Evidence map›Paper›PMID 42784567›Full record

ArticlePloS one2026

Identification of transcriptomic signatures associated with an ac4C related gene set and candidate expression based clusters in osteoarthritis through integrative bioinformatics.

Tianyang Li, Jinpeng Wei, Hua Wu, Ming Zhang

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Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 authors.

Tianyang LiDepartment of Orthopedics, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China.
Jinpeng WeiDepartment of Orthopedics, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China.
Hua WuDepartment of Orthopedics, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China.
Ming ZhangDepartment of Orthopedics, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China.ORCID https://orcid.org/0009-0001-7763-7320

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6 · The paper itself

Abstract

objectiveTo identify osteoarthritis (OA) associated transcripts overlapping a predefined N4-acetylcytidine (ac4C) related gene set and evaluate their potential as an exploratory classification signature and basis for expression based clustering.

methodsFive GEO datasets were analyzed using differential expression, functional enrichment, weighted gene co-expression network analysis, immune-signature scoring, machine learning, SHAP interpretation, consensus clustering, and gene set variation analysis. The predefined 2,135-gene set was derived from a published ac4C-RIP-seq comparison between wild-type and NAT10-deficient HeLa cells and was used only for candidate filtering. Twelve algorithms were combined into 113 two-stage feature-selection/classification pipelines, which were ranked by the mean area under the receiver operating characteristic curve (AUC) across the development cohort and two external evaluation cohorts. Five retained genes were assessed by qRT-PCR in IL-1β-treated primary mouse chondrocytes with three biological replicates per group.

resultsAmong 441 differentially expressed genes, eight overlapped the ac4C related set. Three pipelines shared the highest mean AUC of 0.910. The representative glmBoost-Naive Bayes pipeline achieved AUCs of 0.883 (95% CI, 0.783-0.959), 0.980 (95% CI, 0.880-1.000), and 0.867 (95% CI, 0.600-1.000) in the development cohort, GSE114007, and GSE169077, respectively. Because the two secondary cohorts contributed to pipeline ranking, these estimates represent exploratory evaluation rather than independent validation. Ultimately, five genes were retained, including PCOLCE, KAZALD1, PDE3A, CRIP1, and ID1. Kazald1, Pde3a, Crip1, and Id1 showed nominally significant increases after interleukin-1βtreatment, whereas Pcolce did not. Immune signature differences and the two cluster solution were exploratory.

conclusionsA five gene OA associated transcriptomic signature linked to a predefined ac4C related gene set was identified. These findings are hypothesis generating and do not establish direct ac4C modification, independent clinical validity, or reproducible molecular subtypes.

Indexed as

Computational BiologyOsteoarthritisTranscriptomeAnimalsChondrocytesCluster AnalysisClustering AlgorithmsGene Expression ProfilingGene Expression RegulationGene Regulatory NetworksHeLa CellsHumansInterleukin-1betaMiceInterleukin-1beta

Identifiers

PMID42784567
PMCPMC13606951

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