Identification and verification of diagnostic biomarkers in recurrent pregnancy loss via machine learning algorithm and WGCNA

Frontiers in Immunology

Immunology

Xuemei Tan¹  Changqiang Wei¹  Jinlian Cheng¹  Yiyun Wei²  Lihong Pang³  Zhuolin Zhou⁴  Shanshan Lin⁵ 

[1] Department of Prenatal Diagnosis, The First Afliated Hospital of Guangxi Medical University, Nanning, Guangxi, China;Department of Prenatal Diagnosis, The First Afliated Hospital of Guangxi Medical University, Nanning, Guangxi, China;Guangxi Key Laboratory of Thalassemia Research, Nanning, Guangxi, China;National Health Commission Key Laboratory of Thalassemia Medicine (Guangxi Medical University), Nanning, Guangxi, China;Department of Prenatal Diagnosis, The First Afliated Hospital of Guangxi Medical University, Nanning, Guangxi, China;Guangxi Key Laboratory of Thalassemia Research, Nanning, Guangxi, China;National Health Commission Key Laboratory of Thalassemia Medicine (Guangxi Medical University), Nanning, Guangxi, China;Guangxi Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Nanning, Guangxi, China;Department of Prenatal Diagnosis, The First Afliated Hospital of Guangxi Medical University, Nanning, Guangxi, China;Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, Guangxi, China;Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, Guangxi, China;

关键词: recurrent pregnancy loss;    diagnostic biomarkers;    machine learning;    WGCNA;    immune cell infiltration;   

DOI  :  10.3389/fimmu.2023.1241816

 received in 2023-06-17, accepted in 2023-08-11,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundRecurrent pregnancy loss defined as the occurrence of two or more pregnancy losses before 20-24 weeks of gestation, is a prevalent and significant pathological condition that impacts human reproductive health. However, the underlying mechanism of RPL remains unclear. This study aimed to investigate the biomarkers and molecular mechanisms associated with RPL and explore novel treatment strategies for clinical applications.MethodsThe GEO database was utilized to retrieve the RPL gene expression profile GSE165004. This profile underwent differential expression analysis, WGCNA, functional enrichment, and subsequent analysis of RPL gene expression using LASSO regression, SVM-RFE, and RandomForest algorithms for hub gene screening. ANN model were constructed to assess the performance of hub genes in the dataset. The expression of hub genes in both the RPL and control group samples was validated using RT-qPCR. The immune cell infiltration level of RPL was assessed using CIBERSORT. Additionally, pan-cancer analysis was conducted using Sangerbox, and small-molecule drug screening was performed using CMap.ResultsA total of 352 DEGs were identified, including 198 up-regulated genes and 154 down-regulated genes. Enrichment analysis indicated that the DEGs were primarily associated with Fc gamma R-mediated phagocytosis, the Fc epsilon RI signaling pathway, and various metabolism-related pathways. The turquoise module, which showed the highest relevance to clinical symptoms based on WGCNA results, contained 104 DEGs. Three hub genes, WBP11, ACTR2, and NCSTN, were identified using machine learning algorithms. ROC curves demonstrated a strong diagnostic value when the three hub genes were combined. RT-qPCR confirmed the low expression of WBP11 and ACTR2 in RPL, whereas NCSTN exhibited high expression. The immune cell infiltration analysis results indicated an imbalance of macrophages in RPL. Meanwhile, these three hub genes exhibited aberrant expression in multiple malignancies and were associated with a poor prognosis. Furthermore, we identified several small-molecule drugs.ConclusionThis study identifies and validates hub genes in RPL, which may lead to significant advancements in understanding the molecular mechanisms and treatment strategies for this condition.

【 授权许可】

Unknown   
Copyright © 2023 Wei, Wei, Cheng, Tan, Zhou, Lin and Pang