Evidence map›Paper›PMID 40441264›Full record

ArticleMedicine2025

Identification of key genes regulating colorectal cancer stem cell characteristics by bioinformatics analysis.

Jing Lu, Haotian Zhang, Xiaoyu Gu, Yonghui Liu, Chengwen Zhao, Xudong Wang

Abstract read
In one paragraph

Article in Medicine, 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

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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Jing LuMedical School of Nantong University, Nantong, China.ORCID 0009-0000-1976-2409
Haotian ZhangDepartment of Laboratory Medicine, Affiliated Hospital of Nantong University, Nantong, China.
Xiaoyu GuCenter of Clinical Laboratory Medicine, Zhongda Hospital, Southeast University, Nanjing, China.
Yonghui LiuCenter of Clinical Laboratory Medicine, Zhongda Hospital, Southeast University, Nanjing, China.
Chengwen ZhaoCenter of Clinical Laboratory Medicine, XuZhou Central Hospital, Xuzhou, China.
Xudong WangDepartment of Laboratory Medicine, Affiliated Hospital of Nantong University, Nantong, China.ORCID 0009-0000-0599-4970

Funding

81972015;H2018074 the National Natural Science Foundation;the Social Development Project from Jiangsu Provincial Department of Science and Technology;
6 · The paper itself

Abstract

Cancer stem cells (CSCs), distinguished by their abilities to differentiate and self-renew, play a pivotal role in the progression of colorectal cancer (CRC). However, the mechanisms that sustain CSCs in CRC remain unclear. This study aimed to identify and characterize gene expressions associated with CRC stemness. We applied a 1-class logistic regression machine learning model to calculate the mRNA expression-based stemness index (mRNAsi) for CRC samples from The Cancer Genome Atlas and cBioPortal databases, adjusting the mRNAsi by tumor purity. Clinical features of CRC were considered in assessing both mRNAsi and adjusted mRNAsi levels. Using DESeq2, we screened differentially expressed genes between high and low mRNAsi groups. Enrichment analysis provided functional annotation for these differentially expressed genes. Key genes linked to mRNAsi were identified using the Kaplan-Meier plotter and Cytoscape software, followed by an evaluation of their prognostic significance. Potential small-molecule compounds targeting the CRC stemness signature were explored via L1000FWD, DGIdb, and CMap databases. CRC samples with higher mRNAsi or adjusted mRNAsi values showed improved disease-free survival (DSS) and progression-free survival (PFS). Strong correlation between clinical characteristics of CSCs and mRNAsi was observed; CMS4 subtype CRC patients had lower mRNAsi with worse DSS and PFS. Ten key genes associated with mRNAsi were identified: collagen type I alpha 1, fibrillin 1, matrix metalloproteinase 9, SPP1, BGN, COL5A1, FN1, elastin, matrix metalloproteinase 2, collagen type I alpha 2. Lower expression of these genes correlated with better PFS and DSS. High correlation among these genes was confirmed in the protein-protein interaction network. This study identifies potential small-molecule drugs targeting stemness in CRC and highlights the prognostic value of the 10 key genes, offering insights into therapeutic targets for CRC treatment.

Indexed as

Colorectal NeoplasmsNeoplastic Stem CellsBiomarkers, TumorComputational BiologyFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansKaplan-Meier EstimateMaleMiddle AgedPrognosisRNA, MessengerBiomarkers, TumorRNA, Messengercancer stem cellscolorectal cancerdifferentially expressed genesmRNA expression-based stemness indexThe Cancer Genome Atlastherapeutic targets

Identifiers

PMID40441264
PMCPMC12425111

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.