To study the effect of Gupi Xiaoji Prescription (GXP) on hepatitis B virus(HBV)-related liver cancer through network pharmacology coupled with in vitro experiments and explore their related mechanisms. Gupi Xiaoji Prescription's chemical constituents and the action targets of its six medicinal components were identified using several databases. These included the Traditional Chinese Medicine Systems Pharmacology Database （TCMSP), the Bioinformatics Analysis Tool for Molecular mechANism of TCM (BATMAN-TCM), and the Traditional Chinese Medicine Integrated Database (TCMID), while GeneCards and OMIM were used to compile relevant liver cancer disease targets. Pathway enrichment of gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), analysis of potential targets, and analysis of the enriched pathways in literature were executed in R. The Hepatocellular carcinoma (HCC)-derived HepG2.2.15 cell line stably expresses and replicates HBV. In vitro experiments with HepG2.2.15 were used to verify GXP's effects on HBV-related liver cancer, while the human liver cancer cell line HepG2 was used as the control. 171 active ingredients and 259 potential drug targets were screened from GXP, involving 181 pathways in vitro. These assays identified Polyphyllin I as an effective GXP component. Notably, GXP inhibited cell proliferation and metastasis in a concentration-dependent manner (P < 0.01). In comparison with the vehicle group, the fluorescence intensity of each drug group was significantly weakened (P < 0.01), while the drug group Mitofusins 1(MFN1) and protein expression level of Mitofusins 2 (MFN2) increased significantly. The protein expression level of Mitochondrial fission protein 1 (FIS1) and Optic Atrophy 1 (OPA1) also showed significant decreases (P < 0.01). Molecular docking revealed Fructus saponins I's high affinity with FIS1, MFN1, MFN2, and OPA1. The network pharmacology results indicate that Gupi Xiaoji Prescription may treat liver cancer by regulating mitochondrial division and fusion of key genes to disrupt liver cancer cells' energy metabolism. In vitro experiments also verified that GXP could inhibit the proliferation and migration of HepG2.2.15 cells by up-regulating MFN1 and MFN2, down-regulating the expression of FIS1 and OPA1 in addition to damaging mitochondria. Consistent with network pharmacology and molecular docking results, Polyphyllin I may be the most active compound of the formula's components. It also shows that Traditional Chinese medicine (TCM) plays a significant, targeted role in the treatment of HBV-related liver cancer.

Background: Smoking is the primary risk factor for tracheal, bronchus, and lung (TBL) cancer. Objective: This study aims to explore the epidemiological trends of smoking-attributable TBL cancer from 1990 to 2021. Methods: Mortality and disability-adjusted life-years (DALYs) data for smoking-related TBL cancer from 1990 to 2021 were sourced from the Global Burden of Disease Study (GBD) 2021. Estimated annual percentage changes (EAPCs) were calculated to evaluate trends in age-standardized mortality rates (ASMRs) and age-standardized DALY rates (ASDRs). Additionally, the relationship between disease burden, EAPCs, and the sociodemographic index (SDI) was assessed. Findings: Compared with 1990, both the mortality and DALYs due to smoking-related TBL cancer substantially increased by 2021. However, during this period, ASMR [EAPC: −0.97; 95% confidence interval (CI): −1.05 to −0.89] and ASDR (EAPC: −1.29; 95% CI: −1.37 to −1.22) demonstrated a downward trend. ASMR and ASDR in females were consistently lower than in males. In 2021, East Asia had the highest ASMR, while Central Europe recorded the highest ASDR, with Greenland exhibiting the highest ASMR and ASDR at the national level. Nationally, ASMR for smoking-related TBL cancer in 2021 showed a positive correlation with SDI, while the EAPC of both ASMR and ASDR from 1990 to 2021 displayed a negative correlation with SDI. Furthermore, in 2021, the greatest number of deaths from smoking-related TBL cancer occurred in individuals aged 70–74, while DALYs were highest in the 65–69 age group. Conclusions: The burden of smoking-related TBL cancer varies across age, sex, geography, and SDI regions. Tailored public health interventions aligned with these epidemiological characteristics are essential for alleviating the disease burden.

Background: The individualized prediction and discrimination of precancerous lesions of gastric cancer (PLGC) is critical for the early prevention of gastric cancer (GC). However, accurate non-invasive methods for distinguishing between PLGC and GC are currently lacking. This study therefore aimed to develop a risk prediction model by machine learning and deep learning techniques to aid the early diagnosis of GC. Methods: In this study, a total of 2229 subjects were recruited from nine tertiary hospitals between October 2022 and November 2023. We designed a comprehensive questionnaire, identified statistically significant factors, and created a web-based column chart. Then, a risk prediction model was subsequently developed by machine learning techniques. In addition, a tongue image-based risk prediction model was established by deep learning algorithms. Results: Based on logistic regression analysis, a dynamic web-based nomogram was developed and it is freely accessible at: https://yz6677.shinyapps.io/GC67/. Then, the prediction model was established using ten different machine learning algorithms and the Random Forest (RF) model achieved the highest accuracy at 85.65%. According with the predictive results, the top 10 key risk factors were age, traditional Chinese medicine (TCM) constitution type, tongue coating color, tongue color, irregular meals, pickled food, greasy fur, over-hot eating habit, anxiety and sleep onset latency. These factors are all significant risk indicators for the progression of PLGC patients to GC patients. Subsequently, the Swin Transformer architecture was used to develop a tongue image-based model for predicting the risk for progression of PLGC. The verification set showed an accuracy of 73.33% and an area under curve (AUC) greater than 0.8 across all models. Conclusions: Our study developed machine learning and deep learning-based models for predicting the risk for progression of PLGC to GC, which will offer the assistance to determine the high-risk patients from PLGC and improve the early diagnosis of GC. Graphical

BACKGROUND Ferroptosis is an iron-dependent programmed non-apoptotic cell death characterized by the accumulation of free iron ions and lipid peroxidation. It is associated with the inactivation of glutathione peroxidase (GPX) and the accumulation of lipid peroxides within cells. Ferroptosis is closely related to the occurrence and development of hepatocellular carcinoma (HCC). Chlorogenic acid (CGA), an important bioactive component found in 61 traditional Chinese medicines such as Eucommia ulmoides , has been extensively studied for its effects on various malignant tumors. However, the specific role and potential mechanism of CGA in HCC remain unclear. AIM To elucidate the anti-tumor characteristics and potential mechanisms of CGA in inducing ferroptosis in HCC cells. METHODS The effects of CGA on the proliferation, migration, and invasion of HCC cells were evaluated through in vitro experiments. Bioinformatics analysis combined with network pharmacology was used to study the potential targets and molecular mechanisms of CGA intervention in HCC ferroptosis. In vitro experiments were conducted to verify and explore the anti-HCC effects and mechanisms of CGA through the ferroptosis pathway. RESULTS In vitro experiments showed that CGA dose-dependently inhibited the proliferation, invasion, and migration of HCC cells. Bioinformatics analysis combined with network pharmacology revealed that the pathway of CGA intervention in HCC cell ferroptosis was mainly enriched in the prostaglandin endoperoxide synthase 2 (PTGS2)/aldo-keto reductase family 1 member C3 (AKR1C3)/GPX4 signaling pathway, which was associated with arachidonic acid. In vitro experiments further confirmed that CGA-induced ferroptosis in HCC cells was related to mitochondrial damage through the reprogramming of arachidonic acid metabolism by regulating the PTGS2/AKR1C3/ GPX4 signaling pathway. CONCLUSION This study demonstrates that CGA inhibits HCC cell proliferation, migration, and invasion by inducing ferroptosis through the PTGS2/AKR1C3/GPX4 axis, suggesting its potential as a novel ferroptosis inducer or anti-HCC drug.