ArticleGenes2025
Transcriptome Profiling of the Anterior Cingulate Cortex in a CFA-Induced Inflammatory Pain Model Identifies ECM-Related Genes in a Model of Rheumatoid Arthritis.
Article in Genes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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.
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Who cites it
2 citing papers in PubMed.
- Temporal Transcriptomic Changes in the Cingulate Cortex of Neuropathic Pain Mice.Biomedicines · 2026Article
- Transcriptomic signatures of the insular cortex in a mouse model of neuropathic pain.Frontiers in molecular neuroscience · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundRheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent joint inflammation and progressive bone destruction. However, its complex pathogenesis remains poorly understood, and effective therapeutic targets are still lacking.
objectiveThis study aimed to identify key genes associated with RA and elucidate their biological significance by integrating bioinformatic analysis with experimental validation.
methodsWhole-transcriptome data from the anterior cingulate cortex (ACC) of Complete Freund's Adjuvant (CFA)-induced inflammatory pain and control mice (GSE147216 dataset, GEO database) were collected from NCBI (National Center for Biotechnology Information). Differentially expressed genes (DEGs) were first identified. Subsequent analyses included Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, construction of a protein-protein interaction (PPI) network, and identification of hub genes using a Random Forest machine learning algorithm. Quantitative PCR (qPCR) was performed to validate gene expression levels.
resultsA total of 76 DEGs were identified, including 64 upregulated and 12 downregulated genes. Among them,
conclusionsThis study highlights the potential roles of
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Identifiers
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Registered trials
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.