Evidence map›Paper›PMID 39062881›Full record

ArticleInternational journal of molecular sciences2024

Multi-Omics Characterization of E3 Regulatory Patterns in Different Cancer Types.

Zhongyan Li, Jingting Wan, Shangfu Li, Yun Tang, Yang-Chi-Dung Lin, Jie Ni, Xiaoxuan Cai, Jinhan Yu, Hsien-Da Huang, Tzong-Yi Lee

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

10 authors.

Zhongyan LiWarshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China.
Jingting WanWarshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China.ORCID 0009-0001-3455-9537
Shangfu LiWarshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China.
Yun TangDepartment of Biological Science and Technology, National Yang Ming Chiao Tung University, No. 75, Boai Street, Hsinchu 300, Taiwan.
Yang-Chi-Dung LinWarshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China.
Jie NiWarshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China.
Xiaoxuan CaiWarshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China.
Jinhan YuWarshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China.
Hsien-Da HuangWarshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China.
Tzong-Yi LeeDepartment of Biological Science and Technology, National Yang Ming Chiao Tung University, No. 75, Boai Street, Hsinchu 300, Taiwan.ORCID 0009-0002-0283-7712

Funding

Center for Intelligent Drug Systems and Smart Bio-device IDS2BGuangdong Province Basic and Applied Basic Research Fund 2021A1515012447National Natural Science Foundation of China 32070659National Science and Technology Council NSTC 112-2321-B-A49-01National Science and Technology Council NSTC 113-2634-F-039-001The National Health Research Institutes in Taiwan NHRI-EX113-11320BIYushan Young Fellow Program 112C1N084C
6 · The paper itself

Abstract

Ubiquitination, a post-translational modification, refers to the covalent attachment of ubiquitin molecules to substrates. This modification plays a critical role in diverse cellular processes such as protein degradation. The specificity of ubiquitination for substrates is regulated by E3 ubiquitin ligases. Dysregulation of ubiquitination has been associated with numerous diseases, including cancers. In our study, we first investigated the protein expression patterns of E3 ligases across 12 cancer types. Our findings indicated that E3 ligases tend to be up-regulated and exhibit reduced tissue specificity in tumors. Moreover, the correlation of protein expression between E3 ligases and substrates demonstrated significant changes in cancers, suggesting that E3-substrate specificity alters in tumors compared to normal tissues. By integrating transcriptome, proteome, and ubiquitylome data, we further characterized the E3-substrate regulatory patterns in lung squamous cell carcinoma. Our analysis revealed that the upregulation of the SKP2 E3 ligase leads to excessive degradation of BRCA2, potentially promoting tumor cell proliferation and metastasis. Furthermore, the upregulation of E3 ubiquitin-protein ligase TRIM33 was identified as a biomarker associated with a favorable prognosis by inhibiting the cell cycle. This work exemplifies how leveraging multi-omics data to analyze E3 ligases across various cancers can unveil prognosis biomarkers and facilitate the identification of potential drug targets for cancer therapy.

Indexed as

NeoplasmsUbiquitinationUbiquitin-Protein LigasesBiomarkers, TumorGene Expression Regulation, NeoplasticHumansMultiomicsPrognosisProteomeProteomicsS-Phase Kinase-Associated ProteinsTranscriptomeTripartite Motif ProteinsBiomarkers, TumorProteomeSKP2 protein, humanS-Phase Kinase-Associated ProteinsTripartite Motif ProteinsUbiquitin-Protein LigasescancerE3 ubiquitin ligasesmulti-omics analysisproteomicsubiquitination

Identifiers

PMID39062881
PMCPMC11276688

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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.