In this study, we have evaluated whether 57 genome-wide association studies (GWAS)-identified common variants for type 2 diabetes (T2D) influence the risk of developing prostate cancer (PCa) in a population of 304 Caucasian PCa patients and 686 controls. The association of selected single nucleotide polymorphisms (SNPs) with the risk of PCa was validated through meta-analysis of our data with those from the UKBiobank and FinnGen cohorts, but also previously published genetic studies. We also evaluated whether T2D SNPs associated with PCa risk could influence host immune responses by analysing their correlation with absolute numbers of 91 blood-derived cell populations and circulating levels of 103 immunological proteins and 7 steroid hormones. We also investigated the correlation of the most interesting SNPs with cytokine levels after in vitro stimulation of whole blood, peripheral mononuclear cells (PBMCs), and monocyte-derived macrophages with LPS, PHA, Pam3Cys, and Staphylococcus Aureus. The meta-analysis of our data with those from six large cohorts confirmed that each copy of the FTO_rs9939609A, HNF1B_rs7501939T, HNF1B_rs757210T, HNF1B_rs4430796G, and JAZF1_rs10486567A alleles significantly decreased risk of developing PCa (p = 3.70 × 10⁻⁵, p = 9.39 × 10⁻⁵⁴, p = 5.04 × 10⁻⁵⁴, p = 1.19 × 10⁻⁷¹, and p = 1.66 × 10⁻¹⁸, respectively). Although it was not statistically significant after correction for multiple testing, we also found that the NOTCH2_rs10923931T and RBMS1_rs7593730 SNPs associated with the risk of developing PCa (p = 8.49 × 10⁻⁴ and 0.004). Interestingly, we found that the protective effect attributed to the HFN1B locus could be mediated by the SULT1A1 protein (p = 0.00030), an arylsulfotransferase that catalyzes the sulfate conjugation of many hormones, neurotransmitters, drugs, and xenobiotic compounds. In addition to these results, eQTL analysis revealed that the HNF1B_rs7501939, HNF1B_rs757210, HNF1B_rs4430796, NOTCH2_rs10923931, and RBMS1_rs7593730 SNPs influence the risk of PCa through the modulation of mRNA levels of their respective genes in whole blood and/or liver. These results confirm that functional TD2-related variants influence the risk of developing PCa, but also highlight the need of additional experiments to validate our functional results in a tumoral tissue context.

The COVID-19 pandemic presents a spectrum of clinical outcomes ranging from respiratory conditions to cardiovascular complications that challenge management and resource allocation. Identification of early predictive biomarkers that are easy to detect is a priority to optimize medical care and resources. ACE2 and TMPRSS2 have received special attention due to their role in viral infectivity but also due to their physiological anti-inflammatory activities. CD15 and CD45 are key proteins in the immune response also associated with SARS-CoV-2 response. This study focused on analyzing the expression of ACE2, TMPRSS2, CD15, and CD45 in a cohort of 216 patients (111 mild and 105 severe disease) to ascertain their potential as biomarkers for predicting disease severity. We aimed to assess the correlation between these markers and the severity of symptoms, utilizing qPCR and flow cytometry. We used mixed-effects linear regression models and Receiver Operating Characteristic (ROC) curves to test the performance of the biomarkers in the prediction of the severity of the disease. Significant lower surface expression of CD15 and ACE2 was observed in severe cases in addition to a strong association between aging and the severity of the disease. By integrating these findings, we developed a predictive model achieving 92.9% specificity and 79.3% sensitivity (AUC = 0.91; 95% CI: 0.87–0.96). The study concludes that our combined biomarker model could significantly enhance the management of COVID-19 by enabling early identification of patients at risk for severe outcomes, thus improving treatment strategies and resource distribution.

Authors: Veronica Arenas-Rodriguez, Patricia Maria Porras-Quesada, Victor Sanchez-Conde, Ignacio Puche-Sanz, Fernando Vazquez-Alonso, Sergio Cuenca-Lopez, Blanca Cano-Gutierrez, Sara Martin-Esteban, Maria Jesus Alvarez-Cubero, Luis Javier Martinez-Gonzalez. Affiliations: GENYO (Pfizer-University of Granada-Andalusian Regional Government Centre for Genomics and Oncological

Prostate cancer (PC) is one of the most prevalent tumours in the world, however, the hereditary (Hereditary PC; HPC) form is a rare pathology, that does not exceed 6%. Despite its very low incidence, a family history of PC in a first-degree relative multiplies the risk of suffering from PC by approximately two-fold. Therefore, the search for genetic variables associated with the detection, monitoring and treatment of the condition is paramount. In this study, we conduct deep screening of exomes by next-generation sequencing (NGS) analysis in search of new biomarkers. We performed this analysis in a family with a high incidence of PC. Our data reveal that variants in some genes, such as HIBCH and DPP4, are present in all HPC patients. Moreover, high-risk patients have unique additional variants, such as FANK1, TUBA3FP and ALDH3B2. These results provide a new set of promising biomarkers in HCP.

The androgenic signalling pathway is essential for carcinogenesis and tumour development in prostate cancer (PC). The importance of that pathway makes it the main target of treatments against PC, among which androgen deprivation therapy (ADT) stands out. The heterogeneity of the response against the same treatment shows the importance of the search for molecular biomarkers, which enable the prediction of the response to the therapy in each case. This work focuses on the characterization of the response to treatment in several patients of PC through the analysis of different genetic variants (rs10877012 (CYP27B1); rs3768490 (GSTM5); rs1004446 (IGF2)). The statistical analysis revealed a certain tendency to resistance in A/G genotype carriers in rs1004446 (IGF2). Furthermore, a significant statistical relation between aggressive phenotypes was confirmed in single nucleotide polymorphism (SNP) rs10877012 (CYP27B1, p = 0.013).

Information about the paper titled "APPLICATION OF DIGITAL BOARDS AS AN INNOVATION FOR TEACHING IN BIOCHEMISTRY AND MOLECULAR BIOLOGY" at IATED Digital Library