Integrated analyses for genetic markers of polycystic ovary syndrome with 9 case-control studies of gene expression profiles

Multi-omics insights and therapeutic implications in polycystic ovary syndrome: a review

Polycystic ovary syndrome (PCOS) is a common gynecological disease that causes adverse effects in women in their reproductive phase. Nonetheless, the molecular mechanisms remain unclear. Over the last decade, sequencing and omics approaches have advanced at an increased pace. Omics initiatives have come to the forefront of biomedical research by presenting the significance of biological functions and processes. Thus, multi-omics profiling has yielded important insights into understanding the biology of PCOS by identifying potential biomarkers and therapeutic targets. Multi-omics platforms provide high-throughput data to leverage the molecular mechanisms and pathways involving genetic alteration, epigenetic regulation, transcriptional regulation, protein interaction, and metabolic alterations in PCOS. The purpose of this review is to outline the prospects of multi-omics technologies in PCOS research by revealing novel biomarkers and therapeutic targets. Finally, we address the knowledge gaps and emerging treatment strategies for the management of PCOS. Future PCOS research in multi-omics at the single-cell level may enhance diagnostic and treatment options.

Multiomics Analysis-Based Biomarkers in Diagnosis of Polycystic Ovary Syndrome

Polycystic ovarian syndrome is an utmost communal endocrine, psychological, reproductive, and metabolic disorder that occurs in women of reproductive age with extensive range of clinical manifestations. This may even lead to long-term multiple morbidities including obesity, diabetes mellitus, insulin resistance, cardiovascular disease, infertility, cerebrovascular diseases, and ovarian and endometrial cancer. Women affliction from PCOS in midst assemblage of manifestations allied with menstrual dysfunction and androgen exorbitance, which considerably affects eminence of life. PCOS is recognized as a multifactorial disorder and systemic syndrome in first-degree family members; therefore, the etiology of PCOS syndrome has not been copiously interpreted. The disorder of PCOS comprehends numerous allied health conditions and has influenced various metabolic processes. Due to multifaceted pathophysiology engaging several pathways and proteins, single genetic diagnostic tests cannot be supportive to determine in straight way. Clarification of cellular and biochemical pathways and various genetic players underlying PCOS could upsurge our consideration of pathophysiology of this syndrome. It is requisite to know pathophysiological relationship between biomarker and their reflection towards PCOS disease. Biomarkers deliver vibrantly and potent ways to apprehend the spectrum of PCOS with applications in screening, diagnosis, characterization, and monitoring. This paper relies on the endeavor to point out many candidates as potential biomarkers based on omics technologies, thus highlighting correlation between PCOS disease with innovative technologies. Therefore, the objective of existing review is to encapsulate more findings towards cutting-edge advances in prospective use of biomarkers for PCOS disease. Discussed biomarkers may be fruitful in guiding therapies, addressing disease risk, and predicting clinical outcomes in future.

Oncology and Pharmacogenomics Insights in Polycystic Ovary Syndrome: An Integrative Analysis

Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovaries. Epidemiological findings revealed that women with PCOS are prone to develop certain cancer types due to their shared metabolic and endocrine abnormalities. However, the mechanism that relates PCOS and oncogenesis has not been addressed. Herein, in this review article the genomic status, transcriptional and protein profiles of 264 strongly PCOS related genes (PRG) were evaluated in endometrial cancer (EC), ovarian cancer (OV) and breast cancer (BC) exploring oncogenic databases. The genomic alterations of PRG were significantly higher when compared with a set of non-diseases genes in all cancer types. PTEN had the highest number of mutations in EC, TP53, in OC, and FSHR, in BC. Based on clinical data, women older than 50 years and Black or African American females carried the highest ratio of genomic alterations among all cancer types. The most altered signaling pathways were p53 in EC and OC, while Fc epsilon RI in BC. After evaluating PRG in normal and cancer tissue, downregulation of the differentially expressed genes was a common feature. Less than 30 proteins were up and downregulated in all cancer contexts. We identified 36 highly altered genes, among them 10 were shared between the three cancer types analyzed, which are involved in the cell proliferation regulation, response to hormone and to endogenous stimulus. Despite limited PCOS pharmacogenomics studies, 10 SNPs are reported to be associated with drug response. All were missense mutations, except for rs8111699, an intronic variant characterized as a regulatory element and presumably binding site for transcription factors. In conclusion, in silico analysis revealed key genes that might participate in PCOS and oncogenesis, which could aid in early cancer diagnosis. Pharmacogenomics efforts have implicated SNPs in drug response, yet still remain to be found.

Establishment and Analysis of a Combined Diagnostic Model of Polycystic Ovary Syndrome with Random Forest and Artificial Neural Network

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.

Protein Network Studies on PCOS Biomarkers With S100A8, Druggability Assessment, and RNA Aptamer Designing to Control Its Cyst Migration Effect

The prevalence of polycystic ovary syndrome (PCOS) has been gradually increasing among adult females worldwide. Laparoscopy drilling on ovary is the only available temporary solution with a high incidence of reoccurrence. S100A8 with S100A9 complex is believed to facilitate the cyst migration in PCOS condition. The high evident protein interaction network studies between PCOS biomarkers, cancer invasion markers, and the interactors of S100A8 confirm that this protein has strong interaction with other selective PCOS biomarkers, which may be associative in the immature cyst invasion process. Through the network studies, intensive structural and pathway analysis, S100A8 is identified as a targetable protein. In this research, the non-SELEX in silico method is adapted to construct RNA Library based on the consensus DNA sequence of Glucocorticoid Response Element (GRE) and screened the best nucleotide fragments which are bound within the active sites of the target protein. Selected sequences are joined as a single strand and screened the one which competitively binds with minimal energy. In vitro follow-up of this computational research, the designed RNA aptamer was used to infect the MCF7 cell line through Lipofectamine 2000 mediated delivery to study the anti-cell migration effect. Wound Scratch assay confirms that the synthesized 18-mer oligo has significant inhibition activity toward tumor cell migration at the cellular level.