Evidence map›Paper›PMID 42891308›Full record

ArticleiMetaOmics2026

Comprehensive assessment of unfolded protein response and its association with tumor progression in pan-cancer.

Xinyu Yang, Faming Zhao, Jing Yang, Xinran Xia, Liwei Chen, Peng Zeng, Liang Chen, Yanxia Hu, Xia Sheng

Abstract read
In one paragraph

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

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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

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

9 authors.

Xinyu YangDepartment of Urology, Tongji Hospital, Tongji Medical College Huazhong University of Science and Technology Wuhan China.
Faming ZhaoDepartment of Urology, Tongji Hospital, Tongji Medical College Huazhong University of Science and Technology Wuhan China.ORCID https://orcid.org/0000-0002-8238-3863
Jing YangInstitute of Biomedical Research, School of Life and Health Sciences Hainan University Haikou China.
Xinran XiaInstitute of Biomedical Research, School of Life and Health Sciences Hainan University Haikou China.
Liwei ChenInstitute of Biomedical Research, School of Life and Health Sciences Hainan University Haikou China.
Peng ZengInstitute of Biomedical Research, School of Life and Health Sciences Hainan University Haikou China.
Liang ChenDepartment of Urology, Tongji Hospital, Tongji Medical College Huazhong University of Science and Technology Wuhan China.ORCID https://orcid.org/0000-0001-9720-7099
Yanxia HuInstitute of Biomedical Research, School of Life and Health Sciences Hainan University Haikou China.
Xia ShengInstitute of Biomedical Research, School of Life and Health Sciences Hainan University Haikou China.ORCID https://orcid.org/0000-0002-8742-7343

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Disruption of proteostasis in the endoplasmic reticulum leads to the activation of the unfolded protein response (UPR). UPR activation is common in malignant cells and is often considered a contributor to cancer progression. However, the accurate evaluation of UPR status in pan-cancer is still lacking. Furthermore, the UPR-related key characteristics in cancer progression remain to be explored. To fill in these gaps, we first proposed a UPR signature, according to which a UPR score was assigned to each tumor/tumor cell by gene set variation analysis. Based on this, we depicted the UPR landscape across 33 kinds of tumors and 28 kinds of normal tissues, which revealed significant inter- and intra-tumoral heterogeneity in UPR activity across different cancers. Next, by integrating bulk and single-cell transcriptomic data, we unveiled robust correlations of UPR activation with critical genomic and immune features amongst different cancers. In addition, by employing three machine learning algorithms, we identified and validated a promising biomarker derived from the perspective of UPR for breast cancer prognosis. In summary, our study offers an effective approach for evaluating UPR activity in human cancer and underscores the clinical significance of UPR activity in cancer progression.

Indexed as

endoplasmic reticulum stressmachine learningpan‐cancersingle‐cell transcriptomicsunfolded protein response

Identifiers

PMID42891308
PMCPMC13674402

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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.