Objective: Synsepalum dulcificum Daniell. (SD) is a natural plant fruit and is famous for containing miraculin. It has been reported that SD can be used as an adjuvant treatment to correct patients’ loss of taste during the antitumor process, but the effect of SD itself as an antitumor is not clear. In this study, we investigated the mechanism of action of SD on lung adenocarcinoma using network pharmacology. Materials and Methods. The components of SD were identified by liquid chromatography-mass spectrometry, and then the compounds that affect tumor immunity of SD were screened and the related targets were predicted by TCMIO database. At the same time, the results were associated with lung adenocarcinoma targets included in the MalaCards and CTD databases, so as to construct a compound-target action network diagram and explore the mechanism of SD in the treatment of lung adenocarcinoma. In in vitro experiments, cell viability was determined and western blotting was used to detect the related expression of action targets to determine the therapeutic effect of SD. Results. In this experiment, 335 chemical components were identified in SD, and 107 components were related to tumor immunity. After screening by ADME, it was found that 11 compounds might be inhaled into the human body and affect the growth of lung adenocarcinoma. In vitro experiments showed that SD could inhibit the growth of lung adenocarcinoma A549 cells. SD could reduce the expression of PCNA ( ) and significantly increase the expression of Caspase-3 ( ). The results of further experiments showed that SD could significantly reduce the phosphorylation of EGFR ( ), and SD could also effectively inhibit the expression of JAK and STAT3 phosphorylation ( ) and inhibit the expression of PI3K and AKT phosphorylation ( ). Conclusion. SD can inhibit the growth of lung adenocarcinoma A549 cells and the potential mechanism was found to be the inhibition of EGFR/JAK/STAT3 and EGFR/PI3K/AKT signaling pathway, and the substance basis for SD to exert antitumor effect may be catechin, taxifolin, betaine, epigallocatechin gallate, erucamide, guanosine, kaempferol, lanosterol, morin, oleanolic acid, and quercetin.

Hepatic encephalopathy (HE), a severe neurological complication of liver dysfunction, has long been regarded as a clinical issue confined to liver disease. However, recent clinical observations and basic research have revealed complex pathophysiological connections between HE and Parkinson’s disease (PD), 2 seemingly independent conditions. Patients with HE often exhibit irreversible extrapyramidal symptoms that closely resemble the motor disorders of PD; meanwhile, epidemiological studies suggest that individuals with liver disease—particularly non-alcoholic fatty liver disease (NAFLD)—may face an increased risk of developing PD. From the perspective of the gut–liver–brain axis, this study systematically explores the molecular mechanisms linking HE and PD, proposing a core hypothesis: HE creates a unique “neurotoxic soil” through ammonia toxicity, systemic neuroinflammation, and gut–liver–brain axis dysfunction. This soil may trigger PD in susceptible individuals, accelerate subclinical PD progression, or mimic PD-like pathology. The study analyzes in depth the direct regulatory role of ammonia in α-synuclein (α-syn) aggregation, the impact of liver disease-driven neuroinflammation on microglial activation and α-syn propagation, and the hypothesis of liver-derived α-syn transmission via the gut–liver–brain axis. It further discusses synergistic mechanisms such as manganese deposition, neurotransmitter imbalance, and gut microbiota metabolites. Based on these mechanisms, the study prospects translational medical applications, including the development of diagnostic biomarkers and novel therapeutic strategies such as “ammonia clearance plus” and gut–liver–brain axis targeting. This work provides new insights into how environmental metabolic factors contribute to neurodegenerative diseases and offers a theoretical basis for the combined prevention and treatment of HE and PD.

To identify disease signature genes associated with immune infiltration in nonalcoholic steatohepatitis (NASH), we downloaded 2 publicly available gene expression profiles, GSE164760 and GSE37031, from the gene expression omnibus database. These profiles represent human NASH and control samples and were used for differential genes (DEGs) expression screening. Two machine learning methods, the Least Absolute Shrinkage and Selection Operator regression model and Support Vector Machine Recursive Feature Elimination, were used to identify candidate disease signature genes. The CIBERSORT deconvolution algorithm was employed to analyze the infiltration of 22 immune cell types in NASH. Additionally, we constructed a NASH cell model using HepG2 cells treated with oleic acid and free fatty acids. The construction of the cell model was verified using oil red O staining, and Western blotting was used to detect the protein expression of the disease signature genes in both control and model groups. As a result, a total of 262 DEGs were identified. These DEGs were primarily associated with metal ion transmembrane transporter activity, sodium ion transmembrane transporter protein activity, calcium ion, and neuroactive ligand-receptor interactions. FOS, IGFBP2, dual-specificity phosphatase 1 (DUSP1), and IKZF3 were identified as disease signature genes of NASH by the least absolute shrinkage and selection operator and Support Vector Machine Recursive Feature Elimination algorithms for DEGs analysis. The receiver operating characteristic curves showed that FOS, IGFBP2, DUSP1, and IKZF3 had good diagnostic value (area under receiver operating characteristic curve > 0.8). These findings were validated in the GSE89632 dataset and through cellular assays. Immunocyte infiltration analysis revealed that NASH was associated with CD8 T cells, CD4 T cells, follicular helper T cells, resting NK cells, eosinophils, regulatory T cells, and γδ T cells. The FOS, IGFBP2, DUSP1, and IKZF3 genes were specifically associated with follicular helper T cells. Lipid droplet aggregation significantly increased in HepG2 cells treated with oleic acid and free fatty acids, indicating successful construction of the cell model. In this model, the expression of FOS, IGFBP2, and DUSP1 was significantly decreased, while that of IKZF3 was significantly elevated (P < .01, P < .001) compared with the control group. Therefore, FOS, IGFBP2, DUSP1, and IKZF3 can be considered as disease signature genes associated with immune infiltration in NASH.

Mongolians have high overweight prevalence linked to their nomadic lifestyle and diet, but gut microbiota studies in this population are scarce. This study used fecal metagenomic and serum metabolomic analyses of 96 Mongolian participants (normal-weight n = 55, overweight n = 41) to characterize gut microbiome alterations and identify weight-related biomarkers. The analyses revealed that Parabacteroides distasonis, Barnesiella intestinihominis, and Alistipes onderdonkii were significantly reduced in overweight individuals (p < 0.05). Concurrently, the metabolites such as beta-cryptoxanthin, p-cresol, and ribothymidine were significantly down-regulated in the overweight group (p < 0.05). Random forest models from the three datasets showed a strong diagnostic ability for microbial families (AUC > 0.70). A subsequent integrated multi-kingdom classifier that combined microbiota and metabolite data achieved the highest performance (AUC = 0.818). Key features with high predictive contributions were identified, including Lactobacillus crispatus, Alistipes onderdonkii, and Parabacteroides distasonis, and metabolites, such as beta-cryptoxanthin, p-cresol, and picolinic acid. These results show the random forest model has high predictive value for distinguishing normal weight and overweight individuals. In summary, this study identified specific gut microbiota and serum metabolomic profiles linked to overweight in Mongolians. Multi-omics integration established a diagnostic biomarker model, laying a theoretical basis for microbiome-targeted weight management interventions.