Gene expression alterations in testicular biopsies from males with spermatogenesis arrest identified by transcriptome analysis
Bibliographic record
Abstract
Spermatogenesis is a complex biological process encompasses several stages of cellular divisions, ultimately resulting in producing mature spermatozoa capable of fertilization. Numerous factors involved in the precise regulation of the spermatogenesis, and any disruptions or alterations in these regulatory mechanisms can lead to spermatogenesis arrest, which may result in male infertility. Among these factors, genetic influences play essential role in regulating the process. This study aimed to identify genes that are differentially expressed in relation to spermatogenesis arrest. Testicular biopsy samples were collected from 22 non-obstructive azoospermic patients diagnosed with spermatogenesis arrest (cases) and nine obstructive azoospermic patients (controls). RNA sequencing (RNA-seq) was performed on five samples from the 22 non-obstructive azoospermic patients and compared to previously published transcriptomic data from obstructive azoospermic patients, which served as the control group. Differential expression analysis of the RNA-seq data identified 1,915 differentially expressed genes, comprising 337 upregulated and 1,578 downregulated genes. Among these, several key candidate genes were identified for further analysis, including the upregulation of FOS, FOSB, RGS1, and CXCL8, as well as the downregulation of TNP2, SPRR2C, LINC02314, and C16orf78. RT-qPCR validation confirmed the RNA-seq findings for these genes in the tested samples. Subsequently, RT-qPCR was performed on the remaining 17 non-obstructive (n = 17) and obstructive azoospermic samples (n = 9) collected in this study. The results from these additional samples were consistent with the RNA-seq data, further supporting the findings. Using gene ontology (GO) analysis and published literature, we linked these genes with spermatogenesis arrest, identifying promising targets that could serve as potential biomarkers for this condition in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".