Identification of transcriptomic signatures associated with an ac4C related gene set and candidate expression based clusters in osteoarthritis through integrative bioinformatics
- 1Department of Orthopedics, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China
Abstract
Objective: To 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. Methods: Five 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. Results: Among 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. Conclusions: A 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.
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BibTeX
@article{li2026identification,
title = {{Identification of transcriptomic signatures associated with an ac4C related gene set and candidate expression based clusters in osteoarthritis through integrative bioinformatics}},
author = {Tianyang Li and Jinpeng Wei and Hua Wu and Ming Zhang},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0359336},
url = {https://doi.org/10.1371/journal.pone.0359336},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Identification of transcriptomic signatures associated with an ac4C related gene set and candidate expression based clusters in osteoarthritis through integrative bioinformatics
AU - Tianyang Li
AU - Jinpeng Wei
AU - Hua Wu
AU - Ming Zhang
PY - 2026
DA - 2026/09/24
JO - PLOS One
DO - 10.1371/journal.pone.0359336
UR - https://doi.org/10.1371/journal.pone.0359336
AB - Objective: To 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. Methods: Five 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. Results: Among 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. Conclusions: A 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.
SN - 1932-6203
ER -