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

Abstract

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

Identification of metastasis-related genes for predicting prostate cancer diagnosis, metastasis and immunotherapy drug candidates using machine learning approaches

“…Multi-omics analysis, utilizing artificial intelligence and machine learning techniques, aims to analyze multidimensional data to uncover hidden patterns and correlations related to diseases. These technologies play a crucial role in making clinically relevant decisions and applications [12][13][14]. Martelin et al developed a machine learning model for cost-effective preliminary prostate cancer screening [15].…”