Liu Chunhong, Lu Yulan, Huang Chunchuan, Zeng Yonglong, Zheng Yuye, Wang Chunfang, Huang Huatuo
Center for Medical Laboratory Science, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China.
Baise Key Laboratory for Research and Development on Clinical Molecular Diagnosis for High-Incidence Diseases, Baise, China.
Front Med (Lausanne). 2024 Sep 4;11:1434105. doi: 10.3389/fmed.2024.1434105. eCollection 2024.
Maternal obesity significantly influences fetal development and health later in life; however, the molecular mechanisms behind it remain unclear. This study aims to investigate signature genes related to maternal obesity and fetal programming based on a genomic-wide transcriptional placental study using a combination of different bioinformatics tools.
The dataset (GSE128381) was obtained from Gene Expression Omnibus (GEO). The data of 100 normal body mass index (BMI) and 27 obese mothers were included in the analysis. Differentially expressed genes (DEGs) were evaluated by limma package. Thereafter, functional enrichment analysis was implemented. Then, weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) analysis were used to further screening of signature genes. Simple linear regression analysis was used to assess the correlation between signature genes and newborn birth weight. Gene set enrichment analysis (GSEA) was implemented to study signaling pathways related to signature genes. The expression of the signature genes was also explored in 48 overweight mothers in the same dataset.
A total of 167 DEGs were obtained, of which 122 were up-regulated while 45 were down-regulated. The dataset was then clustered into 11 modules by WGCNA, and the MEbrown was found as the most significant module related to maternal obesity and fetal programming (cor = 0.2, = 0.03). The LASSO analysis showed that , , , , and are signature genes related to maternal obesity and fetal programming, which were increased in the placenta of obese mothers compared to those with normal BMI. The area under the curve (AUC) of the signature genes in the receiver operating characteristic curve (ROC) was 0.709, 0.660, 0.674, 0.667, and 0.717, respectively. Simple linear regression analysis showed that HOXB5 was associated with newborn birth weight. GSEA analysis revealed that these signature genes positively participate in various signaling pathways/functions in the placenta.
, , , , and are novel signature genes related to maternal obesity and fetal programming, of which is implicated in newborn birth weight.
母亲肥胖会显著影响胎儿发育及日后的健康；然而，其背后的分子机制仍不清楚。本研究旨在基于一项使用多种生物信息学工具组合的全基因组胎盘转录研究，探究与母亲肥胖和胎儿编程相关的特征基因。
数据集（GSE128381）取自基因表达综合数据库（GEO）。分析纳入了100名正常体重指数（BMI）母亲和27名肥胖母亲的数据。通过limma软件包评估差异表达基因（DEG）。此后，进行功能富集分析。然后，使用加权基因共表达网络分析（WGCNA）和最小绝对收缩和选择算子（LASSO）分析进一步筛选特征基因。采用简单线性回归分析评估特征基因与新生儿出生体重之间的相关性。实施基因集富集分析（GSEA）以研究与特征基因相关的信号通路。还在同一数据集中的48名超重母亲中探究了特征基因的表达。
共获得167个DEG，其中122个上调，45个下调。然后通过WGCNA将数据集聚类为11个模块，发现MEbrown是与母亲肥胖和胎儿编程最相关的模块（cor = 0.2，P = 0.03）。LASSO分析表明，HOXB5、HOXA10、HOXA11、HOXA13和DLX5是与母亲肥胖和胎儿编程相关的特征基因，与正常BMI母亲相比，肥胖母亲胎盘中这些基因增加。特征基因在受试者工作特征曲线（ROC）中的曲线下面积（AUC）分别为0.709、0.660、0.674、0.667和0.717。简单线性回归分析表明HOXB5与新生儿出生体重相关。GSEA分析显示这些特征基因在胎盘中积极参与各种信号通路/功能。
HOXB5、HOXA10、HOXA11、HOXA13和DLX5是与母亲肥胖和胎儿编程相关的新型特征基因，其中HOXB5与新生儿出生体重有关。
