Abstract
Polycystic ovary syndrome (PCOS) is one of the most common metabolic and reproductive endocrinopathies. However, few studies have tried to develop a diagnostic model based on gene biomarkers. In this study, we applied a computational method by combining two machine learning algorithms, including random forest (RF) and artificial neural network (ANN), to identify gene biomarkers and construct diagnostic model. We collected gene expression data from Gene Expression Omnibus (GEO) database containing 76 PCOS samples and 57 normal samples; five datasets were utilized, including one dataset for screening differentially expressed genes (DEGs), two training datasets, and two validation datasets. Firstly, based on RF, 12 key genes in 264 DEGs were identified to be vital for classification of PCOS and normal samples. Moreover, the weights of these key genes were calculated using ANN with microarray and RNA-seq training dataset, respectively. Furthermore, the diagnostic models for two types of datasets were developed and named neuralPCOS. Finally, two validation datasets were used to test and compare the performance of neuralPCOS with other two set of marker genes by area under curve (AUC). Our model achieved an AUC of 0.7273 in microarray dataset, and 0.6488 in RNA-seq dataset. To conclude, we uncovered gene biomarkers and developed a novel diagnostic model of PCOS, which would be helpful for diagnosis.
Highlights
Polycystic ovary syndrome (PCOS), as a heterogeneous endocrine disorder, is closely associated with menstrual dysfunction, infertility, hirsutism, acne, obesity, and metabolic syndrome [1]
The three major diagnostic criteria of PCOS widely followed are criteria raised by National Institutes of Health (NIH) [2], 2003 Rotterdam Consensus raised by European Society of Human Reproduction and Embryology (ESHRE) and American Society for Reproductive Medicine (ASRM) [3, 4], and criteria raised by Androgen Excess Society (AES) [5]
To validate the accuracy and superiority of the diagnosis model we established, we evaluated the performance with microarray and RNA-seq data and compared them to other marker genes obtained in previous studies [20, 21]
Summary
Polycystic ovary syndrome (PCOS), as a heterogeneous endocrine disorder, is closely associated with menstrual dysfunction, infertility, hirsutism, acne, obesity, and metabolic syndrome [1]. The three major diagnostic criteria of PCOS widely followed are criteria raised by National Institutes of Health (NIH) [2], 2003 Rotterdam Consensus raised by European Society of Human Reproduction and Embryology (ESHRE) and American Society for Reproductive Medicine (ASRM) [3, 4], and criteria raised by Androgen Excess Society (AES) [5]. These criteria have created some controversy in the field [6]. Many studies have successfully used genetic risk scores to explain increasing amounts of variance in diseases [12]
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