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Updated: Nov 19, 2025

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
Published on: March 5, 2022
Integrative analysis of transcriptomic data for identification of T-cell activation-related mRNA signatures
Jae Young Yoo1, Do Young Hyeon2, Yourae Shin2
1Department of Obstetrics and Gynecology, College of Medicine and Ewha Medical Institute, Ewha Womans University, Seoul, 07804, Republic of Korea.
Abstract:
Preterm birth (PTB), defined as birth at less than 37 weeks of gestation, is a major determinant of neonatal mortality and morbidity. Early diagnosis of PTB risk followed by protective interventions are essential to reduce adverse neonatal outcomes. However, due to the redundant nature of the clinical conditions with other diseases, PTB-associated clinical parameters are poor predictors of PTB. To identify molecular signatures predictive of PTB with high accuracy, we performed mRNA sequencing analysis of PTB patients and full-term birth (FTB) controls in Korean population and identified differentially expressed genes (DEGs) as well as cellular pathways represented by the DEGs between PTB and FTB. By integrating the gene expression profiles of different ethnic groups from previous studies, we identified the core T-cell activation pathway associated with PTB, which was shared among all previous datasets, and selected three representative DEGs (CYLD, TFRC, and RIPK2) from the core pathway as mRNA signatures predictive of PTB. We confirmed the dysregulation of the candidate predictors and the core T-cell activation pathway in an independent cohort. Our results suggest that CYLD, TFRC, and RIPK2 are potentially reliable predictors for PTB.
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