Vitamin D has been considered a strong contributing factor to type 1 diabetes mellitus (T1DM). Many studies have investigated polymorphisms in the VDR gene in association with T1DM in different populations, but there are still conflicting findings. This study aimed to evaluate the association of four variants in the VDR gene (rs7975232, rs1544410, rs731236, and rs2228570) with T1DM risk and vitamin D levels within a population from North Region, Brazil, as well as the influence of genomic ancestry on T1DM. A total of 65 T1DM patients and 83 non-T1DM patients were enrolled in this study. VDR gene polymorphisms were assessed using Sanger sequencing analysis. Genomic ancestry was analyzed using a set of 61 ancestry-informative markers. T1DM patients showed higher European genomic contribution and lower Native American genomic contribution when compared to non-T1DM patients. T1DM patients with AA genotype in rs1544410 or CC genotype in rs731236 had significantly lower 25(OH)D levels compared to the other two genotypes (p = 0.013 and p = 0.02, respectively), while T1DM with TT genotype in rs2228570 had higher 25(OH)D levels compared to CC + TC in the same polymorphism (p = 0.011). Our findings suggest that the association between 25(OH)D and T1DM may be modified by VDR variants, possibly influencing the development of this autoimmune disease.

Background: Metastatic castration‐resistant prostate cancer (mCRPC) is an aggressive form of cancer unresponsive to androgen deprivation therapy (ADT) that spreads quickly to other organs. Despite reduced androgen levels after ADT, mCRPC development and lethality continues to be conducted by the androgen receptor (AR) axis. The maintenance of AR signaling in mCRPC is a result of AR alterations, androgen intratumoral production, and the action of regulatory elements, such as noncoding RNAs (ncRNAs). ncRNAs are key elements in cancer signaling, acting in tumor growth, metabolic reprogramming, and tumor progression. In prostate cancer (PCa), the ncRNAs have been reported to be associated with AR expression, PCa proliferation, and castration resistance. In this study, we aimed to reconstruct the lncRNA‐centered regulatory network of mCRPC and identify the lncRNAs which act as master regulators (MRs).Methods: We used publicly available RNA‐sequencing to infer the regulatory network of lncRNAs in mCRPC. Five gene signatures were employed to conduct the master regulator analysis. Inferred MRs were then subjected to functional enrichment and symbolic regression modeling. The latter approach was applied to identify the lncRNAs with greater predictive capacity and potential as a biomarker in mCRPC.Results: We identified 31 lncRNAs involved in cellular proliferation, tumor metabolism, and invasion‐metastasis cascade. SNHG18 and HELLPAR were the highlights of our results. SNHG18 was downregulated in mCRPC and enriched to metastasis signatures. It accurately distinguished both mCRPC and primary CRPC from normal tissue and was associated with epithelial–mesenchymal transition (EMT) and cell‐matrix adhesion pathways. HELLPAR consistently distinguished mCRPC from primary CRPC and normal tissue using only its expression.Conclusion: Our results contribute to understanding the regulatory behavior of lncRNAs in mCRPC and indicate SNHG18 and HELLPAR as master regulators and potential new diagnostic targets in this tumor.

The objective this work was to evaluate genetically the buffaloes populations using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) in the leptin and stearoyl-CoA desaturase (SCD) genes, and associate the genotypes with milk production. Sixty-nine samples of Bubalus bubalis - 38 Murrah breed, 18 Mediterranean and 13 mixed-breed of the Murrah with Mediterranean - were evaluated. Allele and genotype frequencies, the heterozygosity observed and expected, the inbreeding coefficients (FIS), the probabilities of the Hardy-Weinberg equilibrium, the F statistic for population differentiation and Shannon index were calculated using GENEPOP and GenALEx programs. The associations of the different genotypes with a role in milk production were tested by Analysis of Variance (ANOVA) and t-test. We reported the leptin and SCD allele contribution to genetic variability in buffaloes herds in the Brazilian Amazonian region. The A allele was more representative in leptin and SCD genes for all breed groups. No significant effects between genotypes and milk production were found in the present study, but there is an indicative that AA genotype in leptin gene affects milk production in Murrah breed.

Metastasis is not a de novo functional module innovation but rather the pathological redeployment of deeply conserved biological programs. Here, we reconstruct the evolutionary landscape of biological functions involved in the early stages of the metastatic cascade, including cell adhesion, extracellular matrix organization (ECM), regulation of metallopeptidase activity, cell junction organization, epithelial-mesenchymal transition (EMT), and cellular extravasation alongside the physiological constraints that suppress them (Metastasis Suppressor Genes). Using phyletic pattern reconstruction of 787 orthologs across 473 Clusters of Orthologous Groups with the Bridge algorithm, we identified a divergence between the evolutionary timelines of metastasis-enabling programs and metastasis suppressors. Our results indicate that the molecular systems associated with the structural capacity for tumor dissemination are evolutionary ancient. ECM organization traces to the Human-Discoba last common ancestor, EMT to Ctenophora, cell adhesion to Sauropsida, and cellular extravasation to Actinopterygii. In contrast, mechanisms responsible for suppressing genomically unstable cells during continuous tissue renewal emerged later, peaking in the vertebrate lineage. These findings support the Serial Atavism Model and suggest that metastasis arises from the progressive erosion of recently evolved regulatory constraints, allowing the reactivation of ancestral cellular programs that predate complex multicellularity.

Polygenic risk scores (PRS) have emerged as a powerful tool for quantifying genetic susceptibility to complex traits and diseases. However, their calculation and interpretation require standardized data curation, robust statistical methods, and clear reporting strategies. In this work, we present an integrated pipeline designed to address these challenges. The pipeline begins with the construction of a curated genotype/phenotype database derived from public repositories, ensuring that only phenotypes with appropriate metadata, statistical distributions, and ethical suitability are retained. The final dataset comprises 2,346 pheno-types covering 38,256,468 unique SNPs. These phenotypes serve as the final analytical units for PRS calculation, risk stratification, and individual-level interpretation. The generated reports integrate sample-level results, phenotype categorization, risk classification, study references, and variant tables, providing a structured and interpretable output for end users. Together, the curated database and reporting framework establish a comprehensive toolbox for PRS analysis, enhancing reproducibility, transparency, and usability in both research and clinical contexts.