Evidence map›Paper›PMID 41089522›Full record

ArticleFrontiers in oncology2025

Transcriptome analyses identify 10 deregulated hub genes and essential molecular mechanisms in early-onset colorectal cancer.

Hieu Duc Nguyen, Ming-Jenn Chen, Chung-Ying Lee, Yi-Chun Ni, Ke Xin Yee, Yung-Fu Wu, Kuen-Haur Lee

Abstract read
In one paragraph

Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. 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

7 authors.

Hieu Duc NguyenGraduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, New Taipei City, Taiwan.
Ming-Jenn ChenDepartment of Surgery, Chi Mei Medical Center, Tainan, Taiwan.
Chung-Ying LeeDivision of Gastroenterology and Hepatology, Department of Internal Medicine, Taipei Medical University Shuang Ho Hospital Ministry of Health and Welfare, New Taipei City, Taiwan.
Yi-Chun NiPhD Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, New Taipei City, Taiwan.
Ke Xin YeePhD Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, New Taipei City, Taiwan.
Yung-Fu WuNational Defense Medical Center, School of Medicine, Department of Medical Research, Tri-Service General Hospital, Taipei, Taiwan.
Kuen-Haur LeeGraduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, New Taipei City, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The rapid increase in early-onset colorectal cancer (EoCRC) case numbers in recent years indicated an urgent need to identify the essential mechanisms and markers for EoCRC diagnosis and treatment. Therefore, this study aimed to analyze the metadata to overcome the limitation of the sample number of previous EoCRC research and to identify central mechanisms and genes that are crucial for EoCRC. Methods: This study employed statistical analysis of data from the cBioPortal and GEPIA databases to identify overexpressed EoCRC genes. Using a protein-protein interaction map, it identified hub genes. The function of these genes was clarified via risk model, survival, and cell model analysis. Results and Discussion: The results of clinical data analysis showed an increased rate of late-stage diagnosis and a lower overall survival of the EoCRC cohort. A total of 953 enriched gene samples were detected in EoCRC and 89 genes were identified as EoCRC overexpression genes. From the protein-protein interactions among 53 genes, the top 10 hub genes showed potential for EoCRC diagnosis and prognosis by linking gene expression to diagnosis and survival analysis data. The knockdown of four selected hub genes in the cell model identified the association of EoCRC overexpression hub genes with tumor development and suggested their role in mTOR signaling, cell cycle, and apoptosis regulation. In summary, the study analyzed molecular and clinical data to identify hub genes associated with cancer prognosis in patients with EoCRC. These genes may serve as targets for diagnosis and treatment.

Indexed as

apoptosiscell cycleearly-onset colorectal cancerEOCRCEoCRC cell modelmetadatarisk modeltranscriptome

Identifiers

PMID41089522
PMCPMC12515628

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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.