Evidence map›Paper›PMID 39744571›Full record

ArticleJournal of Cancer2025

Pan-cancer analyses reveal cancer-type-specific autophagy signatures with potential implications for prognosis and therapy response.

Hongyan Yuan, Xiaojing Zhu, Jiaxing Zhang, Zixin Zhang, Weihua Li, Jiankai Xu

Abstract read
In one paragraph

Article in Journal of Cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Hongyan YuanCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Xiaojing ZhuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Jiaxing ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Zixin ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Weihua LiMedical imaging department, Shenzhen Second People's Hospital and the First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen 518035, China.
Jiankai XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Autophagy is a common cellular degradation and recycling process that plays crucial roles in the development, progression, immune regulation, and prognosis of various cancers. However, a systematic assessment of the autophagy-related genes (ATGs) across cancer types is deficient. Here, a transcriptome-based pan-cancer analysis of autophagy with potential implications in prognosis and therapy response was performed. About 3 - 32 % of ATGs expressed differentially across 21 human cancers, and the autophagy-related score (ATS) based on differential ATGs could be used to predict the prognosis in 11 cancers, which was validated in multiple independent datasets. Autophagy was found to influence tumor immune microenvironment mainly by regulating tumor-infiltrating lymphocytes and myeloid-derived cells, and interactions between T cells and macrophages with lower ATS was enhanced to improve clinical outcomes by single cell analysis in bladder urothelial carcinoma (BLCA). In addition, the ATS was correlated with drug sensitivity and showed a capacity for prediction of therapy response in diverse cancers. Altogether, the results highlighted robust value of autophagy in cancer prognosis and treatment.

Indexed as

AutophagyCancerPrognosisTherapy responseTumor microenvironment

Identifiers

PMID39744571
PMCPMC11660141

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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 OpenQuestion graph.