ArticleNature communications2018
Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 355 papers, 1 of them a synthesis that pooled it.
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
355 citing papers in PubMed, 1 synthesis or guideline pooled it, 609 citations in OpenAlex.
- The promising role of new molecular biomarkers in prostate cancer: from coding and non-coding genes to artificial intelligence approaches.Prostate cancer and prostatic diseases · 2022Pooled it
- Diffusion MRI reveals amyloid plaque-associated cellular signatures through spatial transcriptomic integration.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Single-cell and spatial RNA sequencing in prostate cancer.Nature reviews. Urology · 2026Review
- Single-cell and spatial transcriptomic profiling identifies a fibroblast-like endothelial state and the MDK-NCL signaling axis in prostate cancer microenvironment remodeling.Translational oncology · 2026Article
- OT-knn: A neighborhood-aware optimal transport framework for aligning spatial transcriptomics data.PLoS computational biology · 2026Article
- Uncovering genes driving developmental stage progression in prostate cancer through spatial transcriptomics.Genes & diseases · 2026Article
- Transcriptomic Challenges We Faced with Animal Models for Neurological Disorders.Current issues in molecular biology · 2026Review
- SRLST: a unified multimodal representation learning framework for spatial transcriptomics analysis.Bioinformatics (Oxford, England) · 2026Article
- The Great Escape: the systems biology of endocrine resistance and lineage plasticity.Endocrine-related cancer · 2026Review
- AddaGCN: Spatial transcriptomics deconvolution using graph convolutional networks with adversarial discriminative domain adaptation.PLoS computational biology · 2026Article
- Morphomechanically-Informed Spatial Curvature Sequencing in Prostate Cancer.bioRxiv : the preprint server for biology · 2026Article
- Article
- Tumor heterogeneity: development, mechanisms, and therapeutic implications.Signal transduction and targeted therapy · 2026Review
- Interpretable and scalable spatial gene set activity analysis with GESSO uncovers functional tissue architecture.bioRxiv : the preprint server for biology · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Advances in spatial omics for the analysis of prostate cancer.Analytical and bioanalytical chemistry · 2026Review
- Combining Spatial Multi-Omics Data to Decipher Spatial Domains and Elucidate Cell Heterogeneity Based on Self-Supervised Graph Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Spatiotemporal cell type deconvolution leveraging tissue structure.Research square · 2026Article
- Review
- Article
295 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
18 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intra-tumor heterogeneity is one of the biggest challenges in cancer treatment today. Here we investigate tissue-wide gene expression heterogeneity throughout a multifocal prostate cancer using the spatial transcriptomics (ST) technology. Utilizing a novel approach for deconvolution, we analyze the transcriptomes of nearly 6750 tissue regions and extract distinct expression profiles for the different tissue components, such as stroma, normal and PIN glands, immune cells and cancer. We distinguish healthy and diseased areas and thereby provide insight into gene expression changes during the progression of prostate cancer. Compared to pathologist annotations, we delineate the extent of cancer foci more accurately, interestingly without link to histological changes. We identify gene expression gradients in stroma adjacent to tumor regions that allow for re-stratification of the tumor microenvironment. The establishment of these profiles is the first step towards an unbiased view of prostate cancer and can serve as a dictionary for future studies.
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.