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Prostate cancer (PCa) is a leading cause of male cancer-related death globally. While the gut microbiota is linked to PCa, its genetic association remains unclear. We screened genetic instruments related to the gut microbiota and paired them with PCa genome-wide association study data to conduct Mendelian randomization (MR) analysis. Positive MR findings were then subjected to colocalization analysis. Subsequently, we utilized the Gene Expression Omnibus (GEO) dataset to perform differential expression analysis, aiming to identify differentially expressed associated genes (DEAGs). We determined the importance scores of these DEAGs through four machine learning models and constructed a nomogram based on these findings, and then validated it in another group of the GEO dataset. MR analysis found 16 gut bacteria causally linked to PCa (7 risk, 9 protective), with 144 related genes. PLCL1, VSNL1, ROR2, NRXN3, and TEAD1 were identified as feature genes for constructing a nomogram that provides a quantitative prediction of the risk of PCa onset. This study indicates that there are causal links between the gut microbiota and PCa. Feature genes may affect the occurrence of PCa by inhibiting the epithelial-mesenchymal transition, proliferation, migration, and invasion of cells.

Prognostic stratification in stage III colon cancer remains poor, despite treatment advances. Tumor-infiltrating lymphocytes, particularly CD3+ T cells, are potential prognostic markers, but manual assessment is labor-intensive and not robust. This study aimed to develop a deep learning model for automated analysis of CD3-stained histological slides to improve prognostic prediction. A total of 1737 patients from three international cohorts (PETACC08, PRODIGE-13, and HARMONY) were analyzed. The deep learning model (VGG19) identified tumor core (TC) and invasive margin (IM) regions on CD3-stained slides. Features from VGG19 and UNI models were used to cluster patients using hierarchical classification. Prognostic performance was evaluated using disease-free survival (DFS) across training, internal validation, and external validation sets. Deep learning classifiers identified distinct patient clusters with significantly different DFS based on TC and IM. For both IM and TC analysis, patients in the favorable group had a better DFS in all sets (IM: p < 0.001, p = 0.04, p = 0.02; TC: p = 0.002, p = 0.01, p = 0.12, respectively). Combining classifiers enhanced prognostic accuracy in all sets (p < 0.001, p = 0.01, p = 0.06, respectively). The model outperformed traditional clinical variables and CD3 enumeration, which demonstrated variability across cohorts. Automated deep learning analysis of CD3-stained slides enables robust and reproducible prognostic stratification in stage III colon cancer, independently of staining and scanning variations. This approach holds promise for guiding personalized treatment strategies.ClinicalTrials.gov Identifiers: NCT00265811, NCT00995202.

The review was based on analysis of 180 sources from databases PubMed, eLibrary, Cohrane Library, MEDLINE for 2015-2025 using keywords precision medicine, personalized medicine, neuro-oncology, oncology, cranio-cerebral injury, neuro-trauma, neuro-proteomics and AI. The purpose of the study was to demonstrate, on the basis of analysis of publications on precision medicine application in neurosurgery, the significance and perspectives of mentioned approach in modern neurosurgical practice. The methods of precision medicine, digital revolution and progress in multi-modal Big Data processing permit to better understand of tumor genesis, their clinical heterogeneity, functional effects and causes underlying their resistance to treatment. The precision medicine methods provide valuable information on pathophysiological mechanisms underlying neuro-trauma through analysis of complex protein interactions and changes. The future of precision medicine in neurosurgical practice is in permanent enhancement of AI and machine learning, permitting rapid and accurate decision-making based on comprehensive molecular data. The future of neurosurgery lies in harmonious integration of such interdisciplinary approaches as precision medicine and clinical neurosurgery to discover new possibilities of targeted and personalized therapy.

The hypothesis-generating case study aimed at identifying those who are sensitive to anti-PD-L1 and TGF-β bifunctional fusion proteins and exploring potential mechanisms in the treatment of recurrent cervical cancer. We report that recurrent cervical cancer treated with anti-PD-L1 and TGF-β bifunctional fusion proteins in Qilu Hospital of Shandong University show distinct clinical therapeutic outcomes. We describe the clinical course, characteristics, and genetic characteristics of the patients and analyzed the differentially expressed genes (DEGs) following treatment. The elevation of peripheral blood lymphocytes after treatment may predict response to anti-PD-L1 and TGF-β bifunctional fusion proteins, since partial response (PR) and progressive disease (PD) exhibit different trends. A total of 4,844 DEGs were selected between PR and PD patients during the anti-PD-L1 and TGF-β bifunctional fusion protein treatments, which are believed to be involved in the regulation of the immune response. We demonstrated that changing-fate genes continuously change during treatment fostering the IL 17 signaling pathway and TGF-β signaling pathways. Finally, we identified the prognostic genes and validated that high expression levels of PMEPA1, FSTL3, SERPINE1, CXCL1, CXCL8, and low expression levels of JUND，MAP2K2 were significantly associated with poor prognosis of cervical cancer patients using the TCGA database. Anti-PD-L1 and TGF-β bifunctional fusion proteins are feasible and effective for recurrent cervical cancer through the IL 17 signaling pathway and TGF-β signaling pathways. A novel immune infiltration-based gene signature consisting of PMEPA1, FSTL3, SERPINE1, CXCL1, CXCL8, JUND, and MAP2K2 plays a crucial role in recurrent cervical cancer patients with anti-PD-L1 and TGF-β bifunctional fusion proteins.

Janus kinase (JAK) dysregulation plays a central role in the pathogenesis of inflammatory, autoimmune, and malignant disorders, making the JAK family an essential therapeutic target across multiple disease domains. Over the past two decades, the field has progressed from the identification of early JAK2 inhibitors to the approval of several first-generation agents, including ruxolitinib, tofacitinib, baricitinib, and fedratinib, which validated the clinical feasibility of JAK blockade. However, limitations related to safety, isoform selectivity, long-term tolerability, and off-target kinase interactions continue to restrict their broader application and highlight the need for next-generation molecules. In this review, we provide a comprehensive and strategic assessment of the molecular features underpinning JAK2 and JAK3 selectivity, including signaling features directly relevant to inhibitor design, mutational landscapes, and structural determinants such as the uniquely targetable Cys909 residue in JAK3. Although the JAK family comprises four kinases, this review intentionally focuses on JAK2 and JAK3, where structural divergence, disease relevance, and emerging selectivity strategies provide the strongest opportunities for next-generation precision inhibitor design. We integrate recent advances in synthetic chemistry, including hinge-binding optimization, heterocyclic diversification, multicomponent reactions, and scaffold-hopping strategies, with computational methodologies such as molecular docking, molecular dynamics simulations, QM/MM calculations, and machine-learning-based predictive modelling. Together, these multidisciplinary approaches have accelerated hit discovery, refined selectivity, and improved the pharmacokinetic and safety profiles of emerging JAK inhibitors. By consolidating progress across medicinal chemistry, structural biology, and computational design, this review outlines key opportunities and remaining challenges in developing next-generation JAK inhibitors with enhanced precision and therapeutic value for oncology, immunology, and chronic inflammatory diseases.

This study aimed to identify neutrophil extracellular trap-related genes (NET-RGs), explore their prognostic significance, and predict drug interactions in peripheral T-cell lymphoma (PTCL). Differentially expressed NET-RGs (DE-NRGs) between PTCL and normal tissues were screened. Functional enrichment analysis was conducted. Bioinformatics and machine learning were used to identify hub genes and assess their diagnostic value. Univariate and multivariate analyses were used to evaluate prognostic roles. Correlation and immune infiltration analyses were performed to explore relationships with the tumor microenvironment (TME). Clinical data were collected from PTCL patients who received potential agents (lenalidomide) as first-line treatment. A total of 31 DE-NRGs were identified (18 upregulated and 13 downregulated), enriched in inflammatory response, extracellular matrix organization, and infection involvement. Four hub genes (AKT2, MAPK14, IRF1, and TNF) were identified as effective PTCL diagnostic markers. Higher AKT2/MAPK14 expression correlated with poorer overall survival (OS), while elevated TNF expression associated with better OS; AKT2 and TNF independently predicted OS. These genes were implicated in modulating TME remodeling. Potential therapeutic agents (e.g. capivasertib, lenalidomide) were predicted, and lenalidomide may represent a feasible initial treatment option for PTCL, with an objective response rate (ORR) of 40.0% and a maximum survival duration exceeding 50 months. NET-RGs play crucial roles in diagnosis, prognosis, and TME regulation, and lenalidomide, a putative TNF-targeting agent, may represent a feasible initial treatment option in PTCL.

Although FGFR2 is a well-validated oncogenic target, no selective FGFR2 inhibitors have been approved for clinical use. In this study, we report the discovery of 2H-pyrazolo[3,4-d]pyrimidin-4-amine derivative as novel, irreversible FGFR2 inhibitors. The optimal compound, PLW559, potently inhibited FGFR2 with an IC50 value of 13.59 nM and demonstrated exceptional selectivity over FGFR1, FGFR3, and FGFR4. Covalent binding to the target was confirmed by mass spectrometry. In cellular models, PLW559 exhibited potent and selective antiproliferative effects against FGFR2-driven cancer cells, effectively suppressed downstream FGFR2 signalling and induced cancer cell apoptosis. Notably, it showed minimal activity in non-FGFR2-dependent cells. This work presents a new class of selective FGFR2 inhibitors based on a novel scaffold, offering promising lead compounds for the development of FGFR2-target therapies.

A series of oxadiazole-based dual inhibitors targeting GSK3 and HDAC6 were rationally designed by integrating key pharmacophores into a single molecule. Among these derivatives, 4-(((5-(benzo[d][1, 3]dioxol-5-yl)-1,3,4-oxadiazol-2-yl)thio)methyl)-N-hydroxybenzamide (15i) was identified as the most potent compound with IC50 of 5.50, 69 nM and 88 nM against HDAC6, GSK3α and GSK3β, respectively. 15i also exhibited potent cytotoxicity against the AGS cancer cell line, with IC50 values in the submicromolar range. Molecular docking simulation confirmed that 15i fitted well into the active sites of both HDAC6 and GSK3β. These findings establish compound 15i as a promising candidate for further evaluation.

FMS-like tyrosine kinase 3 (FLT3/CD135) regulates haematopoiesis and is frequently mutated as FLT3-internal tandem duplication (FLT3-ITD) in acute myeloid leukaemia (AML), associated with poor prognosis. Although FLT3 inhibitors show clinical benefits, resistance remains a challenge. This study hypothesises that antibody-drug conjugate (ADC) efficacy depends on distinct FLT3 trafficking mechanisms in FLT3-wt and FLT3-ITD cells. Confocal imaging showed that in THP-1 (FLT3-wt) cells, FLT3 mAb trafficked to lysosomes, while in MV4-11 (FLT3-ITD) cells, it accumulated in the Golgi. To evaluate the impact of this trafficking difference, we synthesised an anti-FLT3 mAb-MMAE, linked via a Val-Cit-PAB linker at the Fc N-glycan, which exhibited lower cytotoxicity in MV4-11 than THP-1 cells, indicating that the impaired lysosomal trafficking of FLT3-ITD limits drug release and reduces ADC potency. These findings highlight that effective lysosomal targeting is essential for ADC activity and suggest that optimising linker design or restoring lysosome trafficking may enhance FLT3-targeted ADC in AML.

Inadequate adherence to colorectal cancer screening reduces individual and population level health benefits. Blood-based tests offer a new modality that may help patients overcome barriers, but there are concerns about the impact of patients switching from existing guideline-recommended screening modalities. This study estimates the population health outcomes and cost-effectiveness of offering blood-based testing using a validated individual-level simulation model based on patient preference evidence from randomized controlled trials. In this study, a validated discrete-event simulation model was used to evaluate the performance of different combinations of colorectal cancer screening strategy preferences per 10,000 screened individuals beginning at age 45. Preferences for screening options were informed by randomized controlled trials of patients with and without the option of blood-based testing. Adherence to initial patient preferences over a simulated lifetime was modeled as: (1) assumed 100% adherence and (2) longitudinal using a calibrated model. Simulated outcomes included clinical outcomes and cost-effectiveness from a healthcare sector perspective. A strategy was deemed cost-effective at a willingness-to-pay threshold of $100,000 per quality-adjusted life-year gained. The introduction of blood-based testing to an unscreened population with evidence from randomized controlled trials is projected to increase colorectal cancer deaths averted by 35% to 116% and from 68% to 247% relative to no screening, for stated preference and revealed preference scenarios, respectively. These outcomes are cost-effective, with incremental cost-effectiveness ratios ranging from $63,994 to $85,497 and from $30,464 to $54,764 across stated preference and revealed preference scenarios, respectively. Given limited data, natural history and real-world longitudinal adherence to screening are based on evidence-informed assumptions. Using a simulation model to extrapolate data from two recent trials, we demonstrate that the introduction of blood-based tests has the potential to lead to cost-effective increases in the number of CRC deaths averted among the unscreened population.