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Nonobstructive azoospermia (NOA) or testicular failure is the most severe form of male infertility. A variety of conditions, both acquired and congenital, can cause azoospermia. However, in a large number of azoospermia patients who are classified as idiopathic cases, the etiology remains poorly understand mainly due to the lack of knowledge of all the genetic causes and molecular mechanisms responsible for spermatogenesis failure. Identification of the key gene modules and pathways-related spermatogenesis failure might help to reveal the mechanisms of idiopathic azoospermia. Therefore, the expression patterns of spermatogenesis-associated genes in NOA were analyzed by weighted gene coexpression network analysis (WGCNA) based on two public microarray data sets (GSE45885 and GSE45887), which included 51 samples and 32,321 genes. We identified a module (turquoise) that was significantly related to the Johnsen score of the testicular samples. In addition, the results of function and pathway enrichment analyses based on the online bioinformatics database Metascape revealed that genes in the turquoise module were mainly related to the process of spermatogenesis and spermatid development. To further identify spermatogenesis-associated genes, a microarray data set (GSE926) of murine testis at different developmental time points was analyzed by WGCNA. The blue module in GSE926 was significantly related to the time of murine testis development. The overlap study and k-core analysis based on protein-protein interaction network revealed that spermatogenesis-and spermatid development-associated genes, including glyceraldehyde-3-phosphate dehydrogenase, ADAM metallopeptidase domain 2, transition protein 1, testis-specific serine kinase 2, transition protein 2, and germ cell-associated 1 (GSG1), were further identified in the selected modules. The expression profile of GSG1 in human testis was chosen for further study using immunochemistry staining. Taken together, these screened gene modules and pathways provided a more detailed genetic and molecular mechanism underlying spermatogenesis failure occurrence and holds promise as potential diagnosis biomarkers and therapeutic targets.

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Alternative splicing (AS) constitutes a major reason for messenger RNA (mRNA) and protein diversity. Increasing studies have shown a link to splicing dysfunction associated with malignant neoplasia. Systematic analysis of AS events in kidney cancer remains poorly reported. Therefore, we generated AS profiles in 533 kidney renal clear cell carcinoma (KIRC) patients in The Cancer Genome Atlas (TCGA) database using RNA‐seq data. Then, prognostic models were developed in a primary cohort (N = 351) and validated in a validation cohort (N = 182). In addition, splicing networks were built by integrating bioinformatics analyses. A total of 11 268 and 8083 AS variants were significantly associated with patient overall survival time in the primary and validation KIRC cohorts, respectively, including STAT1, DAZAP1, IDS, NUDT7, and KLHDC4. The AS events in the primary KIRC cohorts served as candidate AS events to screen the independent risk factors associated with survival in the primary cohort and to develop prognostic models. The area under the curve of the receiver‐operator characteristic curve for prognostic prediction in the primary and validation KIRC cohorts was 0.84 and 0.82 at 2500 days of overall survival, respectively. In addition, splicing correlation networks revealed key splicing factors (SFs) in KIRC, such as HNRNPH1, HNRNPU, KHDBS1, KHDBS3, SRSF9, RBMX, SFQ, SRP54, HNRNPA0, and SRSF6. In this study, we analyzed the AS landscape in the TCGA KIRC cohort and detected predictors (prognostic) based on AS variants with high performance for risk stratification of the KIRC cohort and revealed key SFs in splicing networks, which could act as underlying mechanisms.

Objectives To investigate the prevalence of symptomatic dry eye (SDE) on women undergoing systemic adjuvant therapy for breast cancer and its association with treatment settings. Methods Woman undergoing breast cancer systemic adjuvant therapy were included in exposure group. An age-matched non-treatment control group was recruited. This cross-sectional questionnaire-based study utilised validated Ocular Surface Disease Index (OSDI) and NCCN-FACT-Breast Cancer Symptom Index (NFBSI-16) questionnaires to determine the presence of SDE and investigate other breast cancer treatment complications. Additionally, demographic data and medical histories were collected. Results Of 423 eligible participants, 200 in each of the control group and the exposure group were included in the final analysis. The prevalence of SDE was 59.0% in breast cancer patients with adjuvant treatment, statistically significantly higher than 25.5% in the control group ( P < 0.01). Additionally, exposure group experienced higher prevalence of moderate and severe SDE, which were 20.0% and 19.5% respectively compared with 9.0% and 4.0% in the control group ( P = 0.002, P < 0.001). There was a significantly high prevalence of SDE among patients who had received over four cycles of systemic therapy (71.0%, P < 0.001) and the application of targeted therapy (71.2%, P = 0.014). The severity of SDE positively correlated with the cycles of treatment administered. Conclusion SDE was significantly predominant in women with breast cancer undergoing systemic adjuvant treatment. Our findings suggest dry eye assessments among patients receiving more than four cycles of chemotherapy or targeted therapy, thus early revealing possible dry eye conditions to both patients and clinicians for further specialized examination and treatment.