A gene expression signature for periodontitis and its cross-cohort validation in public transcriptomic datasets

Flor M Escalante Freitez and Drflorescalante@gmail.com

SAP Artificial Intelligence in Dentistry, 2025

Abstract: Introduction: Periodontitis is a highly prevalent chronic inflammatory disease whose diagnosis relies almost entirely on clinical measures. Gene expression signatures offer a candidate molecular complement, but their transfer across independent cohorts and platforms is seldom tested. Methods: A signature was derived from a public gingival-tissue microarray cohort (GSE10334; 183 periodontitis and 64 healthy sites) by differential expression (FDR below 0.05 and absolute log2 fold change of at least 1). The score, the mean of direction-signed per-gene z-scores, was validated in an independent same-platform microarray cohort (GSE16134; 241 diseased, 69 healthy) and a cross-platform RNA-seq cohort (GSE173078; 12 periodontitis, 12 healthy). Discrimination was quantified by the area under the receiver operating characteristic curve (AUROC) with bootstrap 95% confidence intervals. Results: The signature comprised 190 genes (142 upregulated and 48 downregulated) and was dominated by immunoglobulin and plasma-cell markers. The score reached an AUROC of 0.90 (95% CI 0.85 to 0.95) in internal cross-validation, 0.93 (95% CI 0.89 to 0.96) in the same-platform cohort, and 0.75 (95% CI 0.53 to 0.93) in the cross-platform cohort. Conclusions: A plasma-cell-enriched gene signature generalizes to an independent same-platform cohort but attenuates across platforms, indicating that platform harmonization is required before clinical translation.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:cwf:aidart:aid202520

DOI: 10.62486/aid202520

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