Background and Objective Prostate cancer (PCa) is a severe public health issue and the most common cancer worldwide in men. Early diagnosis can lead to early treatment and long-term survival. The addition of the multiparametric magnetic resonance imaging in combination with ultrasound (mpMRI-U/S fusion) biopsy to the existing diagnostic tools improved prostate cancer detection. Use of both tools gradually increases in every day urological practice. Furthermore, advances in the area of information technology and artificial intelligence have led to the development of software platforms able to support clinical diagnosis and decision-making using patient data from personalized medicine. Methods We investigated the current aspects of implementation, architecture, and design of a health care information system able to handle and store a large number of clinical examination data along with medical images, and produce a risk calculator in a seamless and secure manner complying with data security/accuracy and personal data protection directives and standards simultaneously. Furthermore, we took into account interoperability support and connectivity to legacy and other information management systems. The platform was implemented using open source, modern frameworks, and development tools. Results The application showed that software platforms supporting patient follow-up monitoring can be effective, productive, and of extreme value, while at the same time, aiding toward the betterment medicine clinical workflows. Furthermore, it removes access barriers and restrictions to specialized care, especially for rural areas, providing the exchange of medical images and patient data, among hospitals and physicians. Conclusion This platform handles data to estimate the risk of prostate cancer detection using current state-of-the-art in eHealth systems and services while fusing emerging multidisciplinary and intersectoral approaches. This work offers the research community an open architecture framework that encourages the broader adoption of more robust and comprehensive systems in standard clinical practice.

Background: Hypoxia is recognized as a key feature of cancer growth and is involved in various cellular processes, including proliferation, angiogenesis, and immune surveillance. Besides hypoxia-inducible factor 1-alpha (HIF-1α), which is the main mediator of hypoxia effects and can also be activated under normoxic conditions, little is known about its counterpart, HIF-2. This study focused on investigating the clinical and molecular landscape of HIF-2-altered urothelial carcinoma (UC). Methods: Publicly available next-generation sequencing (NGS) data from muscle-invasive UC cell lines and patient tumor samples from the MSK/TCGA 2020 cohort (n = 476) were interrogated for the level of expression (mRNA, protein) and presence of mutations, copy number variations, structural variants in the EPAS1 gene encoding HIF-2, and findings among various clinical (stage, grade, progression-free and overall survival) and molecular (tumor mutational burden, enriched gene expression) parameters were compared between altered and unaltered tumors. Results: 19% (7/37) of UC cell lines and 7% (27/380) of patients with muscle-invasive UC display high EPAS1 mRNA and protein expression or/and EPAS1 alterations. EPAS1-altered tumors are associated with higher stage, grade, and lymph node metastasis as well as with shorter PFS (14 vs. 51 months, q = 0.01) and OS (15 vs. 55 months, q = 0.01). EPAS1 mRNA expression is directly correlated with that of its target-genes, including VEGF, FLT1, KDR, DLL4, CDH5, ANGPT1 (q < 0.001). While there is a slightly higher tumor mutational burden in EPAS1-altered tumors (9.9 vs. 4.9 mut/Mb), they are enriched in and associated with genes promoting immune evasion, including ARID5B, SPINT1, AAK1, CLIC3, SORT1, SASH1, and FGFR3, respectively (q < 0.001). Conclusions: HIF-2-altered UC has an aggressive clinical and a distinct genomic and immunogenomic profile enriched in angiogenesis- and immune evasion-promoting genes.

MAGE (Meta-Analysis of Gene Expression) is a Python open-source software package designed to perform meta-analysis and functional enrichment analysis of gene expression data. We incorporate standard methods for the meta-analysis of gene expression studies, bootstrap standard errors, corrections for multiple testing, and meta-analysis of multiple outcomes. Importantly, the MAGE toolkit includes additional features for the conversion of probes to gene identifiers, and for conducting functional enrichment analysis, with annotated results, of statistically significant enriched terms in several formats. Along with the tool itself, a web-based infrastructure was also developed to support the features of this package.

Background/Objectives: Humulus lupulus (hops) are a perennial, dioecious plant widely cultivated for beer production, used for their distinguishing aroma and bitterness—traits that confer high added value status. Various hop-derived compounds have been reported to exhibit antioxidant, antimicrobial, antiproliferative and other bioactive effects. This systematic review and meta-analysis assesses the impact of hop compounds on the viability of diverse cancer cell lines. Methods: A comprehensive literature search was performed following PRISMA guidelines. Data were synthesized via multivariate meta-analysis and meta-regression, using IC₅₀ values as the effect size. Key variables included assay type (SRB, tetrazolium salt-based, crystal violet), exposure duration (24, 48, 72 h), specific hop compound and cancer cell line. Results: Of 622 articles identified, 61 met eligibility criteria, yielding 354 individual experiments. Meta-regression of xanthohumol (XN) IC₅₀ values across SRB, tetrazolium and crystal violet assays revealed no statistically significant differences at 24 h (p = 0.77), 48 h (p = 0.35) and 72 h (p = 0.70), supporting the interchangeability of the methods. Meta-analysis confirmed that hop constituents inhibit cancer cell proliferation; XN emerged as the most potent flavonoid (IC₅₀ = 16.89 μM at 72 h), while lupulone was the strongest compound overall (IC₅₀ = 5.00 μM at 72 h). Crude hop extracts demonstrated greater antiproliferative selectivity for cancer versus non-cancer cells (IC₅₀ = 35.23 vs. 43.80 μg/mL at 72 h). Conclusions: Hop compounds, and particularly bitter acids, demonstrate promising antiproliferative activity against cancer cells with comparatively low toxicity to healthy cells. Furthermore, our analysis confirms the comparability of SRB, tetrazolium-based and crystal violet assays, supporting the robust integration of antiproliferative data.

Drought significantly disrupts rice productivity under increasing climate volatility. Identifying robust molecular determinants for resilience remains a critical priority for crop improvement. Following the PRISMA guidelines, we performed a large-scale, dual-platform meta-analysis of RNA-Seq and microarray datasets to elucidate the robust transcriptomic landscape of Oryza sativa underwater deficit. Tissue-specific regulatory pathways were identified using STRING, g:Profiler, and PANTHER. Our analysis resolved distinct functional divergence, where shoots prioritize photosynthetic adjustment while roots emphasize transcriptional and chromatin reprogramming. Beyond validating core ABA signaling, we uncover a novel metabolic pivot: the activation of glyoxylate and dicarboxylate metabolism to mitigate drought-induced carbon starvation. We further identify specialized transport systems for ions and electrons across organelle membranes, alongside cellular reorganization driven by autophagy and actin-dependent cytoskeleton remodeling. These findings highlight a sophisticated network of survival strategies governing energy conservation and structural adaptation. By synthesizing heterogeneous transcriptomics, this study reveals robust pathways that are overlooked in single-platform investigations. This work provides a prioritized roadmap for utilizing functional validation and precision breeding to accelerate the development of climate-resilient rice cultivars.

Background The risk of recurrence after nephrectomy for primary clear cell renal cell carcinoma (ccRCC) is estimated in daily practice solely based on clinical criteria. The aim of this study was to assess the prognostic relevance of common somatic mutations with respect to tumor aggressiveness and outcomes of ccRCC patients after definitive treatment. Methods Primary tumors from 37 patients with ccRCC who underwent radical nephrectomy were analyzed for presence of somatic mutations using a 15-gene targeted next-generation sequencing (NGS) panel. Associations to histopathologic characteristics and outcomes were investigated in the study cohort (n=37) and validated in The Cancer Genome Atlas (TCGA) ccRCC cohort (n=451). Results VHL was the most frequently mutated gene (51%), followed by PBRM1 (27%), BAP1 (13%), SETD2 (13%), KDM5C (5%), ATM (5%), MTOR (5%), and PTEN (3%). One-third of patients did not have any somatic mutations within the 15-gene panel. The vast majority of tumors harboring no mutations at all or VHL-only mutations (51%) were more frequently of smaller size (pT1-2) and earlier stage (I/II), whereas presence of any other gene mutations in various combinations with or without VHL was enriched in larger (pT3) and higher stage tumors (III) (p=0.02). No recurrences were noted in patients with unmutated tumors or VHL-only mutations as opposed to three relapses in patients with non-VHL somatic mutations (p=0.06). Presence of somatic mutations in PBRM1, BAP1, SETD2, KDM5C, ATM, MTOR, or PTEN genes in 451 TCGA ccRCC patients was associated with a significantly shorter disease-free survival (DFS) compared to those with unaltered tumors (q=0.01). Conclusions Preliminary findings from this ongoing study support the prognostic value of non-VHL mutations including PBRM1, BAP1, SETD2, KDM5C, ATM, MTOR, and PTEN in primary ccRCC tumors as surrogates of earlier recurrence and potential selection for adjuvant immune checkpoint inhibition.