ReviewSeminars in cancer biology2023
The promising application of cell-cell interaction analysis in cancer from single-cell and spatial transcriptomics.
Review in Seminars in cancer biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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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.
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Who cites it
51 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deciphering the biological underpinnings behind prognostic MRI-based imaging signatures in breast cancer: a systematic review.Journal of translational medicine · 2025Pooled it
- Addressing multiple facets of ligand-receptor network inference including single-cell proteomics.Nucleic acids research · 2026Article
- Unraveling cell-cell communication through spatial transcriptomics: a review of computational methods.Briefings in bioinformatics · 2026Review
- Inference of spatial chromatin accessibility via integration of spatial transcriptomics and single-cell multi-omics data.Nature communications · 2026Article
- Integrative single-cell and bulk transcriptomics define cell death patterns and ZDHHC22 in gastric cancer progression.BMC medical genomics · 2026Article
- Multi-omics integration identifies ribosome biogenesis-active macrophage subpopulation and its key gene GNL2 in driving liver hepatocellular carcinoma progression and mechanisms.Cancer cell international · 2026Article
- Tracing cell communication programs across conditions at single cell resolution with CCC-RISE.bioRxiv : the preprint server for biology · 2026Article
- scComm: a contrastive learning framework for deciphering cell-cell communications at single-cell resolution.Genome biology · 2026Article
- Single-Nucleus Transcriptomic Sequencing Revealed Cellular and Molecular Changes in a Pilocarpine-Induced Epilepsy Rat Model.Neuroscience bulletin · 2026Article
- Reconstructing single-cell resolution from spatial transcriptomics with CellRefiner.Nature communications · 2026Article
- A Single-Cell Atlas of Pan-Cancer Liver Metastasis Reveals Dynamic Cellular Programs Driving Metastatic Progression and Immune Modulation.Research (Washington, D.C.) · 2026Article
- Spatial transcriptomics in Alzheimer's disease: technologies, challenges and discoveries.Molecular neurodegeneration advances · 2026Review
- Autophagy-dependent secretion of ENO1 mediates chemoresistance of glioblastoma and tumor microenvironment remodeling.Cell death & disease · 2025Article
- MAGNET: Multi-view graph autoencoder with cell-gene attention for cell interaction network reconstruction from spatial transcriptomics.PLoS computational biology · 2025Article
- Integration of genetic, proteomic, and transcriptomic data identifies therapeutic targets and prognostic biomarkers in bladder cancer.Translational andrology and urology · 2025Article
- DD-CC-II: Data Driven Cell-Cell Interaction Inference and Its Application to COVID-19.International journal of molecular sciences · 2025Article
- Single-cell sequencing technology in renal cancer: insights into tumor biology and clinical application.Biomarker research · 2025Review
- Article
- A comparison of integration methods for single-cell RNA sequencing data and ATAC sequencing data.Quantitative biology (Beijing, China) · 2025Review
- An algorithmic perspective on deciphering cell-cell interactions with spatial omics data.Briefings in bioinformatics · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Cell-cell interactions instruct cell fate and function. These interactions are hijacked to promote cancer development. Single-cell transcriptomics and spatial transcriptomics have become powerful new tools for researchers to profile the transcriptional landscape of cancer at unparalleled genetic depth. In this review, we discuss the rapidly growing array of computational tools to infer cell-cell interactions from non-spatial single-cell RNA-sequencing and the limited but growing number of methods for spatial transcriptomics data. Downstream analyses of these computational tools and applications to cancer studies are highlighted. We finish by suggesting several directions for further extensions that anticipate the increasing availability of multi-omics cancer data.
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.