Prostate cancer is the second most diagnosed cancer in men worldwide and a significant cause of cancer-related death. Proteogenomic analysis offers insights into how genomic mutations influence protein expression and can identify novel biomarkers. This study aimed to investigate the impact of missense mutations on protein abundance in prostate cancer versus healthy tissues using SILAC-based quantitative proteomics.

Mass spectrometry data from prostate tumors and adjacent healthy tissues were analyzed using stable isotope labeling. Peptides were classified based on their abundance into RefSeq and Variant Abundant groups. Missense mutations were mapped via RefSeq and dbPepVar databases. Protein intensity metrics were compared, and Spearman’s correlation was used to evaluate the relationship between mutation presence and protein abundance.

Functional enrichment revealed that RefSeq Abundant proteins are involved in normal metabolic and structural functions, while Variant Abundant proteins are enriched in tumor-related pathways such as immune evasion and apoptosis suppression. A significant negative correlation was found between protein intensity difference and ratio (p < 0.05), indicating that missense mutations contribute to altered protein expression. Mutation hotspot analysis identified recurrent alterations in genes such as PPIF and ACTB. PROVEAN was used to evaluate the functional impact of variants, identifying several as deleterious to protein stability and function.

Missense mutations are associated with altered protein abundance and may promote oncogenic processes in prostate cancer. These findings enhance the understanding of genome-proteome interactions and could support the development of targeted biomarkers and therapies.

To support clinicians in making informed decisions regarding the diagnosis and management of inherited hyperbilirubinemia, including Gilbert syndrome, Crigler-Najjar syndrome, Dubin-Johnson syndrome, and Rotor syndrome, the Inherited and Metabolic Liver Disease Collaboration Group of the Hepatology Branch of the Chinese Medical Association convened a panel of Chinese experts in this field. This multidisciplinary consortium developed the present expert consensus by integrating the latest advances in both clinical practice and basic research.

Chronic diabetes mellitus is marked by hyperglycemia and metabolic dysfunction, increasing the risk of complications such as nephropathy. This study aimed to evaluate key biochemical parameters among participants with diabetic nephropathy (DNp), diabetes control (DC), nephropathy control (NC), and healthy control groups.

A prospective case-control study was conducted with 200 participants categorized into four groups: DNp, NC, DC, and healthy controls. Biochemical parameters, including glucose, glycated hemoglobin, waste metabolites, proteins, enzymes, electrolytes, and lipids, were analyzed using an Advia 1800 chemical system analyzer (Siemens, Germany) with standard kits.

Among the four investigated groups, the DNp group exhibited augmented fasting glucose (178.75 ± 61 mg/dL), glycated hemoglobin (8.13 ± 1.7%), creatinine (5.67 ± 1.8 mg/dL), and blood urea nitrogen (72.02 ± 22.8 mg/dL), indicating poor glycemic control and impaired kidney function. In contrast, the DC group showed elevated random glucose levels (280 ± 3.1 mg/dL). Elevated inflammatory markers (C-reactive protein, 6.35 ± 6.3 mg/L; lactate dehydrogenase, 1,216.43 ± 634 U/L) were observed in the NC group. Compared to the other groups, the DC group demonstrated augmented lipid profiles, including elevated triglycerides (230.67 ± 59 mg/dL), very low-density lipoprotein (48.5 ± 16.5 mg/dL), low-density lipoprotein (107.41 ± 16 mg/dL), and cholesterol (169 ± 19 mg/dL). Statistical analysis was performed using one-way analysis of variance followed by a t-test to investigate differences among groups at P < 0.05.

Altered biochemical variations were noted among groups. The DNp group showed renal dysfunction and poor glycemic control, the DC group had dyslipidemia and hyperglycemia, and the NC group showed elevated inflammatory markers. Early testing is indispensable for the timely diagnosis and management of diabetic complications.

Metabolic dysfunction-associated steatohepatitis (MASH) represents a critical step in the progression from simple fatty liver disease to more severe conditions such as cirrhosis and hepatocellular carcinoma, and it remains difficult to treat. Arctigenin (ATG), a monomer of Fructus Arctii, exhibits anti-inflammatory activity. Therefore, we aimed to examine its potential protective role against MASH and explore the underlying mechanisms.

Male C57BL/6 mice were divided into four groups: control, MASH, low-dose ATG (30 mg/kg/day), and high-dose ATG (120 mg/kg/day). MASH was induced through a choline-deficient, L-amino acid-defined high-fat diet for eight weeks, with concurrent preventive ATG administration. Liver injury, lipid metabolism, inflammation, oxidative stress, and fibrosis were assessed. Network pharmacology was employed to identify the potential protective mechanisms of ATG. Key factors were evaluated in vitro to verify the ATG targets.

ATG administration prevented the progression of MASH in a dose-dependent manner. High-dose ATG significantly reduced hepatic macrophage and neutrophil infiltration, serum enzyme levels, and lipid peroxidation, while enhancing antioxidant enzyme activity. Mechanistic network pharmacology identified modulation of the NLR family pyrin domain containing 3 (NLRP3) inflammasome as the central pathway underlying ATG’s bioactivity. Functional analyses in lipopolysaccharide-stimulated RAW264.7 cells confirmed that ATG inhibited NLRP3 expression, pyroptosis-related protein cleavage (hereinafter referred to as GSDMD-N), and pro-inflammatory chemokine production in a concentration-dependent manner. Notably, ATG disrupted NLRP3/GSDMD-N axis activity in macrophages without causing cellular toxicity.

ATG may inhibit the inflammatory cascade primarily by targeting macrophage NLRP3 inflammasomes, thereby preventing the progression of MASH.

In the post-genomic era, long non-coding RNAs (lncRNAs) have emerged as critical regulators in various cancers and hold potential as minimally invasive diagnostic biomarkers. This study aimed to perform microarray analysis of the peripheral blood mononuclear cell (PBMC) transcriptome to evaluate differential lncRNA expression in women with luminal A breast cancer.

A one-color microarray analysis was conducted using SurePrint G3 Human Unrestricted 8×60K arrays and a SureScan Microarray Scanner (Agilent Technologies, USA). The study cohort comprised 16 participants: eight patients diagnosed with luminal A breast cancer and eight healthy controls. Bioinformatic analysis was performed using the “limma” and “tidyverse” packages in the R statistical environment. Functional enrichment analysis was conducted to identify significantly differentially expressed gene clusters. The false discovery rate-adjusted p-value (padj) was applied to ensure methodological rigor. Associations between lncRNAs and disease progression were explored using the LncRNADisease 2.0 database.

Differential expression was observed for long intergenic non-coding (LINC), LOC, and antisense RNA genes. Notably, LINC RNA 974 (LINC00974) exhibited significant differential expression (log fold change > |1.5|, padj < 0.05) after multiple comparison correction. Analysis using the LncRNADisease 2.0 database revealed associations between LINC and antisense RNAs and other oncological disorders.

This study is the first to demonstrate differential lncRNA expression in PBMCs of patients with luminal A breast cancer. Despite the limited sample size, the study demonstrates statistically significant differences between groups, highlighting the potential of PBMC-derived lncRNAs as minimally invasive biomarkers. These findings enhance our understanding of the utility of PBMC-derived lncRNAs as biomarkers for breast cancer.

Regular exercise is fundamental for people with metabolic dysfunction-associated steatotic liver disease (MASLD), yet exercise maintenance is generally poor. This generative co-design process aimed to embed the voices and opinions of people with lived experience of MASLD and their care stakeholders to (i) frame barriers and enablers to exercise maintenance and (ii) highlight priorities for exercise-focused research agendas in MASLD.

A generative co-design framework was applied. Two virtual co-design sessions were undertaken: Session 1 – Framing the issue, where initial discovery was conducted with people with lived experience of MASLD; and Session 2 – Generative design and sharing ideas with lived experience partners and healthcare stakeholders. Sessions were audio-recorded and transcribed, and key determinants and considerations were discerned by two independent researchers.

Lived experience partners (n = 5, 53 ± 16 years, 40% male) ranked five equally important barriers to exercise maintenance: musculoskeletal and pain issues, lack of access to exercise equipment/facilities, cost, competing priorities, and low energy levels, which influenced core positive and negative determinants. Alongside lived experience partners, healthcare stakeholders (hepatologists [n = 3], exercise professionals [n = 3], 67% male) identified three core needs with eight considerations. Some disconnects in priorities were observed. Lived experience partners emphasized affordability, accessibility, and considerations for comorbidities, while healthcare partners advocated for research on natural history, prevention, behavior change, cost-effectiveness, and health system change.

This co-design methodology highlights unique consumer-informed research questions. Exercise interventions and their associated implementation trials will benefit from being co-designed with both people with MASLD and care stakeholders.

Guaiac fecal occult blood test (gFOBT) is often used to evaluate evidence of food protein-induced allergic proctocolitis (FPIAP) in children in primary care and gastroenterology settings; however, it has not been validated for this diagnosis, and little is known about the positivity rates in early infancy. In this study, we used samples from healthy asymptomatic infants aged two weeks to two months to evaluate the gFOBT positivity rate compared to those diagnosed with FPIAP.

This was a nested case-control study. Frozen stool samples from infants aged two days to five months enrolled in the Gastrointestinal Microbiome and Allergic Proctocolitis study were evaluated using gFOBT (n = 123). The results were interpreted by three blinded staff members, including a trained clinical research coordinator, a pediatric gastroenterologist, and an experienced medical assistant. Additionally, the samples were analyzed using a quantitative fecal immunochemical test (FIT) for hemoglobin to compare with gFOBT results.

Eight percent of samples from the 100 healthy asymptomatic infants were gFOBT positive (11% when including positive and equivocal results). Seventy-four percent of samples from infants diagnosed with FPIAP were gFOBT positive. The interrater reliability of gFOBT interpretation was 81%. Of the healthy samples that yielded a positive gFOBT result, 50% also yielded a positive FIT result. Of the 23 FPIAP samples that yielded a positive gFOBT result, 29% yielded a positive FIT result.

Healthy asymptomatic infants in early infancy were gFOBT positive up to 11% of the time. Caution should be used when interpreting gFOBT results in young infants in a diagnostic setting.

Further decompensation in cirrhosis is associated with increased mortality. However, reliable tools to predict further decompensation after transjugular intrahepatic portosystemic shunt (TIPS) are currently limited. This study aimed to investigate the incidence and risk factors of further decompensation within one year post-TIPS in patients with cirrhosis and to develop a predictive model for identifying high-risk individuals.

This retrospective cohort study enrolled 152 patients with cirrhosis undergoing TIPS for variceal bleeding and/or refractory ascites (January 2018–January 2024). Patients were stratified according to one-year decompensation outcomes. LASSO regression and multivariable logistic analysis were used to identify predictors, and a nomogram was constructed and internally validated using bootstrapping (1,000 replicates).

Among the 152 patients (median age 57.5 years [IQR 50.0–66.0]; 58.6% male; 58.6% viral/alcohol-associated etiology), 65.8% (100/152) achieved clinical stability at one year post-TIPS, while 34.2% (52/152) developed further decompensation. LASSO regression identified right hepatic lobe volume, spleen volume, and portal pressure gradient (PPG) reduction as key predictors, all independently associated with further decompensation risk in multivariable analysis (OR [95% CI]: 0.683 [0.535–0.873], 1.435 [1.240–1.661], and 0.961 [0.927–0.996], respectively). The nomogram demonstrated superior discrimination compared with PPG reduction alone and benchmark prognostic scores (AUC 0.854 [0.792–0.915] vs. 0.619–0.652; ΔAUC +0.201–+0.235, p < 0.001) with 92.3% sensitivity. High-risk patients (score > 86) had a 10.7-fold higher risk of further decompensation than low-risk patients (60.0% vs. 5.6%; p < 0.0001).

This validated model, combining hepatosplenic volumetry and PPG reduction, accurately stratifies further decompensation risk post-TIPS and may guide targeted surveillance and preventive interventions.

Nutrition plays a pivotal role in the prevention and management of gastrointestinal and hepatic diseases, yet dietary guidance remains generic, limiting its effectiveness. Conditions such as inflammatory bowel disease, irritable bowel syndrome, metabolic dysfunction-associated steatotic liver disease, celiac disease, and gastroesophageal reflux disease are significantly influenced by dietary factors. Personalized nutrition has emerged as a promising strategy to tailor interventions, but conventional approaches fail to account for individual metabolic, genetic, and microbiome variability, limiting their clinical impact. The rapid rise of artificial intelligence (AI) has transformed precision nutrition by integrating genomics, microbiome profiles, metabolic markers, and real-time dietary tracking to generate individualized recommendations. AI-driven systems are advancing dietary assessment, condition-specific nutrition optimization, and continuous monitoring through tools such as wearable devices and natural language processing-based diet analysis. These innovations hold transformative potential in gastroenterology and hepatology, offering dynamic, patient-specific strategies that may enhance clinical outcomes. However, challenges remain, including the lack of standardized AI-driven protocols, ethical concerns such as bias and data privacy, limited clinical validation, and the underrepresentation of nutrition in many current AI applications. Opportunities for progress include developing federated learning models, expanding real-world validation studies, and designing regulatory and ethical frameworks for safe implementation. This narrative review synthesizes literature published between 2015 and 2025 across five databases, highlighting key applications, limitations, and future directions of AI-driven personalized nutrition in gastroenterology and hepatology. It provides insights into how AI could reshape patient-centered care through more individualized, effective, and scalable dietary strategies.