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Results for “multifacet clustering”

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PubMed2025

Model-based multifacet clustering with high-dimensional omics applications.

High-dimensional omics data often contain intricate and multifaceted information, resulting in the coexistence of multiple plausible sample partitions based on different subsets of selected features. Conventional clustering methods typically yield only one clustering solution, limiting their capacity to fully capture all facets of cluster structures in high-dimensional data. To address this challenge, we propose a model-based multifacet clustering (MFClust) method based on a mixture of Gaussian mixture models, where the former mixture achieves facet assignment for gene features and the latter mixture determines cluster assignment of samples. We demonstrate superior facet and cluster assignment accuracy of MFClust through simulation studies. The proposed method is applied to three transcriptomic applications from postmortem brain and lung disease studies. The result captures multifacet clustering structures associated with critical clinical variables and provides intriguing biological insights for further hypothesis generation and discovery.

Humans↗

PubMed2026

Artificial Intelligence for Colorectal Surgeons-Part II: Research Applications, Challenges in Adoption, and Practical Resources.

BACKGROUND: This is part II of a 2-part series examining artificial intelligence in colorectal surgery. Part I established foundational concepts and clinical applications. Implementation, however, requires understanding research methodologies, available resources, and the specific challenges currently limiting widespread adoption. These topics are the focus of part II. OBJECTIVE: To examine artificial intelligence's transformation of surgical research, provide practical implementation resources, address adoption challenges, and explore future directions in colorectal surgery. METHODS: Comprehensive literature review focusing on artificial intelligence research methodology, implementation barriers, educational resources, and emerging technologies relevant to colorectal surgeons. RESULTS: Artificial intelligence streamlines clinical trial design through predictive modeling and natural language processing, reducing enrollment challenges that contribute to failed or inadequate trial accrual. Machine learning enables heterogeneity analysis within clinical trials, identifying treatment-responsive subgroups. Foundation models unlock analysis of unstructured electronic health record data at scale. Professional societies and universities offer specialized artificial intelligence education programs, with open-access data sets facilitating research participation. However, implementation faces multifaceted challenges: technical infrastructure demands, with real-time processing requiring dedicated graphics processing unit clusters; regulatory frameworks struggling with continuously evolving algorithms; undefined liability distribution for artificial intelligence-assisted decisions; algorithmic bias risking health care disparities; and the "black box" problem limiting clinical trust. Economic barriers include substantial initial costs without clear reimbursement pathways. Future directions include multimodal artificial intelligence integrating imaging, genomics, and histopathology; cognitive robotic systems with real-time decision support; digital twin technology for patient-specific surgical simulation; and global surgical artificial intelligence networks enabling distributed learning across institutions. CONCLUSIONS: Although artificial intelligence offers transformative potential for colorectal surgery research and practice, successful implementation requires addressing technical, regulatory, ethical, and economic challenges. The surgeon's evolving role demands both traditional expertise and computational fluency. Future advances in multimodal integration, autonomous systems, and global collaboration will fundamentally reshape surgical practice but will require thoughtful implementation prioritizing patient benefit and clinical value.

Humans↗

PubMed2026

The application of the CRISPR-Cas system in Pseudomonas aeruginosa infections.

Due to the extensive drug resistance of Pseudomonas aeruginosa (P. aeruginosa), it is still a great clinical challenge. The clustered regularly interspaced short palindromic repeats and associated proteins (CRISPR-Cas) system has become a promising strategy against this pathogen. This review critically evaluates the multifaceted applications of CRISPR-Cas technology in P. aeruginosa, including its role in antimicrobial resistance, diagnostics, genome editing, and emerging therapeutic and vaccine strategies. In addition to conducting a comprehensive analysis of various studies, we also compared the performance and limitations of various CRISPR platforms, and discussed the main technologies and transformation obstacles in this field. Finally, we look forward to the direction of applying these experimental tools to clinical research in the future.

Pseudomonas aeruginosa↗

PubMed2025

Tonic signaling of the B-cell antigen-specific receptor is a common functional hallmark in chronic lymphocytic leukemia cell phosphoproteomes at early disease stages.

B-cell chronic lymphocytic leukemia (B-CLL) is characterized by highly heterogeneous genomic alterations and altered signaling pathways, with limited studies on its proteome. Our study presents a comprehensive analysis of the proteome and phosphoproteome in B-CLL and CLL-like monoclonal B-cell lymphocytosis (MBL) primary cells. Using high-resolution mass spectrometry, we identified 2970 proteins and 316 phosphoproteins across five tumor samples, including 55 newly identified phosphopeptides (ProteomeXchange-PXD005997). Our multifaceted approach also integrated protein microarrays and western blotting for further data validation in a new patient cohort of 14 patients. Despite sharing 73% of their proteomes, the phosphoproteomes varied significantly among samples, independent of cytogenetic alterations and immunoglobulin heavy variable cluster (IGHV) mutational status. We identified common functional hallmarks in B-CLL and MBL phosphoproteomes, notably tonic signaling (low-level, constitutive signaling) of the B-cell antigen-specific receptor (BCR) and nuclear factor NF-kappa-B (NF-kβ)/signal transducer and activator of transcription 3 (STAT3) pathways. Nine phosphoproteins involved in BCR signaling were further validated, showing a high correlation with early disease stages. Our study advances the field by providing a detailed perspective on the proteome and phosphoproteome of B-CLL cells, revealing signaling pathways crucial for disease development and progression. Integrating diverse proteomics techniques and identifying novel phosphopeptides offers new insights into CLL biology, potentially informing future therapeutic strategies and biomarker development for early diagnosis and personalized treatment.

Humans↗

PubMed2025

Nuclear-lamin-guided plastic positioning and folding of the human genome.

The human genome exhibits a highly ordered hierarchical architecture, yet the mechanisms governing its large-scale organization remain poorly understood. Here, we generate lamin single-, double-, and triple-knockout human embryonic and mesenchymal stem cells (hESCs and hMSCs) to investigate the role of lamins in the spatial organization of the human genome. Complete lamin depletion in hMSCs triggers extensive genome repositioning, disrupts chromosome territories, and dissolves long-range compartment clustering and mega-loops. Lamin loss affects both the nuclear periphery and interior, causing partial inversion and dispersion of nuclear speckles, accompanied by reduced global transcription and impaired stem cell homeostasis. Re-expression of wild-type lamin A, which interacts with the speckle scaffold protein SON, partially restores the organizational and transcriptional defects, while the disease-associated E161K mutant disrupts SON binding and shows limited recovery. Our results elucidate the multifaceted roles of lamins in nuclear organization and link their dysfunction to the pathogenesis of laminopathies.

Humans↗

PubMed2025

Integrating necroptosis and immune landscapes: a multi-omics-derived NecropImmScore stratifies prognosis and therapy in ovarian cancer.

BACKGROUND: Ovarian cancer (OC) remains the deadliest gynecologic malignancy, largely due to its immunosuppressive tumor microenvironment (TME) and resistance to therapy. Necroptosis, a regulated lytic cell death pathway mediated by the RIPK1-RIPK3-MLKL axis, can trigger immunogenic cell death, but its specific role in shaping the OC immune landscape and its clinical translation potential are posorly understood. METHODS: We employed multi-omics analysis (transcriptomics, genomics, clinical data) from TCGA-OV (n = 380), ICGC OV-AU, and IMvigor210 cohorts, combined with rigorous in vitro functional validation using OC cell lines (SKOV3, HEY), macrophages (THP-1 derived), and T cells (Jurkat). Computational immunology approaches (ESTIMATE, CIBERSORT, ssGSEA) quantified immune infiltration. We identified MLKL-associated immune genes, performed survival analysis (Kaplan-Meier, Cox regression), and constructed a necroptosis-immune signature (NecropImmScore) using consensus clustering and PCA of 102 prognostic genes. Drug sensitivity was predicted via pRRophetic and CellMiner. RESULTS: MLKL emerged as a protective prognostic biomarker (p = 0.018), significantly correlated with enhanced immune infiltration (ImmuneScore, StromalScore, ESTIMATEScore; p < 2.22e-16), M1 macrophage polarization (p = 0.006), activated CD4 + T cells (p = 0.003), and elevated immune checkpoint expression (PD-L1, CTLA4, LAG3, TIGIT). In vitro, MLKL overexpression in OC cells promoted M1 polarization (p < 0.05), activated Jurkat T cells (upregulated CCR4/5/7/9, CD69, CD3D/E, GZMB; p < 0.05), and induced key chemokines (CXCL9/10/11/13) critical for immune cell recruitment. Integration of MLKL-related and immune-related DEGs (n = 632) revealed enrichment in T-cell activation, chemokine signaling, and antigen presentation pathways (FDR < 0.05). Consensus clustering based on 102 survival-associated genes defined three molecular subtypes (Clusters A-C) with divergent survival (p = 0.019), necroptosis activity, and immune infiltration (Cluster C: best prognosis, highest MLKL/ImmuneScore). The derived NecropImmScore robustly stratified patients: high-score correlated with superior overall survival (TCGA: p < 0.001; ICGC: p = 0.014), inflamed TME phenotype, elevated checkpoint expression, and improved response to anti-PD-L1 in IMvigor210. Critically, high NecropImmScore predicted higher BRCA1 mutation frequency (AUC = 0.802), synergy with BRCA1 status for prognosis, higher homologous recombination deficiency (HRD) score, sensitivity to cisplatin (p = 0.014), paclitaxel (p = 0.016), gemcitabine (p = 0.017), and provided superior prognostic stratification when combined with TMB and HRD score (p < 0.001). CONCLUSION: This study establishes MLKL as a master regulator of anti-tumor immunity in OC, driving chemokine-mediated immune cell recruitment and TME reprogramming. The novel NecropImmScore is a multifaceted biomarker that effectively predicts prognosis, immunotherapy response, BRCA1 deficiency, and chemosensitivity, offering significant potential for guiding precision therapeutic strategies in OC.

Humans↗

PubMed2025

A multifaceted investigation into the impact of m6A methylation-related genes on pancreatic cancer, integrating insights from various databases and foundational experimental research.

BACKGROUND: Despite advances in surgical techniques, immunotherapy, the mortality rate associated with pancreatic cancer (PC) has been on the rise in recent years. Understanding the importance of RNA N6-methyladenosine (m6A) in PC is critical for prognosis, tumor microenvironment, and immunotherapy efficacy. The study aims to identify m6A methylation regulators that play an important role in the development and progression of PC by mining databases. The effect of insulin-like growth factor-binding protein 3 (IGFBP3) on pancreatic tumors was explored, and the related mechanisms were explored. METHODS: We analyzed the expression of m6A regulators in PC by digging deeper into the datasets of The Cancer Genome Atlas and Gene Expression Omnibus (GEO) databases, and analyzed its relationship with the prognosis of patients with PC, looking for m6A methylation regulators that play an important role in the development and progression of PC. Reuse the ConsensusClusterPlus package, Cox analysis, and unsupervised clustering to delineate three distinct m6A clusters - designated as m6A cluster A, m6A cluster B, and m6A cluster C single-sample gene set enrichment analysis, gene set variation analysis, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses evaluated the different pathway roles of these clusters in the development and progression of PC. Finally, the cell lines with IGFBP3 overexpression and knockdown were constructed by lentivirus transfection, the transfection effect was identified by WB, and the effects of IGFBP3 overexpression/knockdown on the survival and growth of PC cell lines were verified by cell cloning experiments and cell counting kit-8 experiments, and the possible related pathways were explored by KEGG. RESULTS: Most m6A regulatory factors are highly expressed in PC, and their high expression is negatively correlated with the prognosis of patients with PC. Furthermore, m6A regulatory factors may influence the occurrence and development of PC through metabolic pathways, stroma activation pathways, immune regulatory processes, and the immune microenvironment. Finally, the overexpression of IGFBP3 promoted the growth of PC cells, and vice versa. CONCLUSIONS: Most m6A regulatory factors are differentially expressed in PC and are associated with the prognosis of patients with PC, potentially influencing the occurrence and development of PC through pathways such as the immune microenvironment. The overexpression of IGFBP3 can promote the growth of PC cells and vice versa.

IGFBP3↗

PubMed2026

Genome-wide identification of CXE gene family in soybean and functional characterization of GmCXE31 in lipid biosynthesis and salt tolerance.

GmCXE31 negatively regulates salt tolerance and lipid synthesis in soybean, and the cxe31-edited lines improve soybean yield and seed quality. Carboxylesterases (CXEs), as essential lipid hydrolases of the α/β-hydrolase fold superfamily, are critical for plant stress responses, hormone signaling and secondary metabolism. The key candidate gene GmCXE31 was previously identified in our laboratory through a genome‑wide association study (GWAS) of soybean lipid‑related traits. In the present study, we further identified 60 GmCXE family genes in soybean. Phylogenetic analysis clustered them into 11 conserved subfamilies. Cis-acting element analysis showed their promoters are enriched with elements related to abiotic stress, growth and hormone signaling, suggesting potential roles in soybean development and stress adaptation. GmCXE31 is highly expressed in seedling roots and responsive to strigolactones (SLs) and salt stress. Functional assays revealed that GmCXE31 negatively regulates soybean salt tolerance: its overexpression reduced salt tolerance in Arabidopsis and soybean under 150 mM NaCl stress, while its knockout enhanced this trait. Lipid profiling revealed GmCXE31-edited lines had higher seed oil content, elevated oleic/linoleic acid ratio and lower saturated fatty acid proportion, which was achieved by regulating lipid synthesis-related genes like GmNFYA. Agronomic trait analysis showed GmCXE31-edited lines had increased nodule number, plant height and single-plant yield at maturity, with opposite phenotypes in overexpression lines. In conclusion, this study elucidates the multifaceted roles of GmCXE31 in coordinating soybean salt tolerance, lipid metabolism and agronomic traits, providing theoretical and genetic resources for salt-tolerant and high-quality soybean molecular breeding.

Glycine max↗

PubMed1991

Insulin resistance. A multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidemia, and atherosclerotic cardiovascular disease.

Diabetes mellitus is commonly associated with systolic/diastolic hypertension, and a wealth of epidemiological data suggest that this association is independent of age and obesity. Much evidence indicates that the link between diabetes and essential hypertension is hyperinsulinemia. Thus, when hypertensive patients, whether obese or of normal body weight, are compared with age- and weight-matched normotensive control subjects, a heightened plasma insulin response to a glucose challenge is consistently found. A state of cellular resistance to insulin action subtends the observed hyperinsulinism. With the insulin/glucose-clamp technique, in combination with tracer glucose infusion and indirect calorimetry, it has been demonstrated that the insulin resistance of essential hypertension is located in peripheral tissues (muscle), is limited to nonoxidative pathways of glucose disposal (glycogen synthesis), and correlates directly with the severity of hypertension. The reasons for the association of insulin resistance and essential hypertension can be sought in at least four general types of mechanisms: Na+ retention, sympathetic nervous system overactivity, disturbed membrane ion transport, and proliferation of vascular smooth muscle cells. Physiological maneuvers, such as calorie restriction (in the overweight patient) and regular physical exercise, can improve tissue sensitivity to insulin; evidence indicates that these maneuvers can also lower blood pressure in both normotensive and hypertensive individuals. Insulin resistance and hyperinsulinemia are also associated with an atherogenic plasma lipid profile. Elevated plasma insulin concentrations enhance very-low-density lipoprotein (VLDL) synthesis, leading to hypertriglyceridemia. Progressive elimination of lipid and apolipoproteins from the VLDL particle leads to an increased formation of intermediate-density and low-density lipoproteins, both of which are atherogenic. Last, insulin, independent of its effects on blood pressure and plasma lipids, is known to be atherogenic. The hormone enhances cholesterol transport into arteriolar smooth muscle cells and increases endogenous lipid synthesis by these cells. Insulin also stimulates the proliferation of arteriolar smooth muscle cells, augments collagen synthesis in the vascular wall, increases the formation of and decreases the regression of lipid plaques, and stimulates the production of various growth factors. In summary, insulin resistance appears to be a syndrome that is associated with a clustering of metabolic disorders, including non-insulin-dependent diabetes mellitus, obesity, hypertension, lipid abnormalities, and atherosclerotic cardiovascular disease.

Arteriosclerosis↗