ReviewBiochemical Society transactions2025
Exploring genome-transcriptome correlations in cancer.
Review in Biochemical Society transactions, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- scRepli-RamDA-seq: a multi-omics technology enabling the analysis of gene expression dynamics during S-phase.Nature communications · 2025Article
- Research on the process and molecular mechanism of inhibiting serous ovarian cancer by PRTN3 and its inhibitor Sivelestat.Journal of ovarian research · 2025Article
- Hybrid BAG-seq: DNA and RNA from the same single nucleus reveals interactions between genomic and transcriptomic landscapes in human tumor samples.Genome biology · 2025Article
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
No grant is acknowledged in the PubMed record.
Abstract
We examine the complex relationship between genomic copy number variation (CNV) and gene expression, highlighting the relevance to cancer biology and other biological contexts. By tracing the history of genometranscriptome correlations, we emphasize the complexity and challenges in understanding these interactions, particularly within the heterogeneous landscape of human cancers. Recent advances in computational algorithms and high-throughput single-cell multi-omic sequencing technologies are discussed, demonstrating their potential to refine our understanding of cancer biology and their limitations. The integration of genomic and transcriptomic analyses, which offers novel insights into tumor evolution and heterogeneity as well as therapeutic strategies, is presented as a crucial approach for advancing cancer research.
Indexed as
Identifiers
What Socratic 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 Socratic graph.