ReviewCells2025
Spatial Transcriptomics in Thyroid Cancer: Applications, Limitations, and Future Perspectives.
Review in Cells, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Bridging the Precision Gap in Rheumatoid Arthritis: Spatial Transcriptomics, Spatial Proteomics, and Artificial Intelligence in Precision Health.Biomedicines · 2026Review
- Peptide Arrays as Tools for Unraveling Tumor Microenvironments and Drug Discovery in Oncology.Cells · 2026Review
- Spatially resolved immune niches in thyroid cancer: from hot-cold-excluded ecosystems to precision immunotherapy.Frontiers in immunology · 2026Review
- Integrating Spatial Omics and Deep Learning: Toward Predictive Models of Cardiomyocyte Differentiation Efficiency.Bioengineering (Basel, Switzerland) · 2025Review
- Decoding the Tumor Microenvironment: Insights and New Targets from Single-Cell Sequencing and Spatial Transcriptomics.Current issues in molecular biology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Spatial transcriptomics (ST) is emerging as a powerful technology that transforms our understanding of thyroid cancer by offering a spatial context of gene expression within the tumor tissue. In this review, we synthesize the recent applications of ST in thyroid cancer research, with a particular focus on the heterogeneity of the tumor microenvironment, tumor evolution, and cellular interactions. Studies have leveraged the spatial information provided by ST to map distinct cell types and expression patterns of genes and pathways across the different regions of thyroid cancer samples. The spatial context also allows a closer examination of invasion and metastasis, especially through the dysregulation at the tumor leading edge. Additionally, signaling pathways are inferred at a more accurate level through the spatial proximity of ligands and receptors. We also discuss the limitations that need to be overcome, including technical limitations like low resolution and sequencing depth, the need for high-quality samples, and complex data handling processes, and suggest future directions for a wider and more efficient application of ST in advancing personalized treatment of thyroid cancer.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.