Spatial Transcriptomics in Thyroid Cancer: A PRISMA-Guided Systematic Review of Platforms, Applications, and Tumor Microenvironment

1. Introduction

Endocrine malignancies are led in frequency by thyroid cancer, whose diagnosis rates have grown considerably across recent decades [1]. Thyroid neoplasms span a diverse histologic landscape under the 2022 World Health Organization framework. Tumors arising from follicular cells include papillary (PTC), follicular (FTC), oncocytic, poorly differentiated (PDTC), and anaplastic (ATC) carcinomas; these are joined by medullary thyroid carcinoma, a neoplasm traced instead to parafollicular C cells [2]. Indolence characterizes the natural history of most differentiated tumors, but not all: some return after treatment, seed distant organs, cease responding to radioiodine, or shed their differentiated phenotype to become PDTC or ATC. Of these, ATC ranks among the deadliest endocrine cancers, and patients frequently survive only months even after aggressive multimodal treatment [3]. Current risk stratification relies heavily on histopathologic features, anatomic staging, and selected molecular alterations; however, these approaches incompletely capture intratumoral heterogeneity, microenvironmental remodeling, and spatially organized tumor-stroma-immune interactions that contribute to invasion, immune escape, metastatic spread, and therapeutic resistance [4].

Traditional transcriptomic approaches have improved understanding of thyroid cancer biology but remain limited by loss of spatial context. Bulk RNA sequencing identifies tumor-level expression programs but returns one averaged profile per specimen, blending malignant, stromal, endothelial, and immune contributions. Single-cell RNA sequencing recovers per-cell resolution, but the dissociation step that makes it possible destroys the record of which cells were neighbors [21]. Spatial transcriptomics (ST) addresses this limitation differently: transcripts are assayed in situ, so every measurement carries the coordinates at which it was made on the histologic section [5,6,21]. The field was designated Nature Methods’ Method of the Year in 2020, reflecting its importance for linking molecular state to tissue architecture [5]. Tumors make this coordinate information consequential. A transcriptional program confined to the invasive boundary, to a lymphovascular interface, or to a dedifferentiated focus is diluted into the tissue average by any assay that discards position.

Several classes of ST platform are now in routine use, and each balance one capability at the expense of another. For example, subcellular localization is generally purchased with a preselected gene panel, while unbiased transcriptome-wide capture is purchased with spot-level averaging [7]. Capture-based platforms, derived from the original spatial transcriptomics approach described by Ståhl et al., profile RNA across spatially barcoded capture regions and are well suited for discovery-oriented whole-transcriptome studies [6]. Imaging-based platforms, including MERFISH, seqFISH-based methods, CosMx, and Xenium, provide single-cell or subcellular localization of targeted transcripts and are particularly useful for resolving cell states and cell-cell interactions at tumor boundaries [8,9,10]. Region-based platforms, such as GeoMx digital spatial profiling, quantify RNA or protein targets within pathologist-defined regions of interest and are compatible with formalin-fixed paraffin-embedded tissue, making them attractive for archival clinical specimens [11]. These complementary approaches are summarized in Table 1 and provide a framework for interpreting the strengths and limitations of the thyroid cancer ST literature.

Interest in applying ST to thyroid cancer has grown rapidly. Recent studies have used ST to characterize tumor-microenvironment remodeling across thyroid cancer differentiation states, fibroblast programs at invasive fronts, immune-evasion niches, spatial patterns of dedifferentiation, lymph node metastasis, and candidate biomarkers in ATC [12,13,14,15,16,17,18,19,20]. For example, ST studies have identified SERPINE1-positive [14] and POSTN-positive [15] cancer-associated fibroblast programs associated with aggressive behavior, APOE-linked malignant or immunosuppressive tumor states [14,18,20], and immune-suppressive, angiogenic, and extracellular-matrix remodeling during dedifferentiation toward ATC [13,16,17]. Narrative reviews have begun to summarize spatial and single-cell approaches in thyroid oncology [21]; however, a systematic, PRISMA-guided synthesis of the most recent advancements in the primary ST literature of thyroid cancer remains needed. Specifically, the field lacks a consolidated review that catalogs the platforms used, thyroid cancer subtypes studied, sample types analyzed, and recurrent biological findings across independent investigations. We therefore conducted a systematic review to map the current ST platform landscape in thyroid cancer and synthesize the major applications, discoveries, limitations, and future directions of spatially resolved transcriptomics across thyroid cancer subtypes.

2. Materials and Methods

This systematic review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement [22]. Eligibility criteria, database search strategy, screening approach, and extraction variables were defined before final study selection. The review was not registered in a public registry, and a formal protocol was not separately published; the a priori criteria described here were applied throughout without amendment. Because the objective was to map the available spatial-transcriptomics literature in thyroid cancer rather than estimate a pooled treatment effect, the review was designed as a qualitative evidence synthesis.

A systematic literature search was performed in PubMed/MEDLINE, Scopus, and Web of Science Core Collection with no lower date restriction; all three databases were last searched on July 10, 2026. Searches were limited to English-language records. Boolean search strategies combined terms related to spatial transcriptomics with terms related to thyroid cancer. Exact search criteria for each individual database are included in Supplemental Table 1 for reference. Search results from all databases were exported in RIS and/or CSV format for screening and de-duplication.

Studies were eligible for inclusion if they met all of the following criteria: primary research; application of a spatially resolved transcriptomic method to thyroid cancer tissue or a thyroid cancer dataset; reporting of spatial gene-expression findings; and inclusion of thyroid cancer-specific results. Studies using human thyroid cancer tissue were prioritized. Reanalyses of publicly available thyroid cancer spatial-transcriptomic datasets were included when they generated thyroid cancer-specific spatial findings.

Studies were excluded if they were reviews, editorials, commentaries, conference abstracts without full-text data, non-thyroid studies, pan-cancer studies without thyroid cancer-specific spatial-transcriptomic analysis, benign thyroid disease studies without cancer tissue, spatial-proteomics-only studies without a transcriptomic readout, technical methods papers without thyroid cancer application, studies without retrievable full text, or non-human model studies that did not include human thyroid cancer tissue.

Records were de-duplicated first by DOI and then by title. Unique records were screened by title and abstract against the eligibility criteria by two reviewers working independently, with disagreements resolved by consensus. Reports that appeared potentially eligible were retrieved for full-text review, and full-text articles were then independently assessed for final inclusion, with disagreements resolved by discussion. No automation tools were used during screening or selection.

Data were extracted into a standardized, pre-piloted extraction form by one reviewer and independently verified by a second reviewer, with disagreements resolved by consensus, and study investigators were not contacted for additional data. For each included study, extracted variables included first author, year of publication, journal, thyroid cancer subtype, cohort or sample size, tissue source, sample preparation method, spatial-transcriptomics platform, complementary methods, analytic approach, and principal spatial findings. Thyroid cancer subtypes were recorded as reported by the original studies, including papillary thyroid carcinoma, follicular thyroid carcinoma, poorly differentiated thyroid carcinoma, anaplastic thyroid carcinoma, medullary thyroid carcinoma, and metastatic thyroid cancer when applicable. Spatial platforms were categorized as capture-based whole-transcriptome methods, region-based digital spatial profiling methods, imaging-based targeted transcriptomic methods, or integrated spatial multi-omic approaches.

Complementary methods were also extracted when reported, including single-cell RNA sequencing, bulk RNA sequencing, whole-exome sequencing, immunohistochemistry, multiplex immunofluorescence, spatial proteomics, metabolomics, or computational deconvolution. Key findings were grouped by biological theme, including tumor-microenvironment remodeling, dedifferentiation, cancer-associated fibroblast programs, immune microenvironment organization, cell–cell communication, invasion, lymph node metastasis, distant metastasis, molecular classification, and candidate biomarker discovery.

Because no validated risk-of-bias instrument exists specifically for descriptive spatial-omics studies, included studies were appraised using the Joanna Briggs Institute (JBI) critical-appraisal checklist most closely matching each study design [23,24]. Single-case spatial-transcriptomics studies were assessed using the JBI checklist for case reports; descriptive multi-sample spatial-transcriptomics studies were assessed using the JBI checklist for case series; and comparative or outcome-oriented cohort studies were assessed using the JBI checklist for analytical cross-sectional studies. If studies did not fit existing clinical JBI checklist, they were appraised using a seven-item framework. This framework assessed data provenance, original-platform quality control, analytic pipeline description, batch-effect handling, independent or functional validation, code/data availability, and parameter transparency.

Methodological appraisal was performed by one reviewer and independently checked by a second reviewer, with disagreements resolved by consensus. Each appraisal item was rated as met, unclear/not reported, not met, or not applicable. An overall level of methodological concern was derived from the proportion of applicable items met: low concern, ≥75%; moderate concern, 50–74%; and high concern, <50%. This appraisal approach was adapted for an emerging spatial-omics literature and was intended to characterize reporting quality and methodological transparency rather than to function as a validated quantitative risk-of-bias score.

Because the included studies were heterogeneous in spatial platform, tissue preparation, thyroid cancer subtype, sample size, analytic pipeline, and reported outcomes, meta-analysis was not appropriate. Findings were therefore synthesized qualitatively. Studies were first summarized descriptively by platform, sample type, thyroid cancer subtype, and use of complementary methods. Principal spatial findings were then organized into recurring biological themes. Convergent findings across independent studies were emphasized, particularly when similar tumor, stromal, immune, or metastatic programs were reported across different cohorts or analytic approaches. Platform-specific strengths and limitations were considered when interpreting the evidence base. Because the synthesis was qualitative and did not include meta-analysis, formal statistical assessment of reporting bias across studies was not performed, and a formal grading of certainty of evidence was not applied; instead, confidence in the findings was contextualized qualitatively using the study-level methodological appraisal described above.

3. Results

3.1. Study Selection

The database search identified 104 total records, including 34 from PubMed/MEDLINE, 39 from Scopus, and 31 from Web of Science Core Collection. After removal of 57 duplicates, 47 unique records underwent title and abstract screening. Twenty-four records were excluded at this stage, most commonly because they were not thyroid cancer-specific, were pan-cancer analyses without a thyroid-specific spatial-transcriptomic readout, were reviews or editorials, involved benign thyroid disease without cancer tissue, or lacked a transcriptomic spatial component.

Twenty-three reports were sought for full-text retrieval. One report could not be retrieved, leaving 22 full-text articles for eligibility assessment. One study using a genetically engineered murine model was excluded by consensus to restrict the synthesis to human disease-focused spatial-transcriptomic studies. The study-selection process is summarized in the PRISMA flow diagram in Figure 1. Ultimately, 21 studies were included in the final qualitative synthesis, including 19 primary human spatial-transcriptomics studies and two reanalyses of publicly available thyroid cancer spatial-transcriptomics datasets. The characteristics, spatial-transcriptomics platforms, thyroid cancer subtypes, complementary methods, and principal findings of these studies are summarized in Table 1.

3.2. Spatial Transcriptomics Platform Landscape

As summarized in Table 2, the spatial-transcriptomics platforms relevant to thyroid cancer can be broadly grouped into capture-based whole-transcriptome methods, region-based digital spatial profiling methods, and imaging-based targeted transcriptomic methods. Capture-based whole-transcriptome sequencing, particularly 10x Visium and Visium CytAssist, was the dominant approach across the included literature; study-level platform assignments are detailed in Table 1 [12,13,14,15,16,17,18,19,20,25,26,27,28,29,30,31,32,33,34,35,36]. This likely reflects the compatibility of capture-based methods with discovery-oriented studies of surgical thyroid specimens, where whole-transcriptome profiling can identify tumor, stromal, immune, and invasive-front programs without requiring preselected gene panels. As shown in Table 2, these platforms offer broad transcriptome coverage but generally operate at spot-level rather than true single-cell resolution

The availability of FFPE-compatible workflows also makes Visium CytAssist particularly attractive for thyroid pathology, where archival tumor blocks are commonly available and histologic context is central to diagnosis [15,17,25,29].

Region-based GeoMx digital spatial profiling was used in studies requiring histology-guided profiling of defined regions of interest, particularly in FFPE-compatible biomarker-discovery settings [12,28]. GeoMx was useful for comparing tumor compartments or preselected histologic regions, but its region-level resolution limits direct single-cell interpretation. Other capture-based methods, including Slide-seqV2, Stereo-seq, and Seq-Scope, remain unreported in thyroid cancer but illustrate the continuing increase in spatial resolution [37,38,39]. Imaging-based platforms such as Xenium, CosMx, MERFISH, and seqFISH+ provide targeted transcript detection at single-cell or subcellular resolution [8,9,10,40]. Although these high-resolution platforms were not widely represented among the included thyroid cancer studies, they may be valuable for validating candidate cell states and ligand-receptor interactions at tumor-immune-stromal interfaces.

Overall, the platform landscape shows that thyroid cancer ST remains concentrated around capture-based whole-transcriptome methods, with limited use of region-based profiling and minimal adoption of high-resolution imaging-based transcriptomic platforms. This concentration has enabled broad discovery across tumor subtypes but also creates limitations in cell-level resolution and cross-platform validation.

3.3. Tumor Microenvironment Remodeling and Dedifferentiation

A dominant theme across the included studies was progressive remodeling of the tumor microenvironment during thyroid cancer progression and dedifferentiation. Several studies used ST, often integrated with single-cell RNA sequencing, to map the transition from differentiated thyroid carcinoma toward poorly differentiated thyroid carcinoma and ATC [13,14,16,17]. Liao et al. generated a spatially resolved transcriptomic landscape across para-tumor tissue, PTC, locally advanced PTC, and ATC, identifying three thyrocyte meta-clusters (TG+IYG+, HLA-DR+, and APOE+APOC1+) that marked successive stages of progression [14]. In the same study, SERPINE1+ fibroblasts increased with malignant progression and were associated with prognosis, supporting a role for fibroblast remodeling in aggressive thyroid cancer biology [14].

Additional studies reinforced the concept that dedifferentiation is accompanied by coordinated changes in stromal, immune, angiogenic, and extracellular-matrix programs. Seok et al. used Visium CytAssist to evaluate PTC, FTC, PDTC, and ATC and demonstrated remodeling of the tumor microenvironment across differentiation states, including pathway-level changes identified through computational deconvolution and pathway analysis [17]. Ning et al. showed that ATC regions were enriched for immune suppression, angiogenesis, and extracellular-matrix remodeling, while adjacent differentiated thyroid carcinoma regions shared high mutational burden with ATC, suggesting clonal evolution during dedifferentiation [16]. Ji et al. further linked cancer-associated fibroblasts to dedifferentiation through a ZFP57-PKM2 axis and lactate secretion, identifying a potential therapeutic role for resveratrol in reversing fibroblast-driven dedifferentiation programs [13].

Cancer-associated fibroblast populations were among the most recurrent stromal findings. Loberg et al. described POSTN+ myofibroblast-like CAFs closely associated with invasive tumor cells and linked to poor prognosis and lymph node metastasis [15]. Together, these studies suggest that dedifferentiation is not solely a malignant-cell-intrinsic process but is spatially coupled to fibroblast activation, extracellular-matrix remodeling, immune suppression, and altered metabolic signaling within the tumor microenvironment.

3.4. Cellular Heterogeneity, Cell States, and Molecular Classification

Several studies used ST to resolve spatial and clonal heterogeneity that is difficult to capture with bulk sequencing or dissociated single-cell approaches alone. Zheng et al. integrated single-cell RNA sequencing with spatially resolved transcriptomics to reconstruct the spatial evolution of PTC and improve molecular classification based on spatially organized malignant and microenvironmental features [36]. Yan et al. similarly used ST to identify prognosis-associated cellular heterogeneity within the PTC microenvironment and derived spatial RNA-clinical signature genes that mapped clinically relevant cell-type distributions across tissue sections [33].

Proteogenomic and multi-omic studies further demonstrated how ST can refine molecular classification in advanced thyroid cancer. Zhang et al. incorporated spatial transcriptomics into a proteogenomic analysis of advanced differentiated thyroid cancer and identified clinically relevant subtypes, including canonical, stromal, and immunogenic groups [34]. Xu et al. developed a Molecular Aggression and Prediction score that integrated immune-microenvironment features and improved outcome prediction in aggressive thyroid malignancy [32]. These studies suggest that spatial data may add clinically meaningful information beyond conventional histology and mutation status by identifying where aggressive, immune-rich, stromal-rich, or dedifferentiated programs occur within tumor tissue.

3.5. Immune Microenvironment and Cell–Cell Interactions

Spatial transcriptomics repeatedly revealed organized immune niches and tumor-immune cell communication patterns in thyroid cancer. Wang et al. showed that interactions between LAMP3+ dendritic cells and T-cell subpopulations promoted immune evasion in progressive PTC [19]. Shobab et al. used GeoMx digital spatial profiling and flow cytometry to identify sex-specific differences in the immune microenvironment of differentiated thyroid cancer, including higher dividing natural killer cells and TIGIT+CD8 T cells in male patients [28].

Several studies identified ligand-receptor signaling axes that may mediate tumor-microenvironment crosstalk. Zhang et al. reported that the PROS1-MERTK axis contributed to tumor-microenvironment interactions and progression in papillary thyroid microcarcinoma [35]. Su et al., using public spatial and single-cell data reanalysis, described an APOE/NCF1-associated immunosuppressive niche with prognostic significance in thyroid cancer [18]. Wang et al. identified a CXCL8+ monocyte and SDC1+ tumor-stem-cell interaction network associated with distant metastatic spread [31]. Collectively, these studies support the value of ST for identifying not only which immune populations are present, but also where they are localized and how they communicate with tumor and stromal compartments.

3.6. Invasion, Lymph Node Metastasis, and Distant Metastasis

Because capsular and vascular invasion are central diagnostic features in follicular-patterned thyroid tumors, several studies focused on spatial programs at the invasive front. Condello et al. used whole-transcriptome Visium profiling in FTC and contrasted invasive capsular and vascular fronts with the relatively indolent tumor core, identifying clone-specific dysregulation of extracellular-matrix genes at the invasive boundary [25]. Suzuki et al. applied spatial transcriptomics to follicular tumors and identified invasive-subpopulation biomarkers, including CD74 expression dynamics that helped distinguish invasive FTC from follicular adenoma [29]. These findings highlight the potential utility of ST in resolving biologically important but spatially restricted invasive regions.

Spatial approaches also provided insight into lymph node metastasis. Xiao et al. identified an APOE-negative tumor-cell subpopulation associated with pathological lymph node metastasis in advanced PTC and showed that APOE modulated proliferation and invasion [20]. Jiang et al. used public ST data integrated with single-cell and bulk transcriptomic datasets to develop a lymph node metastasis signature and machine-learning model for N1 versus N0 stratification [26]. Li et al. combined spatial metabolomics with Visium transcriptomics to map metabolic and transcriptional changes associated with PTC tumorigenesis and lymph node metastasis [27]. These studies suggest that metastatic competence may be spatially encoded through coordinated tumor-cell, immune, stromal, and metabolic programs.

Distant metastatic progression was less commonly studied but was addressed in spatial analyses of dedifferentiated and metastatic thyroid carcinoma. Wang et al. profiled PTC, ATC, ATC lymph node metastasis, and ATC gastric metastasis, identifying SFRP4-high tumor-specific myeloid cells associated with dedifferentiation and poor prognosis and localized near CD44+ tissue-resident memory T cells [30]. Although evidence remains limited, these findings suggest that ST can help characterize the architecture of rare metastatic thyroid cancer sites and identify microenvironmental features associated with aggressive dissemination.

3.7. ATC Biomarker Discovery

Several studies specifically addressed ATC or dedifferentiated thyroid carcinoma, reflecting the urgent need for improved biomarkers and therapeutic targets in this highly aggressive disease. In addition to studies mapping dedifferentiation from differentiated thyroid carcinoma to ATC [13,16,17,30], Haq et al. used GeoMx digital spatial profiling to compare ATC with PTC and other non-anaplastic follicular-cell-derived carcinomas [12]. This study identified eight differentially expressed mRNA and protein markers associated with ATC progression and dedifferentiation, supporting the use of region-based spatial profiling for biomarker discovery in FFPE clinical specimens [12]. Across ATC-focused studies, recurrent themes included immune suppression, extracellular-matrix remodeling, angiogenesis, fibroblast activation, metabolic reprogramming, and myeloid-cell-associated tumor progression.

3.8. Summary of Included Studies

The 21 included studies varied substantially in thyroid cancer subtype, spatial platform, sample preparation, complementary methods, and analytic strategy. Most studies focused on PTC or advanced follicular-cell-derived carcinomas, while fewer studies examined FTC, PDTC, ATC, metastatic disease, or medullary thyroid carcinoma. The most common complementary method was single-cell RNA sequencing, which was frequently integrated with ST to improve cell-type annotation and infer cell-cell communication. Other approaches included whole-exome sequencing, bulk RNA sequencing, proteogenomics, multiplex immunofluorescence, flow cytometry, spatial metabolomics, and computational reanalysis of public datasets. The characteristics and principal findings of the included studies are summarized in Table 1.

3.9. Methodological Quality Appraisal

By design, the evidence base comprised 12 case series, four analytical cross-sectional studies, three single-case reports, and two reanalyses of publicly available spatial-transcriptomics datasets. Applying the design-matched JBI checklists and bespoke omics-reanalysis framework, most studies reached moderate methodological quality. Common strengths included histopathological confirmation of thyroid cancer subtype, use of appropriate spatial-transcriptomic analytic pipelines, and, particularly in larger cohorts, orthogonal validation using immunohistochemistry, multiplex immunofluorescence, functional assays, or independent datasets.

Recurrent weaknesses included unclear consecutive or complete participant enrollment, incomplete reporting of cohort demographics and presenting-site information, and limited clinical outcome or follow-up data. The four analytical cross-sectional cohorts, including the MAP-score and proteogenomic studies, scored highest overall. In contrast, the two public-data reanalyses scored lowest, reflecting reliance on secondary data and limited reporting of code, parameter choices, or original platform-level quality-control details. No study was excluded on quality grounds; rather, quality appraisal was used to contextualize confidence in the reported findings. A design-matched summary of methodological concern is provided in Supplementary Table 2, with per-item scoring reported in the accompanying workbook.

4. Discussion

This systematic review identified 21 studies applying spatial transcriptomics to thyroid cancer, reflecting a rapidly expanding but still early evidence base. Across the included studies, spatial transcriptomics was used to investigate thyroid cancer progression, dedifferentiation, immune organization, stromal remodeling, invasion, lymph node metastasis, distant metastatic spread, and ATC biomarker discovery. The principal finding of this review is that spatial transcriptomics adds a layer of biological information that cannot be fully captured by histology, bulk RNA sequencing, or dissociated single-cell approaches alone. By preserving tissue architecture, these platforms allow tumor, stromal, endothelial, and immune programs to be interpreted in relation to invasive fronts, tumor cores, immune niches, vascular interfaces, and dedifferentiated regions.

A major theme across the literature was that thyroid cancer progression is spatially organized. Several studies demonstrated that dedifferentiation from differentiated thyroid carcinoma toward PDTC or ATC is accompanied by coordinated remodeling of the tumor microenvironment, including fibroblast activation, extracellular-matrix remodeling, angiogenesis, immune suppression, and altered metabolic signaling [13,14,15,16,17,30]. These findings support the concept that aggressive thyroid cancer behavior is not solely driven by malignant epithelial cells, but also by the surrounding stromal and immune architecture. In particular, recurrent identification of SERPINE1-positive and POSTN-positive cancer-associated fibroblast programs suggests that fibroblast populations at invasive or dedifferentiated regions may contribute to aggressive tumor behavior and could represent clinically relevant biomarkers or therapeutic targets [14,15].

Spatial transcriptomics also provided insight into tumor immune organization. Studies identified immune-evasion niches, dendritic-cell and T-cell interaction programs, APOE-linked immunosuppressive states, and myeloid-cell-associated patterns in metastatic or dedifferentiated disease [18,19,30,31]. These observations are clinically relevant because immune-checkpoint inhibition has shown activity in selected aggressive thyroid cancers, especially ATC, although response remains variable [3]. Spatial approaches may help clarify why some tumors remain immune-excluded or immunosuppressed despite immune-cell infiltration. In this setting, spatial transcriptomics may be particularly useful for distinguishing inflamed, immune-excluded, and immune-suppressed tumor regions and for identifying local cell-cell communication pathways that could guide rational combination therapy.

The reviewed studies also highlight the value of spatial methods for evaluating invasion and metastasis. In follicular-patterned tumors, where capsular and vascular invasion define malignant behavior, spatial transcriptomics can directly compare invasive fronts with tumor cores or adjacent noninvasive regions [25,29]. In PTC, studies of lymph node metastasis suggest that metastatic potential may be associated with specific tumor-cell states, immune interactions, stromal programs, and metabolic features [20,26,27]. Although distant metastatic thyroid cancer remains underrepresented, early spatial studies of metastatic ATC suggest that spatial profiling can characterize rare aggressive tumor sites and identify myeloid, immune, and dedifferentiation programs associated with poor prognosis [30].

From a technology standpoint, the current thyroid cancer literature remains heavily concentrated around capture-based whole-transcriptome platforms, especially 10x Visium and Visium CytAssist (Table 1 and Table 2). This has enabled broad discovery across thyroid cancer subtypes, but it also creates important limitations. A single 55 µm spot may overlie several adjacent cells, so its expression profile cannot be assigned with confidence to any one compartment; malignant, stromal, and immune signal arrive summed. Included studies handled this in several ways, such as, single-cell RNA sequencing for annotation, computational deconvolution, immunohistochemistry or multiplex immunofluorescence for orthogonal confirmation, and validation in external datasets. However, single-cell or subcellular imaging-based platforms remain underused in thyroid cancer. Future studies using Xenium, CosMx, MERFISH, seqFISH+, or similar approaches may be especially valuable for validating candidate cell states and ligand-receptor interactions at higher spatial resolution [8,9,10,40].

Several translational opportunities emerge from this review. First, spatial transcriptomics may improve biomarker discovery by identifying not only which genes are expressed, but where they are expressed within tumor tissue. This is particularly important in thyroid cancer, where clinically meaningful biology may be concentrated at invasive margins, lymphovascular spaces, immune-excluded niches, or dedifferentiated foci. Second, FFPE-compatible spatial workflows may allow retrospective analysis of archival pathology specimens linked to clinical outcomes. Third, spatial data may help refine molecular classification beyond mutation status by incorporating stromal, immune, and architectural features. Finally, spatial profiling may support therapeutic target discovery in aggressive thyroid cancers, especially ATC, where improved biomarkers and treatment strategies remain urgently needed.

Despite these strengths, the current evidence base has several limitations. Most studies were small, descriptive, and heterogeneous in platform, sample preparation, tumor subtype, analytic pipeline, and validation strategy. PTC was the most frequently studied subtype, while FTC, PDTC, ATC, medullary thyroid carcinoma, and distant metastatic disease were less consistently represented. Many studies focused on discovery rather than prospective validation, and few linked spatial findings to long-term clinical outcomes, treatment response, recurrence, or survival. In addition, differences in tissue preservation, section selection, spatial resolution, normalization, deconvolution, and cell-type annotation limit direct comparison across studies. These limitations precluded quantitative meta-analysis and required qualitative synthesis. The review process itself also had limitations: the search was restricted to three databases (PubMed/MEDLINE, Scopus, and Web of Science) and to English-language reports, without searching grey literature, trial registers, or conference proceedings, and the review was not prospectively registered; relevant studies may therefore have been missed, and language and publication bias cannot be excluded.

The methodological-quality appraisal further contextualizes these findings. Most studies reached moderate methodological quality, with common strengths including histopathologic confirmation, appropriate spatial-omics analytic workflows, and orthogonal validation in larger studies. However, recurrent weaknesses included incomplete reporting of patient selection, unclear consecutive or complete enrollment, limited demographic and presenting-site details, and sparse outcome or follow-up data. The highest-quality evidence came from comparative or outcome-oriented analytical cross-sectional cohorts, whereas public-data reanalyses were more limited by reliance on secondary data and incomplete reporting of code, parameters, and original quality-control details. No study was excluded on quality grounds, but these appraisal findings suggest that current conclusions should be viewed as hypothesis-generating rather than definitive.

Future thyroid cancer spatial-transcriptomics studies should prioritize larger, clinically annotated cohorts; standardized reporting of tissue processing and analytic pipelines; integration with orthogonal validation; and inclusion of clinically meaningful endpoints such as recurrence, metastasis, treatment response, and survival. Particular attention should be given to aggressive and underrepresented subtypes, including PDTC, ATC, medullary thyroid carcinoma, and distant metastatic disease. Combining whole-transcriptome discovery platforms with high-resolution imaging-based validation may provide the strongest framework for translating spatial findings into reproducible biomarkers and therapeutic hypotheses.

5. Conclusions

Spatial transcriptomics is an emerging and powerful approach for studying thyroid cancer biology in its native tissue context. Across the 21 included studies, spatial methods revealed organized programs of tumor–microenvironment remodeling, dedifferentiation, immune evasion, fibroblast activation, invasion, metastasis, and ATC-associated biomarker expression. Current evidence remains limited by small cohorts, concentration on a limited number of platforms, heterogeneous analytic methods, and incomplete clinical outcome data. Nevertheless, spatial transcriptomics provides a promising framework for linking molecular states to histologic architecture and may ultimately improve thyroid cancer classification, prognostication, and therapeutic targeting.

Future research should move beyond spatial discovery toward biological validation and clinical translation. We plan to apply spatial transcriptomic approaches to larger, well-annotated thyroid cancer cohorts to characterize tumors, immune, stromal, and metabolic niches associated with tumor progression, dedifferentiation, treatment resistance, and patient outcomes. Particular emphasis will be placed on identifying spatially organized, therapeutically actionable biomarkers and cell–cell interactions, followed by orthogonal validation using multiplex imaging and targeted spatial assays. Candidate pathways and targets will subsequently be evaluated in functional models, including thyroid cancer cell models and patient-derived organoids, to determine their biological and therapeutic relevance. Integration of spatial transcriptomics with genomic, proteomic, histopathologic, and clinical data may ultimately enable spatially informed biomarkers for patient stratification and precision therapy. Larger, standardized studies with rigorous validation and longitudinal clinical annotation will be essential to translate these spatial discoveries into clinically actionable strategies.