The molecular characteristics in different procedures of spermatogenesis.

Spermatogenesis is a multistep biological process. In addition to somatic cells, it involves the orderly differentiation of dozens of spermatogenic cells. In this process, the regulatory networks between different spermatogenic cell populations are significantly different. RNA m6A regulators and miRNAs have been found to be closely related to spermatogenesis in recent years, and they are an important part of the above regulatory networks. Understanding gene expression and its rules in different spermatogenic cell populations will help in the in-depth exploration of their detailed roles in spermatogenesis. This study collected a public dataset of nonobstructive azoospermia (NOA). Based on the Johnson score, the testicular samples of NOA were divided into three types, Sertoli-cell only syndrome, meiotic arrest and postmeiotic arrest, which represented the loss of three germ cell populations, including whole spermatogenic cells, postmeiotic spermatogenic cells, and a mixture of late spermatids and spermatozoa, respectively. The aforementioned three types of testis data were compared with normal testis data, and the molecular expression characteristics of the abovementioned three germ cell populations were obtained. Our study showed that different germ cell populations have different active molecules and their pathways. In addition, RNA m6A regulators, including METTL3, IGF2BP2 and PRRC2A, and miRNAs, including hsa-let-7a-2, hsa-let-7f-1, hsa-let-7g, hsa-miR-15a, hsa-miR-197, hsa-miR-21, hsa-miR-30e, hsa-miR-32, hsa-miR-503 and hsa-miR-99a, also presented regulatory roles in almost all germ cells.

A new nomogram for the prediction of bone metastasis in patients with prostate cancer.

OBJECTIVE: This study aimed to establish a new prognostic nomogram for bone metastasis in patients with prostate cancer (PCa). METHODS: This study retrospectively analyzed clinical data from 332 patients diagnosed with PCa from 2014 to 2019, and patients were randomly divided into a training set (n = 184) and a validation set (n = 148). Multivariate logistic regression analysis was used to establish a prediction model based on the training set, and a nomogram was constructed for visual presentation. The calibration, discrimination and clinical usefulness of the model were evaluated using the validation set. RESULTS: Total prostate-specific antigen, clinical tumor stage, Gleason score, prostate volume, red cell distribution width and serum alkaline phosphatase were selected as predictors to develop a prediction model of bone metastasis. After evaluation, the model developed in our study exhibited good discrimination (area under the curve: 0.958; 95% confidence interval: 0.93-0.98), calibration (U = 0.01) and clinical usefulness. CONCLUSIONS: The new proposed model showed high accuracy for bone metastasis prediction in patients with PCa and good clinical usefulness.