Prostate cancer (PCa) is a widespread malignancy with global significance, which substantially affects cancer-related mortality. Its spectrum varies widely, from slow-progressing cases to aggressive or even lethal forms. Effective patient stratification into risk groups is crucial to therapeutic decisions and clinical trials. This review examines a wide range of diagnostic and prognostic biomarkers, several of which are integrated into clinical guidelines, such as the PHI, the 4K score, PCA3, Decipher, and Prolaris. It also explores the emergence of novel biomarkers supported by robust preclinical evidence, including urinary miRNAs and isoprostanes. Genetic alterations frequently identified in PCa, including BRCA1/BRCA2, ETS gene fusions, and AR changes, are also discussed, offering insights into risk assessment and precision treatment strategies. By evaluating the latest developments and applications of PCa biomarkers, this review contributes to an enhanced understanding of their role in disease management.

(1) Background: Recent studies suggest a potential link between gut microbiomes (GMs) and inflammatory diseases, but the role of GMs in lichen sclerosus (LS) remains unclear. This study aims to investigate the causal relationship between GMs and LS, focusing on key GM taxa. (2) Methods: We utilized GWAS summary statistics for 211 GM taxa and their association with 2,445 LS patients and 353,088 healthy controls, employing Mendelian randomization (MR). GWAS data for GM taxa came from the MiBioGen consortium, and for LS from the FinnGen consortium. The primary analytical tools included the inverse-variance weighted (IVW) method, weighted MR, simple mode, weighted median, and MR-Egger methods. Sensitivity analyses included leave-one-out analysis, MR-Egger intercept test, MR-PRESSO global test, and Cochrane’s Q-test. A reverse MR analysis was conducted on bacteria identified in the forward MR study. (3) Results: We identified one strong causal relationship: order Burkholderiales [odds ratio (OR) = 0.420, 95% confidence interval (CI): 0.230 - 0.765, p = 0.005], and three nominally significant relationships: phylum Cyanobacteria (OR = 0.585, 95% CI: 0.373 - 0.919, p = 0.020), class Betaproteobacteria (OR = 0.403, 95% CI: 0.189 - 0.857, p = 0.018), and genus Butyrivibrio (OR = 0.678, 95% CI: 0.507 - 0.907, p = 0.009). Moreover, this MR analysis was not impacted by horizontal pleiotropy, according to the MR-Egger intercept test and MR-PRESSO global test (p > 0.05). Remarkably, the reliability of our results was confirmed by leave-one-out analysis. Reverse MR analysis showed no significant causal relationship between LS and GM. (4) Conclusions: This MR study identifies specific gut flora linked to a lower risk of LS, offering new insights for disease treatment and prevention. Future research should incorporate metagenomics sequencing of extensive microbiome GWAS datasets.