Search Results (48)

Background: Rapid advances in biotechnology provide researchers with the opportunity to integrate omics profiles (genomics, epigenomics, transcriptomics, proteomics, etc.) with multiple phenotypes or experimental conditions. In cancers such as acute myeloid leukemia (AML), where combination therapies are standard of care, identifying genetic drivers of drug resistance requires evaluating how genes are associated with multiple drug response phenotypes. Statistical analyses associating omics profiles with multiple phenotypes yield multiple significance values and rankings for each of many genes. There is a great need to consolidate these multiple rankings into a consensus ranking to prioritize specific genes for detailed follow-up wet-lab or clinical studies. Methods/Results: Here, we evaluate the well-known Fisher’s method, the sum of squared z-statistics (SSz), and the recently published cellMCD method as tools for gene prioritization. In simulation studies, cellMCD showed very similar or highly superior performance to the widely used Fisher’s and SSz methods. These advantages were also observed in an example application involving a CRISPR drug screen of an acute myeloid leukemia cell line. Conclusions: In summary, our results indicate that cellMCD should be more widely used for prioritizing discoveries from multiple omic association studies. These methods are available as an R package on github. Full article

High mobility group A1 (HMGA1) is a chromatin-associated protein that regulates transcription and drives cancer progression. In this pan-cancer study, we analyzed multi-omics data to comprehensively characterize HMGA1’s expression patterns, prognostic significance, epigenetic regulation, and immunotherapy roles. We found that HMGA1 was markedly upregulated in most cancers, mainly driven by promoter hypomethylation and copy number alterations. Elevated HMGA1 expression was consistently associated with unfavorable patient survival, stemness features, and the activation of oncogenic signaling pathways. Crucially, HMGA1 expression correlated with an immune-excluded tumor microenvironment, characterized by suppressed stromal and immune scores. Even in tumors with immune infiltration, high HMGA1 predicted poor prognosis, likely mediated by enhanced regulatory T-cell (Treg) recruitment and impaired effector immune function. Moreover, HMGA1 levels were positively correlated with tumor mutational burden (TMB), and microsatellite instability (MSI), and immunotherapy-related checkpoints including PD-1, CTLA-4, and TIGIT. Drug sensitivity analysis further revealed that HMGA1 predicted resistance to AKT inhibitors, which was experimentally validated in breast cancer cells treated with Capivasertib. Collectively, our findings establish HMGA1 as a pivotal oncogenic regulator and a promising biomarker for prognosis and for guiding strategies in immunotherapy and overcoming targeted therapy resistance. Full article

Microtubule minus-end binding proteins (−TIPs) are critical regulators of microtubule dynamics and stability, whose dysfunctions are increasingly associated with tumorigenesis and cancer progression. This review systematically consolidates current research advances on the molecular characteristics, oncogenic mechanisms, and therapeutic potential of −TIPs in cancer. By integrating preclinical studies, multi-omics data, and clinical evidence, it was found that calmodulin-regulated spectrin-associated proteins (CAMSAPs) and abnormal spindle microtubule assembly (ASPM) primarily exhibit oncogenic properties, whereas CAMSAP3 acts as a tumor suppressor by negatively regulating tumor cell migration. Studies also demonstrate that pharmacological inhibition of the γ-tubulin ring complex (γ-TuRC) effectively attenuates the centrosomal hyper-clustering capacity of malignant cells, thereby suppressing invasive phenotypes. This result underscores the therapeutic value of targeting −TIPs. In summary, −TIPs play critical and complex roles in cancer progression and hold significant potential as prognostic biomarkers and therapeutic targets. Intervention strategies focusing on specific −TIPs, such as γ-TuRC, offer promising strategies for precision cancer therapy; however, the context-dependent functions of these proteins require further investigation to facilitate clinical translation. Full article

Omics sciences are revolutionizing the field of biomedical and dental research by allowing for an integrated understanding of the molecular basis of health and disease. This narrative review analyzes the role of these novel technologies supporting the diagnosis, prognosis, and treatment of the most noteworthy oral diseases, such as dental caries, periodontitis, and oral squamous cell carcinoma. The review discusses the characterization of disease-associated genetic variations and polygenic risk scores as evidenced by genomic studies. It further examines how transcriptomic analyses can identify diagnostic gene expression signatures of immune dysregulation and tumor heterogeneity. The contribution of proteomics and metabolomics studies to the discovery of diagnostic and prognostic protein and metabolites biomarkers is also analyzed. Finally, the integration of different omics datasets within multi-omics frameworks is discussed as a key approach for a holistic interpretation of oral pathogenesis and data-driven precision dentistry. The review also addresses current limitations in the clinical translation of omics sciences into tools for early diagnosis, personalized prevention, and targeted therapy. Full article

Sepsis-associated acute kidney injury (SA-AKI) is a prevalent and life-threatening complication in critically ill children, contributing to high mortality rates (up to 30%) and long-term renal dysfunction in pediatric intensive care units. This review synthesizes recent advances in the signalling pathways underlying SA-AKI, emphasizing pediatric-specific mechanisms, biomarkers, and therapeutic targets. This review covers inflammatory cascades via TLR/NF-κB leading to cytokine storms (IL-6, TNF-α); apoptosis and necrosis involving mitochondrial Bcl-2 dysregulation and OLFM4; and emerging processes like pyroptosis (NF-κB-mediated), metabolic reprogramming (choline deficiency and Nrf2-mitophagy), and novel routes such as cGAS-STING and TGF-β signalling. Biomarkers like urinary OLFM4, DKK3, NGAL, and serum suPAR, alanine, and Penkid enable early diagnosis and risk stratification, with models like PERSEVERE-II enhancing prognostic accuracy. Therapeutic strategies include fluid optimization, renal replacement therapies (CRRT, SLED-f), and pathway-targeted interventions such as choline supplementation, oXiris for cytokine removal, Humanin for immunomodulation, and investigational cGAS-STING inhibitors. Despite progress, challenges persist in translating animal models to pediatric trials and addressing heterogeneity. Integrating multi-omics and precision medicine holds promise for improving outcomes, underscoring the need for multicenter studies in children. Full article

Secondary peritonitis is a life-threatening intra-abdominal condition arising from gastrointestinal perforation, chemical injury, or catheter-related infections, characterized by marked heterogeneity in presentation and progression. Major subtypes include stercoraceous peritonitis with fecal contamination, fibrinous peritonitis triggered by bile or gastric contents, peritoneal dialysis-associated infections, and pancreatitis-associated chemical peritonitis. Regardless of etiology, these conditions share profound local and systemic inflammatory responses, contributing to high morbidity and mortality. Biomarkers such as procalcitonin (PCT), interleukin-6 (IL-6), high mobility group box 1 (HMGB1), C-reactive protein (CRP), lipopolysaccharide (LPS), neutrophil-to-lymphocyte ratio (NLR), and neutrophil gelatinase-associated lipocalin (NGAL) have emerged as tools for early diagnosis, subtype stratification, and monitoring of therapeutic response. Their prognostic value is particularly relevant in peritoneal dialysis and postoperative intensive care. Advances in multi-omics, patient-derived organoids, peritoneum-on-chip models, and microbiota profiling are reshaping understanding of peritoneal pathophysiology, revealing cellular heterogeneity, immune-microenvironment interactions, and mechanisms of fibrotic remodeling. Key translational challenges include assessing whether omics-derived signatures can predict the need for early re-laparotomy or the risk of abdominal compartment syndrome. Integration of high-dimensional biomarker profiling with mechanistic and functional studies promises a new era of precision medicine in secondary peritonitis, enabling risk-adapted interventions, complication prevention, and tailored strategies to improve outcomes. Full article

Lower respiratory tract infections (LRTIs) remain a major cause of global morbidity and mortality, yet accurate pathogen identification and risk stratification continue to pose clinical challenges. Lipidomics—the comprehensive analysis of lipid species within biological systems—has emerged as a promising tool to unravel host–pathogen interactions and reveal novel diagnostic and prognostic biomarkers. This systematic review synthesizes evidence from nine original studies applying mass spectrometry-based lipidomic profiling in human LRTIs, including community-acquired pneumonia (CAP), ventilator-associated pneumonia (VAP), and coronavirus disease 2019 (COVID-19). Across diverse study designs, sample types, and analytical platforms, consistent alterations in lipid metabolism were observed. Perturbations in phospholipid classes, particularly phosphatidylcholines (PCs) and lysophosphatidylcholines (LPCs), were frequently associated with disease severity and immune activation. The ratios of PC to LPC and phosphatidylethanolamine (PE) to lysophosphatidylethanolamine (LPE) emerged as markers of inflammatory remodeling. Sphingolipids—including sphingomyelins (SMs) and sphingosine-1-phosphate (S1P)—were identified as key modulators of monocyte and neutrophil activation. Fatty acid–derived lipid mediators such as oxylipins (e.g., 12,13-epoxyoctadecenoic acid and 15-hydroxyeicosatetraenoic acid) and acylcarnitines reflected pathogen-specific immune responses and mitochondrial dysfunction. Several lipid-based classifiers demonstrated superior diagnostic and prognostic performance compared to conventional clinical scores, including the CURB-65 and pneumonia severity index. However, significant heterogeneity in experimental design, lipid identification workflows, and reporting standards limits inter-study comparability. While preliminary findings support the integration of lipidomics into infectious disease research, larger multi-omic and longitudinal studies are required. This review provides the first comprehensive synthesis of lipidomic alterations in human LRTIs and highlights their emerging translational relevance. Full article

Proteasome subunit beta type-9 (PSMB9), a member of the proteasome beta subunit family, encodes the pivotal β1i component of the immunoproteasome. PSMB9 plays a crucial role in antigen processing and presentation; however, its comprehensive role in orchestrating a tumor-immune landscape and regulating the anti-tumor immune responses remains unexplored. Here we investigated the context-dependent functions of PSMB9 by integrating multi-omics data from The Cancer Genome Atlas, Genotype-Tissue Expression database, Human Protein Atlas, Tumor Immunotherapy Gene Expression Resource, and multiple other databases. Moreover, we explored the predictive value of PSMB9 in multiple immunotherapy cohorts and investigated its functional relevance in CAR-T therapy using genome-scale CRISPR/Cas9 screening, gene knockout cell line in vitro, and clinical cohort validation. We found widespread dysregulation in PSMB9 across cancers, predominantly upregulated in most malignancies and associated with advanced pathological stages in specific contexts. PSMB9 was also broadly and negatively correlated with tumor stemness indices. Crucially, PSMB9 expression was robustly linked to anti-tumor immunity by being significantly correlated with immune-pathway activation (e.g., IFN response, cytokine signaling), immune regulatory and immune checkpoint gene expression, and enhanced infiltration of T cells across nearly all tumor types. Consequently, elevated PSMB9 predicted superior response to immune checkpoint inhibitors in multiple cohorts, showing comparable predictive power to established predictive signatures. Furthermore, CRISPR/Cas9 screening identified PSMB9 loss as a novel mechanism of resistance to CD19 CAR T cell therapy, with PSMB9-deficient tumor cells exhibiting a survival advantage under CAR-T pressure, supported by trends in clinical CAR-T outcomes. Our study uncovers PSMB9 as a previously unrecognized critical regulator of the tumor immune landscape in a pan-cancer scope, whose expression orchestrates key immune processes within the tumor microenvironment and serves as a potent biomarker for patient prognosis. Critically, we first established PSMB9 as a novel prognostic indicator for both checkpoint blockade and CAR-T cell therapies, highlighting its dual role as a crucial immune modulator and a promising biomarker for guiding T cell-based immunotherapy strategies across diverse human cancers. Full article

Background: Glioblastoma multiforme (GBM) is the most common and aggressive primary brain tumour, associated with poor survival outcomes and significant clinical challenges. Conventional diagnostic methods, including MRI, CT, and histopathological analysis of tissue biopsies, are limited by their inability to reliably distinguish treatment effects from true tumour progression, often resulting in misdiagnosis and delayed intervention. Repeated tissue biopsies are also invasive and unsuitable for longitudinal monitoring. Liquid biopsy, a minimally invasive approach analysing tumour-derived material in biofluids such as blood and cerebrospinal fluid (CSF), offers a promising alternative. This review aims to evaluate current evidence on circulating biomarkers including circulating tumour cells (CTCs), circulating tumour DNA (ctDNA), microRNAs (miRNAs), extracellular vesicles (EVs), and proteins in GBM diagnosis and monitoring, and to assess the potential role of artificial intelligence (AI) in enhancing their clinical application. Methods: A narrative synthesis of the literature was undertaken, focusing on studies that have investigated blood- and CSF-derived biomarkers in GBM patients. Key aspects evaluated included biomarker biology, detection techniques, diagnostic and prognostic value, current technical challenges, and progress towards clinical translation. Studies exploring AI and machine learning (ML) approaches for biomarker integration and analysis were also reviewed. Results: Liquid biopsy enables repeated and minimally invasive sampling of tumour-derived material, reflecting the genetic, epigenetic, proteomic, and metabolomic landscape of GBM. Although promising, its translation into routine clinical practice is hindered by the low abundance of circulating biomarkers and lack of standardised collection and analysis protocols. Evidence suggests that combining multiple biomarkers improves sensitivity and specificity compared with single-marker approaches. Emerging AI and ML tools show significant potential for improving biomarker discovery, integrating multi-omic datasets, and enhancing diagnostic and prognostic accuracy. Conclusions: Liquid biopsy represents a transformative tool for GBM management, with the capacity to overcome limitations of conventional diagnostics and provide real-time insights into tumour biology. By integrating multiple circulating biomarkers and leveraging AI-driven approaches, liquid biopsy could enhance diagnostic precision, enable dynamic disease monitoring, and improve clinical decision-making. However, large-scale validation and standardisation are required before routine clinical adoption can be achieved. Full article

Background: Endometrial cancer (EC) remains a major gynecologic malignancy with limited biomarkers for risk stratification. While killer cell lectin-like receptor G2 (KLRG2) exhibits oncogenic properties in other cancers, its clinical significance and mechanistic roles in EC are unknown. This study aims to systematically characterize KLRG2 expression in EC, evaluate its prognostic significance, decipher underlying molecular mechanisms, and explore its role in tumor immune microenvironment regulation. Methods: We performed integrated multi-omics analyses using TCGA-UCEC (n = 552), GTEx, and GEO cohorts (GSE106191), complemented by qPCR validation (14 EC vs. 14 normal samples). Prognostic models were constructed via Cox regression and time-dependent ROC analysis. Epigenetic regulation was assessed through methylation profiling (UALCAN/MethSurv), and immune correlations were evaluated using TIMER/ESTIMATE algorithms. Results: KLRG2 was significantly overexpressed in EC tissues compared to normal endometrium (p < 0.001), validated by immunohistochemistry and qPCR. High KLRG2 expression independently predicted worse overall survival (HR = 3.08, 95% CI = 1.92–4.96) and progression-free interval (HR = 1.98, 95% CI = 1.37–2.87). Furthermore, elevated KLRG2 levels were significantly associated with advanced-stage disease (p < 0.001), deep myometrial invasion (p < 0.05), and high-grade histology (p < 0.001). Mechanistically, promoter hypomethylation was associated with KLRG2 overexpression (p < 0.001), while hypermethylation at three CpG sites (cg04915254, cg04520485, cg23104233) correlated with poor prognosis. Functional enrichment linked KLRG2 to cell cycle checkpoints and G Protein-Coupled Receptor signaling. Immune profiling revealed cytotoxic lymphocyte depletion (CD8+ T cells: Spearman’s ρ = −0.247, p < 0.001; NK CD56bright cells: Spearman’s ρ = −0.276, p < 0.001) and Th2 polarization (Spearman’s ρ = 0.117, p = 0.006). Conclusions: This comprehensive EC study establishes KLRG2 as a dual diagnostic/prognostic biomarker and immunomodulatory target. These findings provide a rationale for developing KLRG2-directed therapies to counteract tumor-intrinsic proliferation and microenvironmental immune suppression. Future single-cell analyses are warranted to dissect KLRG2-mediated tumor-immune crosstalk. Full article

Background and Objectives: Chromobox 1 (CBX1), a key epigenetic regulator involved in chromatin remodeling, has been implicated in various cancers; however, its role in liver hepatocellular carcinoma (LIHC) remains underexplored. This study aimed to investigate the expression patterns, epigenetic regulation, and non-coding RNA (ncRNA) networks involving CBX1 in LIHC, assess their potential as diagnostic and prognostic biomarkers, and explore their relevance as a putative therapeutic target. Materials and Methods: A multi-omics bioinformatics approach was employed using datasets from GEPIA2, OncoDB, UALCAN, Human Protein Atlas, KM Plotter, MethSurv, miRNet, and ENCORI. These databases were used to analyze mRNA and protein expression, DNA methylation, prognosis, and interaction networks involving CBX1 and ncRNAs. Results: CBX1 was significantly upregulated in both the mRNA and protein expression in LIHC. Upregulated CBX1 expression was associated with poor prognosis. DNA methylation analysis revealed that both hypermethylated and hypomethylated probes were significantly associated with CBX1 expression and poor prognosis. hsa-miR-212-3p and hsa-miR-132-3p were significantly upregulated in LIHC and were positively correlated with CBX1 expression and poor prognosis. The ncRNA network was identified, including long ncRNAs, circular RNAs, and pseudogenes, many of which were linked to tumor progression and poor prognosis, and competing endogenous RNAs were associated with tumor progression and poor prognosis in LIHC. Conclusions: CBX1 was significantly overexpressed in LIHC and was regulated by both DNA methylation and ncRNA interactions. Its expression is closely associated with a poor prognosis. The CBX1–micro-RNA–long ncRNA/circular RNA axis is a promising avenue for the development of novel diagnostic and therapeutic strategies. This study provides system-level insights into the regulatory landscape of CBX1 in LIHC and supports its potential role in precision medicine. Full article

This study systematically analyzed the expression and clinical significance of Hydroxysteroid 17-beta dehydrogenase type 10 (HSD17B10) in 33 cancers by integrating TCGA, GTEx, and other multi-omics databases. HSD17B10 was highly expressed in 14 cancers, like GBM and LGG, but low in 5, such as KIRC. Its expression correlated closely with overall survival (OS) and disease-free survival (DFS). In GBM-LGG, LGG, and other cancers, high HSD17B10 expression was linked to lower survival rates, indicating that it could be an independent prognostic marker. HSD17B10 also had a two-way relationship with the tumor’s immune microenvironment. In cancers such as GBM-LGG, high expression correlated positively with immune/stromal scores. However, in most cancers like LUAD, it was negatively associated with B- and T-cell infiltration. Epigenetic analysis showed that low methylation in the HSD17B10 promoter region might drive its high expression in tumors such as SARC, and specific methylation sites (e.g., CG26323797) were significantly related to patient survival. Functional enrichment analysis revealed that HSD17B10 participated in tumor progression by regulating oxidative phosphorylation, mitochondrial metabolism, and RNA methylation. Single-cell and spatial transcriptome data further demonstrated that HSD17B10 had a cell-type-specific expression pattern in colorectal cancer. This study provides a theoretical basis for HSD17B10 as a pan-cancer prognostic marker and therapeutic target. Full article

Cyclin-dependent kinase 1 (CDK1) has emerged as a critical regulator of cell cycle progression, yet its role in liver fibrosis-associated hepatocellular carcinoma (LF-HCC) remains underexplored. This study aimed to systematically evaluate CDK1’s prognostic significance, immune regulatory functions, and therapeutic potential in LF-HCC pathogenesis. Integrated bioinformatics approaches were applied to multi-omics datasets from GEO, TCGA, and TIMER databases. Differentially expressed genes were identified through enrichment analysis and protein–protein interaction networks. Survival outcomes were assessed via Kaplan–Meier analysis, while immune cell infiltration patterns were quantified using CIBERSORT. Molecular docking simulations evaluated CDK1’s binding affinity with pharmacologically active compounds (alvocidib, seliciclib, alsterpaullone) using AutoDock Vina. CDK1 demonstrated significant overexpression in LF-HCC tissues compared to normal controls (p < 0.001). Elevated CDK1 expression correlated with reduced overall survival (HR = 2.41, 95% CI:1.78–3.26, p = 0.003) and advanced tumor staging (p = 0.007). Immune profiling revealed strong associations between CDK1 levels and immunosuppressive cell infiltration, particularly regulatory T cells (r = 0.63, p = 0.001) and myeloid-derived suppressor cells (r = 0.58, p = 0.004). Molecular docking confirmed high-affinity binding of CDK1 to kinase inhibitors through conserved hydrogen-bond interactions (binding energy ≤ −8.5 kcal/mol), with alvocidib showing optimal binding stability. This multimodal analysis establishes CDK1 as both a prognostic biomarker and immunomodulatory regulator in LF-HCC pathogenesis. The enzyme’s dual role in driving tumor progression and reshaping the immune microenvironment positions it as a promising therapeutic target. Computational validation of CDK1 inhibitors provides a rational basis for developing precision therapies against LF-HCC, bridging translational gaps between biomarker discovery and clinical application. Full article

The identification of cancer prognostic biomarkers is crucial for predicting disease progression, optimizing personalized therapies, and improving patient survival. Molecular biomarkers are increasingly being identified for cancer prognosis estimation. However, existing studies and databases often focus on single-type molecular biomarkers, deficient in comprehensive multi-omics data integration, which constrains the comprehensive exploration of biomarkers and underlying mechanisms. To fill this gap, we conducted a systematic prognostic analysis using over 10,000 samples across 33 cancer types from The Cancer Genome Atlas (TCGA). Our study integrated nine types of molecular biomarker-related data: single-nucleotide polymorphism (SNP), copy number variation (CNV), alternative splicing (AS), alternative polyadenylation (APA), coding gene expression, DNA methylation, lncRNA expression, miRNA expression, and protein expression. Using log-rank tests, univariate Cox regression (uni-Cox), and multivariate Cox regression (multi-Cox), we evaluated potential biomarkers associated with four clinical outcome endpoints: overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). As a result, we identified 4,498,523 molecular biomarkers significantly associated with cancer prognosis. Finally, we developed SurvDB, an interactive online database for data retrieval, visualization, and download, providing a comprehensive resource for biomarker discovery and precision oncology research. Full article

Background: Recent studies have identified solute carrier family 19 member 1 (SLC19A1) as a second messenger transporter that regulates massive immune-related signaling cascades, but current studies provide limited information. This study aims to evaluate its role and the potential mechanisms across various cancers. Methods: We analyzed multi-omics data from a pan-cancer cohort to evaluate SLC19A1 expression and its association with multiple features, including prognosis, tumor stemness, genome instability, and immune infiltration. Immunofluorescence staining was performed to validate SLC19A1 expression in tumor tissues and its relationship M2 macrophages. In addition, we used web tools such as ROCplotter to evaluate the association between SLC19A1 and response to chemotherapy and immunotherapy. Results: SLC19A1 was found to be overexpressed in multiple cancer types compared to normal tissues, correlating with poor prognosis. High SLC19A1 levels were associated with increased genomic instability and immune suppression. In addition, SLC19A1 was negatively correlated with CD8+ T-cell infiltration and positively correlated with M2 macrophage infiltration. The association of SLC19A1 with M2 macrophages was confirmed in multiple immunofluorescence staining. Finally, SLC19A1 was associated with the response to chemotherapy and immunotherapy in a variety of tumors. Conclusions: Our findings position SLC19A1 as a novel unfavorable prognostic marker in cancer, closely linked to immune suppression and genomic instability. This study highlights the need for further exploration of SLC19A1 as a therapeutic target and its implications in cancer treatment strategies. Full article