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PubMed2006

Derivation of species-specific hybridization-like knowledge out of cross-species hybridization results.

BACKGROUND: One of the approaches for conducting genomics research in organisms without extant microarray platforms is to profile their expression patterns by using Cross-Species Hybridization (CSH). Several different studies using spotted microarray and CSH produced contradicting conclusions in the ability of CSH to reflect biological processes described by species-specific hybridization (SSH). RESULTS: We used a tomato-spotted cDNA microarray to examine the ability of CSH to reflect SSH data. Potato RNA was hybridized to spotted cDNA tomato and potato microarrays to generate CSH and SSH data, respectively. Difficulties arose in obtaining transcriptomic data from CSH that reflected those obtained from SSH. Nevertheless, once the data was filtered for those corresponding to matching probe sets, by restricting proper cutoffs of probe homology, the CSH transcriptome data showed improved reflection of those of the SSH. CONCLUSIONS: This study evaluated the relative performance of CSH compared to SSH, and proposes methods to ensure that CSH closely reflects the biological process analyzed by SSH.

Cluster Analysis↗

PubMed2025

Extraction, Purification, and Next-Generation Sequencing (NGS) Analysis of DNA and RNA from Formalin-Fixed and Paraffin-Embedded (FFPE) Tissue.

Formalin fixed paraffin embedded (FFPE) tissues have long been used for immunohistological analyses. FFPE tissues can be stored at room temperature for several years enabling analyses to be performed later. Ease of storage and transport makes these tissues an attractive source of biological material. However, formalin fixation results in chemical modifications of proteins and nucleic acids that poses a major challenge to any type of analysis. Recovery of nucleic acids for quantitative assays is rendered difficult due to degradation resulting from fixation and long-term storage, producing low usable yields. Extensive efforts in the last 20 years have led to significant improvements in use of FFPE tissues for DNA and RNA analyses and resulted in development of sensitive assays for a wide range of applications, including next-generation sequencing. In this chapter, we describe the optimization of methods for sequential extraction of DNA and RNA from FFPE tissue and subsequent preparation of DNA-seq and RNA-seq libraries for use with the Illumina platform using commercially available reagents/kits.

Paraffin Embedding↗

PubMed2026

Time-dependent effects of rapid-acting antidepressants in iPSC-derived neurons from treatment-resistant depression and healthy volunteers.

Rapid-acting antidepressants like ketamine and serotonergic psychedelics show promise for treatment-resistant depression (TRD), but the molecular mechanisms that contribute to their therapeutic effects remain unclear. Induced pluripotent stem cells (iPSCs) offer a platform to model human cortical neurons and investigate drug effects in a human-relevant system. Here, iPSCs from individuals with TRD and healthy volunteers (HVs) were differentiated into mature cortical-like neurons and treated for six and 24 h with agents being investigated as rapid-acting antidepressants, including (2 R,6 R)-hydroxynorketamine (HNK), psilocybin, lysergic acid diethylamide (LSD), and 2,5-Dimethoxy-4-iodoamphetamine (DOI). Bulk and single-cell RNA sequencing assessed global and cell-type-specific transcriptomic responses. Synaptic proteins were evaluated via Western blotting and immunocytochemistry. To validate translational relevance, transcriptomic results were compared to CSF proteomics from ketamine-treated HVs. Despite differing initial pharmacological targets, overall gene expression across all compounds was highly correlated at matched timepoints compared to vehicle control, suggesting shared downstream effects. Both glutamatergic and serotonergic drugs converged on pathways involving inflammation, mTORC1 signaling, and cellular growth. At the single-cell level, (2 R,6 R)-HNK showed distinct cell-type specific alterations: upregulation in excitatory neurons and concomitant downregulation of inhibitory neuron populations. Differentially expressed genes from (2 R,6 R)-HNK-treated neurons also overlapped with CSF proteomic signatures from ketamine-treated individuals, supporting the model's translational relevance. This study is the first to assess multiple putative rapid-acting antidepressants in parallel using an iPSC-derived neuron model. Both convergent and drug-specific changes in gene expression and pathway enrichment were observed across diverse compounds, supporting the use of human iPSC-derived neurons in antidepressant drug discovery. Clinical Trial Registry: www.clinical trials.gov, NCT02484456.

Journal Article↗

PubMed2025

Integrating multi-omics approaches in acute myeloid leukemia (AML): Advancements and clinical implications.

Acute myeloid leukemia (AML) is a highly heterogeneous and aggressive hematologic malignancy characterized by clonal proliferation of myeloid precursors. Despite significant advancements in genomic profiling and targeted therapies, patient outcomes remain suboptimal due to disease complexity, resistance mechanisms, and high relapse rates. The integration of multi-omics approaches-spanning genomics, epigenomics, transcriptomics, proteomics, and metabolomics-has revolutionized AML research, offering a comprehensive understanding of leukemogenesis, tumor heterogeneity, and therapeutic vulnerabilities. Recent studies leveraging high-throughput sequencing, mass spectrometry, and advanced computational tools have uncovered novel biomarkers, clonal evolution dynamics, and microenvironmental interactions that drive AML progression and resistance. For instance, single-cell multi-omics has revealed chemotherapy-resistant leukemic stem cell populations, while proteogenomic analyses have identified actionable targets such as MCL1 and metabolic dependencies like OXPHOS. Clinically, integrated omics platforms are refining risk stratification, minimal residual disease (MRD) monitoring, and personalized therapy selection. However, challenges such as data integration complexity, cost barriers, and ethical considerations remain. This review highlights the transformative potential of multi-omics in AML, emphasizing recent advancements in technology, biomarker discovery, and therapeutic innovation. By bridging the gap between molecular insights and clinical practice, multi-omics integration promises to redefine AML management, paving the way for precision oncology and improved patient outcomes.

Humans↗

PubMed2024

Cross-platform proteomics signatures of extreme old age.

In previous work, we used a SomaLogic platform targeting approximately 5000 proteins to generate a serum protein signature of centenarians that we validated in independent studies that used the same technology. We set here to validate and possibly expand the results by profiling the serum proteome of a subset of individuals included in the original study using liquid chromatography tandem mass spectrometry (LC-MS/MS). Following pre-processing, the LC-MS/MS data provided quantification of 398 proteins, with only 266 proteins shared by both platforms. At 1% FDR statistical significance threshold, the analysis of LC-MS/MS data detected 44 proteins associated with extreme old age, including 23 of the original analysis. To identify proteins for which associations between expression and extreme-old age were conserved across platforms, we performed inter-study conservation testing of the 266 proteins quantified by both platforms using a method that accounts for the correlation between the results. From these tests, a total of 80 proteins reached 5% FDR statistical significance, and 26 of these proteins had concordant pattern of gene expression in whole blood generated in an independent set. This signature of 80 proteins points to blood coagulation, IGF signaling, extracellular matrix (ECM) organization, and complement cascade as important pathways whose protein level changes provide evidence for age-related adjustments that distinguish centenarians from younger individuals. The comparison with blood transcriptomics also highlights a possible role for neutrophil degranulation in aging.

Humans↗

PubMed2025

Dual-patterned pluripotent stem cells self-organize into a human embryo model with extended anterior-posterior patterning.

Human gastruloids are a powerful class of stem cell-derived models that recapitulate key features of early embryonic development, including symmetry breaking and the emergence of three germ layers1-3. However, they lack anterior embryonic structures and coordinated axial organization4-6. To address this limitation, we pre-patterned human pluripotent stem cells (hPSCs) by exposing them to either anterior (FGF2) or posterior (CHIR99021 [CHIR] & retinoic acid [RA]) cues. Upon mixing, these dual-patterned hPSCs interacted and self-organized into elongated structures with both anterior and posterior features-which we term anterior-posterior (AP) human gastruloids. Anteriorly pre-treated cells robustly intercalated into posteriorly pre-treated cells, collectively giving rise to a continuum of neural tissues-including a brain-like domain, a neural tube-like structure, and neuro-mesodermal progenitors (NMPs)-with segmented somites arrayed bilaterally. Single cell RNA sequencing (scRNA-seq) revealed that human AP gastruloids contain cell types resembling the midbrain-hindbrain boundary (MHB), regionalized hindbrain structures (i .e. rhombomeres 1-8), regionalized neural crest (i.e. cranial, vagal, trunk)7,8 and head mesoderm. Transcriptomic comparisons to primate embryos revealed that human AP gastruloids most closely resemble Carnegie stage 11 (CS11) embryos. While they lack a notochord and full dorsal-ventral polarity, human AP gastruloids recapitulate key spatial and temporal features of early neurulation and somitogenesis. Perturbation of folic acid metabolism or rho-associated kinase (ROCK) signaling induced spinal cord defects, phenocopying aspects of spina bifida and other neural tube defects, highlighting this model's potential for studying congenital disorders9. AP gastruloids may serve as a simple, robust, scalable platform for modeling coordinated human AP body axis development. More broadly, our results suggest that controlled interactions between differentially prepatterned progenitors can initiate self-organization of complex body axis features. The "pattern-and-mix" strategy may serve as a generalizable framework for assembling spatially organized stem cell models of mammalian development.

Journal Article↗

PubMed2005

Comprehensive expression atlas of fibroblast growth factors and their receptors generated by a novel robotic in situ hybridization platform.

A recently developed robotic platform termed "Genepaint" can carry out large-scale nonradioactive in situ hybridization (ISH) on tissue sections. We report a series of experiments that validate this novel platform. Signal-to-noise ratio and mRNA detection limits were comparable to traditional ISH procedures, and hybridization was transcript-specific, even in cases in which probes could have hybridized to several transcripts of a multigene family. We established an atlas of expression patterns of fibroblast growth factors (Fgfs) and their receptors (Fgfrs) for the embryonic day 14.5 mouse embryo. This atlas provides a comprehensive overview of previously known as well as novel sites of expression for this important family of signaling molecules. The Fgf/Fgfr atlas was integrated into the transcriptome database (www.genepaint.org), where individual Fgf and Fgfr expression patterns can be interactively viewed at cellular resolution and where sites of expressions can be retrieved using an anatomy-based search.

Animals↗

PubMed2026

Mechanism-Driven Diagnostic Development: A Specimen-Aware Framework Illustrated by Colorectal Cancer and Solid Tumours.

Translational oncology has moved rapidly from histopathology and single-analyte biomarkers toward multi-dimensional molecular profiling. Yet many clinically deployed tests still use reductionist biomarker strategies that under-represent cancer complexity. This review examines whether a mechanistic, multi-layered, and specimen-aware approach can improve cancer detection, classification, prognosis, minimal residual disease (MRD) assessment, and therapeutic selection. Evidence across solid tumours shows that genomic alterations alone incompletely explain tumour state, metastatic behaviour, immune evasion, or therapeutic vulnerability. Integrated genome and transcriptome analyses, proteogenomics, single-cell atlases, fragmentomic, methylation based cell-free DNA assays, metabolomics and microbiome assessments reveal clinically relevant biology that single modality tests cannot determine. Minimally invasive collected specimens can extend access to screening, diagnosis and longitudinal monitoring, but the choice of specimen should be matched to disease biology and analytes that represent mechanisms of oncogenesis. However, translation remains constrained by pre-analytical variability, contamination, differences in tumour shedding behaviour, clonal haematopoiesis, translation of generated models, incomplete external validation and uncertain downstream clinical utility for emerging platforms. This review provides a commentary on the future of cancer diagnostics, the considerations and barriers to clinical translation, the relationship between utility and dimensionality of biomarkers assessed and the emerging rationale towards mechanistically grounded integrated models.

biomarkers↗

PubMed2026

Epithelial tumor suppressor deletion promotes neuroendocrine differentiation in bladder cancer and reveals homoharringtonine as a candidate vulnerability.

Neuroendocrine bladder carcinoma (NEBC) is a highly aggressive malignancy with unresolved lineage determinants and limited preclinical models, hindering mechanistic investigation and therapeutic development. Here, we sought to assess whether bladder epithelial-derived models are competent to acquire neuroendocrine lineage programs under defined tumor suppressor alterations and to identify candidate therapeutic vulnerabilities in these systems. We integrated genomic and transcriptomic analyses of human NEBC with genetically engineered mouse models, epithelial-derived bladder organoids, and patient-derived NEBC models. Human NEBC exhibited dominant RB1 and TP53 alterations and an epithelial transcriptional continuum consistent with lineage plasticity. In vivo, intravesical Adeno-Cre-mediated tumor suppressor deletion predominantly generated sarcoma-like tumors, whereas epithelial-restricted organoid models recapitulated the molecular and neuroendocrine features of human NEBC, supporting epithelial lineage competence for neuroendocrine differentiation. Patient-derived models and human NEBC specimens further supported epithelial identity in NEBC. Using these complementary platforms, drug screening identified homoharringtonine (HHT) as a candidate therapeutic vulnerability in the tested NEBC systems. HHT suppressed neuroendocrine marker expression, induced apoptosis, and attenuated IL6-JAK-STAT3 signaling. Together, these findings describe complementary epithelial-derived NEBC models and support further investigation of HHT as a candidate therapeutic vulnerability.

Journal Article↗

PubMed2025

Algorithms and tools for data-driven omics integration to achieve multilayer biological insights: a narrative review.

Systems biology is a holistic approach to biological sciences that combines experimental and computational strategies, aimed at integrating information from different scales of biological processes to unravel pathophysiological mechanisms and behaviours. In this scenario, high-throughput technologies have been playing a major role in providing huge amounts of omics data, whose integration would offer unprecedented possibilities in gaining insights on diseases and identifying potential biomarkers. In the present review, we focus on strategies that have been applied in literature to integrate genomics, transcriptomics, proteomics, and metabolomics in the year range 2018-2024. Integration approaches were divided into three main categories: statistical-based approaches, multivariate methods, and machine learning/artificial intelligence techniques. Among them, statistical approaches (mainly based on correlation) were the ones with a slightly higher prevalence, followed by multivariate approaches, and machine learning techniques. Integrating multiple biological layers has shown great potential in uncovering molecular mechanisms, identifying putative biomarkers, and aid classification, most of the time resulting in better performances when compared to single omics analyses. However, significant challenges remain. The high-throughput nature of omics platforms introduces issues such as variable data quality, missing values, collinearity, and dimensionality. These challenges further increase when combining multiple omics datasets, as the complexity and heterogeneity of the data increase with integration. We report different strategies that have been found in literature to cope with these challenges, but some open issues still remain and should be addressed to disclose the full potential of omics integration.

Algorithms↗

PubMed2022

CRISPRi screens in human iPSC-derived astrocytes elucidate regulators of distinct inflammatory reactive states.

Astrocytes become reactive in response to insults to the central nervous system by adopting context-specific cellular signatures and outputs, but a systematic understanding of the underlying molecular mechanisms is lacking. In this study, we developed CRISPR interference screening in human induced pluripotent stem cell-derived astrocytes coupled to single-cell transcriptomics to systematically interrogate cytokine-induced inflammatory astrocyte reactivity. We found that autocrine-paracrine IL-6 and interferon signaling downstream of canonical NF-κB activation drove two distinct inflammatory reactive signatures, one promoted by STAT3 and the other inhibited by STAT3. These signatures overlapped with those observed in other experimental contexts, including mouse models, and their markers were upregulated in human brains in Alzheimer's disease and hypoxic-ischemic encephalopathy. Furthermore, we validated that markers of these signatures were regulated by STAT3 in vivo using a mouse model of neuroinflammation. These results and the platform that we established have the potential to guide the development of therapeutics to selectively modulate different aspects of inflammatory astrocyte reactivity.

Humans↗

PubMed2026

Long-term rock dove (Columba livia) primordial germ cell culture: A tool towards avian conservation.

Primordial germ cells (PGCs) are critical tools for genome engineering and conservation in birds. Although culture systems for chicken PGCs have been well established for nearly two decades, efforts to propagate PGCs from other avian species have proved exceptionally challenging, limiting the broader application of artificial reproductive technologies in birds. Here we report the first successful derivation and long-term culture of PGCs from the rock dove, or common pigeon (Columba livia). Guided by transcriptomic profiling of PGCs, we developed a species-specific medium that supports PGC maintenance and expansion. We identify insulin signaling as a requirement for survival under the conditions tested, and demonstrate that inhibition of the retinoic acid receptor, in the presence of vitamin A, supports propagation. Supplementation with bone morphogenetic protein 4, leukemia inhibitory factor (LIF), glial cell line-derived neurotrophic factor, and pleiotrophin further enhances PGC proliferation. Cultured cells express canonical germline markers and migrate to the gonads following injection into both rock dove and chicken embryos, confirming functional migratory competency. These findings establish a platform for germline manipulation and biobanking in Columbidae, broadening the potential applicability of reproductive technologies to conservation efforts.

Animals↗

PubMed2006

Group testing for pathway analysis improves comparability of different microarray datasets.

MOTIVATION: The wide use of DNA microarrays for the investigation of the cell transcriptome triggered the invention of numerous methods for the processing of microarray data and lead to a growing number of microarray studies that examine the same biological conditions. However, comparisons made on the level of gene lists obtained by different statistical methods or from different datasets hardly converge. We aimed at examining such discrepancies on the level of apparently affected biologically related groups of genes, e.g. metabolic or signalling pathways. This can be achieved by group testing procedures, e.g. over-representation analysis, functional class scoring (FCS), or global tests. RESULTS: Three public prostate cancer datasets obtained with the same microarray platform (HGU95A/HGU95Av2) were analyzed. Each dataset was subjected to normalization by either variance stabilizing normalization (vsn) or mixed model normalization (MMN). Then, statistical analysis of microarrays was applied to the vsn-normalized data and mixed model analysis to the data normalized by MMN. For multiple testing adjustment the false discovery rate was calculated and the threshold was set to 0.05. Gene lists from the same method applied to different datasets showed overlaps between 42 and 52%, while lists from different methods applied to the same dataset had between 63 and 85% of genes in common. A number of six gene lists obtained by the two statistical methods applied to the three datasets was then subjected to group testing by Fisher's exact test. Group testing by GSEA and global test was applied to the three datasets, as well. Fisher's exact test followed by global test showed more consistent results with respect to the concordance between analyses on gene lists obtained by different methods and different datasets than the GSEA. However, all group testing methods identified pathways that had already been described to be involved in the pathogenesis of prostate cancer. Moreover, pathways recurrently identified in these analyses are more likely to be reliable than those from a single analysis on a single dataset.

Biomarkers, Tumor↗

PubMed2004

Directed evolution of pyruvate decarboxylase-negative Saccharomyces cerevisiae, yielding a C2-independent, glucose-tolerant, and pyruvate-hyperproducing yeast.

The absence of alcoholic fermentation makes pyruvate decarboxylase-negative (Pdc(-)) strains of Saccharomyces cerevisiae an interesting platform for further metabolic engineering of central metabolism. However, Pdc(-) S. cerevisiae strains have two growth defects: (i) growth on synthetic medium in glucose-limited chemostat cultures requires the addition of small amounts of ethanol or acetate and (ii) even in the presence of a C(2) compound, these strains cannot grow in batch cultures on synthetic medium with glucose. We used two subsequent phenotypic selection strategies to obtain a Pdc(-) strain without these growth defects. An acetate-independent Pdc(-) mutant was obtained via (otherwise) glucose-limited chemostat cultivation by progressively lowering the acetate content in the feed. Transcriptome analysis did not reveal the mechanisms behind the C(2) independence. Further selection for glucose tolerance in shake flasks resulted in a Pdc(-) S. cerevisiae mutant (TAM) that could grow in batch cultures ( micro (max) = 0.20 h(-1)) on synthetic medium, with glucose as the sole carbon source. Although the exact molecular mechanisms underlying the glucose-tolerant phenotype were not resolved, transcriptome analysis of the TAM strain revealed increased transcript levels of many glucose-repressible genes relative to the isogenic wild type in nitrogen-limited chemostat cultures with excess glucose. In pH-controlled aerobic batch cultures, the TAM strain produced large amounts of pyruvate. By repeated glucose feeding, a pyruvate concentration of 135 g liter(-1) was obtained, with a specific pyruvate production rate of 6 to 7 mmol g of biomass(-1) h(-1) during the exponential-growth phase and an overall yield of 0.54 g of pyruvate g of glucose(-1).

Culture Media↗

PubMed2026

An Instrumental Optimization of a Label-Free Proteomic Method for Trace Protein Input.

Liquid chromatography-mass spectrometry (LC-MS)-based proteomics of trace-level samples, such as tens of cells or spatially resolved tissue regions, offers unique biological insights but is often constrained by the requirement for specialized, costly instrumentation. In this study, we developed a scalable workflow for the deep proteomic analysis of low- to ultralow-input samples by systematically optimizing a widely adopted Orbitrap and UHPLC platform to maximize sensitivity, precision, and throughput. This optimized workflow identified over 5600 proteins from 5 ng of peptides and 3400 proteins from 20 sorted cells, achieving a throughput of 30 analyses per day while maintaining deep proteome coverage and high quantitative reproducibility. Furthermore, by applying this method to spatially resolved proteomics, we identified over 6100 proteins from microscale regions of interest (ROIs) within a formalin-fixed, paraffin-embedded (FFPE) tissue. A data-driven normalization strategy was employed to correct for variable cellularity across tissue regions, effectively revealing intratumor heterogeneity and distinct molecular and functional signatures, including pathway activations not apparent in parallel spatial transcriptomic analysis. Ultimately, this accessible, high-performance method substantially lowers the instrumentation barrier for the deep proteomic profiling of trace-level biological samples.

Proteomics↗

PubMed2026

A 3D in vitro co-culture model to investigate tumor-endothelial interactions in Neurofibromatosis type 2-associated meningiomas.

BACKGROUND: Neurofibromatosis type 2 (NF2)-associated meningiomas and schwannomas are vascular tumors, and while vascular endothelial growth factor (VEGF) inhibition with bevacizumab has benefited some NF2-related schwannomas, most NF2-associated meningiomas remain nonresponsive. METHODS: Leveraging our transcriptomic data, we performed Gene Ontology (GO) analysis comparing NF2-deficient meningioma cells with NF2-expressing arachnoid cells (ACs). We then established a 3D in vitro angiogenesis model by co-culturing NF2-null meningioma cells with human umbilical vein endothelial cells (HUVECs). Endothelial sprouting was assessed by CD31/PECAM immunostaining. Effects of third-generation mechanistic target of rapamycin complex 1 (mTORC1)-selective inhibitor RMC-6272 as well as APLN knock-out using CRISPR-Cas9 gene editing were also examined. RESULTS: GO analysis identified vascular development among the top significantly upregulated pathways in NF2-deficient cells. In 3D co-culture, ECs formed radially sprouting tube-like networks from the spheroid surface, and our data supports an angiogenesis phenotype driven by meningioma cells. Given these results along with hyperactivation of mTORC1 upon NF2-deficiency, we examined whether RMC-6272 disrupts meningioma-driven angiogenesis. RMC-6272 potently suppressed EC sprouting. Cross-referencing baseline transcriptomic data, we identified Apelin (APLN), the ligand for APLN receptor (APLNR), as a basally upregulated angiogenic factor in NF2-deficient meningiomas. Quantitative RT-PCR (qRT-PCR) confirmed increased APLN expression in NF2-null immortalized and patient-derived meningioma lines, with reduced expression upon mTORC1 inhibition. Apelin-13 stimulation enhanced sprouting, whereas APLN deletion reduced endothelial sprouting. CONCLUSIONS: Here we establish a 3D-tumoroid model and implicate tumor-derived Apelin as an important contributor to NF2-associated meningioma angiogenesis. Our data also suggest that APLN expression is regulated, at least in part, by mTORC1. Together, these results provide a preclinical platform for investigating angiogenic vulnerabilities beyond VEGF in NF2-deficient meningiomas.

3D tumoroid model↗

PubMed2026

Multiomics approaches to cardiovascular disease: technological innovations and clinical translation.

Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, reflecting a persistent gap between clinical phenotyping and the molecular mechanisms that govern disease initiation, progression, and interindividual variability. Recent advances in emerging technologies have fundamentally reshaped cardiovascular physiology by enabling high-resolution, cross-layer profiling of the heart and vasculature across genomic, epigenomic, transcriptomic, proteomic, metabolomic, lipidomic, glycomic, and fluxomic layers, increasingly at single-cell and spatial resolution. These approaches reveal CVD as a coordinated, multilayered process driven by dynamic interactions among cell types, regulatory programs, and metabolic states, rather than isolated gene-level defects. In this review, we synthesize how emerging multiomic, computational, and functional genomic technologies are redefining the study of cardiovascular disease across molecular, cellular, and tissue levels. We highlight recent innovations in single-cell and spatial atlases, long-read sequencing, proteomics and metabolomics, integrative data modeling, and functional omics approaches, including genome-scale perturbation screens and single-cell perturbation frameworks. These platforms enable mechanistic dissection of regulatory circuits, distinguish primary disease drivers from secondary adaptations, and directly assess therapeutic reversibility, advancing the field beyond associative biomarker discovery toward mechanism-guided target prioritization. We further discuss key methodological and translational challenges accompanying high-dimensional cardiovascular data, including preanalytical variability, control selection, temporal misalignment across molecular layers, population diversity, and reference bias. By integrating technological innovation with computational rigor and functional validation, this review frames emerging omics-enabled strategies as a unified, physiologically grounded framework for translating molecular insight into clinically meaningful cardiovascular phenotypes and advancing precision cardiovascular medicine.

Humans↗

PubMed2026

Multi-omics identification of therapeutic targets of compound sappan decoction in hepatocellular carcinoma.

BACKGROUND: Compound sappan decoction (CSD) is a multi-herbal traditional Chinese medicine formulation with clinical relevance in hepatocellular carcinoma (HCC). However, its therapeutic mechanisms remain unclear. METHODS: Bioactive compounds of CSD were identified and standardized using pharmacological and chemical databases. Potential targets were predicted via multiple target inference platforms. HCC-related genes were curated from comprehensive disease databases. Summary-data-based Mendelian randomization (SMR) was conducted to infer causal relationships between compound targets and HCC risk using large-scale quantitative trait loci (QTL) datasets and HCC genome-wide association study data. Colocalization analysis, protein-protein interaction (PPI) network construction, and GO/KEGG enrichment were performed on SMR-identified targets. Molecular docking evaluated binding affinities of representative compounds to prioritized targets. RESULTS: A total of 784 overlapping genes between predicted CSD targets and HCC-related genes were subjected to SMR analysis. Among these, 22 targets were significantly associated with HCC risk based on transcriptomic or proteomic QTLs and showed colocalization evidence. Notably, four targets (ADRB2, APOE, SYK, and PGF) were supported by both replication in an independent cohort and strong colocalization. These 22 targets were enriched in apoptosis, PI3K-Akt signaling, redox metabolism, and detoxification pathways. PPI analysis revealed central hubs including MMP9, BCL2, CASP1, and MCL1. Molecular docking demonstrated strong binding of APOE to quercetin, PGF to luteolin-7-olate, and SYK to kaempferol. CONCLUSIONS: CSD may exert therapeutic effects on HCC through modulation of genetically validated targets involved in tumor progression, inflammation, and metabolic reprogramming, supporting its potential clinical utility as an adjunctive treatment strategy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s12672-026-04740-8.

Caesalpinia↗