Objective: Evidence shows that gene mutation is a significant proportion of genetic factors associated with prostate cancer. The DNA damage response (DDR) is a signal cascade network that aims to maintain genomic integrity in cells. This comprehensive study was performed to determine the link between different DNA damage response gene mutations and prostate cancer. Materials and methods: A systematic literature search was performed using PubMed, Web of Science, and Embase. Papers published up to February 1, 2022 were retrieved. The DDR gene mutations associated with prostate cancer were identified by referring to relevant research and review articles. Data of prostate cancer patients from multiple PCa cohorts were obtained from cBioPortal. The OR or HR and 95% CIs were calculated using both fixed-effects models (FEMs) and random-effects models (REMs). Results: Seventy-four studies were included in this research, and the frequency of 13 DDR genes was examined. Through the analysis of 33 articles that focused on the risk estimates of DDR genes between normal people and PCa patients, DDR genes were found to be more common in prostate cancer patients (OR = 3.6293 95% CI [2.4992; 5.2705]). Also, patients in the mutated group had a worse OS and DFS outcome than those in the unmutated group ( P < .05). Of the 13 DDR genes, the frequency of 9 DDR genes in prostate cancer was less than 1%, and despite differences in race, BRCA2 was the potential gene with the highest frequency (REM Frequency = .0400, 95% CI .0324 - .0541). The findings suggest that mutations in genes such as ATR, BLM, and MLH1 in PCa patients may increase the sensitivity of Olaparib, a PARP inhibitor. Conclusion: These results demonstrate that mutation in any DDR pathway results in a poor prognosis for PCa patients. Furthermore, mutations in ATR, BLM, and MLH1 or the expression of POLR2L, PMS1, FANCE, and other genes significantly influence Olaparib sensitivity, which may be underlying therapeutic targets in the future.

Pyrethroids may be related to male reproductive system damage. However, the results of many previous studies are contradictory and uncertain. Therefore, a systematic review and a meta-analysis were performed to assess the relationship between pyrethroid exposure and male reproductive system damage. A total of 72 articles were identified, among which 57 were selected for meta-analysis, and 15 were selected for qualitative analysis. Pyrethroid exposure affected sperm count (SMD= -2.0424; 95% CI, -2.4699 to -1.6149), sperm motility (SMD=-3.606; 95% CI, -4.5172 to -2.6948), sperm morphology (SMD=2.686; 95% CI, 1.9744 to 3.3976), testis weight (SMD=-1.1591; 95% CI, -1.6145 to -0.7038), epididymal weight (SMD=-1.1576; 95% CI, -1.7455 to -0.5697), and serum testosterone level (SMD=-1.9194; 95% CI, -2.4589 to -1.3798) in the studies of rats. We found that gestational and lactational exposure to pyrethroids can reduce sperm count (SMD=1.8469; 95% CI, -2.9010 to -0.7927), sperm motility (SMD=-2.7151; 95% CI, -3.9574 to -1.4728), testis weight (SMD=-1.4361; 95% CI, -1.8873 to -0.9848), and epididymal weight (SMD=-0.6639; 95% CI, -0.9544 to -0.3733) of F1 offspring. Exposure to pyrethroids can increase malondialdehyde (SMD=3.3451; 95% CI 1.9914 to 4.6988) oxide in testes and can reduce the activities of glutathione (SMD=-2.075; 95% CI -3.0651 to -1.0848), superoxide dismutase (SMD=-2.4856; 95% CI -3.9612 to -1.0100), and catalase (SMD=-2.7564; 95% CI -3.9788 to -1.5340). Pyrethroid exposure and oxidative stress could damage male sperm quality. Gestational and lactational pyrethroid exposure affects the reproductive system of F1 offspring.

Mitotic catastrophe is well-known as a major pathway of endogenous tumor death, but the prognostic significance of its heterogeneity regarding bladder cancer (BLCA) remains unclear. Our study focused on digging deeper into the TCGA and GEO databases. Through differential expression analysis as well as Weighted Gene Co-expression Network Analysis (WGCNA), we identified dysregulated mitotic catastrophe-associated genes, followed by univariate cox regression as well as ten machine learning algorithms to construct robust prognostic models. Based on prognostic stratification, we revealed intergroup differences by enrichment analysis, immune infiltration assessment, and genomic variant analysis. Subsequently by multivariate cox regression as well as survshap(t) model we screened core prognostic gene and identified it by Mendelian randomization. Integration of qRT-PCR, immunohistochemistry, and single-cell analysis explored the core gene expression landscape. In addition, we explored the ceRNA axis containing upstream non-coding RNAs after detailed analysis of pathway activation, immunoregulation, and methylation functions of the core genes. Finally, we performed drug screening and molecular docking experiments based on the core gene in the DSigDB database. Our efforts culminated in the establishment of an accurate prognostic model containing 16 genes based on Coxboost as well as the Random Survival Forest (RSF) algorithm. Detailed analysis from multiple perspectives revealed a strong link between model scores and many key indicators: pathway activation, immune infiltration landscape, genomic variant landscape, and personalized treatment. Subsequently ANLN was identified as the core of the model, and prognostic analysis revealed that it portends a poor prognosis, further corroborated by Mendelian randomization analysis. Interestingly, ANLN expression was significantly upregulated in cancer cells and specifically clustered in epithelial cells and provided multiple pathways to mediate cell division. In addition, ANLN regulated immune infiltration patterns and was also inseparable from overall methylation levels. Further analysis revealed potential regulation of the MIR4435-2HG, hsa-miR-15a-5p, ANLN axis and highlighted a range of potential therapeutic agents including Phytoestrogens. The model we developed was a powerful predictive tool for BLCA prognosis and revealed the impact of mitotic catastrophe heterogeneity on BLCA in multiple dimensions, which then guided clinical decision-making. Furthermore, we highlighted the potential of ANLN as a BLCA target.

Extensive research has revealed that the score derived from the Gleason grading system plays a pivotal role in predicting prostate cancer (PCa) progression. However, the underlying involvement of Gleason-related genes in PCa requires further investigation. This study aimed to identify Gleason-related genes with the potential to guide PCa therapy and future research. Differentially expressed genes (DEGs) were identified by comparing PCa tissues with high or low Gleason scores using the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases. R v3.6.1, SPSS v23, and ImageJ software were used for all analyses. An effective recurrence-free survival (RFS) predictive model based on seven Gleason-related genes was established and validated (TCGA, AUC = 0.803; five years, AUC = 0.740; three years, AUC = 0.722; one year, AUC = 0.711; GSE46602, AUC = 0.766; five years, AUC = 0.808; three years, AUC = 0.723; one year, AUC = 0.656; GSE116918, AUC = 0.788; five years, AUC = 0.704; three years, AUC = 0.693; one year, AUC = 0.996). Calibration and nomogram plots were conducted. Weighted correlation network analysis (WGCNA) was used, and COL5A2 was selected for further analysis. The results from in vitro experiments demonstrated that COL5A2 was upregulated in PCa with high Gleason scores. The knockdown of COL5A2 inhibited cell proliferation and invasion in PC-3 and LNCaP cell lines. Meanwhile, COL5A2 displayed a strong association with immune infiltration, which might be an underlying immunotherapy target for PCa. We successfully established a robust RFS predictive model. The findings from this study indicated that COL5A2 could promote cell proliferation and invasion in PCa.

A synthetic organic substance called bisphenol A (BPA) is used to make polyester, epoxy resin, polyacrylate, and polycarbonate plastic. BPA exposure on a regular basis has increased the risk of developing cancer. Recent research has shown that there is a strong link between BPA exposure and a number of malignancies. We want to investigate any connections between BPA and prostate cancer in this work. The scores of bisphenols in the prostate cancer cohort were obtained using the ssGSEA algorithm. The analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment was used to investigate probable pathways that are closely related to the genes tied to BPA. The BPA-based risk model was built using regression analysis. Additionally, the molecular docking method was employed to assess BPA's capacity to attach to important genes. Finally, we were able to successfully get the BPA cohort ratings for prostate cancer patients. Additionally, the KEGG enrichment study showed that of the malignancies linked to BPA, prostate cancer is the most highly enriched. In a group of men with prostate cancer, the BPA-related prognostic prediction model exhibits good predictive value. The BPA demonstrated strong and efficient binding to the androgen receptor, according to the molecular docking studies. According to cell proliferation and invasion experiments, exposing prostate cancer cells to BPA at a dosage of 10^-7 uM could greatly enhance their ability to proliferate and invade.