transduction, apoptosis, disease, cancer, and cell–extracellular matrix (ECM) interaction and adhesion. MAPPING THE DEGS, TFs AND KINASES IN MTC TO THE CALCIUM SIGNALING PATHWAY The pooled 3054 DEGs in MTC were compared with the calcium signaling pathway gene set (178 genes) provided in Gene Set Enrichment Analysis database (GSEA) [ 30 ]. This analysis revealed forty-six DEGs and six kinases in MTC being associated with calcium signaling pathway as shown in Supplementary Table 7. DEPs that function as Ca 2+ signaling, binding, and transport channels were identified in MTC. The TFs were also directly or indirectly associated with calcium signaling pathway. The DEGs, DEPs, TFs, and kinases associated with calcium signaling pathways significant in the MTC tumorigenesis were mapped. Calcium signaling pathways were found to be linked to the clinical outcomes of MTC and to other significant pathways associated with MTC pathophysiology (Fig. 8 ). DISCUSSION Medullary thyroid cancer being a rare type of thyroid cancer with its unique molecular landscape demands a holistic understanding for its management. In this study, a systems biology approach was adopted to unravel the significant pathways, TFs, and kinases associated with MTC tumorigenesis. In the present study, the most significantly dysregulated pathways in MTC were screened. Calcium signaling pathway was found most significant. Prior research has examined the significance of signaling pathways and its therapeutic implications in MTC [ 31 ]. Calcium signaling pathway is critical in cancer cells controlling the survival, proliferation, angiogenesis, and metastasis. Important processes like telomerase activity, differentiation, and the release of angiogenesis regulators are modulated by calcium binding proteins like S100A8, S100A11, and S100A13 as well as channels like TRPC4. The remodelling of this pathway enables the tumor cells to evade apoptosis and promote angiogenesis [ 32 ] Calcium signaling is of utmost significance in normal thyroid cells as well as thyroid pathologies. It is significant in the proliferation and invasion in thyroid cancer [ 33 ]. Presence of a voltage-gated calcium channel α1H subunit was reported in MTC cell line (TT) [ 34 ]. Calcium-sensing receptor, an ion sensing G protein-coupled receptor promotes cell adhesion and invasion in MTC cells by coupling to the integrin (ECM protein) [ 35 ]. Transcription factors (TFs) are proteins that bind to specific site in DNA and regulate transcription [ 36 ], and are critical in cancer process [ 37 ]. The current research identifies fourteen TFs; AR, ESR, GATA1, GATA2, NANOG, NFE2L2, POU5F1, REST, SALL4, SMAD4, SOX2, SUZ12, TCF3, and TP63 significant in MTC pathogenesis. TFs in cancers can be classified into oncogenic TFs, tumor suppressor TFs, epithelial–mesenchymal transition (EMT) associated TFs, hypoxia inducible factors (HIFs), pluripotency TFs, pro-inflammatory TFs, and nuclear receptors (NRs) based on their pathophysiological implications [ 38 ]. Accordingly, SUZ12 is an oncogenic TF that forms the structural component of Polycomb Repressive Complex 2 which epigenetically represses the tumor suppressor genes and promote tumorigenesis. SMAD4 (Mothers Against Decapentaplegic Homolog 4) and TP63 (Tumor Protein P63) are tumor suppressor TFs having role in checking cancer progression by regulating genes in cell cycle, apoptosis, and DNA repair. SMAD4 is known to mediate the transforming growth factor β (TGF β) signaling to the nucleus and regulate the gene transcription. Despite the expected loss of SMAD4 function in cancers, studies exhibit its expression in thyroid follicular cancer cell lines (NPA papillary carcinoma, WRO follicular carcinoma and ARO anaplastic carcinoma) and control [ 39 ]. C324Y mutation of SMAD4 is associated with progression and metastasis in PTC [ 40 ]. According to earlier research using the PTC cell line TPC-1, SMAD-4 inhibits PTC by blocking the MAPK/JNK pathway and increasing susceptibility to the chemotherapy medications doxorubicin and cisplatin [ 41 ]. Transcription factor TP63 on the other hand, is known for its dual role as oncogene or tumor suppressor gene in different conditions [ 42 ]. Prior research evaluating the expression of TP63 in thyroid neoplasms has revealed that it is highly expressed in Hashimoto's thyroiditis and PTC, while it is uncommon in MTC with least role as a tumor driver in MTC [ 43 ]. GATA1 and GATA2 are epithelial–mesenchymal transition (EMT) associated TFs crucial for erythropoiesis [ 44 ] GATA1 negative regulate the tumor suppressor nuclear receptor binding protein 2 (NRBP2) by assigning histone deacetylase2 (HDAC2) to its promoter region causing histone deacetylation and enhance angiogenesis and TME in PTC [ 45 ]. Previous studies show GATA2 overexpression in malignant FTC compared to the benign follicular thyroid adenoma (FTA) [ 46 ]. NFE2 like BZIP Transcription Factor 2 (NFE2L2) is categorized as hypoxia inducible factor (HIFs) that binds to the promoter of genes possessing antioxidant response elements (ARE). It responds to the reactive oxygen species (ROS) produced due to injury, and inflammation and promote angiogenesis [ 47 ]. The next group of important TFs in MTC are those that cause the tumor's stemness and pluripotency, which can cause it to grow and become resistant to treatment. This includes TFs like SOX2, SALL4 and NANOG. SOX2 (sex-determining region Y-box 2) TFs are known for somatic reprogramming and are implicated in more than 25 cancers [ 48 ]. Earlier studies have also identified SOX2 as biomarker of loss of differentiation in thyroid carcinomas [ 49 ]. Turning off SOX2 has been proven to help ATC overcome the chemotherapy resistance caused by its cancer stem cells (CSCs) [ 50 ]. Latest transcriptomic research in MTC have identified SOX2 overexpression in high grade MTC and its association with lower disease free and overall survival [ 51 ]. Transcription factor SALL4 overexpression is evident in a series of cancers including lung cancer [ 52 ], gastric cancer [ 53 ], and renal cancer [ 54 ]. SALL4 overexpression is associated with cancer progression, invasion, immune evasion and metastasis via modulating PI3K/AKT, Wnt/β-catenin, and Notch signaling pathways [ 55 ]. Another pluripotent TF is the NANOG, which was revealed as a significant thyroid cancer stem cell (CSCs) significant for tumorigenesis [ 56 ]. TCF3 (Transcription Factor 3) is categorized as a proinflammatory TF that regulate inflammatory responses. TCF3 was found to be a DEG in thyroid cancer in a previous investigation [ 57 ], which is consistent with the current study. Transcription factors that act as nuclear receptors (NRs), such as AR (Androgen Receptors) and ESR1 (Estrogen Receptor 1), make up the next group. AR expression determines the gender-biased incidence of the thyroid cancer [ 58 ] whereas ESR1 does not indicate the sex disparity in PTC origin and was observed to be poorly expressed in tumor compared to normal counter parts [ 59 ]. Protein kinases are enzymes responsible to modulate the functions of a protein by transferring a phosphate group to it. Protein kinases play a vital role in the aetiology of MTC by regulating the critical signaling pathways associated with the MTC pathogenesis. Eighty-seven kinases that are important in MTC have been found in this investigation. These kinases were discovered to be involved in key molecular mechanisms and cellular processes in carcinogenesis. These kinases have different functional implications in tumorigenesis. Cell cycle-regulating kinases are among the main types of kinases identified in MTC. These include checkpoint kinases, such as CHEK1, DNAPK, and ATM, as well as several cyclin-dependent kinases (CDC2, CDK1/2/3/4/5/6/8/9). Functions of CDK5[ 60 , 61 ] and CDK8[ 62 ] in MTC is also demonstrated by earlier research. Next category of kinases in MTC play a critical role in signaling pathways responsible for growth and proliferation (AKT1, AXL, EPHA8, ERK1/2, FER, MERTK, PIKFYVE, RET, RAF1, GSK3A/B, PK), stress response and apoptosis (MAPK1/3/8/11/14, MAP2K1/3, MAP3K7, JNK1/2, FOXO3, DYRK1B, HIPK2, NLK), immune response and inflammation (JAK2, IKKα, IKKβ (IKBKB/E), TGFBR2, TRIM33), metabolism (PRKAA1, PDHK1, PIKFYVE), cytoskeletal dynamics (CAM, CAMK family (CAMK2D/4/IV), MARK2), transcription (casein kinase -CK2A/1E) and translation (RPS6KA1/3) regulation and epithelial-mesenchymal transition (TGFβR2, ILK). Literature mining establishes the role of GSK3 in MTC proliferation and survival [ 63 ]. The DEGs, DEPs, TFs, and kinases associated with MTC were found allied with calcium signaling pathway justifying its significance in MTC tumorigenesis. Forty-six DEGs of Ca 2+ signaling pathway were found critical in MTC pathogenesis. The present analysis identified Ca 2+ binding proteins and transport channels, along with the key signaling proteins, as significant component in MTC. The kinases and TFs in Ca 2+ signaling pathway were significant in MTC compared to its normal counterpart. The Ca 2+ signaling integrate with other significant dysregulated pathways in MTC, TFs and kinases in the implication of disease pathophysiology. This study seeks to elucidate the deregulated pathways, TFs, and kinases involved in the pathogenesis of MTC at a systems level through multi-omics data analysis. It is imperative that in vitro and in vivo experiments precede the clinical application of these predictive insights. Another, limitation of this study is the lack of population-based studies involving larger cohorts. Despite these limitations, the identified pathway is significant across datasets, populations, methods, and research setups, confirming its importance as a biomarker and therapeutic target in the context of MTC. The clinical implications of the calcium signaling pathway were linked to MTC based on literature mining. CONCLUSION MTC is a rare neuroendocrine tumor of parafollicular C cells of thyroid gland. A pan-omics analysis of SNPs, DEGs, DEPs and DEMs linked with MTC was conducted to retrieve dysregulated pathways, TFs and kinases in MTC pathogenesis. Calcium signaling pathway was the core dysregulated pathway in MTC. Fourteen TFs and 87 kinases that are essential for the development and progression of MTC were identified by the commonality analysis. Additionally, the findings support the idea that the fundamental pathways are consistent across approaches, populations, and datasets. Proliferation, apoptosis, angiogenesis, EMT, and angiogenesis are among the vital biological processes that are influenced by the core pathway, TFs and kinases. Declarations DECLARATION OF COMPETING INTEREST The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ETHICS DECLARATIONS This study did not involve any human participants or animal subjects, and therefore ethical approval, consent to participate and consent to publish declarations are not applicable. FUNDING This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution FP – Febby Payva, SKS – Santhy KS, and RJ – Remya James. FP & SKS designed the research framework and defined the objectives of the study. FP & RJ collected and curated the data used in the study. FP, SKS, and RJ analysed the data and interpreted the results. FP refined the methodology and ensured its alignment with the study objectives. SKS supervised the overall research project and ensured the availability of tools and software required for data analysis. FP & RJ designed the figures and tables for the manuscript. FP wrote the original draft. All the authors reviewed and edited the manuscript for clarity and scientific rigor. Data Availability Data is provided within the manuscript or supplementary information files References Arrangoiz R, Cordera F, Caba D, Moreno E, Luque De-Leon E, Muñoz M. Medullary Thyroid Carcinoma Literature Review and Current Management. J Clin Endocrinol Diabetes Rev Article Arrangoiz R. 2018;:118. Caillé S, Debreuve-Theresette A, Vitellius G, Deguelte S, La Manna L, Zalzali M. Medullary Thyroid Cancer: Epidemiology and Characteristics According to Data From the Marne-Ardennes Register 1975–2018. J Endocr Soc. 2024;8. Caillé S, Debreuve-Theresette A, Vitellius G, Deguelte S, La Manna L, Zalzali M. 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D’Inzeo S, Nicolussi A, Donini CF, Zani M, Mancini P, Nardi F, et al. A novel human Smad4 mutation is involved in papillary thyroid carcinoma progression. Endocr Relat Cancer. 2012;19:39–55. Cai H, Yang X, Jiang Z, Liang B, Cai Q, Huang H. Upregulation of SMAD4 inhibits thyroid cancer cell growth via MAPK/JNK pathway repression. Trop J Pharm Res. 2019;18:2473–8. Chen Y, Peng Y, Fan S, Li Y, Xiao Z-X, Li C. A double dealing tale of p63: an oncogene or a tumor suppressor. Cell Mol Life Sci. 2018;75:965–73. Unger P, Ewart M, Wang BY, Gan L, Kohtz DS, Burstein DE. Expression of p63 in papillary thyroid carcinoma and in Hashimoto’s thyroiditis: a pathobiologic link? Hum Pathol. 2003;34:764–9. Moriguchi T, Yamamoto M. A regulatory network governing Gata1 and Gata2 gene transcription orchestrates erythroid lineage differentiation. Int J Hematol. 2014;100:417–24. Li M, Jiang H, Chen S, Ma Y. GATA binding protein 1 recruits histone deacetylase 2 to the promoter region of nuclear receptor binding protein 2 to affect the tumor microenvironment and malignancy of thyroid carcinoma. Bioengineered. 2022;13:11320–41. Hossain MA, Asa TA, Rahman MM, Uddin S, Moustafa AA, Quinn JMW, et al. Network-Based Genetic Profiling Reveals Cellular Pathway Differences Between Follicular Thyroid Carcinoma and Follicular Thyroid Adenoma. Int J Environ Res Public Health. 2020;17:1373. Covello KL, Simon MC. HIFs, Hypoxia, and Vascular Development. 2004. pp. 37–54. Wuebben EL, Rizzino A. The dark side of SOX2: cancer - a comprehensive overview. Oncotarget. 2017;8:44917–43. Gomaa W, Marouf A, Alamoudi A, Al-Maghrabi J. SOX2 Is a Potential Novel Marker of Undifferentiated Thyroid Carcinomas. Cureus. 2020. https://doi.org/10.7759/cureus.12102 . Carina V, Zito G, Pizzolanti G, Richiusa P, Criscimanna A, Rodolico V, et al. Multiple Pluripotent Stem Cell Markers in Human Anaplastic Thyroid Cancer: The Putative Upstream Role of SOX2. Thyroid. 2013;23:829–37. Ruz-Caracuel I, Caniego-Casas T, Alonso-Gordoa T, Carretero-Barrio I, Ariño-Palao C, Santón A, et al. Transcriptomic Differences in Medullary Thyroid Carcinoma According to Grade. Endocr Pathol. 2024;35:207–18. Jia X, Qian R, Zhang B, Zhao S. The expression of SALL4 is significantly associated with EGFR, but not KRAS or EML4-ALK mutations in lung cancer. J Thorac Dis. 2016;8:2682–8. Yuan X, Zhang X, Zhang W, Liang W, Zhang P, Shi H, et al. SALL4 promotes gastric cancer progression through activating CD44 expression. Oncogenesis. 2016;5:e268–268. Che J, Wu P, Wang G, Yao X, Zheng J, Guo C. Expression and clinical value of SALL4 in renal cell carcinomas. Mol Med Rep. 2020;22:819–27. Sun B, Xu L, Bi W, Ou W-B. SALL4 Oncogenic Function in Cancers: Mechanisms and Therapeutic Relevance. Int J Mol Sci. 2022;23:2053. Ma R, Minsky N, Morshed SA, Davies TF. Stemness in Human Thyroid Cancers and Derived Cell Lines: The Role of Asymmetrically Dividing Cancer Stem Cells Resistant to Chemotherapy. J Clin Endocrinol Metab. 2014;99:E400–9. Wang Q, Shen Y, Ye B, Hu H, Fan C, Wang T, et al. Gene expression differences between thyroid carcinoma, thyroid adenoma and normal thyroid tissue. Oncol Rep. 2018. https://doi.org/10.3892/or.2018.6717 . Stanley JA, Aruldhas MM, Chandrasekaran M, Neelamohan R, Suthagar E, Annapoorna K, et al. Androgen receptor expression in human thyroid cancer tissues: A potential mechanism underlying the gender bias in the incidence of thyroid cancers. J Steroid Biochem Mol Biol. 2012;130:105–24. Chou C-K, Chi S-Y, Hung Y-Y, Yang Y-C, Fu H-C, Wang J-H, et al. Decreased Expression of Estrogen Receptors Is Associated with Tumorigenesis in Papillary Thyroid Carcinoma. Int J Mol Sci. 2022;23:1015. Yue C-H, Oner M, Chiu C-Y, Chen M-C, Teng C-L, Wang H-Y, et al. RET Regulates Human Medullary Thyroid Cancer Cell Proliferation through CDK5 and STAT3 Activation. Biomolecules. 2021;11:860. Pozo K, Castro-Rivera E, Tan C, Plattner F, Schwach G, Siegl V, et al. The Role of Cdk5 in Neuroendocrine Thyroid Cancer. Cancer Cell. 2013;24:499–511. Liu T, Meng J, Zhang Y. miR-592 acts as an oncogene and promotes medullary thyroid cancer tumorigenesis by targeting cyclindependent kinase 8. Mol Med Rep. 2020. https://doi.org/10.3892/mmr.2020.11392 . Kunnimalaiyaan M, Vaccaro AM, Ndiaye MA, Chen H. Inactivation of glycogen synthase kinase-3β, a downstream target of the raf-1 pathway, is associated with growth suppression in medullary thyroid cancer cells. Mol Cancer Ther. 2007;6:1151–8. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx SupplementaryTable2.docx SupplementaryTable3.xlsx SupplementaryTable4.xlsx SupplementaryTable5.xlsx SupplementaryTable6.docx SupplementaryTable7.xlsx SupplementaryFigure1.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6926629","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":489039040,"identity":"cef94951-3ed8-47d0-a8d8-d0f9ad12ebac","order_by":0,"name":"Febby Payva","email":"","orcid":"","institution":"St. Joseph’s College for Women","correspondingAuthor":false,"prefix":"","firstName":"Febby","middleName":"","lastName":"Payva","suffix":""},{"id":489039041,"identity":"fbd4b22b-4417-494c-8c65-a1014bef0a65","order_by":1,"name":"Santhy K. 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Women","correspondingAuthor":false,"prefix":"","firstName":"Remya","middleName":"","lastName":"James","suffix":""}],"badges":[],"createdAt":"2025-06-19 02:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6926629/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6926629/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87662681,"identity":"3effa889-99bb-4f4a-bf63-f121baed692e","added_by":"auto","created_at":"2025-07-27 10:46:52","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":591543,"visible":true,"origin":"","legend":"\u003cp\u003eSNP enrichment analysis in MTC: a) Predicted\u003cstrong\u003e \u003c/strong\u003efunctional consequence using SNPnexus; b) Gene ontology analysis; and c) Pathway enrichment analysis\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6926629/v1/d67e87f1cdca7006ed17e138.jpg"},{"id":87663845,"identity":"1f7bad6e-eef6-490f-856e-0d893612f037","added_by":"auto","created_at":"2025-07-27 10:54:52","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":659442,"visible":true,"origin":"","legend":"\u003cp\u003eGene expression data analysis of GSE27155 GEO dataset: a) Enrichment analysis of DEGs: KEGG pathway; b) Transcription factor enrichment analysis (through X2k analysis from iLINCS database; c) Kinase enrichment analysis, d) eXpression2Kinases network\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6926629/v1/ad69b9fd4e07bc968c2310c1.jpg"},{"id":87663850,"identity":"be369ac2-48e2-4ec0-9ef9-3069710225af","added_by":"auto","created_at":"2025-07-27 10:54:53","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":708898,"visible":true,"origin":"","legend":"\u003cp\u003eGene expression data analysis of GSE196264 GEO dataset: a) Enrichment analysis of DEGs: KEGG pathway; b) Transcription factor enrichment analysis (through X2k analysis from iLINCS database; c) Kinase enrichment analysis, d) eXpression2Kinases network\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6926629/v1/3f279464d3679f7788c53185.jpg"},{"id":87662689,"identity":"c2039ba3-1a55-425e-bfd2-b9df7be2b76c","added_by":"auto","created_at":"2025-07-27 10:46:53","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":647144,"visible":true,"origin":"","legend":"\u003cp\u003eCommonality analysis of human transcriptomic dataset linked to MTC:\u003cstrong\u003e \u003c/strong\u003ea) KEGG pathways; b) TFs; and c) Kinases\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6926629/v1/b2e019ff172af126eac9934a.jpg"},{"id":87665226,"identity":"ba8c4aa3-e902-43fc-8349-139f05802625","added_by":"auto","created_at":"2025-07-27 11:02:52","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1074758,"visible":true,"origin":"","legend":"\u003cp\u003ePathway analysis of differentially expressed proteins (DEPs) in serum and tissue between healthy controls and PTC patients via Enrichr\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6926629/v1/e94f69be04b1332f00322616.jpg"},{"id":87662718,"identity":"7510280f-949c-4473-be9f-858ceb1e3954","added_by":"auto","created_at":"2025-07-27 10:46:54","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":523014,"visible":true,"origin":"","legend":"\u003cp\u003ePathway enrichment analysis of human metabolomic studies in MTC patients compared with healthy controls\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6926629/v1/1928accabd91061897eae203.jpg"},{"id":87662692,"identity":"02f61b24-e205-4c03-acba-0755b15ee637","added_by":"auto","created_at":"2025-07-27 10:46:53","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":264447,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of dysregulated pathways of MTC associated SNP, transcriptomic, proteomic and metabolomic 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11:02:53","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":226543,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6926629/v1/6de5eb6910e889cc2fa75229.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eA Pan-omics Systems Biology Perspective Identifies Calcium Signaling Pathway Critical in Medullary Thyroid Cancer\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMedullary thyroid cancer (MTC), an uncommon form of thyroid cancer (TC) that affects the thyroid gland's parafollicular C cells, accounts for about 1 to 5% of all thyroid malignancies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The incidence of MTC rose from 0.41 to 0.57/100000 persons between 1975 and 2018, a moderate increase, according to studies. With a sex ratio of 0.73, the average age at which MTC is diagnosed is 53. A complete remission of 58.5% and a five-year survival rate of approximately 88.4% were reported. Between the hereditary and sporadic types of MTC, the prognosis is poorer for sporadic [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Most (70%) of sporadic MTC cases with palpable nodules may have metastases to cervical lymph nodes, with up to 10% harbouring distant metastases. About 20\u0026ndash;25% of cases are hereditary MTC, which is defined by a genetic mutation in RET proto-oncogene and manifests as either familial MTC (FMTC) or multiple endocrine neoplasia (MEN) II A or II B. Early prophylaxis includes family screening of RET mutation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The common symptoms of MTC include nodules and pain in the neck, swallowing difficulty, cough, and dyspnoea followed by some unusual signs like severe diarrhoea, Cushing syndrome, facial flushing, bone pain, lethargy, and weight loss.\u003c/p\u003e\u003cp\u003eCalcitonin and carcinoembryonic antigen (CEA) act as serum biomarkers while RET mutation is a molecular MTC biomarker. The physical examination followed by determination of basal and stimulated calcitonin, CEA, RET mutation analysis, fine-needle aspiration biopsy (FNAB), ultrasonography of the neck, computed tomography (CT) scan, and MRI are the widely used methods of screening MTC [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Positron emission tomography/computed tomography (PET/CT) with radiotracers \u003csup\u003e18\u003c/sup\u003eF-FDG [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], [\u003csup\u003e18\u003c/sup\u003eF] F-DOPA [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and 68Ga-DOTATATE [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] are efficient nuclear medicine techniques for metastatic and recurrent MTC.\u003c/p\u003e\u003cp\u003ePharmacotherapy and surgery are the main techniques used to manage MTC. High levels of toxicity and drug resistance are caused by the pharmacotherapy now in use. Research on the potential application of immunotherapy, radiation, tyrosine kinase inhibitors, and RET-specific kinase inhibitors in the therapeutic management of MTC has advanced recently [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Selpercatinib and Pralsetinib are potent RET-specific kinase inhibitors used in MTC [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Owing to the low tumor mutational burden (TMB) in MTC the efficacy of immunotherapy in MTC management is a challenge [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdvancements in multi-omics analysis and molecular high throughput technologies have enhanced the understanding and management of TC via modifying patient risk stratification, identifying significant target therapy, and facilitating personalized treatment strategies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Multi-omics data analysis was used in various diseases like rheumatoid arthritis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and PTC [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Notably, an integrated proteogenomic analysis has been conducted for MTC [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, comprehensive system-based multi-omics studies remain limited for MTC.\u003c/p\u003e\u003cp\u003eThe present study aims to address this gap by identifying the key dysregulated metabolic pathways in the pathophysiology of MTC at multiple omics levels by assembling MTC-associated single nucleotide polymorphisms (SNPs), differentially expressed genes (DEGs), proteins (DEPs), and metabolites (DEMs). Furthermore, the kinases and transcription factors (TFs) essential to MTC were found. The clinical outcome in MTC was associated with these putative pathways. Hence, a systems biology approach provides a comprehensive overview of the MTC, offering insights that could potentially be translated into clinical applications for improved patient care and management.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eMTC ASSOCIATED SINGLE NUCLEOTIDE POLYMORPHISM (SNP) ANALYSIS\u003c/h2\u003e\u003cp\u003eSNPs allied to MTC were screened from DisGeNET (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.disgenet.org/\u003c/span\u003e\u003cspan address=\"http://www.disgenet.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and verified with ClinVar database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/clinvar/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/clinvar/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for its association with MTC or MEN 2A/ 2B. One hundred fifty-one missense mutations were curated. DisGeNET is a comprehensive platform for the disease associated genes and variants [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The variant analysis was performed by SIFT and PolyPhen-2.\u003c/p\u003e\u003cp\u003eSIFT (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sift.bii.a-star.edu.sg/\u003c/span\u003e\u003cspan address=\"https://sift.bii.a-star.edu.sg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) forecasts how a nucleotide alteration in the coding area will affect a protein's function [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The curated dbSNPs were predicted in this study based on their potential influence on MTC genesis. A SIFT value of less than 0.05 was considered \"deleterious,\" demonstrating a high conservation, while a value of greater than 0.05 was considered \"tolerated,\" indicating low conservation, for amino acid changes. PolyPhen-2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genetics.bwh.harvard.edu/pph2/\u003c/span\u003e\u003cspan address=\"http://genetics.bwh.harvard.edu/pph2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a program for identifying the SNPs in the coding sequence. It examines how the SNVs affect the protein's functionality. SNVs with scores near 1.0 are most likely harmful, while those near 0.0 are projected to be benign [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAnother annotation tool for predicting genome sequence variation is SNPnexus (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.snp-nexus.org\u003c/span\u003e\u003cspan address=\"https://www.snp-nexus.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Among the several applications of SNPnexus are genome mapping, impacts on protein function, connections between phenotype and disease, structural variation, pathway analysis, and clinical interpretation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEnrichr (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/Enrichr/\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/Enrichr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was applied for the pathway enrichment and gene ontology (GO) of the biological process (BP), molecular function (MF), and cellular component (CC) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMTC TRANSCRIPTOMIC DATA ANALYSIS\u003c/h3\u003e\n\u003cp\u003eTwo gene expression datasets from the Gene Expression Omnibus (GEO) repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo)\u0026mdash;GSE2715\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo)\u0026mdash;GSE2715\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e5[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and GSE196264 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u0026mdash;were used to analyse MTC transcriptome data by comparing sick and healthy controls. Significant DEGs for GSE27155 were identified using GEO2R (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/geo2r\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/geo2r\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with an adjusted P-value threshold of \u0026lt;\u0026thinsp;0.05, while DEGs for GSE196264 were obtained from the corresponding published study. The online tool Enrichr (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/Enrichr/\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/Enrichr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to do the enrichment analysis of the DEGs. CC, BP, and MF were annotated by a gene ontology (GO) analysis. Molbiotools(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/Enrichr/\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/Enrichr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to identify the dysregulated pathways that were shared by the datasets.\u003c/p\u003e\n\u003ch3\u003eIDENIFICATION OF TRANSCRIPTION FACTORS (TFS) AND KINASES IN MTC\u003c/h3\u003e\n\u003cp\u003eDEGs were examined using eXpression2Kinases (X2K) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/X2K/\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/X2K/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to identify potential TFs and kinases associated with MTC. It is a web resource for transcription factor enrichment analysis (TFEA), kinase enrichment research, protein-protein interactions subnetwork building, and TFs-kinase interaction network design [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eMTC PROTEOMIC DATA ANALYSIS\u003c/h3\u003e\n\u003cp\u003eThe proteomic data decipher the amount, nature, location, interaction, post translational modification, and biological function of protein in an organism. Two published MTC-related proteomic data sets in serum [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and tissue [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], were mined from the EMBL-EBI (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database to identify MTC-associated significant DEPs (adj. P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with a two-fold change. To determine the dysregulated pathways in MTC, DEPs were assessed using Enrichr.\u003c/p\u003e\n\u003ch3\u003eMTC METABOLOMIC DATA ANALYSIS\u003c/h3\u003e\n\u003cp\u003eThe metabolomic analysis is a bio-analytical tool for comprehending the biological functionality within an organism in a healthy state instead of a diseased state [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This article analysed two metabolomic studies, one conducted in serum[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and the other in plasma [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Together with other omics analyses, metabolomics\u0026mdash;the final stage of the gene expression process\u0026mdash;is essential to comprehensively understanding the illness [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. To determine the key metabolic pathways linked to MTC, metabolite enrichment analysis was performed using the web-based Metaboanalyst 6.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metaboanalyst.ca/\u003c/span\u003e\u003cspan address=\"https://www.metaboanalyst.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eMULTI-OMICS DATA INTEGRATION IN MTC\u003c/h2\u003e\u003cp\u003eDysregulated pathways in the SNP analysis, transcriptomics, proteomics, and metabolomics were collated. Pathways potentially involved in the MTC disease mechanism were identified through an analysis of shared pathways across MTC-linked human SNPs, DEGs, DEPs, and DEMs. The key altered pathways were further correlated with clinical parameters in MTC.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULT","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eSTUDY OF SNP REVEAL SIGNIFICANT MUTATIONS AND ALTERED PATHWAYS IN MTC PATHOGENESIS\u003c/h2\u003e\u003cp\u003eThe DisGeNET database was used to source the SNPs linked to MTC tumorigenesis. A total of 151 missense variants were chosen for SNP analysis and critical pathway enrichment. SNP analysis explains the mutation led functional modifications of the proteins resulting in MTC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and Supplementary Table\u0026nbsp;1). Enrichment analysis identifies potential dysregulated pathways associated with MTC and streamline the MF, CC, and BP in MTC pathogenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). GO MF observed were transmembrane receptor protein kinase activity, transmembrane receptor protein tyrosine kinase activity, protein tyrosine kinase activity, neurotrophin binding and calcium ion binding. However, the cellular components enriched were axon, endosome membrane, cytoplasmic vesicle membrane, neuron projection, bounding membrane of organelle, and endosome. Positive regulation of neuron projection development and cell projection organization, enzyme-linked receptor protein signaling pathway, regulation of phosphatidylinositol 3-kinase/protein kinase B signal transduction, cell surface receptor protein tyrosine kinase signaling pathway, phosphorylation, positive regulation of MAPK cascade and protein phosphorylation were the primary biological processes involved. Twelve KEGG pathways were found significant in the pathway enrichment analysis of MTC associated SNPs as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and Supplementary table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSIGNIFICANT DIFFERENTIALLY EXPRESSED GENES (DEGS), TRANSCRIPTION FACTORS, KINASES, AND DYSREGULATED PATHWAYS IN MTC IDENTIFIED USING TRANSCRIPTOMIC DATA ANALYSIS\u003c/p\u003e\u003cp\u003eSignificant DEGs associated with transcriptomic datasets GSE27155 and GSE196264 comparing the diseased with the normal were used for the study. GSE27155 and GSE196264 analysis resulted 2013 and 1666 DEGs respectively. The DEGs were mapped to pathways potentially implicated in MTC. GO assessment of the DEGs were also conducted for both the GEO datasets. (Supplementary Table\u0026nbsp;3)\u003c/p\u003e\u003cp\u003eDEGs from GSE27155 dataset was binned into one hundred fifty-eight significant KEGG pathways including MAPK signaling pathway, pathways in cancer, focal adhesion, PI3K-Akt signaling pathway, axon guidance, dopaminergic synapse, Rap1 signaling pathway, proteoglycans in cancer, human papillomavirus infection, and relaxin signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and Supplementary Table\u0026nbsp;3b). S-methylmatonate semialdehyde, gamma- aminobutyric acid, NAD\u003csup\u003e+\u003c/sup\u003e, 5-hydroxyindolacetaldehyde and beta alanine were the predicted metabolites (Supplementary Fig.\u0026nbsp;1A).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGSE196264 dataset was enriched in major KEGG pathways like dopaminergic synapse, circadian entrainment, adrenergic signaling in cardiomyocytes, calcium signaling pathway, thyroid hormone synthesis, and ECM-receptor interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and Supplementary Table\u0026nbsp;3b). Glycine, glucose 1 phosphate, retinal, 9-cis retina, and tyrosine were among the predicted metabolites (Supplementary Fig.\u0026nbsp;1B).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA comparative analysis of transcriptomic dataset resulted in fifteen common pathways like adrenergic signaling in cardiomyocytes, arrhythmogenic right ventricular cardiomyopathy, calcium signaling pathway, circadian entrainment, cocaine addiction, dilated cardiomyopathy, dopaminergic synapse, ECM-receptor interaction, gastric acid secretion, glutamatergic synapse, hypertrophic cardiomyopathy, protein digestion and absorption, Rap1 signaling pathway, thyroid hormone synthesis, and cAMP signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The key TFs and kinases linked to MTC carcinogenesis were identified in two datasets (Supplementary Table\u0026nbsp;3e), and it was observed that fourteen transcription factors were common (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Between the two MTC datasets, eighty-seven kinases were remarkable (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eSIGNIFICANT DIFFERENTIALLY EXPRESSED PROTEINS (DEP) AND DYSREGULATED PATHWAYS ASSOCIATED WITH MTC FROM PROTEOMIC DATA ANALYSIS\u003c/h2\u003e\u003cp\u003eMTC proteomic data comparing the diseased with normal in serum and tissue samples were analysed to derive DEPs. The serum and tissue datasets yielded 73 and 338 significant DEPs, respectively. The DEPs were evaluated for the critical pathways dysregulated in MTC (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Supplementary Table\u0026nbsp;4).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eSIGNIFICANT MTC-ASSOCIATED METABOLITES AND METABOLIC PATHWAYS FROM METABOLOMIC DATA ANALYSIS\u003c/h2\u003e\u003cp\u003eAnalysing metabolomic data is essential to comprehending how the main metabolic pathways contribute to the pathophysiology of disease. Significant metabolites were screened in the MTC-based metabolomic datasets. According to Supplementary Table\u0026nbsp;5, these metabolites were mapped into important metabolic pathways associated with MTC tumorigenesis (adj. P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.25) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Glycerophospholipid metabolism; sphingolipid metabolism; biosynthesis of unsaturated fatty acids; arginine biosynthesis, alanine aspartate and glutamate metabolism; and valine leucine and isoleucine biosynthesis, were the pooled significant metabolic pathways.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eDECIPHERING DYSREGULATED PATHWAYS IN MTC USING AN INTEGRATED MULTI-OMIC DATA ANALYSIS\u003c/h2\u003e\u003cp\u003eTo identify the primary dysregulated pathways in MTC, the important pathways at the multi-omics level were combined. Calcium signaling pathway was the most significant pathway in MTC, common to transcriptomic, proteomic, and SNP datasets of MTC (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The other significant dysregulated pathways common to at least two omics data were enlisted and categorised according to their parent pathways as shown in Supplementary Table\u0026nbsp;6. The parent pathways linked with MTC oncogenesis were metabolism, signal transduction, apoptosis, disease, cancer, and cell\u0026ndash;extracellular matrix (ECM) interaction and adhesion.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMAPPING THE DEGS, TFs AND KINASES IN MTC TO THE CALCIUM SIGNALING PATHWAY\u003c/p\u003e\u003cp\u003eThe pooled 3054 DEGs in MTC were compared with the calcium signaling pathway gene set (178 genes) provided in Gene Set Enrichment Analysis database (GSEA) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This analysis revealed forty-six DEGs and six kinases in MTC being associated with calcium signaling pathway as shown in Supplementary Table\u0026nbsp;7. DEPs that function as Ca\u003csup\u003e2+\u003c/sup\u003e signaling, binding, and transport channels were identified in MTC. The TFs were also directly or indirectly associated with calcium signaling pathway. The DEGs, DEPs, TFs, and kinases associated with calcium signaling pathways significant in the MTC tumorigenesis were mapped. Calcium signaling pathways were found to be linked to the clinical outcomes of MTC and to other significant pathways associated with MTC pathophysiology (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eMedullary thyroid cancer being a rare type of thyroid cancer with its unique molecular landscape demands a holistic understanding for its management. In this study, a systems biology approach was adopted to unravel the significant pathways, TFs, and kinases associated with MTC tumorigenesis. In the present study, the most significantly dysregulated pathways in MTC were screened. Calcium signaling pathway was found most significant. Prior research has examined the significance of signaling pathways and its therapeutic implications in MTC [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCalcium signaling pathway is critical in cancer cells controlling the survival, proliferation, angiogenesis, and metastasis. Important processes like telomerase activity, differentiation, and the release of angiogenesis regulators are modulated by calcium binding proteins like S100A8, S100A11, and S100A13 as well as channels like TRPC4. The remodelling of this pathway enables the tumor cells to evade apoptosis and promote angiogenesis [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eCalcium signaling is of utmost significance in normal thyroid cells as well as thyroid pathologies. It is significant in the proliferation and invasion in thyroid cancer [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Presence of a voltage-gated calcium channel α1H subunit was reported in MTC cell line (TT) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Calcium-sensing receptor, an ion sensing G protein-coupled receptor promotes cell adhesion and invasion in MTC cells by coupling to the integrin (ECM protein) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTranscription factors (TFs) are proteins that bind to specific site in DNA and regulate transcription [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and are critical in cancer process [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The current research identifies fourteen TFs; AR, ESR, GATA1, GATA2, NANOG, NFE2L2, POU5F1, REST, SALL4, SMAD4, SOX2, SUZ12, TCF3, and TP63 significant in MTC pathogenesis. TFs in cancers can be classified into oncogenic TFs, tumor suppressor TFs, epithelial\u0026ndash;mesenchymal transition (EMT) associated TFs, hypoxia inducible factors (HIFs), pluripotency TFs, pro-inflammatory TFs, and nuclear receptors (NRs) based on their pathophysiological implications [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Accordingly, SUZ12 is an oncogenic TF that forms the structural component of Polycomb Repressive Complex 2 which epigenetically represses the tumor suppressor genes and promote tumorigenesis.\u003c/p\u003e\u003cp\u003eSMAD4 (Mothers Against Decapentaplegic Homolog 4) and TP63 (Tumor Protein P63) are tumor suppressor TFs having role in checking cancer progression by regulating genes in cell cycle, apoptosis, and DNA repair. SMAD4 is known to mediate the transforming growth factor β (TGF β) signaling to the nucleus and regulate the gene transcription. Despite the expected loss of SMAD4 function in cancers, studies exhibit its expression in thyroid follicular cancer cell lines (NPA papillary carcinoma, WRO follicular carcinoma and ARO anaplastic carcinoma) and control [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. C324Y mutation of SMAD4 is associated with progression and metastasis in PTC [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. According to earlier research using the PTC cell line TPC-1, SMAD-4 inhibits PTC by blocking the MAPK/JNK pathway and increasing susceptibility to the chemotherapy medications doxorubicin and cisplatin [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Transcription factor TP63 on the other hand, is known for its dual role as oncogene or tumor suppressor gene in different conditions [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Prior research evaluating the expression of TP63 in thyroid neoplasms has revealed that it is highly expressed in Hashimoto's thyroiditis and PTC, while it is uncommon in MTC with least role as a tumor driver in MTC [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGATA1 and GATA2 are epithelial\u0026ndash;mesenchymal transition (EMT) associated TFs crucial for erythropoiesis [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] GATA1 negative regulate the tumor suppressor nuclear receptor binding protein 2 (NRBP2) by assigning histone deacetylase2 (HDAC2) to its promoter region causing histone deacetylation and enhance angiogenesis and TME in PTC [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Previous studies show GATA2 overexpression in malignant FTC compared to the benign follicular thyroid adenoma (FTA) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. NFE2 like BZIP Transcription Factor 2 (NFE2L2) is categorized as hypoxia inducible factor (HIFs) that binds to the promoter of genes possessing antioxidant response elements (ARE). It responds to the reactive oxygen species (ROS) produced due to injury, and inflammation and promote angiogenesis [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe next group of important TFs in MTC are those that cause the tumor's stemness and pluripotency, which can cause it to grow and become resistant to treatment. This includes TFs like SOX2, SALL4 and NANOG. SOX2 (sex-determining region Y-box 2) TFs are known for somatic reprogramming and are implicated in more than 25 cancers [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Earlier studies have also identified SOX2 as biomarker of loss of differentiation in thyroid carcinomas [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Turning off SOX2 has been proven to help ATC overcome the chemotherapy resistance caused by its cancer stem cells (CSCs) [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Latest transcriptomic research in MTC have identified SOX2 overexpression in high grade MTC and its association with lower disease free and overall survival [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Transcription factor SALL4 overexpression is evident in a series of cancers including lung cancer [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], gastric cancer [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], and renal cancer [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. SALL4 overexpression is associated with cancer progression, invasion, immune evasion and metastasis via modulating PI3K/AKT, Wnt/β-catenin, and Notch signaling pathways [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Another pluripotent TF is the NANOG, which was revealed as a significant thyroid cancer stem cell (CSCs) significant for tumorigenesis [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTCF3 (Transcription Factor 3) is categorized as a proinflammatory TF that regulate inflammatory responses. TCF3 was found to be a DEG in thyroid cancer in a previous investigation [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], which is consistent with the current study. Transcription factors that act as nuclear receptors (NRs), such as AR (Androgen Receptors) and ESR1 (Estrogen Receptor 1), make up the next group. AR expression determines the gender-biased incidence of the thyroid cancer [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] whereas ESR1 does not indicate the sex disparity in PTC origin and was observed to be poorly expressed in tumor compared to normal counter parts [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eProtein kinases are enzymes responsible to modulate the functions of a protein by transferring a phosphate group to it. Protein kinases play a vital role in the aetiology of MTC by regulating the critical signaling pathways associated with the MTC pathogenesis. Eighty-seven kinases that are important in MTC have been found in this investigation. These kinases were discovered to be involved in key molecular mechanisms and cellular processes in carcinogenesis. These kinases have different functional implications in tumorigenesis. Cell cycle-regulating kinases are among the main types of kinases identified in MTC. These include checkpoint kinases, such as CHEK1, DNAPK, and ATM, as well as several cyclin-dependent kinases (CDC2, CDK1/2/3/4/5/6/8/9). Functions of CDK5[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] and CDK8[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] in MTC is also demonstrated by earlier research. Next category of kinases in MTC play a critical role in signaling pathways responsible for growth and proliferation (AKT1, AXL, EPHA8, ERK1/2, FER, MERTK, PIKFYVE, RET, RAF1, GSK3A/B, PK), stress response and apoptosis (MAPK1/3/8/11/14, MAP2K1/3, MAP3K7, JNK1/2, FOXO3, DYRK1B, HIPK2, NLK), immune response and inflammation (JAK2, IKKα, IKKβ (IKBKB/E), TGFBR2, TRIM33), metabolism (PRKAA1, PDHK1, PIKFYVE), cytoskeletal dynamics (CAM, CAMK family (CAMK2D/4/IV), MARK2), transcription (casein kinase -CK2A/1E) and translation (RPS6KA1/3) regulation and epithelial-mesenchymal transition (TGFβR2, ILK). Literature mining establishes the role of GSK3 in MTC proliferation and survival [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe DEGs, DEPs, TFs, and kinases associated with MTC were found allied with calcium signaling pathway justifying its significance in MTC tumorigenesis. Forty-six DEGs of Ca\u003csup\u003e2+\u003c/sup\u003e signaling pathway were found critical in MTC pathogenesis. The present analysis identified Ca\u003csup\u003e2+\u003c/sup\u003e binding proteins and transport channels, along with the key signaling proteins, as significant component in MTC. The kinases and TFs in Ca\u003csup\u003e2+\u003c/sup\u003e signaling pathway were significant in MTC compared to its normal counterpart. The Ca\u003csup\u003e2+\u003c/sup\u003e signaling integrate with other significant dysregulated pathways in MTC, TFs and kinases in the implication of disease pathophysiology.\u003c/p\u003e\u003cp\u003eThis study seeks to elucidate the deregulated pathways, TFs, and kinases involved in the pathogenesis of MTC at a systems level through multi-omics data analysis. It is imperative that in vitro and in vivo experiments precede the clinical application of these predictive insights. Another, limitation of this study is the lack of population-based studies involving larger cohorts. Despite these limitations, the identified pathway is significant across datasets, populations, methods, and research setups, confirming its importance as a biomarker and therapeutic target in the context of MTC. The clinical implications of the calcium signaling pathway were linked to MTC based on literature mining.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eMTC is a rare neuroendocrine tumor of parafollicular C cells of thyroid gland. A pan-omics analysis of SNPs, DEGs, DEPs and DEMs linked with MTC was conducted to retrieve dysregulated pathways, TFs and kinases in MTC pathogenesis. Calcium signaling pathway was the core dysregulated pathway in MTC. Fourteen TFs and 87 kinases that are essential for the development and progression of MTC were identified by the commonality analysis. Additionally, the findings support the idea that the fundamental pathways are consistent across approaches, populations, and datasets. Proliferation, apoptosis, angiogenesis, EMT, and angiogenesis are among the vital biological processes that are influenced by the core pathway, TFs and kinases.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eDECLARATION OF COMPETING INTEREST\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eETHICS DECLARATIONS\u003c/h2\u003e\u003cp\u003eThis study did not involve any human participants or animal subjects, and therefore ethical approval, consent to participate and consent to publish declarations are not applicable.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFUNDING\u003c/h2\u003e\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eFP \u0026ndash; Febby Payva, SKS \u0026ndash; Santhy KS, and RJ \u0026ndash; Remya James. FP \u0026amp; SKS designed the research framework and defined the objectives of the study. FP \u0026amp; RJ collected and curated the data used in the study. FP, SKS, and RJ analysed the data and interpreted the results. FP refined the methodology and ensured its alignment with the study objectives. SKS supervised the overall research project and ensured the availability of tools and software required for data analysis. FP \u0026amp; RJ designed the figures and tables for the manuscript. FP wrote the original draft. All the authors reviewed and edited the manuscript for clarity and scientific rigor.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArrangoiz R, Cordera F, Caba D, Moreno E, Luque De-Leon E, Mu\u0026ntilde;oz M. Medullary Thyroid Carcinoma Literature Review and Current Management. 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Mol Cancer Ther. 2007;6:1151\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Medullary thyroid cancer, systems biology, multi-omics, calcium signaling pathway","lastPublishedDoi":"10.21203/rs.3.rs-6926629/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6926629/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Medullary thyroid carcinoma (MTC) is an uncommon type of thyroid cancer that occurs in the thyroid gland's parafollicular C cell. The pan-omics approach aids in determining the molecular pathways and elements important to MTC carcinogenesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology\u003c/strong\u003e: SNPs associated with medullary thyroid carcinoma (MTC) were extracted from the DisGeNET database, while differentially expressed genes were obtained from the Gene Expression Omnibus (GEO). Proteomic and metabolomic datasets were retrieved from published studies available in the EMBL-EBI database. The functional implications of the SNPs; and the pathways and gene ontology analysis of SNPs, differentially expressed genes (DEGs), proteins (DEPs), and metabolites (DEMs) were performed. Important kinases and transcription factors (TFs) were also found. To identify the core dysregulated pathways in MTC carcinogenesis, an integrated approach was employed to study the pan-omics data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Calcium signaling pathway was identified as the core dysregulated pathway in MTC. The parent pathways linked with MTC oncogenesis were metabolism, signal transduction, apoptosis, disease, cancer, and cell–extracellular matrix (ECM) interaction and adhesion. Fourteen TFs and eighty-seven kinases that are essential for the development and progression of MTC were identified by the commonality analysis. The core pathway, TFs, and kinases were influential on the clinical outputs like proliferation, metastasis, angiogenesis, EMT, and apoptosis in MTC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e- The core deregulated pathway, TFs, and kinases deciphered may help identify potential biomarkers and druggable targets for improved patient care and management in MTC.\u003c/p\u003e","manuscriptTitle":"A Pan-omics Systems Biology Perspective Identifies Calcium Signaling Pathway Critical in Medullary Thyroid Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-27 10:46:47","doi":"10.21203/rs.3.rs-6926629/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":"968f3cf6-9afc-4326-b754-74d926afa8f9","owner":[],"postedDate":"July 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-24T13:38:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-27 10:46:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6926629","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6926629","identity":"rs-6926629","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}