Testicular germ cell tumors (TGCT) are the most common tumor in young white men and have a high heritability. In this study, the international Testicular Cancer Consortium assemble 10,156 and 179,683 men with and without TGCT, respectively, for a genome-wide association study. This meta-analysis identifies 22 TGCT susceptibility loci, bringing the total to 78, which account for 44% of disease heritability. Men with a polygenic risk score (PRS) in the 95^th percentile have a 6.8-fold increased risk of TGCT compared to men with median scores. Among men with independent TGCT risk factors such as cryptorchidism, the PRS may guide screening decisions with the goal of reducing treatment-related complications causing long-term morbidity in survivors. These findings emphasize the interconnected nature of two known pathways that promote TGCT susceptibility: male germ cell development within its somatic niche and regulation of chromosomal division and structure, and implicate an additional biological pathway, mRNA translation.

Biomedical science is rapidly developing in terms of more transparency, openness and reproducibility of scientific publications. This is even more important for all studies that are based on results from basic semen examination. Recently two concordant documents have been published: the 6th edition of the WHO Laboratory Manual for the Examination and Processing of Human Semen, and the International Standard ISO 23162:2021. With these tools, we propose that authors should be instructed to follow these laboratory methods in order to publish studies in peer-reviewed journals, preferable by using a checklist as suggested in an Appendix to this article.

Semen analysis is central in infertility investigation. Manual assessment of sperm motility according to the WHO recommendations is the golden standard, and extensive training is a requirement for accurate and reproducible results. Deep convolutional neural networks (DCNN) are especially suitable for image classification. In this study, we evaluated the performance of the DCNN ResNet-50 in predicting the proportion of sperm in the WHO motility categories. Two models were evaluated using tenfold cross-validation with 65 video recordings of wet semen preparations from an external quality assessment programme for semen analysis. The corresponding manually assessed data was obtained from several of the reference laboratories, and the mean values were used for training of the DCNN models. One model was trained to predict the three categories progressive motility, non-progressive motility, and immotile spermatozoa. Another model was used in predicting four categories, where progressive motility was differentiated into rapid and slow. The resulting average mean absolute error (MAE) was 0.05 and 0.07, and the average ZeroR baseline was 0.09 and 0.10 for the three-category and the four-category model, respectively. Manual and DCNN-predicted motility was compared by Pearson’s correlation coefficient and by difference plots. The strongest correlation between the mean manually assessed values and DCNN-predicted motility was observed for % progressively motile spermatozoa (Pearson’s r = 0.88, p < 0.001) and % immotile spermatozoa (r = 0.89, p < 0.001). For rapid progressive motility, the correlation was moderate (Pearson’s r = 0.673, p < 0.001). The median difference between manual and predicted progressive motility was 0 and 2 for immotile spermatozoa. The largest bias was observed at high and low percentages of progressive and immotile spermatozoa. The DCNN-predicted value was within the range of the interlaboratory variation of the results for most of the samples. In conclusion, DCNN models were able to predict the proportion of spermatozoa into the WHO motility categories with significantly lower error than the baseline. The best correlation between the manual and the DCNN-predicted motility values was found for the categories progressive and immotile. Of note, there was considerable variation between the mean motility values obtained for each category by the reference laboratories, especially for rapid progressive motility, which impacts the training of the DCNN models.

Guanine nucleotide-binding proteins (G proteins) are key components in membrane signal transduction that may play an important role in testis function. The present study is the first description of cell-specific differences in the contents of G protein alpha-subunits and their mRNAs in isolated rat testicular cells (pachytene spermatocytes, round spermatids, Sertoli cells, peritubular cells). By using Western blot techniques G1-3 alpha was shown to be the only pertussis toxin (PTX) substrate present in all the testicular cells examined. Surprisingly, we observed a lack of immunoreactive Gi-1 alpha/Gi-2 alpha protein in pachytene spermatocytes and round spermatids in spite of significant levels of the corresponding mRNAs as revealed by Northern analysis. No immunoreactive Gs alpha was detected in germ cells, in agreement with previous findings that the hormone-sensitive adenylyl cyclase is absent in these cell types. Peritubular cells and Sertoli cells contained no Go alpha, whereas high levels of both immunoreactive protein and mRNA were found in pachytene spermatocytes. This indicates that the Go protein may play a role at this stage of spermatogenesis. The stimulation of phospholipase C (PLC) in germ cell membranes by 5'-guanylyl imidophosphate indicates that PTX-sensitive PLC activation may be mediated by Go alpha or Gi-3 alpha.

Testicular germ cell tumour (TGCT) is the most common cancer type among young adults in many parts of the world. Although the pathogenesis of TGCT is not well understood, the involvement of heritable components is evident, and the risk is polygenic. Genome-wide association studies have so far found 78 susceptibility loci for TGCT, and many of the loci are in non-coding regions indicating the involvement of non-coding RNAs in TGCT pathogenesis. MicroRNAs (miRNAs), a class of non-coding RNAs, have emerged as important gene regulators at the post-transcriptional level. They are crucial in controlling many cellular processes, such as proliferation, differentiation, and apoptosis, and an aberrant miRNA expression may contribute to the pathogenesis of several cancers, including TGCT. In support of this notion, several studies reported differential expression of miRNAs in TGCTs. We previously demonstrated that miRNAs were the most common group of small non-coding RNAs in TGCTs, and several functional studies of miRNAs in TGCTs suggest that they may act as either oncogene or tumour suppressors. Moreover, individual miRNA targets and downstream pathways in the context of TGCT development have been explored. In this review, we will focus on the diverse roles and targets of miRNAs in TGCT pathogenesis.