Evidence map›Paper›PMID 41660258›Full record

ArticleiScience2026

Consensus machine learning identifies cell death gene signature for carotid artery stenosis diagnosis.

Chunguang Guo, Kun Fang, Gaopo Cai, Yi Liu, Weichang Zhang, Linfeng Zhang, Ziting Wu, Mingyao Luo, Chang Shu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2026. 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

9 authors.

Chunguang GuoState Key Laboratory of Cardiovascular Disease, Center of Vascular Surgery, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical Colege, Beijing 100037, China.
Kun FangState Key Laboratory of Cardiovascular Disease, Center of Vascular Surgery, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical Colege, Beijing 100037, China.
Gaopo CaiDepartment of Endovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China.
Yi LiuState Key Laboratory of Cardiovascular Disease, Center of Vascular Surgery, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical Colege, Beijing 100037, China.
Weichang ZhangState Key Laboratory of Cardiovascular Disease, Center of Vascular Surgery, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical Colege, Beijing 100037, China.
Linfeng ZhangDepartment of Endovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China.
Ziting WuState Key Laboratory of Cardiovascular Disease, Center of Vascular Surgery, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical Colege, Beijing 100037, China.
Mingyao LuoState Key Laboratory of Cardiovascular Disease, Center of Vascular Surgery, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical Colege, Beijing 100037, China.
Chang ShuState Key Laboratory of Cardiovascular Disease, Center of Vascular Surgery, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical Colege, Beijing 100037, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Carotid artery stenosis (CAS) is a major contributor to ischemic stroke, and molecular tools for its early detection remain limited. To address this need, we integrated one in-house RNA-seq cohort with eight public datasets comprising 696 samples, together with proteomic profiling, RT-qPCR, single-cell sequencing, and FYCO1 silencing experiments. From 1,258 curated cell death-related genes, candidates were filtered by logistic regression across cohorts, and ten machine learning algorithms were combined into 105 model configurations to derive a consensus diagnostic classifier. Fourteen genes showed consistent associations with CAS, and the machine learning-derived diagnostic signature (MLDS), consisting of IRF1, FYCO1, and FDFT1, demonstrated the highest cross-cohort performance. FYCO1 downregulation was validated in plaques and blood and supported by single-cell analysis, while functional assays indicated impaired autophagic flux and heightened inflammatory signaling. These findings highlight MLDS as a robust molecular tool that may enhance the precision diagnosis of CAS.

Indexed as

health sciences

Identifiers

PMID41660258
PMCPMC12874109

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.