Evidence map›Paper›PMID 40411705›Full record

ArticleDiscover oncology2025

Prognostic model identification of ribosome biogenesis-related genes in pancreatic cancer based on multiple machine learning analyses.

Yuan Sun, Yan Li, Anlan Zhang, Tao Hu, Ming Li

Abstract read
In one paragraph

Article in Discover oncology, 2025. 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

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2 · The registry

The trial behind it

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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
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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

5 authors.

Yuan SunDepartment of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Yan LiDepartment of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Anlan ZhangDepartment of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Tao HuDepartment of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Ming LiDepartment of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China. liming@hospital.cqmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPancreatic cancer is a highly aggressive cancer characterized by low survival rate. Enhanced ribosome biogenesis may be associated with tumor drug resistance and malignant phenotypes, representing a potential therapeutic target in pancreatic cancer. Therefore, exploring the molecular mechanisms of ribosome biogenesis in pancreatic cancer may uncover new biomarkers and potential therapeutic targets, facilitating the development of personalized treatment strategies.

methodsRibosome biogenesis-related gene signatures were acquired from TCGA and Gene Cards databases. Prognostic gene sets were screened using machine learning algorithms to construct a risk model, which was externally validated via GEO database. Single-cell RNA sequencing analysis (GSE155698 dataset) was performed to assess gene expression patterns and module scores.

resultsSixty ribosome biogenesis-related prognostic genes were identified in pancreatic cancer. Cox regression and machine learning algorithms selected nine pivotal biomarkers (ECT2; CKB; HMGA2; TPX2; ERBB3; SLC2A1; KRT13; PRSS3; CRABP2) with high diagnostic and prognostic specificity for PAAD. The machine learning-derived risk score correlated strongly with tumor proliferation pathways and immunosuppression, suggesting dual roles in tumor promotion and immunosuppressive microenvironment remodeling. Single-cell analysis highlighted predominant expression of CKB, SLC2A1, ERBB3, CRABP2, and PRSS3 in pancreatic ductal epithelial cells.

conclusionsOur results shed light on the potential connections between ribosome biogenesis-related molecular characteristics and clinical features, the tumor microenvironment, and clinical drug responses. The research underscores the critical role of ribosome biogenesis in the progression and treatment resistance of pancreatic cancer, offering valuable new perspectives for prognostic evaluation and therapeutic response prediction in pancreatic cancer.

Indexed as

Drug sensitivityPancreatic cancerPrognosisRibosome biogenesisTumor microenvironment

Identifiers

PMID40411705
PMCPMC12103412

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.