High-throughput transcriptomic technologies have made differential gene expression analysis a cornerstone of cancer research by generating extensive lists of differentially expressed genes across tumor types and conditions. However, such lists provide limited biological insight without functional and mechanistic interpretation. This review examines the transition from descriptive gene expression profiles to a mechanistic understanding of cancer biology. We discuss integrative approaches that place expression changes within signaling pathways, transcriptional regulatory networks, and protein–protein interaction networks, thereby helping to identify functional modules and candidate upstream regulators. We emphasize the context-dependent nature of gene expression, which is shaped by genetic alterations, epigenetic landscapes, microenvironmental signals, and cellular heterogeneity. We also examine methodological advances, including gene set enrichment analysis, network-based modeling, and integration of genomic, epigenomic, proteomic, metabolomic, and single-cell transcriptomic data. Case studies across cancer types illustrate how mechanistic analyses can reveal context-specific transcriptional programs associated with oncogenic signaling, tumor suppression, metabolic reprogramming, epithelial–mesenchymal transition, and tumor–immune interactions. We highlight potential translational applications, including candidate biomarker discovery, prioritization of druggable targets, rational design of combination therapies, and investigation of therapeutic resistance. Finally, we discuss current challenges and emerging technologies, such as spatial transcriptomics and clustered regularly interspaced short palindromic repeats (CRISPR)-based perturbation screens, that are advancing the field toward dynamic, systems-level models of tumor biology. Integrating computational analyses with experimental validation can help translate transcriptomic data into clinically relevant hypotheses for precision oncology and personalized cancer therapy.

Hepatitis B virus (HBV) genotype C is common in East Asia and is associated with poor outcomes, particularly liver cirrhosis and hepatocellular carcinoma (HCC). Clinically, genotype C infection has been associated with persistent viral replication, delayed HBeAg seroconversion, more active hepatic inflammation, and an increased risk of HCC. Current evidence suggests that these features are driven by several key molecular events, including the A1762T/G1764A double mutation in the basal core promoter, the G1896A mutation in the precore region, abnormal hepatitis B virus X protein function, and viral integration. Together, these changes may reshape viral transcription, antigen expression, host immune interactions, and oncogenic signaling, thereby contributing to disease progression and hepatocarcinogenesis. Other factors, such as epigenetic changes, dysregulated DNA damage responses, impaired tumor protein p53 function, and disrupted autophagy, may also be involved, although their exact roles remain unclear. Notably, even after effective viral suppression with potent nucleos(t)ide analogs, patients with genotype C may still have a relatively high residual risk of HCC. This review summarizes the molecular virological features, pathogenic mechanisms, immune dysregulation, and clinical significance of HBV genotype C, and discusses the potential value of genotype information in risk stratification, long-term surveillance, and clinical assessment of chronic hepatitis B.

In contemporary practice, elevated 24-hour urinary copper excretion (24-h UCE) often triggers referral for suspected Wilson disease (WD). In this hypercupriuric referral setting, interpretation of 24-h UCE may be distorted by spectrum effects. In this study, we aimed to compare conventional copper biomarkers in hypercupriuric referrals and evaluate whether a Leipzig-aligned ceruloplasmin (Cp) framework could provide a clinically useful triage approach.

We retrospectively studied consecutive, untreated patients evaluated for suspected WD with hypercupriuria between February 2017 and February 2025. The final diagnosis was established using a prespecified Leipzig-based algorithm, with ATP7B testing when indicated. Diagnostic performance of Cp and 24-h UCE was compared. A prespecified Cp three-zone framework was evaluated using <0.10 g/L, 0.10–0.20 g/L, and >0.20 g/L as high-probability, indeterminate, and low-probability zones, respectively.

Among 541 untreated hypercupriuric patients, 65 had WD and 476 had adjudicated non-WD liver disease. Cp outperformed 24-h UCE for diagnosing WD (AUROC, 0.988 vs. 0.762). The optimal Cp cutoff was 0.15 g/L, with 90.8% sensitivity and 97.7% specificity. Cp < 0.10 g/L defined a high-probability zone with 98.0% WD prevalence, whereas Cp > 0.20 g/L defined a low-probability zone with 0.5% WD prevalence. Among non-WD controls, higher urinary copper was independently associated with higher bilirubin, prolonged international normalized ratio, and lower albumin.

In hypercupriuric referrals for suspected WD, Cp retained strong diagnostic performance and outperformed 24-h UCE. A Leipzig-aligned Cp three-zone framework may support probability-based triage in contemporary referral practice.

Primary biliary cholangitis (PBC) significantly impairs health-related quality of life (HRQL), yet the impact of disease stage and fatigue on HRQL and psychological status remains insufficiently quantified. This study aimed to investigate differences in HRQL across disease stages and the impact of fatigue in patients with PBC.

This cross-sectional study recruited 219 patients with PBC from two Chinese tertiary hospitals (2011–2024). After excluding one preclinical case, 218 patients were analyzed. Quality of life was assessed using the validated Chinese versions of the SF-36 and Chronic Liver Disease Questionnaire (CLDQ); psychological status was assessed using the Self-Rating Anxiety Scale and Self-Rating Depression Scale. Between-group differences were quantified by mean differences (MDs) and odds ratios (ORs) with 95% confidence intervals (CIs). Baseline characteristics were balanced across stages (all P > 0.05).

Of the 218 patients (90.4% female; mean age, 57.2 ± 10.3 years), 41 were in the clinical stage, 75 in the fibrosis stage, and 102 in the cirrhosis stage. SF-36 scores were lowest in the cirrhosis stage (e.g., Physical Functioning MD, 17.26; 95% CI, 6.93–27.59 vs. clinical stage), with similar declines in CLDQ domains. Anxiety was highest in the clinical stage (58.5%; OR vs. cirrhosis, 4.13; 95% CI, 1.92–8.92), whereas depression was highest in the cirrhosis stage (55.9%; OR vs. clinical stage, 4.50; 95% CI, 1.95–10.38). Fatigue prevalence was 66.1% and increased with disease stage. Patients with fatigue had lower SF-36 scores in Physical Functioning, Bodily Pain, Vitality, Mental Health, and Physical Component Summary (e.g., Physical Component Summary MD, 38.22; 95% CI, 10.41–66.02).

HRQL declines progressively with PBC stage. Fatigue is strongly associated with impaired HRQL and is closely interrelated with anxiety and depression. Stage-specific psychological patterns suggest the need for tailored supportive interventions.

Bioactive peptides encrypted within food proteins and released by enzymatic hydrolysis or gastrointestinal digestion represent potential functional ingredients. Amaranth is an underutilized pseudocereal with a balanced amino acid profile and a protein composition that may yield peptides with diverse biological activities. However, translation of amaranth-derived peptides remains limited by low or uncertain bioavailability, variable yields, extraction and purification challenges, incomplete sequence identification, insufficient genotype screening, limited understanding of structure-activity relationships, and scarce in vivo and clinical validation. This review summarizes current evidence on the production, characterization, and pharmacological potential of amaranth-derived bioactive peptides. Enzymatic hydrolysis, fermentation, gastrointestinal digestion, and protein engineering have generated peptide fractions or sequences with antioxidant, antimicrobial, angiotensin-converting enzyme-inhibitory, dipeptidyl peptidase IV-inhibitory, hypocholesterolemic, anti-inflammatory, antithrombotic, and anticancer activities, primarily in in silico, biochemical, cell-based, and animal models. Analytical workflows involving chromatographic separation, mass spectrometry, and bioinformatic prediction have improved peptide discovery, but results remain difficult to compare because processing conditions and activity assays are not standardized. Available evidence suggests that amaranth proteins are promising sources of multifunctional peptides; nevertheless, these findings do not yet establish clinical efficacy. Future work should optimize extraction and identification methods, clarify sequence-structure-activity relationships, evaluate stability and intestinal absorption, compare genotypes and non-seed tissues, and conduct well-designed in vivo studies, safety assessments, and clinical trials. Scalable processing and formulation strategies will also be required before amaranth-derived peptides can be developed as reliable functional food, nutraceutical, or pharmaceutical ingredients.

Bacterial infection is a key cause of mortality in patients with acute-on-chronic liver failure (ACLF). In this study, we aimed to identify metabolite biomarkers and develop a novel machine learning model for early identification of bacterial infection in ACLF.

Based on a prospective multicenter cohort from 14 centers, 1,314 patients with acute-on-chronic liver disease were enrolled, including those with ACLF and non-ACLF. Plasma samples at admission were collected for metabolomics profiling. Patients were randomly divided into discovery (n = 921) and validation (n = 393) sets. Machine learning was used to develop diagnostic models. The win ratio method was employed to assess the risk stratification capability of the models.

Bacterial infection occurred in 198 of the 451 ACLF patients and 132 of the 863 non-ACLF patients. Infection altered the plasma metabolome, especially in lipid, amino acid, and xenobiotic metabolic pathways. Models for bacterial infection in ACLF (five metabolites) and non-ACLF (six metabolites) demonstrated superior discrimination in the discovery (AUCs: 0.881 and 0.935, respectively) and validation sets (AUCs: 0.835 and 0.889, respectively) compared with C-reactive protein, white blood cell count, procalcitonin, and the best composite clinical model. Metabolic risk stratification based on the models effectively predicted 90-day outcomes (all-cause death, organ failure, sepsis, new-onset acute decompensation, and systemic inflammatory response syndrome).

Our models based on novel metabolic biomarkers enable identification of patients at high risk of bacterial infection and support risk stratification of 90-day outcomes.