Unravelling the Tumourigenesis Mechanisms of Oncocytic Cell Tumours: Discoveries from a Comparative Omics Study

Unravelling the Tumourigenesis Mechanisms of Oncocytic Cell Tumours: Discoveries from a Comparative Omics Study | 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 Unravelling the Tumourigenesis Mechanisms of Oncocytic Cell Tumours: Discoveries from a Comparative Omics Study SULE CANBERK, MARTA FERREIRA, Arnaud Da Cruz Paula, LUÍSA PEREIRA, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5337626/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 Oncocytic cell tumours (OCTs), formerly known as Hürthle cell tumours in thyroid, are a subset of thyroid and other endocrine neoplasms that present diagnostic and therapeutic challenges due to their variable clinical behaviour. Considering the limited exploration of transcriptomic and proteomic profiles of OCTs compared to MRNs in the literature, we conducted RNA and protein sequencing on 12 OCTs (5 oncocytic adenomas and 7 oncocytic carcinomas) and 6 MRNs, based on the fact that oncocytic morphology alone does not determine biological behavior. RNA sequencing data analysis revealed the presence of 47 downregulated and 38 upregulated differentially expressed genes (DEGs) in OCTs when compared to MRNs. Significant signalling pathways affecting OCTs were associated with the heme metabolism. Protein sequencing data analysis showed the presence of 20 underexpressed and 64 overexpressed differentially expressed proteins (DEPs) in OCTs than in MRNs, and all of the OCAs were found to cluster together, constituting a distinct cluster than the one comprising the MRNs. The majority of DEPs affected three major cellular pathways in OCTs, including epigenetic modifications, tumor microenvironment, and protein biogenesis, that may shape the behavior and morphology of these tumors. Hence, further research into these mechanisms and their impact on tumour phenotype and behaviour may lead to better diagnostic and therapeutic strategies for patients with OCTs. Oncocytic cell tumors mitochondrion-rich neoplasms RNA and protein sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Oncocytic cell tumors (OCTs) of the thyroid remain understudied at the multi-omics level, with limited comparative studies focused on the genomics of these tumors that could offer initial molecular insights into their tumorigenesis [ 1 , 2 ]. This underrepresentation may stem from the rarity of OCTs (3–7% of all thyroid cancers) compared to non-OCTs and their recognition as a distinct tumor class only in the 4th edition of the WHO Classification of Endocrine Tumors [ 3 – 6 ].Previously, oncocytic adenoma (OA) and oncocytic carcinoma (OCA) were considered morphological subtypes of follicular neoplasms, often leading to their oversight within this broader category[ 7 ]. In the 5th WHO Endocrine Tumor classification however, the term "Hürthle" was replaced with "oncocytic" [ 3 ], even though such last term is still used for subtypes of other follicular cell-derived thyroid tumors, such as papillary thyroid carcinoma (PTC), medullary thyroid carcinoma (MTC), and poorly differentiated thyroid carcinoma (PDTC), potentially causing confusion [ 5 ]. Additionally, we classify other histotypes with oncocytic features (≥ 75% of the tumor), such as oncocytic-PTC (O-PTC) and non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP), as "mitochondrion-rich neoplasms (MTRNs)" for clearer terminology. As reported in the literature, common genetic alterations frequently observed in thyroid tumors are considerably less prevalent in OCTs. For instance, RAS mutations, typically found in 30–45% of follicular-patterned thyroid tumors (follicular adenomas (FA), follicular thyroid carcinomas (FTC), and the infiltrative follicular variant of PTC), and in 20–40% of PDTCs, are notably reduced to 6% in OA and 11% in OCA [ 5 , 8 ]. Likewise, the BRAF V600E hotspot mutation, a well-established hallmark in sporadic PTCs, present in 30–90% of cases and associated with more aggressive subtypes, has been largely absent in many studies focused on OCTs. Rearrangements affecting RET/PTC are also underrepresented in these tumors [ 5 , 8 ]. TERT promoter mutations show variable prevalence in OCTs, ranging from 0–32%, with a higher occurrence in widely invasive OCA (wi-OCA) compared to minimally invasive OCA (mi-OCA) [ 5 ]. In contrast, TP53 (25%) and PTEN (41%) pathogenic mutations have been found more frequently in OCTs than in non-oncocytic thyroid neoplasms [ 5 ]. The absence of classical genetic drivers of thyroid tumorigenesis in OCTs suggests that answers may lie beyond thyroid pathology and within broader experimental models. Hence, endocrine pathologists may need to explore these areas more deeply to better understand the biology and behavior of OCTs. For instance, in vitro and in vivo studies have demonstrated complete suppression of malignancy in nuclei transplanted cells (cybrids), with tumorigenic nuclei but with normal cytoplasm, which might indicate that non-nuclear factors are key to OCT behavior [ 9 – 11 ]. This tumor-suppressor effect has been linked to Warburg’s theory of cancer metabolism [ 12 – 15 ] Seyfried et al recently expanded on this by proposing the "cancer as a mitochondrial metabolic disease" (MMT) theory, integrating cybrid studies and Warburg’s findings [ 14 , 16 ]. Warburg demonstrated that cancer cells favor glycolysis for energy production, even in oxygen-rich conditions [ 12 ]. Indeed, Maximo et al [ 17 – 20 ] and others [ 21 – 24 ] have shown that due to insufficient Oxidative Phosphorylation (OXPHOS) activity in OCTs, their metabolism shifts to glycolysis, thus illustrating the Warburg effect. Studies focusing on OCTs showed that mitochondrial DNA (mtDNA) alterations indeed stand out as a distinctive feature of these tumors, along with a high incidence of aneuploidy [ 20 , 25 , 26 ]. Additional studies, including those from our own group, have identified mutations in genes belonging to the Complex I (CI) subunits of the OXPHOS system in OCTs [ 19 , 21 , 22 , 27 ]. These pioneering investigations in these tumors have shed light on the underlying mechanism of mitochondrial dysfunction, revealing an increased mitochondrial proliferation and biogenesis as a compensatory response to oxidative stress. Máximo et al first observed a 4977bp deletion in mtDNA, commonly referred to as the "common deletion", which affects genes encoding for CI, complex IV (CIV), ATPase subunits, and five tRNAs, and was associated with malignancy in OCTs [ 17 ]. Based on the findings from Maximo and his group, Gasparre et al have confirmed a high prevalence of disruptive mutations in CI subunit genes in 45 oncocytic thyroid lesions [ 22 ]. More recently, Gopal et al reported recurrent homoplasmic mutations in CI subunits specific to OCA [ 28 , 29 ] and have also observed that a significantly larger fraction of mtDNA of OCA/OCTs displayed deleterious mutations when compared with the mtDNA of other cancers [ 28 – 31 ]. Regarding copy number variations, several studies have consistently reported gains of chromosome 7 and losses of chromosome 22 in OCTs [ 5 , 32 – 35 ]. Near-whole genome haploidization and subsequent endoreduplication was also described in OCTs. For a comprehensive review, please refer to articles by Canberk et al and Asa and Mete et al [ 5 , 8 ] . While mtDNA alterations affecting OCTs have been extensively studied, information on the transcriptomic and proteomic profiles of these tumors is rather scarce. Acknowledging the fact that oncocytic morphology alone does not indicate biological behavior of OCTs, we aimed to compare OCTs and MRNs, regardless of their benign or malignant status, at transcriptomic and proteomic level. To the best of our knowledge, this is the first transcriptomic and proteomic study investigating the distinctions and similarities between OCTs and MRNs, aiming to elucidate the molecular mechanisms underlying OCT tumorigenesis. Materials and Methods The study protocol was approved by the Ethical Committee of Centro Hospitalar Universitário de São João in 2017 as part of the project titled: “Diabetes & Obesity at the Intersection of Oncological and Cardiovascular Diseases—a Systems Analysis Network for Precision Medicine (DOCnet). Pathology reports from the institutional database with a diagnosis of "oncocytic and/or Hürthle" thyroid tumors, derived from follicular cells and with representative samples in the institutional frozen tumor bank, were identified and retrieved. All frozen cases were reviewed by an endocrine pathologist (S.C.) according to the 5th edition of the WHO thyroid tumor classification [ 3 ]. The final cohort consisted of 18 cases of purely oncocytic tumors including 12 OCTs and 6 MRNs, none of which displayed high-grade features (Supplementary Fig. 1). Of the 12 OCTs identified, 5 were OAs and 7 were OCAs, and of the 6 MRNs identified, 2 were of oncocytic subtype of papillary thyroid carcinoma (O-PTC) and 4 were low-risk neoplasms with oncocytic morphology (Table 1 ). Total RNA was extracted from all 18 frozen samples (four sections, 10 µm thick, each) using the miRNeasy Mini Kit (Qiagen, Valencia, CA, USA) in accordance to the manufacturer’s instructions and as previously described (36) The RNA from each sample was then sequenced using the Ion AmpliSeq™ Transcriptome Human Gene Expression Kit (ThermoFisher Scientific). The library qualities were checked by running on a BioAnalyzer 2100 and the concentrations were determined from the analysis profiles. Ten barcoded libraries were pooled together on an equimolar basis and run using three PIv3 chips on an Ion Torrent Proton using HiQ chemistry. Generated files were analyzed using the Transcriptome Analysis Console (TAC) Software to assess the quality metrics of each sequenced sample. After preliminary analysis, we obtained the list of genes with the fold-change associated with each comparison (Supplementary Fig. 1, Supplementary Tables 1 and 2). For such comparison, we selected differentially expressed genes (DEGs) using a p-value threshold of 5% and a module of log2 fold-change higher than 2, as previously described [ 37 ]. The protein extraction was performed as previously described [ 36 ]. In brief, each frozen sample was processed with lysing matrix A (MP Biomedicals, Irvine, CA, USA) and lysis buffer (100 mM Tris-HCl ph 8.5, 1% sodium deoxycholate, 10 mM tris (2-carboxtethyl) phosphine, 40 mM chloroacetamide, and protéase inhibitors). Protein homogenization was performed using FastPrep-24 equipment (MP Biomedicals). The protein extracts were centrifuged, incubated and sonicated. After measuring the protein concentration, one-hundred micrograms from each sample were processed for proteomic analysis following solid-phase-enhanced sample preparation (SP3) protocol as described by Hughes et al (PMID: 30464214). Protein identification and quantitation were performed via nano liquid Chromatography Analysis - Mass Spectrometry (LC-MS). This equipment is composed of an Ultimate 3000 liquid chromatography system coupled to a Q-Exactive Hybrid Quadrupole-Orbitrap mass spectrometer (Thermo Scientific, Bremen, Germany). The resulting data was analyzed using the software Proteome Discoverer 2.4.0.305 (Thermo Scientific). The protein identification was performed taken into account the UniProt protein sequence database for the Homo sapiens Proteome (2019_09) together with a mass spectra library (NIST_Humale_Orbitrap_HCD_20160923) using the SequestHT and MSPepSearch software, respectively. The protein quantification was performed using the Label Free Quantification (LFQ) methodology. The criteria used for selection of differentially expressed proteins (DEPs) were: protein with at least 2 unique peptides; p-value adjusted by FDR ≤ 0.05 in the t-Student test; identification and quantification of each protein performed in at least 50% of the samples in one of the groups. The enrichment analysis for all RNA and protein measurements was performed using the package enrichGO and with annotation of the org.Hs.eg.db package. All plots presented here were performed using the R package “ggplot2”. Statistical analyses were performed also using R, in particular the nonparametric Wilcoxon rank-sum test. We used the IntAct database via Cytoscape [ 38 , 39 ] to create network visualizations and identify key genes from transcriptomics and proteomics data (Supplementary Fig. 1). Cytohubba [ 40 ] was used to select the top 10 hub genes based on maximum clique centrality (MCC) scores, yielding 40 critical up- and downregulated genes. These were integrated and analyzed using the Functional Interaction (FI) app [ 39 ] for network module based clustering and pathway enrichment, with modules filtered by FDR > 0. Results Demographics and clinical data Our cohort included 12 OCTs and 6 MRNs (Table 1 ). The mean patient age was 65.6 years, with a predominance of females (15 females, 3 males). All OA cases were female, while the OCAs included six females and one male. The two oncocytic-papillary thyroid carcinoma (O-PTC) cases were female, the oncocytic-well-differentiated tumour with uncertain malignant potential (O-WT-UMPs) comprised two males and one female, and the single oncocytic O-NIFTP case was female. Tumor size greatly varied by subtype: the OAs and OCAs had a mean tumor size of 22.6 mm and 40.2 mm, respectively. The O-PTCs, O-WT-UMPs and O-NIFTP had a mean tumor size of 10.5 mm, 23.6 mm and 30 mm, respectively. Oncocytic morphology was consistently 100% across all subtypes, with no high-grade features (necrosis/mitosis) observed in malignant cases. Table 1 Dataset and clinicopathological characteristics of all cases included in this study. F, female; M, male; OA, oncocytic adenoma; OCA, oncocytic carcinoma; O-PTC, oncocytic-papillary thyroid carcinoma; O-WT-UMP, oncocytic-well differentiated tumour-with uncertain malignant potential. Variable Entire cohort (n = 18) Oncocytic tumours* Mitochondrion-rich tumours* OA (n = 5) OCA (n = 7) O-PTC (n = 2) O-WT-UMP (n = 3) O-NIFTP (n = 1) Age (mean) 65.6 55 71.1 68.5 69 65 Gender (F/M) 15 F/ 3 M 5 F 6 F/ 1 M 2 F 1 F/2 M 1 F Size (mm) 28.7 22.6 40.2 10.5 23.6 30 Biological behaviour - Benign Malignant Malignant Low-risk Low-risk Subtype - 5 2 widely invasive/2 angioinvasive encapsulated/3 minimally invasive 2 3 1 Oncocytic morphology 100% 100% 100% 100% 100% 100% Dominant growth pattern 13 follicular/ 5 trabecular 5 follicular 3 follicular/ 4 trabecular 1 follicular/ 1 trabecular 3 follicular 1 follicular Differentially expressed genes in oncocytic tumors and associated signalling pathways RNA sequencing data analysis revealed the presence of 85 DEGs in OCTs when compared to MRNs, including 38 upregulated and 47 downregulated differentially expressed genes (DEGs) (Fig. 1 a, Supplementary Table 1). Clustering analysis between the two cohorts, although quite scattered, showed that 8 out of 12 OCTs clustered together, along with 2 MRNs (Fig. 1 b). When looking at upregulated DEGs, only one was considered to be in the top 10 hub genes, and with the highest MCC score ( LCN2 , Fig. 1 c). However, and from the downregulated DEGs, nine were present in the top 10 hub genes, with JUNB , HBA2 , FXYD6 having the highest rank (Fig. 1 d). Signalling pathway analysis from the downregulated DEGs revealed significant signaling pathways associated with heme metabolism, such as scavenging of heme from plasma and binding and uptake of ligands by scavenger receptors, among others (Fig. 1 e, Supplementary Table 2). Differentially expressed proteins in oncocytic tumors and associated signalling pathways When compared to MRNs, protein sequencing data analysis showed the presence of 84 DEPs in OCTs, including 64 overexpressed and 20 underexpressed differentially expressed proteins (DEPs) (Fig. 2 a, Supplementary Table 3). Interestingly, clustering analysis between OCTs and MRNs demonstrated that all of the OCAs (n = 7) clustered together, along with 3 OAs, constituting a completely different cluster than the one comprising the 6 MRNs (Fig. 2 b). The Cytohubba analysis revealed that from all underexpressed proteins, all top ten hub genes were DEPs (Fig. 2 c), with QPRT, CD44 and EEF1A2 proteins having the highest MCC score. In addition, signalling pathway analysis from underexpressed DEPs rendered significant pathways associated with extracellular matrix and structure organization, mainly affected by CD44, ELANE, FBLN5 and MFAP4 (Fig. 2 d, Supplementary Table 4). When looking at overexpressed DEPs, six were considered to be in the top 10 hub genes, namely NCL, EHMT2, RPS25, MRPL12, NEDD8, RPS15 and NPM1, being the last two proteins associated with cancer-related genes (PMID: 29625053) (Fig. 2 e, Supplementary Table 4). Signalling pathway analysis from overexpressed DEPs provided several significant pathways related with protein biogenesis, epigenetic regulation and tumor microenviroment (Fig. 3 , Supplementary Table 5). More specifically, and looking at signaling pathways from the protein biogenesis, those associated with mitochondrial translation initiation, elongation and termination were considered to be of interest (Fig. 3 a, Supplementary Table 5). From the signaling pathways belonging to epigenetic regulation, the chromatin silencing and chromatin organization involved in regulation of transcription were those found to be of interest, being underpinned by DEPs such as H1-10, H2AC2, H2AZ2, HMGA1 and HMGB1 (Fig. 3 b, Supplementary Table 5). From the tumor microenvironment, the signaling pathways associated to T-helper 1 type immune response, and to T cell mediated immune response to tumor cells, were considered to be the most interesting pathways, and harboring DEPs such as HLA-DRB1, HMGB1 and RIPK2 (Fig. 3 c, Supplementary Table 5). Integrative pathways in OCTs revealed by module clustering analysis Module-based analysis using the Reactome functional interaction from all DEGs and DEPs identified six distinct modules, which align with three core molecular networks in OCTs: epigenetic regulation (extracellular matrix organization), Protein biogenesis (mitochondrial translation), and Tumor microenvironment (immune response) (Fig. 1 . E, Fig. 2 d, Fig. 3 , Fig. 4 a, Supplementary Tables 4 and 5). Module A harbored genes related to extracellular matrix organization and carbohydrate metabolism, as indicated by biological processes (BP), cellular compartment CC, and Reactome orthologies. Such module is underpinned by CD44 (found to be underexpressed) and NEDD8 (found to be overexpressed), highlighting an overall upregulation in key molecules of the EGFR-related tyrosine kinase family and the ubiquitination pathway (Fig. 4 a, b). Module B was mostly centered around genes involved in the heme metabolism, such as HBB and HBA2 (found to be downregulated by RNA sequencing analysis), and affects processes such as carbon dioxide exchange, oxygen release, and heme scavenging (Fig. 4 a). Such marked downregulation, combined with the upregulation of HMGB1 in proteomics, strongly indicates the activation of the heme scavenging pathway. Specifically, the Hb: Hp-CD163 pathway, responsible for clearing free hemoglobin (Hb) to repair oxidative damage, emerges as a key pathway in this study (Fig. 1 e and Fig. 4 b). Module C comprises genes related to protein biogenesis, including EEF1A2 (found to be underexpressed), NPM1 and RPS15 (found to be overexpressed and considered cancer-related genes) (Fig. 4 a). Other overexpressed proteins, such as nucleolin (NCL), H1-10, and MRPL12, play roles in mitochondrial translation, ribosome assembly, and chromatin silencing. A key observation in Module C is the convergence of two critical processes: the upregulation of both mito-nuclear protein translational machinery and epigenetic regulation in OCTs compared to MRNs. NPM1 emerges as a crucial link between these processes, bridging protein translation, transcriptional machinery, and epigenetic regulation (Fig. 4 a, b). Discussion With the purpose of acquiring insights into the transcriptomic and proteomic profiles of OCTs, we subjected 12 OCTs and 6 MRNs to RNA and protein sequencing and compared their expression profiles. The resulting data revealed a marked variation between OCTs and MRNs, both at RNA and protein level, revealing several DEGs and DEPs being up- or downregulated in OCTs when compared to MRNs. In addition, signalling pathway analysis also revealed numerous cellular mechanisms associated with epigenetic modifications, tumor microenvironment, and protein biogenesis affecting OCTs, that can explain their behavior and phenotype. Although the clustering analysis from gene expression levels in both cohorts showed a moderate specificity comprising OCT samples, the same analysis from protein expression levels revealed a clear-cut separation between OCAs and MRNs, thus reinforcing the MMT theory happening in these tumors, as proposed by Seyfried et al [ 16 ]. Despite their shared trait of high mitochondrial abundance, these tumour types likely rely on specific DEGs and DEPs to drive their distinct features. These distinct DEGs and DEPs between the two groups, appear to be significantly different through three main mechanisms related to epigenetic modifications, tumour microenvironment, and protein biogenesis. In OCTs, unlike in MRNs, the role of increased ubiquitination in EGFR degradation, driven by CBL, CBLB, and VAV2, with underexpressed CD44 initiating the pathway, merits attention. CD44, acting as a co-receptor for growth factors like EGFR and TGFβR1 through its interaction with hyaluronic acid (HA), enhances receptor tyrosine kinase signaling [ 41 , 42 ]. HA-CD44 binding can either activate the RAS-MAPK pathway via GRB2 , as seen in non-oncocytic thyroid tumorigenesis [ 5 , 43 ], or promote EGFR degradation via CBL, thereby inhibiting the MAPK pathway. Downregulation of CD44 leads to NEDD8-mediated c-CBL neddylation by UBE2M, further facilitating EGFR degradation (Fig. 4 b). This selective modulation could explain the limited activation of the MAPK pathway in OCTs. The observed downregulation of the TGFβ signaling pathway in RNA sequencing for oncocytic tumors supports these findings, given that increased ubiquitination has been shown to negatively affect TGFβ’s tumor-suppressive function [ 44 ]. Notably, VAV2 , CBL , CBLB , and LCN2 are among the top-10 upregulated hub genes, with LCN2 , a known hub gene linked to oncocytic morphology in FTC, being identified by our group before the 4th WHO edition [ 45 ]. CD44 has been primarily analyzed immunohistochemically in PTC, where high CD44 expression, especially CD44s and CD44v6, is more frequent than in other thyroid neoplasms [ 46 , 47 ]. In our results, however, CD44 is underexpressed in OCTs, when compared to MRNs, leading to attenuation of EGFR activation via ubiquitination. This mechanism aligns with the 2% EGFR mutation rate found within the 20% of receptor thyrosine kinase (RTK) mutation-positive Hürthle cell carcinoma (HCC) cases reported by Ganly et al [ 1 ] and could explain the resistance of OCTs to therapy [ 48 – 50 ], similar to the chemotherapy resistance seen in other cancers [ 51 , 52 ] The "scavenging of heme from plasma" signaling pathway, identified consistently through RNA sequencing data analysis, from low expression levels of both the HBB and HBA2 genes in OCTs, highlights excessive plasma heme. Under normal conditions, heme is synthesized in mitochondria, essential for hemoproteins in the OXPHOS machinery [ 53 , 54 ]. OXPHOS complexes, especially III and IV, are mtDNA-encoded, and OCTs have been linked to a large fraction of deleterious mtDNA mutations, which disturb cellular respiration and oxygenation due to electron transport chain (ETC) impairment [ 17 , 19 , 20 , 22 , 24 , 55 , 56 ]. Oxidative stress can release heme from these hemoproteins into the extracellular pool as labile heme, a toxic form with significant biological roles [ 57 – 64 ]. In OCTs, two key functions of labile heme are relevant based on OMICs data: (i) intracellular heme deficiency due to labialization can increase labile heme levels, affecting downstream pathways by binding to cellular factors such as transcription factors and kinases [ 65 ], and (ii) labile heme acts as a potent proinflammatory damage-associated molecular pattern (DAMP), influencing both innate and adaptive immunity through chemokines and receptors like Toll-like receptors (TLRs) [ 62 , 63 , 66 ]. In a recent study by Martínez-Aguilar et al [ 67 ] using data-independent acquisition mass spectrometry, downregulation of HBB and HBA was observed in thyroid tumors compared to normal tissue, which the authors attributed to lower erythrocyte abundance. In parallel, Ganly et al. [ 68 ] highlighted alterations in heme signaling in their analysis of the immune microenvironment of HCCs, which aligns with our findings of downregulated heme metabolism in OCTs. In addition, the Hb: Hp-CD163 scavenging complex is also activated in response to elevated plasma heme [ 65 , 66 ] but saturation of this complex, as indicated by the downregulated "scavenging of heme from plasma" pathway, attracts the proinflammatory molecule HMGB1 (Fig. 4 b) [ 69 – 72 ] .This process may trigger sterile inflammation, as our proteomics data show upregulation of HMGB1 and HLA-DR1 in immune response-related pathways, suggesting an inflammatory tumor microenvironment in OCTs. Notably, heme deficiency negatively regulates the Ras-ERK1/2 pathway, and emerges as a second potential mechanism that came out through our results, aligning with the low frequency of MAPK pathway activation in OCTs [ 56 , 73 ]. Proteins for the mitochondrial OXPHOS system are synthesized by cytosolic ribosomes and transported into mitochondria, where chaperones ensure proper targeting and folding [ 74 ]. This Mito-Cytosolic Translational Balance is essential for mitochondrial functionality and coordination between mitochondrial and nuclear genomes [ 74 , 75 ]. Disruptions can trigger the mitochondrial unfolded protein response (UPRmt) to restore function[ 75 – 77 ]. If inadequate, mitochondrial precursor overaccumulation stress (mPOS) may occur, particularly in cells with mtDNA mutations and respiratory impairment, potentially leading to novel cell death mechanisms [ 28 , 76 , 77 ]. Given that OCTs exhibit reduced apoptosis, this may represent one of the primary or alternative cell death mechanisms in these tumors (1]. In OCTs, increased cytosolic translational machinery, marked by overexpressed proteins RPS19, RPS15, and EEF1A2, reflects this imbalance. Enriched GO terms and Reactome pathways highlighted ribosomal proteins, including their mitochondrial counterparts MRPL12 and MRPL40, indicating elevated proteostatic flow between the cytosol and mitochondria (Fig. 4 b). The UPRmt involvement in OCTs was also evident through the overexpressed DEPs with the presence of chaperones (TRAPPC8, TIMM8A, and UQCRH) and ribosomal quality control components, such as TIMM10, a subunit of TIMM23 [ 78 ]. EEF1A2 depletion, associated with mitochondrial complex I deficiency, that serves as a marker of protein toxicity and a trigger for UPRmt was also found to be underexpressed at protein level [ 79 ]. Mitochondrial dysfunction from mtDNA mutations, particularly affecting complex I, has been well-documented in OCTs [ 20 , 28 , 29 , 78 ]. Increased translational activity may act as a compensatory mechanism, where NPM1, a cancer-related gene found to be an overexpressed DEP in OCTs, emerges as a critical player linking protein biogenesis and epigenetic changes. NPM1 and NCL, nuclear chaperones are involved in ribosome biogenesis and chromatin remodeling and behave as shuttles for ribosomal proteins and core histones between the nucleus and cytosol in response to cell proliferation or stress [ 80 – 83 ]. NPM1 supports ribosome biogenesis, mRNA processing, and chromatin remodeling, while managing mitochondrial p53 degradation to promote apoptosis resistance [ 80 – 84 ]. NCL also aids ribosome biogenesis and histone chaperoning for chromatin remodeling, with both proteins connected through the overexpressed protein H1-10 [ 83 – 87 ]. The EHMT1/2 complex, responsible for H3K9me1/2 methylation, influences transcriptional regulation via ubiquitination mechanisms, involving enzymes like CBX3, UBE2M, and NEDD8 [ 88 ]. We have previously demonstrated the loss of 5hmC in oncocytic morphology, suggesting its involvement in OCTs [ 89 ]. The results obtained reveal a potential link between 5hmC and NPM1, similar to what was observed in AML, where 5hmC is negatively correlated with NPM1 mutations. This suggests that NPM1 may play a role in modulating 5hmC levels in OCTs, warranting further investigation [ 89 , 90 ]. As we circle back to the conundrum posed at the onset of such discussion, the scarcity of nuclear genetic mutations vis-à-vis the presence of characteristic disruptive mutations in mtDNA within oncocytic neoplasms - we might hypothesize that the mutational landscape of mtDNA might be, in fact, a pivotal contributor to the phenomena delineated in our transcriptomic and proteomic analyses in OCTs versus MRNs. Within OCTs, these mtDNA mutations seem to cause a distinctive tumoral phenotype/behavior, hallmarked by aberrant epigenetic modifications, that can re-modulate tumor microenvironment, and increase protein biogenesis as a compensatory mechanism. This context may effectively displace the need for alterations in canonical oncogenes and tumor suppressor genes or render such genetic variances redundant or incompatible, in this singular epigenetic framework. Our study has some relevant limitations. Although the rarity of OCTs in thyroid cancers, our cohort is rather small, precluding us of performing additional detailed sequencing analysis from both the transcriptomic and proteomic data. As we did not sequence the DNA (targeted or whole exome sequencing) of the present samples, we cannot exclude the possibility of genetic-derived alterations (somatic mutations and copy number variations) to be the main drivers of the OCTs here included. In conclusion, and to the best of our knowledge, this was the first transcriptomic and proteomic study focused on the molecular profiles of OCTs, and on how such profiles differ from those observed in MRNs. We were able to demonstrate, mainly at protein level, that OCTs do cluster together and differ from MRNs, and that relevant signaling pathways affecting epigenetic modifications, tumor microenvironment and protein biogenesis may shape the behavior and morphology of these oncocytic tumors. These pathways might hold significant potential for diagnostic purposes and may serve as targets for future therapeutic interventions that needs to be further investigated through in vitro and in vivo studies. Declarations Author Contribution Conceptualization : S.C. and V.M. Methodology : S.C., L.P., and A.C.P. Formal analysis and investigation : S.C., M.F., A.C.P., C.O., P.S., and V.M. Writing : S.C. and V.M. Review and editing : S.C., M.F., A.C.P., L.P., C.O., H.O., P.S., and V.M. All authors reviewed the manuscript. Funding This work was supported by Portuguese funds through FCT—Fundação para a Ciência e a Tecnologia—in the framework of a Ph.D. grant to SC (SFRH/BD/147650/2019). This article is partly supported by the project “Cancer Research on Therapy Resistance: From Basic Mechanisms to Novel Targets”—NORTE-01-0145-FEDER-000051, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This research received partial support from Portuguese funds through FCT (Fundação para a Ciência e a Tecnologia) within the scope of the project, accessible athttps://doi.org/10.54499/2022.05763.PTDC . Availability of Data and Materials Dataset was deposited in the files of “Cancer signaling and Metabolism” research group of i3s. The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Ethical Approval The study design was approved by the Ethical Committee of Centro Hospitalar Universitário de São João at 16 March 2017 under the Project entitled “Diabetes & obesity at the crossroads between Oncological and Cardiovascular diseases—a system analysis NETwork towards precision medicine (DOCnet). Conflict of Interest The authors declare no competing interests . Disclaimer The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. References Ganly I, Makarov V, Deraje S, Dong Y, Reznik E, Seshan V, et al. Integrated Genomic Analysis of Hurthle Cell Cancer Reveals Oncogenic Drivers, Recurrent Mitochondrial Mutations, and Unique Chromosomal Landscapes. Cancer Cell. 2018;34(2):256-70 e5. Ganly I, Ricarte Filho J, Eng S, Ghossein R, Morris LG, Liang Y, et al. Genomic dissection of Hurthle cell carcinoma reveals a unique class of thyroid malignancy. 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The Role of 5-Hydroxymethylcytosine as a Potential Epigenetic Biomarker in a Large Series of Thyroid Neoplasms. Endocr Pathol. 2024;35(1):25-39. Magotra M, Sakhdari A, Lee PJ, Tomaszewicz K, Dresser K, Hutchinson LM, et al. Immunohistochemical loss of 5-hydroxymethylcytosine expression in acute myeloid leukaemia: relationship to somatic gene mutations affecting epigenetic pathways. Histopathology. 2016;69(6):1055-65. Additional Declarations No competing interests reported. Supplementary Files TableS1.docx Supplementary Table 1: Differentially expressed genes in oncocytic cell tumors compared to mitochondrion-rich neoplasms, from RNA sequencing data analysis. TableS2.docx Supplementary Table 2: Significant signalling pathways from downregulated differentially expressed genes in oncocytic cell tumors. CC, cellular component; MF, molecular functions; Reac., Reactome. TableS3.docx Supplementary Table 3: Differentially expressed proteins in oncocytic cell tumors compared to mitochondrion-rich neoplasms, from protein sequencing data analysis. TableS4.docx Supplementary Table 4: Significant signalling pathways from underexpressed differentially expressed proteins in oncocytic cell tumors. BP, biological process; Reac., Reactome. TableS5.docx Supplementary Table 5: Significant signalling pathways from overexpressed differentially expressed proteins in oncocytic cell tumors. BP; biological process, CC; cellular component; Reac., Reactome. SupplementaryFigure1.pdf Supplementary Figure 1: Schematic representation of the tissues samples, sequencing methods employed, and bioinformatics analysis. Depiction of the sample cohort of oncocytic cell tumors and mitochondrion-rich neoplasms including in the study, and the sequencing and bioinformatics methods employed. MRN, mitochondrion-rich neoplasm; OCT, oncocytic cell tumor. 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The y-axis represents p-value and the x-axis represents the log fold-change of each gene. Each dot represents a particular gene. Upregulated and downregulated genes are depicted in green and red, respectively. (\u003cstrong\u003eb\u003c/strong\u003e), heatmap depicting the clustering analysis of OCTs and MRNs according to each DEG. Each column is a sample and each row is a gene. The fold-change values and patient type are color-coded according to the legend. (\u003cstrong\u003ec\u003c/strong\u003e) network interaction between the top 10 hub genes from upregulated DEGs, ranked according to maximum clique centrality score (MCC score) and color-coded according to the legend. (\u003cstrong\u003ed\u003c/strong\u003e) network interaction between the top 10 hub genes from downregulated DEGs, ranked according to the MCC score, and color-coded according to the legend. (\u003cstrong\u003ee\u003c/strong\u003e), horizontal bar plot depicting the signalling pathways (y-axis) most significantly altered (x-axis) in OCTs according to downregulated DEGs. DEG, differentially expressed gene; FC, fold-change; MCC maximum clique centrality; MRN, mitochondrion-rich neoplasm; OCT; oncocytic cell tumor.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5337626/v1/34d99cef8df92085c91fae56.png"},{"id":70038398,"identity":"9a8cf27a-2478-4dd5-99f2-36cdf92d79fd","added_by":"auto","created_at":"2024-11-27 17:36:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1109424,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferentially expressed proteins identified in oncocytic cell tumors when compared to mitochondrion-rich neoplasms, and associated signaling pathways.\u003c/strong\u003e (\u003cstrong\u003ea\u003c/strong\u003e), volcano plot depicting the significant differentially expressed proteins (DEPs) in oncocytic cells tumors (OCTs) when compared to mitochondrion-neoplasms (MRNs). The y-axis represents p-value and the x-axis represents the log fold-change of each protein. Each dot represents a particular protein. Overexpressed and underexpressedproteins are depicted in green and red, respectively. (\u003cstrong\u003eb\u003c/strong\u003e), heatmap depicting the clustering analysis of OCTs and MRNs according to each DEG. Each column is a sample and each row is a gene. The fold-change values and patient type are color-coded according to the legend. (\u003cstrong\u003ec\u003c/strong\u003e) network interaction between the top 10 hub genes from underexpressedDEPs, ranked according to maximum clique centrality score (MCC score), and color-coded according to the legend. (\u003cstrong\u003ed\u003c/strong\u003e), horizontal bar plot depicting the signalling pathways (y-axis) most significantly altered (x-axis) in OCTs according to underexpressed DEPs. (\u003cstrong\u003ee\u003c/strong\u003e), network interaction between the top 10 hub genes from overexpressed DEPs, ranked according to the MCC score, and color-coded according to the legend. DEP, differentially expressed protein; FC, fold-change; MRN, mitochondrion-rich neoplasm; OCT; oncocytic cell tumor.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5337626/v1/4408abdb99710495a073ce13.png"},{"id":70037763,"identity":"33afa20f-da2c-44d8-9d60-caa317031b00","added_by":"auto","created_at":"2024-11-27 17:28:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":895819,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMost affected signalling pathways in oncocytic cell tumors according to overexpressed differentially expressed proteins. \u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e), horizontal bar plot depicting the signalling pathways of protein biogenesis (y-axis) most significantly altered (x-axis) in OCTs according to overexpressed differentially expressed proteins (DEPs). (\u003cstrong\u003eb\u003c/strong\u003e), horizontal bar plot depicting the signalling pathways of epigenetic regulation (y-axis) most significantly altered (x-axis) in OCTs according to overexpressed DEPs. (\u003cstrong\u003ec\u003c/strong\u003e), horizontal bar plot depicting the signalling pathways of the tumor microenvironment (y-axis) most significantly altered (x-axis) in OCTs according to overexpressed DEPs. DEP, differentially expressed protein.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5337626/v1/dc2e252493ae0a44628158f3.png"},{"id":70037760,"identity":"e924dc28-bd43-4911-ac09-b34810050ba0","added_by":"auto","created_at":"2024-11-27 17:28:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1219456,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeneSet-based analysis from differentially expressed genes and differentially expressed proteins, and general hypothesis for the oncocytic morphology in oncocytic cell tumors. \u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e), GeneSet based analysis from differentially expressed genes (DEG) and differentially expressed proteins (DEP) present in the top 10 hub genes. The up- and downregulated DEGs and DEPs, as well as the modules are color-coded according to the legend. (\u003cstrong\u003eb\u003c/strong\u003e), overall mechanisms demonstrating the possible oncocytic morphology in oncocytic cell tumors, such as the CD44-EGFR interaction (left), the Hb: Hp-CD163 and HMGB1 interaction (middle), and the mito-cytosolic protein imbalance mechanism (right). DEG, differentially expressed gene; DEP, differentially expressed protein; OCT, oncocytic cell tumor.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5337626/v1/0999c992f6fde71cf88bce40.png"},{"id":70039788,"identity":"79ea6bcb-b6c4-4e8b-bcca-e02802fc211d","added_by":"auto","created_at":"2024-11-27 17:52:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5772637,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5337626/v1/028c7328-34a1-47a1-bec4-d2da6053f42f.pdf"},{"id":70038399,"identity":"5b8c8456-8dbf-43a6-9a3d-11b1e9e8d363","added_by":"auto","created_at":"2024-11-27 17:36:24","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":104667,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e: Differentially expressed genes in oncocytic cell tumors compared to mitochondrion-rich neoplasms, from RNA sequencing data analysis.\u003c/p\u003e","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5337626/v1/fc47df11ef1841c65570a117.docx"},{"id":70038397,"identity":"cdb6144d-7e69-450e-b0bc-1fec2995c8eb","added_by":"auto","created_at":"2024-11-27 17:36:24","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":103971,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 2: \u003c/strong\u003eSignificant signalling pathways from downregulated differentially expressed genes in oncocytic cell tumors. CC, cellular component; MF, molecular functions; Reac., Reactome.\u003c/p\u003e","description":"","filename":"TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5337626/v1/ce0e940cf395012963d71a88.docx"},{"id":70037759,"identity":"2ad1e115-1f84-4944-b478-d7b47c70567a","added_by":"auto","created_at":"2024-11-27 17:28:24","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":139797,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 3\u003c/strong\u003e: Differentially expressed proteins in oncocytic cell tumors compared to mitochondrion-rich neoplasms, from protein sequencing data analysis.\u003c/p\u003e","description":"","filename":"TableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-5337626/v1/5bc3f78ae8f811d576d9d0ec.docx"},{"id":70037764,"identity":"c2f128f6-d48d-4a1f-a9e0-5590b0c42a2d","added_by":"auto","created_at":"2024-11-27 17:28:24","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":69624,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 4: \u003c/strong\u003eSignificant signalling pathways from underexpressed differentially expressed proteins in oncocytic cell tumors. BP, biological process; Reac., Reactome.\u003c/p\u003e","description":"","filename":"TableS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-5337626/v1/f9935abd410960a816b81171.docx"},{"id":70037761,"identity":"303e3d7f-61ca-45d5-b221-df0563ec3d09","added_by":"auto","created_at":"2024-11-27 17:28:24","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":124589,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 5: \u003c/strong\u003eSignificant signalling pathways from overexpressed differentially expressed proteins in oncocytic cell tumors. BP; biological process, CC; cellular component; Reac., Reactome.\u003c/p\u003e","description":"","filename":"TableS5.docx","url":"https://assets-eu.researchsquare.com/files/rs-5337626/v1/982a59938d4fa2c5ab9cd3e4.docx"},{"id":70037765,"identity":"f192d007-37f6-4dd6-b396-66461f65ca65","added_by":"auto","created_at":"2024-11-27 17:28:25","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":14289287,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1: Schematic representation of the tissues samples, sequencing methods employed, and bioinformatics analysis.\u003c/strong\u003e Depiction of the sample cohort of oncocytic cell tumors and mitochondrion-rich neoplasms including in the study, and the sequencing and bioinformatics methods employed. MRN, mitochondrion-rich neoplasm; OCT, oncocytic cell tumor.\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5337626/v1/290370a25a5bbffbdb5db508.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unravelling the Tumourigenesis Mechanisms of Oncocytic Cell Tumours: Discoveries from a Comparative Omics Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOncocytic cell tumors (OCTs) of the thyroid remain understudied at the multi-omics level, with limited comparative studies focused on the genomics of these tumors that could offer initial molecular insights into their tumorigenesis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This underrepresentation may stem from the rarity of OCTs (3\u0026ndash;7% of all thyroid cancers) compared to non-OCTs and their recognition as a distinct tumor class only in the 4th edition of the WHO Classification of Endocrine Tumors [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].Previously, oncocytic adenoma (OA) and oncocytic carcinoma (OCA) were considered morphological subtypes of follicular neoplasms, often leading to their oversight within this broader category[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the 5th WHO Endocrine Tumor classification however, the term \"H\u0026uuml;rthle\" was replaced with \"oncocytic\" [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], even though such last term is still used for subtypes of other follicular cell-derived thyroid tumors, such as papillary thyroid carcinoma (PTC), medullary thyroid carcinoma (MTC), and poorly differentiated thyroid carcinoma (PDTC), potentially causing confusion [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Additionally, we classify other histotypes with oncocytic features (\u0026ge;\u0026thinsp;75% of the tumor), such as oncocytic-PTC (O-PTC) and non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP), as \"mitochondrion-rich neoplasms (MTRNs)\" for clearer terminology.\u003c/p\u003e \u003cp\u003eAs reported in the literature, common genetic alterations frequently observed in thyroid tumors are considerably less prevalent in OCTs. For instance, RAS mutations, typically found in 30\u0026ndash;45% of follicular-patterned thyroid tumors (follicular adenomas (FA), follicular thyroid carcinomas (FTC), and the infiltrative follicular variant of PTC), and in 20\u0026ndash;40% of PDTCs, are notably reduced to 6% in OA and 11% in OCA [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Likewise, the \u003cem\u003eBRAF V600E\u003c/em\u003e hotspot mutation, a well-established hallmark in sporadic PTCs, present in 30\u0026ndash;90% of cases and associated with more aggressive subtypes, has been largely absent in many studies focused on OCTs. Rearrangements affecting RET/PTC are also underrepresented in these tumors [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. \u003cem\u003eTERT\u003c/em\u003e promoter mutations show variable prevalence in OCTs, ranging from 0\u0026ndash;32%, with a higher occurrence in widely invasive OCA (wi-OCA) compared to minimally invasive OCA (mi-OCA) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In contrast, \u003cem\u003eTP53\u003c/em\u003e (25%) and \u003cem\u003ePTEN\u003c/em\u003e (41%) pathogenic mutations have been found more frequently in OCTs than in non-oncocytic thyroid neoplasms [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe absence of classical genetic drivers of thyroid tumorigenesis in OCTs suggests that answers may lie beyond thyroid pathology and within broader experimental models. Hence, endocrine pathologists may need to explore these areas more deeply to better understand the biology and behavior of OCTs. For instance, \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e studies have demonstrated complete suppression of malignancy in nuclei transplanted cells (cybrids), with tumorigenic nuclei but with normal cytoplasm, which might indicate that non-nuclear factors are key to OCT behavior [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This tumor-suppressor effect has been linked to Warburg\u0026rsquo;s theory of cancer metabolism [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eSeyfried et al recently expanded on this by proposing the \"cancer as a mitochondrial metabolic disease\" (MMT) theory, integrating cybrid studies and Warburg\u0026rsquo;s findings [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Warburg demonstrated that cancer cells favor glycolysis for energy production, even in oxygen-rich conditions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Indeed, Maximo et al [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and others [\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] have shown that due to insufficient Oxidative Phosphorylation (OXPHOS) activity in OCTs, their metabolism shifts to glycolysis, thus illustrating the Warburg effect.\u003c/p\u003e \u003cp\u003eStudies focusing on OCTs showed that mitochondrial DNA (mtDNA) alterations indeed stand out as a distinctive feature of these tumors, along with a high incidence of aneuploidy [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Additional studies, including those from our own group, have identified mutations in genes belonging to the Complex I (CI) subunits of the OXPHOS system in OCTs [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These pioneering investigations in these tumors have shed light on the underlying mechanism of mitochondrial dysfunction, revealing an increased mitochondrial proliferation and biogenesis as a compensatory response to oxidative stress. M\u0026aacute;ximo et al first observed a 4977bp deletion in mtDNA, commonly referred to as the \"common deletion\", which affects genes encoding for CI, complex IV (CIV), ATPase subunits, and five tRNAs, and was associated with malignancy in OCTs [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Based on the findings from Maximo and his group, Gasparre et al have confirmed a high prevalence of disruptive mutations in CI subunit genes in 45 oncocytic thyroid lesions [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. More recently, Gopal et al reported recurrent homoplasmic mutations in CI subunits specific to OCA [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and have also observed that a significantly larger fraction of mtDNA of OCA/OCTs displayed deleterious mutations when compared with the mtDNA of other cancers [\u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Regarding copy number variations, several studies have consistently reported gains of chromosome 7 and losses of chromosome 22 in OCTs [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Near-whole genome haploidization and subsequent endoreduplication was also described in OCTs. For a comprehensive review, please refer to articles by Canberk et al and Asa and Mete et al [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003eWhile mtDNA alterations affecting OCTs have been extensively studied, information on the transcriptomic and proteomic profiles of these tumors is rather scarce. Acknowledging the fact that oncocytic morphology alone does not indicate biological behavior of OCTs, we aimed to compare OCTs and MRNs, regardless of their benign or malignant status, at transcriptomic and proteomic level. To the best of our knowledge, this is the first transcriptomic and proteomic study investigating the distinctions and similarities between OCTs and MRNs, aiming to elucidate the molecular mechanisms underlying OCT tumorigenesis.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThe study protocol was approved by the Ethical Committee of Centro Hospitalar Universit\u0026aacute;rio de S\u0026atilde;o Jo\u0026atilde;o in 2017 as part of the project titled: \u0026ldquo;Diabetes \u0026amp; Obesity at the Intersection of Oncological and Cardiovascular Diseases\u0026mdash;a Systems Analysis Network for Precision Medicine (DOCnet). Pathology reports from the institutional database with a diagnosis of \"oncocytic and/or H\u0026uuml;rthle\" thyroid tumors, derived from follicular cells and with representative samples in the institutional frozen tumor bank, were identified and retrieved. All frozen cases were reviewed by an endocrine pathologist (S.C.) according to the 5th edition of the WHO thyroid tumor classification [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The final cohort consisted of 18 cases of purely oncocytic tumors including 12 OCTs and 6 MRNs, none of which displayed high-grade features (Supplementary Fig.\u0026nbsp;1). Of the 12 OCTs identified, 5 were OAs and 7 were OCAs, and of the 6 MRNs identified, 2 were of oncocytic subtype of papillary thyroid carcinoma (O-PTC) and 4 were low-risk neoplasms with oncocytic morphology (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTotal RNA was extracted from all 18 frozen samples (four sections, 10 \u0026micro;m thick, each) using the miRNeasy Mini Kit (Qiagen, Valencia, CA, USA) in accordance to the manufacturer\u0026rsquo;s instructions and as previously described (36) The RNA from each sample was then sequenced using the Ion AmpliSeq\u0026trade; Transcriptome Human Gene Expression Kit (ThermoFisher Scientific). The library qualities were checked by running on a BioAnalyzer 2100 and the concentrations were determined from the analysis profiles. Ten barcoded libraries were pooled together on an equimolar basis and run using three PIv3 chips on an Ion Torrent Proton using HiQ chemistry.\u003c/p\u003e \u003cp\u003eGenerated files were analyzed using the Transcriptome Analysis Console (TAC) Software to assess the quality metrics of each sequenced sample. After preliminary analysis, we obtained the list of genes with the fold-change associated with each comparison (Supplementary Fig.\u0026nbsp;1, Supplementary Tables\u0026nbsp;1 and 2). For such comparison, we selected differentially expressed genes (DEGs) using a p-value threshold of 5% and a module of log2 fold-change higher than 2, as previously described [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe protein extraction was performed as previously described [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In brief, each frozen sample was processed with lysing matrix A (MP Biomedicals, Irvine, CA, USA) and lysis buffer (100 mM Tris-HCl ph 8.5, 1% sodium deoxycholate, 10 mM tris (2-carboxtethyl) phosphine, 40 mM chloroacetamide, and prot\u0026eacute;ase inhibitors). Protein homogenization was performed using FastPrep-24 equipment (MP Biomedicals). The protein extracts were centrifuged, incubated and sonicated. After measuring the protein concentration, one-hundred micrograms from each sample were processed for proteomic analysis following solid-phase-enhanced sample preparation (SP3) protocol as described by Hughes et al (PMID: 30464214).\u003c/p\u003e \u003cp\u003eProtein identification and quantitation were performed via nano liquid Chromatography Analysis - Mass Spectrometry (LC-MS). This equipment is composed of an Ultimate 3000 liquid chromatography system coupled to a Q-Exactive Hybrid Quadrupole-Orbitrap mass spectrometer (Thermo Scientific, Bremen, Germany).\u003c/p\u003e \u003cp\u003eThe resulting data was analyzed using the software Proteome Discoverer 2.4.0.305 (Thermo Scientific). The protein identification was performed taken into account the UniProt protein sequence database for the Homo sapiens Proteome (2019_09) together with a mass spectra library (NIST_Humale_Orbitrap_HCD_20160923) using the SequestHT and MSPepSearch software, respectively. The protein quantification was performed using the Label Free Quantification (LFQ) methodology. The criteria used for selection of differentially expressed proteins (DEPs) were: protein with at least 2 unique peptides; p-value adjusted by FDR\u0026thinsp;\u0026le;\u0026thinsp;0.05 in the t-Student test; identification and quantification of each protein performed in at least 50% of the samples in one of the groups.\u003c/p\u003e \u003cp\u003eThe enrichment analysis for all RNA and protein measurements was performed using the package enrichGO and with annotation of the org.Hs.eg.db package. All plots presented here were performed using the R package \u0026ldquo;ggplot2\u0026rdquo;. Statistical analyses were performed also using R, in particular the nonparametric Wilcoxon rank-sum test.\u003c/p\u003e \u003cp\u003eWe used the IntAct database via Cytoscape [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] to create network visualizations and identify key genes from transcriptomics and proteomics data (Supplementary Fig.\u0026nbsp;1). Cytohubba [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] was used to select the top 10 hub genes based on maximum clique centrality (MCC) scores, yielding 40 critical up- and downregulated genes. These were integrated and analyzed using the Functional Interaction (FI) app [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] for network module based clustering and pathway enrichment, with modules filtered by FDR\u0026thinsp;\u0026gt;\u0026thinsp;0.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDemographics and clinical data\u003c/h2\u003e \u003cp\u003eOur cohort included 12 OCTs and 6 MRNs (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean patient age was 65.6 years, with a predominance of females (15 females, 3 males). All OA cases were female, while the OCAs included six females and one male. The two oncocytic-papillary thyroid carcinoma (O-PTC) cases were female, the oncocytic-well-differentiated tumour with uncertain malignant potential (O-WT-UMPs) comprised two males and one female, and the single oncocytic O-NIFTP case was female. Tumor size greatly varied by subtype: the OAs and OCAs had a mean tumor size of 22.6 mm and 40.2 mm, respectively. The O-PTCs, O-WT-UMPs and O-NIFTP had a mean tumor size of 10.5 mm, 23.6 mm and 30 mm, respectively. Oncocytic morphology was consistently 100% across all subtypes, with no high-grade features (necrosis/mitosis) observed in malignant cases.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDataset and clinicopathological characteristics of all cases included in this study. F, female; M, male; OA, oncocytic adenoma; OCA, oncocytic carcinoma; O-PTC, oncocytic-papillary thyroid carcinoma; O-WT-UMP, oncocytic-well differentiated tumour-with uncertain malignant potential.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEntire cohort\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOncocytic tumours*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMitochondrion-rich tumours*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eOA (n\u0026thinsp;=\u0026thinsp;5)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eOCA (n\u0026thinsp;=\u0026thinsp;7)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eO-PTC (n\u0026thinsp;=\u0026thinsp;2)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eO-WT-UMP (n\u0026thinsp;=\u0026thinsp;3)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eO-NIFTP (n\u0026thinsp;=\u0026thinsp;1)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (mean)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender (F/M)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 F/ 3 M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 F/ 1 M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 F/2 M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 F\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSize (mm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBiological behaviour\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBenign\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMalignant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMalignant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow-risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow-risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubtype\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 widely invasive/2 angioinvasive encapsulated/3 minimally invasive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOncocytic morphology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDominant growth pattern\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 follicular/ 5 trabecular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 follicular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 follicular/ 4 trabecular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 follicular/ 1 trabecular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 follicular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 follicular\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDifferentially expressed genes in oncocytic tumors and associated signalling pathways\u003c/h3\u003e\n\u003cp\u003eRNA sequencing data analysis revealed the presence of 85 DEGs in OCTs when compared to MRNs, including 38 upregulated and 47 downregulated differentially expressed genes (DEGs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, Supplementary Table\u0026nbsp;1). Clustering analysis between the two cohorts, although quite scattered, showed that 8 out of 12 OCTs clustered together, along with 2 MRNs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). When looking at upregulated DEGs, only one was considered to be in the top 10 hub genes, and with the highest MCC score (\u003cem\u003eLCN2\u003c/em\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). However, and from the downregulated DEGs, nine were present in the top 10 hub genes, with \u003cem\u003eJUNB\u003c/em\u003e, \u003cem\u003eHBA2\u003c/em\u003e, \u003cem\u003eFXYD6\u003c/em\u003e having the highest rank (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Signalling pathway analysis from the downregulated DEGs revealed significant signaling pathways associated with heme metabolism, such as scavenging of heme from plasma and binding and uptake of ligands by scavenger receptors, among others (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee, Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eDifferentially expressed proteins in oncocytic tumors and associated signalling pathways\u003c/h3\u003e\n\u003cp\u003eWhen compared to MRNs, protein sequencing data analysis showed the presence of 84 DEPs in OCTs, including 64 overexpressed and 20 underexpressed differentially expressed proteins (DEPs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Supplementary Table\u0026nbsp;3). Interestingly, clustering analysis between OCTs and MRNs demonstrated that all of the OCAs (n\u0026thinsp;=\u0026thinsp;7) clustered together, along with 3 OAs, constituting a completely different cluster than the one comprising the 6 MRNs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The Cytohubba analysis revealed that from all underexpressed proteins, all top ten hub genes were DEPs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec), with QPRT, CD44 and EEF1A2 proteins having the highest MCC score. In addition, signalling pathway analysis from underexpressed DEPs rendered significant pathways associated with extracellular matrix and structure organization, mainly affected by CD44, ELANE, FBLN5 and MFAP4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, Supplementary Table\u0026nbsp;4). When looking at overexpressed DEPs, six were considered to be in the top 10 hub genes, namely NCL, EHMT2, RPS25, MRPL12, NEDD8, RPS15 and NPM1, being the last two proteins associated with cancer-related genes (PMID: 29625053) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee, Supplementary Table\u0026nbsp;4). Signalling pathway analysis from overexpressed DEPs provided several significant pathways related with protein biogenesis, epigenetic regulation and tumor microenviroment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary Table\u0026nbsp;5). More specifically, and looking at signaling pathways from the protein biogenesis, those associated with mitochondrial translation initiation, elongation and termination were considered to be of interest (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, Supplementary Table\u0026nbsp;5). From the signaling pathways belonging to epigenetic regulation, the chromatin silencing and chromatin organization involved in regulation of transcription were those found to be of interest, being underpinned by DEPs such as H1-10, H2AC2, H2AZ2, HMGA1 and HMGB1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, Supplementary Table\u0026nbsp;5). From the tumor microenvironment, the signaling pathways associated to T-helper 1 type immune response, and to T cell mediated immune response to tumor cells, were considered to be the most interesting pathways, and harboring DEPs such as HLA-DRB1, HMGB1 and RIPK2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, Supplementary Table\u0026nbsp;5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eIntegrative pathways in OCTs revealed by module clustering analysis\u003c/h3\u003e\n\u003cp\u003eModule-based analysis using the Reactome functional interaction from all DEGs and DEPs identified six distinct modules, which align with three core molecular networks in OCTs: epigenetic regulation (extracellular matrix organization), Protein biogenesis (mitochondrial translation), and Tumor microenvironment (immune response) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. E, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, Supplementary Tables\u0026nbsp;4 and 5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eModule A harbored genes related to extracellular matrix organization and carbohydrate metabolism, as indicated by biological processes (BP), cellular compartment CC, and Reactome orthologies. Such module is underpinned by CD44 (found to be underexpressed) and NEDD8 (found to be overexpressed), highlighting an overall upregulation in key molecules of the EGFR-related tyrosine kinase family and the ubiquitination pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, b).\u003c/p\u003e \u003cp\u003eModule B was mostly centered around genes involved in the heme metabolism, such as \u003cem\u003eHBB\u003c/em\u003e and \u003cem\u003eHBA2\u003c/em\u003e (found to be downregulated by RNA sequencing analysis), and affects processes such as carbon dioxide exchange, oxygen release, and heme scavenging (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Such marked downregulation, combined with the upregulation of HMGB1 in proteomics, strongly indicates the activation of the heme scavenging pathway. Specifically, the Hb: Hp-CD163 pathway, responsible for clearing free hemoglobin (Hb) to repair oxidative damage, emerges as a key pathway in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eModule C comprises genes related to protein biogenesis, including EEF1A2 (found to be underexpressed), NPM1 and RPS15 (found to be overexpressed and considered cancer-related genes) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Other overexpressed proteins, such as nucleolin (NCL), H1-10, and MRPL12, play roles in mitochondrial translation, ribosome assembly, and chromatin silencing. A key observation in Module C is the convergence of two critical processes: the upregulation of both mito-nuclear protein translational machinery and epigenetic regulation in OCTs compared to MRNs. NPM1 emerges as a crucial link between these processes, bridging protein translation, transcriptional machinery, and epigenetic regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, b).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWith the purpose of acquiring insights into the transcriptomic and proteomic profiles of OCTs, we subjected 12 OCTs and 6 MRNs to RNA and protein sequencing and compared their expression profiles. The resulting data revealed a marked variation between OCTs and MRNs, both at RNA and protein level, revealing several DEGs and DEPs being up- or downregulated in OCTs when compared to MRNs. In addition, signalling pathway analysis also revealed numerous cellular mechanisms associated with epigenetic modifications, tumor microenvironment, and protein biogenesis affecting OCTs, that can explain their behavior and phenotype.\u003c/p\u003e \u003cp\u003eAlthough the clustering analysis from gene expression levels in both cohorts showed a moderate specificity comprising OCT samples, the same analysis from protein expression levels revealed a clear-cut separation between OCAs and MRNs, thus reinforcing the MMT theory happening in these tumors, as proposed by Seyfried et al [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Despite their shared trait of high mitochondrial abundance, these tumour types likely rely on specific DEGs and DEPs to drive their distinct features. These distinct DEGs and DEPs between the two groups, appear to be significantly different through three main mechanisms related to epigenetic modifications, tumour microenvironment, and protein biogenesis.\u003c/p\u003e \u003cp\u003eIn OCTs, unlike in MRNs, the role of increased ubiquitination in EGFR degradation, driven by CBL, CBLB, and VAV2, with underexpressed CD44 initiating the pathway, merits attention. CD44, acting as a co-receptor for growth factors like EGFR and TGFβR1 through its interaction with hyaluronic acid (HA), enhances receptor tyrosine kinase signaling [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. HA-CD44 binding can either activate the RAS-MAPK pathway via \u003cem\u003eGRB2\u003c/em\u003e, as seen in non-oncocytic thyroid tumorigenesis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], or promote EGFR degradation via CBL, thereby inhibiting the MAPK pathway. Downregulation of CD44 leads to NEDD8-mediated c-CBL neddylation by UBE2M, further facilitating EGFR degradation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). This selective modulation could explain the limited activation of the MAPK pathway in OCTs. The observed downregulation of the TGFβ signaling pathway in RNA sequencing for oncocytic tumors supports these findings, given that increased ubiquitination has been shown to negatively affect TGFβ\u0026rsquo;s tumor-suppressive function [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Notably, \u003cem\u003eVAV2\u003c/em\u003e, \u003cem\u003eCBL\u003c/em\u003e, \u003cem\u003eCBLB\u003c/em\u003e, and \u003cem\u003eLCN2\u003c/em\u003e are among the top-10 upregulated hub genes, with \u003cem\u003eLCN2\u003c/em\u003e, a known hub gene linked to oncocytic morphology in FTC, being identified by our group before the 4th WHO edition [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. CD44 has been primarily analyzed immunohistochemically in PTC, where high CD44 expression, especially CD44s and CD44v6, is more frequent than in other thyroid neoplasms [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In our results, however, CD44 is underexpressed in OCTs, when compared to MRNs, leading to attenuation of EGFR activation via ubiquitination. This mechanism aligns with the 2% EGFR mutation rate found within the 20% of receptor thyrosine kinase (RTK) mutation-positive H\u0026uuml;rthle cell carcinoma (HCC) cases reported by Ganly et al [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and could explain the resistance of OCTs to therapy [\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], similar to the chemotherapy resistance seen in other cancers [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe \"scavenging of heme from plasma\" signaling pathway, identified consistently through RNA sequencing data analysis, from low expression levels of both the \u003cem\u003eHBB\u003c/em\u003e and \u003cem\u003eHBA2\u003c/em\u003e genes in OCTs, highlights excessive plasma heme. Under normal conditions, heme is synthesized in mitochondria, essential for hemoproteins in the OXPHOS machinery [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. OXPHOS complexes, especially III and IV, are mtDNA-encoded, and OCTs have been linked to a large fraction of deleterious mtDNA mutations, which disturb cellular respiration and oxygenation due to electron transport chain (ETC) impairment [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Oxidative stress can release heme from these hemoproteins into the extracellular pool as labile heme, a toxic form with significant biological roles [\u003cspan additionalcitationids=\"CR58 CR59 CR60 CR61 CR62 CR63\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. In OCTs, two key functions of labile heme are relevant based on OMICs data: (i) intracellular heme deficiency due to labialization can increase labile heme levels, affecting downstream pathways by binding to cellular factors such as transcription factors and kinases [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], and (ii) labile heme acts as a potent proinflammatory damage-associated molecular pattern (DAMP), influencing both innate and adaptive immunity through chemokines and receptors like Toll-like receptors (TLRs) [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. In a recent study by Mart\u0026iacute;nez-Aguilar et al [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] using data-independent acquisition mass spectrometry, downregulation of HBB and HBA was observed in thyroid tumors compared to normal tissue, which the authors attributed to lower erythrocyte abundance. In parallel, Ganly et al. [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] highlighted alterations in heme signaling in their analysis of the immune microenvironment of HCCs, which aligns with our findings of downregulated heme metabolism in OCTs.\u003c/p\u003e \u003cp\u003eIn addition, the Hb: Hp-CD163 scavenging complex is also activated in response to elevated plasma heme [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] but saturation of this complex, as indicated by the downregulated \"scavenging of heme from plasma\" pathway, attracts the proinflammatory molecule HMGB1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) [\u003cspan additionalcitationids=\"CR70 CR71\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e] .This process may trigger sterile inflammation, as our proteomics data show upregulation of HMGB1 and HLA-DR1 in immune response-related pathways, suggesting an inflammatory tumor microenvironment in OCTs. Notably, heme deficiency negatively regulates the Ras-ERK1/2 pathway, and emerges as a second potential mechanism that came out through our results, aligning with the low frequency of MAPK pathway activation in OCTs [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eProteins for the mitochondrial OXPHOS system are synthesized by cytosolic ribosomes and transported into mitochondria, where chaperones ensure proper targeting and folding [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. This Mito-Cytosolic Translational Balance is essential for mitochondrial functionality and coordination between mitochondrial and nuclear genomes [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Disruptions can trigger the mitochondrial unfolded protein response (UPRmt) to restore function[\u003cspan additionalcitationids=\"CR76\" citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. If inadequate, mitochondrial precursor overaccumulation stress (mPOS) may occur, particularly in cells with mtDNA mutations and respiratory impairment, potentially leading to novel cell death mechanisms [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Given that OCTs exhibit reduced apoptosis, this may represent one of the primary or alternative cell death mechanisms in these tumors (1]. In OCTs, increased cytosolic translational machinery, marked by overexpressed proteins RPS19, RPS15, and EEF1A2, reflects this imbalance. Enriched GO terms and Reactome pathways highlighted ribosomal proteins, including their mitochondrial counterparts MRPL12 and MRPL40, indicating elevated proteostatic flow between the cytosol and mitochondria (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eThe UPRmt involvement in OCTs was also evident through the overexpressed DEPs with the presence of chaperones (TRAPPC8, TIMM8A, and UQCRH) and ribosomal quality control components, such as TIMM10, a subunit of TIMM23 [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. EEF1A2 depletion, associated with mitochondrial complex I deficiency, that serves as a marker of protein toxicity and a trigger for UPRmt was also found to be underexpressed at protein level [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Mitochondrial dysfunction from mtDNA mutations, particularly affecting complex I, has been well-documented in OCTs [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Increased translational activity may act as a compensatory mechanism, where NPM1, a cancer-related gene found to be an overexpressed DEP in OCTs, emerges as a critical player linking protein biogenesis and epigenetic changes.\u003c/p\u003e \u003cp\u003eNPM1 and NCL, nuclear chaperones are involved in ribosome biogenesis and chromatin remodeling and behave as shuttles for ribosomal proteins and core histones between the nucleus and cytosol in response to cell proliferation or stress [\u003cspan additionalcitationids=\"CR81 CR82\" citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. NPM1 supports ribosome biogenesis, mRNA processing, and chromatin remodeling, while managing mitochondrial p53 degradation to promote apoptosis resistance [\u003cspan additionalcitationids=\"CR81 CR82 CR83\" citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. NCL also aids ribosome biogenesis and histone chaperoning for chromatin remodeling, with both proteins connected through the overexpressed protein H1-10 [\u003cspan additionalcitationids=\"CR84 CR85 CR86\" citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. The EHMT1/2 complex, responsible for H3K9me1/2 methylation, influences transcriptional regulation via ubiquitination mechanisms, involving enzymes like CBX3, UBE2M, and NEDD8 [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. We have previously demonstrated the loss of 5hmC in oncocytic morphology, suggesting its involvement in OCTs [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. The results obtained reveal a potential link between 5hmC and NPM1, similar to what was observed in AML, where 5hmC is negatively correlated with \u003cem\u003eNPM1\u003c/em\u003e mutations. This suggests that NPM1 may play a role in modulating 5hmC levels in OCTs, warranting further investigation [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs we circle back to the conundrum posed at the onset of such discussion, the scarcity of nuclear genetic mutations vis-\u0026agrave;-vis the presence of characteristic disruptive mutations in mtDNA within oncocytic neoplasms - we might hypothesize that the mutational landscape of mtDNA might be, in fact, a pivotal contributor to the phenomena delineated in our transcriptomic and proteomic analyses in OCTs versus MRNs. Within OCTs, these mtDNA mutations seem to cause a distinctive tumoral phenotype/behavior, hallmarked by aberrant epigenetic modifications, that can re-modulate tumor microenvironment, and increase protein biogenesis as a compensatory mechanism. This context may effectively displace the need for alterations in canonical oncogenes and tumor suppressor genes or render such genetic variances redundant or incompatible, in this singular epigenetic framework.\u003c/p\u003e \u003cp\u003eOur study has some relevant limitations. Although the rarity of OCTs in thyroid cancers, our cohort is rather small, precluding us of performing additional detailed sequencing analysis from both the transcriptomic and proteomic data. As we did not sequence the DNA (targeted or whole exome sequencing) of the present samples, we cannot exclude the possibility of genetic-derived alterations (somatic mutations and copy number variations) to be the main drivers of the OCTs here included.\u003c/p\u003e \u003cp\u003eIn conclusion, and to the best of our knowledge, this was the first transcriptomic and proteomic study focused on the molecular profiles of OCTs, and on how such profiles differ from those observed in MRNs. We were able to demonstrate, mainly at protein level, that OCTs do cluster together and differ from MRNs, and that relevant signaling pathways affecting epigenetic modifications, tumor microenvironment and protein biogenesis may shape the behavior and morphology of these oncocytic tumors. These pathways might hold significant potential for diagnostic purposes and may serve as targets for future therapeutic interventions that needs to be further investigated through \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization\u003c/strong\u003e: S.C. and V.M. \u003cstrong\u003eMethodology\u003c/strong\u003e: S.C., L.P., and A.C.P. \u003cstrong\u003eFormal analysis and investigation\u003c/strong\u003e: S.C., M.F., A.C.P., C.O., P.S., and V.M. \u003cstrong\u003eWriting\u003c/strong\u003e: S.C. and V.M. \u003cstrong\u003eReview and editing\u003c/strong\u003e: S.C., M.F., A.C.P., L.P., C.O., H.O., P.S., and V.M. \u003cstrong\u003eAll authors reviewed the manuscript.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Portuguese funds through FCT\u0026mdash;Funda\u0026ccedil;\u0026atilde;o para a Ci\u0026ecirc;ncia e a Tecnologia\u0026mdash;in the framework of a Ph.D. grant to SC (SFRH/BD/147650/2019). This article is partly supported by the project \u0026ldquo;Cancer Research on Therapy Resistance: From Basic Mechanisms to Novel Targets\u0026rdquo;\u0026mdash;NORTE-01-0145-FEDER-000051, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).\u0026nbsp;This research received partial support from Portuguese funds through FCT (Funda\u0026ccedil;\u0026atilde;o para a Ci\u0026ecirc;ncia e a Tecnologia) within the scope of the project, accessible athttps://doi.org/10.54499/2022.05763.PTDC\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDataset was deposited in the files of \u0026ldquo;Cancer signaling and Metabolism\u0026rdquo; research group of i3s. The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study design was approved by the Ethical Committee of Centro Hospitalar Universit\u0026aacute;rio de S\u0026atilde;o Jo\u0026atilde;o at 16 March 2017 under the Project entitled \u0026ldquo;Diabetes \u0026amp; obesity at the crossroads between Oncological and Cardiovascular diseases\u0026mdash;a system analysis NETwork towards precision medicine (DOCnet).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGanly I, Makarov V, Deraje S, Dong Y, Reznik E, Seshan V, et al. 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Histopathology. 2016;69(6):1055-65.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Oncocytic cell tumors, mitochondrion-rich neoplasms, RNA and protein sequencing","lastPublishedDoi":"10.21203/rs.3.rs-5337626/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5337626/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOncocytic cell tumours (OCTs), formerly known as H\u0026uuml;rthle cell tumours in thyroid, are a subset of thyroid and other endocrine neoplasms that present diagnostic and therapeutic challenges due to their variable clinical behaviour. Considering the limited exploration of transcriptomic and proteomic profiles of OCTs compared to MRNs in the literature, we conducted RNA and protein sequencing on 12 OCTs (5 oncocytic adenomas and 7 oncocytic carcinomas) and 6 MRNs, based on the fact that oncocytic morphology alone does not determine biological behavior. RNA sequencing data analysis revealed the presence of 47 downregulated and 38 upregulated differentially expressed genes (DEGs) in OCTs when compared to MRNs. Significant signalling pathways affecting OCTs were associated with the heme metabolism. Protein sequencing data analysis showed the presence of 20 underexpressed and 64 overexpressed differentially expressed proteins (DEPs) in OCTs than in MRNs, and all of the OCAs were found to cluster together, constituting a distinct cluster than the one comprising the MRNs. The majority of DEPs affected three major cellular pathways in OCTs, including epigenetic modifications, tumor microenvironment, and protein biogenesis, that may shape the behavior and morphology of these tumors. Hence, further research into these mechanisms and their impact on tumour phenotype and behaviour may lead to better diagnostic and therapeutic strategies for patients with OCTs.\u003c/p\u003e","manuscriptTitle":"Unravelling the Tumourigenesis Mechanisms of Oncocytic Cell Tumours: Discoveries from a Comparative Omics Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-27 17:28:19","doi":"10.21203/rs.3.rs-5337626/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":"8a893cf2-9c70-4b6d-bc9a-06a4548563ff","owner":[],"postedDate":"November 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-27T17:28:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-27 17:28:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5337626","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5337626","identity":"rs-5337626","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}