Evidence map›Paper›PMID 39313491›Full record

ArticleMolecular pain

Pain sensitivity genes as therapeutic targets in knee osteoarthritis: A comprehensive analysis.

Zirui Li, Haicheng Chen, Chujie Chen

Abstract read
In one paragraph

Article in Molecular pain. 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. Article
  2. 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

3 authors.

Zirui LiSchool of Physical Education and Health, Guangzhou University of Chinese Medicine, Guangzhou, China.
Haicheng ChenThe Department of Orthopedics, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Chujie ChenSchool of Physical Education and Health, Guangzhou University of Chinese Medicine, Guangzhou, China.ORCID 0009-0008-3750-7282

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pain sensitivity is a significant factor in knee osteoarthritis (KOA), influencing patient outcomes and complicating treatment. Genetic differences, particularly in pain-sensing genes (PSRGs), are known to contribute to the variability in pain experiences among KOA patients. This study aims to systematically analyze PSRGs in KOA to better understand their role and potential as therapeutic targets. We utilized bulk RNA-seq data from the GSE114007 and GSE169077 datasets to identify differentially expressed genes, with 20 genes found to be significantly altered. Key PSRGs, including PENK, NGF, HOXD1, and TRPA1, were identified using LASSO, SVM, and random forest algorithms. Further, KEGG and GO enrichment analyses revealed pathways such as "Neuroactive ligand-receptor interaction" and "ECM-receptor interaction," which were validated through external datasets. Single-cell RNA-seq analysis from GSE152805, GSE133449, and GSE104782 datasets demonstrated the heterogeneity and dynamic expression of PSRGs across different cell subpopulations in synovium, meniscus, and cartilage samples. UMAP and pseudotime analyses were used to visualize spatial distribution and developmental trajectories of these genes. The findings emphasize the critical roles of PSRGs in KOA, highlighting their potential as therapeutic targets and suggesting that integrating genetic information into clinical practice could significantly improve pain management and treatment strategies for KOA.

Indexed as

Osteoarthritis, KneeDatabases, GeneticGene Expression ProfilingGene Expression RegulationHumansPainPain ThresholdBulk RNA-seqknee osteoarthritispain sensory related genessingle-cell RNA-seq analysis

Identifiers

PMID39313491
PMCPMC11456193

What Socratic holds

Textmetadata
LicenceCC BY-NC
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.