Evidence map›Paper›PMID 41552300›Full record

ArticleAmerican journal of translational research2025

Ferroptosis-related genes and pathways in knee osteoarthritis cartilage degeneration: discovered by bioinformatics technology and in vivo experimental verification.

Jun Dong, Hui Su, Tingting Zhou, Xiaoming Li, Hui Li

Abstract read
In one paragraph

Article in American journal of translational research, 2025. 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
–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

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

5 authors.

Jun DongDepartment of Joint Surgery, Tianjin Medical University General Hospital Tianjin 300052, China.
Hui SuDepartment of Orthopedics, The First School of Clinical Medicine, Zhejiang Chinese Medical University Hangzhou 310053, Zhejiang, China.
Tingting ZhouDepartment of Joint Surgery, Cangzhou Hospital of Integrated TCM-WM Cangzhou 061001, Hebei, China.
Xiaoming LiDepartment of Joint Surgery, Cangzhou Hospital of Integrated TCM-WM Cangzhou 061001, Hebei, China.
Hui LiDepartment of Joint Surgery, Tianjin Medical University General Hospital Tianjin 300052, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study aimed to investigate the role of ferroptosis-related genes and pathways in cartilage degeneration in knee osteoarthritis (KOA) and to identify potential therapeutic targets.

methodsIntegrated bioinformatics analysis of public KOA transcriptomic datasets was performed to identify differentially expressed ferroptosis-related genes. Key pathways and hub genes were further validated using a surgically-induced KOA mouse model and clinical cartilage samples through histology, immunohistochemistry, micro-CT, ELISA, western blot, and qPCR.

resultsSignificant iron deposition, mitochondrial damage, and dysregulation of ferroptosis markers (e.g., decreased GPX4 and increased p53) were observed in KOA cartilage. Key genes such as MMP2, SPP1, MYC, and BNIP3 were identified, and pathways including mTOR and HIF-1 signaling were enriched. Experimental results consistently indicated enhanced ferroptosis and matrix degradation in both mouse and human OA samples.

conclusionFerroptosis contributes significantly to KOA progression through iron overload, lipid peroxidation, and dysregulated metabolic pathways, providing new insight for targeted therapeutic strategies.

Indexed as

bioinformaticsexperimentalFerroptosisosteoarthritisverification

Identifiers

PMID41552300
PMCPMC12808055

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.