Identification of cell senescence-related genes in spontaneous preterm birth based on bioinformatics analysis and machine learning - Guo Juan; Li Ting; Wang Yahui; Zuo Luguang (PLoS ONE, 2026)
Spontaneous premature birth (SPTB) is a common pregnancy complication; however, few studies have explored cell senescence-related markers in SPTB. Bioinformatics and machine learning approaches were used to predict potential biomarkers associated with SPTB. Normal and SPTB gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database, and cell senescence-associated genes from the Human Aging Genomic Resources (HAGR) database. Functional enrichment analysis and protein-protein interaction (PPI) network analysis of differentially expressed senescence-related genes in SPTB were conducted using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and STRING databases. The infiltration of 22 types of immune cells in SPTB was calculated using the CIBERSORT deconvolution algorithm. Machine learning methods were employed to identify hub differentially expressed genes (DEGs). Datasets GSE174415 and GSE118442 were extracted for validation to determine the final hub genes. Additionally, receiver operating characteristic (ROC) curves were constructed to assess the diagnostic potential of the hub genes, and significant pathways associated with the final hub gene were explored by Gene Set Enrichment Analysis (GSEA). Finally, real-time quantitative polymerase chain reaction (RT-qPCR) was performed to validate the hub gene in clinical specimens. A total of 923 DEGs were identified, including 525 upregulated and 398 downregulated in the SPTB group. These 923 genes were intersected with 866 cell senescence-related genes, yielding 48 intersection genes. Functional enrichment analysis indicated that these intersection genes were primarily associated with cytokine–cytokine receptor interactions and the PI3K-Akt signaling pathway. The expression of activated dendritic cells and follicular helper T cells was significantly lower in the SPTB group compared to the full-term pregnancy group. A total of six hub genes, LGALS3, ESR1, PLA2G2A, TWIST1, CBS, and PLA2R1, were identified by machine learning. According to dataset validation, TWIST1 was identified as the final hub gene. TWIST1 was downregulated in placental tissues of the SPTB group and demonstrated high diagnostic value for SPTB. Thus, TWIST1 may be a novel molecular target for predicting and diagnosing SPTB, providing diagnostic value and novel insights into this condition.