Precision Clinical Medicine最新文献

Background: Single-cell RNA-sequencing (scRNA-seq) has emerged as a powerful tool for cancer research, enabling in-depth characterization of tumor heterogeneity at the single-cell level. Recently, several scRNA-seq copy number variation (scCNV) inference methods have been developed, expanding the application of scRNA-seq to study genetic heterogeneity in cancer using transcriptomic data. However, the fidelity of these methods has not been investigated systematically.

Methods: We benchmarked five commonly used scCNV inference methods: HoneyBADGER, CopyKAT, CaSpER, inferCNV, and sciCNV. We evaluated their performance across four different scRNA-seq platforms using data from our previous multicenter study. We evaluated scCNV performance further using scRNA-seq datasets derived from mixed samples consisting of five human lung adenocarcinoma cell lines and also sequenced tissues from a small cell lung cancer patient and used the data to validate our findings with a clinical scRNA-seq dataset.

Results: We found that the sensitivity and specificity of the five scCNV inference methods varied, depending on the selection of reference data, sequencing depth, and read length. CopyKAT and CaSpER outperformed other methods overall, while inferCNV, sciCNV, and CopyKAT performed better than other methods in subclone identification. We found that batch effects significantly affected the performance of subclone identification in mixed datasets in most methods we tested.

Conclusion: Our benchmarking study revealed the strengths and weaknesses of each of these scCNV inference methods and provided guidance for selecting the optimal CNV inference method using scRNA-seq data.

The gut virome, an essential component of the intestinal microbiome, constitutes ∼0.1% of the total microbial biomass but contains a far greater number of particles than bacteria, with phages making up 90%-95% of this virome. This review systematically examines the developmental patterns of the gut virome, focusing on factors influencing its composition, including diet, environment, host genetics, and immunity. Additionally, it explores the gut virome's associations with various diseases, its interactions with gut bacteria and the immune system, and its emerging clinical applications.

Objectives: To investigate the risk factors in patients with drug-resistant tuberculosis (DR-TB) and clinical characteristics related to unfavorable anti-TB treatment outcomes.

Methods: A total of 961 pulmonary tuberculosis (TB) patients were included at West China Hospital of Sichuan University from January 2008 to November 2023. We analyzed the differences of clinical characteristics between DR-TB and drug-sensitive tuberculosis (DS-TB), and then compared these features in DR-TB patients with different outcomes. Multivariable logistic regression models were employed to quantify risk factors associated with DR-TB and adverse treatment outcomes.

Results: Among 961 pulmonary TB patients, a history of anti-TB treatment [odds ratio (OR), 3.289; 95% confidence interval (CI), 2.359-4.604] and CT-scan cavities (OR, 1.512; 95% CI, 1.052-2.168) increased DR-TB risk. A total of 214 DR-TB patients were followed for a median of 24.5 months. Among them, 116/214 (54.2%) patients achieved favorable outcomes. Prior anti-TB treatment (OR, 1.927; 95% CI, 1.033-3.640), multidrug-resistant tuberculosis (MDR-TB) (OR, 2.558; 95% CI, 1.272-5.252), positive sputum bacteriology (OR, 2.116; 95% CI, 1.100-4.134), and pleural effusion (OR, 2.097; 95% CI, 1.093-4.082) were associated with unfavorable outcomes, while isoniazid-resistant TB patients showed better outcomes (OR, 0.401; 95% CI, 0.181-0.853). The clinical model for unfavorable outcome prediction of DR-TB achieved an area under the curve (AUC) of 0.754 (95% CI, 0.690-0.818).

Conclusions: Treatment history of anti-TB not only increases the risk of the emergence of DR-TB, but also potentially leads to treatment failure during re-treatment in DR-TB patients. Drug resistance subtypes, radiological characteristics, and the results of sputum smear or culture may affect the treatment outcome of DR-TB.

Pharmacogenomics, therapeutic drug monitoring, and the assessments of hepatic and renal function have made significant contributions to the advancement of individualized medicine. However, their lack of direct correlation with protein abundance/non-genetic factors, target drug concentration, and drug metabolism/excretion significantly limits their application in precision drug therapy. The primary task of precision medicine is to accurately determine drug dosage, which depends on a precise assessment of the ability to handle drugs in vivo, and drug metabolizing enzymes and transporters are critical determinants of drug disposition in the body. Therefore, accurately evaluating the functions of these enzymes and transporters is key to assessing the capacity to handle drugs and predicting drug concentrations in target organs. Recent advancements in the evaluation of enzyme and transporter functions using exogenous probes and endogenous biomarkers show promise in advancing personalized medicine. This article aims to provide a comprehensive overview of the latest research on markers used for the functional evaluation of drug-metabolizing enzymes and transporters. It also explores the application of marker omics in systematically assessing their functions, thereby laying a foundation for advancing precision pharmacotherapy.

This study introduces a novel Transformer-based time-series framework designed to revolutionize risk stratification in Intensive Care Units (ICUs) by predicting patient outcomes with high temporal precision. Leveraging sequential data from the eICU database, our two-stage architecture dynamically captures evolving health trajectories throughout a patient's ICU stay, enabling real-time identification of high-risk individuals and actionable insights for personalized interventions. The model demonstrated exceptional predictive power, achieving a progressive AUC increase from 0.87 (±0.021) on admission day to 0.92 (±0.009) by day 5, reflecting its capacity to assimilate longitudinal physiological patterns. Rigorous external validation across geographically diverse cohorts-including an 81.8% accuracy on Chinese sepsis data (AUC=0.73) and 76.56% accuracy on MIMIC-IV-3.1 (AUC=0.84)-confirmed robust generalizability. Crucially, SHAP-derived temporal heatmaps unveiled mortality-associated feature dynamics over time, bridging the gap between model predictions and clinically interpretable biomarkers. These findings establish a new paradigm for ICU prognostics, where data-driven temporal modeling synergizes with clinician expertise to optimize triage, reduce diagnostic latency, and ultimately improve survival outcomes in critical care.

Background: Epstein-Barr virus (EBV) infection is associated with clinical symptoms, treatment response, need for surgical intervention, and an enhanced likelihood of lymphoma among patients with ulcerative colitis (UC). However, existing studies have primarily concentrated on the epidemiological and clinical associations between EBV and UC, leaving the mechanisms by which EBV exacerbates colitis poorly understood.

Methods: Clinical specimens of UC patients with EBV infection and a mouse model of dextran sulfate sodium-induced colitis with concurrent murine γ-herpesvirus 68 (MHV-68) infection were utilized to investigate the relationship between EBV infection and macrophage pyroptosis. In vivo, adoptive transfer of MHV-68-induced macrophages and macrophage depletion were performed to elucidate the underlying mechanisms. In vitro, myeloid leukemia mononuclear cells of human (THP-1) and macrophages derived from mouse bone marrow (BMDMs) were stimulated with EBV and MHV-68, respectively, to assess macrophage pyroptosis and glycolysis.

Results: EBV-induced activation of macrophage pyroptosis was positively correlated with clinical disease activity in UC patients. Furthermore, MHV-68 infection activated pyroptosis by upregulating gasdermin D, NLRP3, interleukin-1β, and interleukin-18 in colonic tissues and peritoneal macrophages of mice with colitis. In vitro, EBV and MHV-68 also mediated activation of pyroptosis in human THP-1 cells and mouse BMDMs, respectively. Additionally, the adoptive transfer of MHV-68-induced BMDMs aggravated murine colitis, whereas macrophage depletion attenuated MHV-68-induced intestinal injury. Mechanistically, MHV-68 promoted macrophage pyroptosis by upregulating glycolysis, while the glycolysis inhibitor, 2-deoxy-D-glucose, blocked this process in vitro.

Conclusion: EBV infection exacerbates UC by driving macrophage pyroptosis through upregulation of glycolysis, indicating a potential therapeutic approach to mitigate EBV-induced intestinal inflammation.

Background: Intratumor heterogeneity is common in cancers, with different cell subtypes supporting each other to become more malignant. Nasopharyngeal carcinoma (NPC), a highly metastatic cancer, shows significant heterogeneity among its cells. This study investigates how NPC cell subtypes with varying metastatic potentials influence each other through exosome-transmitted molecules.

Methods: Exosomes were purified and characterized. MicroRNA expression was analyzed via sequencing and qRT-PCR. The effects of miR-30a-5p on migration, invasion, and metastasis were evaluated in vitro and in vivo. Its impact on desmoglein glycoprotein (DSG2) was assessed using dual-luciferase assays and Western blotting. Immunohistochemistry (IHC) and statistical models linked miR-30a-5p/DSG2 levels to patient prognosis.

Results: Different NPC cell subtypes transmit metastatic potential via exosomes. High-metastatic cells enhance the migration, invasion, and metastasis of low-metastatic cells through exosome-transmitted miR-30a-5p. Plasma levels of exosomal miR-30a-5p are reliable indicators of NPC prognosis. miR-30a-5p may promote metastasis by targeting DSG2 and modulating Wnt signaling. Plasma exosomal miR-30a-5p inversely correlates with DSG2 levels, predicting patient outcomes.

Conclusion: High-metastatic NPC cells can increase the metastatic potential of low-metastatic cells through exosome-transmitted miR-30a-5p, which is a valuable prognostic marker assessable via liquid biopsy.

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract, and its pathogenesis is believed to be associated with an imbalance between commensal organisms and the intestinal immune system. This imbalance is significantly influenced by the intestinal microbiota and metabolites and plays a critical role in maintaining intestinal mucosal homeostasis. However, disturbances in the intestinal microbiota cause dysregulated immune responses and consequently induce intestinal inflammation. Recent studies have illustrated the roles of the intestinal microbiota in the pathogenesis of IBD and underscored the potential of precision diagnosis and therapy. This work summarises recent progress in this field and particularly focuses on the application of the intestinal microbiota and metabolites in the precision diagnosis, prognosis assessment, treatment effectiveness evaluation, and therapeutic management of IBD.