Despite rapid advances in whole-genome sequencing (WGS), translating genomic findings into individualized insights remains challenging. We present GenRiskPro, a clinical decision-support and research platform, which automates WGS variant calling, annotation, prioritization, and reporting to deliver actionable findings and facilitate precision wellness. (To test the GenRiskPro platform, log on to https://www.phenomeportal.org/dashboard using the following credentials: Username: user@test.com; Password: test.) GenRiskPro integrates rare and common variant prioritization in a unified pipeline and in-house database, enabling both rare and complex disease and trait association analyses. Variant reporting is supported via LongevityCloud, which features a web portal for clinicians to review, adjust, and authorize the return of results in tabular and PDF formats, alongside a mobile app with artificial intelligence (AI) integration for sequenced individuals. Case studies using Turkish (TR, n = 275) and Swedish (SW, n = 101) WGS data assessed platform performance and variant prioritization: (a) predefined gene panels yielded a 1.82% positive rate for actionable findings per American College of Medical Genetics and Genomics (ACMG) secondary findings guidelines; (b) phenotype-driven support diagnosed cases including muscular dystrophy and microcephaly; (c) cohort-level ClinVar reassessment identified potentially misclassified pathogenic variants; (d) rare variant burden analysis revealed enrichment in ABCA4 for TR and SMPD1 in SW; and (e) population analysis highlighted carrier differences in trait-associated SNPs (rs12913832 and rs4988235) and PGx variants (CYP2B64 and CYP2B66). GenRiskPro unifies databases, literature, web development, and AI for rapid, user-friendly genomic analysis and reporting, which fosters collaboration among hospitals, researchers, clinicians, and patients.

Achieving minimally invasive and rapid detection is a crucial goal in modern medicine. The comprehensive characterization of the blood proteome holds great promise in advancing our understanding of disease etiology, facilitating early diagnosis, risk stratification, and improved monitoring across various diseases and their subtypes. In this study, we collected plasma proteomes from over 3000 patients, representing 23 distinct diseases, encompassing a total of 1462 proteins. Based on histological knowledge, we developed a two-stage hierarchical multi-disease classifier and applied it to perform multi-disease classification on the collected proteomic data. Our results demonstrate that this empirically guided two-stage hierarchical multi-disease classifier outperforms traditional machine learning algorithms in terms of prediction performance, showing better balance and more meaningful feature selections. This finding highlights the positive role that domain expertise can play in machine learning-based disease detection, and underscores the potential of plasma proteomics for multi-disease screening.

Accurate interpretation of genomic variants remains a major bottleneck in precision oncology, due in part to fragmented knowledge across databases and limited integration between clinical evidence and population-scale genomic datasets. Here we present OncoRisk, a stand-alone, user-friendly web server that unifies data from over ten oncogenic databases and seven large-scale pan-cancer cohorts, enabling rapid multi-database queries and network-based exploration of genomic variants, gene-gene interactions, and therapy associations. The platform features a semi-automated reporting workflow that generates comprehensive, patient-specific clinical reports from raw tissue sequencing data and categorizes variants into actionable tiers. For translational research, OncoRisk provides modules for data-driven exploration, allowing users to validate findings by interrogating mutation frequencies and clinical associations across real-world patient data. Furthermore, an integrated suite of analytical tools enables comprehensive, cohort-level investigations of mutational landscapes, prognostic biomarkers, and oncogenic signaling pathways. By providing a unified ecosystem that bridges curated knowledge with large-scale cohort data, OncoRisk serves as an effective catalyst for both discovery research and clinical application in oncology. OncoRisk is publicly available at https://www.phenomeportal.org/oncorisk.

Hepatocellular carcinoma (HCC) remains difficult to treat due to its limited targets. Hence, we introduced phosphorylated c-Jun N-terminal kinase (p-JNK) as an anti-HCC target protein and investigated JNK-IN-5A and six derivatives (SET135, SET156, SET158, SET159, SET171, and SET172) which stabilize p-JNK. In vitro, these compounds outperformed sorafenib and regorafenib, inducing stronger p53-mediated cell-cycle arrest, autophagy, apoptosis, and reduced invasiveness via JNK/c-Jun pathways. RNA-seq profiling revealed distinct mechanisms: SET135 triggered autophagic necrosis via p62/SQSTM1, while SET171 induced reactive oxygen species (ROS)-driven necrosis. Systems biology analysis confirmed their enhanced efficacy. A 7-day GLP-like rat toxicity study showed SET135 and SET171 were well-tolerated. In vivo study performed with 21-day treatment of SET135 or SET171 showed superior anti-tumor effects compared to sorafenib via apoptotic mechanisms in HCC-transplanted mice. These findings highlight JNK-IN-5A derivatives as promising HCC therapeutic candidates capable of inducing both apoptotic and necrotic cell death.

Background Pyruvate kinase liver and red blood cells (PKLR) is linked to metabolic dysfunction-associated steatotic liver disease (MASLD). Previous study, we identified JNK-IN-5A, a c-Jun N-terminal kinase (JNK) inhibitor that suppresses PKL expression in HepG2 cells using computational drug repurposing and screened out four hit JNK-IN-5A derivatives (SET-151, SET-152, SET-162, SET-130).Materials and Methods We validated therapeutic efficacy of JNK-IN-5A and four derivative (SET-151, SET-152, SET-162, SET-130). HepG2 de novo lipogenesis (DNL) steatosis model was used in vitro validation. RNA sequencing data were analysed using systems biology approaches, including transcriptomic profiling and COMPASS analysis. GLP-like toxicity assessment in rat model shows in vivo safety and MASLD rat model revealed in vivo therapeutic effect to MASLD and MASH.Results In a HepG2 DNL steatosis model, all compounds reduced intracellular triacylglycerol (TAG) and inhibited key DNL proteins (PKL, FASN, ACACA, SCD1, SREBP1-c, ChREBP). Transcriptomic profiling revealed stronger anti-steatotic effects with SET-151, SET-152, and SET-162, which uniquely downregulated genes in pyruvate metabolism, bile acid synthesis, fatty acid metabolism, and glycolysis. Compass analysis showed these derivatives significantly altered lipid-related metabolic reactions, unlike JNK-IN-5A. In a high-sucrose, high-fat diet-induced MASLD rat model, JNK-IN-5A and SET-152 reduced hepatic lipid accumulation, liver stiffness, and MASLD biomarkers.Conclusion Our findings identify PKLR as a promising therapeutic target for MASLD and MASH. SET-152 suppressing PKLR through JNK inhibition highlights its potential as a new drug for MASLD and MASH therapy.

Numerous web-based tools have been developed to support large-scale genomics research, whereas challenges remain due to their limited functionality. Therefore, we developed VarXOmics, an end-to-end, versatile web server for querying variants and genes, streamlining germline variant analysis, prioritizing variants with multi-omics insights, and providing interactive visualizations. The utility of VarXOmics was demonstrated by analyzing multiple small variants of the whole genome sequencing data from a breast cancer patient. It prioritized BRCA2 c.3751dup as the most likely pathogenic variant, and highlighted disease associations with cell cycle regulation, DNA repair pathways, and type 2 diabetes through multi-omics evidence, gene set enrichment, and network analysis. Overall, VarXOmics serves as a practical genomics platform for researchers and clinicians. It shows potential in identifying pathogenic variants and causal genes, uncovering the molecular mechanisms of disease pathogenesis, providing valuable references for clinical decision-making and therapeutic strategies, thus advancing precision medicine. VarXOmics is publicly available at https://www.phenomeportal.org/varxomics.

Congenital heart disease (CHD) is the most common birth defect, yet its molecular etiology remains poorly understood. Recent advances in sequencing technology offer opportunities to uncover genetic and transcriptomic contributions to CHD. We performed an integrative multi-omics study on a pediatric CHD cohort (n=211) using whole-genome sequencing (WGS) and paired whole-blood transcriptomics (n=100). WGS identified approximately 28 million variants, including 309 known pathogenic and 724 protein-loss-of-function (pLoF) variants. Within a curated CHD gene list, 5 patients carried known pathogenic variants in EVC, HSPA9, DNAH11, PTPN11, and FBN1. Rare-variant burden analysis through Fisher's exact tests identified a significant enrichment of damaging missense mutations in CHD cases, primarily affecting early embryonic programs such as pattern specification and heart morphogenesis. In contrast, blood transcriptomics highlighted systemic functional shifts, specifically the suppression of mitochondrial oxidative phosphorylation and activation of interferon-mediated immune responses, reflecting downstream perturbations following developmental failure.

Crucially, multi-omics integration identified core drivers supported by multiple lines of evidence: a four-way intersection (literature, variant burden, eQTLs, and DEGs) highlighted COL6A2, PKD2, and PKD1L1, while three-way intersections identified key regulators like SALL4, GLI1, ANK3, and ALMS1. Furthermore, functional enrichment analysis specifically targeting the 626 genes overlapping between eGenes and DEGs revealed significant involvement in small GTPase-mediated signal transduction and cytoskeleton organization. These findings demonstrate that blood-based multi-omics can effectively capture cardiac-relevant regulatory signals, providing a non-invasive framework to elucidate the molecular landscape of CHD.

Accurate interpretation of genomic variants remains a major bottleneck in precision oncology, due in part to fragmented knowledge across databases and limited integration between clinical evidence and population-scale genomic datasets. Here we present OncoRisk, a stand-alone, user-friendly web server that unifies data from over ten oncogenic databases and seven large-scale pan-cancer cohorts, enabling rapid multi-database queries and network-based exploration of genomic variants, gene-gene interactions, and therapy associations. The platform features a semi-automated reporting workflow that generates comprehensive, patient-specific clinical reports from raw tissue sequencing data and categorizes variants into actionable tiers. For translational research, OncoRisk provides modules for data-driven exploration, allowing users to validate findings by interrogating mutation frequencies and clinical associations across real-world patient data. Furthermore, an integrated suite of analytical tools enables comprehensive, cohort-level investigations of mutational landscapes, prognostic biomarkers, and oncogenic signaling pathways. By providing a unified ecosystem that bridges curated knowledge with large-scale cohort data, OncoRisk serves as an effective catalyst for both discovery research and clinical application in oncology. OncoRisk is publicly available at https://www.phenomeportal.org/oncorisk.