ReviewJournal of cellular and molecular medicine2025
Tumour Single-Cell Bioinformatics: From Immune Profiling to Molecular Dynamics.
Review in Journal of cellular and molecular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Molecular mechanisms of KMT2C alterations in gastrointestinal cancers: enhancer network destabilization, lineage plasticity, and clinical translation.Frontiers in immunology · 2026Review
- Tumour Single-Cell Bioinformatics: From Immune Profiling to Molecular Dynamics.Journal of cellular and molecular medicine · 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
10 authors.
Funding
Abstract
Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of tumours by enabling high-resolution profiling of their cellular composition. Traditionally perceived as masses of homogeneous cancer cells, tumours are now recognised as complex ecosystems shaped by the tumour microenvironment (TME), which includes diverse immune cells, cancer-associated fibroblasts and extracellular matrix components. scRNA-seq has revealed remarkable heterogeneity within the TME, identifying novel or rare immune cell subsets and delineating their dynamic functional states. In particular, it has illuminated intercellular signalling networks and temporal cell-state transitions that drive tumour progression and immune evasion. Moreover, the integration of scRNA-seq data with clinical information has highlighted its potential in improving patient stratification, biomarker discovery and therapeutic target identification. Here, we systematically summarise recent advances in applying scRNA-seq to dissect the TME, discuss the implications of these findings for immunotherapy resistance and precision oncology, and outline future opportunities for integrating scRNA-seq with emerging technologies to develop more effective and personalised cancer treatment strategies.
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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.