Evidence map›Paper›PMID 39924344›Full record

ArticleCNS neuroscience & therapeutics2025

Discovery of Novel Pain Regulators Through Integration of Cross-Species High-Throughput Data.

Ying Chen, Akhilesh K Bajpai, Nan Li, Jiahui Xiang, Angelina Wang, Qingqing Gu, Junpu Ruan, Ran Zhang, Gang Chen, Lu Lu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in CNS neuroscience & therapeutics, 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. 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

10 authors.

Ying ChenDepartment of Histology and Embryology, Medical College, Nantong University, Nantong, Jiangsu, China.ORCID 0000-0003-2363-0550
Akhilesh K BajpaiDepartment of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA.
Nan LiDepartment of Histology and Embryology, Medical College, Nantong University, Nantong, Jiangsu, China.
Jiahui XiangMedical College, Nantong University, Nantong, Jiangsu, China.
Angelina WangDepartment of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA.
Qingqing GuDepartment of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA.
Junpu RuanMedical College, Nantong University, Nantong, Jiangsu, China.
Ran ZhangMedical College, Nantong University, Nantong, Jiangsu, China.
Gang ChenDepartment of Histology and Embryology, Medical College, Nantong University, Nantong, Jiangsu, China.
Lu LuDepartment of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA.

Funding

"333 Project" of Jiangsu Province BRA2020076National Natural Science Foundation of China 32070998National Natural Science Foundation of China 32271054Postgraduate Research & Practice Innovation Program of Jiangsu Province KYCX24 3566
6 · The paper itself

Abstract

aimsChronic pain is an impeding condition that affects day-to-day life and poses a substantial economic burden, surpassing many other health conditions. This study employs a cross-species integrated approach to uncover novel pain mediators/regulators.

methodsWe used weighted gene coexpression network analysis to identify pain-enriched gene module. Functional analysis and protein-protein interaction (PPI) network analysis of the module genes were conducted. RNA sequencing compared pain model and control mice. PheWAS was performed to link genes to pain-related GWAS traits. Finally, candidates were prioritized based on node degree, differential expression, GWAS associations, and phenotype correlations.

resultsA gene module significantly over-enriched with the pain reference set was identified (referred to as "pain module"). Analysis revealed 141 pain module genes interacting with 46 pain reference genes in the PPI network, which included 88 differentially expressed genes. PheWAS analysis linked 53 of these genes to pain-related GWAS traits. Expression correlation analysis identified Vdac1, Add2, Syt2, and Syt4 as significantly correlated with pain phenotypes across eight brain regions. NCAM1, VAMP2, SYT2, ADD2, and KCND3 were identified as top pain response/regulator genes.

conclusionThe identified genes and molecular mechanisms may enhance understanding of pain pathways and contribute to better drug target identification.

Indexed as

Chronic PainPainAnimalsGene Regulatory NetworksGenome-Wide Association StudyHigh-Throughput Screening AssaysMaleMiceMice, Inbred C57BLProtein Interaction MapsSpecies SpecificityBXD micecross‐species integrated approachpainRNA sequencingspared nerve injury

Identifiers

PMID39924344
PMCPMC11807727

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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.