Yue Jin

Snail is a core inducer of epithelial-to-mesenchymal transition. Here, we show that UBR5 promotes ubiquitin-mediated degradation of Snail and regulates the progression of colorectal cancer cells through its E3 ubiquitin ligase function. UBR5 specifically binds to Snail in vitro, but not Slug, and its degradation depends on snail phosphorylation. Depletion of endogenous UBR5 causes Snail protein accumulation, epithelial-to-mesenchymal transition, and tumor invasion in colorectal cancer cells. Conversely, the overexpression of UBR5 reduces Snail protein abundance and cellular invasiveness. The activity-deficient mutant UBR5 C2768S disrupts its binding and degradation to Snail, thereby losing the ability to regulate epithelial-to-mesenchymal transition in colorectal cancer cells. UBR5 is lowly expressed in human colorectal cancer versus normal tissues, and high UBR5 levels correlate with favorable prognosis, suggesting that UBR5 sustains the epithelial state and inhibits cancer progression. These findings establish the UBR5-Snail axis as a mechanism of post-translational regulation of epithelial-to-mesenchymal transition and colorectal cancer metastasis.

Chronic atrophic gastritis (CAG) is a precancerous gastric condition with limited therapeutic interventions, and the mechanisms underlying the benefits of Coptis chinensis Franch. (CCF) remain insufficiently defined. This study employed an integrated computational strategy to clarify the molecular basis of CCF activity against CAG. Network pharmacology was used to identify potential targets of the major CCF constituents berberine, coptisine, and palmatine, followed by molecular docking, machine learning-based IC₅₀ prediction, and molecular dynamics simulations. Fifty-eight overlapping targets between CCF compounds and CAG-related genes were identified, highlighting SRC, STAT3, MAPK1, and NFKB1 as central nodes enriched in inflammatory and immune pathways, including TNF and MAPK signaling. Docking analyses revealed strong interactions between all three compounds and SRC kinase, and machine learning models predicted IC₅₀ values in the low micromolar range (1.38–1.82 μM). Molecular dynamics simulations further suggest that berberine may stabilize the crucial regulatory regions of SRC, specifically the activation loop. It is hypothesized that this stabilization maintains the inactive conformation of the kinase domain and potentially shields Tyr416 from phosphorylation, thus potentially influencing kinase activation. These findings suggest that CCF may modulate key inflammatory and immune pathways implicated in CAG progression, with SRC emerging as a central node for further investigation.

With rapid industrialization and urbanization, the increasing prevalence of air and water pollutants poses a significant threat to public health. Traditional research methods, such as epidemiological studies and in vitro/in vivo experiments, provide valuable biological insights but are often costly, time-consuming, and limited in scale. To address this gap, this study develops a machine learning-based approach to predict the carcinogenicity of pollutants. Using the dataset of carcinogenic and non-carcinogenic molecules that we collected, the pretrained KPGT model trained with molecular fingerprints and descriptors achieved an AUC of 0.83, surpassing traditional machine learning models. To validate this model, common pollutants from air and water sources were analyzed. Further clustering classified these pollutants into five distinct groups. Target prediction analysis identified key genes associated with representative pollutant molecules, such as MAPK1, MTOR, and PTPN11. GO and KEGG pathway analyses, along with survival analysis, revealed potential carcinogenic mechanisms and prognostic implications. Our findings contribute to improved pollution risk assessment and evidence-based environmental policy development, ultimately aiding in the mitigation of pollutant-related health risks.

Integrating network pharmacological analysis and bioinformatic techniques, this study systematically investigated the molecular mechanisms of six medicinal food homologous plants (Astragalus membranaceus, Ganoderma lucidum, Dioscorea opposite, Curcuma longa, Glycyrrhiza uralensis, and Pueraria lobata) against colorectal cancer. Through screening the TCMSP database, 303 active compounds and 453 drug targets were identified. By integrating differential expression gene analysis with WGCNA on the GSE41258 dataset from the GEO database, 49 potential therapeutic targets were identified. GO and KEGG enrichment analyses demonstrated that these targets are primarily involved in drug response, fatty acid metabolism, and key cancer-related pathways. Cross-validation using three machine learning algorithms—LASSO regression, SVM-RFE, and Random Forest—pinpointed four critical target genes: CA1, CCND1, CXCL2, and EIF6. Further, CIBERSORT immune infiltration analysis revealed strong associations between these core genes and the tumor immune microenvironment in colorectal cancer patients, notably in modulating M0 macrophage infiltration and mast cell activity. Molecular docking analyses confirmed robust binding interactions between active compounds and core target proteins. This study systematically elucidated the molecular mechanisms of six medicinal food homologous plants against colorectal cancer, providing scientific evidence for their rational clinical application.

The rise in human activity has intensified environmental pollution, posing a global public health challenge. Understanding the intricate mechanisms by which pollutants impact health is crucial. Traditional research, often limited to specific techniques and short-term exposures, fails to capture the full complexity of these interactions. This study integrates machine-learning, quantum chemical computing, physicochemical properties, target prediction, KEGG and GO pathway analyses and survival analysis to examine the effects of air and water pollutants on human health. We chose knowledge-guided pre-trained graph transformer (KPGT) framework with an AUC of 0.83 knowledge bootstrap to predict the carcinogenic potential of pollutants and clustered environmental pollutants into seven different groups. For each group, quantum chemical and physicochemical properties, target prediction, KEGG and GO pathway analyses further revealed links between pollutants and cancer-related factors, such as MicroRNA, PD-L1, the PD-1 checkpoint pathway, and HIF-1 signaling. Survival analysis identified key proteins associated with poor cancer prognosis. These findings provide insights into the complex effects of pollutants on health and contribute to public health research.

Epithelial-to-mesenchymal transition (EMT) affects the invasion and migration of cancer cells. Here, we show that FBXO11 recognizes and promotes ubiquitin-mediated degradation of ZEB1. There is a strong association between FBXO11 and ZEB1 in non-small cell lung cancer (NSLC) in a clinical database. FBXO11 interacts with ZEB1, a core inducer of EMT. FBXO11 leads to increased ubiquitination and proteasomal degradation of ZEB1. Depletion of endogenous FBXO11 causes ZEB1 protein accumulation and EMT in A549 and H1299 cells, while overexpression of FBXO11 reduces ZEB1 protein abundance and cellular invasiveness. Importantly, the depletion of ZEB1 suppresses the increased migration and invasion of A549 and H1299 cells promoted by the depletion of FBXO11. The same results are shown in xenograft tumors. High FBXO11 expression is associated with a favorable prognosis in NSLC. Collectively, our study demonstrates that FBXO11 modulates EMT by mediating the stability of ZEB1 in lung cancer cells.