Clinical and Experimental Medicine最新文献

Although most studies have reported that a high number of negative lymph nodes (NLNs) at surgery can be associated with improved overall survival (OS) in patients with breast cancer (BC), the effect size was reported differently in several studies, which may be due to the small sample size of the primary studies. This systematic review and meta-analysis aimed to investigate the association of a high number of NLNs removed during surgery with OS and recurrence-free survival (RFS) in BC patients who are candidates for axillary lymph node dissection. We searched the PubMed, Embase, Scopus, Google Scholar, and Web of Science databases, as well as study references, to identify related articles published from the beginning of 2000 to October 2024. Based on sensitivity analysis, the removal of ≥ 10 NLNs was defined as the high number of NLNs removed group, and the removal of < 10 NLNs was defined as the low number of NLNs removed group. The heterogeneity between studies was assessed using Cochran's Q and I2 tests. Publication bias was assessed using Egger's test. Ultimately, 14 studies encompassing 36,576 BC patients were included. A pooled estimate of 14 studies showed that a high number of NLN removed compared to a low number of NLN removed was significantly associated with improved 5-year OS (HR: 0.82, 95% CI: 0.74, 0.90), I2 = 93.8) and RFS rate (HR:0.76, CI: 0.765, 0.86), I2 = 86.3). A higher number of NLNs removed during surgery in BC patients who are candidates for axillary lymph node dissection appears to be associated with improved OS and RFS.

PANoptosis is a novel form of programmed cell death that integrates pyroptosis, apoptosis, and necroptosis; in this study, we combined the single-cell RNA sequencing (scRNA-seq) dataset GSE227136 with transcriptome data to elucidate its role in idiopathic pulmonary fibrosis (IPF) pathogenesis. PANoptosis-related genes were compiled from GeneCards and published literature. Consensus clustering was used to identify distinct PANoptosis-related clusters of IPF in the GEO dataset based on filtered PANoptosis-related differentially expressed genes (PRDEGs). Specific hub genes were identified using weighted gene co-expression network analysis (WGCNA) and two machine learning methodologies, which were used to develop predictive models. The inflammatory programmed cell death score (PANoptosis score, Ps) for each IPF patient was calculated based on nine PRDEGs, followed by analyses of these PRDEGs' expression differences and their ROC curves. PRDEG expression was confirmed in murine pulmonary tissues using quantitative real-time polymerase chain reaction (qRT-PCR). We successfully identified nine PRDEGs and two distinct PANoptosis-related clusters with these PRDEGs. Using WGCNA and machine learning approaches, we constructed a nomogram with robust predictive capacity for diagnosis of IPF. In addition, immune infiltration analysis among different molecular groups and single cell analysis revealed that increased PANoptosis activity was closely associated with immune activation. Finally, results from qRT-PCR showed a significant increase in the expression of MLKL and AIM2 in the lung tissue of the IPF animal model.

Hepatocellular carcinoma (HCC) has a poor prognosis, particularly with spinal metastases. Current prognostic scores (e.g., Revised Tokuhashi, New England Spinal Metastasis Score) lack integration of tumor microenvironment (TME)-based molecular subtypes, limiting their utility in precision medicine. This study evaluated the prognostic value of these subtypes and whether they enhance established scoring systems. In a single-center retrospective cohort of 117 HCC patients undergoing surgery for spinal metastases (2009-2024), patients were stratified into three TME subtypes: immune-inflamed (n = 39), immune-excluded (n = 53), and immune-desert (n = 25). Overall survival (OS) was analyzed using Kaplan-Meier and Cox regression. The discriminative ability of four prognostic scores was assessed with time-dependent ROC curves. Recursive partitioning analysis (RPA) integrated molecular subtypes with clinical scores to develop novel decision trees. Median OS for the cohort was 13.1 months. TME subtype was a powerful independent prognostic factor, with immune-inflamed, immune-excluded, and immune-desert subtypes showing median OS of 17.2, 12.1, and 8.8 months, respectively (P < 0.001). Multivariable analysis confirmed this association (e.g., immune-desert aHR = 9.52, P < 0.001). The Revised Tokuhashi score showed the highest baseline discriminative ability for 1-year survival (AUROC = 0.726). Integrating TME subtype and postoperative systemic therapy significantly improved predictive accuracy across all models (AUROCs > 0.92). RPA generated clinically actionable decision trees, defining three distinct prognostic groups. TME-based molecular subtypes are critical independent survival determinants in HCC with spinal metastases. Their integration with clinical scores using RPA produces highly accurate predictive models and practical decision aids, advocating for a biology-augmented approach to personalize patient management.

Prognostic stratification of Hodgkin lymphoma (HL) patients in ICU remains challenging, with conventional scoring systems often overlooking pathophysiological biomarkers. This retrospective cohort study analyzed 1,908 HL patients from the MIMIC-IV database. Multivariate logistic regression and machine learning (ML, gradient-boosting (GBM) was optimized with LASSO regularization) were employed to identify 30-day mortality predictors, validated through SHAP interpretability, calibration curves, and decision curve analysis. Multi-organ dysfunction (AST, BUN, T-Bil), systemic inflammation (NLR, WBC) and APTT emerged as critical mortality determinants, and selected for model construction. GBM achieved superior discrimination (training AUC = 0.89; test AUC = 0.75), SHAP analysis, calibration curve and decision curve analysis (DCA) confirmed clinical utility, outperforming empirical intervention strategies. This study establishes a biomarker-driven ML framework for HL prognosis, integrating renal, hepatic, and inflammatory markers into actionable risk stratification. thereby providing a scientific basis for comprehensive HL management.

Monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma (SMM), and multiple myeloma (MM) form a continuum of plasma cell disorders, with progression from MGUS to MM being difficult to predict. Current risk stratification models, largely based on clinical, laboratory, and cytogenetic markers, fail to capture the molecular complexity underlying disease progression, limiting their predictive accuracy. Recent advancements in multi-omics technologies, encompassing genomics, transcriptomics, proteomics, and metabolomics, have provided deeper insights into the molecular drivers of these conditions. The integration of artificial intelligence (AI) and machine learning (ML) further enhances this understanding, offering new avenues for dynamic, personalized risk prediction. AI-based approaches that incorporate multi-omics data have the potential to identify novel biomarkers and predict disease outcomes with greater precision. These advancements could revolutionize risk stratification by providing a more individualized and dynamic framework for patient monitoring and treatment. However, the clinical adoption of AI and multi-omics tools is fraught with challenges, including the integration of complex data types, the need for standardized protocols, and concerns surrounding data privacy and algorithmic bias. Furthermore, evolving regulatory frameworks must accommodate the continuous learning capabilities of AI systems. This article explores the current limitations of risk stratification models in MGUS and SMM and examines the potential of multi-omics and AI to improve predictive accuracy. It also discusses the technical, ethical, and regulatory hurdles that must be overcome to enable the clinical implementation of these technologies, offering a roadmap for their future integration into patient care.

Chronic Kidney Disease (CKD) is a progressive condition characterized by the gradual loss of renal function over time, affecting millions worldwide and representing a significant public health challenge. CKD is associated with increased morbidity and mortality, primarily due to cardiovascular complications, and its prevalence continues to rise due to factors such as diabetes, hypertension, and aging populations. Despite advances in understanding its etiology, early detection remains a challenge, and current diagnostic methods often identify the disease at advanced stages, limiting therapeutic options and impacting patient outcomes. Early diagnosis of CKD is crucial for implementing interventions that can slow disease progression, prevent complications, and improve quality of life. Consequently, there is a growing emphasis on personalized management strategies tailored to the unique molecular and clinical profiles of patients. Personalized approaches enable targeted therapies, optimize treatment efficacy, and reduce adverse effects, ultimately transforming CKD care from a one-size-fits-all model to precision medicine. Multi-omics approaches have emerged as powerful tools in modern medicine, offering comprehensive insights into the molecular landscape of diseases like CKD. By integrating data from various biological layers, such as genomics, transcriptomics, proteomics, epigenomics, and metabolomics, researchers can achieve a holistic understanding of disease mechanisms, identify novel biomarkers, and uncover therapeutic targets. This systems biology perspective enables the characterization of individual variability, facilitating the development of personalized treatment strategies. In conclusion, multi-omics has the potential to revolutionize early diagnosis, refine patient stratification, and guide the design of targeted pharmacological interventions, paving the way for a new paradigm in disease management.

Skin cutaneous melanoma (SKCM) represents a highly aggressive malignancy with rising incidence, characterized by early metastatic potential and development of treatment resistance in advanced stages. While ion channel-related genes (ICRGs) demonstrate therapeutic relevance across cancers, their role in SKCM remains incompletely defined. A comprehensive assessment of ICRG regulatory patterns was conducted in SKCM samples using single-cell spatial transcriptomic and multi-omics data. These patterns were correlated with tumor microenvironment (TME) cell infiltration characteristics to construct ICRG scores quantifying tumor-specific ICRG modification patterns. The ICRG.Gene.cluster stratifies samples into two distinct subpopulations representing divergent immune phenotypes. An ICRG scoring system is constructed based on ICRG phenotype genes and validated in independent cohorts. Through ICRG-associated gene profiling, CD8⁺ T cells are categorized into five subsets, all exhibiting significant temporal dynamics in pseudotime analysis. Spatial transcriptomics confirms prominent co-localization spots between the C3 CD8⁺ T cell subset and melanoma cells. SCENIC analysis identifies that specific ICRG genes function as target nodes regulated by transcription factors. Core ICRG genes demonstrate elevated expression in both cell lines and clinical specimens, supporting their potential role as disease-associated genetic risk loci. ICRG modification patterns provide critical insights into TME infiltration heterogeneity, enabling refined prognostic assessment and therapeutic targeting strategies.

Sepsis remains a Major global health burden, accounting for an estimated 11 million deaths annually, and is characterized by a profoundly dysregulated host immune response to infection. Despite its significant morbidity and mortality, the immunopathogenesis of sepsis-particularly within underrepresented populations-remains inadequately understood. Here we report distinct immunological signatures associated with clinical outcomes among Vietnamese septic patients. To our knowledge, this represents the first comprehensive investigation of both cellular and cytokine immune parameters in the sepsis population. Our findings demonstrate that survivors of sepsis exhibited a higher proportion of circulating B cells, whereas non-survivors showed increased activation of T and natural killer (NK) cells, marked by elevated expression of activation molecules such as CD69 and GITR. There was a notable reduction in B cell numbers, and further phenotypic analysis revealed signs of B cell exhaustion, indicated by increased CD21low expression, as well as depletion of both memory and naïve B cell subsets. Collectively, these results establish compromised humoral immunity in septic patients. T cells in septic patients displayed a skewing toward effector memory phenotypes, and NK cells demonstrated impaired cytotoxic potential, as evidenced by decreased expression of key the key activating receptors including NKG2D and DNAM-1. Concurrent cytokine profiling revealed significantly elevated concentrations of both pro- and anti-inflammatory mediators in septic patients. A significantly diminished percentage of CD8⁺CD45RA⁺CD197⁻ T cells, alongside markedly elevated interleukin-6 (IL-6) levels, was observed in non-survivors, strongly supporting their role as key prognostic biomarkers for predicting sepsis-related mortality. Interestingly, tumor necrosis factor-alpha (TNF-α) levels did not significantly differ between those who survived and those who did not, a result that diverges from some prior reports and highlights the possibility of population-specific immunological nuances.

Cigarette smoking, a leading cause of preventable morbidity and mortality worldwide, has increasingly been recognized as a significant and independent risk factor for the development and progression of various liver diseases. Historically, the direct impact of smoking on liver health received limited attention compared to its well-established effects on the respiratory and cardiovascular systems. However, a growing body of evidence now unequivocally demonstrates that smoking negatively influences the incidence, severity, and outcomes of a wide spectrum of hepatic conditions, including metabolic dysfunction-associated steatotic liver disease (MASLD), alcohol-related liver disease (ALD), chronic viral hepatitis (HBV and HCV), primary biliary cholangitis (PBC), and hepatocellular carcinoma (HCC). The detrimental effects of tobacco extend to patients undergoing liver transplantation, where smoking is associated with increased post-transplant complications and mortality. The underlying mechanisms are complex, involving direct and indirect toxic effects, immunologic dysregulation, and oncogenic pathways, primarily driven by oxidative stress, systemic inflammation, insulin resistance, and the presence of numerous carcinogens in tobacco smoke. This comprehensive review synthesizes current knowledge, highlighting the multifaceted ways in which smoking impacts liver health, from cellular injury and fibrosis progression to increased cancer risk and compromised transplant outcomes. In addition, we explore the rising prevalence of electronic cigarette use and present the latest evidence regarding their potential impact on liver health. We emphasize the critical importance of smoking cessation as a therapeutic intervention across all stages of liver disease and discuss the challenges and strategies for its implementation. By integrating the updated research data and clinical insights, this review aims to underscore the urgent need for greater awareness among healthcare professionals and patients regarding the profound and pervasive link between smoking and liver disease, advocating for targeted interventions to alleviate this preventable burden.