Xiaoning Chen

Chlorpyrifos (CPF) is a widely used insecticide known for its extended persistence in soil and water. It can induce non-alcoholic fatty liver disease (NAFLD) in mice. However, the specific molecular mechanisms responsible for CPF-induced NAFLD remain partially understood. Utilizing network toxicology analysis, we have identified TP53, HSP90AA1, AKT1, and JUN as pivotal driver genes orchestrating NAFLD progression triggered by CPF. A predictive nomogram constructed on these core genes demonstrates promising translational prospects for NAFLD. Gene-set enrichment analysis (GSEA) underscores the significance of the tricarboxylic acid (TCA) cycle and histidine metabolism as critical pathways associated with CPF-induced NAFLD. Evaluation of immune infiltration patterns reveals notable changes in the immunological milieu and accelerated disease advancement in CPF-induced NAFLD. Molecular docking and dynamic simulations provide support for the formation of stable complexes between CPF and proteins encoded by the identified core genes. Ultimately, biomedical experiments confirmed that CPF exacerbates the pathological progression of NAFLD by stabilizing HSP90AA1 protein and promoting the phosphorylation of TP53 and JUN. This study elucidates the molecular mechanisms underpinning CPF-induced NAFLD, providing a theoretical foundation for comprehending its pathogenesis.

Aim: Despite the significant therapeutic outcomes achieved in systemic treatments for liver hepatocellular carcinoma (LIHC), it is an objective reality that only a low proportion of patients exhibit an improved objective response rate (ORR) to current immunotherapies. Antibody-dependent cellular phagocytosis (ADCP) immunotherapy is considered the new engine for precision immunotherapy. Based on this, we aim to develop an ADCP-based LIHC risk stratification system and screen for relevant targets.Method: Utilizing a combination of single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data, we screened for ADCP modulating factors in LIHC and identified differentially expressed genes along with their involved functional pathways. A risk scoring model was established by identifying ADCP-related genes with prognostic value through LASSO Cox regression analysis. The risk scoring model was then subjected to evaluations of immune infiltration and immunotherapy relevance, with pan-cancer analysis and in vitro experimental studies conducted on key targets.Results: Building on the research by Kamber RA et al., we identified GYPA, CLDN18, and IRX5 as potential key target genes regulating ADCP in LIHC. These genes demonstrated significant correlations with immune infiltration cells, such as M1-type macrophages, and the effectiveness of immunotherapy in LIHC, as well as a close association with clinical pathological staging and patient prognosis. Pan-cancer analysis revealed that CLDN18 was prognostically and immunologically relevant across multiple types of cancer. Validation through tissue and cell samples confirmed that GYPA and CLDN18 were upregulated in liver cancer tissues and cells. Furthermore, in vitro knockdown of CLDN18 inhibited the malignancy capabilities of liver cancer cells.Conclusion: We have identified an ADCP signature in LIHC comprising three genes. Analysis based on a risk scoring model derived from these three genes, coupled with subsequent experimental validation, confirmed the pivotal role of M1-type macrophages in ADCP within LIHC, establishing CLDN18 as a critical ADCP regulatory target in LIHC.

Objective: Nonalcoholic fatty liver disease (NAFLD) is a serious threat to human health worldwide. In this study, the aim is to analyze diagnosis biomarkers in NAFLD and its relationship with the immune microenvironment based on bioinformatics analysis.Methods: We downloaded microarray datasets (GSE48452 and GSE63067) from the Gene Expression Omnibus (GEO) database for screening differentially expressed genes (DEGs). The hub genes were screened by a series of machine learning analyses, such as support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and weighted gene co-expression network analysis (WGCNA). It is worth mentioning that we used the gene enrichment analysis to explore the driver pathways of NAFLD occurrence. Subsequently, the aforementioned genes were validated by external datasets (GSE66676). Moreover, the CIBERSORT algorithm was used to estimate the proportion of different types of immune cells. Finally, the Spearman analysis was used to verify the relationship between hub genes and immune cells.Results: Hub genes (CAMK1D, CENPV, and TRHDE) were identified. In addition, we found that the pathogenesis of NAFLD is mainly related to nutrient metabolism and the immune system. In correlation analysis, CENPV expression had a strong negative correlation with resting memory CD4 T cells, and TRHDE expression had a strong positive correlation with naive B cells.Conclusion: CAMK1D, CENPV, and TRHDE play regulatory roles in NAFLD. In particular, CENPV and TRHDE may regulate the immune microenvironment by mediating resting memory CD4 T cells and naive B cells, respectively, and thus influence disease progression.