ReviewTrends in genetics : TIG2015
Single-Cell Analysis in Cancer Genomics.
Review in Trends in genetics : TIG, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 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
82 citing papers in PubMed.
- The arsenic proteome in cancer: Unravelling molecular mechanisms of anticancer drugs.Journal of biological inorganic chemistry : JBIC : a publication of the Society of Biological Inorganic Chemistry · 2026Review
- A metrological foundation for absolute transcriptomics using International System of Units-anchored calibrators.Nature communications · 2026Article
- Single-Nucleus Multi-Omics Reveals Hypoxia-Driven Angiogenic Programs and Their Epigenetic Control in Sinonasal Squamous Cell Carcinoma.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Cracking the resistance code: The molecular reclassification of cancer and precision therapy strategies.Seminars in cancer biology · 2025Review
- Single-Cell Sequencing: Genomic and Transcriptomic Approaches in Cancer Cell Biology.International journal of molecular sciences · 2025Review
- Topological data analysis in single cell biology.Frontiers in immunology · 2025Review
- Constructing Cell-Specific Causal Networks of Individual Cells for Depicting Dynamical Biological Processes.Research (Washington, D.C.) · 2025Article
- A metabolic map and artificial intelligence-aided identification of nasopharyngeal carcinoma via a single-cell Raman platform.British journal of cancer · 2024Article
- Morphological Stability for in silico Models of Avascular Tumors.Bulletin of mathematical biology · 2024Article
- Recent advances in enzyme-free and enzyme-mediated single-nucleotide variation assayNational science review · 2024Review
- Single-Cell Recovery from Tumor Cell Xenotransplanted Zebrafish Embryos for the Study of Metastasis-Initiating Cells.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Potential value of high-throughput single-cell DNA sequencing of Juvenile myelomonocytic leukemia: report of two cases.NPJ systems biology and applications · 2023Article
- Harnessing the potential of CAR-T cell therapy: progress, challenges, and future directions in hematological and solid tumor treatments.Journal of translational medicine · 2023Review
- Mid-Infrared Photothermal-Fluorescence In Situ Hybridization for Functional Analysis and Genetic Identification of Single Cells.Analytical chemistry · 2023Article
- Single-Cell Classification Based on Population Nucleus Size Combining Microwave Impedance Spectroscopy and Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- Cancer: slaying the nine-headed Hydra.Annals of oncology : official journal of the European Society for Medical Oncology · 2023Review
- Droplets microfluidics platform-A tool for single cell research.Frontiers in bioengineering and biotechnology · 2023Review
- Mass Spectrometry Imaging-Based Single-Cell Lipidomics Profiles Metabolic Signatures of Heart Failure.Research (Washington, D.C.) · 2023Article
- Review
- Article
22 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
4 authors.
Funding
Abstract
Genetic changes and environmental differences result in cellular heterogeneity among cancer cells within the same tumor, thereby complicating treatment outcomes. Recent advances in single-cell technologies have opened new avenues to characterize the intra-tumor cellular heterogeneity, identify rare cell types, measure mutation rates, and, ultimately, guide diagnosis and treatment. In this paper we review the recent single-cell technological and computational advances at the genomic, transcriptomic, and proteomic levels, and discuss their applications in cancer research.
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.