Shijun Zhang

Background: Early-onset gastric cancer (EOGC) represents a clinically and biologically distinct subtype of gastric cancer characterized by unique molecular features. However, its underlying molecular mechanisms remain unclear. This study aimed to identify EOGC-associated hub genes using an integrated bioinformatics and machine learning framework. Methods: Differentially expressed genes (DEGs) between EOGC and late-onset gastric cancer (LOGC) were analyzed using functional enrichment, weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) regression, and random forest algorithms. Immune infiltration was characterized using CIBERSORT. Candidate hub genes were validated using an independent external cohort, clinical specimens, single-cell transcriptomic datasets, and in vitro functional assays. A five-gene logistic regression model was established and evaluated using ROC-AUC, calibration analysis, and bootstrap resampling. Results: Transcriptomic analysis identified 227 DEGs, and WGCNA identified seven co-expression modules, with the green module showing the strongest association with EOGC. Machine learning identified five hub genes: CCR7, CD1D, PTGER2, PIK3CG, and EBI3. The combined model showed good discriminatory performance in the training, internal validation, and independent external validation cohorts (AUC = 0.909, 0.859, and 0.802, respectively). Bootstrap resampling yielded AUC estimates of 0.908, 0.852, and 0.789, respectively, supporting model stability. All five genes showed higher expression in EOGC and were validated at the mRNA and protein levels. Immune profiling and single-cell analysis revealed an immune-associated molecular phenotype and distinct expression patterns across immune cell subsets. Silencing the hub genes suppressed gastric cancer cell proliferation and migration in vitro . Conclusion: We identified five EOGC-associated hub genes and revealed an immune-associated molecular landscape. The five-gene model represents an EOGC-associated molecular signature distinguishing EOGC from LOGC rather than a stand-alone clinical diagnostic tool. These findings provide insights into the molecular and immune biology of EOGC and may inform future studies of molecular stratification and treatment-related outcomes.

Background: The immune system is central to host defense against pathogens and the maintenance of organismal homeostasis. Immune cells often reside in or migrate through tissue microenvironments characterized by significant mechanical activity, where they are continuously subjected to diverse mechanical stimuli. Despite the ubiquity of mechanical signals in such contexts, the precise regulatory mechanisms by which these signals control immune cell function in a context-dependent manner remain incompletely understood. Main body: In recent years, the mechanosensitive ion channel Piezo1 has emerged as a core component of cellular mechanical sensing system, attracting considerable attention in immunology. This review integrates the latest research advances to systematically analyze the role of Piezo1 in both innate and adaptive immune cells. We discuss how Piezo1 is widely involved in key immune processes, including cell activation, differentiation, and functional regulation, and highlight the context-dependent nature of Piezo1 signaling, which can drive divergent immune outcomes depending on cellular lineage and pathological microenvironment. Conclusions: By synthesizing the current evidence, this review provides a theoretical foundation for understanding mechanical signaling in immunity. Crucially, this underscores that the functional duality and context-dependency of Piezo1 present both opportunities and challenges for drug targeting, offering insights for the development of precision immunotherapies that account for specific pathological settings.

Aristolochic acid I (AAI) is a known liver carcinogen, but its molecular mechanisms in hepatocellular carcinoma (HCC) remain incompletely understood. This study aimed to systematically elucidate these mechanisms. This study employed a multidisciplinary strategy: assessing AAI carcinogenicity via ProTox-3.0; identifying its targets using PubChem; and screening HCC-related genes from GEO. Core genes were filtered through network toxicology and Lasso-Cox regression. Molecular docking and dynamics simulations analyzed AAI-protein interactions, while GO, KEGG, and GSEA enrichment revealed relevant pathways. Key gene expression and clinical relevance were validated via TCGA and HPA databases. The carcinogenic potential of the AAI was 0.77. Fifty-two potential targets of AAI-induced HCC were identified. Network toxicology analysis identified 12 core genes including EZH2, FABP5, and RXRA. Lasso-Cox regression analysis identified four core genes that drive HCC progression, including EZH2. Functional enrichment analysis revealed that AAI-HCC targets were significantly enriched in lipid metabolism and the PPAR signaling pathway (p < 0.05), with the PPAR signaling pathway being significantly activated in HCC. Molecular docking and MD simulations confirmed the high affinity and stable binding of AAI to EZH2, FABP5, and RXRA. Through in silico and database-based validation, biological samples and clinical data confirmed that these genes were significantly overexpressed in HCC (p < 0.001) and were correlated with poor prognosis (p < 0.01). AAI may synergistically activate the PPAR signaling pathway by targeting EZH2, FABP5, and RXRA, driving lipid metabolism reprogramming and promoting the occurrence and development of HCC. Graphical Abstract Flowchart of the study in this paper

Hepatocellular carcinoma (HCC) is a major contributor to global cancer-related mortality. Serglycin (SRGN) is involved in the progression of various cancers, and its overexpression is related to poor prognosis in HCC patients. Its biological role in HCC aggressiveness is unknown. This study aims to elucidate the mechanism of SRGN in HCC. Via in vitro and vivo experiments, we identified SRGN as a critical regulator of HCC cells migratory capability and metastasis. SRGN significantly correlated with maintaining stemness-like characteristics, emphasizing its central role in HCC progression. Mechanistically, SRGN activated YAP into the tumor cells' nucleus. Moreover, SRGN selectively upregulated CRISPLD2, establishing the SRGN/YAP/CRISPLD2 axis and promoting metastatic behavior in HCC cells. Our results revealed that CRISPLD2 is a direct target of the SRGN-mediated YAP/TEAD1 complex. SRGN orchestrated stemness maintenance, tumorigenesis, and metastasis in an autocrine way by selectively reactivating the novel YAP/CRISPLD2 axis. Besides, sorafenib with verteporfin showed a certain therapeutic effect in SRGN-positive individuals. Our work clarifies the mechanism by which SRGN promotes the invasiveness of HCC. It provides insights into targeting the SRGN-triggered signaling pathway as a potential new direction in treating HCC.

Background: Hepatocellular carcinoma (HCC) continues to increase in morbidity and mortality among all types of cancer. DNA methylation, an important epigenetic modification, is associated with cancer occurrence and progression. The objective of this study was to establish a model based on DNA methylation risk scores for identifying new potential therapeutic targets in HCC and preventing cancer progression.Methods: Transcriptomic, clinical, and DNA methylation data on 374 tumor tissues and 50 adjacent normal tissues were downloaded from The Cancer Genome Atlas–Liver Hepatocellular Carcinoma database. The gene expression profiles of the GSE54236 liver cancer dataset, which contains data on 161 liver tissue samples, were obtained from the Gene Expression Omnibus database. We analyzed the relationship between DNA methylation and gene expression levels after identifying the differentially methylated and expressed genes. Then, we developed and validated a risk score model based on the DNA methylation-driven genes. A tissue array consisting of 30 human hepatocellular carcinoma samples and adjacent normal tissues was used to assess the protein and mRNA expression levels of the marker genes by immunohistochemistry and qRT-PCR, respectively.Results: Three methylation-related differential genes were identified in our study: GLS, MEX3B, and GNA14. The results revealed that their DNA methylation levels were negatively correlated with local gene expression regulation. The gene methylation levels correlated strongly with the prognosis of patients with liver cancer. This was confirmed by qRT-PCR and immunohistochemical verification of the expression of these genes or proteins in tumors and adjacent tissues. These results revealed the relationship between the level of relevant gene methylation and the prognosis of patients with liver cancer as well as the underlying cellular and biological mechanisms. This allows our gene signature to provide more accurate and appropriate predictions for clinical applications.Conclusion: Through bioinformatics analysis and experimental validation, we obtained three DNA methylation marker: GLS, MEX3B, and GNA14. This helps to predict the prognosis and may be a potential therapeutic target for HCC patients.

Network traffic detection systems play a critical role in the security of smart grid networks, as they are responsible for detecting and defending against various external attacks. Accurate network traffic anomaly detection is essential to ensure secure communication between power devices. However, traditional abnormal detection models often suffer from high false positive rates and inadequate detection performance. To address these issues, we propose an enhanced Bi-directional Generative Adversarial Network (BGAN) for network traffic anomaly detection. The BGAN model leverages the foundational framework of GAN to learn the distribution of complex traffic data. In this model, the discriminator incorporates a Bidirectional Gated Recurrent Unit (BiGRU) network to capture the temporal correlations of sequential distributions. Additionally, we introduce an attention mechanism to enhance the model's ability to identify relevant information between input data and current output data, thereby improving overall detection performance. To evaluate the effectiveness of our proposed method, we conducted experiments using multiple datasets. The results demonstrate that our approach surpasses the performance of other compared traffic abnormal detection methods.

Background: Hepatocellular Carcinoma (HCC) is a common lethal digestive system tumor. The oxidative stress mechanism is crucial in the HCC genesis and progression.Methods: Our study analyzed single-cell and bulk sequencing data to compare the microenvironment of non-tumor liver tissues and HCC tissues. Through these analyses, we aimed to investigate the effect of oxidative stress on cells in the HCC microenvironment and identify critical oxidative stress response-related genes that impact the survival of HCC patients.Results: Our results showed increased oxidative stress in HCC tissue compared to non-tumor tissue. Immune cells in the HCC microenvironment exhibited higher oxidative detoxification capacity, and oxidative stress-induced cell death of dendritic cells was attenuated. HCC cells demonstrated enhanced communication with immune cells through the MIF pathway in a highly oxidative hepatoma microenvironment. Meanwhile, using machine learning and Cox regression screening, we identified PRDX1 as a predictor of early occurrence and prognosis in patients with HCC. The expression level of PRDX1 in HCC was related to dysregulated ribosome biogenesis and positively correlated with the expression of immunological checkpoints (PDCD1LG2, CTLA4, TIGIT, LAIR1). High PRDX1 expression in HCC patients correlated with better sensitivity to immunotherapy agents such as sorafenib, IGF-1R inhibitor, and JAK inhibitor.Conclusion: In conclusion, our study unveiled variations in oxidative stress levels between non-tumor liver and HCC tissues. And we identified oxidative stress gene markers associated with hepatocarcinogenesis development, offering novel insights into the oxidative stress response mechanism in HCC.