Evidence map›Paper›PMID 39849508›Full record

ArticleJournal of orthopaedic surgery and research2025

Integrated analysis of bioinformatics, mendelian randomization, and experimental validation reveals novel diagnostic and therapeutic targets for osteoarthritis: progesterone as a potential therapeutic agent.

Ziyu Weng, Chenzhong Wang, Bo Liu, Yi Yang, Yueqi Zhang, Chi Zhang

Abstract read
In one paragraph

Article in Journal of orthopaedic surgery and research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. 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.

Ziyu Weng *Department of Orthopedic Surgery, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Chenzhong Wang *Department of Orthopedic Surgery, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Bo Liu *Department of Orthopedic Surgery, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Yi YangDepartment of Orthopedic Surgery, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Yueqi ZhangDepartment of Traumatic Surgery, School of Medicine, Shanghai East Hospital, Tongji University, Shanghai, China. zhangyueqi0302@163.com.
Chi ZhangDepartment of Orthopedic Surgery, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. zhang.chi@zs-hospital.sh.cn.

Funding

National Natural Science Foundation of China 81971308National Natural Science Foundation of China 82302038
6 · The paper itself

Abstract

backgroundOsteoarthritis (OA), characterized by progressive degeneration of cartilage and reactive proliferation of subchondral bone, stands as a prevalent condition in orthopedic clinics. However, the precise mechanisms underlying OA pathogenesis remain inadequately explored.

methodsIn this study, Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) machine learning techniques were employed to identify hub genes. Based on these hub genes, an Artificial Neural Network (ANN) diagnostic model was constructed. The Drug Signatures Database (DSigDB) was utilized to screen small-molecule drugs targeting these hub genes, and molecular docking analyses and molecular dynamics simulations were employed to explore and validate the binding interactions between proteins and small-molecule drugs. Expression changes of the hub genes under inflammatory conditions were validated through in vitro experiments, including RT-qPCR and Western blotting, and the therapeutic effects of the identified small-molecule drug on chondrocytes under inflammatory conditions were further verified in vitro. Lastly, Mendelian randomization analysis was conducted to examine the causal association between progesterone levels and various OA phenotypes.

resultsIn this study, we identified three hub genes: interleukin 1 receptor-associated kinase 3 (IRAK3), integrin subunit beta-like 1 (ITGBL1), and Ras homolog family member U (RHOU). An Artificial Neural Network (ANN) diagnostic model constructed based on these hub genes demonstrated excellent performance in both training and validation phases. Screening with the Drug Signatures Database (DSigDB) identified progesterone as a small-molecule drug targeting these key proteins. Molecular docking analysis using AutoDock Vina revealed that progesterone exhibited binding energies of ≤ -7 kcal/mol with each of the key proteins, indicating strong binding affinity. Furthermore, molecular dynamics simulations validated the stability and strength of these interactions. RT-qPCR and Western blotting confirmed the downregulation of the hub genes in IL-1β-treated chondrocytes. Western blotting also demonstrated the potential therapeutic effects of progesterone on IL-1β-treated chondrocytes. Finally, Mendelian randomization analysis established a significant association between progesterone levels and multiple OA phenotypes.

conclusionIn our study, IRAK3, ITGBL1, and RHOU were identified as potential novel diagnostic and therapeutic targets for OA. Progesterone was preliminarily validated as a small-molecule drug with potential effects on OA. Further research is crucial to elucidate the pathogenesis of OA and the specific therapeutic mechanisms involved.

Indexed as

Computational BiologyOsteoarthritisProgesteroneChondrocytesHumansInterleukin-1 Receptor-Associated KinasesMolecular Docking SimulationMolecular Targeted TherapyNeural Networks, ComputerInterleukin-1 Receptor-Associated KinasesProgesteroneArtificial neural networksBiomarkerDrug predictionIRAK3Machine learningMendelian randomizationMolecular dockingOsteoarthritisProgesterone

Identifiers

PMID39849508
PMCPMC11755849

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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.