Identification of diagnostic genes and the miRNA‒mRNA‒TF regulatory network in human oocyte aging via machine learning methods

Oocyte aging is a significant factor in the negative reproductive outcomes of older women. However, the pathogenesis of oocyte aging remains unclear. This study aimed to identify the hub genes involved in oocyte aging via bioinformatics methods.

The oocyte aging datasets GSE155179 and GSE158802 were obtained from the GEO database and analyzed as the training set. The GSE164371 dataset was then defined as the validation set. Differentially expressed genes were analyzed via the limma package and weighted gene coexpression network analysis, and intersected with cellular senescence-associated genes from the Cell Senescence database. The hub genes were identified via three machine learning algorithms, namely, support vector machine recursive feature elimination, random forest, and least absolute shrinkage and selection operator logistic, which were also confirmed via the validation set. Finally, a microRNA–mRNA‒transcription factor regulatory network and single-gene gene set enrichment analysis were performed to clarify the pathogenesis of oocyte aging.

A competing endogenous RNA network of GSE155179 and GSE158802 with 124 mRNAs, 31 long noncoding RNAs, and 31 miRNAs was constructed. Two modules with 814 genes were considered the key modules of oocyte aging. PDIK1L, SIRT1, and MCU were subsequently identified as hub genes; on the basis of these hub genes, a regulatory network of oocyte aging with 8 miRNAs, 3 mRNAs, and 227 TFs was ultimately constructed.

This study contributes to a deeper understanding of oocyte aging and may aid in the development of therapeutic approaches to improve reproductive outcomes in older women.