Construction and analysis of a joint diagnostic model of machine learning for cryptorchidism based on single-cell sequencing

IF 1.6 4区医学 Q4 DEVELOPMENTAL BIOLOGY

Birth Defects Research Pub Date : 2024-03-08 DOI:10.1002/bdr2.2316

Yuehua Chen, Xiaomeng Zhou, Linghua Ji, Jun Zhao, Hua Xian, Yunzhao Xu, Ziheng Wang, Wenliang Ge

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引用次数: 0

Abstract

Background

Cryptorchidism is a condition in which one or both of a baby's testicles do not fully descend into the bottom of the scrotum. Newborns with cryptorchidism are at increased risk of developing infertility later in life. The aim of this study was to develop a novel diagnostic model for cryptorchidism and to identify new biomarkers associated with cryptorchidism.

Methods

The study data were obtained from RNA sequencing data of cryptorchid patients from Nantong University Hospital and the Gene Expression Omnibus (GEO) database. Differential expression analysis was used to obtain differentially expressed genes (DEGs) between the control and cryptorchid groups. These DEGs were analyzed for their functions by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment using GSEA software. Random Forest algorithm was used to screen central genes based on these DEGs. Neuralnet software package was used to develop artificial neural network models. Based on clinical data, receiver operating characteristic (ROC) was used to validate the models. Single-cell sequencing analysis was used for the pathogenesis of cryptorchidism.

Results

We obtained a total of 525 important DEGs related to cryptorchidism, which are mainly associated with biological functions such as supramolecular complexes and microtubule cytoskeleton. Random forest approach screening obtained eight hub genes. A neural network based on the hub genes showed a 100% success rate of the model. Finally, single-cell sequencing analysis validated the hub genes.

Conclusion

We developed a novel diagnostic model for cryptorchidism using artificial neural networks and validated its utility as an effective diagnostic tool.

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基于单细胞测序的隐睾症机器学习联合诊断模型的构建与分析

背景介绍隐睾症是指婴儿的一个或两个睾丸没有完全下降到阴囊底部。患有隐睾症的新生儿日后患不育症的风险会增加。这项研究的目的是开发一种新型的隐睾症诊断模型，并确定与隐睾症相关的新生物标志物：研究数据来自南通大学附属医院隐睾患者的 RNA 测序数据和基因表达总库（GEO）数据库。差异表达分析用于获得对照组和隐睾组之间的差异表达基因（DEGs）。利用GSEA软件，通过基因本体（GO）和京都基因组百科全书（KEGG）富集分析这些DEGs的功能。使用随机森林算法根据这些 DEGs 筛选中心基因。使用 Neuralnet 软件包开发人工神经网络模型。根据临床数据，使用接收器操作特征（ROC）来验证模型。单细胞测序分析用于研究隐睾症的发病机制：结果：我们共获得了525个与隐睾症相关的重要DEGs，它们主要与超分子复合物和微管细胞骨架等生物功能有关。随机森林法筛选获得了 8 个枢纽基因。基于枢纽基因的神经网络显示该模型的成功率为100%。最后，单细胞测序分析验证了枢纽基因：我们利用人工神经网络开发了一种新型的隐睾症诊断模型，并验证了其作为有效诊断工具的实用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Birth Defects Research Medicine-Embryology

CiteScore

3.60

自引率

9.50%

发文量

153

期刊介绍： The journal Birth Defects Research publishes original research and reviews in areas related to the etiology of adverse developmental and reproductive outcome. In particular the journal is devoted to the publication of original scientific research that contributes to the understanding of the biology of embryonic development and the prenatal causative factors and mechanisms leading to adverse pregnancy outcomes, namely structural and functional birth defects, pregnancy loss, postnatal functional defects in the human population, and to the identification of prenatal factors and biological mechanisms that reduce these risks. Adverse reproductive and developmental outcomes may have genetic, environmental, nutritional or epigenetic causes. Accordingly, the journal Birth Defects Research takes an integrated, multidisciplinary approach in its organization and publication strategy. The journal Birth Defects Research contains separate sections for clinical and molecular teratology, developmental and reproductive toxicology, and reviews in developmental biology to acknowledge and accommodate the integrative nature of research in this field. Each section has a dedicated editor who is a leader in his/her field and who has full editorial authority in his/her area.