Testicular piRNA Analysis Identified Dysregulated piRNAs in Non-obstructive Azoospermia.

Male infertility has remained idiopathic in a remarkable proportion of all cases. Gonadal expression of PIWI-interacting RNAs (piRNAs) has been shown to be vital to normal spermatogenesis, as they are expressed in almost all types of testicular germ cells. These molecules and their related Piwi proteins strictly regulate transposable elements' activity and gene expression. We aimed to identify dysregulated piRNAs in idiopathic non-obstructive azoospermic (NOA) testis by global expression analysis. Testis tissue samples from 18 azoospermic patients (ten NOA and eight OA) were studied by small RNA sequencing. To validate high-throughput sequencing data, quantitative real-time polymerase chain reactions for two differentially altered piRNAs were performed. Bioinformatics analyses were undertaken to identify pathways affected by piRNA dysregulation. In the NOA group, 1328 piRNAs were identified to be differentially expressed, of which 1322 were downregulated and 6 were upregulated. Bioinformatics analysis corroborated the involvement of dysregulated piRNA in spermatogenesis. We also identified 64 clusters of differentially expressed piRNAs, of which 42 clusters had a minimum of ten absolute piRNA hits. Our study suggests that piRNAs show significant dysregulation in infertility. Their target genes play a role in their self-biogenesis, probably by regulating their own production through a feedback mechanism. The downregulated piRNAs may find value as biomarkers for the presence of spermatozoa in the testis of azoospermic individuals, while the upregulated piRNAs are great candidates for further investigation of their precise functions in spermatogenesis.

Meta-analysis of integrated ChIP-seq and transcriptome data revealed genomic regions affected by estrogen receptor alpha in breast cancer.

BACKGROUND: The largest group of patients with breast cancer are estrogen receptor-positive (ER+) type. The estrogen receptor acts as a transcription factor and triggers cell proliferation and differentiation. Hence, investigating ER-DNA interaction genomic regions can help identify genes directly regulated by ER and understand the mechanism of ER action in cancer progression. METHODS: In the present study, we employed a workflow to do a meta-analysis of ChIP-seq data of ER+ cell lines stimulated with 10 nM and 100 nM of E2. All publicly available data sets were re-analyzed with the same platform. Then, the known and unknown batch effects were removed. Finally, the meta-analysis was performed to obtain meta-differentially bound sites in estrogen-treated MCF7 cell lines compared to vehicles (as control). Also, the meta-analysis results were compared with the results of T47D cell lines for more precision. Enrichment analyses were also employed to find the functional importance of common meta-differentially bound sites and associated genes among both cell lines. RESULTS: Remarkably, POU5F1B, ZNF662, ZNF442, KIN, ZNF410, and SGSM2 transcription factors were recognized in the meta-analysis but not in individual studies. Enrichment of the meta-differentially bound sites resulted in the candidacy of pathways not previously reported in breast cancer. PCGF2, HNF1B, and ZBED6 transcription factors were also predicted through the enrichment analysis of associated genes. In addition, comparing the meta-analysis results of both ChIP-seq and RNA-seq data showed that many transcription factors affected by ER were up-regulated. CONCLUSION: The meta-analysis of ChIP-seq data of estrogen-treated MCF7 cell line leads to the identification of new binding sites of ER that have not been previously reported. Also, enrichment of the meta-differentially bound sites and their associated genes revealed new terms and pathways involved in the development of breast cancer which should be examined in future in vitro and in vivo studies.

Unlocking the potential of microRNAs: machine learning identifies key biomarkers for myocardial infarction diagnosis.

BACKGROUND: MicroRNAs (miRNAs) play a crucial role in regulating adaptive and maladaptive responses in cardiovascular diseases, making them attractive targets for potential biomarkers. However, their potential as novel biomarkers for diagnosing cardiovascular diseases requires systematic evaluation. METHODS: In this study, we aimed to identify a key set of miRNA biomarkers using integrated bioinformatics and machine learning analysis. We combined and analyzed three gene expression datasets from the Gene Expression Omnibus (GEO) database, which contains peripheral blood mononuclear cell (PBMC) samples from individuals with myocardial infarction (MI), stable coronary artery disease (CAD), and healthy individuals. Additionally, we selected a set of miRNAs based on their area under the receiver operating characteristic curve (AUC-ROC) for separating the CAD and MI samples. We designed a two-layer architecture for sample classification, in which the first layer isolates healthy samples from unhealthy samples, and the second layer classifies stable CAD and MI samples. We trained different machine learning models using both biomarker sets and evaluated their performance on a test set. RESULTS: We identified hsa-miR-21-3p, hsa-miR-186-5p, and hsa-miR-32-3p as the differentially expressed miRNAs, and a set including hsa-miR-186-5p, hsa-miR-21-3p, hsa-miR-197-5p, hsa-miR-29a-5p, and hsa-miR-296-5p as the optimum set of miRNAs selected by their AUC-ROC. Both biomarker sets could distinguish healthy from not-healthy samples with complete accuracy. The best performance for the classification of CAD and MI was achieved with an SVM model trained using the biomarker set selected by AUC-ROC, with an AUC-ROC of 0.96 and an accuracy of 0.94 on the test data. CONCLUSIONS: Our study demonstrated that miRNA signatures derived from PBMCs could serve as valuable novel biomarkers for cardiovascular diseases.