Objective: Esophageal cancer (EC) ranks among the most prevalent malignancies globally and represents a significant and growing public health burden. This study aimed to construct a prognostic model leveraging anoikis-related genes (ARGs) to predict patient survival and elucidate the immunological microenvironment in EC. The findings are anticipated to enhance prognostic accuracy and inform therapeutic strategies, ultimately improving patient outcomes and treatment efficacy.Methods: A comprehensive analysis was conducted using 11 control samples and 159 EC samples obtained from The Cancer Genome Atlas (TCGA) database, alongside associated clinical features. A total of 794 ARGs were curated from GeneCards database. Functional enrichment analyses of EC-related differentially expressed ARGs were performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Prognostic differential ARGs associated with EC were identified through univariate Cox regression analysis, while LASSO regression was employed to minimize overfitting and construct a robust risk prognostic model. The EC cohort was stratified into training and testing groups for model development and verification. Model performance was evaluated through risk curves, survival curves, time-dependent receiver operating characteristic (ROC) curves, ROC curves for the riskscore and clinical features, and independent prognostic analysis. A nomogram with high predictive accuracy was also developed to estimate the prognosis of EC patients. To assess the impact of the risk prognosis model on the immune microenvironment of EC, analyses included tumor microenvironment analysis, single-sample gene set enrichment analysis (ssGSEA), immune cell infiltration correlation analysis, and differential analysis of immune checkpoint expression. Drug sensitivity profiling was conducted to identify potential therapeutic agents for EC. Finally, the expression of selected ARGs was validated at the mRNA level in EC cell lines using real-time quantitative PCR (RT-qPCR).Results: The ARG-based risk prognostic model was constructed incorporating four high-risk ARGs (CDK1, IL17A, FOXC2, and OLFM3) and two low-risk ARGs (PIP5K1C and MAPK1). This model demonstrated strong predictive accuracy for the survival outcomes of EC patients. Immune correlation analyses revealed that the high-risk group exhibited significantly lower immunological scores compared to the low-risk group. Notably, immune cells such as macrophages and mast cells were markedly downregulated in the high-risk group. Additionally, key immunological functions, including APC co-inhibition, parainflammation, Type I IFN Response, and Type II IFN Response, were significantly suppressed in the high-risk group. Eight immune checkpoint-related genes (TNFRSF25, TNFRSF14, CD70, TNFSF15, TMIGD2, CD160, TNFSF18, and HHLA2) displayed distinct expression differences between high- and low-risk groups. The nomogram developed from this model demonstrated high efficacy in predicting EC patient prognosis. Furthermore, six potential therapeutic agents for EC were identified: BIRB.0796, Camptothecin, CHIR.99021, Methotrexate, PF.4708671, and Vorinostat. Finally, the mRNA expression levels of ARGs were validated using RT-qPCR in EC cell lines. Compared to normal esophageal epithelial cells (NE-2), CDK1 and MAPK1 were significantly upregulated in two EC cell lines (KYSE-30 and KYSE-180).Conclusion: This study provides valuable insights into the prognostic outcomes and immune microenvironment of EC through the analysis of ARGs. Furthermore, several potential therapeutic agents for EC were identified, offering promising avenues for treatment. These findings hold significant potential for enhancing the survival outcomes of EC patients and provide meaningful guidance for clinical decision-making in managing this malignancy.

Intervertebral disc degeneration (IDD) is the main cause of low back pain (LBP), which imposes a heavy burden on individuals and society. At present, the treatment methods for IDD aim to control clinical symptoms, and it is difficult to fundamentally reverse IDD and reconstruct its mechanical function. Related studies have shown that oxidative stress is a key factor in promoting intervertebral disc (IVD) degeneration and impeding its repair, so antioxidant therapy may be effective in improving or reversing IDD progression. However, considering the non-targeted systemic cytotoxicity and limited bioavailability of antioxidant biopharmaceuticals, it is urgent to establish an efficient drug delivery system. With the development and innovation of nanomedicine and materials science, nano-drug delivery systems (NDDSs) have shown broad application prospects in various diseases. Relying on the characteristics of nanoscale materials, NDDSs can achieve good biodegradability, biocompatibility, targeted drug delivery, and controlled drug release, thereby enhancing the efficacy of antioxidant preparations. This review not only summarizes the research progress of NDDSs in the treatment of IDD, but also evaluates the advantages and challenges faced by various NDDSs. We anticipate that the perspectives articulated in this review will offer valuable insights for the precise management of IDD.

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental pollutants commonly involved in multiple health risks. Epidemiological studies have confirmed a direct connection between PFAS exposure(including PFOA and PFOS) with the prevalence of osteoarthritis(OA), but the underlying mechanisms and key targets remain unclear. This study integrates network toxicology, transcriptomic analysis, machine learning, and experimental validation to systematically investigate the role of PFOA/PFOS in OA pathogenesis. We identified 38 common targets shared between PFOA/PFOS exposure and OA, enriched in oxidative stress, endocrine metabolism, and senescence pathways. We further screened seven diagnostic biomarkers using machine learning to construct a nomogram for predicting the early onset of OA. Among these, ESR1 emerged as the core target associated with PFOA/PFOS exposure-related OA through multi-algorithm integration. Moreover, molecular docking and molecular dynamics simulations established that PFOA and PFOS could directly bind to ESR1 in a stable manner. In vitro experiments and single-cell transcriptomic analysis indicated that PFOA and PFOS exacerbate oxidative stress and senescence by downregulating ESR1 expression on chondrocytes, resulting in cartilage matrix degradation. Importantly, the ESR1 agonist raloxifene effectively reversed these detrimental effects to maintain cartilage homeostasis. Overall, these findings revealed ESR1 dysfunction as a critical molecular event in PFAS exposure-related OA, providing novel insights into environmental etiologies of OA and potential therapeutic strategies.

Objective: Esophageal cancer (EC) is characterized by a high degree of malignancy and poor prognosis. N6-methyladenosine (m6A), a prominent post-transcriptional modification of mRNA in mammalian cells, plays a pivotal role in regulating various cellular and biological processes. Similarly, cuproptosis has garnered attention for its potential implications in cancer biology. This study seeks to elucidate the impact of m6A- and cuproptosis-related long non-coding RNAs (m6aCRLncs) on the prognosis of patients with EC.Methods: The EC transcriptional data and corresponding clinical information were retrieved from The Cancer Genome Atlas (TCGA) database, comprising 11 normal samples and 159 EC samples. Data on 23 m6A regulators and 25 cuproptosis-related genes were sourced from the latest literature. The m6aCRLncs linked to EC were identified through co-expression analysis. Differentially expressed m6aCRLncs associated with EC prognosis were screened using the limma package in R and univariate Cox regression analysis. Subtype clustering was performed to classify EC patients, enabling the investigation of differences in clinical outcomes and immune microenvironment across patient clusters. A risk prognostic model was constructed using least absolute shrinkage and selection operator (LASSO) regression. Its robustness was evaluated through survival analysis, risk stratification curves, and receiver operating characteristic (ROC) curves. Additionally, the model’s applicability across various clinical features and molecular subtypes of EC patients was assessed. To further explore the model’s utility in predicting the immune microenvironment, single-sample gene set enrichment analysis (ssGSEA), immune cell infiltration analysis, and immune checkpoint differential expression analysis were conducted. Drug sensitivity analysis was performed to identify potential therapeutic agents for EC. Finally, the mRNA expression levels of m6aCRLncs in EC cell lines were validated using reverse transcription quantitative polymerase chain reaction (RT-qPCR).Results: We developed a prognostic risk model based on five m6aCRLncs, namely ELF3-AS1, HNF1A-AS1, LINC00942, LINC01389, and MIR181A2HG, to predict survival outcomes and characterize the immune microenvironment in EC patients. Analysis of molecular subtypes and clinical features revealed significant differences in cluster distribution, disease stage, and N stage between high- and low-risk groups. Immune profiling further identified distinct immune cell populations and functional pathways associated with risk scores, including positive correlations with naive B cells, resting CD4+ T cells, and plasma cells, and negative correlations with macrophages M0 and M1. Additionally, we identified key immune checkpoint-related genes with significant differential expression between risk groups, including TNFRSF14, TNFSF15, TNFRSF18, LGALS9, CD44, HHLA2, and CD40. Furthermore, nine candidate drugs with potential therapeutic efficacy in EC were identified: Bleomycin, Cisplatin, Cyclopamine, PLX4720, Erlotinib, Gefitinib, RO.3306, XMD8.85, and WH.4.023. Finally, RT-qPCR validation of the mRNA expression levels of m6aCRLncs in EC cell lines demonstrated that ELF3-AS1 expression was significantly upregulated in the EC cell lines KYSE-30 and KYSE-180 compared to normal esophageal epithelial cells.Conclusion: This study elucidates the role of m6aCRLncs in shaping the prognostic outcomes and immune microenvironment of EC. Furthermore, it identifies potential therapeutic agents with efficacy against EC. These findings hold significant promise for enhancing the survival of EC patients and provide valuable insights to inform clinical decision-making in the management of this disease.

Piezo1 is a mechanically activated, non-selective cation channel characterized by its exquisite sensitivity to membrane tension and high permeability to calcium(Ca²⁺⁾, enabling the conversion of mechanical stimuli into intracellular signaling events. Activation of Piezo1 leads to Ca²⁺ influx, which initiates iron metabolism reprogramming—including transferrin receptor 1(TfR1)-dependent iron uptake, divalent metal transporter 1(DMT1)-mediated iron transport, and Nuclear Receptor Coactivator 4(NCOA4)-regulated ferritinophagy—thereby promoting the accumulation of reactive oxygen species (ROS) and lipid peroxidation. Ultimately, these events culminate in ferroptosis by suppressing glutathione peroxidase 4 (GPX4) activity. The “mechanical force–Piezo1–Ca²⁺–iron/lipid metabolism” axis establishes mechanical stress as a pivotal upstream regulator of ferroptosis. This axis facilitates the functional integration of mechanotransduction into inflammatory mediator production, vascular and extracellular matrix(ECM) remodeling, and metabolic reprogramming. Furthermore, this signaling pathway exerts context-dependent pathogenic or protective effects across diverse pathological conditions, including musculoskeletal degeneration, ischemia-reperfusion injury, inflammatory bowel disease, neurovascular disorders, and cancer. This review provides a comprehensive overview of the molecular mechanisms and clinical evidence governing Piezo1-mediated ferroptosis. We summarize current pharmacological and genetic interventions for its inhibition—along with associated limitations such as selectivity and pharmacokinetic challenges—and explores interventions targeting the channel itself, Ca²⁺signaling, and downstream ferroptotic processes, including iron chelation, lipid peroxidation suppression, and preservation of the GPX4/coenzyme Q10 (CoQ10) axis. Furthermore, the potential for integrating these interventions with established therapeutic modalities is also discussed. A profound understanding of the druggability and context-dependent dynamics of the Piezo1-ferroptosis axis is expected to facilitate the discovery of novel therapeutic targets and combinatorial regimens for the precision management of mechanosensitive diseases.

Objective: Piceatannol (PIC) exhibits antioxidant and anti-inflammatory activities. This study integrates network pharmacology and experimental validation to investigate its potential role in rheumatoid arthritis (RA). Methods: PIC targets were predicted using public databases. RA-related differentially expressed genes (DEGs) were identified from Gene Expression Omnibus (GEO) datasets (|logFC| ≥ 1 and P-value < 0.05). Intersection genes were analyzed via protein-protein interaction (PPI) network (hub gene selection), molecular docking (binding affinity < -5.0 kcal/mol as threshold), ConnectivityMap and molecular dynamics simulation. Experimental validation included CCK8, flow cytometry, real-time quantitative PCR (RT-qPCR), Western blotting, and an adjuvant-induced arthritis (AIA) rat model. Results: 35 intersecting genes were identified, from which 6 hub genes (SYK, CXCL8, TNF, NFKB1, PPARG, and CASP8) were selected. PIC showed stable binding to all hub genes (affinities: -5.6 to -7.8 kcal/mol). ConnectivityMap suggested a regulatory relationship between PIC and SYK. Molecular dynamics simulations demonstrate that the PIC–SYK complex maintains stable structural integrity. Experimental validation showed that PIC reduced MH7A cell viability, induced G2/M arrest and apoptosis, and downregulated mRNA levels of SYK, NFKB1, and CASP8, consistent with predictions. In vivo , PIC alleviated AIA severity. Conclusion: These preliminary findings suggest that PIC exerts therapeutic effects in RA models, potentially via SYK/NFKB1/CASP8. The study provides a theoretical basis for further evaluation of PIC in RA, while acknowledging the exploratory nature of network pharmacology and preclinical models.