Evidence map›Paper›PMID 40702779›Full record

ArticleProtein & cell2025

Systematic characterization of full-length RNA isoforms in human colorectal cancer at single-cell resolution.

Ping Lu, Yu Zhang, Yueli Cui, Yuhan Liao, Zhenyu Liu, Zhi-Jie Cao, Jun-E Liu, Lu Wen, Xin Zhou, Wei Fu and 1 more

Abstract read
In one paragraph

Article in Protein & cell, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Long-Read Sequencing Reveals RNA Splicing Complexity in Human Diseases.Computational and structural biotechnology journal · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Ping LuBiomedical Pioneering Innovative Center, School of Life Sciences, Department of General Surgery, Third Hospital, Peking University, Beijing 100871, China.ORCID 0000-0002-4340-2670
Yu ZhangBiomedical Pioneering Innovative Center, School of Life Sciences, Department of General Surgery, Third Hospital, Peking University, Beijing 100871, China.ORCID 0000-0002-2548-5580
Yueli CuiBiomedical Pioneering Innovative Center, School of Life Sciences, Department of General Surgery, Third Hospital, Peking University, Beijing 100871, China.ORCID 0000-0002-2892-4729
Yuhan LiaoBiomedical Pioneering Innovative Center, School of Life Sciences, Department of General Surgery, Third Hospital, Peking University, Beijing 100871, China.ORCID 0000-0002-3151-2063
Zhenyu LiuBiomedical Pioneering Innovative Center, School of Life Sciences, Department of General Surgery, Third Hospital, Peking University, Beijing 100871, China.ORCID 0000-0001-6144-8018
Zhi-Jie CaoBiomedical Pioneering Innovative Center, School of Life Sciences, Department of General Surgery, Third Hospital, Peking University, Beijing 100871, China.ORCID 0000-0002-0026-671X
Jun-E LiuBiomedical Pioneering Innovative Center, School of Life Sciences, Department of General Surgery, Third Hospital, Peking University, Beijing 100871, China.ORCID 0000-0001-9051-159X
Lu WenBiomedical Pioneering Innovative Center, School of Life Sciences, Department of General Surgery, Third Hospital, Peking University, Beijing 100871, China.ORCID 0000-0002-1773-1876
Xin ZhouBiomedical Pioneering Innovative Center, School of Life Sciences, Department of General Surgery, Third Hospital, Peking University, Beijing 100871, China.ORCID 0000-0002-4048-4017
Wei FuBiomedical Pioneering Innovative Center, School of Life Sciences, Department of General Surgery, Third Hospital, Peking University, Beijing 100871, China.ORCID 0000-0001-5248-7891
Fuchou TangBiomedical Pioneering Innovative Center, School of Life Sciences, Department of General Surgery, Third Hospital, Peking University, Beijing 100871, China.ORCID 0000-0002-8625-7717

Funding

Beijing Nova Program 2022029National Key Research and Development Program of China 2023ZD0520000National Natural Science Foundation of China 81972702
6 · The paper itself

Abstract

Dysregulated RNA splicing is a well-recognized characteristic of colorectal cancer (CRC); however, its intricacies remain obscure, partly due to challenges in profiling full-length transcript variants at the single-cell level. Here, we employ high-depth long-read scRNA-seq to define the full-length transcriptome of colorectal epithelial cells in 12 CRC patients, revealing extensive isoform diversities and splicing alterations. Cancer cells exhibited increased transcript complexity, with widespread 3'-UTR shortening and reduced intron retention. Distinct splicing regulation patterns were observed between intrinsic-consensus molecular subtypes (iCMS), with iCMS3 displaying even higher splicing factor activities and more pronounced 3'-UTR shortening. Furthermore, we revealed substantial shifts in isoform usage that result in alterations of protein sequences from the same gene with distinct carcinogenic effects during tumorigenesis of CRC. Allele-specific expression analysis revealed dominant mutant allele expression in key oncogenes and tumor suppressors. Moreover, mutated PPIG was linked to widespread splicing dysregulation, and functional validation experiments confirmed its critical role in modulating RNA splicing and tumor-associated processes. Our findings highlight the transcriptomic plasticity in CRC and suggest novel candidate targets for splicing-based therapeutic strategies.

Indexed as

Colorectal NeoplasmsRNA IsoformsRNA, NeoplasmSingle-Cell AnalysisGene Expression Regulation, NeoplasticHumansRNA SplicingTranscriptomeRNA IsoformsRNA, Neoplasmcolorectal cancerdifferential transcript usagefull-length RNA isoformslong-read scRNA-seqRNA splicing

Identifiers

PMID40702779
PMCPMC12578292

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.