Evidence map›Paper›PMID 40001145›Full record

ArticleJournal of translational medicine2025

Revolutionizing the treatment of intervertebral disc degeneration: an approach based on molecular typing.

Shaofeng Chen, Wei Zhang, Yifan Liu, Runzhi Huang, Xiaoyi Zhou, Xianzhao Wei

Abstract read
In one paragraph

Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

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5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Shaofeng Chen *Department of Orthopaedic Surgery, Changhai Hospital, Shanghai, China.
Wei Zhang *Department of Burn Surgery, Changhai Hospital, Shanghai, China.
Yifan Liu *Department of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Runzhi HuangDepartment of Burn Surgery, Changhai Hospital, Shanghai, China. runzhihuang2022@163.com.
Xiaoyi ZhouDepartment of Orthopaedic Surgery, Changhai Hospital, Shanghai, China. 13818909826@163.com.
Xianzhao WeiDepartment of Orthopaedic Surgery, Changhai Hospital, Shanghai, China. weixianzhao@126.com.

Funding

Shanghai Rising-Star Program 23YF1458400
6 · The paper itself

Abstract

backgroundIntervertebral disc degeneration (IVDD) is a significant cause of global disability, reducing labor productivity, increasing the burden on public health, and affecting socio-economic well-being. Currently, there is a lack of recognized clinical approaches for molecular classification and precision therapy.

methodsChondrocyte differentiation and prognosis-related genes were extracted from single-cell RNA sequencing and multi-omics data in the Gene Expression Omnibus (GEO) database through chondrocyte trajectory analysis and non-parametric tests. Subsequently, a precise IVDD risk stratification system was developed using ConsensusClusterPlus analysis. The clinical significance of molecular typing was demonstrated through case-control trials involving IVDD patients. Specific inhibitors of molecular typing were predicted using the pRRophetic package in R language and then validated in vitro.

resultsA stratified model for IVDD, considering chondrocyte differentiation and demonstrating high clinical relevance, was developed using a set of 44 chondrocyte fate genes. Extensive analyses of multi-omics data confirmed the clinical relevance of this model, indicating that cases in the High Chondrocyte Scoring Classification (HCSC) group had the most favorable prognosis, whereas those in the Low Chondrocyte Scoring Classification (LCSC) group had the worst prognosis. Additionally, clinical case-control studies provided evidence of the utility of IVDD molecular typing in translational medicine. A gene expression-based molecular typing approach was used to create a matrix identifying potential inhibitors specific to each IVDD subtype. In vitro experiments revealed that gefitinib, a drug designed for LCSC, not only had protective effects on chondrocytes but also could induce the conversion of LCSC into the HCSC subgroup. Therefore, IVDD molecular typing played a critical role in assisting clinicians with risk stratification and enabling personalized treatment decisions.

conclusionThe results of the study have provided a comprehensive and clinically relevant molecular typing for IVDD, involving a precise stratification system that offers a new opportunity for customizing personalized treatments for IVDD.

Indexed as

Intervertebral Disc DegenerationAdultCase-Control StudiesCell DifferentiationChondrocytesFemaleGene Expression ProfilingHumansMaleMiddle AgedPrognosisCase–control trialGefitinibIVDDMolecular subtypeSingle-cell RNA sequencing

Identifiers

PMID40001145
PMCPMC11863857

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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.