Evidence map›Paper›PMID 41982476›Full record

ArticleJournal of inflammation research2026

Identification of a Diagnostic Gene Signature Associated with Centrosome Amplification in Pressure Injuries: A Cross-Sectional Transcriptome and Machine Learning Study.

Sen Li, Haowei Shen, Kunlin Li, Xin Wang, Zhaofei Sun, Yanping Li, Yuhuan Yuan

Abstract read
In one paragraph

Article in Journal of inflammation 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.

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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

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

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

7 authors.

Sen Li *Department of Cardiology, Henan Provincial People's Hospital, Zhengzhou, Henan, People's Republic of China.ORCID 0009-0007-5728-3629
Haowei Shen *Department of General Surgery, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, People's Republic of China.ORCID 0009-0005-5339-8213
Kunlin LiDepartment of General Surgery, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, People's Republic of China.
Xin WangDepartment of General Surgery, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, People's Republic of China.
Zhaofei SunDepartment of Hepato-Pancreato-Biliary Surgery, Henan Provincial People's Hospital, Zhengzhou, Henan, People's Republic of China.
Yanping LiDepartment of Hepato-Pancreato-Biliary Surgery, Henan Provincial People's Hospital, Zhengzhou, Henan, People's Republic of China.
Yuhuan YuanDepartment of Hepato-Pancreato-Biliary Surgery, Henan Provincial People's Hospital, Zhengzhou, Henan, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Centrosome amplification (CA) contributes to cancer but remains poorly characterized in non-neoplastic conditions such as pressure injuries (PI). This study investigated CA-related genes (CARGs) in PI to identify potential therapeutic targets. Patients and Methods: Transcriptomic data from 15 patients with PI and 15 healthy control blood samples were analyzed. Differentially expressed genes were intersected with CARGs, and key genes were identified using machine learning algorithms and receiver operating characteristic curve analysis. A nomogram was constructed, and underlying mechanisms were investigated using functional enrichment, immune infiltration, drug prediction, and quantitative reverse transcription polymerase chain reaction (RT-qPCR) analyses. Results: Conclusion: This study preliminarily identifies

Indexed as

bioinformaticscentrosome amplificationmachine learningpressure injuries

Identifiers

PMID41982476
PMCPMC13075428

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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.