ReviewNon-coding RNA2025
Single-Cell Transcriptomic Approaches for Decoding Non-Coding RNA Mechanisms in Colorectal Cancer.
Review in Non-coding RNA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Comprehensive single-cell analysis reveals cellular heterogeneity and immune interactions in colorectal cancer.Translational oncology · 2026Article
- Review
- Computational identification and validation of non-coding rna biomarkers in gastrointestinal cancer.Functional & integrative genomics · 2025Review
- Exploring microRNAs, One Cell at a Time.Non-coding RNA · 2025Article
- Diagnostic and Therapeutic Potential of Selected microRNAs in Colorectal Cancer: A Literature Review.Cancers · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-coding RNAs (ncRNAs) play crucial roles in colorectal cancer (CRC) development and progression. Recent developments in single-cell transcriptome profiling methods have revealed surprising levels of expression variability among seemingly homogeneous cells, suggesting the existence of many more cell types than previously estimated. This review synthesizes recent advances in ncRNA research in CRC, emphasizing single-cell bioinformatics approaches for their analysis. We explore computational methods and tools used for ncRNA identification, characterization, and functional prediction in CRC, with a focus on single-cell RNA sequencing (scRNA-seq) data. The review highlights key bioinformatics strategies, including sequence-based and structure-based approaches, machine learning applications, and multi-omics data integration. We discuss how these computational techniques can be applied to analyze differential expression, perform functional enrichment, and construct regulatory networks involving ncRNAs in CRC. Additionally, we examine the role of bioinformatics in leveraging ncRNAs as diagnostic and prognostic biomarkers for CRC. We also discuss recent scRNA-seq studies revealing ncRNA heterogeneity in CRC. This review aims to provide a comprehensive overview of the current state of single-cell bioinformatics in ncRNA CRC research and outline future directions in this rapidly evolving field, emphasizing the integration of computational approaches with experimental validation to advance our understanding of ncRNA biology in CRC.
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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.