Identification and validation of cell senescence genes in recurrent spontaneous abortion via multiple bioinformatics algorithms

Recurrent spontaneous abortion (RSA) represents a significant challenge in reproductive obstetrics, affecting approximately 5% of couples globally. Despite various treatments, the effectiveness of these interventions remains highly contentious. Emerging evidence suggests that cellular senescence pla...

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Vydáno v:	Scientific reports Ročník 15; číslo 1; s. 35457 - 18
Hlavní autoři:	Wei, Yiyun, Zhou, Zhuolin, Wei, Changqiang, Cheng, Jinlian, Lin, Shanshan, Zhu, Zhiwei, Pang, Lihong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 10.10.2025 Nature Publishing Group Nature Portfolio
Témata:	631/1647/2217 631/1647/514 631/80/304 631/80/82 Abortion, Habitual - genetics Algorithms Bioinformatics Body mass index Cell cycle Cell interactions Cell senescence Cellular Senescence - genetics Computational Biology - methods Datasets Disease Endocrine disorders Extracellular matrix Feature selection Female Gene expression Gene Expression Profiling Gene Regulatory Networks Gene set enrichment analysis Genes Humanities and Social Sciences Humans Immunofluorescence Machine Learning MAP kinase Miscarriage Molecular modelling multidisciplinary Neural networks Neural Networks, Computer Obstetrics Pathogenesis Pregnancy Ribonucleic acid RNA RSA Science Science (multidisciplinary) Senescence Sensitivity analysis Signal transduction SRSF3 Support vector machines TBX2 TNRC6B Transcriptome Transcriptomes RSA SRSF3 Cell senescence Machine learning TBX2 TNRC6B
ISSN:	2045-2322, 2045-2322
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Shrnutí:	Recurrent spontaneous abortion (RSA) represents a significant challenge in reproductive obstetrics, affecting approximately 5% of couples globally. Despite various treatments, the effectiveness of these interventions remains highly contentious. Emerging evidence suggests that cellular senescence plays a critical role in the pathogenesis of multiple diseases, potentially implicating its involvement in RSA as well. This study integrated two RNA sequencing datasets and employed Weighted Gene Co-Expression Network Analysis (WGCNA) alongside five machine learning algorithms (XGBoost, Boruta, LASSO, SVM-RFE, and Random Forest) to identify cellular senescence genes linked to RSA. Gene expression was validated at both the transcriptome and protein levels using qPCR, Western blot, and immunofluorescence techniques. An Artificial Neural Network (ANN) model was implemented to evaluate their diagnostic value. Additionally, functional enrichment and single-cell RNA sequencing analyses were conducted to investigate the biological functions of these genes. Three cellular senescence genes—TBX2, SRSF3, and TNRC6B—were identified and found to be upregulated in RSA patients. Functional enrichment analysis revealed these genes’ involvement in the MAPK signaling pathway, ECM-receptor interaction, and cell-cell communication. Single-cell RNA sequencing demonstrated the distribution of these genes across various cell types, underscoring their significance in RSA. Furthermore, drug sensitivity analysis identified potential small molecule therapeutics for RSA. Cellular senescence may play a central role in the pathology of RSA, with TBX2, SRSF3, and TNRC6B emerging as potential diagnostic biomarkers. This research enhances our understanding of the molecular mechanisms underlying RSA and lays the groundwork for new diagnostic and therapeutic strategies. Nonetheless, further experimental studies are required to elucidate the specific roles and mechanisms of these genes in RSA, with the ultimate goal of achieving precise prevention and personalized treatment.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2045-2322 2045-2322
DOI:	10.1038/s41598-025-19251-9