ReviewMolecular cancer2024
Advances in spatial transcriptomics and its applications in cancer research.
Review in Molecular cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 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
105 citing papers in PubMed.
- Serval: A modular framework for decoding imaging based spatial transcriptomics data.Bioinformatics (Oxford, England) · 2026Article
- CoexpressDeconvolve enables reference-free single-cell-resolution deconvolution from spot-based spatial transcriptomics.iScience · 2026Article
- Spatial-EV-seq: Mapping the spatial organisation of extracellular vesicles in tissues.Clinical and translational medicine · 2026Article
- ProST: an image prompt-guided multimodal representation learning framework for spatial domain identification.Bioinformatics (Oxford, England) · 2026Article
- Image-guided spatial omics enhancement reveals hidden spatial microstructures.Bioinformatics (Oxford, England) · 2026Article
- SPP1-driven spatial niche remodelling defines aggressive progression in the basal subtype of upper tract urothelial carcinoma.Clinical and translational medicine · 2026Article
- STAID: A Self-Refining Deep Learning Framework for Spatial Cell-Type Deconvolution with Biologically Informed Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- RETROFIT: Reference-free deconvolution of cell-type mixtures in spatial transcriptomics.Nature communications · 2026Article
- Article
- Proteoglycans as Molecular Regulators of Bone Metastasis: Extracellular Matrix Remodeling, Tumor-Bone Crosstalk, Dormancy, and Therapeutic Opportunities.Biomolecules · 2026Review
- Review
- The applications of single-cell and spatial transcriptomics in neuroscience and brain disorders.Neuroscience and biobehavioral reviews · 2026Review
- SCRUM-Japan MONSTAR3 hematology cohort: a nationwide multi-omics integrated platform for next-generation precision medicine in hematologic malignancies.International journal of clinical oncology · 2026Article
- Regulatory B-cell states in NSCLC immunotherapy resistance: mechanisms, spatial context and translational implications.Cancer immunology, immunotherapy : CII · 2026Review
- Mapping the spatial landscape of extracellular vesicles in tissues with Spatial-EV-seq.Nature biotechnology · 2026Article
- ResSAT: enhancing spatial transcriptomics prediction from H&E-stained histology images with an interactive spot transformer.Genome biology · 2026Article
- Multidrug resistance in cancer: current understandings and future perspective.Molecular biomedicine · 2026Review
- Spatially resolved profiling of extracellular vesicles in tissues with Spatial-EV-seq.Nature biotechnology · 2026Article
- Spatial and Phenotypic Heterogeneity of ILC Subsets in Mouse Lung Under Type 2 Inflammatory Conditions.European journal of immunology · 2026Article
- Unraveling the Molecular Mechanisms of Glioma Recurrence: A Study Integrating Single-Cell and Spatial Transcriptomics.Annals of clinical and translational neurology · 2026Article
45 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Malignant tumors have increasing morbidity and high mortality, and their occurrence and development is a complicate process. The development of sequencing technologies enabled us to gain a better understanding of the underlying genetic and molecular mechanisms in tumors. In recent years, the spatial transcriptomics sequencing technologies have been developed rapidly and allow the quantification and illustration of gene expression in the spatial context of tissues. Compared with the traditional transcriptomics technologies, spatial transcriptomics technologies not only detect gene expression levels in cells, but also inform the spatial location of genes within tissues, cell composition of biological tissues, and interaction between cells. Here we summarize the development of spatial transcriptomics technologies, spatial transcriptomics tools and its application in cancer research. We also discuss the limitations and challenges of current spatial transcriptomics approaches, as well as future development and prospects.
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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.