C/EBPβ Drives Anaplastic Thyroid Cancer Dedifferentiation and Radioiodine Resistance via IL-6/JAK/STAT and EMT Activation

C/EBPβ Drives Anaplastic Thyroid Cancer Dedifferentiation and Radioiodine Resistance via IL-6/JAK/STAT and EMT Activation | 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 Article C/EBPβ Drives Anaplastic Thyroid Cancer Dedifferentiation and Radioiodine Resistance via IL-6/JAK/STAT and EMT Activation Jungmin Choi, So Hee Dho, Kwangmin Yoo, Jun Sung Lee, Hyo Jin Park, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9248842/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Anaplastic thyroid carcinoma (ATC) is among the most lethal human malignancies, with a median survival of four months. While ATC is thought to arise from papillary thyroid carcinoma (PTC) through dedifferentiation, the molecular drivers underlying this transition remain poorly defined. Here, we integrated single-cell transcriptomics (43,866 cells; PTC, n = 5; ATC, n = 3) with spatial transcriptomics from independent cohorts across the disease spectrum. We identified CCAAT/enhancer-binding protein beta (C/EBPβ) as a transcriptionally hyperactivated, genomically amplified master regulator of thyroid cancer dedifferentiation. Trajectory analysis positioned C/EBPβ upregulation as an early molecular switch. Mechanistically, C/EBPβ activates IL-6/JAK/STAT signaling and epithelial-mesenchymal transition (EMT). In PTC cell lines, inducible C/EBPβ expression phenocopied ATC-like aggressive features, enhancing proliferation, migration, and invasion, and conferring radioiodine refractoriness by suppressing the sodium-iodide symporter (NIS). These phenotypes were abrogated by a DNA-binding-deficient mutant and reversed upon withdrawal of C/EBPβ. In vivo , induction of C/EBPβ accelerated xenograft tumor growth. Furthermore, immunohistochemistry confirmed marked C/EBPβ overexpression in clinical ATC specimens (mean H-score: 166.7) compared with advanced PTC (mean H-score: 18.8). Spatial analysis revealed ATC-associated microenvironmental remodeling, highlighted by CST1-expressing myofibroblastic cancer-associated fibroblasts (myCAFs) at tumor boundaries and the expansion of SPP1 + tumor-associated macrophages linked to hypoxia and EMT. Collectively, these integrated findings establish C/EBPβ as a critical mechanistic driver and candidate therapeutic target in aggressive thyroid cancer. Biological sciences/Cancer/Cancer genomics Biological sciences/Cell biology/Mechanisms of disease Anaplastic thyroid carcinoma papillary thyroid carcinoma single-cell RNA sequencing spatial transcriptomics C/EBPβ dedifferentiation IL-6/JAK/STAT EMT radioiodine resistance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION Anaplastic thyroid carcinoma (ATC) is among the most lethal human malignancies, with a median survival of approximately four months 1 , despite accounting for only 1.3% of thyroid cancers in the United States 2 . In contrast, papillary thyroid carcinoma (PTC) usually has an excellent prognosis 3 . This striking clinical divergence has focused attention on the molecular events underlying progression from differentiated thyroid cancer to ATC. Although the cellular origin of ATC remains debated 4 , shared oncogenic alterations and mixed histology support the idea that a substantial proportion of ATCs arise through dedifferentiation from pre-existing PTCs 5 . Genomic studies have identified recurrent alterations involving TP53, the TERT promoter, PI3K/AKT/mTOR signaling, SWI/SNF components, and chromatin modifiers 6 , 7 . Shared BRAF V600E mutations between PTC and ATC further support a common evolutionary origin 7 , and co-occurring BRAF and TERT alterations can accelerate dedifferentiation and progression 8 . However, targeted monotherapies directed at individual oncogenic lesions often have limited durability due to acquired resistance. These observations raise the possibility that broader transcriptional regulators, rather than single downstream effectors, help establish and maintain the dedifferentiated ATC state 9 . Beyond tumor-intrinsic programs, the tumor microenvironment (TME) 10 contributes significantly to ATC growth, invasion, and immune evasion. While single-cell transcriptomics has provided valuable insights into the cellular composition of thyroid tumors, it lacks the spatial context needed to localize tumor-stroma interfaces and organized cellular niches. Integrating spatial and single-cell transcriptomics enables the identification of tissue architecture, intercellular signaling, and microenvironmental niches driving disease progression. In this study, we integrated single-cell RNA sequencing (scRNA-seq) from PTC and ATC tumors with Visium spatial transcriptomics from an independent cross-sectional cohort. This analysis identified CCAAT/enhancer-binding protein beta ( CEBPB ; C/EBPβ) as a transcriptionally hyperactivated, genomically amplified master regulator of thyroid cancer dedifferentiation. C/EBPβ, a transcription factor known to be involved in inflammation and proliferation 11 , 12 , is selectively hyperactivated in ATC. Functional assays in PTC cell lines supported a role for C/EBPβ in promoting proliferation, migration, invasion, and radioiodine resistance, whereas spatial analyses implicated myofibroblastic cancer-associated fibroblasts (myCAFs), specifically a CST1-upregulated subpopulation at tumor boundaries, and SPP1 + macrophages in the ATC microenvironment. Together, these data define a mechanistically grounded framework for aggressive thyroid cancer that links tumor-cell dedifferentiation to stromal remodeling and clinical aggressiveness. MATERIALS AND METHODS Thyroid cancer patient cohort Tumor tissues were collected from 32 patients with ATC or PTC at Gangnam Severance Hospital, Seoul, South Korea. scRNA-seq was performed on eight samples: PTC from the thyroid gland ( n = 4, including one early-onset PTC case), PTC from a lymph node ( n = 1), ATC from the thyroid gland ( n = 2), and ATC from a lymph node ( n = 1) (Supplementary Table 1). Immunohistochemical (IHC) analysis was performed on 25 specimens stratified by clinical group: ATC ( n = 10; 1-year survival, n = 5), PTC N1bM0 ( n = 5), PTC N1bM1 ( n = 5), and early-onset PTC (PTCEO) N1b ( n = 5). Visium spatial transcriptomics data (GSE250521) were obtained from a public repository and deconvoluted using our scRNA-seq reference; these samples were not patient-matched to our cohort. Bulk RNA-seq and functional assays were performed in thyroid cancer cell lines with inducible C/EBPβ expression. ENCODE ChIP-seq data from K562 cells (ENCSR000EHE) were used as orthogonal evidence for candidate C/EBPβ-bound loci. Ethics approval and consent to participate Collection and use of human specimens were approved by the Institutional Review Board of Gangnam Severance Hospital (IRB number: 3-2020-0309). All patients provided written informed consent for research use of their tissues before sample collection. scRNA-seq analysis FASTQ files (PTC, n = 5; ATC, n = 3) were aligned to the hg19 reference genome using Cell Ranger (v3.1.0). Count matrices were analyzed with Seurat (v4.4.0). Cells expressing at least 200 genes were retained, sample-specific mitochondrial-read thresholds (15–25%) were applied during quality control, genes detected in fewer than three cells were excluded, and doublets were removed using scDblFinder. After filtering, 43,866 cells and 22,272 genes remained for downstream analysis. Data were log-normalized with a scale factor of 10,000, and the top 2,000 variable genes were used for principal component analysis. Clustering was performed at resolution 0.5, with additional subclustering at resolution 5.0 where indicated. UMAP visualization used the first 15 principal components. Differentially expressed genes (DEGs) were identified with NEBULA using adjusted P 0.5. Key findings were additionally examined using pseudobulk aggregation and mixed-effects models with patient as a random effect. Visium spatial transcriptomics analysis Visium gene-spot matrices for para-tumor thyroid (PT), PTC, locally advanced PTC (LPTC), and ATC ( n = 4 per group) were obtained from GEO (GSE250521). Data were processed in Seurat, including normalization, variable-gene selection, principal component analysis, and clustering using the top 50 principal components. Cell-type composition within spatial spots was inferred using RCTD (spacexr). The reference dataset was built from the top 300 marker genes per cell type derived from our scRNA-seq data. RCTD was run in full mode to estimate cell-type likelihood weights, and spots were assigned the cell type with the highest likelihood weight for comparative analyses. Gene set and enrichment analysis Functional activities were quantified using Seurat’s AddModuleScore(). Thyroid differentiation score (TDS), IL-6/JAK/STAT, and EMT signatures were calculated using SCENIC-derived CEBPB regulons and MSigDB Hallmark gene sets 13 (Supplementary Tables 2–4). ATC signatures were curated from MSigDB C2. Additionally, BRAF and RAS signature scores were calculated using gene sets derived from genes upregulated in BRAF and RAS mutant samples from the TCGA thyroid cancer bulk RNA-seq dataset 14 (Supplementary Table 5). Gene set enrichment analysis (GSEA) was performed using fgsea with differentially expressed genes (DEGs) ranked by log2 fold change. Gene Ontology Biological Process gene sets were analyzed and summarized using the GOREA package. CNV inference and heterogeneity analysis CNVs in epithelial cells were inferred using inferCNV, with myeloid cells serving as a reference. Differences in CEBPB CNV values were evaluated using a one-way ANOVA with Tukey’s post hoc test. Clonal diversity within patients was quantified using the Shannon diversity index. Gene regulatory network and protein activity analysis Transcription factor activity was inferred using SCENIC 15 , and regulon activity was quantified with AUCell. Protein activity was independently estimated using ARACNe3 16,17 to infer transcription factor targets, followed by Nonparametric Analytical Rank-based Enrichment Analysis (NaRnEA) 18 . Results were reported as normalized enrichment scores (NES) and proportional enrichment scores (PES). Cell culture and inducible cell lines Human PTC cell lines (TPC-1 and SNU-790) were cultured in RPMI-1640 supplemented with 10% FBS. Inducible C/EBPβ wild-type and mutant (Δ284-319) lines were generated using the pCW57-GFP-2A lentiviral system. CEBPB cDNA was cloned from SW579 cells and inserted into the vector, and mutants were generated using site-directed mutagenesis. Stable lines were selected with puromycin. Functional assays Cell proliferation was measured using CCK-8 assays and IncuCyte live-cell imaging following doxycycline induction. Migration and invasion assays were performed using Transwell chambers coated with gelatin or Matrigel and incubated for 48 h toward a 10% FBS chemoattractant. Radioiodine uptake assays were conducted using carrier-free ¹³¹I and quantified with a gamma counter. Molecular and histologic analysis RNA was isolated using TRIzol and analyzed by qRT-PCR using specific primers (Supplementary Table 6) and SYBR Green, with GAPDH as an internal control. Protein lysates were prepared using RIPA buffer and analyzed by immunoblotting. IHC staining for C/EBPβ was performed on formalin-fixed paraffin-embedded sections, and staining intensity was quantified using H-scores 19,20 (Supplementary Tables 7–8). Statistical analysis Data are presented as the mean ± SEM from independent biological replicates (typically n ≥ 3). Two-group comparisons were performed using two-tailed Student’s t -tests, and multiple groups were analyzed using one-way ANOVA with appropriate post hoc tests. A P value < 0.05 was considered statistically significant. RESULTS Single-cell and spatial profiling define tumor and microenvironmental states in PTC and ATC To investigate cellular heterogeneity and lineage plasticity in PTC and ATC, we profiled tissue samples from conventional PTC patients (thyroid gland, n = 3; lymph node, n = 1), an early-onset PTC patient (thyroid gland, n = 1), and ATC patients (thyroid gland, n = 2; lymph node, n = 1) (Fig. 1a, Supplementary Table 1). After removing low-quality cells and doublets, scRNA-seq yielded 43,866 cells. Unsupervised clustering and marker-based annotation identified 11 major cell populations comprising epithelial, immune, stromal, and proliferating cells (Fig. 1b, c; Supplementary Fig. 1). Cell-type composition varied across patients and sampling sites (Fig. 1d). Lymph node samples (PTC1 and ATC1) contained lower proportions of tumor cells and higher immune cell abundance than primary tumor specimens. Notably, we observed an inverse relationship between the proportion of tumor cells and T/Natural Killer (NK) cell levels across patients; samples with lower tumor-cell fractions exhibited increased T and NK cell levels. Conversely, the proportion of myeloid cells was elevated in ATC samples (particularly ATC1), suggesting a potential role for myeloid-driven immune remodeling in the ATC tumor microenvironment (Fig. 1e). To add spatial context to these microenvironmental changes, we analyzed Visium spatial transcriptomics datasets comprising four samples each from para-tumor thyroid (PT), PTC, locally advanced PTC (LPTC), and ATC from an independent study 21 . Integration of the Visium and scRNA-seq datasets enabled the deconvolution of spot-level cell mixtures using RCTD, with each spot annotated according to the cell type with the highest likelihood weight (Fig. 1f). In the ATC4 sample, most spots within tumor regions were labeled as PTC cells. This labeling indicates that PTC cells are the dominant cell type in those spots, consistent with a previous pathological examination that reported mixed PTC and ATC histology in this case 21 . Across all malignant states, myofibroblastic cancer-associated fibroblasts (myCAFs) were abundant components of the spatial TME. However, differences in overall myCAF abundance across PT, PTC, LPTC, and ATC did not reach statistical significance (adjusted P = 0.087; Fig. 1g). We therefore focused our subsequent spatial analyses on specific spatial localization and inferred cell-cell interactions rather than bulk abundance alone. Epithelial-state analysis reveals progressive loss of thyroid differentiation We further analyzed epithelial cells using sub-clustering and identified four distinct transcriptional states: normal follicular cells, PTC type 1, PTC type 2, and ATC cells (Fig. 2a). To assess the degree of differentiation among these cell types, we calculated the TDS 14 , 22 (Supplementary Table 2). A significant decrease in TDS from follicular cells to PTC and then to ATC cells confirmed progressive dedifferentiation, consistent with a stepwise loss of thyroid-lineage identity (Fig. 2b). Gene expression marker analysis revealed subtype-specific signatures (Fig. 2c, Supplementary Table 9). Normal follicular cells highly expressed differentiated thyroid genes ( TG , TPO , and TFF3 ). Both PTC and ATC cells shared expression of the thyroid cancer marker KRT19 , while ATC cells uniquely expressed genes linked to proliferation and invasion ( UBE2C , S100A10 , and TFPI2 ). The two PTC states were also distinct: PTC type 1 preferentially expressed FN1 , GDF15 , and SLC34A2 , whereas PTC type 2 expressed S100A1 , EEF1A1 , and RPS4X . GSEA further emphasized the biological differences among these epithelial states (Fig. 2d, Supplementary Fig. 2). Follicular cells were enriched for oxidative phosphorylation and thyroid hormone metabolic pathways. PTC type 1 cells displayed enrichment in pathways related to growth hormone response, cell junction assembly, and stem cell differentiation. PTC type 2 cells were enriched for ribosome biogenesis and cytoplasmic translation, reflecting a metabolically active epithelial population. In contrast, ATC cells exhibited enrichment in pathways associated with cell division, proliferation, and regulation of cell-cell adhesion, consistent with aggressive stem cell-like properties. To further characterize activated pathways, we employed the Hotspot Python package to identify gene modules 23 (Supplementary Fig. 3a, b). Consistent with the GSEA results, follicular cells were significantly enriched in thyroid gland development (odds ratio = 29.665; P = 1.232 × 10⁻²; Supplementary Fig. 3c). Both PTC subtypes were enriched in Myc targets, oxidative phosphorylation, and reactive oxygen species pathways. Hotspot analysis independently highlighted hypoxia, EMT, and interferon-response modules in ATC cells (Supplementary Fig. 3d), supporting a dedifferentiated, invasive, and stress-adapted state. Inferred CEBPB amplification and overexpression characterize ATC cells To identify genomic alterations distinguishing ATC from PTC, we inferred CNVs from epithelial scRNA-seq profiles using myeloid cells as a reference population (Fig. 2e). Our analysis replicated previously reported altered genes and chromosomal regions, with a notable exclusive amplification of the CEBPB locus on chromosome 20 in ATC cells. Inferred CEBPB CNV values were significantly higher in ATC than in follicular or PTC cells (one-way ANOVA; F -statistic = 711.2; degrees of freedom = 3; P < 2 × 10⁻¹⁶), supporting increased CEBPB expression (Fig. 2f). Consistent with the inferCNV results, CEBPB expression was enriched in ATC cells in the scRNA-seq data and was also significantly elevated in ATC spots from the independent Visium dataset compared with spots corresponding to follicular cells from PT ( P = 5.847 × 10⁻⁴) or PTC cells from PTC ( P = 6.454 × 10⁻⁴) and LPTC ( P = 1.759 × 10⁻³) samples (Fig. 2g, h; Supplementary Fig. 4a). CREB3L1 , another factor previously implicated in the dedifferentiation process from PTC to ATC 4 (cytogenetic band: 11p11.2), likewise showed significant amplification in ATC cells (one-way ANOVA; F = 437.8; degrees of freedom = 3; P < 2 × 10⁻¹⁶). The expression levels of CEBPB and CREB3L1 were positively correlated across epithelial cells (Spearman’s r = 0.317, P < 2.2 × 10⁻¹⁶; Supplementary Fig. 4b). Intratumoral heterogeneity was assessed using Shannon diversity indices calculated from the CNV-defined tumor clones. PTC tumors exhibited higher clonal diversity than ATC tumors (Fig. 2i), consistent with the selective expansion of specific malignant clones during ATC progression. Of note, one ATC sample (ATC2) retained relatively high diversity and a more PTC-like transcriptional profile, suggesting it represents an intermediate state rather than a fully established ATC program. C/EBPβ regulates inflammatory, IL-6/JAK/STAT, and EMT programs in ATC To identify key transcription factors (TFs) involved in thyroid cancer progression, we performed SCENIC analysis and identified TFs exclusively activated in each epithelial cell subpopulation. Notably, CEBPB was highly activated in ATC cells (Wilcoxon rank-sum test, P < 2 × 10⁻¹⁶; Fig. 3a), consistent with its inferred CNV amplification. Regulatory network inference further revealed distinct pathway activation patterns across cancer subtypes (Supplementary Fig. 4c). While PTC type 2 cells showed activation of cell cycle-related pathways (including Myc targets, E2F targets, the mitotic spindle, and the G2M checkpoint), ATC-enriched regulons were associated with aggressive cancer behavior, including TNF-α signaling, glycolysis, hypoxia, angiogenesis, IL-6/JAK/STAT signaling, inflammatory response, and epithelial-mesenchymal transition (EMT). Over-representation analysis (ORA) of the CEBPB regulon showed significant regulation of the inflammatory response (odds ratio = 6.349; P = 2.093 × 10⁻⁶), IL-6/JAK/STAT signaling (odds ratio = 7.203; P = 3.87 × 10⁻⁴), hypoxia (odds ratio = 12.656; P = 7.556 × 10⁻¹⁷), and EMT (odds ratio = 11.408; P = 7.579 × 10⁻¹⁵) pathways (Fig. 3b). CEBPB is a known regulator of inflammatory genes, including IL-6 . Consistent with this role, IL-6 expression was elevated in ATC cells (Supplementary Fig. 4d, e). Among 74 curated ligand-receptor pairs capable of activating JAK/STAT signaling 24 , four were linked to the CEBPB regulon, with LIF-IL6ST showing prominent expression in ATC cells (Supplementary Fig. 4f). Across epithelial cells, IL-6/JAK/STAT signature scores showed strong correlations with EMT and ATC-state scores (EMT: r = 0.976, P = 3.18 x 10 − 7 and ATC: r = 0.672, P = 2.34 x 10 − 2 ; Fig. 3c; Supplementary Tables 3 and 4), and these correlations were comparable to or stronger than those observed for BRAF- or RAS-associated transcriptional signatures ( r = -0.68, P = 2.13 x 10 − 2 ; Supplementary Fig. 4g, h). Candidate C/EBPβ target genes overexpressed in ATC included EMP3 , CD44 , MMP14 , ITGA5 , and INHBA at the intersection of inflammatory and EMT programs, and CXCL3 , VEGFA , and LOX within IL-6/JAK/STAT-associated genes (Fig. 3d). The spatial cohort independently showed increased expression of these genes and progressively higher EMT, inflammatory-response, and IL-6/JAK/STAT signature scores from PTC to LPTC to ATC (Fig. 3e–g; Supplementary Fig. 5a, b). Because the spatial data are cross-sectional and not patient-matched, these gradients should be interpreted as state associations rather than direct temporal progression. Finally, protein-activity inference using ARACNe/NaRnEA supported C/EBPβ enrichment in ATC cells (Supplementary Fig. 5c), along with higher inferred activity of EMT- and inflammation-related proteins, including EMP3, CD44, INHBA, and CXCL3. Taken together, these integrative analyses nominate C/EBPβ as a central upstream regulator of an ATC-associated inflammatory and mesenchymal program. C/EBPβ overexpression recapitulates ATC-associated transcriptional programs in vitro To functionally validate the role of C/EBPβ in thyroid cancer progression, we overexpressed C/EBPβ in the PTC cell line TPC-1 and performed bulk RNA sequencing 25 – 27 . Gene Set Enrichment Analysis (GSEA) using the DEGs between C/EBPβ-overexpressing TPC-1 cells and controls revealed significant enrichment of pathways characteristic of ATC. These included TNF-α signaling via NF-κB (NES = 2.474; P = 7.364 × 10⁻³), hypoxia (NES = 2.269; P = 7.364 × 10⁻³), EMT (NES = 2.115; P = 7.364 × 10⁻³), inflammatory responses (NES = 2.101; P = 7.364 × 10⁻³), and IL-6/JAK/STAT signaling (NES = 1.919; P = 7.364 × 10⁻³; Fig. 3h). We also observed significant upregulation of CEBPB itself (Log2 Fold Change = 1.623), EMT-associated target genes ( MMP2 , MMP14 , and SNAI2 ), and IL-6/JAK/STAT signaling-associated target genes ( CXCL3 and VEGFA ) in the overexpressing cells (Supplementary Fig. 5d). These results are consistent with our earlier CEBPB regulon analysis and provide experimental support for the role of C/EBPβ in driving the transition from PTC to a more aggressive ATC-like transcriptional phenotype. To obtain orthogonal evidence for candidate downstream targets, we analyzed chromatin immunoprecipitation sequencing (ChIP-Seq) data for C/EBPβ in the K562 cell line, obtained from the ENCODE project (ENCSR000EHE). This analysis revealed significant binding of C/EBPβ to the promoters and enhancers of several genes associated with the inflammatory response, IL-6/JAK/STAT signaling, and EMT (Fig. 3i, Supplementary Fig. 5e). Notably, binding peaks were detected in genes associated with both EMT and the inflammatory response, such as CD44 (signal value = 40.682; q -value = 1.991 × 10⁻⁵) and ITGA5 (signal value = 278.697; q -value = 1.452 × 10⁻⁴), while HAS2 (signal value = 32.785; q -value = 1.479 × 10⁻⁴) and NFKBIA (signal value = 458.506; q -value = 1.991 × 10⁻⁵) were primarily linked to EMT. Furthermore, C/EBPβ bound to the promoter of VEGFA (signal value = 140.235; q -value = 1.991 × 10⁻⁵), the enhancer of MMP2 (signal value = 46.146; q -value = 1.991 × 10⁻⁵), the IL6ST enhancer, and LOX (signal value = 30.591; q -value = 1.358 × 10⁻⁴), which regulates IL-6/JAK/STAT signaling. Importantly, binding was also observed in the regulatory region of SLC5A5 (signal value = 46.029; q -value = 1.991 × 10⁻⁵), which encodes the sodium/iodide symporter. Because these data were generated in a non-thyroid lineage, we interpret them as supportive evidence for plausible C/EBPβ-bound loci rather than definitive thyroid-specific direct targets 28 . Cross-sectional trajectory analysis positions C/EBPβ early in the dedifferentiation continuum To investigate the dynamic process of PTC-to-ATC transformation, we performed pseudotime analysis to order epithelial cells along a developmental trajectory based on their gene expression profiles 29 . Force-directed layouts of epithelial cells revealed a trajectory consistent with a cross-sectional ordering from differentiated follicular/PTC-like states toward ATC-like states, supporting a dedifferentiation model (Fig. 4a). Because these data are not longitudinally sampled from the same tumors, trajectory inference is interpreted as a transcriptional continuum rather than definitive lineage tracing (Fig. 4b). To further confirm the identified trajectory and connectivity between different cell states, we performed a partition-based graph abstraction analysis 30 , which confirmed the connectivity between these states (Supplementary Fig. 6a). To investigate dynamic changes in TF activity during this progression, we clustered significantly activated TFs based on their expression trends along pseudotime. We then identified hallmark pathways significantly enriched in the regulons of each cluster using enrichR (Fig. 4c–e; Supplementary Tables 10–11). Notably, cluster 0 TFs, which included CEBPB , showed a significant increase in expression (Log2 Fold Change = 1.453) early along the transition. These early-peaking TFs regulate key pathways associated with aggressive cancer progression, including TNF-α signaling via NF-κB, hypoxia, and EMT. In contrast, cluster 2 TFs, which are enriched for cell-cycle regulators, were more prominent in the later stages of the trajectory. The expression profile of CEBPB , peaking during the initiation of ATC-like states, supports its association with an early transition program. Furthermore, potency scores calculated using CytoTRACE2 to assess the decrease in differentiation and increase in stemness correlated strongly with CEBPB expression ( r = 0.778, P = 4.84 x 10 − 3 ; Fig. 4f, g; Supplementary Fig. 6b). These data link C/EBPβ to a less differentiated, more stem-like epithelial state and nominate it as a candidate upstream regulator of early dedifferentiation, although longitudinal validation will be needed to establish definitive temporal causality. C/EBPβ promotes aggressive phenotypes and loss of iodine-handling features in vitro To validate the functional significance of C/EBPβ in ATC development, we generated C/EBPβ-low PTC cell lines (TPC-1 and SNU-790) with inducible wild-type C/EBPβ expression (Fig. 5a). Additionally, we developed a human C/EBPβ mutant construct (Δ284–319) to disrupt its DNA-binding and dimerization functions by deleting the DNA-binding and leucine zipper domains. The protein levels of this C/EBPβ mutant were lower than the wild-type, consistent with a previously observed link between dimerization and C/EBPβ stability 31 . We therefore conservatively interpret this mutant as a partial loss-of-function construct. Nuclear localization of the induced C/EBPβ supported its activity as a transcription factor (Supplementary Fig. 7a). Wild-type C/EBPβ significantly increased cell proliferation in both TPC-1 and SNU-790 cells compared to controls, whereas the Δ284–319 construct attenuated this effect in CCK-8 and live-cell imaging assays (Fig. 5b, c). Consistent with the clinical characteristics of ATC, wild-type C/EBPβ also prominently enhanced migration and invasion in Transwell and live-cell migration assays, with weaker effects observed for the Δ284–319 mutant (Fig. 5d, e). These data indicate that C/EBPβ DNA-binding and transcriptional activity are essential for promoting aggressive behavior in PTC cells. Because impaired iodine handling is a hallmark of dedifferentiated thyroid cancer and drives radioiodine refractoriness, we next examined iodine uptake. In TPC-1 cells, wild-type C/EBPβ reduced iodine uptake and concomitantly decreased the expression of SLC5A5 (NIS) and PAX8 32 , whereas the Δ284–319 mutant did not reproduce this phenotype (Fig. 5f, g; Supplementary Fig. 7b). SNU-790 cells, which have intrinsically poor iodine uptake 33 , showed a similar decrease in NIS and PAX8 transcripts following C/EBPβ induction (Supplementary Fig. 7c). These results support a role for C/EBPβ in suppressing thyroid-differentiation features and generating a radioiodine-refractory-like state in vitro . C/EBPβ-dependent phenotypes are reversible and associated with aggressive disease in vivo To validate our in vitro findings, we first examined the reversibility of the C/EBPβ-induced changes. Following doxycycline withdrawal, immunoblotting confirmed reduced levels of C/EBPβ in both TPC-1 and SNU-790 cells (Fig. 6a). Proliferation rates subsequently returned toward baseline levels after C/EBPβ removal (Fig. 6b), indicating that its effects depend on sustained expression. To assess the oncogenic potential of C/EBPβ in vivo , we used a TPC-1 xenograft model. Induction of C/EBPβ resulted in a significant increase in tumor size relative to the control group (Supplementary Fig. 8a), providing in vivo evidence that C/EBPβ drives tumor progression. As an independent clinical assessment, exploratory analysis of data from The Cancer Genome Atlas (TCGA) and Genomic Data Commons (GDC) revealed that patients with PTC ( N = 496) whose CEBPB expression exceeded the third quartile exhibited significantly shorter overall survival than those with lower expression ( P = 3.1 × 10⁻²; Fig. 6c). Because event rates in PTC are low and this analysis was not performed in ATC, this association should be interpreted as supportive rather than definitive evidence of prognostic value. We extended our analysis to patient-derived samples to explore the clinical relevance of C/EBPβ expression in aggressive thyroid cancer. IHC analysis of ATC specimens ( n = 10) revealed strong C/EBPβ positivity, with an average H-score of 166.7 (Fig. 6d, Supplementary Fig. 8b, and Supplementary Table 7). Notably, C/EBPβ expression was consistently elevated across all ATC tissues regardless of survival outcomes. In contrast, specimens from 15 patients with N1b-stage PTC, representing the most advanced stage of PTC, exhibited minimal C/EBPβ staining, with an average H-score of only 18.8 (Fig. 6d, Supplementary Fig. 8b, Supplementary Table 8). Within the limits of this cohort size, the independence of PTC metastasis from C/EBPβ expression suggests that C/EBPβ is a critical driver and candidate tissue biomarker of dedifferentiated ATC, rather than a general marker of metastatic PTC. myCAFs and SPP1⁺ macrophages define permissive ATC microenvironmental niches To investigate tumor-microenvironment interactions, we inferred cell–cell communication from the scRNA-seq and Visium data using CellChat 34 . Compared with PTC, ATC showed stronger myCAF–tumor interactions, particularly collagen-based signaling axes such as COL1A1/2–CD44 and COL1A1/2–ITGA3/ITGB1 (Fig. 7a; Supplementary Fig. 9). These pathways, involved in adhesion, migration, proliferation, and differentiation, were consistently more active in ATC than in PTC. To further characterize these interactions, we employed NicheNet 35 , which prioritized GAS6-AXL and CXCL12-CXCR4 as candidate myCAF-to-ATC signaling axes (Fig. 7b, c; Supplementary Fig. 9e–g). The CXCL12 signaling pathway targets molecules such as MMP2 and VEGFA, which are involved in EMT and angiogenesis 36 . Notably, CXCL12 has been reported to induce C/EBPβ in other cellular contexts 37 , suggesting a paracrine feed-forward loop that could amplify C/EBPβ expression in ATC cells. Additionally, GAS6 induces IL-6, a known promoter of tumorigenesis and metastasis 38 , 39 . Because CellChat and NicheNet infer communication from expression patterns, we conservatively interpret these pathways as testable hypotheses for ATC stromal crosstalk. Spatial niche analysis using the Python package scNiche 40 localized ATC cells and myCAFs at tumor boundaries in the ATC4 Visium sample, where both cell types showed significantly higher RCTD likelihood weights than in non-boundary regions (Wilcoxon rank-sum test, P = 1.584 × 10⁻¹⁹ for ATC; P = 6.176 × 10⁻²⁷ for myCAF; Fig. 7d–f). Furthermore, CST1 expression was significantly upregulated in these ATC–myCAF boundary regions (Log2 Fold Change = 0.964, P = 3.736 × 10⁻³⁸; Fig. 7g) and was also elevated in ATC1, while remaining minimal in PT and PTC samples. scRNA-seq further validated preferential CST1 expression in ATC-associated myCAFs, and CST1 ⁺ myCAFs were enriched for collagen fibril organization (Supplementary Fig. 10c, d), consistent with a boundary-specific extracellular matrix-remodeling phenotype 41 . Immune composition also shifted across disease states. PTC samples contained more CD4⁺ cytotoxic T lymphocytes expressing GZMA and GZMK , whereas ATC showed an expansion of SPP1⁺ and C1QC⁺ tumor-associated macrophages (TAMs) (Supplementary Fig. 11). Relative to C1QC⁺ TAMs, SPP1⁺ TAMs were enriched for angiogenesis, EMT, and hypoxia pathways (Fig. 7h). Predicted SPP1⁺ TAM-to-ATC interactions involved ICAM1-ITGB2, MMP9-LRP1, and ANXA1-FPR1, and converged on targets related to TIMP1, IL-6, MMP2, VEGFA, and CXCL1 (Fig. 7i, j), further supporting an ATC microenvironment organized around inflammatory and mesenchymal programs. DISCUSSION Our study identifies C/EBPβ as a CNV-amplified, transcriptionally hyperactive master regulator driving dedifferentiation from PTC-like to ATC-like states (Fig. 7k). Integrating single-cell, spatial, and functional data, we demonstrate that C/EBPβ links inflammatory signaling to mesenchymal reprogramming. Functional induction of C/EBPβ in PTC cells was sufficient to reproduce several hallmarks of aggressive disease, including increased proliferation, migration, invasion, and radioiodine refractoriness. Trajectory analysis supports a dedifferentiation continuum, and together, these results support a model in which C/EBPβ establishes and stabilizes an ATC-like phenotypic state. These data refine the mechanistic link between inflammatory signaling and dedifferentiation in thyroid cancer. Previous studies have implicated C/EBPβ in thyroid cancer progression; notably, its cytoplasmic accumulation in PTC has been associated with advanced disease and reduced apoptosis 42 , but its precise role in dedifferentiation remained unclear. The strong association between C/EBPβ activity, IL-6/JAK/STAT signaling, and EMT across both single-cell and spatial datasets suggests that C/EBPβ sits near the interface of inflammatory transcriptional control and mesenchymal reprogramming. Consistent with findings in other systems, JAK/STAT activation can precede EMT, suggesting a coordinated cascade. Furthermore, the observation that C/EBPβ induction suppresses SLC5A5 (NIS) and PAX8 directly connects this network to the loss of thyroid-specific function, a clinically vital feature of radioiodine-refractory disease. While current data do not establish a linear causal chain in vivo , they define a coherent regulatory module that warrants targeted perturbation in future studies. A second major contribution of this study is the spatial framing of the ATC microenvironment. Beyond tumor-intrinsic effects, spatial data show myCAFs as dominant stromal components interacting with ATC cells via collagen signaling. Although bulk myCAF abundance differences were not statistically significant across malignant states, boundary-associated CST1 ⁺ myCAFs displayed a spatially restricted ECM-remodeling program linked to aggressiveness. In parallel, immune profiling revealed cytotoxic T lymphocyte (CTL) enrichment in PTC, which stood in stark contrast to the expansion of angiogenesis-, hypoxia-, and EMT-associated SPP1⁺ TAMs in ATC. These findings support a cooperative model in which C/EBPβ-driven tumor-cell dedifferentiation and spatially organized microenvironmental remodeling mutually amplify to promote ATC progression. From a translational perspective, C/EBPβ is highly attractive because it links the mechanism directly to the clinical phenotype. High C/EBPβ protein expression robustly distinguished ATC from advanced PTC in patient tissues, and exploratory survival analysis in PTC suggested that elevated CEBPB expression is associated with poorer outcomes. While the current clinical evidence requires expansion before C/EBPβ can be established as a validated prognostic biomarker, our data strongly support it as a candidate biomarker of dedifferentiated disease and a therapeutic vulnerability. Consequently, C/EBPβ-directed strategies, including experimental antagonists such as ST101, merit rigorous evaluation in models of aggressive thyroid cancer 43 . Finally, this study has important limitations. First, the scRNA-seq ATC cohort was small ( n = 3) and included samples from both primary tumors and lymph nodes, meaning some compositional differences may reflect sampling site rather than pure histology. Second, the Visium data were obtained from an independent, non-patient-matched cohort; while this supports cross-cohort reproducibility, it precludes direct within-patient evolutionary inference. Third, the evidence for CEBPB copy-number gain derives from inferCNV rather than direct DNA-based profiling. Fourth, candidate downstream targets were supported by non-thyroid ENCODE ChIP-seq and computational network inference, both of which require direct thyroid-lineage validation. Fifth, our functional work relied predominantly on gain-of-function models; although the Δ284–319 construct attenuated C/EBPβ-associated phenotypes, its lower stability suggests it acts as a partial loss-of-function rather than a clean separation-of-function mutant. Declarations DATA AVAILABILITY All data are available in the main text or Supplementary Information. Sequencing datasets were deposited in GEO (RRID:SCR_005012) under accession numbers GSE277750 (RNA-seq) and GSE277751 (scRNA-seq). Analysis code is available at the GitHub repository: https://github.com/KuChoiLab/2026_THCA_methods_cdd_KM ACKNOWLEDGEMENTS This work was supported by National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (RS-2023-00212238 to J.C., RS-2024-00408822 to L.K.K., and NRF-2021R1I1A1A01044274 to S.H.D.). Medical Illustration & Design (MID), a member of the Medical Research Support Services of Yonsei University College of Medicine, provided support with medical illustrations. AUTHOR CONTRIBUTIONS S. H. Dho: Conceptualization, Methodology, Validation, Investigation, Writing - original draft, Visualization. K. Yoo: Conceptualization, Methodology, Software, Validation, Investigation, Writing - original draft, Visualization. J. S. Lee and H. J. Park: Investigation, Visualization. W. Woo, M. Cho, J. Song, and Y. S. Lee: Investigation, Visualization. S.-M. Kim: Conceptualization, Writing - review and editing, Supervision, Funding acquisition. L. K. Kim: Conceptualization, Writing - review and editing, Supervision, Funding acquisition. J. Choi: Conceptualization, Writing - review and editing, Supervision, Funding acquisition. COMPETING INTERESTS The authors declare no competing interests. References Maniakas, A. et al. 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PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol 20, 59 (2019). Hattori, T., Ohoka, N., Inoue, Y., Hayashi, H. & Onozaki, K. C/EBP family transcription factors are degraded by the proteasome but stabilized by forming dimer. Oncogene 22, 1273–1280 (2003). Riesco-Eizaguirre, G., Wert-Lamas, L., Perales-Paton, J., Sastre-Perona, A., Fernandez, L. P. & Santisteban, P. The miR-146b-3p/PAX8/NIS Regulatory Circuit Modulates the Differentiation Phenotype and Function of Thyroid Cells during Carcinogenesis. Cancer Res 75, 4119–4130 (2015). Koh, C. S. et al. Establishment and characterization of cell lines from three human thyroid carcinomas: responses to all-trans-retinoic acid and mutations in the BRAF gene. Mol Cell Endocrinol 264, 118–127 (2007). Jin, S. et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun 12, 1088 (2021). Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat Methods 17, 159–162 (2020). Shi, Y., Riese, D. J., 2nd & Shen, J. The Role of the CXCL12/CXCR4/CXCR7 Chemokine Axis in Cancer. Front Pharmacol 11, 574667 (2020). Dinic, S. et al. CXCL12 protects pancreatic beta-cells from oxidative stress by a Nrf2-induced increase in catalase expression and activity. Proc Jpn Acad Ser B Phys Biol Sci 92, 436–454 (2016). Tanaka, M. & Siemann, D. W. Gas6/Axl Signaling Pathway in the Tumor Immune Microenvironment. Cancers (Basel) 12 (2020). Zheng, R., Chen, G., Li, X., Wei, X., Liu, C. & Derwahl, M. Effect of IL-6 on proliferation of human thyroid anaplastic cancer stem cells. Int J Clin Exp Pathol 12, 3992–4001 (2019). Qian, J. et al. Identification and characterization of cell niches in tissue from spatial omics data at single-cell resolution. Nat Commun 16, 1693 (2025). Dinh, H. Q. et al. Integrated single-cell transcriptome analysis reveals heterogeneity of esophageal squamous cell carcinoma microenvironment. Nat Commun 12, 7335 (2021). Xu, F. et al. C/EBPbeta mediates anti-proliferative effects of 1,25(OH)2D on differentiated thyroid carcinoma cells. Endocr Relat Cancer 29, 321–334 (2022). Darvishi, E. et al. Anticancer Activity of ST101, A Novel Antagonist of CCAAT/Enhancer Binding Protein beta. Mol Cancer Ther 21, 1632–1644 (2022). Additional Declarations (Not answered) Supplementary Files 20260327SupplementaryTable.xls Supplementary tables 20260327SupplementaryInformation.docx Supplementary information SupplementaryMaterial.docx Supplementary materials originaldata.pptx Supplementary information Cite Share Download PDF Status: Under Review Version 1 posted Reviewer # 2 agreed at journal 12 May, 2026 Reviewer # 1 agreed at journal 08 May, 2026 Reviewers invited by journal 08 May, 2026 Submission checks completed at journal 23 Apr, 2026 First submitted to journal 21 Apr, 2026 Unknown event 20 Apr, 2026 Editor assigned by journal 16 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9248842","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":636710147,"identity":"5bf411bc-abfb-49e1-99f7-b321db00f6a0","order_by":0,"name":"Jungmin 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Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lark","middleName":"Kyun","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2026-03-28 01:05:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9248842/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9248842/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109763568,"identity":"9f249f35-74fb-40e4-9051-6a9e96059c61","added_by":"auto","created_at":"2026-05-22 07:35:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1529980,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-cell and spatial atlas of papillary and anaplastic thyroid carcinoma. a Workflow for generating scRNA-seq data and integrative single-cell/spatial analysis of PTC, PTCEO, and ATC specimens. b UMAP visualization of 43,866 cells annotated into 11 major cell types. c Dot plot displaying representative marker genes for each major cell type. Dot size indicates the percentage of cells expressing each marker gene, and color indicates the average expression level. d Cell-type proportions for each patient sample. e Aggregated cell-type proportions for PTC and ATC groups. f Visium spatial transcriptomics spots labeled by the cell type with the highest likelihood weight inferred from RCTD deconvolution for each sample. Only cell types represented by \u0026gt; 100 spots across all samples are included in the legend. g Boxplots showing proportional changes in dominant Visium spot annotations across cancer states (Wilcoxon rank-sum test with Benjamini–Hochberg correction). Only cell types accounting for \u0026gt; 5% of total spots in at least one sample are shown.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9248842/v1/6efc9b3adc2ba8eae55a2701.png"},{"id":109764905,"identity":"515ab65f-722b-4336-a757-b5ab78c1d6cc","added_by":"auto","created_at":"2026-05-22 07:38:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2166974,"visible":true,"origin":"","legend":"\u003cp\u003eEpithelial-state characterization reveals progressive dedifferentiation and \u003cem\u003eCEBPB\u003c/em\u003eamplification. a UMAP of epithelial cells, showing follicular cells, PTC type 1, PTC type 2, and ATC cells. b Boxplot showing Thyroid Differentiation Scores (TDS) for each epithelial cell subpopulation (*** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; Wilcoxon rank-sum test). c Dot plot illustrating expression levels of marker genes for each epithelial subpopulation. Dot size reflects the proportion of cells expressing the feature, and color indicates the average expression level. d Dot plot illustrating representative Gene Ontology Biological Process (GOBP) terms enriched in each epithelial subpopulation. Color scales indicate normalized enrichment score (NES), and dot size reflects -log10(adjusted \u003cem\u003eP\u003c/em\u003e value). e Heatmap of CNVs in epithelial cells inferred by inferCNV, using myeloid cells as a reference population; blue indicates inferred deletion and red indicates inferred amplification. f UMAP showing inferred \u003cem\u003eCEBPB\u003c/em\u003e CNV values across epithelial states. g Integrated violin and box plots showing \u003cem\u003eCEBPB\u003c/em\u003eexpression across epithelial states and patient samples. h Visium slides displaying \u003cem\u003eCEBPB\u003c/em\u003e expression across disease states. i Bar plot displaying Shannon’s diversity index for CNV-defined clones in each patient.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9248842/v1/9bddfb8b6946fd79fdc2a887.png"},{"id":109762934,"identity":"ac8d9bf1-a7c2-41e4-a183-1316cf7e3485","added_by":"auto","created_at":"2026-05-22 07:32:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1318582,"visible":true,"origin":"","legend":"\u003cp\u003eC/EBPβ regulates inflammatory, IL-6/JAK/STAT, and EMT programs in ATC. a Heatmap of SCENIC-inferred transcription factor activities across epithelial subpopulations. b Hallmark pathways enriched in the \u003cem\u003eCEBPB\u003c/em\u003e regulon via over-representation analysis. Dot size represents gene count, and color represents adjusted \u003cem\u003eP\u003c/em\u003e value. c Scatterplots showing the relationships between the IL-6/JAK/STAT signature and EMT or ATC-state signatures in epithelial cells. d Heatmap of scaled expression for representative \u003cem\u003eCEBPB\u003c/em\u003e-associated target genes in EMT, inflammatory response, and IL-6/JAK/STAT programs. e Heatmap comparing scaled expression of these target genes across Visium samples. Red stars indicate genes significantly upregulated in ATC samples compared with PT, PTC, and LPTC samples (* \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Wilcoxon rank-sum test with Benjamini–Hochberg correction). f Spatial visualization of EMT, inflammatory response, and IL-6/JAK/STAT signature scores across Visium slides. g Comparison of these signature scores across disease states (*** \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001; Wilcoxon rank-sum test with Benjamini–Hochberg correction). h Bar plot showing Hallmark pathways enriched in control and C/EBPβ-overexpressing TPC-1 cells by bulk RNA-seq. i Representative ENCODE K562 C/EBPβ ChIP-seq tracks across candidate target loci. Red peaks indicate significant C/EBPβ binding; because these data derive from a non-thyroid context, they provide supportive rather than definitive evidence for direct thyroid-specific binding.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9248842/v1/11878c1b78b84e780dce1a02.png"},{"id":109763723,"identity":"242763fd-80de-4c5d-9ae7-e7a392464bc5","added_by":"auto","created_at":"2026-05-22 07:35:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":566672,"visible":true,"origin":"","legend":"\u003cp\u003eCross-sectional trajectory analysis positions C/EBPβ early in the dedifferentiation continuum. a Force-directed layout of epithelial cells colored by pseudotime. b Force-directed layout annotated by epithelial state. c Heatmap of transcription factors showing dynamic changes along pseudotime. d Smoothed expression trends for transcription-factor clusters along pseudotime, with the top enriched hallmark pathways listed for each cluster. e Module scores for each transcription-factor cluster along pseudotime. f Force-directed layout visualizing the expression levels of \u003cem\u003eCEBPB\u003c/em\u003e in epithelial cells. g Scatterplot showing the Pearson correlation between \u003cem\u003eCEBPB\u003c/em\u003e expression levels and CytoTRACE2 potency scores in epithelial cells.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9248842/v1/25291908d0e1a4e7a0f71715.png"},{"id":109800002,"identity":"55208ed4-f9b8-415a-bee9-1a27232cdcf8","added_by":"auto","created_at":"2026-05-22 15:35:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":885616,"visible":true,"origin":"","legend":"\u003cp\u003eC/EBPβ promotes aggressive phenotypes and loss of iodine-handling features \u003cem\u003ein vitro\u003c/em\u003e. a Immunoblots for p-C/EBPβ, C/EBPβ, and GAPDH in TPC-1 and SNU-790 cells expressing empty vector, inducible wild-type C/EBPβ, or the partial loss-of-function C/EBPβ (Δ284-319) mutant. b Cell proliferation assays in PTC cell lines expressing vector, wild-type C/EBPβ, or C/EBPβ (Δ284-319). Data are presented as mean ± SEM (\u003cem\u003en\u003c/em\u003e = 3 independent experiments; ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 versus vector or mutant; two-tailed Student’s \u003cem\u003et\u003c/em\u003e-test). c IncuCyte live-cell proliferation assays in TPC-1 and SNU-790 cells (\u003cem\u003en\u003c/em\u003e= 3). d Transwell migration and invasion assays. Panels display migrated and invaded cells stained with crystal violet. Quantification was performed by counting cells in eight randomly chosen fields per condition (\u003cem\u003en\u003c/em\u003e = 3). e IncuCyte live-cell migration assays in TPC-1 and SNU-790 cells (\u003cem\u003en\u003c/em\u003e = 3). f Radioiodine (¹³¹I) uptake assays in TPC-1 cells expressing vector, wild-type C/EBPβ, or C/EBPβ (Δ284-319) (\u003cem\u003en\u003c/em\u003e = 2). g qRT-PCR analysis of \u003cem\u003eSLC5A5\u003c/em\u003e(NIS) and \u003cem\u003ePAX8\u003c/em\u003e expression following C/EBPβ induction in TPC-1 cells (\u003cem\u003en\u003c/em\u003e= 3).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9248842/v1/33200d55759f6e0dfbb805b2.png"},{"id":109762937,"identity":"ce4f0370-fcf5-43a9-a79a-122c2106fdf4","added_by":"auto","created_at":"2026-05-22 07:33:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2532622,"visible":true,"origin":"","legend":"\u003cp\u003eC/EBPβ-dependent phenotypes are reversible and associated with aggressive disease \u003cem\u003ein vivo\u003c/em\u003e. a Immunoblots for C/EBPβ and GAPDH in TPC-1 and SNU-790 cells following doxycycline induction (ON), withdrawal (washoff), or no treatment (OFF). b Cell proliferation assays demonstrating phenotypic reversibility following doxycycline washoff. Data are presented as mean ± SEM (\u003cem\u003en\u003c/em\u003e = 3; ** \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01 versus vector; two-tailed Student’s \u003cem\u003et\u003c/em\u003e-test). c Kaplan-Meier curves illustrating overall survival in PTC patients (\u003cem\u003eN\u003c/em\u003e = 496) from the TCGA/GDC cohorts, stratified into low and high (exceeding the third quartile) \u003cem\u003eCEBPB\u003c/em\u003eexpression groups. d Comparative analysis of C/EBPβ immunohistochemistry H-scores between ATC (\u003cem\u003en\u003c/em\u003e = 10) and advanced N1b-stage PTC (\u003cem\u003en\u003c/em\u003e = 15) clinical specimens, alongside representative staining images.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-9248842/v1/8d9b05582801bf6f2f671cd3.png"},{"id":109763008,"identity":"6ba311ce-7571-473a-b74e-da55cb6999ff","added_by":"auto","created_at":"2026-05-22 07:33:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1032835,"visible":true,"origin":"","legend":"\u003cp\u003emyCAFs and SPP1⁺ macrophages define permissive ATC microenvironmental niches. a Dot plot comparing CellChat-inferred myCAF–tumor cell interaction probabilities across cancer states (PTC, LPTC, and ATC) using Visium spatial transcriptomics data. b, c NicheNet analysis heatmaps depicting the regulatory potential between myCAF ligands and ATC cell receptors (b) or downstream target genes (c) in ATC samples. d Visium slides mapping RCTD likelihood weights of ATC cells and myCAFs in the ATC4 sample. e Identification of the spatially restricted ATC–myCAF boundary niche in the ATC4 sample using scNiche. f Comparison of RCTD likelihood weights for ATC cells and myCAFs between boundary and non-boundary regions (Wilcoxon rank-sum test). g Spatial visualization demonstrating \u003cem\u003eCST1\u003c/em\u003eexpression enriched specifically within the ATC–myCAF boundary niche. h Bar plot showing Hallmark pathways significantly enriched in SPP1⁺ TAMs (positive NES) versus C1QC⁺ TAMs (negative NES) by GSEA. i, j Heatmaps displaying the CellChat-inferred interaction strengths between SPP1⁺ TAM ligands and ATC cell receptors (i) or downstream target genes (j) in ATC samples. k Proposed mechanistic model of ATC dedifferentiation driven by C/EBPβ and localized intercellular TME communications.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-9248842/v1/d3ba012b8cc18419238d0da5.png"},{"id":109800013,"identity":"74cb15fb-a74a-4216-9094-4d8cd95e55b2","added_by":"auto","created_at":"2026-05-22 15:35:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9862552,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9248842/v1/59259d19-4d27-41f1-a6cf-6309b31c2964.pdf"},{"id":109762940,"identity":"d9633cbe-5f83-4681-9fd7-cdeabec3fadd","added_by":"auto","created_at":"2026-05-22 07:33:08","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":116224,"visible":true,"origin":"","legend":"Supplementary tables","description":"","filename":"20260327SupplementaryTable.xls","url":"https://assets-eu.researchsquare.com/files/rs-9248842/v1/d9680c8fa55596b951a243a9.xls"},{"id":109763682,"identity":"56753d10-2c28-4eec-948a-bb899ac5f593","added_by":"auto","created_at":"2026-05-22 07:35:24","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":11032958,"visible":true,"origin":"","legend":"Supplementary information","description":"","filename":"20260327SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-9248842/v1/4ec00e4ad34d166bb771134e.docx"},{"id":109762931,"identity":"b6108a35-284a-4217-a23e-7caeaa3f5961","added_by":"auto","created_at":"2026-05-22 07:32:53","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":12064007,"visible":true,"origin":"","legend":"Supplementary materials","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9248842/v1/03a53b4f4e97d11e07cbfff5.docx"},{"id":109763726,"identity":"4d973d86-7336-4fbc-8d15-b4372f89993d","added_by":"auto","created_at":"2026-05-22 07:35:34","extension":"pptx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":38857814,"visible":true,"origin":"","legend":"Supplementary information","description":"","filename":"originaldata.pptx","url":"https://assets-eu.researchsquare.com/files/rs-9248842/v1/24e274c86c283d5ab646b899.pptx"}],"financialInterests":"(Not answered)","formattedTitle":"C/EBPβ Drives Anaplastic Thyroid Cancer Dedifferentiation and Radioiodine Resistance via IL-6/JAK/STAT and EMT Activation","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAnaplastic thyroid carcinoma (ATC) is among the most lethal human malignancies, with a median survival of approximately four months\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, despite accounting for only 1.3% of thyroid cancers in the United States\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In contrast, papillary thyroid carcinoma (PTC) usually has an excellent prognosis\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. This striking clinical divergence has focused attention on the molecular events underlying progression from differentiated thyroid cancer to ATC. Although the cellular origin of ATC remains debated\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, shared oncogenic alterations and mixed histology support the idea that a substantial proportion of ATCs arise through dedifferentiation from pre-existing PTCs\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGenomic studies have identified recurrent alterations involving TP53, the TERT promoter, PI3K/AKT/mTOR signaling, SWI/SNF components, and chromatin modifiers\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Shared BRAF V600E mutations between PTC and ATC further support a common evolutionary origin\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, and co-occurring BRAF and TERT alterations can accelerate dedifferentiation and progression\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, targeted monotherapies directed at individual oncogenic lesions often have limited durability due to acquired resistance. These observations raise the possibility that broader transcriptional regulators, rather than single downstream effectors, help establish and maintain the dedifferentiated ATC state\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBeyond tumor-intrinsic programs, the tumor microenvironment (TME)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e contributes significantly to ATC growth, invasion, and immune evasion. While single-cell transcriptomics has provided valuable insights into the cellular composition of thyroid tumors, it lacks the spatial context needed to localize tumor-stroma interfaces and organized cellular niches. Integrating spatial and single-cell transcriptomics enables the identification of tissue architecture, intercellular signaling, and microenvironmental niches driving disease progression.\u003c/p\u003e \u003cp\u003eIn this study, we integrated single-cell RNA sequencing (scRNA-seq) from PTC and ATC tumors with Visium spatial transcriptomics from an independent cross-sectional cohort. This analysis identified CCAAT/enhancer-binding protein beta (\u003cem\u003eCEBPB\u003c/em\u003e; C/EBPβ) as a transcriptionally hyperactivated, genomically amplified master regulator of thyroid cancer dedifferentiation. C/EBPβ, a transcription factor known to be involved in inflammation and proliferation\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, is selectively hyperactivated in ATC. Functional assays in PTC cell lines supported a role for C/EBPβ in promoting proliferation, migration, invasion, and radioiodine resistance, whereas spatial analyses implicated myofibroblastic cancer-associated fibroblasts (myCAFs), specifically a CST1-upregulated subpopulation at tumor boundaries, and SPP1\u0026thinsp;+\u0026thinsp;macrophages in the ATC microenvironment. Together, these data define a mechanistically grounded framework for aggressive thyroid cancer that links tumor-cell dedifferentiation to stromal remodeling and clinical aggressiveness.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003eThyroid cancer patient cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTumor tissues were collected from 32 patients with ATC or PTC at Gangnam Severance Hospital, Seoul, South Korea. scRNA-seq was performed on eight samples: PTC from the thyroid gland (\u003cem\u003en\u003c/em\u003e = 4, including one early-onset PTC case), PTC from a lymph node (\u003cem\u003en\u003c/em\u003e = 1), ATC from the thyroid gland (\u003cem\u003en\u003c/em\u003e = 2), and ATC from a lymph node (\u003cem\u003en\u003c/em\u003e = 1) (Supplementary Table 1). Immunohistochemical (IHC) analysis was performed on 25 specimens stratified by clinical group: ATC (\u003cem\u003en\u003c/em\u003e = 10; \u0026lt;1-year survival, \u003cem\u003en\u003c/em\u003e = 5; \u0026gt;1-year survival, \u003cem\u003en\u003c/em\u003e = 5), PTC N1bM0 (\u003cem\u003en\u003c/em\u003e = 5), PTC N1bM1 (\u003cem\u003en\u003c/em\u003e = 5), and early-onset PTC (PTCEO) N1b (\u003cem\u003en\u003c/em\u003e = 5).\u003c/p\u003e\n\u003cp\u003eVisium spatial transcriptomics data (GSE250521) were obtained from a public repository and deconvoluted using our scRNA-seq reference; these samples were not patient-matched to our cohort. Bulk RNA-seq and functional assays were performed in thyroid cancer cell lines with inducible C/EBP\u0026beta; expression. ENCODE ChIP-seq data from K562 cells (ENCSR000EHE) were used as orthogonal evidence for candidate C/EBP\u0026beta;-bound loci.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCollection and use of human specimens were approved by the Institutional Review Board of Gangnam Severance Hospital (IRB number: 3-2020-0309). All patients provided written informed consent for research use of their tissues before sample collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003escRNA-seq analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFASTQ files (PTC, \u003cem\u003en\u003c/em\u003e = 5; ATC, \u003cem\u003en\u003c/em\u003e = 3) were aligned to the hg19 reference genome using Cell Ranger (v3.1.0). Count matrices were analyzed with Seurat (v4.4.0). Cells expressing at least 200 genes were retained, sample-specific mitochondrial-read thresholds (15\u0026ndash;25%) were applied during quality control, genes detected in fewer than three cells were excluded, and doublets were removed using scDblFinder. After filtering, 43,866 cells and 22,272 genes remained for downstream analysis.\u003c/p\u003e\n\u003cp\u003eData were log-normalized with a scale factor of 10,000, and the top 2,000 variable genes were used for principal component analysis. Clustering was performed at resolution 0.5, with additional subclustering at resolution 5.0 where indicated. UMAP visualization used the first 15 principal components. Differentially expressed genes (DEGs) were identified with NEBULA using adjusted \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 and log2 fold change \u0026gt; 0.5. Key findings were additionally examined using pseudobulk aggregation and mixed-effects models with patient as a random effect.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVisium spatial transcriptomics analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVisium gene-spot matrices for para-tumor thyroid (PT), PTC, locally advanced PTC (LPTC), and ATC (\u003cem\u003en\u003c/em\u003e = 4 per group) were obtained from GEO (GSE250521). Data were processed in Seurat, including normalization, variable-gene selection, principal component analysis, and clustering using the top 50 principal components.\u003c/p\u003e\n\u003cp\u003eCell-type composition within spatial spots was inferred using RCTD (spacexr). The reference dataset was built from the top 300 marker genes per cell type derived from our scRNA-seq data. RCTD was run in full mode to estimate cell-type likelihood weights, and spots were assigned the cell type with the highest likelihood weight for comparative analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene set and enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunctional activities were quantified using Seurat\u0026rsquo;s AddModuleScore(). Thyroid differentiation score (TDS), IL-6/JAK/STAT, and EMT signatures were calculated using SCENIC-derived \u003cem\u003eCEBPB\u003c/em\u003e regulons and MSigDB Hallmark gene sets\u003csup\u003e13\u003c/sup\u003e (Supplementary Tables 2\u0026ndash;4). ATC signatures were curated from MSigDB C2. Additionally, BRAF and RAS signature scores were calculated using gene sets derived from genes upregulated in BRAF and RAS mutant samples from the TCGA thyroid cancer bulk RNA-seq dataset\u003csup\u003e14\u003c/sup\u003e (Supplementary Table 5). Gene set enrichment analysis (GSEA) was performed using fgsea with differentially expressed genes (DEGs) ranked by log2 fold change. Gene Ontology Biological Process gene sets were analyzed and summarized using the GOREA package.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCNV inference and heterogeneity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCNVs in epithelial cells were inferred using inferCNV, with myeloid cells serving as a reference. Differences in \u003cem\u003eCEBPB\u003c/em\u003e CNV values were evaluated using a one-way ANOVA with Tukey\u0026rsquo;s post hoc test. Clonal diversity within patients was quantified using the Shannon diversity index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene regulatory network and protein activity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTranscription factor activity was inferred using SCENIC\u003csup\u003e15\u003c/sup\u003e, and regulon activity was quantified with AUCell. Protein activity was independently estimated using ARACNe3\u003csup\u003e16,17\u003c/sup\u003e to infer transcription factor targets, followed by Nonparametric Analytical Rank-based Enrichment Analysis (NaRnEA)\u003csup\u003e18\u003c/sup\u003e. Results were reported as normalized enrichment scores (NES) and proportional enrichment scores (PES).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell culture and inducible cell lines\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuman PTC cell lines (TPC-1 and SNU-790) were cultured in RPMI-1640 supplemented with 10% FBS. Inducible C/EBP\u0026beta; wild-type and mutant (\u0026Delta;284-319) lines were generated using the pCW57-GFP-2A lentiviral system. \u003cem\u003eCEBPB\u003c/em\u003e cDNA was cloned from SW579 cells and inserted into the vector, and mutants were generated using site-directed mutagenesis. Stable lines were selected with puromycin.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional assays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCell proliferation was measured using CCK-8 assays and IncuCyte live-cell imaging following doxycycline induction. Migration and invasion assays were performed using Transwell chambers coated with gelatin or Matrigel and incubated for 48 h toward a 10% FBS chemoattractant. Radioiodine uptake assays were conducted using carrier-free \u0026sup1;\u0026sup3;\u0026sup1;I and quantified with a gamma counter.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular and histologic analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA was isolated using TRIzol and analyzed by qRT-PCR using specific primers (Supplementary Table 6) and SYBR Green, with \u003cem\u003eGAPDH\u003c/em\u003e as an internal control. Protein lysates were prepared using RIPA buffer and analyzed by immunoblotting. IHC staining for C/EBP\u0026beta; was performed on formalin-fixed paraffin-embedded sections, and staining intensity was quantified using H-scores\u003csup\u003e19,20\u003c/sup\u003e (Supplementary Tables 7\u0026ndash;8).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are presented as the mean \u0026plusmn; SEM from independent biological replicates (typically \u003cem\u003en\u003c/em\u003e \u0026ge; 3). Two-group comparisons were performed using two-tailed Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-tests, and multiple groups were analyzed using one-way ANOVA with appropriate post hoc tests. A \u003cem\u003eP\u003c/em\u003e value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cstrong\u003eSingle-cell and spatial profiling define tumor and microenvironmental states in PTC and ATC\u003c/strong\u003e \u003cp\u003eTo investigate cellular heterogeneity and lineage plasticity in PTC and ATC, we profiled tissue samples from conventional PTC patients (thyroid gland, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3; lymph node, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1), an early-onset PTC patient (thyroid gland, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1), and ATC patients (thyroid gland, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2; lymph node, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1) (Fig.\u0026nbsp;1a, Supplementary Table\u0026nbsp;1). After removing low-quality cells and doublets, scRNA-seq yielded 43,866 cells. Unsupervised clustering and marker-based annotation identified 11 major cell populations comprising epithelial, immune, stromal, and proliferating cells (Fig.\u0026nbsp;1b, c; Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eCell-type composition varied across patients and sampling sites (Fig.\u0026nbsp;1d). Lymph node samples (PTC1 and ATC1) contained lower proportions of tumor cells and higher immune cell abundance than primary tumor specimens. Notably, we observed an inverse relationship between the proportion of tumor cells and T/Natural Killer (NK) cell levels across patients; samples with lower tumor-cell fractions exhibited increased T and NK cell levels. Conversely, the proportion of myeloid cells was elevated in ATC samples (particularly ATC1), suggesting a potential role for myeloid-driven immune remodeling in the ATC tumor microenvironment (Fig.\u0026nbsp;1e).\u003c/p\u003e \u003cp\u003eTo add spatial context to these microenvironmental changes, we analyzed Visium spatial transcriptomics datasets comprising four samples each from para-tumor thyroid (PT), PTC, locally advanced PTC (LPTC), and ATC from an independent study\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Integration of the Visium and scRNA-seq datasets enabled the deconvolution of spot-level cell mixtures using RCTD, with each spot annotated according to the cell type with the highest likelihood weight (Fig.\u0026nbsp;1f). In the ATC4 sample, most spots within tumor regions were labeled as PTC cells. This labeling indicates that PTC cells are the dominant cell type in those spots, consistent with a previous pathological examination that reported mixed PTC and ATC histology in this case\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAcross all malignant states, myofibroblastic cancer-associated fibroblasts (myCAFs) were abundant components of the spatial TME. However, differences in overall myCAF abundance across PT, PTC, LPTC, and ATC did not reach statistical significance (adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.087; Fig.\u0026nbsp;1g). We therefore focused our subsequent spatial analyses on specific spatial localization and inferred cell-cell interactions rather than bulk abundance alone.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEpithelial-state analysis reveals progressive loss of thyroid differentiation\u003c/strong\u003e \u003cp\u003eWe further analyzed epithelial cells using sub-clustering and identified four distinct transcriptional states: normal follicular cells, PTC type 1, PTC type 2, and ATC cells (Fig.\u0026nbsp;2a). To assess the degree of differentiation among these cell types, we calculated the TDS\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e (Supplementary Table\u0026nbsp;2). A significant decrease in TDS from follicular cells to PTC and then to ATC cells confirmed progressive dedifferentiation, consistent with a stepwise loss of thyroid-lineage identity (Fig.\u0026nbsp;2b).\u003c/p\u003e \u003cp\u003eGene expression marker analysis revealed subtype-specific signatures (Fig.\u0026nbsp;2c, Supplementary Table\u0026nbsp;9). Normal follicular cells highly expressed differentiated thyroid genes (\u003cem\u003eTG\u003c/em\u003e, \u003cem\u003eTPO\u003c/em\u003e, and \u003cem\u003eTFF3\u003c/em\u003e). Both PTC and ATC cells shared expression of the thyroid cancer marker \u003cem\u003eKRT19\u003c/em\u003e, while ATC cells uniquely expressed genes linked to proliferation and invasion (\u003cem\u003eUBE2C\u003c/em\u003e, \u003cem\u003eS100A10\u003c/em\u003e, and \u003cem\u003eTFPI2\u003c/em\u003e). The two PTC states were also distinct: PTC type 1 preferentially expressed \u003cem\u003eFN1\u003c/em\u003e, \u003cem\u003eGDF15\u003c/em\u003e, and \u003cem\u003eSLC34A2\u003c/em\u003e, whereas PTC type 2 expressed \u003cem\u003eS100A1\u003c/em\u003e, \u003cem\u003eEEF1A1\u003c/em\u003e, and \u003cem\u003eRPS4X\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eGSEA further emphasized the biological differences among these epithelial states (Fig.\u0026nbsp;2d, Supplementary Fig.\u0026nbsp;2). Follicular cells were enriched for oxidative phosphorylation and thyroid hormone metabolic pathways. PTC type 1 cells displayed enrichment in pathways related to growth hormone response, cell junction assembly, and stem cell differentiation. PTC type 2 cells were enriched for ribosome biogenesis and cytoplasmic translation, reflecting a metabolically active epithelial population. In contrast, ATC cells exhibited enrichment in pathways associated with cell division, proliferation, and regulation of cell-cell adhesion, consistent with aggressive stem cell-like properties.\u003c/p\u003e \u003cp\u003eTo further characterize activated pathways, we employed the Hotspot Python package to identify gene modules\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e (Supplementary Fig.\u0026nbsp;3a, b). Consistent with the GSEA results, follicular cells were significantly enriched in thyroid gland development (odds ratio\u0026thinsp;=\u0026thinsp;29.665; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.232 \u0026times; 10⁻\u0026sup2;; Supplementary Fig.\u0026nbsp;3c). Both PTC subtypes were enriched in Myc targets, oxidative phosphorylation, and reactive oxygen species pathways. Hotspot analysis independently highlighted hypoxia, EMT, and interferon-response modules in ATC cells (Supplementary Fig.\u0026nbsp;3d), supporting a dedifferentiated, invasive, and stress-adapted state.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInferred CEBPB amplification and overexpression characterize ATC cells\u003c/strong\u003e \u003cp\u003eTo identify genomic alterations distinguishing ATC from PTC, we inferred CNVs from epithelial scRNA-seq profiles using myeloid cells as a reference population (Fig.\u0026nbsp;2e). Our analysis replicated previously reported altered genes and chromosomal regions, with a notable exclusive amplification of the \u003cem\u003eCEBPB\u003c/em\u003e locus on chromosome 20 in ATC cells. Inferred \u003cem\u003eCEBPB\u003c/em\u003e CNV values were significantly higher in ATC than in follicular or PTC cells (one-way ANOVA; \u003cem\u003eF\u003c/em\u003e-statistic\u0026thinsp;=\u0026thinsp;711.2; degrees of freedom\u0026thinsp;=\u0026thinsp;3; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;2 \u0026times; 10⁻\u0026sup1;⁶), supporting increased \u003cem\u003eCEBPB\u003c/em\u003e expression (Fig.\u0026nbsp;2f).\u003c/p\u003e \u003cp\u003eConsistent with the inferCNV results, \u003cem\u003eCEBPB\u003c/em\u003e expression was enriched in ATC cells in the scRNA-seq data and was also significantly elevated in ATC spots from the independent Visium dataset compared with spots corresponding to follicular cells from PT (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.847 \u0026times; 10⁻⁴) or PTC cells from PTC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.454 \u0026times; 10⁻⁴) and LPTC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.759 \u0026times; 10⁻\u0026sup3;) samples (Fig.\u0026nbsp;2g, h; Supplementary Fig.\u0026nbsp;4a). \u003cem\u003eCREB3L1\u003c/em\u003e, another factor previously implicated in the dedifferentiation process from PTC to ATC\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e (cytogenetic band: 11p11.2), likewise showed significant amplification in ATC cells (one-way ANOVA; \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;437.8; degrees of freedom\u0026thinsp;=\u0026thinsp;3; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;2 \u0026times; 10⁻\u0026sup1;⁶). The expression levels of \u003cem\u003eCEBPB\u003c/em\u003e and \u003cem\u003eCREB3L1\u003c/em\u003e were positively correlated across epithelial cells (Spearman\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.317, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;2.2 \u0026times; 10⁻\u0026sup1;⁶; Supplementary Fig.\u0026nbsp;4b).\u003c/p\u003e \u003cp\u003eIntratumoral heterogeneity was assessed using Shannon diversity indices calculated from the CNV-defined tumor clones. PTC tumors exhibited higher clonal diversity than ATC tumors (Fig.\u0026nbsp;2i), consistent with the selective expansion of specific malignant clones during ATC progression. Of note, one ATC sample (ATC2) retained relatively high diversity and a more PTC-like transcriptional profile, suggesting it represents an intermediate state rather than a fully established ATC program.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eC/EBPβ regulates inflammatory, IL-6/JAK/STAT, and EMT programs in ATC\u003c/strong\u003e \u003cp\u003eTo identify key transcription factors (TFs) involved in thyroid cancer progression, we performed SCENIC analysis and identified TFs exclusively activated in each epithelial cell subpopulation. Notably, \u003cem\u003eCEBPB\u003c/em\u003e was highly activated in ATC cells (Wilcoxon rank-sum test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;2 \u0026times; 10⁻\u0026sup1;⁶; Fig.\u0026nbsp;3a), consistent with its inferred CNV amplification. Regulatory network inference further revealed distinct pathway activation patterns across cancer subtypes (Supplementary Fig.\u0026nbsp;4c). While PTC type 2 cells showed activation of cell cycle-related pathways (including Myc targets, E2F targets, the mitotic spindle, and the G2M checkpoint), ATC-enriched regulons were associated with aggressive cancer behavior, including TNF-α signaling, glycolysis, hypoxia, angiogenesis, IL-6/JAK/STAT signaling, inflammatory response, and epithelial-mesenchymal transition (EMT).\u003c/p\u003e \u003cp\u003eOver-representation analysis (ORA) of the \u003cem\u003eCEBPB\u003c/em\u003e regulon showed significant regulation of the inflammatory response (odds ratio\u0026thinsp;=\u0026thinsp;6.349; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.093 \u0026times; 10⁻⁶), IL-6/JAK/STAT signaling (odds ratio\u0026thinsp;=\u0026thinsp;7.203; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.87 \u0026times; 10⁻⁴), hypoxia (odds ratio\u0026thinsp;=\u0026thinsp;12.656; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.556 \u0026times; 10⁻\u0026sup1;⁷), and EMT (odds ratio\u0026thinsp;=\u0026thinsp;11.408; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.579 \u0026times; 10⁻\u0026sup1;⁵) pathways (Fig.\u0026nbsp;3b). \u003cem\u003eCEBPB\u003c/em\u003e is a known regulator of inflammatory genes, including \u003cem\u003eIL-6\u003c/em\u003e. Consistent with this role, \u003cem\u003eIL-6\u003c/em\u003e expression was elevated in ATC cells (Supplementary Fig.\u0026nbsp;4d, e). Among 74 curated ligand-receptor pairs capable of activating JAK/STAT signaling\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, four were linked to the \u003cem\u003eCEBPB\u003c/em\u003e regulon, with LIF-IL6ST showing prominent expression in ATC cells (Supplementary Fig.\u0026nbsp;4f). Across epithelial cells, IL-6/JAK/STAT signature scores showed strong correlations with EMT and ATC-state scores (EMT: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.976, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.18 x 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e and ATC: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.672, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.34 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e ; Fig.\u0026nbsp;3c; Supplementary Tables\u0026nbsp;3 and 4), and these correlations were comparable to or stronger than those observed for BRAF- or RAS-associated transcriptional signatures (\u003cem\u003er\u003c/em\u003e = -0.68, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.13 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e ; Supplementary Fig.\u0026nbsp;4g, h).\u003c/p\u003e \u003cp\u003eCandidate C/EBPβ target genes overexpressed in ATC included \u003cem\u003eEMP3\u003c/em\u003e, \u003cem\u003eCD44\u003c/em\u003e, \u003cem\u003eMMP14\u003c/em\u003e, \u003cem\u003eITGA5\u003c/em\u003e, and \u003cem\u003eINHBA\u003c/em\u003e at the intersection of inflammatory and EMT programs, and \u003cem\u003eCXCL3\u003c/em\u003e, \u003cem\u003eVEGFA\u003c/em\u003e, and \u003cem\u003eLOX\u003c/em\u003e within IL-6/JAK/STAT-associated genes (Fig.\u0026nbsp;3d). The spatial cohort independently showed increased expression of these genes and progressively higher EMT, inflammatory-response, and IL-6/JAK/STAT signature scores from PTC to LPTC to ATC (Fig.\u0026nbsp;3e\u0026ndash;g; Supplementary Fig.\u0026nbsp;5a, b). Because the spatial data are cross-sectional and not patient-matched, these gradients should be interpreted as state associations rather than direct temporal progression.\u003c/p\u003e \u003cp\u003eFinally, protein-activity inference using ARACNe/NaRnEA supported C/EBPβ enrichment in ATC cells (Supplementary Fig.\u0026nbsp;5c), along with higher inferred activity of EMT- and inflammation-related proteins, including EMP3, CD44, INHBA, and CXCL3. Taken together, these integrative analyses nominate C/EBPβ as a central upstream regulator of an ATC-associated inflammatory and mesenchymal program.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eC/EBPβ overexpression recapitulates ATC-associated transcriptional programs in vitro\u003c/strong\u003e \u003cp\u003eTo functionally validate the role of C/EBPβ in thyroid cancer progression, we overexpressed C/EBPβ in the PTC cell line TPC-1 and performed bulk RNA sequencing\u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Gene Set Enrichment Analysis (GSEA) using the DEGs between C/EBPβ-overexpressing TPC-1 cells and controls revealed significant enrichment of pathways characteristic of ATC. These included TNF-α signaling via NF-κB (NES\u0026thinsp;=\u0026thinsp;2.474; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.364 \u0026times; 10⁻\u0026sup3;), hypoxia (NES\u0026thinsp;=\u0026thinsp;2.269; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.364 \u0026times; 10⁻\u0026sup3;), EMT (NES\u0026thinsp;=\u0026thinsp;2.115; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.364 \u0026times; 10⁻\u0026sup3;), inflammatory responses (NES\u0026thinsp;=\u0026thinsp;2.101; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.364 \u0026times; 10⁻\u0026sup3;), and IL-6/JAK/STAT signaling (NES\u0026thinsp;=\u0026thinsp;1.919; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.364 \u0026times; 10⁻\u0026sup3;; Fig.\u0026nbsp;3h). We also observed significant upregulation of \u003cem\u003eCEBPB\u003c/em\u003e itself (Log2 Fold Change\u0026thinsp;=\u0026thinsp;1.623), EMT-associated target genes (\u003cem\u003eMMP2\u003c/em\u003e, \u003cem\u003eMMP14\u003c/em\u003e, and \u003cem\u003eSNAI2\u003c/em\u003e), and IL-6/JAK/STAT signaling-associated target genes (\u003cem\u003eCXCL3\u003c/em\u003e and \u003cem\u003eVEGFA\u003c/em\u003e) in the overexpressing cells (Supplementary Fig.\u0026nbsp;5d). These results are consistent with our earlier \u003cem\u003eCEBPB\u003c/em\u003e regulon analysis and provide experimental support for the role of C/EBPβ in driving the transition from PTC to a more aggressive ATC-like transcriptional phenotype.\u003c/p\u003e \u003cp\u003eTo obtain orthogonal evidence for candidate downstream targets, we analyzed chromatin immunoprecipitation sequencing (ChIP-Seq) data for C/EBPβ in the K562 cell line, obtained from the ENCODE project (ENCSR000EHE). This analysis revealed significant binding of C/EBPβ to the promoters and enhancers of several genes associated with the inflammatory response, IL-6/JAK/STAT signaling, and EMT (Fig.\u0026nbsp;3i, Supplementary Fig.\u0026nbsp;5e). Notably, binding peaks were detected in genes associated with both EMT and the inflammatory response, such as \u003cem\u003eCD44\u003c/em\u003e (signal value\u0026thinsp;=\u0026thinsp;40.682; \u003cem\u003eq\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;1.991 \u0026times; 10⁻⁵) and \u003cem\u003eITGA5\u003c/em\u003e (signal value\u0026thinsp;=\u0026thinsp;278.697; \u003cem\u003eq\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;1.452 \u0026times; 10⁻⁴), while \u003cem\u003eHAS2\u003c/em\u003e (signal value\u0026thinsp;=\u0026thinsp;32.785; \u003cem\u003eq\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;1.479 \u0026times; 10⁻⁴) and \u003cem\u003eNFKBIA\u003c/em\u003e (signal value\u0026thinsp;=\u0026thinsp;458.506; \u003cem\u003eq\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;1.991 \u0026times; 10⁻⁵) were primarily linked to EMT. Furthermore, C/EBPβ bound to the promoter of \u003cem\u003eVEGFA\u003c/em\u003e (signal value\u0026thinsp;=\u0026thinsp;140.235; \u003cem\u003eq\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;1.991 \u0026times; 10⁻⁵), the enhancer of \u003cem\u003eMMP2\u003c/em\u003e (signal value\u0026thinsp;=\u0026thinsp;46.146; \u003cem\u003eq\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;1.991 \u0026times; 10⁻⁵), the \u003cem\u003eIL6ST\u003c/em\u003e enhancer, and \u003cem\u003eLOX\u003c/em\u003e (signal value\u0026thinsp;=\u0026thinsp;30.591; \u003cem\u003eq\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;1.358 \u0026times; 10⁻⁴), which regulates IL-6/JAK/STAT signaling. Importantly, binding was also observed in the regulatory region of \u003cem\u003eSLC5A5\u003c/em\u003e (signal value\u0026thinsp;=\u0026thinsp;46.029; \u003cem\u003eq\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;1.991 \u0026times; 10⁻⁵), which encodes the sodium/iodide symporter. Because these data were generated in a non-thyroid lineage, we interpret them as supportive evidence for plausible C/EBPβ-bound loci rather than definitive thyroid-specific direct targets\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCross-sectional trajectory analysis positions C/EBPβ early in the dedifferentiation continuum\u003c/strong\u003e \u003cp\u003eTo investigate the dynamic process of PTC-to-ATC transformation, we performed pseudotime analysis to order epithelial cells along a developmental trajectory based on their gene expression profiles\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Force-directed layouts of epithelial cells revealed a trajectory consistent with a cross-sectional ordering from differentiated follicular/PTC-like states toward ATC-like states, supporting a dedifferentiation model (Fig.\u0026nbsp;4a). Because these data are not longitudinally sampled from the same tumors, trajectory inference is interpreted as a transcriptional continuum rather than definitive lineage tracing (Fig.\u0026nbsp;4b). To further confirm the identified trajectory and connectivity between different cell states, we performed a partition-based graph abstraction analysis\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, which confirmed the connectivity between these states (Supplementary Fig.\u0026nbsp;6a).\u003c/p\u003e \u003cp\u003eTo investigate dynamic changes in TF activity during this progression, we clustered significantly activated TFs based on their expression trends along pseudotime. We then identified hallmark pathways significantly enriched in the regulons of each cluster using enrichR (Fig.\u0026nbsp;4c\u0026ndash;e; Supplementary Tables\u0026nbsp;10\u0026ndash;11). Notably, cluster 0 TFs, which included \u003cem\u003eCEBPB\u003c/em\u003e, showed a significant increase in expression (Log2 Fold Change\u0026thinsp;=\u0026thinsp;1.453) early along the transition. These early-peaking TFs regulate key pathways associated with aggressive cancer progression, including TNF-α signaling via NF-κB, hypoxia, and EMT. In contrast, cluster 2 TFs, which are enriched for cell-cycle regulators, were more prominent in the later stages of the trajectory.\u003c/p\u003e \u003cp\u003eThe expression profile of \u003cem\u003eCEBPB\u003c/em\u003e, peaking during the initiation of ATC-like states, supports its association with an early transition program. Furthermore, potency scores calculated using CytoTRACE2 to assess the decrease in differentiation and increase in stemness correlated strongly with \u003cem\u003eCEBPB\u003c/em\u003e expression (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.778, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.84 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e; Fig.\u0026nbsp;4f, g; Supplementary Fig.\u0026nbsp;6b). These data link C/EBPβ to a less differentiated, more stem-like epithelial state and nominate it as a candidate upstream regulator of early dedifferentiation, although longitudinal validation will be needed to establish definitive temporal causality.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eC/EBPβ promotes aggressive phenotypes and loss of iodine-handling features in vitro\u003c/strong\u003e \u003cp\u003eTo validate the functional significance of C/EBPβ in ATC development, we generated C/EBPβ-low PTC cell lines (TPC-1 and SNU-790) with inducible wild-type C/EBPβ expression (Fig.\u0026nbsp;5a). Additionally, we developed a human C/EBPβ mutant construct (Δ284\u0026ndash;319) to disrupt its DNA-binding and dimerization functions by deleting the DNA-binding and leucine zipper domains. The protein levels of this C/EBPβ mutant were lower than the wild-type, consistent with a previously observed link between dimerization and C/EBPβ stability\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. We therefore conservatively interpret this mutant as a partial loss-of-function construct.\u003c/p\u003e \u003cp\u003eNuclear localization of the induced C/EBPβ supported its activity as a transcription factor (Supplementary Fig.\u0026nbsp;7a). Wild-type C/EBPβ significantly increased cell proliferation in both TPC-1 and SNU-790 cells compared to controls, whereas the Δ284\u0026ndash;319 construct attenuated this effect in CCK-8 and live-cell imaging assays (Fig.\u0026nbsp;5b, c). Consistent with the clinical characteristics of ATC, wild-type C/EBPβ also prominently enhanced migration and invasion in Transwell and live-cell migration assays, with weaker effects observed for the Δ284\u0026ndash;319 mutant (Fig.\u0026nbsp;5d, e). These data indicate that C/EBPβ DNA-binding and transcriptional activity are essential for promoting aggressive behavior in PTC cells.\u003c/p\u003e \u003cp\u003eBecause impaired iodine handling is a hallmark of dedifferentiated thyroid cancer and drives radioiodine refractoriness, we next examined iodine uptake. In TPC-1 cells, wild-type C/EBPβ reduced iodine uptake and concomitantly decreased the expression of \u003cem\u003eSLC5A5\u003c/em\u003e (NIS) and \u003cem\u003ePAX8\u003c/em\u003e\u003csup\u003e32\u003c/sup\u003e, whereas the Δ284\u0026ndash;319 mutant did not reproduce this phenotype (Fig.\u0026nbsp;5f, g; Supplementary Fig.\u0026nbsp;7b). SNU-790 cells, which have intrinsically poor iodine uptake\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, showed a similar decrease in \u003cem\u003eNIS\u003c/em\u003e and \u003cem\u003ePAX8\u003c/em\u003e transcripts following C/EBPβ induction (Supplementary Fig.\u0026nbsp;7c). These results support a role for C/EBPβ in suppressing thyroid-differentiation features and generating a radioiodine-refractory-like state \u003cem\u003ein vitro\u003c/em\u003e.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eC/EBPβ-dependent phenotypes are reversible and associated with aggressive disease in vivo\u003c/strong\u003e \u003cp\u003eTo validate our \u003cem\u003ein vitro\u003c/em\u003e findings, we first examined the reversibility of the C/EBPβ-induced changes. Following doxycycline withdrawal, immunoblotting confirmed reduced levels of C/EBPβ in both TPC-1 and SNU-790 cells (Fig.\u0026nbsp;6a). Proliferation rates subsequently returned toward baseline levels after C/EBPβ removal (Fig.\u0026nbsp;6b), indicating that its effects depend on sustained expression.\u003c/p\u003e \u003cp\u003eTo assess the oncogenic potential of C/EBPβ \u003cem\u003ein vivo\u003c/em\u003e, we used a TPC-1 xenograft model. Induction of C/EBPβ resulted in a significant increase in tumor size relative to the control group (Supplementary Fig.\u0026nbsp;8a), providing \u003cem\u003ein vivo\u003c/em\u003e evidence that C/EBPβ drives tumor progression. As an independent clinical assessment, exploratory analysis of data from The Cancer Genome Atlas (TCGA) and Genomic Data Commons (GDC) revealed that patients with PTC (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;496) whose \u003cem\u003eCEBPB\u003c/em\u003e expression exceeded the third quartile exhibited significantly shorter overall survival than those with lower expression (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.1 \u0026times; 10⁻\u0026sup2;; Fig.\u0026nbsp;6c). Because event rates in PTC are low and this analysis was not performed in ATC, this association should be interpreted as supportive rather than definitive evidence of prognostic value.\u003c/p\u003e \u003cp\u003eWe extended our analysis to patient-derived samples to explore the clinical relevance of C/EBPβ expression in aggressive thyroid cancer. IHC analysis of ATC specimens (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10) revealed strong C/EBPβ positivity, with an average H-score of 166.7 (Fig.\u0026nbsp;6d, Supplementary Fig.\u0026nbsp;8b, and Supplementary Table\u0026nbsp;7). Notably, C/EBPβ expression was consistently elevated across all ATC tissues regardless of survival outcomes. In contrast, specimens from 15 patients with N1b-stage PTC, representing the most advanced stage of PTC, exhibited minimal C/EBPβ staining, with an average H-score of only 18.8 (Fig.\u0026nbsp;6d, Supplementary Fig.\u0026nbsp;8b, Supplementary Table\u0026nbsp;8). Within the limits of this cohort size, the independence of PTC metastasis from C/EBPβ expression suggests that C/EBPβ is a critical driver and candidate tissue biomarker of dedifferentiated ATC, rather than a general marker of metastatic PTC.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003emyCAFs and SPP1⁺ macrophages define permissive ATC microenvironmental niches\u003c/strong\u003e \u003cp\u003eTo investigate tumor-microenvironment interactions, we inferred cell\u0026ndash;cell communication from the scRNA-seq and Visium data using CellChat\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Compared with PTC, ATC showed stronger myCAF\u0026ndash;tumor interactions, particularly collagen-based signaling axes such as COL1A1/2\u0026ndash;CD44 and COL1A1/2\u0026ndash;ITGA3/ITGB1 (Fig.\u0026nbsp;7a; Supplementary Fig.\u0026nbsp;9). These pathways, involved in adhesion, migration, proliferation, and differentiation, were consistently more active in ATC than in PTC.\u003c/p\u003e \u003cp\u003eTo further characterize these interactions, we employed NicheNet\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, which prioritized GAS6-AXL and CXCL12-CXCR4 as candidate myCAF-to-ATC signaling axes (Fig.\u0026nbsp;7b, c; Supplementary Fig.\u0026nbsp;9e\u0026ndash;g). The CXCL12 signaling pathway targets molecules such as MMP2 and VEGFA, which are involved in EMT and angiogenesis\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Notably, CXCL12 has been reported to induce C/EBPβ in other cellular contexts\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, suggesting a paracrine feed-forward loop that could amplify C/EBPβ expression in ATC cells. Additionally, GAS6 induces IL-6, a known promoter of tumorigenesis and metastasis\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Because CellChat and NicheNet infer communication from expression patterns, we conservatively interpret these pathways as testable hypotheses for ATC stromal crosstalk.\u003c/p\u003e \u003cp\u003eSpatial niche analysis using the Python package scNiche\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e localized ATC cells and myCAFs at tumor boundaries in the ATC4 Visium sample, where both cell types showed significantly higher RCTD likelihood weights than in non-boundary regions (Wilcoxon rank-sum test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.584 \u0026times; 10⁻\u0026sup1;⁹ for ATC; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.176 \u0026times; 10⁻\u0026sup2;⁷ for myCAF; Fig.\u0026nbsp;7d\u0026ndash;f). Furthermore, \u003cem\u003eCST1\u003c/em\u003e expression was significantly upregulated in these ATC\u0026ndash;myCAF boundary regions (Log2 Fold Change\u0026thinsp;=\u0026thinsp;0.964, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.736 \u0026times; 10⁻\u0026sup3;⁸; Fig.\u0026nbsp;7g) and was also elevated in ATC1, while remaining minimal in PT and PTC samples. scRNA-seq further validated preferential \u003cem\u003eCST1\u003c/em\u003e expression in ATC-associated myCAFs, and \u003cem\u003eCST1\u003c/em\u003e⁺ myCAFs were enriched for collagen fibril organization (Supplementary Fig.\u0026nbsp;10c, d), consistent with a boundary-specific extracellular matrix-remodeling phenotype\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eImmune composition also shifted across disease states. PTC samples contained more CD4⁺ cytotoxic T lymphocytes expressing \u003cem\u003eGZMA\u003c/em\u003e and \u003cem\u003eGZMK\u003c/em\u003e, whereas ATC showed an expansion of SPP1⁺ and C1QC⁺ tumor-associated macrophages (TAMs) (Supplementary Fig.\u0026nbsp;11). Relative to C1QC⁺ TAMs, SPP1⁺ TAMs were enriched for angiogenesis, EMT, and hypoxia pathways (Fig.\u0026nbsp;7h). Predicted SPP1⁺ TAM-to-ATC interactions involved ICAM1-ITGB2, MMP9-LRP1, and ANXA1-FPR1, and converged on targets related to TIMP1, IL-6, MMP2, VEGFA, and CXCL1 (Fig.\u0026nbsp;7i, j), further supporting an ATC microenvironment organized around inflammatory and mesenchymal programs.\u003c/p\u003e \u003c/p\u003e "},{"header":"DISCUSSION","content":"\u003cp\u003eOur study identifies C/EBPβ as a CNV-amplified, transcriptionally hyperactive master regulator driving dedifferentiation from PTC-like to ATC-like states (Fig.\u0026nbsp;7k). Integrating single-cell, spatial, and functional data, we demonstrate that C/EBPβ links inflammatory signaling to mesenchymal reprogramming. Functional induction of C/EBPβ in PTC cells was sufficient to reproduce several hallmarks of aggressive disease, including increased proliferation, migration, invasion, and radioiodine refractoriness. Trajectory analysis supports a dedifferentiation continuum, and together, these results support a model in which C/EBPβ establishes and stabilizes an ATC-like phenotypic state.\u003c/p\u003e \u003cp\u003eThese data refine the mechanistic link between inflammatory signaling and dedifferentiation in thyroid cancer. Previous studies have implicated C/EBPβ in thyroid cancer progression; notably, its cytoplasmic accumulation in PTC has been associated with advanced disease and reduced apoptosis\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, but its precise role in dedifferentiation remained unclear. The strong association between C/EBPβ activity, IL-6/JAK/STAT signaling, and EMT across both single-cell and spatial datasets suggests that C/EBPβ sits near the interface of inflammatory transcriptional control and mesenchymal reprogramming. Consistent with findings in other systems, JAK/STAT activation can precede EMT, suggesting a coordinated cascade. Furthermore, the observation that C/EBPβ induction suppresses \u003cem\u003eSLC5A5\u003c/em\u003e (NIS) and \u003cem\u003ePAX8\u003c/em\u003e directly connects this network to the loss of thyroid-specific function, a clinically vital feature of radioiodine-refractory disease. While current data do not establish a linear causal chain \u003cem\u003ein vivo\u003c/em\u003e, they define a coherent regulatory module that warrants targeted perturbation in future studies.\u003c/p\u003e \u003cp\u003eA second major contribution of this study is the spatial framing of the ATC microenvironment. Beyond tumor-intrinsic effects, spatial data show myCAFs as dominant stromal components interacting with ATC cells via collagen signaling. Although bulk myCAF abundance differences were not statistically significant across malignant states, boundary-associated \u003cem\u003eCST1\u003c/em\u003e⁺ myCAFs displayed a spatially restricted ECM-remodeling program linked to aggressiveness. In parallel, immune profiling revealed cytotoxic T lymphocyte (CTL) enrichment in PTC, which stood in stark contrast to the expansion of angiogenesis-, hypoxia-, and EMT-associated SPP1⁺ TAMs in ATC. These findings support a cooperative model in which C/EBPβ-driven tumor-cell dedifferentiation and spatially organized microenvironmental remodeling mutually amplify to promote ATC progression.\u003c/p\u003e \u003cp\u003eFrom a translational perspective, C/EBPβ is highly attractive because it links the mechanism directly to the clinical phenotype. High C/EBPβ protein expression robustly distinguished ATC from advanced PTC in patient tissues, and exploratory survival analysis in PTC suggested that elevated \u003cem\u003eCEBPB\u003c/em\u003e expression is associated with poorer outcomes. While the current clinical evidence requires expansion before C/EBPβ can be established as a validated prognostic biomarker, our data strongly support it as a candidate biomarker of dedifferentiated disease and a therapeutic vulnerability. Consequently, C/EBPβ-directed strategies, including experimental antagonists such as ST101, merit rigorous evaluation in models of aggressive thyroid cancer\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFinally, this study has important limitations. First, the scRNA-seq ATC cohort was small (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3) and included samples from both primary tumors and lymph nodes, meaning some compositional differences may reflect sampling site rather than pure histology. Second, the Visium data were obtained from an independent, non-patient-matched cohort; while this supports cross-cohort reproducibility, it precludes direct within-patient evolutionary inference. Third, the evidence for \u003cem\u003eCEBPB\u003c/em\u003e copy-number gain derives from inferCNV rather than direct DNA-based profiling. Fourth, candidate downstream targets were supported by non-thyroid ENCODE ChIP-seq and computational network inference, both of which require direct thyroid-lineage validation. Fifth, our functional work relied predominantly on gain-of-function models; although the Δ284\u0026ndash;319 construct attenuated C/EBPβ-associated phenotypes, its lower stability suggests it acts as a partial loss-of-function rather than a clean separation-of-function mutant.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data are available in the main text or Supplementary Information. Sequencing datasets were deposited in GEO (RRID:SCR_005012) under accession numbers GSE277750 (RNA-seq) and GSE277751 (scRNA-seq). Analysis code is available at the GitHub repository: https://github.com/KuChoiLab/2026_THCA_methods_cdd_KM\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (RS-2023-00212238 to J.C., RS-2024-00408822 to L.K.K., and NRF-2021R1I1A1A01044274 to S.H.D.). Medical Illustration \u0026amp; Design (MID), a member of the Medical Research Support Services of Yonsei University College of Medicine, provided support with medical illustrations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS. H. Dho: Conceptualization, Methodology, Validation, Investigation, Writing - original draft, Visualization. K. Yoo: Conceptualization, Methodology, Software, Validation, Investigation, Writing - original draft, Visualization. J. S. Lee and H. J. Park: Investigation, Visualization. W. Woo, M. Cho, J. Song, and Y. S. Lee: Investigation, Visualization. S.-M. Kim: Conceptualization, Writing - review and editing, Supervision, Funding acquisition. L. K. Kim: Conceptualization, Writing - review and editing, Supervision, Funding acquisition. J. Choi: Conceptualization, Writing - review and editing, Supervision, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eManiakas, A. et al. Evaluation of Overall Survival in Patients With Anaplastic Thyroid Carcinoma, 2000\u0026ndash;2019. \u003cem\u003eJAMA Oncol\u003c/em\u003e 6, 1397\u0026ndash;1404 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLim, H., Devesa, S. S., Sosa, J. A., Check, D. \u0026amp; Kitahara, C. M. 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Anticancer Activity of ST101, A Novel Antagonist of CCAAT/Enhancer Binding Protein beta. \u003cem\u003eMol Cancer Ther\u003c/em\u003e 21, 1632\u0026ndash;1644 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cell-death-and-disease","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"cddis","sideBox":"Learn more about [Cell Death \u0026 Disease](http://www.nature.com/cddis/)","snPcode":"41419","submissionUrl":"https://mts-cddis.nature.com/cgi-bin/main.plex","title":"Cell Death \u0026 Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anaplastic thyroid carcinoma, papillary thyroid carcinoma, single-cell RNA sequencing, spatial transcriptomics, C/EBPβ, dedifferentiation, IL-6/JAK/STAT, EMT, radioiodine resistance ","lastPublishedDoi":"10.21203/rs.3.rs-9248842/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9248842/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnaplastic thyroid carcinoma (ATC) is among the most lethal human malignancies, with a median survival of four months. While ATC is thought to arise from papillary thyroid carcinoma (PTC) through dedifferentiation, the molecular drivers underlying this transition remain poorly defined. Here, we integrated single-cell transcriptomics (43,866 cells; PTC, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5; ATC, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3) with spatial transcriptomics from independent cohorts across the disease spectrum. We identified CCAAT/enhancer-binding protein beta (C/EBPβ) as a transcriptionally hyperactivated, genomically amplified master regulator of thyroid cancer dedifferentiation. Trajectory analysis positioned C/EBPβ upregulation as an early molecular switch. Mechanistically, C/EBPβ activates IL-6/JAK/STAT signaling and epithelial-mesenchymal transition (EMT). In PTC cell lines, inducible C/EBPβ expression phenocopied ATC-like aggressive features, enhancing proliferation, migration, and invasion, and conferring radioiodine refractoriness by suppressing the sodium-iodide symporter (NIS). These phenotypes were abrogated by a DNA-binding-deficient mutant and reversed upon withdrawal of C/EBPβ. \u003cem\u003eIn vivo\u003c/em\u003e, induction of C/EBPβ accelerated xenograft tumor growth. Furthermore, immunohistochemistry confirmed marked C/EBPβ overexpression in clinical ATC specimens (mean H-score: 166.7) compared with advanced PTC (mean H-score: 18.8). Spatial analysis revealed ATC-associated microenvironmental remodeling, highlighted by CST1-expressing myofibroblastic cancer-associated fibroblasts (myCAFs) at tumor boundaries and the expansion of SPP1\u0026thinsp;+\u0026thinsp;tumor-associated macrophages linked to hypoxia and EMT. Collectively, these integrated findings establish C/EBPβ as a critical mechanistic driver and candidate therapeutic target in aggressive thyroid cancer.\u003c/p\u003e","manuscriptTitle":"C/EBPβ Drives Anaplastic Thyroid Cancer Dedifferentiation and Radioiodine Resistance via IL-6/JAK/STAT and EMT Activation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-18 10:05:40","doi":"10.21203/rs.3.rs-9248842/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-05-12T04:50:58+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-05-08T09:34:25+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2026-05-08T08:28:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-23T13:25:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cell Death \u0026 Disease","date":"2026-04-21T14:29:56+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2026-04-20T15:53:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-16T12:46:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cell-death-and-disease","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"cddis","sideBox":"Learn more about [Cell Death \u0026 Disease](http://www.nature.com/cddis/)","snPcode":"41419","submissionUrl":"https://mts-cddis.nature.com/cgi-bin/main.plex","title":"Cell Death \u0026 Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fa4e91b6-9a70-48cb-a7cd-e128b9ba36af","owner":[],"postedDate":"May 18th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-05-12T04:50:58+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-05-08T09:34:25+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"16","date":"2026-05-08T08:28:53+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67768117,"name":"Biological sciences/Cancer/Cancer genomics"},{"id":67768118,"name":"Biological sciences/Cell biology/Mechanisms of disease"}],"tags":[],"updatedAt":"2026-05-18T10:05:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-18 10:05:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9248842","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9248842","identity":"rs-9248842","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}