Small for gestational age children at risk: Identifying placenta-brain axis genes as biomarkers for early prediction of neurodevelopmental delay

高危小于胎龄儿：识别胎盘-脑轴基因作为神经发育迟缓早期预测的生物标志物

Authors：Jingjing Cheng, Heyue Jin, Yimin Zhang, Jiawen Ren, Kun Huang, Juan Tong, Hong Gan, Jia Lv, Qu'nan Wang, Fangbiao Tao, Yumin Zhu

Source：Life Sciences

DOI: 10.1016/j.lfs.2025.123450

Abstract

Aims: Small for gestational age (SGA) is a prevalent issue in global public health. The relationship between SGA and neurodevelopmental delay remains a topic of debate and the exploration of potential biomarkers is crucial. The identification of placental-brain axis genes offers novel perspectives for anticipating neurodevelopmental delay.

Main methods: First, we utilized multiple logistic regression to assess Ages and Stages Questionnaire of China (ASQ-C) scores in children at 6 months, 18 months, and 48 months of age. Next, we analyzed the placental transcriptome data from SGA and appropriate for gestational age (AGA) children in the Ma'anshan Birth Cohort (MABC) and validated it through Real-time quantitative PCR (RT-qPCR). Finally, we combined the experimental data with clinical data to establish a predictive model.

Key findings: SGA children were found to have a higher risk of neurodevelopmental delay at 6 months and 18 months of age. Further experimental validation found that decreased RPS27A gene expression was associated with developmental delay in solving-problem and personal-social domain at 6 months of age in SGA children.

Significance: Our study focused on the neurodevelopmental results of children from three time points, analyzed the mechanism of neurodevelopmental delay in SGA from the perspective of placenta-brain axis, and conducted experimental verification of the selected biomarkers. Therefore, our study has certain novelty and persuasive, providing new insights for early detection of neurodevelopmental delay in children with SGA.

Keywords: Bioinformatics; Machine learning; Neurodevelopment; Placenta; Prediction model; Small for gestational age.

摘要
目的: 小于胎龄儿（SGA）是全球公共卫生领域的普遍问题。SGA与神经发育迟缓的关系仍存在争议，探索潜在生物标志物至关重要。胎盘-脑轴基因的识别为预测神经发育迟缓提供了新视角。
主要方法： 首先，我们采用多元逻辑回归评估中国年龄与发育进程问卷（ASQ-C）在6月龄、18月龄及48月龄儿童中的评分。随后，基于马鞍山优生优育队列（MABC）的SGA与适于胎龄儿（AGA）胎盘转录组数据进行分析，并通过实时定量PCR（RT-qPCR）进行验证。最后，结合实验数据与临床数据建立预测模型。
主要发现： SGA儿童在6月龄和18月龄时出现神经发育迟缓的风险更高。实验验证发现，RPS27A基因表达下调与SGA儿童6月龄时解决问题能力及个人-社会领域发育迟缓相关。
意义： 本研究聚焦三个时间点的儿童神经发育结果，从胎盘-脑轴视角解析SGA神经发育迟缓机制，并对筛选的生物标志物进行实验验证。因此，本研究具有一定新颖性和说服力，为SGA儿童神经发育迟缓的早期识别提供了新思路。

关键词: 生物信息学；机器学习；神经发育；胎盘；预测模型；小于胎龄