Evidence map›Paper›PMID 42007143›Full record

ArticleAmerican journal of translational research2026

Metabolic reprogramming-associated genomic instability drives colorectal cancer progression via the UBXN1-NF-κB axis.

Long Qian, Ziyi Li, Tangtang Yang, Shasha Xia, Lei Jin, Chengyu Zhu, Wenshan Jing, Yue Wang, Yun Ye, Yi Shen and 3 more

Abstract read
In one paragraph

Article in American journal of translational research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

13 authors.

Long QianDepartment of General Surgery, The First Affiliated Hospital of Anhui University of Chinese Medicine Hefei 230031, Anhui, P. R. China.
Ziyi LiDepartment of General Surgery, The First Affiliated Hospital of Anhui University of Chinese Medicine Hefei 230031, Anhui, P. R. China.
Tangtang YangDepartment of General Surgery, The First Affiliated Hospital of Anhui University of Chinese Medicine Hefei 230031, Anhui, P. R. China.
Shasha XiaInstitute of Data Space, Hefei Comprehensive National Science Center Hefei 230000, Anhui, P. R. China.
Lei JinDepartment of General Surgery, The First Affiliated Hospital of Anhui University of Chinese Medicine Hefei 230031, Anhui, P. R. China.
Chengyu ZhuDepartment of General Surgery, The First Affiliated Hospital of Anhui University of Chinese Medicine Hefei 230031, Anhui, P. R. China.
Wenshan JingDepartment of General Surgery, The First Affiliated Hospital of Anhui University of Chinese Medicine Hefei 230031, Anhui, P. R. China.
Yue WangDepartment of General Surgery, The First Affiliated Hospital of Anhui University of Chinese Medicine Hefei 230031, Anhui, P. R. China.
Yun YeDepartment of Traditional Chinese Medicine, Shexian Guilin Town Health Center Huangshan 245000, Anhui, P. R. China.
Yi ShenDepartment of General Surgery, The First Affiliated Hospital of Anhui University of Chinese Medicine Hefei 230031, Anhui, P. R. China.
Lixiang LiInstitute of Chinese Medicine Surgery, Anhui Academy of Chinese Medicine Hefei 230031, Anhui, P. R. China.
Hui PengDepartment of General Surgery, The First Affiliated Hospital of Anhui University of Chinese Medicine Hefei 230031, Anhui, P. R. China.
Qingsheng YuDepartment of General Surgery, The First Affiliated Hospital of Anhui University of Chinese Medicine Hefei 230031, Anhui, P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Colorectal cancer (CRC) is a common malignancy in clinical practice, and its treatment is greatly challenged by tumor heterogeneity. The most prominent features of CRC heterogeneity are differences in metabolic states and genomic instability, which ultimately lead to unfavorable clinical outcomes. Based on this, the present study aimed to investigate the association between metabolic reprogramming and copy number variation (CNV) in CRC using single-cell datasets. By integrating four publicly available single-cell RNA sequencing datasets, a comprehensive single-cell atlas of CRC was constructed. Subsequently, epithelial cells were specifically analyzed, and consensus non-negative matrix factorization (cNMF) was applied to identify six gene expression programs, covering functional modules such as cell cycle, metabolism, inflammatory stress, and immune interaction. Genomic instability was assessed using the inference of copy number variations (InferCNV) analytical tool, which identified malignant epithelial cells characterized by large-scale CNVs. Meanwhile, metabolic pathway activity at the single-cell level was evaluated using the area under the curve cell (AUCell) method, and predictive performance was further assessed using machine learning algorithms. The results demonstrated that metabolic features could effectively predict the malignant state defined by CNVs, achieving an area under the curve (AUC) of 0.985, with protein metabolism and TP53-related pathways contributing most significantly. Further integrative analysis identified 13 metabolism-related genes associated with clinical prognosis, among which UBXN1 was identified as a central node in the protein-protein interaction network. Functional analysis of UBXN1 revealed that it suppresses the NF-κB signaling pathway, thereby regulating the malignant phenotype of CRC cells. In conclusion, this study systematically elucidates the critical link between metabolic features and genomic instability in CRC, suggesting that UBXN1 may serve as a potential therapeutic target.

Indexed as

CRCNF-κB signal pathwaysingle-cell data analysisUBXN1

Identifiers

PMID42007143
PMCPMC13090884

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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.