Hua Zhang

Cancer remains a leading cause of global morbidity and mortality, yet conventional therapies, including surgery, radiotherapy, and chemotherapy, are often limited by invasiveness, systemic toxicity, and drug resistance. In this context, extracellular vesicles (EVs) have emerged as a promising cell-free nanotherapeutic platform. As endogenous nanocarriers, EVs enable precise, targeted delivery of diverse bioactive cargoes (eg, nucleic acids, chemotherapeutics, immunomodulators) to tumor tissues, thereby enhancing therapeutic efficacy while minimizing off-target effects, which is the key advantages for their application in tumor targeted therapy. This review systematically summarizes the characteristics of animal-derived and plant-derived EVs and highlights their translational applications in multiple cancers via immune activation, targeted delivery, tumor microenvironment remodeling, and anti-angiogenesis. We further introduce advanced bioengineering strategies for EV modification to optimize cargo loading, targeting specificity, and in vivo stability, particularly frontier innovations such as artificial intelligence-assisted design and microfluidic manufacturing that improve the precision, controllability, and scalability of engineered EVs. Compared to synthetic nanocarriers, EVs exhibit unique advantages, including excellent biocompatibility, low immunogenicity, and superior ability to cross biological barriers. However, the clinical application of EV-based therapies faces notable challenges, including EV heterogeneity, scalability of production, standardization of characterization methods, cargo loading efficiency, and long-term safety concerns. This review emphasizes the transformative potential of engineered EVs in advancing tumor targeted therapy and improving outcomes for patients with refractory or metastatic tumors.

Objective: To assess the overall survival (OS) and cancer-specific survival (CSS) in patients with different primary locations of hypopharyngeal squamous cell carcinoma (HSCC) and to develop a predictive model incorporating key clinical and treatment variables (including systemic therapy) to serve as a prognostic reference for patients with locally advanced HSCC. Methods: This retrospective cohort study extracted data for 1,591 patients with locally advanced HSCC from the Surveillance, Epidemiology, and End Results (SEER) database. X-tile software was used to determine optimal cutoff values for continuous variables. Kaplan–Meier analysis compared survival by primary site. Independent risk factors were identified using stepwise Cox regression, and a nomogram was constructed. Model performance was assessed using the concordance index (C-index), area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). Results: OS and CSS differed significantly by primary site. Stepwise Cox analysis identified 13 independent prognostic factors. The nomogram showed a C-index of 0.727 in both training and validation sets. The AUCs for 1-, 3-, and 5-year OS were 0.792, 0.791, 0.788 (training) and 0.780, 0.796, 0.789 (validation). Calibration curves indicated good agreement between predicted and observed outcomes. DCA demonstrated superior net benefit over TNM staging alone. Conclusion: Prognosis in locally advanced HSCC varies by primary site. The developed nomogram provides moderate prognostic accuracy and may serve as a supplementary tool for risk stratification and clinical discussion, though external validation is warranted.

Cisplatin is one of the most widely used chemotherapeutic drugs in treating malignant tumors. However, its clinical efficacy is severely compromised by the emergence of drug resistance. Recent studies have highlighted N6-methyladenosine (m6A) modification, a key epigenetic regulatory mechanism, as playing a crucial role in the development of cisplatin resistance. m6A modification regulates essential RNA processes, including RNA stability, translation efficiency, and subcellular localization, through the coordinated activities of “writers” (e.g., METTL3, WTAP), “erasers” (e.g., FTO, ALKBH5), and “readers” (e.g., YTH family, GF2BPfamily). These regulatory effects ultimately affect cancer cell sensitivity to cisplatin. Furthermore, m6A modification is critically involved in the regulation of fundamental cellular processes such as autophagy, apoptosis, and tumor metabolism, thereby contributing to the reinforcement of resistance mechanisms. This review provides a comprehensive summary of the mechanisms through which m6A modification mediates cisplatin resistance and discusses its potential implications for precision cancer therapy. This review consolidates the scientific foundation for potential future strategies and innovative approaches targeting chemoresistance.

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Head and neck cancer (HNC) is a prevalent malignancy with a rising global incidence. While traditional risk factors such as tobacco use and viral infections are well-established, the dysbiosis of oral and gut microbiota has recently emerged as a pivotal contributor to HNC pathogenesis. The oral-gut axis serves as a critical conduit for bidirectional microbial crosstalk, facilitated by bacterial translocation, metabolic exchange, and immune modulation, collectively fostering a pro-tumorigenic microenvironment. Key oral pathogens, including Fusobacterium nucleatum and Porphyromonas gingivalis , exacerbate chronic inflammation, promote immune evasion, and activate oncogenic signaling pathways such as Wnt/β-catenin, MAPK/ERK, and PD-1/PD-L1. In parallel, gut dysbiosis influences HNC progression by altering the production of microbial metabolites, including short-chain fatty acids, bile acids, and tryptophan derivatives, which systemically regulate inflammation and anti-tumor immunity. Growing evidence also implicates the microbiota in modulating responses to radiotherapy, chemotherapy, and immunotherapy. Therapeutic strategies targeting the oral-gut axis, including probiotics and antimicrobial peptides, hold promise for alleviating treatment-induced mucosal injury and improving therapeutic outcomes. Nonetheless, significant challenges persist, including elucidating network-level microbial interactions, validating robust biomarkers, and advancing these findings into clinical practice. Future multidisciplinary collaborations are essential to fully leverage the oral-gut microbiota axis for precision oncology in HNC.

In the published article, there was an error. [The content presented in Paragraphs 5 and 6 of the Conclusion section (Page 10) has been incorrectly placed. This material is the figure notes accompanying the two relevant figures. Its inclusion in the Conclusion is both redundant and unsuitable. It is therefore recommended that this content be removed from its current location].A correction has been made to [Conclusion], [Paragraphs 5 and6]. This sentence previously stated:In recent years, with the rapid advancement of urbanization and changes in lifestyle, there has been a significant increase in the incidence of allergic diseases. The WHO predicts that allergic diseases associated with industrialization and Western lifestyles will double in the future, potentially due to gut microbiota imbalance. In contemporary medicine and life sciences, research into gut microbiota imbalance and its role in the prevention of allergic diseases has gained increasing attention. Investigation the interactions between the microbiome, metabolome, and host, as well as their collaborative mechanisms in maintaining intestinal homeostasis, has become a critical factor in developing more convenient and efficient treatment methods, as well as enabling early disease prevention. With the advent of high-throughput sequencing technology, we have achieved a deeper understanding of the composition and diversity of gut microbiota. The extensive application of multi-omics technologies, including metagenomics, metatranscriptomics, metaproteomics, and metabolomics, enables us to study the interactions between gut microbiota and hosts at multiple levels. These studies provide a theoretical foundation for the development of diagnostic markers and therapeutic strategies based on gut microbiota.In the context of personalized treatment, a randomized controlled trial (N = 1591 participants) demonstrated that eight out of nine probiotics could alleviate at least one clinical symptom of allergic rhinitis. Furthermore, lactate, ornithine, and six additional metabolites were identified as potential predictors of the efficacy of sublingual immunotherapy for allergic rhinitis. These metabolites regulate metabolic pathways, modulate immune system function, and mitigate symptoms of allergic rhinitis. Conversely, another double-blind randomized controlled study revealed that a probiotic mixture did not exhibit significant therapeutic effects on moderate to severe atopic dermatitis in children. Regarding the overall treatment process for allergic diseases, probiotics can alleviate allergy symptoms, reduce medication usage, and lower medical expenses. However, prolonged use may increase costs. Metabolite supplementation can enhance treatment effectiveness, decrease disease recurrence, and reduce long-term medical expenses. Nevertheless, the high cost of metabolite supplements may also lead to increased treatment expenses.Notwithstanding these findings, there are notable limitations in the gut microbiota research. Significant inter-individual variability in gut microbiota communities limits the generalizability of research findings. Therefore, mother-infant cohort studies and cross-regional, multi-center collaborations are essential for addressing these generalizability issues. Individual genetic background, dietary habits, lifestyle, and environmental factors all influence the composition and function of the gut microbiota, adding complexity and challenges to the research. Currently, most studies can only establish correlations between gut microbiota and disease, making it difficult to determine causation. Although some intervention studies (e.g., probiotics, prebiotics, fecal microbiota transplantation) offer insights into causal relationships, these studies often suffer from small sample sizes and lack of long-term follow-up, hindering definitive conclusions. The interaction between gut microbiota and the host is a complex process influenced by multiple factors, involving systems such as the immune, metabolic, and nervous systems. Current research often focuses on single factor or pathway, which makes it challenging to comprehensively elucidate the intricate interactive mechanisms between gut microbiota and the host.Therefore, future research should place greater emphasis on individual differences and actively pursue personalized investigations based on individual characteristics. By integrating multiple-omics datasets, incorporating individual genetic, dietary, lifestyle information, and constructing personalized gut microbiota models, a foundation for precision diagnosis and treatment can be established. Through the application of advanced experimental designs and analysis methods, such as randomized controlled trials and causal inference analyses, further exploration of the causal relationship between gut microbiota and diseases can be achieved. Simultaneously, combining animal models with clinical studies will help validate the reliability and reproducibility of these causal relationships. Based on findings regarding the relationship between gut microbiota and diseases, novel intervention strategies have been developed, including personalized probiotics formulations, prebiotics, fecal microbiota transplantation, and microbiota-derived metabolic products. Their safety and efficacy can be confirmed through rigorous clinical trials, providing new avenues for diseases prevention and treatment. Long-term follow-up studies should be conducted to monitor the dynamic changes in gut microbiotacommunities and their impact on host health and diseases progression. Through continuous monitoring and data analysis, the long-term associations between gut microbiota communities and disease development can be elucidated, offering a basis for early warning and intervention strategies. In conclusion, while significant advancements have been made in the study of gut microbiota and metabolites, numerous challenges remain. Future research should aim...

This study was designed to identify immune-related biomarkers associated with allergic rhinitis (AR) and construct a robust a diagnostic model. Two datasets (GSE5010 and GSE50223) were downloaded from the NCBI GEO database, containing 38 and 84 blood CD4 + T cell samples, respectively. To eliminate batch effects, the surrogate variable analysis (sva) R package (version 3.38.0) was employed, enabling the integration of data for subsequent analysis. Immune cell infiltration profiles were assessed using the Gene Set Variation Analysis (GSVA) R package (version 1.36.3). A gene co-expression network was constructed via the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to identify disease-related modules. Differentially expressed genes (DEGs) were identified using the linear models for microarray data (limma) R package (version 3.34.7), followed by functional enrichment analysis using DAVID. Protein-protein interaction (PPI) networks were constructed based on the STRING database to highlight key genes. A diagnostic model was subsequently developed utilizing the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm and Support Vector Machine (SVM) method, with its discriminative capacity assessed via Receiver Operating Characteristic (ROC) curves. A total of twenty-eight immune cell types were analyzed, revealing significant differences in eight types between the AR and control groups. Through WGCNA, three disease-related modules comprising 4278 candidate genes were identified. Differential expression analysis identified 326 significant DEGs, of which 257 overlapped with WGCNA-selected genes. These genes exhibited significant enrichment in immune-related pathways, including “cytokine-cytokine receptor interaction” and “chemokine signaling pathway.” Gene Set Enrichment Analysis (GSEA) further uncovered 12 KEGG pathways significantly associated with disease risk scores. Drug screening identified 24 small molecule drugs related to key genes. A diagnostic model incorporating five genes (RFC4, LYN, IL3, TNFRSF1B, and RBBP7) was constructed, demonstrating diagnostic efficiencies of 0.843 and 0.739 in the training and validation sets, respectively. An AR mouse model was successfully established, and the expression levels of relevant genes were validated through RT-qPCR experiments. The five-gene diagnostic model established in this study exhibits strong predictive ability in distinguishing AR patients from healthy controls, with potential clinical applications in diagnosing AR and advancing novel diagnostic and therapeutic strategies.

The gut microbiota and its metabolites play important roles in the pathogenesis of various diseases. The diversity of the gut microbiota is closely related to the development and function of the human immune system. Dysbiosis, characterized by alterations in the species, quantity, and distribution of microbial community, may disrupt immune tolerance mechanisms, thereby inducing excessive immune responses to allergens and increasing the risk of allergic diseases. Various metabolites, such as short-chain fatty acids (SCFAs), bile acids, and amino acid metabolites, exert significant regulatory effects on the development of allergic diseases by modulating immune cell function, maintaining intestinal barrier integrity, and participating in signal transduction pathways. A comprehensive investigation into the relationship between allergic diseases and gut microbiota and their metabolites not only aids in elucidating the pathogenesis of allergic diseases but also provides novel insights and a theoretical foundation for developing innovative diagnostic methods, preventive strategies, and therapeutic options. This article systematically reviews the latest findings regarding the mutual influence between gut microbiota and the metabolome in host immune regulation, as well as the impact of this interaction on the development of allergic diseases, aiming to offering new strategies for the prevention and treatment of allergic diseases.