ABSTRACT
Male infertility, age-related changes, and tumors have been increasingly studied in the field of male reproductive health due to the emergence of environmental stressors, declining fertility rates, and aging populations. Numerous studies have demonstrated that the ERK1/2 signaling pathway plays a significant role in male reproduction. The ERK1/2 pathway is associated with several signaling pathways and has a complex interplay that influences the spermatogenic microenvironment, sperm viability, gonadal axis regulation, as well as resistance to testicular aging and tumors. Moreover, the ERK1/2 pathway directly or indirectly regulates testicular somatic cells, which are crucial for maintaining spermatogenesis and microenvironment regulation. Given the critical role of the ERK1/2 signaling pathway in male reproductive health, comprehensive exploration of its multifaceted effects on male reproduction and underlying mechanisms is necessary. This study aims to provide a solid foundation for in-depth research in the field of male reproduction and further enhance the reproductive health of males.
Subject(s)
Infertility, Male , Neoplasms , Male , Humans , Fertility/physiology , MAP Kinase Signaling System , Semen/metabolism , Reproduction , Testis/metabolism , Infertility, Male/genetics , Infertility, Male/metabolism , Infertility, Male/pathology , Signal Transduction , Neoplasms/metabolism , Tumor MicroenvironmentABSTRACT
Recent advances in spatially resolved transcriptomic technologies have enabled unprecedented opportunities to elucidate tissue architecture and function in situ. Spatial transcriptomics can provide multimodal and complementary information simultaneously, including gene expression profiles, spatial locations, and histology images. However, most existing methods have limitations in efficiently utilizing spatial information and matched high-resolution histology images. To fully leverage the multi-modal information, we propose a SPAtially embedded Deep Attentional graph Clustering (SpaDAC) method to identify spatial domains while reconstructing denoised gene expression profiles. This method can efficiently learn the low-dimensional embeddings for spatial transcriptomics data by constructing multi-view graph modules to capture both spatial location connectives and morphological connectives. Benchmark results demonstrate that SpaDAC outperforms other algorithms on several recent spatial transcriptomics datasets. SpaDAC is a valuable tool for spatial domain detection, facilitating the comprehension of tissue architecture and cellular microenvironment. The source code of SpaDAC is freely available at Github (https://github.com/huoyuying/SpaDAC.git).
Subject(s)
Gene Expression Profiling , Transcriptome , Transcriptome/genetics , Algorithms , Cluster Analysis , SoftwareABSTRACT
BACKGROUND: Gastric cancer is a malignant tumor with high morbidity and mortality. Therefore, the accurate recognition of prognostic molecular markers is the key to improving treatment efficacy and prognosis. METHODS: In this study, we developed a stable and robust signature through a series of processes using machine-learning approaches. This PRGS was further experimentally validated in clinical samples and a gastric cancer cell line. RESULTS: The PRGS is an independent risk factor for overall survival that performs reliably and has a robust utility. Notably, PRGS proteins promote cancer cell proliferation by regulating the cell cycle. Besides, the high-risk group displayed a lower tumor purity, higher immune cell infiltration, and lower oncogenic mutation than the low-PRGS group. CONCLUSIONS: This PRGS could be a powerful and robust tool to improve clinical outcomes for individual gastric cancer patients.
